diff --git a/.buildinfo b/.buildinfo new file mode 100644 index 0000000..170b7cb --- /dev/null +++ b/.buildinfo @@ -0,0 +1,4 @@ +# Sphinx build info version 1 +# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. +config: 744e9e08e89a895865344cc21e3f0de7 +tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/.github/workflows/build-docs.yml b/.github/workflows/build-docs.yml deleted file mode 100644 index 441cecf..0000000 --- a/.github/workflows/build-docs.yml +++ /dev/null @@ -1,33 +0,0 @@ -name: deploy-docs -on: - push: - branches: - - master -jobs: - check-bats-version: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v2 - with: - persist-credentials: false - - name: Set up Python 3.7 - uses: actions/setup-python@v1 - with: - python-version: 3.7 - - name: Install dependencies - run: | - sudo apt install doxygen - python -m pip install --upgrade pip - pip install sphinx breathe sphinx_rtd_theme sphinxcontrib-googleanalytics - - name: Build Documentation - run: | - cd docs - make html - - - name: Deploy - uses: JamesIves/github-pages-deploy-action@3.7.1 - with: - GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - BRANCH: docs - FOLDER: docs/build/html - CLEAN: true diff --git a/.gitignore b/.gitignore deleted file mode 100644 index fca176b..0000000 --- a/.gitignore +++ /dev/null @@ -1,42 +0,0 @@ -# Prerequisites -*.d - -# Compiled Object files -*.slo -*.lo -*.o -*.obj - -# Precompiled Headers -*.gch -*.pch - -# Compiled Dynamic libraries -*.so -*.dylib -*.dll - -# Fortran module files -*.mod -*.smod - -# Compiled Static libraries -*.lai -*.la -*.a -*.lib - -# Executables -*.exe -*.out -*.app - -# Build directories -build -examples/*/build - -# Libtensorflow -libtensorflow - -# Downloaded model -examples/efficientnet/model diff --git a/.nojekyll b/.nojekyll new file mode 100644 index 0000000..e69de29 diff --git a/CITATION.cff b/CITATION.cff deleted file mode 100644 index 8c707b2..0000000 --- a/CITATION.cff +++ /dev/null @@ -1,11 +0,0 @@ -cff-version: 1.2.0 -message: "If you use this software, please cite it as below." -authors: -- family-names: "Izquierdo" - given-names: "Sergio" - orcid: "https://orcid.org/0000-0002-5639-5035" -title: "cppflow: Run TensorFlow models in C++ without installation and without Bazel" -version: 2.0.0 -doi: 10.5281/zenodo.7107618 -date-released: 2019-05-16 -url: "https://github.com/serizba/cppflow" diff --git a/CMakeLists.txt b/CMakeLists.txt deleted file mode 100644 index 0731fc2..0000000 --- a/CMakeLists.txt +++ /dev/null @@ -1,65 +0,0 @@ -cmake_minimum_required(VERSION 3.8 FATAL_ERROR) -project(cppflow LANGUAGES CXX) - -include(GNUInstallDirs) -set(INSTALL_CONFIGDIR ${CMAKE_INSTALL_LIBDIR}/${PROJECT_NAME}/cmake) - -set(CMAKE_MODULE_PATH "${CMAKE_MODULE_PATH};${CMAKE_SOURCE_DIR}/cmake/modules") - - -# Library target -find_package(tensorflow REQUIRED) -add_library(cppflow INTERFACE) -target_include_directories(cppflow - INTERFACE - ${tensorflow_INCLUDE_DIRS} - $ - $ -) -target_compile_features(cppflow INTERFACE cxx_std_17) -target_link_libraries(cppflow INTERFACE - ${tensorflow_LIBRARIES} -) -install( - TARGETS cppflow - EXPORT install_targets -) - - -# Build examples -option(BUILD_EXAMPLES "Build examples" ON) -if(BUILD_EXAMPLES) - add_subdirectory(examples) -endif() - - -# Install headers -install(DIRECTORY include/ DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}) - - -# Install targets file -install(EXPORT install_targets - FILE - ${PROJECT_NAME}Targets.cmake - NAMESPACE - ${PROJECT_NAME}:: - DESTINATION - ${INSTALL_CONFIGDIR} -) - - -# Install cppflowConfig.cmake -include(CMakePackageConfigHelpers) -configure_package_config_file( - ${CMAKE_CURRENT_SOURCE_DIR}/cmake/${PROJECT_NAME}Config.cmake.in - ${CMAKE_CURRENT_BINARY_DIR}/${PROJECT_NAME}Config.cmake - INSTALL_DESTINATION ${INSTALL_CONFIGDIR} -) -install(FILES - ${CMAKE_CURRENT_BINARY_DIR}/${PROJECT_NAME}Config.cmake - DESTINATION ${INSTALL_CONFIGDIR} -) - - -# Install find modules -install(DIRECTORY cmake/modules/ DESTINATION ${INSTALL_CONFIGDIR}/modules) diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 83981dd..0000000 --- a/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2019 Sergio Izquierdo - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/README.md b/README.md deleted file mode 100644 index 8eb17cf..0000000 --- a/README.md +++ /dev/null @@ -1,94 +0,0 @@ -# ![cppflow](docs/source/cppflow.svg) - -Run TensorFlow models in c++ without Bazel, without TensorFlow installation and without compiling Tensorflow. Perform tensor manipulation, use eager execution and run saved models directly from C++. - -```c++ -// Read the graph -cppflow::model model("saved_model_folder"); - -// Load an image -auto input = cppflow::decode_jpeg(cppflow::read_file(std::string("image.jpg"))); - -// Cast it to float, normalize to range [0, 1], and add batch_dimension -input = cppflow::cast(input, TF_UINT8, TF_FLOAT); -input = input / 255.f; -input = cppflow::expand_dims(input, 0); - -// Run -auto output = model(input); - -// Show the predicted class -std::cout << cppflow::arg_max(output, 1) << std::endl; -``` - -You can take a look to the [examples](https://github.com/serizba/cppflow/tree/master/examples/) to see a full example on how to load a deep network and feed it with a sample image. - -CppFlow uses [Tensorflow C API](https://www.tensorflow.org/install/lang_c) to run the models, meaning you can use it without installing Tensorflow and without compiling the whole Tensorflow repository with bazel, you just need to download the C API. With this project you can manage and run your models in C++ without worrying about void, malloc or free. With CppFlow you easily can: - -* Open saved models created with Python -* Execute Tensorflow neural networks in C++ -* Perform tensor manipulation directly from C++ - -## How To Run It - -Since it uses TensorFlow 2 C API you just have to [download it](https://www.tensorflow.org/install/lang_c), check the [docs](https://serizba.github.io/cppflow/installation.html) to see a guide on how to do it. - -Afterwards, you can install the library: - -```sh -git clone git@github.com:serizba/cppflow.git -cd cppflow/examples/load_model -mkdir build -cd build -cmake .. -make -j -make install -``` - -Now you can check the [quickstart guide](https://serizba.github.io/cppflow/quickstart.html) to run a program using cppflow. - - -## Documentation - -Check the docs at [https://serizba.github.io/cppflow/](https://serizba.github.io/cppflow/). - -There you can find quickstart guides and more information about how to install the library and run the examples. - -## Development - -CppFlow is basically a wrapper over Tensorflow C API. The basic class, [tensor](https://github.com/serizba/cppflow/blob/master/include/cppflow/tensor.h) is a wrapper of a TF eager tensor, and it just constains a pointer to its TF representation. - -The TF C API provides the tools to call all the TF [raw ops](https://www.tensorflow.org/api_docs/python/tf/raw_ops), but using them is confusing. CppFlow includes a facade over these functions, so they can be called easily as normal C++ functions. To achieve this, the file [ops](https://github.com/serizba/cppflow/blob/master/include/cppflow/raw_ops.h) contains (mostly) all the TF raw ops functions, but with a simple C++ interface. This file has been generated automatically using a [small script](https://github.com/serizba/cppflow/blob/master/include/cppflow/ops_generator/generator.py). - -CppFlow also includes a wrapper on TF saved models, the [model](https://github.com/serizba/cppflow/blob/master/include/cppflow/model.h) class, so they can be easily opened and executed. - -## Contributors - -If you are willing to contribute to this project, please go ahead an visit the [development roadmap of cppflow](https://github.com/users/serizba/projects/3). Specially `contributor_wanted` labelled PR or issues are very welcome to new contributors. - -# Citation - -If you use this code or find this work useful in your research, please cite us: - -``` -@software{ - izquierdo2019cppflow, - author = {Izquierdo, Sergio}, - doi = {10.5281/zenodo.7107618}, - title = {{cppflow: Run TensorFlow models in C++ without installation and without Bazel}}, - url = {https://github.com/serizba/cppflow}, - version = {2.0.0}, - month = {5}, - year = {2019} -} -``` - -## Style guide - -We use the [Google's C++ style guide](https://google.github.io/styleguide/cppguide.html) using static code linker [cpplint](https://github.com/cpplint/cpplint). -We use the [Google's Python style guide](https://google.github.io/styleguide/pyguide.html) using static code linker [pylint](https://pylint.pycqa.org/en/latest/user_guide/installation/index.html) using attached pylintrc configuration. - - -## Remark - -CppFlow is not related with TensorFlow. The CppFlow icon is a modified version of the TensorFlow logo. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc. diff --git a/docs/source/cppflow.svg b/_images/cppflow.svg similarity index 100% rename from docs/source/cppflow.svg rename to _images/cppflow.svg diff --git a/docs/source/my_cat.jpg b/_images/my_cat.jpg similarity index 100% rename from docs/source/my_cat.jpg rename to _images/my_cat.jpg diff --git a/docs/source/examples.rst b/_sources/examples.rst.txt similarity index 100% rename from docs/source/examples.rst rename to _sources/examples.rst.txt diff --git a/docs/source/index.rst b/_sources/index.rst.txt similarity index 100% rename from docs/source/index.rst rename to _sources/index.rst.txt diff --git a/docs/source/installation.rst b/_sources/installation.rst.txt similarity index 100% rename from docs/source/installation.rst rename to _sources/installation.rst.txt diff --git a/docs/source/quickstart.rst b/_sources/quickstart.rst.txt similarity index 100% rename from docs/source/quickstart.rst rename to _sources/quickstart.rst.txt diff --git a/_static/_sphinx_javascript_frameworks_compat.js b/_static/_sphinx_javascript_frameworks_compat.js new file mode 100644 index 0000000..8549469 --- /dev/null +++ b/_static/_sphinx_javascript_frameworks_compat.js @@ -0,0 +1,134 @@ +/* + * _sphinx_javascript_frameworks_compat.js + * ~~~~~~~~~~ + * + * Compatability shim for jQuery and underscores.js. + * + * WILL BE REMOVED IN Sphinx 6.0 + * xref RemovedInSphinx60Warning + * + */ + +/** + * select a different prefix for underscore + */ +$u = _.noConflict(); + + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. 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b/_static/doctools.js @@ -0,0 +1,264 @@ +/* + * doctools.js + * ~~~~~~~~~~~ + * + * Base JavaScript utilities for all Sphinx HTML documentation. + * + * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); + } +}; + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + parent.insertBefore( + span, + parent.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const Documentation = { + init: () => { + Documentation.highlightSearchWords(); + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } + }, + + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; + }, + + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; + }, + + /** + * highlight the search words provided in the url in the text + */ + highlightSearchWords: () => { + const highlight = + new URLSearchParams(window.location.search).get("highlight") || ""; + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

' + + '' + + Documentation.gettext("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + const url = new URL(window.location); + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); + }, + + /** + * Initialise the domain index toggle buttons + */ + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; + + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); + }, + + initOnKeyListeners: () => { + // only install a listener if it is really needed + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; + + const blacklistedElements = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", + ]); + document.addEventListener("keydown", (event) => { + if (blacklistedElements.has(document.activeElement.tagName)) return; // bail for input elements + if (event.altKey || event.ctrlKey || event.metaKey) return; // bail with special keys + + if (!event.shiftKey) { + switch (event.key) { + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); + } + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); + } + break; + case "Escape": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.hideSearchWords(); + event.preventDefault(); + } + } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } + }); + }, +}; + +// quick alias for translations +const _ = Documentation.gettext; + +_ready(Documentation.init); diff --git a/_static/documentation_options.js b/_static/documentation_options.js new file mode 100644 index 0000000..2313d6d --- /dev/null +++ b/_static/documentation_options.js @@ -0,0 +1,14 @@ +var DOCUMENTATION_OPTIONS = { + URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), + VERSION: '2.0', + LANGUAGE: 'en', + COLLAPSE_INDEX: false, + BUILDER: 'html', + FILE_SUFFIX: '.html', + LINK_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '.txt', + NAVIGATION_WITH_KEYS: false, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: true, +}; \ No newline at end of file diff --git a/_static/file.png b/_static/file.png new file mode 100644 index 0000000..a858a41 Binary files /dev/null and b/_static/file.png differ diff --git a/_static/jquery-3.6.0.js b/_static/jquery-3.6.0.js new file mode 100644 index 0000000..fc6c299 --- /dev/null +++ b/_static/jquery-3.6.0.js @@ -0,0 +1,10881 @@ +/*! + * jQuery JavaScript Library v3.6.0 + * https://jquery.com/ + * + * Includes Sizzle.js + * https://sizzlejs.com/ + * + * Copyright OpenJS Foundation and other contributors + * Released under the MIT license + * https://jquery.org/license + * + * Date: 2021-03-02T17:08Z + */ +( function( global, factory ) { + + "use strict"; + + if ( typeof module === "object" && typeof module.exports === "object" ) { + + // For CommonJS and CommonJS-like environments where a proper `window` + // is present, execute the factory and get jQuery. + // For environments that do not have a `window` with a `document` + // (such as Node.js), expose a factory as module.exports. + // This accentuates the need for the creation of a real `window`. + // e.g. var jQuery = require("jquery")(window); + // See ticket #14549 for more info. + module.exports = global.document ? + factory( global, true ) : + function( w ) { + if ( !w.document ) { + throw new Error( "jQuery requires a window with a document" ); + } + return factory( w ); + }; + } else { + factory( global ); + } + +// Pass this if window is not defined yet +} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { + +// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 +// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode +// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common +// enough that all such attempts are guarded in a try block. +"use strict"; + +var arr = []; + +var getProto = Object.getPrototypeOf; + +var slice = arr.slice; + +var flat = arr.flat ? function( array ) { + return arr.flat.call( array ); +} : function( array ) { + return arr.concat.apply( [], array ); +}; + + +var push = arr.push; + +var indexOf = arr.indexOf; + +var class2type = {}; + +var toString = class2type.toString; + +var hasOwn = class2type.hasOwnProperty; + +var fnToString = hasOwn.toString; + +var ObjectFunctionString = fnToString.call( Object ); + +var support = {}; + +var isFunction = function isFunction( obj ) { + + // Support: Chrome <=57, Firefox <=52 + // In some browsers, typeof returns "function" for HTML elements + // (i.e., `typeof document.createElement( "object" ) === "function"`). + // We don't want to classify *any* DOM node as a function. + // Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5 + // Plus for old WebKit, typeof returns "function" for HTML collections + // (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756) + return typeof obj === "function" && typeof obj.nodeType !== "number" && + typeof obj.item !== "function"; + }; + + +var isWindow = function isWindow( obj ) { + return obj != null && obj === obj.window; + }; + + +var document = window.document; + + + + var preservedScriptAttributes = { + type: true, + src: true, + nonce: true, + noModule: true + }; + + function DOMEval( code, node, doc ) { + doc = doc || document; + + var i, val, + script = doc.createElement( "script" ); + + script.text = code; + if ( node ) { + for ( i in preservedScriptAttributes ) { + + // Support: Firefox 64+, Edge 18+ + // Some browsers don't support the "nonce" property on scripts. + // On the other hand, just using `getAttribute` is not enough as + // the `nonce` attribute is reset to an empty string whenever it + // becomes browsing-context connected. + // See https://github.com/whatwg/html/issues/2369 + // See https://html.spec.whatwg.org/#nonce-attributes + // The `node.getAttribute` check was added for the sake of + // `jQuery.globalEval` so that it can fake a nonce-containing node + // via an object. + val = node[ i ] || node.getAttribute && node.getAttribute( i ); + if ( val ) { + script.setAttribute( i, val ); + } + } + } + doc.head.appendChild( script ).parentNode.removeChild( script ); + } + + +function toType( obj ) { + if ( obj == null ) { + return obj + ""; + } + + // Support: Android <=2.3 only (functionish RegExp) + return typeof obj === "object" || typeof obj === "function" ? + class2type[ toString.call( obj ) ] || "object" : + typeof obj; +} +/* global Symbol */ +// Defining this global in .eslintrc.json would create a danger of using the global +// unguarded in another place, it seems safer to define global only for this module + + + +var + version = "3.6.0", + + // Define a local copy of jQuery + jQuery = function( selector, context ) { + + // The jQuery object is actually just the init constructor 'enhanced' + // Need init if jQuery is called (just allow error to be thrown if not included) + return new jQuery.fn.init( selector, context ); + }; + +jQuery.fn = jQuery.prototype = { + + // The current version of jQuery being used + jquery: version, + + constructor: jQuery, + + // The default length of a jQuery object is 0 + length: 0, + + toArray: function() { + return slice.call( this ); + }, + + // Get the Nth element in the matched element set OR + // Get the whole matched element set as a clean array + get: function( num ) { + + // Return all the elements in a clean array + if ( num == null ) { + return slice.call( this ); + } + + // Return just the one element from the set + return num < 0 ? this[ num + this.length ] : this[ num ]; + }, + + // Take an array of elements and push it onto the stack + // (returning the new matched element set) + pushStack: function( elems ) { + + // Build a new jQuery matched element set + var ret = jQuery.merge( this.constructor(), elems ); + + // Add the old object onto the stack (as a reference) + ret.prevObject = this; + + // Return the newly-formed element set + return ret; + }, + + // Execute a callback for every element in the matched set. + each: function( callback ) { + return jQuery.each( this, callback ); + }, + + map: function( callback ) { + return this.pushStack( jQuery.map( this, function( elem, i ) { + return callback.call( elem, i, elem ); + } ) ); + }, + + slice: function() { + return this.pushStack( slice.apply( this, arguments ) ); + }, + + first: function() { + return this.eq( 0 ); + }, + + last: function() { + return this.eq( -1 ); + }, + + even: function() { + return this.pushStack( jQuery.grep( this, function( _elem, i ) { + return ( i + 1 ) % 2; + } ) ); + }, + + odd: function() { + return this.pushStack( jQuery.grep( this, function( _elem, i ) { + return i % 2; + } ) ); + }, + + eq: function( i ) { + var len = this.length, + j = +i + ( i < 0 ? len : 0 ); + return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); + }, + + end: function() { + return this.prevObject || this.constructor(); + }, + + // For internal use only. + // Behaves like an Array's method, not like a jQuery method. + push: push, + sort: arr.sort, + splice: arr.splice +}; + +jQuery.extend = jQuery.fn.extend = function() { + var options, name, src, copy, copyIsArray, clone, + target = arguments[ 0 ] || {}, + i = 1, + length = arguments.length, + deep = false; + + // Handle a deep copy situation + if ( typeof target === "boolean" ) { + deep = target; + + // Skip the boolean and the target + target = arguments[ i ] || {}; + i++; + } + + // Handle case when target is a string or something (possible in deep copy) + if ( typeof target !== "object" && !isFunction( target ) ) { + target = {}; + } + + // Extend jQuery itself if only one argument is passed + if ( i === length ) { + target = this; + i--; + } + + for ( ; i < length; i++ ) { + + // Only deal with non-null/undefined values + if ( ( options = arguments[ i ] ) != null ) { + + // Extend the base object + for ( name in options ) { + copy = options[ name ]; + + // Prevent Object.prototype pollution + // Prevent never-ending loop + if ( name === "__proto__" || target === copy ) { + continue; + } + + // Recurse if we're merging plain objects or arrays + if ( deep && copy && ( jQuery.isPlainObject( copy ) || + ( copyIsArray = Array.isArray( copy ) ) ) ) { + src = target[ name ]; + + // Ensure proper type for the source value + if ( copyIsArray && !Array.isArray( src ) ) { + clone = []; + } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) { + clone = {}; + } else { + clone = src; + } + copyIsArray = false; + + // Never move original objects, clone them + target[ name ] = jQuery.extend( deep, clone, copy ); + + // Don't bring in undefined values + } else if ( copy !== undefined ) { + target[ name ] = copy; + } + } + } + } + + // Return the modified object + return target; +}; + +jQuery.extend( { + + // Unique for each copy of jQuery on the page + expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), + + // Assume jQuery is ready without the ready module + isReady: true, + + error: function( msg ) { + throw new Error( msg ); + }, + + noop: function() {}, + + isPlainObject: function( obj ) { + var proto, Ctor; + + // Detect obvious negatives + // Use toString instead of jQuery.type to catch host objects + if ( !obj || toString.call( obj ) !== "[object Object]" ) { + return false; + } + + proto = getProto( obj ); + + // Objects with no prototype (e.g., `Object.create( null )`) are plain + if ( !proto ) { + return true; + } + + // Objects with prototype are plain iff they were constructed by a global Object function + Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; + return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; + }, + + isEmptyObject: function( obj ) { + var name; + + for ( name in obj ) { + return false; + } + return true; + }, + + // Evaluates a script in a provided context; falls back to the global one + // if not specified. + globalEval: function( code, options, doc ) { + DOMEval( code, { nonce: options && options.nonce }, doc ); + }, + + each: function( obj, callback ) { + var length, i = 0; + + if ( isArrayLike( obj ) ) { + length = obj.length; + for ( ; i < length; i++ ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } else { + for ( i in obj ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } + + return obj; + }, + + // results is for internal usage only + makeArray: function( arr, results ) { + var ret = results || []; + + if ( arr != null ) { + if ( isArrayLike( Object( arr ) ) ) { + jQuery.merge( ret, + typeof arr === "string" ? + [ arr ] : arr + ); + } else { + push.call( ret, arr ); + } + } + + return ret; + }, + + inArray: function( elem, arr, i ) { + return arr == null ? -1 : indexOf.call( arr, elem, i ); + }, + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + merge: function( first, second ) { + var len = +second.length, + j = 0, + i = first.length; + + for ( ; j < len; j++ ) { + first[ i++ ] = second[ j ]; + } + + first.length = i; + + return first; + }, + + grep: function( elems, callback, invert ) { + var callbackInverse, + matches = [], + i = 0, + length = elems.length, + callbackExpect = !invert; + + // Go through the array, only saving the items + // that pass the validator function + for ( ; i < length; i++ ) { + callbackInverse = !callback( elems[ i ], i ); + if ( callbackInverse !== callbackExpect ) { + matches.push( elems[ i ] ); + } + } + + return matches; + }, + + // arg is for internal usage only + map: function( elems, callback, arg ) { + var length, value, + i = 0, + ret = []; + + // Go through the array, translating each of the items to their new values + if ( isArrayLike( elems ) ) { + length = elems.length; + for ( ; i < length; i++ ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + + // Go through every key on the object, + } else { + for ( i in elems ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + } + + // Flatten any nested arrays + return flat( ret ); + }, + + // A global GUID counter for objects + guid: 1, + + // jQuery.support is not used in Core but other projects attach their + // properties to it so it needs to exist. + support: support +} ); + +if ( typeof Symbol === "function" ) { + jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; +} + +// Populate the class2type map +jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), + function( _i, name ) { + class2type[ "[object " + name + "]" ] = name.toLowerCase(); + } ); + +function isArrayLike( obj ) { + + // Support: real iOS 8.2 only (not reproducible in simulator) + // `in` check used to prevent JIT error (gh-2145) + // hasOwn isn't used here due to false negatives + // regarding Nodelist length in IE + var length = !!obj && "length" in obj && obj.length, + type = toType( obj ); + + if ( isFunction( obj ) || isWindow( obj ) ) { + return false; + } + + return type === "array" || length === 0 || + typeof length === "number" && length > 0 && ( length - 1 ) in obj; +} +var Sizzle = +/*! + * Sizzle CSS Selector Engine v2.3.6 + * https://sizzlejs.com/ + * + * Copyright JS Foundation and other contributors + * Released under the MIT license + * https://js.foundation/ + * + * Date: 2021-02-16 + */ +( function( window ) { +var i, + support, + Expr, + getText, + isXML, + tokenize, + compile, + select, + outermostContext, + sortInput, + hasDuplicate, + + // Local document vars + setDocument, + document, + docElem, + documentIsHTML, + rbuggyQSA, + rbuggyMatches, + matches, + contains, + + // Instance-specific data + expando = "sizzle" + 1 * new Date(), + preferredDoc = window.document, + dirruns = 0, + done = 0, + classCache = createCache(), + tokenCache = createCache(), + compilerCache = createCache(), + nonnativeSelectorCache = createCache(), + sortOrder = function( a, b ) { + if ( a === b ) { + hasDuplicate = true; + } + return 0; + }, + + // Instance methods + hasOwn = ( {} ).hasOwnProperty, + arr = [], + pop = arr.pop, + pushNative = arr.push, + push = arr.push, + slice = arr.slice, + + // Use a stripped-down indexOf as it's faster than native + // https://jsperf.com/thor-indexof-vs-for/5 + indexOf = function( list, elem ) { + var i = 0, + len = list.length; + for ( ; i < len; i++ ) { + if ( list[ i ] === elem ) { + return i; + } + } + return -1; + }, + + booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" + + "ismap|loop|multiple|open|readonly|required|scoped", + + // Regular expressions + + // http://www.w3.org/TR/css3-selectors/#whitespace + whitespace = "[\\x20\\t\\r\\n\\f]", + + // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram + identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace + + "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+", + + // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors + attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + + + // Operator (capture 2) + "*([*^$|!~]?=)" + whitespace + + + // "Attribute values must be CSS identifiers [capture 5] + // or strings [capture 3 or capture 4]" + "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + + whitespace + "*\\]", + + pseudos = ":(" + identifier + ")(?:\\((" + + + // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: + // 1. quoted (capture 3; capture 4 or capture 5) + "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + + + // 2. simple (capture 6) + "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + + + // 3. anything else (capture 2) + ".*" + + ")\\)|)", + + // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter + rwhitespace = new RegExp( whitespace + "+", "g" ), + rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + + whitespace + "+$", "g" ), + + rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), + rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + + "*" ), + rdescend = new RegExp( whitespace + "|>" ), + + rpseudo = new RegExp( pseudos ), + ridentifier = new RegExp( "^" + identifier + "$" ), + + matchExpr = { + "ID": new RegExp( "^#(" + identifier + ")" ), + "CLASS": new RegExp( "^\\.(" + identifier + ")" ), + "TAG": new RegExp( "^(" + identifier + "|[*])" ), + "ATTR": new RegExp( "^" + attributes ), + "PSEUDO": new RegExp( "^" + pseudos ), + "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + + whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + + whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), + "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), + + // For use in libraries implementing .is() + // We use this for POS matching in `select` + "needsContext": new RegExp( "^" + whitespace + + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace + + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) + }, + + rhtml = /HTML$/i, + rinputs = /^(?:input|select|textarea|button)$/i, + rheader = /^h\d$/i, + + rnative = /^[^{]+\{\s*\[native \w/, + + // Easily-parseable/retrievable ID or TAG or CLASS selectors + rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, + + rsibling = /[+~]/, + + // CSS escapes + // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters + runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ), + funescape = function( escape, nonHex ) { + var high = "0x" + escape.slice( 1 ) - 0x10000; + + return nonHex ? + + // Strip the backslash prefix from a non-hex escape sequence + nonHex : + + // Replace a hexadecimal escape sequence with the encoded Unicode code point + // Support: IE <=11+ + // For values outside the Basic Multilingual Plane (BMP), manually construct a + // surrogate pair + high < 0 ? + String.fromCharCode( high + 0x10000 ) : + String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); + }, + + // CSS string/identifier serialization + // https://drafts.csswg.org/cssom/#common-serializing-idioms + rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, + fcssescape = function( ch, asCodePoint ) { + if ( asCodePoint ) { + + // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER + if ( ch === "\0" ) { + return "\uFFFD"; + } + + // Control characters and (dependent upon position) numbers get escaped as code points + return ch.slice( 0, -1 ) + "\\" + + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; + } + + // Other potentially-special ASCII characters get backslash-escaped + return "\\" + ch; + }, + + // Used for iframes + // See setDocument() + // Removing the function wrapper causes a "Permission Denied" + // error in IE + unloadHandler = function() { + setDocument(); + }, + + inDisabledFieldset = addCombinator( + function( elem ) { + return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; + }, + { dir: "parentNode", next: "legend" } + ); + +// Optimize for push.apply( _, NodeList ) +try { + push.apply( + ( arr = slice.call( preferredDoc.childNodes ) ), + preferredDoc.childNodes + ); + + // Support: Android<4.0 + // Detect silently failing push.apply + // eslint-disable-next-line no-unused-expressions + arr[ preferredDoc.childNodes.length ].nodeType; +} catch ( e ) { + push = { apply: arr.length ? + + // Leverage slice if possible + function( target, els ) { + pushNative.apply( target, slice.call( els ) ); + } : + + // Support: IE<9 + // Otherwise append directly + function( target, els ) { + var j = target.length, + i = 0; + + // Can't trust NodeList.length + while ( ( target[ j++ ] = els[ i++ ] ) ) {} + target.length = j - 1; + } + }; +} + +function Sizzle( selector, context, results, seed ) { + var m, i, elem, nid, match, groups, newSelector, + newContext = context && context.ownerDocument, + + // nodeType defaults to 9, since context defaults to document + nodeType = context ? context.nodeType : 9; + + results = results || []; + + // Return early from calls with invalid selector or context + if ( typeof selector !== "string" || !selector || + nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { + + return results; + } + + // Try to shortcut find operations (as opposed to filters) in HTML documents + if ( !seed ) { + setDocument( context ); + context = context || document; + + if ( documentIsHTML ) { + + // If the selector is sufficiently simple, try using a "get*By*" DOM method + // (excepting DocumentFragment context, where the methods don't exist) + if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) { + + // ID selector + if ( ( m = match[ 1 ] ) ) { + + // Document context + if ( nodeType === 9 ) { + if ( ( elem = context.getElementById( m ) ) ) { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( elem.id === m ) { + results.push( elem ); + return results; + } + } else { + return results; + } + + // Element context + } else { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( newContext && ( elem = newContext.getElementById( m ) ) && + contains( context, elem ) && + elem.id === m ) { + + results.push( elem ); + return results; + } + } + + // Type selector + } else if ( match[ 2 ] ) { + push.apply( results, context.getElementsByTagName( selector ) ); + return results; + + // Class selector + } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName && + context.getElementsByClassName ) { + + push.apply( results, context.getElementsByClassName( m ) ); + return results; + } + } + + // Take advantage of querySelectorAll + if ( support.qsa && + !nonnativeSelectorCache[ selector + " " ] && + ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) && + + // Support: IE 8 only + // Exclude object elements + ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) { + + newSelector = selector; + newContext = context; + + // qSA considers elements outside a scoping root when evaluating child or + // descendant combinators, which is not what we want. + // In such cases, we work around the behavior by prefixing every selector in the + // list with an ID selector referencing the scope context. + // The technique has to be used as well when a leading combinator is used + // as such selectors are not recognized by querySelectorAll. + // Thanks to Andrew Dupont for this technique. + if ( nodeType === 1 && + ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) { + + // Expand context for sibling selectors + newContext = rsibling.test( selector ) && testContext( context.parentNode ) || + context; + + // We can use :scope instead of the ID hack if the browser + // supports it & if we're not changing the context. + if ( newContext !== context || !support.scope ) { + + // Capture the context ID, setting it first if necessary + if ( ( nid = context.getAttribute( "id" ) ) ) { + nid = nid.replace( rcssescape, fcssescape ); + } else { + context.setAttribute( "id", ( nid = expando ) ); + } + } + + // Prefix every selector in the list + groups = tokenize( selector ); + i = groups.length; + while ( i-- ) { + groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " + + toSelector( groups[ i ] ); + } + newSelector = groups.join( "," ); + } + + try { + push.apply( results, + newContext.querySelectorAll( newSelector ) + ); + return results; + } catch ( qsaError ) { + nonnativeSelectorCache( selector, true ); + } finally { + if ( nid === expando ) { + context.removeAttribute( "id" ); + } + } + } + } + } + + // All others + return select( selector.replace( rtrim, "$1" ), context, results, seed ); +} + +/** + * Create key-value caches of limited size + * @returns {function(string, object)} Returns the Object data after storing it on itself with + * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) + * deleting the oldest entry + */ +function createCache() { + var keys = []; + + function cache( key, value ) { + + // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) + if ( keys.push( key + " " ) > Expr.cacheLength ) { + + // Only keep the most recent entries + delete cache[ keys.shift() ]; + } + return ( cache[ key + " " ] = value ); + } + return cache; +} + +/** + * Mark a function for special use by Sizzle + * @param {Function} fn The function to mark + */ +function markFunction( fn ) { + fn[ expando ] = true; + return fn; +} + +/** + * Support testing using an element + * @param {Function} fn Passed the created element and returns a boolean result + */ +function assert( fn ) { + var el = document.createElement( "fieldset" ); + + try { + return !!fn( el ); + } catch ( e ) { + return false; + } finally { + + // Remove from its parent by default + if ( el.parentNode ) { + el.parentNode.removeChild( el ); + } + + // release memory in IE + el = null; + } +} + +/** + * Adds the same handler for all of the specified attrs + * @param {String} attrs Pipe-separated list of attributes + * @param {Function} handler The method that will be applied + */ +function addHandle( attrs, handler ) { + var arr = attrs.split( "|" ), + i = arr.length; + + while ( i-- ) { + Expr.attrHandle[ arr[ i ] ] = handler; + } +} + +/** + * Checks document order of two siblings + * @param {Element} a + * @param {Element} b + * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b + */ +function siblingCheck( a, b ) { + var cur = b && a, + diff = cur && a.nodeType === 1 && b.nodeType === 1 && + a.sourceIndex - b.sourceIndex; + + // Use IE sourceIndex if available on both nodes + if ( diff ) { + return diff; + } + + // Check if b follows a + if ( cur ) { + while ( ( cur = cur.nextSibling ) ) { + if ( cur === b ) { + return -1; + } + } + } + + return a ? 1 : -1; +} + +/** + * Returns a function to use in pseudos for input types + * @param {String} type + */ +function createInputPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for buttons + * @param {String} type + */ +function createButtonPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return ( name === "input" || name === "button" ) && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for :enabled/:disabled + * @param {Boolean} disabled true for :disabled; false for :enabled + */ +function createDisabledPseudo( disabled ) { + + // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable + return function( elem ) { + + // Only certain elements can match :enabled or :disabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled + if ( "form" in elem ) { + + // Check for inherited disabledness on relevant non-disabled elements: + // * listed form-associated elements in a disabled fieldset + // https://html.spec.whatwg.org/multipage/forms.html#category-listed + // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled + // * option elements in a disabled optgroup + // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled + // All such elements have a "form" property. + if ( elem.parentNode && elem.disabled === false ) { + + // Option elements defer to a parent optgroup if present + if ( "label" in elem ) { + if ( "label" in elem.parentNode ) { + return elem.parentNode.disabled === disabled; + } else { + return elem.disabled === disabled; + } + } + + // Support: IE 6 - 11 + // Use the isDisabled shortcut property to check for disabled fieldset ancestors + return elem.isDisabled === disabled || + + // Where there is no isDisabled, check manually + /* jshint -W018 */ + elem.isDisabled !== !disabled && + inDisabledFieldset( elem ) === disabled; + } + + return elem.disabled === disabled; + + // Try to winnow out elements that can't be disabled before trusting the disabled property. + // Some victims get caught in our net (label, legend, menu, track), but it shouldn't + // even exist on them, let alone have a boolean value. + } else if ( "label" in elem ) { + return elem.disabled === disabled; + } + + // Remaining elements are neither :enabled nor :disabled + return false; + }; +} + +/** + * Returns a function to use in pseudos for positionals + * @param {Function} fn + */ +function createPositionalPseudo( fn ) { + return markFunction( function( argument ) { + argument = +argument; + return markFunction( function( seed, matches ) { + var j, + matchIndexes = fn( [], seed.length, argument ), + i = matchIndexes.length; + + // Match elements found at the specified indexes + while ( i-- ) { + if ( seed[ ( j = matchIndexes[ i ] ) ] ) { + seed[ j ] = !( matches[ j ] = seed[ j ] ); + } + } + } ); + } ); +} + +/** + * Checks a node for validity as a Sizzle context + * @param {Element|Object=} context + * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value + */ +function testContext( context ) { + return context && typeof context.getElementsByTagName !== "undefined" && context; +} + +// Expose support vars for convenience +support = Sizzle.support = {}; + +/** + * Detects XML nodes + * @param {Element|Object} elem An element or a document + * @returns {Boolean} True iff elem is a non-HTML XML node + */ +isXML = Sizzle.isXML = function( elem ) { + var namespace = elem && elem.namespaceURI, + docElem = elem && ( elem.ownerDocument || elem ).documentElement; + + // Support: IE <=8 + // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes + // https://bugs.jquery.com/ticket/4833 + return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); +}; + +/** + * Sets document-related variables once based on the current document + * @param {Element|Object} [doc] An element or document object to use to set the document + * @returns {Object} Returns the current document + */ +setDocument = Sizzle.setDocument = function( node ) { + var hasCompare, subWindow, + doc = node ? node.ownerDocument || node : preferredDoc; + + // Return early if doc is invalid or already selected + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) { + return document; + } + + // Update global variables + document = doc; + docElem = document.documentElement; + documentIsHTML = !isXML( document ); + + // Support: IE 9 - 11+, Edge 12 - 18+ + // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( preferredDoc != document && + ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) { + + // Support: IE 11, Edge + if ( subWindow.addEventListener ) { + subWindow.addEventListener( "unload", unloadHandler, false ); + + // Support: IE 9 - 10 only + } else if ( subWindow.attachEvent ) { + subWindow.attachEvent( "onunload", unloadHandler ); + } + } + + // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only, + // Safari 4 - 5 only, Opera <=11.6 - 12.x only + // IE/Edge & older browsers don't support the :scope pseudo-class. + // Support: Safari 6.0 only + // Safari 6.0 supports :scope but it's an alias of :root there. + support.scope = assert( function( el ) { + docElem.appendChild( el ).appendChild( document.createElement( "div" ) ); + return typeof el.querySelectorAll !== "undefined" && + !el.querySelectorAll( ":scope fieldset div" ).length; + } ); + + /* Attributes + ---------------------------------------------------------------------- */ + + // Support: IE<8 + // Verify that getAttribute really returns attributes and not properties + // (excepting IE8 booleans) + support.attributes = assert( function( el ) { + el.className = "i"; + return !el.getAttribute( "className" ); + } ); + + /* getElement(s)By* + ---------------------------------------------------------------------- */ + + // Check if getElementsByTagName("*") returns only elements + support.getElementsByTagName = assert( function( el ) { + el.appendChild( document.createComment( "" ) ); + return !el.getElementsByTagName( "*" ).length; + } ); + + // Support: IE<9 + support.getElementsByClassName = rnative.test( document.getElementsByClassName ); + + // Support: IE<10 + // Check if getElementById returns elements by name + // The broken getElementById methods don't pick up programmatically-set names, + // so use a roundabout getElementsByName test + support.getById = assert( function( el ) { + docElem.appendChild( el ).id = expando; + return !document.getElementsByName || !document.getElementsByName( expando ).length; + } ); + + // ID filter and find + if ( support.getById ) { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + return elem.getAttribute( "id" ) === attrId; + }; + }; + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var elem = context.getElementById( id ); + return elem ? [ elem ] : []; + } + }; + } else { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + var node = typeof elem.getAttributeNode !== "undefined" && + elem.getAttributeNode( "id" ); + return node && node.value === attrId; + }; + }; + + // Support: IE 6 - 7 only + // getElementById is not reliable as a find shortcut + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var node, i, elems, + elem = context.getElementById( id ); + + if ( elem ) { + + // Verify the id attribute + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + + // Fall back on getElementsByName + elems = context.getElementsByName( id ); + i = 0; + while ( ( elem = elems[ i++ ] ) ) { + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + } + } + + return []; + } + }; + } + + // Tag + Expr.find[ "TAG" ] = support.getElementsByTagName ? + function( tag, context ) { + if ( typeof context.getElementsByTagName !== "undefined" ) { + return context.getElementsByTagName( tag ); + + // DocumentFragment nodes don't have gEBTN + } else if ( support.qsa ) { + return context.querySelectorAll( tag ); + } + } : + + function( tag, context ) { + var elem, + tmp = [], + i = 0, + + // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too + results = context.getElementsByTagName( tag ); + + // Filter out possible comments + if ( tag === "*" ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem.nodeType === 1 ) { + tmp.push( elem ); + } + } + + return tmp; + } + return results; + }; + + // Class + Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) { + if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { + return context.getElementsByClassName( className ); + } + }; + + /* QSA/matchesSelector + ---------------------------------------------------------------------- */ + + // QSA and matchesSelector support + + // matchesSelector(:active) reports false when true (IE9/Opera 11.5) + rbuggyMatches = []; + + // qSa(:focus) reports false when true (Chrome 21) + // We allow this because of a bug in IE8/9 that throws an error + // whenever `document.activeElement` is accessed on an iframe + // So, we allow :focus to pass through QSA all the time to avoid the IE error + // See https://bugs.jquery.com/ticket/13378 + rbuggyQSA = []; + + if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) { + + // Build QSA regex + // Regex strategy adopted from Diego Perini + assert( function( el ) { + + var input; + + // Select is set to empty string on purpose + // This is to test IE's treatment of not explicitly + // setting a boolean content attribute, + // since its presence should be enough + // https://bugs.jquery.com/ticket/12359 + docElem.appendChild( el ).innerHTML = "" + + ""; + + // Support: IE8, Opera 11-12.16 + // Nothing should be selected when empty strings follow ^= or $= or *= + // The test attribute must be unknown in Opera but "safe" for WinRT + // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section + if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) { + rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); + } + + // Support: IE8 + // Boolean attributes and "value" are not treated correctly + if ( !el.querySelectorAll( "[selected]" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); + } + + // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ + if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { + rbuggyQSA.push( "~=" ); + } + + // Support: IE 11+, Edge 15 - 18+ + // IE 11/Edge don't find elements on a `[name='']` query in some cases. + // Adding a temporary attribute to the document before the selection works + // around the issue. + // Interestingly, IE 10 & older don't seem to have the issue. + input = document.createElement( "input" ); + input.setAttribute( "name", "" ); + el.appendChild( input ); + if ( !el.querySelectorAll( "[name='']" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" + + whitespace + "*(?:''|\"\")" ); + } + + // Webkit/Opera - :checked should return selected option elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + // IE8 throws error here and will not see later tests + if ( !el.querySelectorAll( ":checked" ).length ) { + rbuggyQSA.push( ":checked" ); + } + + // Support: Safari 8+, iOS 8+ + // https://bugs.webkit.org/show_bug.cgi?id=136851 + // In-page `selector#id sibling-combinator selector` fails + if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { + rbuggyQSA.push( ".#.+[+~]" ); + } + + // Support: Firefox <=3.6 - 5 only + // Old Firefox doesn't throw on a badly-escaped identifier. + el.querySelectorAll( "\\\f" ); + rbuggyQSA.push( "[\\r\\n\\f]" ); + } ); + + assert( function( el ) { + el.innerHTML = "" + + ""; + + // Support: Windows 8 Native Apps + // The type and name attributes are restricted during .innerHTML assignment + var input = document.createElement( "input" ); + input.setAttribute( "type", "hidden" ); + el.appendChild( input ).setAttribute( "name", "D" ); + + // Support: IE8 + // Enforce case-sensitivity of name attribute + if ( el.querySelectorAll( "[name=d]" ).length ) { + rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); + } + + // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) + // IE8 throws error here and will not see later tests + if ( el.querySelectorAll( ":enabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: IE9-11+ + // IE's :disabled selector does not pick up the children of disabled fieldsets + docElem.appendChild( el ).disabled = true; + if ( el.querySelectorAll( ":disabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: Opera 10 - 11 only + // Opera 10-11 does not throw on post-comma invalid pseudos + el.querySelectorAll( "*,:x" ); + rbuggyQSA.push( ",.*:" ); + } ); + } + + if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches || + docElem.webkitMatchesSelector || + docElem.mozMatchesSelector || + docElem.oMatchesSelector || + docElem.msMatchesSelector ) ) ) ) { + + assert( function( el ) { + + // Check to see if it's possible to do matchesSelector + // on a disconnected node (IE 9) + support.disconnectedMatch = matches.call( el, "*" ); + + // This should fail with an exception + // Gecko does not error, returns false instead + matches.call( el, "[s!='']:x" ); + rbuggyMatches.push( "!=", pseudos ); + } ); + } + + rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) ); + rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) ); + + /* Contains + ---------------------------------------------------------------------- */ + hasCompare = rnative.test( docElem.compareDocumentPosition ); + + // Element contains another + // Purposefully self-exclusive + // As in, an element does not contain itself + contains = hasCompare || rnative.test( docElem.contains ) ? + function( a, b ) { + var adown = a.nodeType === 9 ? a.documentElement : a, + bup = b && b.parentNode; + return a === bup || !!( bup && bup.nodeType === 1 && ( + adown.contains ? + adown.contains( bup ) : + a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 + ) ); + } : + function( a, b ) { + if ( b ) { + while ( ( b = b.parentNode ) ) { + if ( b === a ) { + return true; + } + } + } + return false; + }; + + /* Sorting + ---------------------------------------------------------------------- */ + + // Document order sorting + sortOrder = hasCompare ? + function( a, b ) { + + // Flag for duplicate removal + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + // Sort on method existence if only one input has compareDocumentPosition + var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; + if ( compare ) { + return compare; + } + + // Calculate position if both inputs belong to the same document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ? + a.compareDocumentPosition( b ) : + + // Otherwise we know they are disconnected + 1; + + // Disconnected nodes + if ( compare & 1 || + ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) { + + // Choose the first element that is related to our preferred document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( a == document || a.ownerDocument == preferredDoc && + contains( preferredDoc, a ) ) { + return -1; + } + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( b == document || b.ownerDocument == preferredDoc && + contains( preferredDoc, b ) ) { + return 1; + } + + // Maintain original order + return sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + } + + return compare & 4 ? -1 : 1; + } : + function( a, b ) { + + // Exit early if the nodes are identical + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + var cur, + i = 0, + aup = a.parentNode, + bup = b.parentNode, + ap = [ a ], + bp = [ b ]; + + // Parentless nodes are either documents or disconnected + if ( !aup || !bup ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + return a == document ? -1 : + b == document ? 1 : + /* eslint-enable eqeqeq */ + aup ? -1 : + bup ? 1 : + sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + + // If the nodes are siblings, we can do a quick check + } else if ( aup === bup ) { + return siblingCheck( a, b ); + } + + // Otherwise we need full lists of their ancestors for comparison + cur = a; + while ( ( cur = cur.parentNode ) ) { + ap.unshift( cur ); + } + cur = b; + while ( ( cur = cur.parentNode ) ) { + bp.unshift( cur ); + } + + // Walk down the tree looking for a discrepancy + while ( ap[ i ] === bp[ i ] ) { + i++; + } + + return i ? + + // Do a sibling check if the nodes have a common ancestor + siblingCheck( ap[ i ], bp[ i ] ) : + + // Otherwise nodes in our document sort first + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + ap[ i ] == preferredDoc ? -1 : + bp[ i ] == preferredDoc ? 1 : + /* eslint-enable eqeqeq */ + 0; + }; + + return document; +}; + +Sizzle.matches = function( expr, elements ) { + return Sizzle( expr, null, null, elements ); +}; + +Sizzle.matchesSelector = function( elem, expr ) { + setDocument( elem ); + + if ( support.matchesSelector && documentIsHTML && + !nonnativeSelectorCache[ expr + " " ] && + ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && + ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { + + try { + var ret = matches.call( elem, expr ); + + // IE 9's matchesSelector returns false on disconnected nodes + if ( ret || support.disconnectedMatch || + + // As well, disconnected nodes are said to be in a document + // fragment in IE 9 + elem.document && elem.document.nodeType !== 11 ) { + return ret; + } + } catch ( e ) { + nonnativeSelectorCache( expr, true ); + } + } + + return Sizzle( expr, document, null, [ elem ] ).length > 0; +}; + +Sizzle.contains = function( context, elem ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( context.ownerDocument || context ) != document ) { + setDocument( context ); + } + return contains( context, elem ); +}; + +Sizzle.attr = function( elem, name ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( elem.ownerDocument || elem ) != document ) { + setDocument( elem ); + } + + var fn = Expr.attrHandle[ name.toLowerCase() ], + + // Don't get fooled by Object.prototype properties (jQuery #13807) + val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? + fn( elem, name, !documentIsHTML ) : + undefined; + + return val !== undefined ? + val : + support.attributes || !documentIsHTML ? + elem.getAttribute( name ) : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; +}; + +Sizzle.escape = function( sel ) { + return ( sel + "" ).replace( rcssescape, fcssescape ); +}; + +Sizzle.error = function( msg ) { + throw new Error( "Syntax error, unrecognized expression: " + msg ); +}; + +/** + * Document sorting and removing duplicates + * @param {ArrayLike} results + */ +Sizzle.uniqueSort = function( results ) { + var elem, + duplicates = [], + j = 0, + i = 0; + + // Unless we *know* we can detect duplicates, assume their presence + hasDuplicate = !support.detectDuplicates; + sortInput = !support.sortStable && results.slice( 0 ); + results.sort( sortOrder ); + + if ( hasDuplicate ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem === results[ i ] ) { + j = duplicates.push( i ); + } + } + while ( j-- ) { + results.splice( duplicates[ j ], 1 ); + } + } + + // Clear input after sorting to release objects + // See https://github.com/jquery/sizzle/pull/225 + sortInput = null; + + return results; +}; + +/** + * Utility function for retrieving the text value of an array of DOM nodes + * @param {Array|Element} elem + */ +getText = Sizzle.getText = function( elem ) { + var node, + ret = "", + i = 0, + nodeType = elem.nodeType; + + if ( !nodeType ) { + + // If no nodeType, this is expected to be an array + while ( ( node = elem[ i++ ] ) ) { + + // Do not traverse comment nodes + ret += getText( node ); + } + } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { + + // Use textContent for elements + // innerText usage removed for consistency of new lines (jQuery #11153) + if ( typeof elem.textContent === "string" ) { + return elem.textContent; + } else { + + // Traverse its children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + ret += getText( elem ); + } + } + } else if ( nodeType === 3 || nodeType === 4 ) { + return elem.nodeValue; + } + + // Do not include comment or processing instruction nodes + + return ret; +}; + +Expr = Sizzle.selectors = { + + // Can be adjusted by the user + cacheLength: 50, + + createPseudo: markFunction, + + match: matchExpr, + + attrHandle: {}, + + find: {}, + + relative: { + ">": { dir: "parentNode", first: true }, + " ": { dir: "parentNode" }, + "+": { dir: "previousSibling", first: true }, + "~": { dir: "previousSibling" } + }, + + preFilter: { + "ATTR": function( match ) { + match[ 1 ] = match[ 1 ].replace( runescape, funescape ); + + // Move the given value to match[3] whether quoted or unquoted + match[ 3 ] = ( match[ 3 ] || match[ 4 ] || + match[ 5 ] || "" ).replace( runescape, funescape ); + + if ( match[ 2 ] === "~=" ) { + match[ 3 ] = " " + match[ 3 ] + " "; + } + + return match.slice( 0, 4 ); + }, + + "CHILD": function( match ) { + + /* matches from matchExpr["CHILD"] + 1 type (only|nth|...) + 2 what (child|of-type) + 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) + 4 xn-component of xn+y argument ([+-]?\d*n|) + 5 sign of xn-component + 6 x of xn-component + 7 sign of y-component + 8 y of y-component + */ + match[ 1 ] = match[ 1 ].toLowerCase(); + + if ( match[ 1 ].slice( 0, 3 ) === "nth" ) { + + // nth-* requires argument + if ( !match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + // numeric x and y parameters for Expr.filter.CHILD + // remember that false/true cast respectively to 0/1 + match[ 4 ] = +( match[ 4 ] ? + match[ 5 ] + ( match[ 6 ] || 1 ) : + 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) ); + match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" ); + + // other types prohibit arguments + } else if ( match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + return match; + }, + + "PSEUDO": function( match ) { + var excess, + unquoted = !match[ 6 ] && match[ 2 ]; + + if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) { + return null; + } + + // Accept quoted arguments as-is + if ( match[ 3 ] ) { + match[ 2 ] = match[ 4 ] || match[ 5 ] || ""; + + // Strip excess characters from unquoted arguments + } else if ( unquoted && rpseudo.test( unquoted ) && + + // Get excess from tokenize (recursively) + ( excess = tokenize( unquoted, true ) ) && + + // advance to the next closing parenthesis + ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) { + + // excess is a negative index + match[ 0 ] = match[ 0 ].slice( 0, excess ); + match[ 2 ] = unquoted.slice( 0, excess ); + } + + // Return only captures needed by the pseudo filter method (type and argument) + return match.slice( 0, 3 ); + } + }, + + filter: { + + "TAG": function( nodeNameSelector ) { + var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); + return nodeNameSelector === "*" ? + function() { + return true; + } : + function( elem ) { + return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; + }; + }, + + "CLASS": function( className ) { + var pattern = classCache[ className + " " ]; + + return pattern || + ( pattern = new RegExp( "(^|" + whitespace + + ")" + className + "(" + whitespace + "|$)" ) ) && classCache( + className, function( elem ) { + return pattern.test( + typeof elem.className === "string" && elem.className || + typeof elem.getAttribute !== "undefined" && + elem.getAttribute( "class" ) || + "" + ); + } ); + }, + + "ATTR": function( name, operator, check ) { + return function( elem ) { + var result = Sizzle.attr( elem, name ); + + if ( result == null ) { + return operator === "!="; + } + if ( !operator ) { + return true; + } + + result += ""; + + /* eslint-disable max-len */ + + return operator === "=" ? result === check : + operator === "!=" ? result !== check : + operator === "^=" ? check && result.indexOf( check ) === 0 : + operator === "*=" ? check && result.indexOf( check ) > -1 : + operator === "$=" ? check && result.slice( -check.length ) === check : + operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : + operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : + false; + /* eslint-enable max-len */ + + }; + }, + + "CHILD": function( type, what, _argument, first, last ) { + var simple = type.slice( 0, 3 ) !== "nth", + forward = type.slice( -4 ) !== "last", + ofType = what === "of-type"; + + return first === 1 && last === 0 ? + + // Shortcut for :nth-*(n) + function( elem ) { + return !!elem.parentNode; + } : + + function( elem, _context, xml ) { + var cache, uniqueCache, outerCache, node, nodeIndex, start, + dir = simple !== forward ? "nextSibling" : "previousSibling", + parent = elem.parentNode, + name = ofType && elem.nodeName.toLowerCase(), + useCache = !xml && !ofType, + diff = false; + + if ( parent ) { + + // :(first|last|only)-(child|of-type) + if ( simple ) { + while ( dir ) { + node = elem; + while ( ( node = node[ dir ] ) ) { + if ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) { + + return false; + } + } + + // Reverse direction for :only-* (if we haven't yet done so) + start = dir = type === "only" && !start && "nextSibling"; + } + return true; + } + + start = [ forward ? parent.firstChild : parent.lastChild ]; + + // non-xml :nth-child(...) stores cache data on `parent` + if ( forward && useCache ) { + + // Seek `elem` from a previously-cached index + + // ...in a gzip-friendly way + node = parent; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex && cache[ 2 ]; + node = nodeIndex && parent.childNodes[ nodeIndex ]; + + while ( ( node = ++nodeIndex && node && node[ dir ] || + + // Fallback to seeking `elem` from the start + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + // When found, cache indexes on `parent` and break + if ( node.nodeType === 1 && ++diff && node === elem ) { + uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; + break; + } + } + + } else { + + // Use previously-cached element index if available + if ( useCache ) { + + // ...in a gzip-friendly way + node = elem; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex; + } + + // xml :nth-child(...) + // or :nth-last-child(...) or :nth(-last)?-of-type(...) + if ( diff === false ) { + + // Use the same loop as above to seek `elem` from the start + while ( ( node = ++nodeIndex && node && node[ dir ] || + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + if ( ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) && + ++diff ) { + + // Cache the index of each encountered element + if ( useCache ) { + outerCache = node[ expando ] || + ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + uniqueCache[ type ] = [ dirruns, diff ]; + } + + if ( node === elem ) { + break; + } + } + } + } + } + + // Incorporate the offset, then check against cycle size + diff -= last; + return diff === first || ( diff % first === 0 && diff / first >= 0 ); + } + }; + }, + + "PSEUDO": function( pseudo, argument ) { + + // pseudo-class names are case-insensitive + // http://www.w3.org/TR/selectors/#pseudo-classes + // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters + // Remember that setFilters inherits from pseudos + var args, + fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || + Sizzle.error( "unsupported pseudo: " + pseudo ); + + // The user may use createPseudo to indicate that + // arguments are needed to create the filter function + // just as Sizzle does + if ( fn[ expando ] ) { + return fn( argument ); + } + + // But maintain support for old signatures + if ( fn.length > 1 ) { + args = [ pseudo, pseudo, "", argument ]; + return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? + markFunction( function( seed, matches ) { + var idx, + matched = fn( seed, argument ), + i = matched.length; + while ( i-- ) { + idx = indexOf( seed, matched[ i ] ); + seed[ idx ] = !( matches[ idx ] = matched[ i ] ); + } + } ) : + function( elem ) { + return fn( elem, 0, args ); + }; + } + + return fn; + } + }, + + pseudos: { + + // Potentially complex pseudos + "not": markFunction( function( selector ) { + + // Trim the selector passed to compile + // to avoid treating leading and trailing + // spaces as combinators + var input = [], + results = [], + matcher = compile( selector.replace( rtrim, "$1" ) ); + + return matcher[ expando ] ? + markFunction( function( seed, matches, _context, xml ) { + var elem, + unmatched = matcher( seed, null, xml, [] ), + i = seed.length; + + // Match elements unmatched by `matcher` + while ( i-- ) { + if ( ( elem = unmatched[ i ] ) ) { + seed[ i ] = !( matches[ i ] = elem ); + } + } + } ) : + function( elem, _context, xml ) { + input[ 0 ] = elem; + matcher( input, null, xml, results ); + + // Don't keep the element (issue #299) + input[ 0 ] = null; + return !results.pop(); + }; + } ), + + "has": markFunction( function( selector ) { + return function( elem ) { + return Sizzle( selector, elem ).length > 0; + }; + } ), + + "contains": markFunction( function( text ) { + text = text.replace( runescape, funescape ); + return function( elem ) { + return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; + }; + } ), + + // "Whether an element is represented by a :lang() selector + // is based solely on the element's language value + // being equal to the identifier C, + // or beginning with the identifier C immediately followed by "-". + // The matching of C against the element's language value is performed case-insensitively. + // The identifier C does not have to be a valid language name." + // http://www.w3.org/TR/selectors/#lang-pseudo + "lang": markFunction( function( lang ) { + + // lang value must be a valid identifier + if ( !ridentifier.test( lang || "" ) ) { + Sizzle.error( "unsupported lang: " + lang ); + } + lang = lang.replace( runescape, funescape ).toLowerCase(); + return function( elem ) { + var elemLang; + do { + if ( ( elemLang = documentIsHTML ? + elem.lang : + elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) { + + elemLang = elemLang.toLowerCase(); + return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; + } + } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 ); + return false; + }; + } ), + + // Miscellaneous + "target": function( elem ) { + var hash = window.location && window.location.hash; + return hash && hash.slice( 1 ) === elem.id; + }, + + "root": function( elem ) { + return elem === docElem; + }, + + "focus": function( elem ) { + return elem === document.activeElement && + ( !document.hasFocus || document.hasFocus() ) && + !!( elem.type || elem.href || ~elem.tabIndex ); + }, + + // Boolean properties + "enabled": createDisabledPseudo( false ), + "disabled": createDisabledPseudo( true ), + + "checked": function( elem ) { + + // In CSS3, :checked should return both checked and selected elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + var nodeName = elem.nodeName.toLowerCase(); + return ( nodeName === "input" && !!elem.checked ) || + ( nodeName === "option" && !!elem.selected ); + }, + + "selected": function( elem ) { + + // Accessing this property makes selected-by-default + // options in Safari work properly + if ( elem.parentNode ) { + // eslint-disable-next-line no-unused-expressions + elem.parentNode.selectedIndex; + } + + return elem.selected === true; + }, + + // Contents + "empty": function( elem ) { + + // http://www.w3.org/TR/selectors/#empty-pseudo + // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), + // but not by others (comment: 8; processing instruction: 7; etc.) + // nodeType < 6 works because attributes (2) do not appear as children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + if ( elem.nodeType < 6 ) { + return false; + } + } + return true; + }, + + "parent": function( elem ) { + return !Expr.pseudos[ "empty" ]( elem ); + }, + + // Element/input types + "header": function( elem ) { + return rheader.test( elem.nodeName ); + }, + + "input": function( elem ) { + return rinputs.test( elem.nodeName ); + }, + + "button": function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === "button" || name === "button"; + }, + + "text": function( elem ) { + var attr; + return elem.nodeName.toLowerCase() === "input" && + elem.type === "text" && + + // Support: IE<8 + // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" + ( ( attr = elem.getAttribute( "type" ) ) == null || + attr.toLowerCase() === "text" ); + }, + + // Position-in-collection + "first": createPositionalPseudo( function() { + return [ 0 ]; + } ), + + "last": createPositionalPseudo( function( _matchIndexes, length ) { + return [ length - 1 ]; + } ), + + "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) { + return [ argument < 0 ? argument + length : argument ]; + } ), + + "even": createPositionalPseudo( function( matchIndexes, length ) { + var i = 0; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "odd": createPositionalPseudo( function( matchIndexes, length ) { + var i = 1; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "lt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? + argument + length : + argument > length ? + length : + argument; + for ( ; --i >= 0; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "gt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? argument + length : argument; + for ( ; ++i < length; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ) + } +}; + +Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ]; + +// Add button/input type pseudos +for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { + Expr.pseudos[ i ] = createInputPseudo( i ); +} +for ( i in { submit: true, reset: true } ) { + Expr.pseudos[ i ] = createButtonPseudo( i ); +} + +// Easy API for creating new setFilters +function setFilters() {} +setFilters.prototype = Expr.filters = Expr.pseudos; +Expr.setFilters = new setFilters(); + +tokenize = Sizzle.tokenize = function( selector, parseOnly ) { + var matched, match, tokens, type, + soFar, groups, preFilters, + cached = tokenCache[ selector + " " ]; + + if ( cached ) { + return parseOnly ? 0 : cached.slice( 0 ); + } + + soFar = selector; + groups = []; + preFilters = Expr.preFilter; + + while ( soFar ) { + + // Comma and first run + if ( !matched || ( match = rcomma.exec( soFar ) ) ) { + if ( match ) { + + // Don't consume trailing commas as valid + soFar = soFar.slice( match[ 0 ].length ) || soFar; + } + groups.push( ( tokens = [] ) ); + } + + matched = false; + + // Combinators + if ( ( match = rcombinators.exec( soFar ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + + // Cast descendant combinators to space + type: match[ 0 ].replace( rtrim, " " ) + } ); + soFar = soFar.slice( matched.length ); + } + + // Filters + for ( type in Expr.filter ) { + if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] || + ( match = preFilters[ type ]( match ) ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + type: type, + matches: match + } ); + soFar = soFar.slice( matched.length ); + } + } + + if ( !matched ) { + break; + } + } + + // Return the length of the invalid excess + // if we're just parsing + // Otherwise, throw an error or return tokens + return parseOnly ? + soFar.length : + soFar ? + Sizzle.error( selector ) : + + // Cache the tokens + tokenCache( selector, groups ).slice( 0 ); +}; + +function toSelector( tokens ) { + var i = 0, + len = tokens.length, + selector = ""; + for ( ; i < len; i++ ) { + selector += tokens[ i ].value; + } + return selector; +} + +function addCombinator( matcher, combinator, base ) { + var dir = combinator.dir, + skip = combinator.next, + key = skip || dir, + checkNonElements = base && key === "parentNode", + doneName = done++; + + return combinator.first ? + + // Check against closest ancestor/preceding element + function( elem, context, xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + return matcher( elem, context, xml ); + } + } + return false; + } : + + // Check against all ancestor/preceding elements + function( elem, context, xml ) { + var oldCache, uniqueCache, outerCache, + newCache = [ dirruns, doneName ]; + + // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching + if ( xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + if ( matcher( elem, context, xml ) ) { + return true; + } + } + } + } else { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + outerCache = elem[ expando ] || ( elem[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ elem.uniqueID ] || + ( outerCache[ elem.uniqueID ] = {} ); + + if ( skip && skip === elem.nodeName.toLowerCase() ) { + elem = elem[ dir ] || elem; + } else if ( ( oldCache = uniqueCache[ key ] ) && + oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { + + // Assign to newCache so results back-propagate to previous elements + return ( newCache[ 2 ] = oldCache[ 2 ] ); + } else { + + // Reuse newcache so results back-propagate to previous elements + uniqueCache[ key ] = newCache; + + // A match means we're done; a fail means we have to keep checking + if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) { + return true; + } + } + } + } + } + return false; + }; +} + +function elementMatcher( matchers ) { + return matchers.length > 1 ? + function( elem, context, xml ) { + var i = matchers.length; + while ( i-- ) { + if ( !matchers[ i ]( elem, context, xml ) ) { + return false; + } + } + return true; + } : + matchers[ 0 ]; +} + +function multipleContexts( selector, contexts, results ) { + var i = 0, + len = contexts.length; + for ( ; i < len; i++ ) { + Sizzle( selector, contexts[ i ], results ); + } + return results; +} + +function condense( unmatched, map, filter, context, xml ) { + var elem, + newUnmatched = [], + i = 0, + len = unmatched.length, + mapped = map != null; + + for ( ; i < len; i++ ) { + if ( ( elem = unmatched[ i ] ) ) { + if ( !filter || filter( elem, context, xml ) ) { + newUnmatched.push( elem ); + if ( mapped ) { + map.push( i ); + } + } + } + } + + return newUnmatched; +} + +function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { + if ( postFilter && !postFilter[ expando ] ) { + postFilter = setMatcher( postFilter ); + } + if ( postFinder && !postFinder[ expando ] ) { + postFinder = setMatcher( postFinder, postSelector ); + } + return markFunction( function( seed, results, context, xml ) { + var temp, i, elem, + preMap = [], + postMap = [], + preexisting = results.length, + + // Get initial elements from seed or context + elems = seed || multipleContexts( + selector || "*", + context.nodeType ? [ context ] : context, + [] + ), + + // Prefilter to get matcher input, preserving a map for seed-results synchronization + matcherIn = preFilter && ( seed || !selector ) ? + condense( elems, preMap, preFilter, context, xml ) : + elems, + + matcherOut = matcher ? + + // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, + postFinder || ( seed ? preFilter : preexisting || postFilter ) ? + + // ...intermediate processing is necessary + [] : + + // ...otherwise use results directly + results : + matcherIn; + + // Find primary matches + if ( matcher ) { + matcher( matcherIn, matcherOut, context, xml ); + } + + // Apply postFilter + if ( postFilter ) { + temp = condense( matcherOut, postMap ); + postFilter( temp, [], context, xml ); + + // Un-match failing elements by moving them back to matcherIn + i = temp.length; + while ( i-- ) { + if ( ( elem = temp[ i ] ) ) { + matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem ); + } + } + } + + if ( seed ) { + if ( postFinder || preFilter ) { + if ( postFinder ) { + + // Get the final matcherOut by condensing this intermediate into postFinder contexts + temp = []; + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) ) { + + // Restore matcherIn since elem is not yet a final match + temp.push( ( matcherIn[ i ] = elem ) ); + } + } + postFinder( null, ( matcherOut = [] ), temp, xml ); + } + + // Move matched elements from seed to results to keep them synchronized + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) && + ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) { + + seed[ temp ] = !( results[ temp ] = elem ); + } + } + } + + // Add elements to results, through postFinder if defined + } else { + matcherOut = condense( + matcherOut === results ? + matcherOut.splice( preexisting, matcherOut.length ) : + matcherOut + ); + if ( postFinder ) { + postFinder( null, results, matcherOut, xml ); + } else { + push.apply( results, matcherOut ); + } + } + } ); +} + +function matcherFromTokens( tokens ) { + var checkContext, matcher, j, + len = tokens.length, + leadingRelative = Expr.relative[ tokens[ 0 ].type ], + implicitRelative = leadingRelative || Expr.relative[ " " ], + i = leadingRelative ? 1 : 0, + + // The foundational matcher ensures that elements are reachable from top-level context(s) + matchContext = addCombinator( function( elem ) { + return elem === checkContext; + }, implicitRelative, true ), + matchAnyContext = addCombinator( function( elem ) { + return indexOf( checkContext, elem ) > -1; + }, implicitRelative, true ), + matchers = [ function( elem, context, xml ) { + var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( + ( checkContext = context ).nodeType ? + matchContext( elem, context, xml ) : + matchAnyContext( elem, context, xml ) ); + + // Avoid hanging onto element (issue #299) + checkContext = null; + return ret; + } ]; + + for ( ; i < len; i++ ) { + if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) { + matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ]; + } else { + matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches ); + + // Return special upon seeing a positional matcher + if ( matcher[ expando ] ) { + + // Find the next relative operator (if any) for proper handling + j = ++i; + for ( ; j < len; j++ ) { + if ( Expr.relative[ tokens[ j ].type ] ) { + break; + } + } + return setMatcher( + i > 1 && elementMatcher( matchers ), + i > 1 && toSelector( + + // If the preceding token was a descendant combinator, insert an implicit any-element `*` + tokens + .slice( 0, i - 1 ) + .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } ) + ).replace( rtrim, "$1" ), + matcher, + i < j && matcherFromTokens( tokens.slice( i, j ) ), + j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ), + j < len && toSelector( tokens ) + ); + } + matchers.push( matcher ); + } + } + + return elementMatcher( matchers ); +} + +function matcherFromGroupMatchers( elementMatchers, setMatchers ) { + var bySet = setMatchers.length > 0, + byElement = elementMatchers.length > 0, + superMatcher = function( seed, context, xml, results, outermost ) { + var elem, j, matcher, + matchedCount = 0, + i = "0", + unmatched = seed && [], + setMatched = [], + contextBackup = outermostContext, + + // We must always have either seed elements or outermost context + elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ), + + // Use integer dirruns iff this is the outermost matcher + dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ), + len = elems.length; + + if ( outermost ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + outermostContext = context == document || context || outermost; + } + + // Add elements passing elementMatchers directly to results + // Support: IE<9, Safari + // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id + for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) { + if ( byElement && elem ) { + j = 0; + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( !context && elem.ownerDocument != document ) { + setDocument( elem ); + xml = !documentIsHTML; + } + while ( ( matcher = elementMatchers[ j++ ] ) ) { + if ( matcher( elem, context || document, xml ) ) { + results.push( elem ); + break; + } + } + if ( outermost ) { + dirruns = dirrunsUnique; + } + } + + // Track unmatched elements for set filters + if ( bySet ) { + + // They will have gone through all possible matchers + if ( ( elem = !matcher && elem ) ) { + matchedCount--; + } + + // Lengthen the array for every element, matched or not + if ( seed ) { + unmatched.push( elem ); + } + } + } + + // `i` is now the count of elements visited above, and adding it to `matchedCount` + // makes the latter nonnegative. + matchedCount += i; + + // Apply set filters to unmatched elements + // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` + // equals `i`), unless we didn't visit _any_ elements in the above loop because we have + // no element matchers and no seed. + // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that + // case, which will result in a "00" `matchedCount` that differs from `i` but is also + // numerically zero. + if ( bySet && i !== matchedCount ) { + j = 0; + while ( ( matcher = setMatchers[ j++ ] ) ) { + matcher( unmatched, setMatched, context, xml ); + } + + if ( seed ) { + + // Reintegrate element matches to eliminate the need for sorting + if ( matchedCount > 0 ) { + while ( i-- ) { + if ( !( unmatched[ i ] || setMatched[ i ] ) ) { + setMatched[ i ] = pop.call( results ); + } + } + } + + // Discard index placeholder values to get only actual matches + setMatched = condense( setMatched ); + } + + // Add matches to results + push.apply( results, setMatched ); + + // Seedless set matches succeeding multiple successful matchers stipulate sorting + if ( outermost && !seed && setMatched.length > 0 && + ( matchedCount + setMatchers.length ) > 1 ) { + + Sizzle.uniqueSort( results ); + } + } + + // Override manipulation of globals by nested matchers + if ( outermost ) { + dirruns = dirrunsUnique; + outermostContext = contextBackup; + } + + return unmatched; + }; + + return bySet ? + markFunction( superMatcher ) : + superMatcher; +} + +compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { + var i, + setMatchers = [], + elementMatchers = [], + cached = compilerCache[ selector + " " ]; + + if ( !cached ) { + + // Generate a function of recursive functions that can be used to check each element + if ( !match ) { + match = tokenize( selector ); + } + i = match.length; + while ( i-- ) { + cached = matcherFromTokens( match[ i ] ); + if ( cached[ expando ] ) { + setMatchers.push( cached ); + } else { + elementMatchers.push( cached ); + } + } + + // Cache the compiled function + cached = compilerCache( + selector, + matcherFromGroupMatchers( elementMatchers, setMatchers ) + ); + + // Save selector and tokenization + cached.selector = selector; + } + return cached; +}; + +/** + * A low-level selection function that works with Sizzle's compiled + * selector functions + * @param {String|Function} selector A selector or a pre-compiled + * selector function built with Sizzle.compile + * @param {Element} context + * @param {Array} [results] + * @param {Array} [seed] A set of elements to match against + */ +select = Sizzle.select = function( selector, context, results, seed ) { + var i, tokens, token, type, find, + compiled = typeof selector === "function" && selector, + match = !seed && tokenize( ( selector = compiled.selector || selector ) ); + + results = results || []; + + // Try to minimize operations if there is only one selector in the list and no seed + // (the latter of which guarantees us context) + if ( match.length === 1 ) { + + // Reduce context if the leading compound selector is an ID + tokens = match[ 0 ] = match[ 0 ].slice( 0 ); + if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" && + context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) { + + context = ( Expr.find[ "ID" ]( token.matches[ 0 ] + .replace( runescape, funescape ), context ) || [] )[ 0 ]; + if ( !context ) { + return results; + + // Precompiled matchers will still verify ancestry, so step up a level + } else if ( compiled ) { + context = context.parentNode; + } + + selector = selector.slice( tokens.shift().value.length ); + } + + // Fetch a seed set for right-to-left matching + i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length; + while ( i-- ) { + token = tokens[ i ]; + + // Abort if we hit a combinator + if ( Expr.relative[ ( type = token.type ) ] ) { + break; + } + if ( ( find = Expr.find[ type ] ) ) { + + // Search, expanding context for leading sibling combinators + if ( ( seed = find( + token.matches[ 0 ].replace( runescape, funescape ), + rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) || + context + ) ) ) { + + // If seed is empty or no tokens remain, we can return early + tokens.splice( i, 1 ); + selector = seed.length && toSelector( tokens ); + if ( !selector ) { + push.apply( results, seed ); + return results; + } + + break; + } + } + } + } + + // Compile and execute a filtering function if one is not provided + // Provide `match` to avoid retokenization if we modified the selector above + ( compiled || compile( selector, match ) )( + seed, + context, + !documentIsHTML, + results, + !context || rsibling.test( selector ) && testContext( context.parentNode ) || context + ); + return results; +}; + +// One-time assignments + +// Sort stability +support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando; + +// Support: Chrome 14-35+ +// Always assume duplicates if they aren't passed to the comparison function +support.detectDuplicates = !!hasDuplicate; + +// Initialize against the default document +setDocument(); + +// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) +// Detached nodes confoundingly follow *each other* +support.sortDetached = assert( function( el ) { + + // Should return 1, but returns 4 (following) + return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1; +} ); + +// Support: IE<8 +// Prevent attribute/property "interpolation" +// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx +if ( !assert( function( el ) { + el.innerHTML = ""; + return el.firstChild.getAttribute( "href" ) === "#"; +} ) ) { + addHandle( "type|href|height|width", function( elem, name, isXML ) { + if ( !isXML ) { + return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); + } + } ); +} + +// Support: IE<9 +// Use defaultValue in place of getAttribute("value") +if ( !support.attributes || !assert( function( el ) { + el.innerHTML = ""; + el.firstChild.setAttribute( "value", "" ); + return el.firstChild.getAttribute( "value" ) === ""; +} ) ) { + addHandle( "value", function( elem, _name, isXML ) { + if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { + return elem.defaultValue; + } + } ); +} + +// Support: IE<9 +// Use getAttributeNode to fetch booleans when getAttribute lies +if ( !assert( function( el ) { + return el.getAttribute( "disabled" ) == null; +} ) ) { + addHandle( booleans, function( elem, name, isXML ) { + var val; + if ( !isXML ) { + return elem[ name ] === true ? name.toLowerCase() : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; + } + } ); +} + +return Sizzle; + +} )( window ); + + + +jQuery.find = Sizzle; +jQuery.expr = Sizzle.selectors; + +// Deprecated +jQuery.expr[ ":" ] = jQuery.expr.pseudos; +jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; +jQuery.text = Sizzle.getText; +jQuery.isXMLDoc = Sizzle.isXML; +jQuery.contains = Sizzle.contains; +jQuery.escapeSelector = Sizzle.escape; + + + + +var dir = function( elem, dir, until ) { + var matched = [], + truncate = until !== undefined; + + while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { + if ( elem.nodeType === 1 ) { + if ( truncate && jQuery( elem ).is( until ) ) { + break; + } + matched.push( elem ); + } + } + return matched; +}; + + +var siblings = function( n, elem ) { + var matched = []; + + for ( ; n; n = n.nextSibling ) { + if ( n.nodeType === 1 && n !== elem ) { + matched.push( n ); + } + } + + return matched; +}; + + +var rneedsContext = jQuery.expr.match.needsContext; + + + +function nodeName( elem, name ) { + + return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); + +} +var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); + + + +// Implement the identical functionality for filter and not +function winnow( elements, qualifier, not ) { + if ( isFunction( qualifier ) ) { + return jQuery.grep( elements, function( elem, i ) { + return !!qualifier.call( elem, i, elem ) !== not; + } ); + } + + // Single element + if ( qualifier.nodeType ) { + return jQuery.grep( elements, function( elem ) { + return ( elem === qualifier ) !== not; + } ); + } + + // Arraylike of elements (jQuery, arguments, Array) + if ( typeof qualifier !== "string" ) { + return jQuery.grep( elements, function( elem ) { + return ( indexOf.call( qualifier, elem ) > -1 ) !== not; + } ); + } + + // Filtered directly for both simple and complex selectors + return jQuery.filter( qualifier, elements, not ); +} + +jQuery.filter = function( expr, elems, not ) { + var elem = elems[ 0 ]; + + if ( not ) { + expr = ":not(" + expr + ")"; + } + + if ( elems.length === 1 && elem.nodeType === 1 ) { + return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; + } + + return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { + return elem.nodeType === 1; + } ) ); +}; + +jQuery.fn.extend( { + find: function( selector ) { + var i, ret, + len = this.length, + self = this; + + if ( typeof selector !== "string" ) { + return this.pushStack( jQuery( selector ).filter( function() { + for ( i = 0; i < len; i++ ) { + if ( jQuery.contains( self[ i ], this ) ) { + return true; + } + } + } ) ); + } + + ret = this.pushStack( [] ); + + for ( i = 0; i < len; i++ ) { + jQuery.find( selector, self[ i ], ret ); + } + + return len > 1 ? jQuery.uniqueSort( ret ) : ret; + }, + filter: function( selector ) { + return this.pushStack( winnow( this, selector || [], false ) ); + }, + not: function( selector ) { + return this.pushStack( winnow( this, selector || [], true ) ); + }, + is: function( selector ) { + return !!winnow( + this, + + // If this is a positional/relative selector, check membership in the returned set + // so $("p:first").is("p:last") won't return true for a doc with two "p". + typeof selector === "string" && rneedsContext.test( selector ) ? + jQuery( selector ) : + selector || [], + false + ).length; + } +} ); + + +// Initialize a jQuery object + + +// A central reference to the root jQuery(document) +var rootjQuery, + + // A simple way to check for HTML strings + // Prioritize #id over to avoid XSS via location.hash (#9521) + // Strict HTML recognition (#11290: must start with <) + // Shortcut simple #id case for speed + rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, + + init = jQuery.fn.init = function( selector, context, root ) { + var match, elem; + + // HANDLE: $(""), $(null), $(undefined), $(false) + if ( !selector ) { + return this; + } + + // Method init() accepts an alternate rootjQuery + // so migrate can support jQuery.sub (gh-2101) + root = root || rootjQuery; + + // Handle HTML strings + if ( typeof selector === "string" ) { + if ( selector[ 0 ] === "<" && + selector[ selector.length - 1 ] === ">" && + selector.length >= 3 ) { + + // Assume that strings that start and end with <> are HTML and skip the regex check + match = [ null, selector, null ]; + + } else { + match = rquickExpr.exec( selector ); + } + + // Match html or make sure no context is specified for #id + if ( match && ( match[ 1 ] || !context ) ) { + + // HANDLE: $(html) -> $(array) + if ( match[ 1 ] ) { + context = context instanceof jQuery ? context[ 0 ] : context; + + // Option to run scripts is true for back-compat + // Intentionally let the error be thrown if parseHTML is not present + jQuery.merge( this, jQuery.parseHTML( + match[ 1 ], + context && context.nodeType ? context.ownerDocument || context : document, + true + ) ); + + // HANDLE: $(html, props) + if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { + for ( match in context ) { + + // Properties of context are called as methods if possible + if ( isFunction( this[ match ] ) ) { + this[ match ]( context[ match ] ); + + // ...and otherwise set as attributes + } else { + this.attr( match, context[ match ] ); + } + } + } + + return this; + + // HANDLE: $(#id) + } else { + elem = document.getElementById( match[ 2 ] ); + + if ( elem ) { + + // Inject the element directly into the jQuery object + this[ 0 ] = elem; + this.length = 1; + } + return this; + } + + // HANDLE: $(expr, $(...)) + } else if ( !context || context.jquery ) { + return ( context || root ).find( selector ); + + // HANDLE: $(expr, context) + // (which is just equivalent to: $(context).find(expr) + } else { + return this.constructor( context ).find( selector ); + } + + // HANDLE: $(DOMElement) + } else if ( selector.nodeType ) { + this[ 0 ] = selector; + this.length = 1; + return this; + + // HANDLE: $(function) + // Shortcut for document ready + } else if ( isFunction( selector ) ) { + return root.ready !== undefined ? + root.ready( selector ) : + + // Execute immediately if ready is not present + selector( jQuery ); + } + + return jQuery.makeArray( selector, this ); + }; + +// Give the init function the jQuery prototype for later instantiation +init.prototype = jQuery.fn; + +// Initialize central reference +rootjQuery = jQuery( document ); + + +var rparentsprev = /^(?:parents|prev(?:Until|All))/, + + // Methods guaranteed to produce a unique set when starting from a unique set + guaranteedUnique = { + children: true, + contents: true, + next: true, + prev: true + }; + +jQuery.fn.extend( { + has: function( target ) { + var targets = jQuery( target, this ), + l = targets.length; + + return this.filter( function() { + var i = 0; + for ( ; i < l; i++ ) { + if ( jQuery.contains( this, targets[ i ] ) ) { + return true; + } + } + } ); + }, + + closest: function( selectors, context ) { + var cur, + i = 0, + l = this.length, + matched = [], + targets = typeof selectors !== "string" && jQuery( selectors ); + + // Positional selectors never match, since there's no _selection_ context + if ( !rneedsContext.test( selectors ) ) { + for ( ; i < l; i++ ) { + for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { + + // Always skip document fragments + if ( cur.nodeType < 11 && ( targets ? + targets.index( cur ) > -1 : + + // Don't pass non-elements to Sizzle + cur.nodeType === 1 && + jQuery.find.matchesSelector( cur, selectors ) ) ) { + + matched.push( cur ); + break; + } + } + } + } + + return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); + }, + + // Determine the position of an element within the set + index: function( elem ) { + + // No argument, return index in parent + if ( !elem ) { + return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; + } + + // Index in selector + if ( typeof elem === "string" ) { + return indexOf.call( jQuery( elem ), this[ 0 ] ); + } + + // Locate the position of the desired element + return indexOf.call( this, + + // If it receives a jQuery object, the first element is used + elem.jquery ? elem[ 0 ] : elem + ); + }, + + add: function( selector, context ) { + return this.pushStack( + jQuery.uniqueSort( + jQuery.merge( this.get(), jQuery( selector, context ) ) + ) + ); + }, + + addBack: function( selector ) { + return this.add( selector == null ? + this.prevObject : this.prevObject.filter( selector ) + ); + } +} ); + +function sibling( cur, dir ) { + while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} + return cur; +} + +jQuery.each( { + parent: function( elem ) { + var parent = elem.parentNode; + return parent && parent.nodeType !== 11 ? parent : null; + }, + parents: function( elem ) { + return dir( elem, "parentNode" ); + }, + parentsUntil: function( elem, _i, until ) { + return dir( elem, "parentNode", until ); + }, + next: function( elem ) { + return sibling( elem, "nextSibling" ); + }, + prev: function( elem ) { + return sibling( elem, "previousSibling" ); + }, + nextAll: function( elem ) { + return dir( elem, "nextSibling" ); + }, + prevAll: function( elem ) { + return dir( elem, "previousSibling" ); + }, + nextUntil: function( elem, _i, until ) { + return dir( elem, "nextSibling", until ); + }, + prevUntil: function( elem, _i, until ) { + return dir( elem, "previousSibling", until ); + }, + siblings: function( elem ) { + return siblings( ( elem.parentNode || {} ).firstChild, elem ); + }, + children: function( elem ) { + return siblings( elem.firstChild ); + }, + contents: function( elem ) { + if ( elem.contentDocument != null && + + // Support: IE 11+ + // elements with no `data` attribute has an object + // `contentDocument` with a `null` prototype. + getProto( elem.contentDocument ) ) { + + return elem.contentDocument; + } + + // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only + // Treat the template element as a regular one in browsers that + // don't support it. + if ( nodeName( elem, "template" ) ) { + elem = elem.content || elem; + } + + return jQuery.merge( [], elem.childNodes ); + } +}, function( name, fn ) { + jQuery.fn[ name ] = function( until, selector ) { + var matched = jQuery.map( this, fn, until ); + + if ( name.slice( -5 ) !== "Until" ) { + selector = until; + } + + if ( selector && typeof selector === "string" ) { + matched = jQuery.filter( selector, matched ); + } + + if ( this.length > 1 ) { + + // Remove duplicates + if ( !guaranteedUnique[ name ] ) { + jQuery.uniqueSort( matched ); + } + + // Reverse order for parents* and prev-derivatives + if ( rparentsprev.test( name ) ) { + matched.reverse(); + } + } + + return this.pushStack( matched ); + }; +} ); +var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); + + + +// Convert String-formatted options into Object-formatted ones +function createOptions( options ) { + var object = {}; + jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { + object[ flag ] = true; + } ); + return object; +} + +/* + * Create a callback list using the following parameters: + * + * options: an optional list of space-separated options that will change how + * the callback list behaves or a more traditional option object + * + * By default a callback list will act like an event callback list and can be + * "fired" multiple times. + * + * Possible options: + * + * once: will ensure the callback list can only be fired once (like a Deferred) + * + * memory: will keep track of previous values and will call any callback added + * after the list has been fired right away with the latest "memorized" + * values (like a Deferred) + * + * unique: will ensure a callback can only be added once (no duplicate in the list) + * + * stopOnFalse: interrupt callings when a callback returns false + * + */ +jQuery.Callbacks = function( options ) { + + // Convert options from String-formatted to Object-formatted if needed + // (we check in cache first) + options = typeof options === "string" ? + createOptions( options ) : + jQuery.extend( {}, options ); + + var // Flag to know if list is currently firing + firing, + + // Last fire value for non-forgettable lists + memory, + + // Flag to know if list was already fired + fired, + + // Flag to prevent firing + locked, + + // Actual callback list + list = [], + + // Queue of execution data for repeatable lists + queue = [], + + // Index of currently firing callback (modified by add/remove as needed) + firingIndex = -1, + + // Fire callbacks + fire = function() { + + // Enforce single-firing + locked = locked || options.once; + + // Execute callbacks for all pending executions, + // respecting firingIndex overrides and runtime changes + fired = firing = true; + for ( ; queue.length; firingIndex = -1 ) { + memory = queue.shift(); + while ( ++firingIndex < list.length ) { + + // Run callback and check for early termination + if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && + options.stopOnFalse ) { + + // Jump to end and forget the data so .add doesn't re-fire + firingIndex = list.length; + memory = false; + } + } + } + + // Forget the data if we're done with it + if ( !options.memory ) { + memory = false; + } + + firing = false; + + // Clean up if we're done firing for good + if ( locked ) { + + // Keep an empty list if we have data for future add calls + if ( memory ) { + list = []; + + // Otherwise, this object is spent + } else { + list = ""; + } + } + }, + + // Actual Callbacks object + self = { + + // Add a callback or a collection of callbacks to the list + add: function() { + if ( list ) { + + // If we have memory from a past run, we should fire after adding + if ( memory && !firing ) { + firingIndex = list.length - 1; + queue.push( memory ); + } + + ( function add( args ) { + jQuery.each( args, function( _, arg ) { + if ( isFunction( arg ) ) { + if ( !options.unique || !self.has( arg ) ) { + list.push( arg ); + } + } else if ( arg && arg.length && toType( arg ) !== "string" ) { + + // Inspect recursively + add( arg ); + } + } ); + } )( arguments ); + + if ( memory && !firing ) { + fire(); + } + } + return this; + }, + + // Remove a callback from the list + remove: function() { + jQuery.each( arguments, function( _, arg ) { + var index; + while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { + list.splice( index, 1 ); + + // Handle firing indexes + if ( index <= firingIndex ) { + firingIndex--; + } + } + } ); + return this; + }, + + // Check if a given callback is in the list. + // If no argument is given, return whether or not list has callbacks attached. + has: function( fn ) { + return fn ? + jQuery.inArray( fn, list ) > -1 : + list.length > 0; + }, + + // Remove all callbacks from the list + empty: function() { + if ( list ) { + list = []; + } + return this; + }, + + // Disable .fire and .add + // Abort any current/pending executions + // Clear all callbacks and values + disable: function() { + locked = queue = []; + list = memory = ""; + return this; + }, + disabled: function() { + return !list; + }, + + // Disable .fire + // Also disable .add unless we have memory (since it would have no effect) + // Abort any pending executions + lock: function() { + locked = queue = []; + if ( !memory && !firing ) { + list = memory = ""; + } + return this; + }, + locked: function() { + return !!locked; + }, + + // Call all callbacks with the given context and arguments + fireWith: function( context, args ) { + if ( !locked ) { + args = args || []; + args = [ context, args.slice ? args.slice() : args ]; + queue.push( args ); + if ( !firing ) { + fire(); + } + } + return this; + }, + + // Call all the callbacks with the given arguments + fire: function() { + self.fireWith( this, arguments ); + return this; + }, + + // To know if the callbacks have already been called at least once + fired: function() { + return !!fired; + } + }; + + return self; +}; + + +function Identity( v ) { + return v; +} +function Thrower( ex ) { + throw ex; +} + +function adoptValue( value, resolve, reject, noValue ) { + var method; + + try { + + // Check for promise aspect first to privilege synchronous behavior + if ( value && isFunction( ( method = value.promise ) ) ) { + method.call( value ).done( resolve ).fail( reject ); + + // Other thenables + } else if ( value && isFunction( ( method = value.then ) ) ) { + method.call( value, resolve, reject ); + + // Other non-thenables + } else { + + // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: + // * false: [ value ].slice( 0 ) => resolve( value ) + // * true: [ value ].slice( 1 ) => resolve() + resolve.apply( undefined, [ value ].slice( noValue ) ); + } + + // For Promises/A+, convert exceptions into rejections + // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in + // Deferred#then to conditionally suppress rejection. + } catch ( value ) { + + // Support: Android 4.0 only + // Strict mode functions invoked without .call/.apply get global-object context + reject.apply( undefined, [ value ] ); + } +} + +jQuery.extend( { + + Deferred: function( func ) { + var tuples = [ + + // action, add listener, callbacks, + // ... .then handlers, argument index, [final state] + [ "notify", "progress", jQuery.Callbacks( "memory" ), + jQuery.Callbacks( "memory" ), 2 ], + [ "resolve", "done", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 0, "resolved" ], + [ "reject", "fail", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 1, "rejected" ] + ], + state = "pending", + promise = { + state: function() { + return state; + }, + always: function() { + deferred.done( arguments ).fail( arguments ); + return this; + }, + "catch": function( fn ) { + return promise.then( null, fn ); + }, + + // Keep pipe for back-compat + pipe: function( /* fnDone, fnFail, fnProgress */ ) { + var fns = arguments; + + return jQuery.Deferred( function( newDefer ) { + jQuery.each( tuples, function( _i, tuple ) { + + // Map tuples (progress, done, fail) to arguments (done, fail, progress) + var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; + + // deferred.progress(function() { bind to newDefer or newDefer.notify }) + // deferred.done(function() { bind to newDefer or newDefer.resolve }) + // deferred.fail(function() { bind to newDefer or newDefer.reject }) + deferred[ tuple[ 1 ] ]( function() { + var returned = fn && fn.apply( this, arguments ); + if ( returned && isFunction( returned.promise ) ) { + returned.promise() + .progress( newDefer.notify ) + .done( newDefer.resolve ) + .fail( newDefer.reject ); + } else { + newDefer[ tuple[ 0 ] + "With" ]( + this, + fn ? [ returned ] : arguments + ); + } + } ); + } ); + fns = null; + } ).promise(); + }, + then: function( onFulfilled, onRejected, onProgress ) { + var maxDepth = 0; + function resolve( depth, deferred, handler, special ) { + return function() { + var that = this, + args = arguments, + mightThrow = function() { + var returned, then; + + // Support: Promises/A+ section 2.3.3.3.3 + // https://promisesaplus.com/#point-59 + // Ignore double-resolution attempts + if ( depth < maxDepth ) { + return; + } + + returned = handler.apply( that, args ); + + // Support: Promises/A+ section 2.3.1 + // https://promisesaplus.com/#point-48 + if ( returned === deferred.promise() ) { + throw new TypeError( "Thenable self-resolution" ); + } + + // Support: Promises/A+ sections 2.3.3.1, 3.5 + // https://promisesaplus.com/#point-54 + // https://promisesaplus.com/#point-75 + // Retrieve `then` only once + then = returned && + + // Support: Promises/A+ section 2.3.4 + // https://promisesaplus.com/#point-64 + // Only check objects and functions for thenability + ( typeof returned === "object" || + typeof returned === "function" ) && + returned.then; + + // Handle a returned thenable + if ( isFunction( then ) ) { + + // Special processors (notify) just wait for resolution + if ( special ) { + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ) + ); + + // Normal processors (resolve) also hook into progress + } else { + + // ...and disregard older resolution values + maxDepth++; + + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ), + resolve( maxDepth, deferred, Identity, + deferred.notifyWith ) + ); + } + + // Handle all other returned values + } else { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Identity ) { + that = undefined; + args = [ returned ]; + } + + // Process the value(s) + // Default process is resolve + ( special || deferred.resolveWith )( that, args ); + } + }, + + // Only normal processors (resolve) catch and reject exceptions + process = special ? + mightThrow : + function() { + try { + mightThrow(); + } catch ( e ) { + + if ( jQuery.Deferred.exceptionHook ) { + jQuery.Deferred.exceptionHook( e, + process.stackTrace ); + } + + // Support: Promises/A+ section 2.3.3.3.4.1 + // https://promisesaplus.com/#point-61 + // Ignore post-resolution exceptions + if ( depth + 1 >= maxDepth ) { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Thrower ) { + that = undefined; + args = [ e ]; + } + + deferred.rejectWith( that, args ); + } + } + }; + + // Support: Promises/A+ section 2.3.3.3.1 + // https://promisesaplus.com/#point-57 + // Re-resolve promises immediately to dodge false rejection from + // subsequent errors + if ( depth ) { + process(); + } else { + + // Call an optional hook to record the stack, in case of exception + // since it's otherwise lost when execution goes async + if ( jQuery.Deferred.getStackHook ) { + process.stackTrace = jQuery.Deferred.getStackHook(); + } + window.setTimeout( process ); + } + }; + } + + return jQuery.Deferred( function( newDefer ) { + + // progress_handlers.add( ... ) + tuples[ 0 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onProgress ) ? + onProgress : + Identity, + newDefer.notifyWith + ) + ); + + // fulfilled_handlers.add( ... ) + tuples[ 1 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onFulfilled ) ? + onFulfilled : + Identity + ) + ); + + // rejected_handlers.add( ... ) + tuples[ 2 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onRejected ) ? + onRejected : + Thrower + ) + ); + } ).promise(); + }, + + // Get a promise for this deferred + // If obj is provided, the promise aspect is added to the object + promise: function( obj ) { + return obj != null ? jQuery.extend( obj, promise ) : promise; + } + }, + deferred = {}; + + // Add list-specific methods + jQuery.each( tuples, function( i, tuple ) { + var list = tuple[ 2 ], + stateString = tuple[ 5 ]; + + // promise.progress = list.add + // promise.done = list.add + // promise.fail = list.add + promise[ tuple[ 1 ] ] = list.add; + + // Handle state + if ( stateString ) { + list.add( + function() { + + // state = "resolved" (i.e., fulfilled) + // state = "rejected" + state = stateString; + }, + + // rejected_callbacks.disable + // fulfilled_callbacks.disable + tuples[ 3 - i ][ 2 ].disable, + + // rejected_handlers.disable + // fulfilled_handlers.disable + tuples[ 3 - i ][ 3 ].disable, + + // progress_callbacks.lock + tuples[ 0 ][ 2 ].lock, + + // progress_handlers.lock + tuples[ 0 ][ 3 ].lock + ); + } + + // progress_handlers.fire + // fulfilled_handlers.fire + // rejected_handlers.fire + list.add( tuple[ 3 ].fire ); + + // deferred.notify = function() { deferred.notifyWith(...) } + // deferred.resolve = function() { deferred.resolveWith(...) } + // deferred.reject = function() { deferred.rejectWith(...) } + deferred[ tuple[ 0 ] ] = function() { + deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); + return this; + }; + + // deferred.notifyWith = list.fireWith + // deferred.resolveWith = list.fireWith + // deferred.rejectWith = list.fireWith + deferred[ tuple[ 0 ] + "With" ] = list.fireWith; + } ); + + // Make the deferred a promise + promise.promise( deferred ); + + // Call given func if any + if ( func ) { + func.call( deferred, deferred ); + } + + // All done! + return deferred; + }, + + // Deferred helper + when: function( singleValue ) { + var + + // count of uncompleted subordinates + remaining = arguments.length, + + // count of unprocessed arguments + i = remaining, + + // subordinate fulfillment data + resolveContexts = Array( i ), + resolveValues = slice.call( arguments ), + + // the primary Deferred + primary = jQuery.Deferred(), + + // subordinate callback factory + updateFunc = function( i ) { + return function( value ) { + resolveContexts[ i ] = this; + resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; + if ( !( --remaining ) ) { + primary.resolveWith( resolveContexts, resolveValues ); + } + }; + }; + + // Single- and empty arguments are adopted like Promise.resolve + if ( remaining <= 1 ) { + adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject, + !remaining ); + + // Use .then() to unwrap secondary thenables (cf. gh-3000) + if ( primary.state() === "pending" || + isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { + + return primary.then(); + } + } + + // Multiple arguments are aggregated like Promise.all array elements + while ( i-- ) { + adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject ); + } + + return primary.promise(); + } +} ); + + +// These usually indicate a programmer mistake during development, +// warn about them ASAP rather than swallowing them by default. +var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; + +jQuery.Deferred.exceptionHook = function( error, stack ) { + + // Support: IE 8 - 9 only + // Console exists when dev tools are open, which can happen at any time + if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { + window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); + } +}; + + + + +jQuery.readyException = function( error ) { + window.setTimeout( function() { + throw error; + } ); +}; + + + + +// The deferred used on DOM ready +var readyList = jQuery.Deferred(); + +jQuery.fn.ready = function( fn ) { + + readyList + .then( fn ) + + // Wrap jQuery.readyException in a function so that the lookup + // happens at the time of error handling instead of callback + // registration. + .catch( function( error ) { + jQuery.readyException( error ); + } ); + + return this; +}; + +jQuery.extend( { + + // Is the DOM ready to be used? Set to true once it occurs. + isReady: false, + + // A counter to track how many items to wait for before + // the ready event fires. See #6781 + readyWait: 1, + + // Handle when the DOM is ready + ready: function( wait ) { + + // Abort if there are pending holds or we're already ready + if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { + return; + } + + // Remember that the DOM is ready + jQuery.isReady = true; + + // If a normal DOM Ready event fired, decrement, and wait if need be + if ( wait !== true && --jQuery.readyWait > 0 ) { + return; + } + + // If there are functions bound, to execute + readyList.resolveWith( document, [ jQuery ] ); + } +} ); + +jQuery.ready.then = readyList.then; + +// The ready event handler and self cleanup method +function completed() { + document.removeEventListener( "DOMContentLoaded", completed ); + window.removeEventListener( "load", completed ); + jQuery.ready(); +} + +// Catch cases where $(document).ready() is called +// after the browser event has already occurred. +// Support: IE <=9 - 10 only +// Older IE sometimes signals "interactive" too soon +if ( document.readyState === "complete" || + ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { + + // Handle it asynchronously to allow scripts the opportunity to delay ready + window.setTimeout( jQuery.ready ); + +} else { + + // Use the handy event callback + document.addEventListener( "DOMContentLoaded", completed ); + + // A fallback to window.onload, that will always work + window.addEventListener( "load", completed ); +} + + + + +// Multifunctional method to get and set values of a collection +// The value/s can optionally be executed if it's a function +var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { + var i = 0, + len = elems.length, + bulk = key == null; + + // Sets many values + if ( toType( key ) === "object" ) { + chainable = true; + for ( i in key ) { + access( elems, fn, i, key[ i ], true, emptyGet, raw ); + } + + // Sets one value + } else if ( value !== undefined ) { + chainable = true; + + if ( !isFunction( value ) ) { + raw = true; + } + + if ( bulk ) { + + // Bulk operations run against the entire set + if ( raw ) { + fn.call( elems, value ); + fn = null; + + // ...except when executing function values + } else { + bulk = fn; + fn = function( elem, _key, value ) { + return bulk.call( jQuery( elem ), value ); + }; + } + } + + if ( fn ) { + for ( ; i < len; i++ ) { + fn( + elems[ i ], key, raw ? + value : + value.call( elems[ i ], i, fn( elems[ i ], key ) ) + ); + } + } + } + + if ( chainable ) { + return elems; + } + + // Gets + if ( bulk ) { + return fn.call( elems ); + } + + return len ? fn( elems[ 0 ], key ) : emptyGet; +}; + + +// Matches dashed string for camelizing +var rmsPrefix = /^-ms-/, + rdashAlpha = /-([a-z])/g; + +// Used by camelCase as callback to replace() +function fcamelCase( _all, letter ) { + return letter.toUpperCase(); +} + +// Convert dashed to camelCase; used by the css and data modules +// Support: IE <=9 - 11, Edge 12 - 15 +// Microsoft forgot to hump their vendor prefix (#9572) +function camelCase( string ) { + return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); +} +var acceptData = function( owner ) { + + // Accepts only: + // - Node + // - Node.ELEMENT_NODE + // - Node.DOCUMENT_NODE + // - Object + // - Any + return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); +}; + + + + +function Data() { + this.expando = jQuery.expando + Data.uid++; +} + +Data.uid = 1; + +Data.prototype = { + + cache: function( owner ) { + + // Check if the owner object already has a cache + var value = owner[ this.expando ]; + + // If not, create one + if ( !value ) { + value = {}; + + // We can accept data for non-element nodes in modern browsers, + // but we should not, see #8335. + // Always return an empty object. + if ( acceptData( owner ) ) { + + // If it is a node unlikely to be stringify-ed or looped over + // use plain assignment + if ( owner.nodeType ) { + owner[ this.expando ] = value; + + // Otherwise secure it in a non-enumerable property + // configurable must be true to allow the property to be + // deleted when data is removed + } else { + Object.defineProperty( owner, this.expando, { + value: value, + configurable: true + } ); + } + } + } + + return value; + }, + set: function( owner, data, value ) { + var prop, + cache = this.cache( owner ); + + // Handle: [ owner, key, value ] args + // Always use camelCase key (gh-2257) + if ( typeof data === "string" ) { + cache[ camelCase( data ) ] = value; + + // Handle: [ owner, { properties } ] args + } else { + + // Copy the properties one-by-one to the cache object + for ( prop in data ) { + cache[ camelCase( prop ) ] = data[ prop ]; + } + } + return cache; + }, + get: function( owner, key ) { + return key === undefined ? + this.cache( owner ) : + + // Always use camelCase key (gh-2257) + owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; + }, + access: function( owner, key, value ) { + + // In cases where either: + // + // 1. No key was specified + // 2. A string key was specified, but no value provided + // + // Take the "read" path and allow the get method to determine + // which value to return, respectively either: + // + // 1. The entire cache object + // 2. The data stored at the key + // + if ( key === undefined || + ( ( key && typeof key === "string" ) && value === undefined ) ) { + + return this.get( owner, key ); + } + + // When the key is not a string, or both a key and value + // are specified, set or extend (existing objects) with either: + // + // 1. An object of properties + // 2. A key and value + // + this.set( owner, key, value ); + + // Since the "set" path can have two possible entry points + // return the expected data based on which path was taken[*] + return value !== undefined ? value : key; + }, + remove: function( owner, key ) { + var i, + cache = owner[ this.expando ]; + + if ( cache === undefined ) { + return; + } + + if ( key !== undefined ) { + + // Support array or space separated string of keys + if ( Array.isArray( key ) ) { + + // If key is an array of keys... + // We always set camelCase keys, so remove that. + key = key.map( camelCase ); + } else { + key = camelCase( key ); + + // If a key with the spaces exists, use it. + // Otherwise, create an array by matching non-whitespace + key = key in cache ? + [ key ] : + ( key.match( rnothtmlwhite ) || [] ); + } + + i = key.length; + + while ( i-- ) { + delete cache[ key[ i ] ]; + } + } + + // Remove the expando if there's no more data + if ( key === undefined || jQuery.isEmptyObject( cache ) ) { + + // Support: Chrome <=35 - 45 + // Webkit & Blink performance suffers when deleting properties + // from DOM nodes, so set to undefined instead + // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) + if ( owner.nodeType ) { + owner[ this.expando ] = undefined; + } else { + delete owner[ this.expando ]; + } + } + }, + hasData: function( owner ) { + var cache = owner[ this.expando ]; + return cache !== undefined && !jQuery.isEmptyObject( cache ); + } +}; +var dataPriv = new Data(); + +var dataUser = new Data(); + + + +// Implementation Summary +// +// 1. Enforce API surface and semantic compatibility with 1.9.x branch +// 2. Improve the module's maintainability by reducing the storage +// paths to a single mechanism. +// 3. Use the same single mechanism to support "private" and "user" data. +// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) +// 5. Avoid exposing implementation details on user objects (eg. expando properties) +// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 + +var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, + rmultiDash = /[A-Z]/g; + +function getData( data ) { + if ( data === "true" ) { + return true; + } + + if ( data === "false" ) { + return false; + } + + if ( data === "null" ) { + return null; + } + + // Only convert to a number if it doesn't change the string + if ( data === +data + "" ) { + return +data; + } + + if ( rbrace.test( data ) ) { + return JSON.parse( data ); + } + + return data; +} + +function dataAttr( elem, key, data ) { + var name; + + // If nothing was found internally, try to fetch any + // data from the HTML5 data-* attribute + if ( data === undefined && elem.nodeType === 1 ) { + name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); + data = elem.getAttribute( name ); + + if ( typeof data === "string" ) { + try { + data = getData( data ); + } catch ( e ) {} + + // Make sure we set the data so it isn't changed later + dataUser.set( elem, key, data ); + } else { + data = undefined; + } + } + return data; +} + +jQuery.extend( { + hasData: function( elem ) { + return dataUser.hasData( elem ) || dataPriv.hasData( elem ); + }, + + data: function( elem, name, data ) { + return dataUser.access( elem, name, data ); + }, + + removeData: function( elem, name ) { + dataUser.remove( elem, name ); + }, + + // TODO: Now that all calls to _data and _removeData have been replaced + // with direct calls to dataPriv methods, these can be deprecated. + _data: function( elem, name, data ) { + return dataPriv.access( elem, name, data ); + }, + + _removeData: function( elem, name ) { + dataPriv.remove( elem, name ); + } +} ); + +jQuery.fn.extend( { + data: function( key, value ) { + var i, name, data, + elem = this[ 0 ], + attrs = elem && elem.attributes; + + // Gets all values + if ( key === undefined ) { + if ( this.length ) { + data = dataUser.get( elem ); + + if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { + i = attrs.length; + while ( i-- ) { + + // Support: IE 11 only + // The attrs elements can be null (#14894) + if ( attrs[ i ] ) { + name = attrs[ i ].name; + if ( name.indexOf( "data-" ) === 0 ) { + name = camelCase( name.slice( 5 ) ); + dataAttr( elem, name, data[ name ] ); + } + } + } + dataPriv.set( elem, "hasDataAttrs", true ); + } + } + + return data; + } + + // Sets multiple values + if ( typeof key === "object" ) { + return this.each( function() { + dataUser.set( this, key ); + } ); + } + + return access( this, function( value ) { + var data; + + // The calling jQuery object (element matches) is not empty + // (and therefore has an element appears at this[ 0 ]) and the + // `value` parameter was not undefined. An empty jQuery object + // will result in `undefined` for elem = this[ 0 ] which will + // throw an exception if an attempt to read a data cache is made. + if ( elem && value === undefined ) { + + // Attempt to get data from the cache + // The key will always be camelCased in Data + data = dataUser.get( elem, key ); + if ( data !== undefined ) { + return data; + } + + // Attempt to "discover" the data in + // HTML5 custom data-* attrs + data = dataAttr( elem, key ); + if ( data !== undefined ) { + return data; + } + + // We tried really hard, but the data doesn't exist. + return; + } + + // Set the data... + this.each( function() { + + // We always store the camelCased key + dataUser.set( this, key, value ); + } ); + }, null, value, arguments.length > 1, null, true ); + }, + + removeData: function( key ) { + return this.each( function() { + dataUser.remove( this, key ); + } ); + } +} ); + + +jQuery.extend( { + queue: function( elem, type, data ) { + var queue; + + if ( elem ) { + type = ( type || "fx" ) + "queue"; + queue = dataPriv.get( elem, type ); + + // Speed up dequeue by getting out quickly if this is just a lookup + if ( data ) { + if ( !queue || Array.isArray( data ) ) { + queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); + } else { + queue.push( data ); + } + } + return queue || []; + } + }, + + dequeue: function( elem, type ) { + type = type || "fx"; + + var queue = jQuery.queue( elem, type ), + startLength = queue.length, + fn = queue.shift(), + hooks = jQuery._queueHooks( elem, type ), + next = function() { + jQuery.dequeue( elem, type ); + }; + + // If the fx queue is dequeued, always remove the progress sentinel + if ( fn === "inprogress" ) { + fn = queue.shift(); + startLength--; + } + + if ( fn ) { + + // Add a progress sentinel to prevent the fx queue from being + // automatically dequeued + if ( type === "fx" ) { + queue.unshift( "inprogress" ); + } + + // Clear up the last queue stop function + delete hooks.stop; + fn.call( elem, next, hooks ); + } + + if ( !startLength && hooks ) { + hooks.empty.fire(); + } + }, + + // Not public - generate a queueHooks object, or return the current one + _queueHooks: function( elem, type ) { + var key = type + "queueHooks"; + return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { + empty: jQuery.Callbacks( "once memory" ).add( function() { + dataPriv.remove( elem, [ type + "queue", key ] ); + } ) + } ); + } +} ); + +jQuery.fn.extend( { + queue: function( type, data ) { + var setter = 2; + + if ( typeof type !== "string" ) { + data = type; + type = "fx"; + setter--; + } + + if ( arguments.length < setter ) { + return jQuery.queue( this[ 0 ], type ); + } + + return data === undefined ? + this : + this.each( function() { + var queue = jQuery.queue( this, type, data ); + + // Ensure a hooks for this queue + jQuery._queueHooks( this, type ); + + if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { + jQuery.dequeue( this, type ); + } + } ); + }, + dequeue: function( type ) { + return this.each( function() { + jQuery.dequeue( this, type ); + } ); + }, + clearQueue: function( type ) { + return this.queue( type || "fx", [] ); + }, + + // Get a promise resolved when queues of a certain type + // are emptied (fx is the type by default) + promise: function( type, obj ) { + var tmp, + count = 1, + defer = jQuery.Deferred(), + elements = this, + i = this.length, + resolve = function() { + if ( !( --count ) ) { + defer.resolveWith( elements, [ elements ] ); + } + }; + + if ( typeof type !== "string" ) { + obj = type; + type = undefined; + } + type = type || "fx"; + + while ( i-- ) { + tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); + if ( tmp && tmp.empty ) { + count++; + tmp.empty.add( resolve ); + } + } + resolve(); + return defer.promise( obj ); + } +} ); +var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; + +var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); + + +var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; + +var documentElement = document.documentElement; + + + + var isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ); + }, + composed = { composed: true }; + + // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only + // Check attachment across shadow DOM boundaries when possible (gh-3504) + // Support: iOS 10.0-10.2 only + // Early iOS 10 versions support `attachShadow` but not `getRootNode`, + // leading to errors. We need to check for `getRootNode`. + if ( documentElement.getRootNode ) { + isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ) || + elem.getRootNode( composed ) === elem.ownerDocument; + }; + } +var isHiddenWithinTree = function( elem, el ) { + + // isHiddenWithinTree might be called from jQuery#filter function; + // in that case, element will be second argument + elem = el || elem; + + // Inline style trumps all + return elem.style.display === "none" || + elem.style.display === "" && + + // Otherwise, check computed style + // Support: Firefox <=43 - 45 + // Disconnected elements can have computed display: none, so first confirm that elem is + // in the document. + isAttached( elem ) && + + jQuery.css( elem, "display" ) === "none"; + }; + + + +function adjustCSS( elem, prop, valueParts, tween ) { + var adjusted, scale, + maxIterations = 20, + currentValue = tween ? + function() { + return tween.cur(); + } : + function() { + return jQuery.css( elem, prop, "" ); + }, + initial = currentValue(), + unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), + + // Starting value computation is required for potential unit mismatches + initialInUnit = elem.nodeType && + ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && + rcssNum.exec( jQuery.css( elem, prop ) ); + + if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { + + // Support: Firefox <=54 + // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) + initial = initial / 2; + + // Trust units reported by jQuery.css + unit = unit || initialInUnit[ 3 ]; + + // Iteratively approximate from a nonzero starting point + initialInUnit = +initial || 1; + + while ( maxIterations-- ) { + + // Evaluate and update our best guess (doubling guesses that zero out). + // Finish if the scale equals or crosses 1 (making the old*new product non-positive). + jQuery.style( elem, prop, initialInUnit + unit ); + if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { + maxIterations = 0; + } + initialInUnit = initialInUnit / scale; + + } + + initialInUnit = initialInUnit * 2; + jQuery.style( elem, prop, initialInUnit + unit ); + + // Make sure we update the tween properties later on + valueParts = valueParts || []; + } + + if ( valueParts ) { + initialInUnit = +initialInUnit || +initial || 0; + + // Apply relative offset (+=/-=) if specified + adjusted = valueParts[ 1 ] ? + initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : + +valueParts[ 2 ]; + if ( tween ) { + tween.unit = unit; + tween.start = initialInUnit; + tween.end = adjusted; + } + } + return adjusted; +} + + +var defaultDisplayMap = {}; + +function getDefaultDisplay( elem ) { + var temp, + doc = elem.ownerDocument, + nodeName = elem.nodeName, + display = defaultDisplayMap[ nodeName ]; + + if ( display ) { + return display; + } + + temp = doc.body.appendChild( doc.createElement( nodeName ) ); + display = jQuery.css( temp, "display" ); + + temp.parentNode.removeChild( temp ); + + if ( display === "none" ) { + display = "block"; + } + defaultDisplayMap[ nodeName ] = display; + + return display; +} + +function showHide( elements, show ) { + var display, elem, + values = [], + index = 0, + length = elements.length; + + // Determine new display value for elements that need to change + for ( ; index < length; index++ ) { + elem = elements[ index ]; + if ( !elem.style ) { + continue; + } + + display = elem.style.display; + if ( show ) { + + // Since we force visibility upon cascade-hidden elements, an immediate (and slow) + // check is required in this first loop unless we have a nonempty display value (either + // inline or about-to-be-restored) + if ( display === "none" ) { + values[ index ] = dataPriv.get( elem, "display" ) || null; + if ( !values[ index ] ) { + elem.style.display = ""; + } + } + if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { + values[ index ] = getDefaultDisplay( elem ); + } + } else { + if ( display !== "none" ) { + values[ index ] = "none"; + + // Remember what we're overwriting + dataPriv.set( elem, "display", display ); + } + } + } + + // Set the display of the elements in a second loop to avoid constant reflow + for ( index = 0; index < length; index++ ) { + if ( values[ index ] != null ) { + elements[ index ].style.display = values[ index ]; + } + } + + return elements; +} + +jQuery.fn.extend( { + show: function() { + return showHide( this, true ); + }, + hide: function() { + return showHide( this ); + }, + toggle: function( state ) { + if ( typeof state === "boolean" ) { + return state ? this.show() : this.hide(); + } + + return this.each( function() { + if ( isHiddenWithinTree( this ) ) { + jQuery( this ).show(); + } else { + jQuery( this ).hide(); + } + } ); + } +} ); +var rcheckableType = ( /^(?:checkbox|radio)$/i ); + +var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); + +var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); + + + +( function() { + var fragment = document.createDocumentFragment(), + div = fragment.appendChild( document.createElement( "div" ) ), + input = document.createElement( "input" ); + + // Support: Android 4.0 - 4.3 only + // Check state lost if the name is set (#11217) + // Support: Windows Web Apps (WWA) + // `name` and `type` must use .setAttribute for WWA (#14901) + input.setAttribute( "type", "radio" ); + input.setAttribute( "checked", "checked" ); + input.setAttribute( "name", "t" ); + + div.appendChild( input ); + + // Support: Android <=4.1 only + // Older WebKit doesn't clone checked state correctly in fragments + support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; + + // Support: IE <=11 only + // Make sure textarea (and checkbox) defaultValue is properly cloned + div.innerHTML = ""; + support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; + + // Support: IE <=9 only + // IE <=9 replaces "; + support.option = !!div.lastChild; +} )(); + + +// We have to close these tags to support XHTML (#13200) +var wrapMap = { + + // XHTML parsers do not magically insert elements in the + // same way that tag soup parsers do. So we cannot shorten + // this by omitting or other required elements. + thead: [ 1, "", "
" ], + col: [ 2, "", "
" ], + tr: [ 2, "", "
" ], + td: [ 3, "", "
" ], + + _default: [ 0, "", "" ] +}; + +wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; +wrapMap.th = wrapMap.td; + +// Support: IE <=9 only +if ( !support.option ) { + wrapMap.optgroup = wrapMap.option = [ 1, "" ]; +} + + +function getAll( context, tag ) { + + // Support: IE <=9 - 11 only + // Use typeof to avoid zero-argument method invocation on host objects (#15151) + var ret; + + if ( typeof context.getElementsByTagName !== "undefined" ) { + ret = context.getElementsByTagName( tag || "*" ); + + } else if ( typeof context.querySelectorAll !== "undefined" ) { + ret = context.querySelectorAll( tag || "*" ); + + } else { + ret = []; + } + + if ( tag === undefined || tag && nodeName( context, tag ) ) { + return jQuery.merge( [ context ], ret ); + } + + return ret; +} + + +// Mark scripts as having already been evaluated +function setGlobalEval( elems, refElements ) { + var i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + dataPriv.set( + elems[ i ], + "globalEval", + !refElements || dataPriv.get( refElements[ i ], "globalEval" ) + ); + } +} + + +var rhtml = /<|&#?\w+;/; + +function buildFragment( elems, context, scripts, selection, ignored ) { + var elem, tmp, tag, wrap, attached, j, + fragment = context.createDocumentFragment(), + nodes = [], + i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + elem = elems[ i ]; + + if ( elem || elem === 0 ) { + + // Add nodes directly + if ( toType( elem ) === "object" ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); + + // Convert non-html into a text node + } else if ( !rhtml.test( elem ) ) { + nodes.push( context.createTextNode( elem ) ); + + // Convert html into DOM nodes + } else { + tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); + + // Deserialize a standard representation + tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); + wrap = wrapMap[ tag ] || wrapMap._default; + tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; + + // Descend through wrappers to the right content + j = wrap[ 0 ]; + while ( j-- ) { + tmp = tmp.lastChild; + } + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, tmp.childNodes ); + + // Remember the top-level container + tmp = fragment.firstChild; + + // Ensure the created nodes are orphaned (#12392) + tmp.textContent = ""; + } + } + } + + // Remove wrapper from fragment + fragment.textContent = ""; + + i = 0; + while ( ( elem = nodes[ i++ ] ) ) { + + // Skip elements already in the context collection (trac-4087) + if ( selection && jQuery.inArray( elem, selection ) > -1 ) { + if ( ignored ) { + ignored.push( elem ); + } + continue; + } + + attached = isAttached( elem ); + + // Append to fragment + tmp = getAll( fragment.appendChild( elem ), "script" ); + + // Preserve script evaluation history + if ( attached ) { + setGlobalEval( tmp ); + } + + // Capture executables + if ( scripts ) { + j = 0; + while ( ( elem = tmp[ j++ ] ) ) { + if ( rscriptType.test( elem.type || "" ) ) { + scripts.push( elem ); + } + } + } + } + + return fragment; +} + + +var rtypenamespace = /^([^.]*)(?:\.(.+)|)/; + +function returnTrue() { + return true; +} + +function returnFalse() { + return false; +} + +// Support: IE <=9 - 11+ +// focus() and blur() are asynchronous, except when they are no-op. +// So expect focus to be synchronous when the element is already active, +// and blur to be synchronous when the element is not already active. +// (focus and blur are always synchronous in other supported browsers, +// this just defines when we can count on it). +function expectSync( elem, type ) { + return ( elem === safeActiveElement() ) === ( type === "focus" ); +} + +// Support: IE <=9 only +// Accessing document.activeElement can throw unexpectedly +// https://bugs.jquery.com/ticket/13393 +function safeActiveElement() { + try { + return document.activeElement; + } catch ( err ) { } +} + +function on( elem, types, selector, data, fn, one ) { + var origFn, type; + + // Types can be a map of types/handlers + if ( typeof types === "object" ) { + + // ( types-Object, selector, data ) + if ( typeof selector !== "string" ) { + + // ( types-Object, data ) + data = data || selector; + selector = undefined; + } + for ( type in types ) { + on( elem, type, selector, data, types[ type ], one ); + } + return elem; + } + + if ( data == null && fn == null ) { + + // ( types, fn ) + fn = selector; + data = selector = undefined; + } else if ( fn == null ) { + if ( typeof selector === "string" ) { + + // ( types, selector, fn ) + fn = data; + data = undefined; + } else { + + // ( types, data, fn ) + fn = data; + data = selector; + selector = undefined; + } + } + if ( fn === false ) { + fn = returnFalse; + } else if ( !fn ) { + return elem; + } + + if ( one === 1 ) { + origFn = fn; + fn = function( event ) { + + // Can use an empty set, since event contains the info + jQuery().off( event ); + return origFn.apply( this, arguments ); + }; + + // Use same guid so caller can remove using origFn + fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); + } + return elem.each( function() { + jQuery.event.add( this, types, fn, data, selector ); + } ); +} + +/* + * Helper functions for managing events -- not part of the public interface. + * Props to Dean Edwards' addEvent library for many of the ideas. + */ +jQuery.event = { + + global: {}, + + add: function( elem, types, handler, data, selector ) { + + var handleObjIn, eventHandle, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.get( elem ); + + // Only attach events to objects that accept data + if ( !acceptData( elem ) ) { + return; + } + + // Caller can pass in an object of custom data in lieu of the handler + if ( handler.handler ) { + handleObjIn = handler; + handler = handleObjIn.handler; + selector = handleObjIn.selector; + } + + // Ensure that invalid selectors throw exceptions at attach time + // Evaluate against documentElement in case elem is a non-element node (e.g., document) + if ( selector ) { + jQuery.find.matchesSelector( documentElement, selector ); + } + + // Make sure that the handler has a unique ID, used to find/remove it later + if ( !handler.guid ) { + handler.guid = jQuery.guid++; + } + + // Init the element's event structure and main handler, if this is the first + if ( !( events = elemData.events ) ) { + events = elemData.events = Object.create( null ); + } + if ( !( eventHandle = elemData.handle ) ) { + eventHandle = elemData.handle = function( e ) { + + // Discard the second event of a jQuery.event.trigger() and + // when an event is called after a page has unloaded + return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? + jQuery.event.dispatch.apply( elem, arguments ) : undefined; + }; + } + + // Handle multiple events separated by a space + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // There *must* be a type, no attaching namespace-only handlers + if ( !type ) { + continue; + } + + // If event changes its type, use the special event handlers for the changed type + special = jQuery.event.special[ type ] || {}; + + // If selector defined, determine special event api type, otherwise given type + type = ( selector ? special.delegateType : special.bindType ) || type; + + // Update special based on newly reset type + special = jQuery.event.special[ type ] || {}; + + // handleObj is passed to all event handlers + handleObj = jQuery.extend( { + type: type, + origType: origType, + data: data, + handler: handler, + guid: handler.guid, + selector: selector, + needsContext: selector && jQuery.expr.match.needsContext.test( selector ), + namespace: namespaces.join( "." ) + }, handleObjIn ); + + // Init the event handler queue if we're the first + if ( !( handlers = events[ type ] ) ) { + handlers = events[ type ] = []; + handlers.delegateCount = 0; + + // Only use addEventListener if the special events handler returns false + if ( !special.setup || + special.setup.call( elem, data, namespaces, eventHandle ) === false ) { + + if ( elem.addEventListener ) { + elem.addEventListener( type, eventHandle ); + } + } + } + + if ( special.add ) { + special.add.call( elem, handleObj ); + + if ( !handleObj.handler.guid ) { + handleObj.handler.guid = handler.guid; + } + } + + // Add to the element's handler list, delegates in front + if ( selector ) { + handlers.splice( handlers.delegateCount++, 0, handleObj ); + } else { + handlers.push( handleObj ); + } + + // Keep track of which events have ever been used, for event optimization + jQuery.event.global[ type ] = true; + } + + }, + + // Detach an event or set of events from an element + remove: function( elem, types, handler, selector, mappedTypes ) { + + var j, origCount, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); + + if ( !elemData || !( events = elemData.events ) ) { + return; + } + + // Once for each type.namespace in types; type may be omitted + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // Unbind all events (on this namespace, if provided) for the element + if ( !type ) { + for ( type in events ) { + jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); + } + continue; + } + + special = jQuery.event.special[ type ] || {}; + type = ( selector ? special.delegateType : special.bindType ) || type; + handlers = events[ type ] || []; + tmp = tmp[ 2 ] && + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); + + // Remove matching events + origCount = j = handlers.length; + while ( j-- ) { + handleObj = handlers[ j ]; + + if ( ( mappedTypes || origType === handleObj.origType ) && + ( !handler || handler.guid === handleObj.guid ) && + ( !tmp || tmp.test( handleObj.namespace ) ) && + ( !selector || selector === handleObj.selector || + selector === "**" && handleObj.selector ) ) { + handlers.splice( j, 1 ); + + if ( handleObj.selector ) { + handlers.delegateCount--; + } + if ( special.remove ) { + special.remove.call( elem, handleObj ); + } + } + } + + // Remove generic event handler if we removed something and no more handlers exist + // (avoids potential for endless recursion during removal of special event handlers) + if ( origCount && !handlers.length ) { + if ( !special.teardown || + special.teardown.call( elem, namespaces, elemData.handle ) === false ) { + + jQuery.removeEvent( elem, type, elemData.handle ); + } + + delete events[ type ]; + } + } + + // Remove data and the expando if it's no longer used + if ( jQuery.isEmptyObject( events ) ) { + dataPriv.remove( elem, "handle events" ); + } + }, + + dispatch: function( nativeEvent ) { + + var i, j, ret, matched, handleObj, handlerQueue, + args = new Array( arguments.length ), + + // Make a writable jQuery.Event from the native event object + event = jQuery.event.fix( nativeEvent ), + + handlers = ( + dataPriv.get( this, "events" ) || Object.create( null ) + )[ event.type ] || [], + special = jQuery.event.special[ event.type ] || {}; + + // Use the fix-ed jQuery.Event rather than the (read-only) native event + args[ 0 ] = event; + + for ( i = 1; i < arguments.length; i++ ) { + args[ i ] = arguments[ i ]; + } + + event.delegateTarget = this; + + // Call the preDispatch hook for the mapped type, and let it bail if desired + if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { + return; + } + + // Determine handlers + handlerQueue = jQuery.event.handlers.call( this, event, handlers ); + + // Run delegates first; they may want to stop propagation beneath us + i = 0; + while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { + event.currentTarget = matched.elem; + + j = 0; + while ( ( handleObj = matched.handlers[ j++ ] ) && + !event.isImmediatePropagationStopped() ) { + + // If the event is namespaced, then each handler is only invoked if it is + // specially universal or its namespaces are a superset of the event's. + if ( !event.rnamespace || handleObj.namespace === false || + event.rnamespace.test( handleObj.namespace ) ) { + + event.handleObj = handleObj; + event.data = handleObj.data; + + ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || + handleObj.handler ).apply( matched.elem, args ); + + if ( ret !== undefined ) { + if ( ( event.result = ret ) === false ) { + event.preventDefault(); + event.stopPropagation(); + } + } + } + } + } + + // Call the postDispatch hook for the mapped type + if ( special.postDispatch ) { + special.postDispatch.call( this, event ); + } + + return event.result; + }, + + handlers: function( event, handlers ) { + var i, handleObj, sel, matchedHandlers, matchedSelectors, + handlerQueue = [], + delegateCount = handlers.delegateCount, + cur = event.target; + + // Find delegate handlers + if ( delegateCount && + + // Support: IE <=9 + // Black-hole SVG instance trees (trac-13180) + cur.nodeType && + + // Support: Firefox <=42 + // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) + // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click + // Support: IE 11 only + // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) + !( event.type === "click" && event.button >= 1 ) ) { + + for ( ; cur !== this; cur = cur.parentNode || this ) { + + // Don't check non-elements (#13208) + // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) + if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { + matchedHandlers = []; + matchedSelectors = {}; + for ( i = 0; i < delegateCount; i++ ) { + handleObj = handlers[ i ]; + + // Don't conflict with Object.prototype properties (#13203) + sel = handleObj.selector + " "; + + if ( matchedSelectors[ sel ] === undefined ) { + matchedSelectors[ sel ] = handleObj.needsContext ? + jQuery( sel, this ).index( cur ) > -1 : + jQuery.find( sel, this, null, [ cur ] ).length; + } + if ( matchedSelectors[ sel ] ) { + matchedHandlers.push( handleObj ); + } + } + if ( matchedHandlers.length ) { + handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); + } + } + } + } + + // Add the remaining (directly-bound) handlers + cur = this; + if ( delegateCount < handlers.length ) { + handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); + } + + return handlerQueue; + }, + + addProp: function( name, hook ) { + Object.defineProperty( jQuery.Event.prototype, name, { + enumerable: true, + configurable: true, + + get: isFunction( hook ) ? + function() { + if ( this.originalEvent ) { + return hook( this.originalEvent ); + } + } : + function() { + if ( this.originalEvent ) { + return this.originalEvent[ name ]; + } + }, + + set: function( value ) { + Object.defineProperty( this, name, { + enumerable: true, + configurable: true, + writable: true, + value: value + } ); + } + } ); + }, + + fix: function( originalEvent ) { + return originalEvent[ jQuery.expando ] ? + originalEvent : + new jQuery.Event( originalEvent ); + }, + + special: { + load: { + + // Prevent triggered image.load events from bubbling to window.load + noBubble: true + }, + click: { + + // Utilize native event to ensure correct state for checkable inputs + setup: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Claim the first handler + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + // dataPriv.set( el, "click", ... ) + leverageNative( el, "click", returnTrue ); + } + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Force setup before triggering a click + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + leverageNative( el, "click" ); + } + + // Return non-false to allow normal event-path propagation + return true; + }, + + // For cross-browser consistency, suppress native .click() on links + // Also prevent it if we're currently inside a leveraged native-event stack + _default: function( event ) { + var target = event.target; + return rcheckableType.test( target.type ) && + target.click && nodeName( target, "input" ) && + dataPriv.get( target, "click" ) || + nodeName( target, "a" ); + } + }, + + beforeunload: { + postDispatch: function( event ) { + + // Support: Firefox 20+ + // Firefox doesn't alert if the returnValue field is not set. + if ( event.result !== undefined && event.originalEvent ) { + event.originalEvent.returnValue = event.result; + } + } + } + } +}; + +// Ensure the presence of an event listener that handles manually-triggered +// synthetic events by interrupting progress until reinvoked in response to +// *native* events that it fires directly, ensuring that state changes have +// already occurred before other listeners are invoked. +function leverageNative( el, type, expectSync ) { + + // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add + if ( !expectSync ) { + if ( dataPriv.get( el, type ) === undefined ) { + jQuery.event.add( el, type, returnTrue ); + } + return; + } + + // Register the controller as a special universal handler for all event namespaces + dataPriv.set( el, type, false ); + jQuery.event.add( el, type, { + namespace: false, + handler: function( event ) { + var notAsync, result, + saved = dataPriv.get( this, type ); + + if ( ( event.isTrigger & 1 ) && this[ type ] ) { + + // Interrupt processing of the outer synthetic .trigger()ed event + // Saved data should be false in such cases, but might be a leftover capture object + // from an async native handler (gh-4350) + if ( !saved.length ) { + + // Store arguments for use when handling the inner native event + // There will always be at least one argument (an event object), so this array + // will not be confused with a leftover capture object. + saved = slice.call( arguments ); + dataPriv.set( this, type, saved ); + + // Trigger the native event and capture its result + // Support: IE <=9 - 11+ + // focus() and blur() are asynchronous + notAsync = expectSync( this, type ); + this[ type ](); + result = dataPriv.get( this, type ); + if ( saved !== result || notAsync ) { + dataPriv.set( this, type, false ); + } else { + result = {}; + } + if ( saved !== result ) { + + // Cancel the outer synthetic event + event.stopImmediatePropagation(); + event.preventDefault(); + + // Support: Chrome 86+ + // In Chrome, if an element having a focusout handler is blurred by + // clicking outside of it, it invokes the handler synchronously. If + // that handler calls `.remove()` on the element, the data is cleared, + // leaving `result` undefined. We need to guard against this. + return result && result.value; + } + + // If this is an inner synthetic event for an event with a bubbling surrogate + // (focus or blur), assume that the surrogate already propagated from triggering the + // native event and prevent that from happening again here. + // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the + // bubbling surrogate propagates *after* the non-bubbling base), but that seems + // less bad than duplication. + } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { + event.stopPropagation(); + } + + // If this is a native event triggered above, everything is now in order + // Fire an inner synthetic event with the original arguments + } else if ( saved.length ) { + + // ...and capture the result + dataPriv.set( this, type, { + value: jQuery.event.trigger( + + // Support: IE <=9 - 11+ + // Extend with the prototype to reset the above stopImmediatePropagation() + jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), + saved.slice( 1 ), + this + ) + } ); + + // Abort handling of the native event + event.stopImmediatePropagation(); + } + } + } ); +} + +jQuery.removeEvent = function( elem, type, handle ) { + + // This "if" is needed for plain objects + if ( elem.removeEventListener ) { + elem.removeEventListener( type, handle ); + } +}; + +jQuery.Event = function( src, props ) { + + // Allow instantiation without the 'new' keyword + if ( !( this instanceof jQuery.Event ) ) { + return new jQuery.Event( src, props ); + } + + // Event object + if ( src && src.type ) { + this.originalEvent = src; + this.type = src.type; + + // Events bubbling up the document may have been marked as prevented + // by a handler lower down the tree; reflect the correct value. + this.isDefaultPrevented = src.defaultPrevented || + src.defaultPrevented === undefined && + + // Support: Android <=2.3 only + src.returnValue === false ? + returnTrue : + returnFalse; + + // Create target properties + // Support: Safari <=6 - 7 only + // Target should not be a text node (#504, #13143) + this.target = ( src.target && src.target.nodeType === 3 ) ? + src.target.parentNode : + src.target; + + this.currentTarget = src.currentTarget; + this.relatedTarget = src.relatedTarget; + + // Event type + } else { + this.type = src; + } + + // Put explicitly provided properties onto the event object + if ( props ) { + jQuery.extend( this, props ); + } + + // Create a timestamp if incoming event doesn't have one + this.timeStamp = src && src.timeStamp || Date.now(); + + // Mark it as fixed + this[ jQuery.expando ] = true; +}; + +// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding +// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html +jQuery.Event.prototype = { + constructor: jQuery.Event, + isDefaultPrevented: returnFalse, + isPropagationStopped: returnFalse, + isImmediatePropagationStopped: returnFalse, + isSimulated: false, + + preventDefault: function() { + var e = this.originalEvent; + + this.isDefaultPrevented = returnTrue; + + if ( e && !this.isSimulated ) { + e.preventDefault(); + } + }, + stopPropagation: function() { + var e = this.originalEvent; + + this.isPropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopPropagation(); + } + }, + stopImmediatePropagation: function() { + var e = this.originalEvent; + + this.isImmediatePropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopImmediatePropagation(); + } + + this.stopPropagation(); + } +}; + +// Includes all common event props including KeyEvent and MouseEvent specific props +jQuery.each( { + altKey: true, + bubbles: true, + cancelable: true, + changedTouches: true, + ctrlKey: true, + detail: true, + eventPhase: true, + metaKey: true, + pageX: true, + pageY: true, + shiftKey: true, + view: true, + "char": true, + code: true, + charCode: true, + key: true, + keyCode: true, + button: true, + buttons: true, + clientX: true, + clientY: true, + offsetX: true, + offsetY: true, + pointerId: true, + pointerType: true, + screenX: true, + screenY: true, + targetTouches: true, + toElement: true, + touches: true, + which: true +}, jQuery.event.addProp ); + +jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { + jQuery.event.special[ type ] = { + + // Utilize native event if possible so blur/focus sequence is correct + setup: function() { + + // Claim the first handler + // dataPriv.set( this, "focus", ... ) + // dataPriv.set( this, "blur", ... ) + leverageNative( this, type, expectSync ); + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function() { + + // Force setup before trigger + leverageNative( this, type ); + + // Return non-false to allow normal event-path propagation + return true; + }, + + // Suppress native focus or blur as it's already being fired + // in leverageNative. + _default: function() { + return true; + }, + + delegateType: delegateType + }; +} ); + +// Create mouseenter/leave events using mouseover/out and event-time checks +// so that event delegation works in jQuery. +// Do the same for pointerenter/pointerleave and pointerover/pointerout +// +// Support: Safari 7 only +// Safari sends mouseenter too often; see: +// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 +// for the description of the bug (it existed in older Chrome versions as well). +jQuery.each( { + mouseenter: "mouseover", + mouseleave: "mouseout", + pointerenter: "pointerover", + pointerleave: "pointerout" +}, function( orig, fix ) { + jQuery.event.special[ orig ] = { + delegateType: fix, + bindType: fix, + + handle: function( event ) { + var ret, + target = this, + related = event.relatedTarget, + handleObj = event.handleObj; + + // For mouseenter/leave call the handler if related is outside the target. + // NB: No relatedTarget if the mouse left/entered the browser window + if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { + event.type = handleObj.origType; + ret = handleObj.handler.apply( this, arguments ); + event.type = fix; + } + return ret; + } + }; +} ); + +jQuery.fn.extend( { + + on: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn ); + }, + one: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn, 1 ); + }, + off: function( types, selector, fn ) { + var handleObj, type; + if ( types && types.preventDefault && types.handleObj ) { + + // ( event ) dispatched jQuery.Event + handleObj = types.handleObj; + jQuery( types.delegateTarget ).off( + handleObj.namespace ? + handleObj.origType + "." + handleObj.namespace : + handleObj.origType, + handleObj.selector, + handleObj.handler + ); + return this; + } + if ( typeof types === "object" ) { + + // ( types-object [, selector] ) + for ( type in types ) { + this.off( type, selector, types[ type ] ); + } + return this; + } + if ( selector === false || typeof selector === "function" ) { + + // ( types [, fn] ) + fn = selector; + selector = undefined; + } + if ( fn === false ) { + fn = returnFalse; + } + return this.each( function() { + jQuery.event.remove( this, types, fn, selector ); + } ); + } +} ); + + +var + + // Support: IE <=10 - 11, Edge 12 - 13 only + // In IE/Edge using regex groups here causes severe slowdowns. + // See https://connect.microsoft.com/IE/feedback/details/1736512/ + rnoInnerhtml = /\s*$/g; + +// Prefer a tbody over its parent table for containing new rows +function manipulationTarget( elem, content ) { + if ( nodeName( elem, "table" ) && + nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { + + return jQuery( elem ).children( "tbody" )[ 0 ] || elem; + } + + return elem; +} + +// Replace/restore the type attribute of script elements for safe DOM manipulation +function disableScript( elem ) { + elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; + return elem; +} +function restoreScript( elem ) { + if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { + elem.type = elem.type.slice( 5 ); + } else { + elem.removeAttribute( "type" ); + } + + return elem; +} + +function cloneCopyEvent( src, dest ) { + var i, l, type, pdataOld, udataOld, udataCur, events; + + if ( dest.nodeType !== 1 ) { + return; + } + + // 1. Copy private data: events, handlers, etc. + if ( dataPriv.hasData( src ) ) { + pdataOld = dataPriv.get( src ); + events = pdataOld.events; + + if ( events ) { + dataPriv.remove( dest, "handle events" ); + + for ( type in events ) { + for ( i = 0, l = events[ type ].length; i < l; i++ ) { + jQuery.event.add( dest, type, events[ type ][ i ] ); + } + } + } + } + + // 2. Copy user data + if ( dataUser.hasData( src ) ) { + udataOld = dataUser.access( src ); + udataCur = jQuery.extend( {}, udataOld ); + + dataUser.set( dest, udataCur ); + } +} + +// Fix IE bugs, see support tests +function fixInput( src, dest ) { + var nodeName = dest.nodeName.toLowerCase(); + + // Fails to persist the checked state of a cloned checkbox or radio button. + if ( nodeName === "input" && rcheckableType.test( src.type ) ) { + dest.checked = src.checked; + + // Fails to return the selected option to the default selected state when cloning options + } else if ( nodeName === "input" || nodeName === "textarea" ) { + dest.defaultValue = src.defaultValue; + } +} + +function domManip( collection, args, callback, ignored ) { + + // Flatten any nested arrays + args = flat( args ); + + var fragment, first, scripts, hasScripts, node, doc, + i = 0, + l = collection.length, + iNoClone = l - 1, + value = args[ 0 ], + valueIsFunction = isFunction( value ); + + // We can't cloneNode fragments that contain checked, in WebKit + if ( valueIsFunction || + ( l > 1 && typeof value === "string" && + !support.checkClone && rchecked.test( value ) ) ) { + return collection.each( function( index ) { + var self = collection.eq( index ); + if ( valueIsFunction ) { + args[ 0 ] = value.call( this, index, self.html() ); + } + domManip( self, args, callback, ignored ); + } ); + } + + if ( l ) { + fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); + first = fragment.firstChild; + + if ( fragment.childNodes.length === 1 ) { + fragment = first; + } + + // Require either new content or an interest in ignored elements to invoke the callback + if ( first || ignored ) { + scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); + hasScripts = scripts.length; + + // Use the original fragment for the last item + // instead of the first because it can end up + // being emptied incorrectly in certain situations (#8070). + for ( ; i < l; i++ ) { + node = fragment; + + if ( i !== iNoClone ) { + node = jQuery.clone( node, true, true ); + + // Keep references to cloned scripts for later restoration + if ( hasScripts ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( scripts, getAll( node, "script" ) ); + } + } + + callback.call( collection[ i ], node, i ); + } + + if ( hasScripts ) { + doc = scripts[ scripts.length - 1 ].ownerDocument; + + // Reenable scripts + jQuery.map( scripts, restoreScript ); + + // Evaluate executable scripts on first document insertion + for ( i = 0; i < hasScripts; i++ ) { + node = scripts[ i ]; + if ( rscriptType.test( node.type || "" ) && + !dataPriv.access( node, "globalEval" ) && + jQuery.contains( doc, node ) ) { + + if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { + + // Optional AJAX dependency, but won't run scripts if not present + if ( jQuery._evalUrl && !node.noModule ) { + jQuery._evalUrl( node.src, { + nonce: node.nonce || node.getAttribute( "nonce" ) + }, doc ); + } + } else { + DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); + } + } + } + } + } + } + + return collection; +} + +function remove( elem, selector, keepData ) { + var node, + nodes = selector ? jQuery.filter( selector, elem ) : elem, + i = 0; + + for ( ; ( node = nodes[ i ] ) != null; i++ ) { + if ( !keepData && node.nodeType === 1 ) { + jQuery.cleanData( getAll( node ) ); + } + + if ( node.parentNode ) { + if ( keepData && isAttached( node ) ) { + setGlobalEval( getAll( node, "script" ) ); + } + node.parentNode.removeChild( node ); + } + } + + return elem; +} + +jQuery.extend( { + htmlPrefilter: function( html ) { + return html; + }, + + clone: function( elem, dataAndEvents, deepDataAndEvents ) { + var i, l, srcElements, destElements, + clone = elem.cloneNode( true ), + inPage = isAttached( elem ); + + // Fix IE cloning issues + if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && + !jQuery.isXMLDoc( elem ) ) { + + // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 + destElements = getAll( clone ); + srcElements = getAll( elem ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + fixInput( srcElements[ i ], destElements[ i ] ); + } + } + + // Copy the events from the original to the clone + if ( dataAndEvents ) { + if ( deepDataAndEvents ) { + srcElements = srcElements || getAll( elem ); + destElements = destElements || getAll( clone ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + cloneCopyEvent( srcElements[ i ], destElements[ i ] ); + } + } else { + cloneCopyEvent( elem, clone ); + } + } + + // Preserve script evaluation history + destElements = getAll( clone, "script" ); + if ( destElements.length > 0 ) { + setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); + } + + // Return the cloned set + return clone; + }, + + cleanData: function( elems ) { + var data, elem, type, + special = jQuery.event.special, + i = 0; + + for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { + if ( acceptData( elem ) ) { + if ( ( data = elem[ dataPriv.expando ] ) ) { + if ( data.events ) { + for ( type in data.events ) { + if ( special[ type ] ) { + jQuery.event.remove( elem, type ); + + // This is a shortcut to avoid jQuery.event.remove's overhead + } else { + jQuery.removeEvent( elem, type, data.handle ); + } + } + } + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataPriv.expando ] = undefined; + } + if ( elem[ dataUser.expando ] ) { + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataUser.expando ] = undefined; + } + } + } + } +} ); + +jQuery.fn.extend( { + detach: function( selector ) { + return remove( this, selector, true ); + }, + + remove: function( selector ) { + return remove( this, selector ); + }, + + text: function( value ) { + return access( this, function( value ) { + return value === undefined ? + jQuery.text( this ) : + this.empty().each( function() { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + this.textContent = value; + } + } ); + }, null, value, arguments.length ); + }, + + append: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.appendChild( elem ); + } + } ); + }, + + prepend: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.insertBefore( elem, target.firstChild ); + } + } ); + }, + + before: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this ); + } + } ); + }, + + after: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this.nextSibling ); + } + } ); + }, + + empty: function() { + var elem, + i = 0; + + for ( ; ( elem = this[ i ] ) != null; i++ ) { + if ( elem.nodeType === 1 ) { + + // Prevent memory leaks + jQuery.cleanData( getAll( elem, false ) ); + + // Remove any remaining nodes + elem.textContent = ""; + } + } + + return this; + }, + + clone: function( dataAndEvents, deepDataAndEvents ) { + dataAndEvents = dataAndEvents == null ? false : dataAndEvents; + deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; + + return this.map( function() { + return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); + } ); + }, + + html: function( value ) { + return access( this, function( value ) { + var elem = this[ 0 ] || {}, + i = 0, + l = this.length; + + if ( value === undefined && elem.nodeType === 1 ) { + return elem.innerHTML; + } + + // See if we can take a shortcut and just use innerHTML + if ( typeof value === "string" && !rnoInnerhtml.test( value ) && + !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { + + value = jQuery.htmlPrefilter( value ); + + try { + for ( ; i < l; i++ ) { + elem = this[ i ] || {}; + + // Remove element nodes and prevent memory leaks + if ( elem.nodeType === 1 ) { + jQuery.cleanData( getAll( elem, false ) ); + elem.innerHTML = value; + } + } + + elem = 0; + + // If using innerHTML throws an exception, use the fallback method + } catch ( e ) {} + } + + if ( elem ) { + this.empty().append( value ); + } + }, null, value, arguments.length ); + }, + + replaceWith: function() { + var ignored = []; + + // Make the changes, replacing each non-ignored context element with the new content + return domManip( this, arguments, function( elem ) { + var parent = this.parentNode; + + if ( jQuery.inArray( this, ignored ) < 0 ) { + jQuery.cleanData( getAll( this ) ); + if ( parent ) { + parent.replaceChild( elem, this ); + } + } + + // Force callback invocation + }, ignored ); + } +} ); + +jQuery.each( { + appendTo: "append", + prependTo: "prepend", + insertBefore: "before", + insertAfter: "after", + replaceAll: "replaceWith" +}, function( name, original ) { + jQuery.fn[ name ] = function( selector ) { + var elems, + ret = [], + insert = jQuery( selector ), + last = insert.length - 1, + i = 0; + + for ( ; i <= last; i++ ) { + elems = i === last ? this : this.clone( true ); + jQuery( insert[ i ] )[ original ]( elems ); + + // Support: Android <=4.0 only, PhantomJS 1 only + // .get() because push.apply(_, arraylike) throws on ancient WebKit + push.apply( ret, elems.get() ); + } + + return this.pushStack( ret ); + }; +} ); +var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); + +var getStyles = function( elem ) { + + // Support: IE <=11 only, Firefox <=30 (#15098, #14150) + // IE throws on elements created in popups + // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" + var view = elem.ownerDocument.defaultView; + + if ( !view || !view.opener ) { + view = window; + } + + return view.getComputedStyle( elem ); + }; + +var swap = function( elem, options, callback ) { + var ret, name, + old = {}; + + // Remember the old values, and insert the new ones + for ( name in options ) { + old[ name ] = elem.style[ name ]; + elem.style[ name ] = options[ name ]; + } + + ret = callback.call( elem ); + + // Revert the old values + for ( name in options ) { + elem.style[ name ] = old[ name ]; + } + + return ret; +}; + + +var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); + + + +( function() { + + // Executing both pixelPosition & boxSizingReliable tests require only one layout + // so they're executed at the same time to save the second computation. + function computeStyleTests() { + + // This is a singleton, we need to execute it only once + if ( !div ) { + return; + } + + container.style.cssText = "position:absolute;left:-11111px;width:60px;" + + "margin-top:1px;padding:0;border:0"; + div.style.cssText = + "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + + "margin:auto;border:1px;padding:1px;" + + "width:60%;top:1%"; + documentElement.appendChild( container ).appendChild( div ); + + var divStyle = window.getComputedStyle( div ); + pixelPositionVal = divStyle.top !== "1%"; + + // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 + reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; + + // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 + // Some styles come back with percentage values, even though they shouldn't + div.style.right = "60%"; + pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; + + // Support: IE 9 - 11 only + // Detect misreporting of content dimensions for box-sizing:border-box elements + boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; + + // Support: IE 9 only + // Detect overflow:scroll screwiness (gh-3699) + // Support: Chrome <=64 + // Don't get tricked when zoom affects offsetWidth (gh-4029) + div.style.position = "absolute"; + scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; + + documentElement.removeChild( container ); + + // Nullify the div so it wouldn't be stored in the memory and + // it will also be a sign that checks already performed + div = null; + } + + function roundPixelMeasures( measure ) { + return Math.round( parseFloat( measure ) ); + } + + var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, + reliableTrDimensionsVal, reliableMarginLeftVal, + container = document.createElement( "div" ), + div = document.createElement( "div" ); + + // Finish early in limited (non-browser) environments + if ( !div.style ) { + return; + } + + // Support: IE <=9 - 11 only + // Style of cloned element affects source element cloned (#8908) + div.style.backgroundClip = "content-box"; + div.cloneNode( true ).style.backgroundClip = ""; + support.clearCloneStyle = div.style.backgroundClip === "content-box"; + + jQuery.extend( support, { + boxSizingReliable: function() { + computeStyleTests(); + return boxSizingReliableVal; + }, + pixelBoxStyles: function() { + computeStyleTests(); + return pixelBoxStylesVal; + }, + pixelPosition: function() { + computeStyleTests(); + return pixelPositionVal; + }, + reliableMarginLeft: function() { + computeStyleTests(); + return reliableMarginLeftVal; + }, + scrollboxSize: function() { + computeStyleTests(); + return scrollboxSizeVal; + }, + + // Support: IE 9 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Behavior in IE 9 is more subtle than in newer versions & it passes + // some versions of this test; make sure not to make it pass there! + // + // Support: Firefox 70+ + // Only Firefox includes border widths + // in computed dimensions. (gh-4529) + reliableTrDimensions: function() { + var table, tr, trChild, trStyle; + if ( reliableTrDimensionsVal == null ) { + table = document.createElement( "table" ); + tr = document.createElement( "tr" ); + trChild = document.createElement( "div" ); + + table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate"; + tr.style.cssText = "border:1px solid"; + + // Support: Chrome 86+ + // Height set through cssText does not get applied. + // Computed height then comes back as 0. + tr.style.height = "1px"; + trChild.style.height = "9px"; + + // Support: Android 8 Chrome 86+ + // In our bodyBackground.html iframe, + // display for all div elements is set to "inline", + // which causes a problem only in Android 8 Chrome 86. + // Ensuring the div is display: block + // gets around this issue. + trChild.style.display = "block"; + + documentElement + .appendChild( table ) + .appendChild( tr ) + .appendChild( trChild ); + + trStyle = window.getComputedStyle( tr ); + reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) + + parseInt( trStyle.borderTopWidth, 10 ) + + parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight; + + documentElement.removeChild( table ); + } + return reliableTrDimensionsVal; + } + } ); +} )(); + + +function curCSS( elem, name, computed ) { + var width, minWidth, maxWidth, ret, + + // Support: Firefox 51+ + // Retrieving style before computed somehow + // fixes an issue with getting wrong values + // on detached elements + style = elem.style; + + computed = computed || getStyles( elem ); + + // getPropertyValue is needed for: + // .css('filter') (IE 9 only, #12537) + // .css('--customProperty) (#3144) + if ( computed ) { + ret = computed.getPropertyValue( name ) || computed[ name ]; + + if ( ret === "" && !isAttached( elem ) ) { + ret = jQuery.style( elem, name ); + } + + // A tribute to the "awesome hack by Dean Edwards" + // Android Browser returns percentage for some values, + // but width seems to be reliably pixels. + // This is against the CSSOM draft spec: + // https://drafts.csswg.org/cssom/#resolved-values + if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { + + // Remember the original values + width = style.width; + minWidth = style.minWidth; + maxWidth = style.maxWidth; + + // Put in the new values to get a computed value out + style.minWidth = style.maxWidth = style.width = ret; + ret = computed.width; + + // Revert the changed values + style.width = width; + style.minWidth = minWidth; + style.maxWidth = maxWidth; + } + } + + return ret !== undefined ? + + // Support: IE <=9 - 11 only + // IE returns zIndex value as an integer. + ret + "" : + ret; +} + + +function addGetHookIf( conditionFn, hookFn ) { + + // Define the hook, we'll check on the first run if it's really needed. + return { + get: function() { + if ( conditionFn() ) { + + // Hook not needed (or it's not possible to use it due + // to missing dependency), remove it. + delete this.get; + return; + } + + // Hook needed; redefine it so that the support test is not executed again. + return ( this.get = hookFn ).apply( this, arguments ); + } + }; +} + + +var cssPrefixes = [ "Webkit", "Moz", "ms" ], + emptyStyle = document.createElement( "div" ).style, + vendorProps = {}; + +// Return a vendor-prefixed property or undefined +function vendorPropName( name ) { + + // Check for vendor prefixed names + var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), + i = cssPrefixes.length; + + while ( i-- ) { + name = cssPrefixes[ i ] + capName; + if ( name in emptyStyle ) { + return name; + } + } +} + +// Return a potentially-mapped jQuery.cssProps or vendor prefixed property +function finalPropName( name ) { + var final = jQuery.cssProps[ name ] || vendorProps[ name ]; + + if ( final ) { + return final; + } + if ( name in emptyStyle ) { + return name; + } + return vendorProps[ name ] = vendorPropName( name ) || name; +} + + +var + + // Swappable if display is none or starts with table + // except "table", "table-cell", or "table-caption" + // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display + rdisplayswap = /^(none|table(?!-c[ea]).+)/, + rcustomProp = /^--/, + cssShow = { position: "absolute", visibility: "hidden", display: "block" }, + cssNormalTransform = { + letterSpacing: "0", + fontWeight: "400" + }; + +function setPositiveNumber( _elem, value, subtract ) { + + // Any relative (+/-) values have already been + // normalized at this point + var matches = rcssNum.exec( value ); + return matches ? + + // Guard against undefined "subtract", e.g., when used as in cssHooks + Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : + value; +} + +function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { + var i = dimension === "width" ? 1 : 0, + extra = 0, + delta = 0; + + // Adjustment may not be necessary + if ( box === ( isBorderBox ? "border" : "content" ) ) { + return 0; + } + + for ( ; i < 4; i += 2 ) { + + // Both box models exclude margin + if ( box === "margin" ) { + delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); + } + + // If we get here with a content-box, we're seeking "padding" or "border" or "margin" + if ( !isBorderBox ) { + + // Add padding + delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + + // For "border" or "margin", add border + if ( box !== "padding" ) { + delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + + // But still keep track of it otherwise + } else { + extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + + // If we get here with a border-box (content + padding + border), we're seeking "content" or + // "padding" or "margin" + } else { + + // For "content", subtract padding + if ( box === "content" ) { + delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + } + + // For "content" or "padding", subtract border + if ( box !== "margin" ) { + delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + } + } + + // Account for positive content-box scroll gutter when requested by providing computedVal + if ( !isBorderBox && computedVal >= 0 ) { + + // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border + // Assuming integer scroll gutter, subtract the rest and round down + delta += Math.max( 0, Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + computedVal - + delta - + extra - + 0.5 + + // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter + // Use an explicit zero to avoid NaN (gh-3964) + ) ) || 0; + } + + return delta; +} + +function getWidthOrHeight( elem, dimension, extra ) { + + // Start with computed style + var styles = getStyles( elem ), + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). + // Fake content-box until we know it's needed to know the true value. + boxSizingNeeded = !support.boxSizingReliable() || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + valueIsBorderBox = isBorderBox, + + val = curCSS( elem, dimension, styles ), + offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); + + // Support: Firefox <=54 + // Return a confounding non-pixel value or feign ignorance, as appropriate. + if ( rnumnonpx.test( val ) ) { + if ( !extra ) { + return val; + } + val = "auto"; + } + + + // Support: IE 9 - 11 only + // Use offsetWidth/offsetHeight for when box sizing is unreliable. + // In those cases, the computed value can be trusted to be border-box. + if ( ( !support.boxSizingReliable() && isBorderBox || + + // Support: IE 10 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Interestingly, in some cases IE 9 doesn't suffer from this issue. + !support.reliableTrDimensions() && nodeName( elem, "tr" ) || + + // Fall back to offsetWidth/offsetHeight when value is "auto" + // This happens for inline elements with no explicit setting (gh-3571) + val === "auto" || + + // Support: Android <=4.1 - 4.3 only + // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) + !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && + + // Make sure the element is visible & connected + elem.getClientRects().length ) { + + isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; + + // Where available, offsetWidth/offsetHeight approximate border box dimensions. + // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the + // retrieved value as a content box dimension. + valueIsBorderBox = offsetProp in elem; + if ( valueIsBorderBox ) { + val = elem[ offsetProp ]; + } + } + + // Normalize "" and auto + val = parseFloat( val ) || 0; + + // Adjust for the element's box model + return ( val + + boxModelAdjustment( + elem, + dimension, + extra || ( isBorderBox ? "border" : "content" ), + valueIsBorderBox, + styles, + + // Provide the current computed size to request scroll gutter calculation (gh-3589) + val + ) + ) + "px"; +} + +jQuery.extend( { + + // Add in style property hooks for overriding the default + // behavior of getting and setting a style property + cssHooks: { + opacity: { + get: function( elem, computed ) { + if ( computed ) { + + // We should always get a number back from opacity + var ret = curCSS( elem, "opacity" ); + return ret === "" ? "1" : ret; + } + } + } + }, + + // Don't automatically add "px" to these possibly-unitless properties + cssNumber: { + "animationIterationCount": true, + "columnCount": true, + "fillOpacity": true, + "flexGrow": true, + "flexShrink": true, + "fontWeight": true, + "gridArea": true, + "gridColumn": true, + "gridColumnEnd": true, + "gridColumnStart": true, + "gridRow": true, + "gridRowEnd": true, + "gridRowStart": true, + "lineHeight": true, + "opacity": true, + "order": true, + "orphans": true, + "widows": true, + "zIndex": true, + "zoom": true + }, + + // Add in properties whose names you wish to fix before + // setting or getting the value + cssProps: {}, + + // Get and set the style property on a DOM Node + style: function( elem, name, value, extra ) { + + // Don't set styles on text and comment nodes + if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { + return; + } + + // Make sure that we're working with the right name + var ret, type, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ), + style = elem.style; + + // Make sure that we're working with the right name. We don't + // want to query the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Gets hook for the prefixed version, then unprefixed version + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // Check if we're setting a value + if ( value !== undefined ) { + type = typeof value; + + // Convert "+=" or "-=" to relative numbers (#7345) + if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { + value = adjustCSS( elem, name, ret ); + + // Fixes bug #9237 + type = "number"; + } + + // Make sure that null and NaN values aren't set (#7116) + if ( value == null || value !== value ) { + return; + } + + // If a number was passed in, add the unit (except for certain CSS properties) + // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append + // "px" to a few hardcoded values. + if ( type === "number" && !isCustomProp ) { + value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); + } + + // background-* props affect original clone's values + if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { + style[ name ] = "inherit"; + } + + // If a hook was provided, use that value, otherwise just set the specified value + if ( !hooks || !( "set" in hooks ) || + ( value = hooks.set( elem, value, extra ) ) !== undefined ) { + + if ( isCustomProp ) { + style.setProperty( name, value ); + } else { + style[ name ] = value; + } + } + + } else { + + // If a hook was provided get the non-computed value from there + if ( hooks && "get" in hooks && + ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { + + return ret; + } + + // Otherwise just get the value from the style object + return style[ name ]; + } + }, + + css: function( elem, name, extra, styles ) { + var val, num, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ); + + // Make sure that we're working with the right name. We don't + // want to modify the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Try prefixed name followed by the unprefixed name + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // If a hook was provided get the computed value from there + if ( hooks && "get" in hooks ) { + val = hooks.get( elem, true, extra ); + } + + // Otherwise, if a way to get the computed value exists, use that + if ( val === undefined ) { + val = curCSS( elem, name, styles ); + } + + // Convert "normal" to computed value + if ( val === "normal" && name in cssNormalTransform ) { + val = cssNormalTransform[ name ]; + } + + // Make numeric if forced or a qualifier was provided and val looks numeric + if ( extra === "" || extra ) { + num = parseFloat( val ); + return extra === true || isFinite( num ) ? num || 0 : val; + } + + return val; + } +} ); + +jQuery.each( [ "height", "width" ], function( _i, dimension ) { + jQuery.cssHooks[ dimension ] = { + get: function( elem, computed, extra ) { + if ( computed ) { + + // Certain elements can have dimension info if we invisibly show them + // but it must have a current display style that would benefit + return rdisplayswap.test( jQuery.css( elem, "display" ) ) && + + // Support: Safari 8+ + // Table columns in Safari have non-zero offsetWidth & zero + // getBoundingClientRect().width unless display is changed. + // Support: IE <=11 only + // Running getBoundingClientRect on a disconnected node + // in IE throws an error. + ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? + swap( elem, cssShow, function() { + return getWidthOrHeight( elem, dimension, extra ); + } ) : + getWidthOrHeight( elem, dimension, extra ); + } + }, + + set: function( elem, value, extra ) { + var matches, + styles = getStyles( elem ), + + // Only read styles.position if the test has a chance to fail + // to avoid forcing a reflow. + scrollboxSizeBuggy = !support.scrollboxSize() && + styles.position === "absolute", + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) + boxSizingNeeded = scrollboxSizeBuggy || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + subtract = extra ? + boxModelAdjustment( + elem, + dimension, + extra, + isBorderBox, + styles + ) : + 0; + + // Account for unreliable border-box dimensions by comparing offset* to computed and + // faking a content-box to get border and padding (gh-3699) + if ( isBorderBox && scrollboxSizeBuggy ) { + subtract -= Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + parseFloat( styles[ dimension ] ) - + boxModelAdjustment( elem, dimension, "border", false, styles ) - + 0.5 + ); + } + + // Convert to pixels if value adjustment is needed + if ( subtract && ( matches = rcssNum.exec( value ) ) && + ( matches[ 3 ] || "px" ) !== "px" ) { + + elem.style[ dimension ] = value; + value = jQuery.css( elem, dimension ); + } + + return setPositiveNumber( elem, value, subtract ); + } + }; +} ); + +jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, + function( elem, computed ) { + if ( computed ) { + return ( parseFloat( curCSS( elem, "marginLeft" ) ) || + elem.getBoundingClientRect().left - + swap( elem, { marginLeft: 0 }, function() { + return elem.getBoundingClientRect().left; + } ) + ) + "px"; + } + } +); + +// These hooks are used by animate to expand properties +jQuery.each( { + margin: "", + padding: "", + border: "Width" +}, function( prefix, suffix ) { + jQuery.cssHooks[ prefix + suffix ] = { + expand: function( value ) { + var i = 0, + expanded = {}, + + // Assumes a single number if not a string + parts = typeof value === "string" ? value.split( " " ) : [ value ]; + + for ( ; i < 4; i++ ) { + expanded[ prefix + cssExpand[ i ] + suffix ] = + parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; + } + + return expanded; + } + }; + + if ( prefix !== "margin" ) { + jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; + } +} ); + +jQuery.fn.extend( { + css: function( name, value ) { + return access( this, function( elem, name, value ) { + var styles, len, + map = {}, + i = 0; + + if ( Array.isArray( name ) ) { + styles = getStyles( elem ); + len = name.length; + + for ( ; i < len; i++ ) { + map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); + } + + return map; + } + + return value !== undefined ? + jQuery.style( elem, name, value ) : + jQuery.css( elem, name ); + }, name, value, arguments.length > 1 ); + } +} ); + + +function Tween( elem, options, prop, end, easing ) { + return new Tween.prototype.init( elem, options, prop, end, easing ); +} +jQuery.Tween = Tween; + +Tween.prototype = { + constructor: Tween, + init: function( elem, options, prop, end, easing, unit ) { + this.elem = elem; + this.prop = prop; + this.easing = easing || jQuery.easing._default; + this.options = options; + this.start = this.now = this.cur(); + this.end = end; + this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); + }, + cur: function() { + var hooks = Tween.propHooks[ this.prop ]; + + return hooks && hooks.get ? + hooks.get( this ) : + Tween.propHooks._default.get( this ); + }, + run: function( percent ) { + var eased, + hooks = Tween.propHooks[ this.prop ]; + + if ( this.options.duration ) { + this.pos = eased = jQuery.easing[ this.easing ]( + percent, this.options.duration * percent, 0, 1, this.options.duration + ); + } else { + this.pos = eased = percent; + } + this.now = ( this.end - this.start ) * eased + this.start; + + if ( this.options.step ) { + this.options.step.call( this.elem, this.now, this ); + } + + if ( hooks && hooks.set ) { + hooks.set( this ); + } else { + Tween.propHooks._default.set( this ); + } + return this; + } +}; + +Tween.prototype.init.prototype = Tween.prototype; + +Tween.propHooks = { + _default: { + get: function( tween ) { + var result; + + // Use a property on the element directly when it is not a DOM element, + // or when there is no matching style property that exists. + if ( tween.elem.nodeType !== 1 || + tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { + return tween.elem[ tween.prop ]; + } + + // Passing an empty string as a 3rd parameter to .css will automatically + // attempt a parseFloat and fallback to a string if the parse fails. + // Simple values such as "10px" are parsed to Float; + // complex values such as "rotate(1rad)" are returned as-is. + result = jQuery.css( tween.elem, tween.prop, "" ); + + // Empty strings, null, undefined and "auto" are converted to 0. + return !result || result === "auto" ? 0 : result; + }, + set: function( tween ) { + + // Use step hook for back compat. + // Use cssHook if its there. + // Use .style if available and use plain properties where available. + if ( jQuery.fx.step[ tween.prop ] ) { + jQuery.fx.step[ tween.prop ]( tween ); + } else if ( tween.elem.nodeType === 1 && ( + jQuery.cssHooks[ tween.prop ] || + tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { + jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); + } else { + tween.elem[ tween.prop ] = tween.now; + } + } + } +}; + +// Support: IE <=9 only +// Panic based approach to setting things on disconnected nodes +Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { + set: function( tween ) { + if ( tween.elem.nodeType && tween.elem.parentNode ) { + tween.elem[ tween.prop ] = tween.now; + } + } +}; + +jQuery.easing = { + linear: function( p ) { + return p; + }, + swing: function( p ) { + return 0.5 - Math.cos( p * Math.PI ) / 2; + }, + _default: "swing" +}; + +jQuery.fx = Tween.prototype.init; + +// Back compat <1.8 extension point +jQuery.fx.step = {}; + + + + +var + fxNow, inProgress, + rfxtypes = /^(?:toggle|show|hide)$/, + rrun = /queueHooks$/; + +function schedule() { + if ( inProgress ) { + if ( document.hidden === false && window.requestAnimationFrame ) { + window.requestAnimationFrame( schedule ); + } else { + window.setTimeout( schedule, jQuery.fx.interval ); + } + + jQuery.fx.tick(); + } +} + +// Animations created synchronously will run synchronously +function createFxNow() { + window.setTimeout( function() { + fxNow = undefined; + } ); + return ( fxNow = Date.now() ); +} + +// Generate parameters to create a standard animation +function genFx( type, includeWidth ) { + var which, + i = 0, + attrs = { height: type }; + + // If we include width, step value is 1 to do all cssExpand values, + // otherwise step value is 2 to skip over Left and Right + includeWidth = includeWidth ? 1 : 0; + for ( ; i < 4; i += 2 - includeWidth ) { + which = cssExpand[ i ]; + attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; + } + + if ( includeWidth ) { + attrs.opacity = attrs.width = type; + } + + return attrs; +} + +function createTween( value, prop, animation ) { + var tween, + collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), + index = 0, + length = collection.length; + for ( ; index < length; index++ ) { + if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { + + // We're done with this property + return tween; + } + } +} + +function defaultPrefilter( elem, props, opts ) { + var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, + isBox = "width" in props || "height" in props, + anim = this, + orig = {}, + style = elem.style, + hidden = elem.nodeType && isHiddenWithinTree( elem ), + dataShow = dataPriv.get( elem, "fxshow" ); + + // Queue-skipping animations hijack the fx hooks + if ( !opts.queue ) { + hooks = jQuery._queueHooks( elem, "fx" ); + if ( hooks.unqueued == null ) { + hooks.unqueued = 0; + oldfire = hooks.empty.fire; + hooks.empty.fire = function() { + if ( !hooks.unqueued ) { + oldfire(); + } + }; + } + hooks.unqueued++; + + anim.always( function() { + + // Ensure the complete handler is called before this completes + anim.always( function() { + hooks.unqueued--; + if ( !jQuery.queue( elem, "fx" ).length ) { + hooks.empty.fire(); + } + } ); + } ); + } + + // Detect show/hide animations + for ( prop in props ) { + value = props[ prop ]; + if ( rfxtypes.test( value ) ) { + delete props[ prop ]; + toggle = toggle || value === "toggle"; + if ( value === ( hidden ? "hide" : "show" ) ) { + + // Pretend to be hidden if this is a "show" and + // there is still data from a stopped show/hide + if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { + hidden = true; + + // Ignore all other no-op show/hide data + } else { + continue; + } + } + orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); + } + } + + // Bail out if this is a no-op like .hide().hide() + propTween = !jQuery.isEmptyObject( props ); + if ( !propTween && jQuery.isEmptyObject( orig ) ) { + return; + } + + // Restrict "overflow" and "display" styles during box animations + if ( isBox && elem.nodeType === 1 ) { + + // Support: IE <=9 - 11, Edge 12 - 15 + // Record all 3 overflow attributes because IE does not infer the shorthand + // from identically-valued overflowX and overflowY and Edge just mirrors + // the overflowX value there. + opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; + + // Identify a display type, preferring old show/hide data over the CSS cascade + restoreDisplay = dataShow && dataShow.display; + if ( restoreDisplay == null ) { + restoreDisplay = dataPriv.get( elem, "display" ); + } + display = jQuery.css( elem, "display" ); + if ( display === "none" ) { + if ( restoreDisplay ) { + display = restoreDisplay; + } else { + + // Get nonempty value(s) by temporarily forcing visibility + showHide( [ elem ], true ); + restoreDisplay = elem.style.display || restoreDisplay; + display = jQuery.css( elem, "display" ); + showHide( [ elem ] ); + } + } + + // Animate inline elements as inline-block + if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { + if ( jQuery.css( elem, "float" ) === "none" ) { + + // Restore the original display value at the end of pure show/hide animations + if ( !propTween ) { + anim.done( function() { + style.display = restoreDisplay; + } ); + if ( restoreDisplay == null ) { + display = style.display; + restoreDisplay = display === "none" ? "" : display; + } + } + style.display = "inline-block"; + } + } + } + + if ( opts.overflow ) { + style.overflow = "hidden"; + anim.always( function() { + style.overflow = opts.overflow[ 0 ]; + style.overflowX = opts.overflow[ 1 ]; + style.overflowY = opts.overflow[ 2 ]; + } ); + } + + // Implement show/hide animations + propTween = false; + for ( prop in orig ) { + + // General show/hide setup for this element animation + if ( !propTween ) { + if ( dataShow ) { + if ( "hidden" in dataShow ) { + hidden = dataShow.hidden; + } + } else { + dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); + } + + // Store hidden/visible for toggle so `.stop().toggle()` "reverses" + if ( toggle ) { + dataShow.hidden = !hidden; + } + + // Show elements before animating them + if ( hidden ) { + showHide( [ elem ], true ); + } + + /* eslint-disable no-loop-func */ + + anim.done( function() { + + /* eslint-enable no-loop-func */ + + // The final step of a "hide" animation is actually hiding the element + if ( !hidden ) { + showHide( [ elem ] ); + } + dataPriv.remove( elem, "fxshow" ); + for ( prop in orig ) { + jQuery.style( elem, prop, orig[ prop ] ); + } + } ); + } + + // Per-property setup + propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); + if ( !( prop in dataShow ) ) { + dataShow[ prop ] = propTween.start; + if ( hidden ) { + propTween.end = propTween.start; + propTween.start = 0; + } + } + } +} + +function propFilter( props, specialEasing ) { + var index, name, easing, value, hooks; + + // camelCase, specialEasing and expand cssHook pass + for ( index in props ) { + name = camelCase( index ); + easing = specialEasing[ name ]; + value = props[ index ]; + if ( Array.isArray( value ) ) { + easing = value[ 1 ]; + value = props[ index ] = value[ 0 ]; + } + + if ( index !== name ) { + props[ name ] = value; + delete props[ index ]; + } + + hooks = jQuery.cssHooks[ name ]; + if ( hooks && "expand" in hooks ) { + value = hooks.expand( value ); + delete props[ name ]; + + // Not quite $.extend, this won't overwrite existing keys. + // Reusing 'index' because we have the correct "name" + for ( index in value ) { + if ( !( index in props ) ) { + props[ index ] = value[ index ]; + specialEasing[ index ] = easing; + } + } + } else { + specialEasing[ name ] = easing; + } + } +} + +function Animation( elem, properties, options ) { + var result, + stopped, + index = 0, + length = Animation.prefilters.length, + deferred = jQuery.Deferred().always( function() { + + // Don't match elem in the :animated selector + delete tick.elem; + } ), + tick = function() { + if ( stopped ) { + return false; + } + var currentTime = fxNow || createFxNow(), + remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), + + // Support: Android 2.3 only + // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) + temp = remaining / animation.duration || 0, + percent = 1 - temp, + index = 0, + length = animation.tweens.length; + + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( percent ); + } + + deferred.notifyWith( elem, [ animation, percent, remaining ] ); + + // If there's more to do, yield + if ( percent < 1 && length ) { + return remaining; + } + + // If this was an empty animation, synthesize a final progress notification + if ( !length ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + } + + // Resolve the animation and report its conclusion + deferred.resolveWith( elem, [ animation ] ); + return false; + }, + animation = deferred.promise( { + elem: elem, + props: jQuery.extend( {}, properties ), + opts: jQuery.extend( true, { + specialEasing: {}, + easing: jQuery.easing._default + }, options ), + originalProperties: properties, + originalOptions: options, + startTime: fxNow || createFxNow(), + duration: options.duration, + tweens: [], + createTween: function( prop, end ) { + var tween = jQuery.Tween( elem, animation.opts, prop, end, + animation.opts.specialEasing[ prop ] || animation.opts.easing ); + animation.tweens.push( tween ); + return tween; + }, + stop: function( gotoEnd ) { + var index = 0, + + // If we are going to the end, we want to run all the tweens + // otherwise we skip this part + length = gotoEnd ? animation.tweens.length : 0; + if ( stopped ) { + return this; + } + stopped = true; + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( 1 ); + } + + // Resolve when we played the last frame; otherwise, reject + if ( gotoEnd ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + deferred.resolveWith( elem, [ animation, gotoEnd ] ); + } else { + deferred.rejectWith( elem, [ animation, gotoEnd ] ); + } + return this; + } + } ), + props = animation.props; + + propFilter( props, animation.opts.specialEasing ); + + for ( ; index < length; index++ ) { + result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); + if ( result ) { + if ( isFunction( result.stop ) ) { + jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = + result.stop.bind( result ); + } + return result; + } + } + + jQuery.map( props, createTween, animation ); + + if ( isFunction( animation.opts.start ) ) { + animation.opts.start.call( elem, animation ); + } + + // Attach callbacks from options + animation + .progress( animation.opts.progress ) + .done( animation.opts.done, animation.opts.complete ) + .fail( animation.opts.fail ) + .always( animation.opts.always ); + + jQuery.fx.timer( + jQuery.extend( tick, { + elem: elem, + anim: animation, + queue: animation.opts.queue + } ) + ); + + return animation; +} + +jQuery.Animation = jQuery.extend( Animation, { + + tweeners: { + "*": [ function( prop, value ) { + var tween = this.createTween( prop, value ); + adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); + return tween; + } ] + }, + + tweener: function( props, callback ) { + if ( isFunction( props ) ) { + callback = props; + props = [ "*" ]; + } else { + props = props.match( rnothtmlwhite ); + } + + var prop, + index = 0, + length = props.length; + + for ( ; index < length; index++ ) { + prop = props[ index ]; + Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; + Animation.tweeners[ prop ].unshift( callback ); + } + }, + + prefilters: [ defaultPrefilter ], + + prefilter: function( callback, prepend ) { + if ( prepend ) { + Animation.prefilters.unshift( callback ); + } else { + Animation.prefilters.push( callback ); + } + } +} ); + +jQuery.speed = function( speed, easing, fn ) { + var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { + complete: fn || !fn && easing || + isFunction( speed ) && speed, + duration: speed, + easing: fn && easing || easing && !isFunction( easing ) && easing + }; + + // Go to the end state if fx are off + if ( jQuery.fx.off ) { + opt.duration = 0; + + } else { + if ( typeof opt.duration !== "number" ) { + if ( opt.duration in jQuery.fx.speeds ) { + opt.duration = jQuery.fx.speeds[ opt.duration ]; + + } else { + opt.duration = jQuery.fx.speeds._default; + } + } + } + + // Normalize opt.queue - true/undefined/null -> "fx" + if ( opt.queue == null || opt.queue === true ) { + opt.queue = "fx"; + } + + // Queueing + opt.old = opt.complete; + + opt.complete = function() { + if ( isFunction( opt.old ) ) { + opt.old.call( this ); + } + + if ( opt.queue ) { + jQuery.dequeue( this, opt.queue ); + } + }; + + return opt; +}; + +jQuery.fn.extend( { + fadeTo: function( speed, to, easing, callback ) { + + // Show any hidden elements after setting opacity to 0 + return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() + + // Animate to the value specified + .end().animate( { opacity: to }, speed, easing, callback ); + }, + animate: function( prop, speed, easing, callback ) { + var empty = jQuery.isEmptyObject( prop ), + optall = jQuery.speed( speed, easing, callback ), + doAnimation = function() { + + // Operate on a copy of prop so per-property easing won't be lost + var anim = Animation( this, jQuery.extend( {}, prop ), optall ); + + // Empty animations, or finishing resolves immediately + if ( empty || dataPriv.get( this, "finish" ) ) { + anim.stop( true ); + } + }; + + doAnimation.finish = doAnimation; + + return empty || optall.queue === false ? + this.each( doAnimation ) : + this.queue( optall.queue, doAnimation ); + }, + stop: function( type, clearQueue, gotoEnd ) { + var stopQueue = function( hooks ) { + var stop = hooks.stop; + delete hooks.stop; + stop( gotoEnd ); + }; + + if ( typeof type !== "string" ) { + gotoEnd = clearQueue; + clearQueue = type; + type = undefined; + } + if ( clearQueue ) { + this.queue( type || "fx", [] ); + } + + return this.each( function() { + var dequeue = true, + index = type != null && type + "queueHooks", + timers = jQuery.timers, + data = dataPriv.get( this ); + + if ( index ) { + if ( data[ index ] && data[ index ].stop ) { + stopQueue( data[ index ] ); + } + } else { + for ( index in data ) { + if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { + stopQueue( data[ index ] ); + } + } + } + + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && + ( type == null || timers[ index ].queue === type ) ) { + + timers[ index ].anim.stop( gotoEnd ); + dequeue = false; + timers.splice( index, 1 ); + } + } + + // Start the next in the queue if the last step wasn't forced. + // Timers currently will call their complete callbacks, which + // will dequeue but only if they were gotoEnd. + if ( dequeue || !gotoEnd ) { + jQuery.dequeue( this, type ); + } + } ); + }, + finish: function( type ) { + if ( type !== false ) { + type = type || "fx"; + } + return this.each( function() { + var index, + data = dataPriv.get( this ), + queue = data[ type + "queue" ], + hooks = data[ type + "queueHooks" ], + timers = jQuery.timers, + length = queue ? queue.length : 0; + + // Enable finishing flag on private data + data.finish = true; + + // Empty the queue first + jQuery.queue( this, type, [] ); + + if ( hooks && hooks.stop ) { + hooks.stop.call( this, true ); + } + + // Look for any active animations, and finish them + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && timers[ index ].queue === type ) { + timers[ index ].anim.stop( true ); + timers.splice( index, 1 ); + } + } + + // Look for any animations in the old queue and finish them + for ( index = 0; index < length; index++ ) { + if ( queue[ index ] && queue[ index ].finish ) { + queue[ index ].finish.call( this ); + } + } + + // Turn off finishing flag + delete data.finish; + } ); + } +} ); + +jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { + var cssFn = jQuery.fn[ name ]; + jQuery.fn[ name ] = function( speed, easing, callback ) { + return speed == null || typeof speed === "boolean" ? + cssFn.apply( this, arguments ) : + this.animate( genFx( name, true ), speed, easing, callback ); + }; +} ); + +// Generate shortcuts for custom animations +jQuery.each( { + slideDown: genFx( "show" ), + slideUp: genFx( "hide" ), + slideToggle: genFx( "toggle" ), + fadeIn: { opacity: "show" }, + fadeOut: { opacity: "hide" }, + fadeToggle: { opacity: "toggle" } +}, function( name, props ) { + jQuery.fn[ name ] = function( speed, easing, callback ) { + return this.animate( props, speed, easing, callback ); + }; +} ); + +jQuery.timers = []; +jQuery.fx.tick = function() { + var timer, + i = 0, + timers = jQuery.timers; + + fxNow = Date.now(); + + for ( ; i < timers.length; i++ ) { + timer = timers[ i ]; + + // Run the timer and safely remove it when done (allowing for external removal) + if ( !timer() && timers[ i ] === timer ) { + timers.splice( i--, 1 ); + } + } + + if ( !timers.length ) { + jQuery.fx.stop(); + } + fxNow = undefined; +}; + +jQuery.fx.timer = function( timer ) { + jQuery.timers.push( timer ); + jQuery.fx.start(); +}; + +jQuery.fx.interval = 13; +jQuery.fx.start = function() { + if ( inProgress ) { + return; + } + + inProgress = true; + schedule(); +}; + +jQuery.fx.stop = function() { + inProgress = null; +}; + +jQuery.fx.speeds = { + slow: 600, + fast: 200, + + // Default speed + _default: 400 +}; + + +// Based off of the plugin by Clint Helfers, with permission. +// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ +jQuery.fn.delay = function( time, type ) { + time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; + type = type || "fx"; + + return this.queue( type, function( next, hooks ) { + var timeout = window.setTimeout( next, time ); + hooks.stop = function() { + window.clearTimeout( timeout ); + }; + } ); +}; + + +( function() { + var input = document.createElement( "input" ), + select = document.createElement( "select" ), + opt = select.appendChild( document.createElement( "option" ) ); + + input.type = "checkbox"; + + // Support: Android <=4.3 only + // Default value for a checkbox should be "on" + support.checkOn = input.value !== ""; + + // Support: IE <=11 only + // Must access selectedIndex to make default options select + support.optSelected = opt.selected; + + // Support: IE <=11 only + // An input loses its value after becoming a radio + input = document.createElement( "input" ); + input.value = "t"; + input.type = "radio"; + support.radioValue = input.value === "t"; +} )(); + + +var boolHook, + attrHandle = jQuery.expr.attrHandle; + +jQuery.fn.extend( { + attr: function( name, value ) { + return access( this, jQuery.attr, name, value, arguments.length > 1 ); + }, + + removeAttr: function( name ) { + return this.each( function() { + jQuery.removeAttr( this, name ); + } ); + } +} ); + +jQuery.extend( { + attr: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set attributes on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + // Fallback to prop when attributes are not supported + if ( typeof elem.getAttribute === "undefined" ) { + return jQuery.prop( elem, name, value ); + } + + // Attribute hooks are determined by the lowercase version + // Grab necessary hook if one is defined + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + hooks = jQuery.attrHooks[ name.toLowerCase() ] || + ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); + } + + if ( value !== undefined ) { + if ( value === null ) { + jQuery.removeAttr( elem, name ); + return; + } + + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + elem.setAttribute( name, value + "" ); + return value; + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + ret = jQuery.find.attr( elem, name ); + + // Non-existent attributes return null, we normalize to undefined + return ret == null ? undefined : ret; + }, + + attrHooks: { + type: { + set: function( elem, value ) { + if ( !support.radioValue && value === "radio" && + nodeName( elem, "input" ) ) { + var val = elem.value; + elem.setAttribute( "type", value ); + if ( val ) { + elem.value = val; + } + return value; + } + } + } + }, + + removeAttr: function( elem, value ) { + var name, + i = 0, + + // Attribute names can contain non-HTML whitespace characters + // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 + attrNames = value && value.match( rnothtmlwhite ); + + if ( attrNames && elem.nodeType === 1 ) { + while ( ( name = attrNames[ i++ ] ) ) { + elem.removeAttribute( name ); + } + } + } +} ); + +// Hooks for boolean attributes +boolHook = { + set: function( elem, value, name ) { + if ( value === false ) { + + // Remove boolean attributes when set to false + jQuery.removeAttr( elem, name ); + } else { + elem.setAttribute( name, name ); + } + return name; + } +}; + +jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { + var getter = attrHandle[ name ] || jQuery.find.attr; + + attrHandle[ name ] = function( elem, name, isXML ) { + var ret, handle, + lowercaseName = name.toLowerCase(); + + if ( !isXML ) { + + // Avoid an infinite loop by temporarily removing this function from the getter + handle = attrHandle[ lowercaseName ]; + attrHandle[ lowercaseName ] = ret; + ret = getter( elem, name, isXML ) != null ? + lowercaseName : + null; + attrHandle[ lowercaseName ] = handle; + } + return ret; + }; +} ); + + + + +var rfocusable = /^(?:input|select|textarea|button)$/i, + rclickable = /^(?:a|area)$/i; + +jQuery.fn.extend( { + prop: function( name, value ) { + return access( this, jQuery.prop, name, value, arguments.length > 1 ); + }, + + removeProp: function( name ) { + return this.each( function() { + delete this[ jQuery.propFix[ name ] || name ]; + } ); + } +} ); + +jQuery.extend( { + prop: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set properties on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + + // Fix name and attach hooks + name = jQuery.propFix[ name ] || name; + hooks = jQuery.propHooks[ name ]; + } + + if ( value !== undefined ) { + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + return ( elem[ name ] = value ); + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + return elem[ name ]; + }, + + propHooks: { + tabIndex: { + get: function( elem ) { + + // Support: IE <=9 - 11 only + // elem.tabIndex doesn't always return the + // correct value when it hasn't been explicitly set + // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ + // Use proper attribute retrieval(#12072) + var tabindex = jQuery.find.attr( elem, "tabindex" ); + + if ( tabindex ) { + return parseInt( tabindex, 10 ); + } + + if ( + rfocusable.test( elem.nodeName ) || + rclickable.test( elem.nodeName ) && + elem.href + ) { + return 0; + } + + return -1; + } + } + }, + + propFix: { + "for": "htmlFor", + "class": "className" + } +} ); + +// Support: IE <=11 only +// Accessing the selectedIndex property +// forces the browser to respect setting selected +// on the option +// The getter ensures a default option is selected +// when in an optgroup +// eslint rule "no-unused-expressions" is disabled for this code +// since it considers such accessions noop +if ( !support.optSelected ) { + jQuery.propHooks.selected = { + get: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent && parent.parentNode ) { + parent.parentNode.selectedIndex; + } + return null; + }, + set: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent ) { + parent.selectedIndex; + + if ( parent.parentNode ) { + parent.parentNode.selectedIndex; + } + } + } + }; +} + +jQuery.each( [ + "tabIndex", + "readOnly", + "maxLength", + "cellSpacing", + "cellPadding", + "rowSpan", + "colSpan", + "useMap", + "frameBorder", + "contentEditable" +], function() { + jQuery.propFix[ this.toLowerCase() ] = this; +} ); + + + + + // Strip and collapse whitespace according to HTML spec + // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace + function stripAndCollapse( value ) { + var tokens = value.match( rnothtmlwhite ) || []; + return tokens.join( " " ); + } + + +function getClass( elem ) { + return elem.getAttribute && elem.getAttribute( "class" ) || ""; +} + +function classesToArray( value ) { + if ( Array.isArray( value ) ) { + return value; + } + if ( typeof value === "string" ) { + return value.match( rnothtmlwhite ) || []; + } + return []; +} + +jQuery.fn.extend( { + addClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + if ( cur.indexOf( " " + clazz + " " ) < 0 ) { + cur += clazz + " "; + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + removeClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + if ( !arguments.length ) { + return this.attr( "class", "" ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + + // This expression is here for better compressibility (see addClass) + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + + // Remove *all* instances + while ( cur.indexOf( " " + clazz + " " ) > -1 ) { + cur = cur.replace( " " + clazz + " ", " " ); + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + toggleClass: function( value, stateVal ) { + var type = typeof value, + isValidValue = type === "string" || Array.isArray( value ); + + if ( typeof stateVal === "boolean" && isValidValue ) { + return stateVal ? this.addClass( value ) : this.removeClass( value ); + } + + if ( isFunction( value ) ) { + return this.each( function( i ) { + jQuery( this ).toggleClass( + value.call( this, i, getClass( this ), stateVal ), + stateVal + ); + } ); + } + + return this.each( function() { + var className, i, self, classNames; + + if ( isValidValue ) { + + // Toggle individual class names + i = 0; + self = jQuery( this ); + classNames = classesToArray( value ); + + while ( ( className = classNames[ i++ ] ) ) { + + // Check each className given, space separated list + if ( self.hasClass( className ) ) { + self.removeClass( className ); + } else { + self.addClass( className ); + } + } + + // Toggle whole class name + } else if ( value === undefined || type === "boolean" ) { + className = getClass( this ); + if ( className ) { + + // Store className if set + dataPriv.set( this, "__className__", className ); + } + + // If the element has a class name or if we're passed `false`, + // then remove the whole classname (if there was one, the above saved it). + // Otherwise bring back whatever was previously saved (if anything), + // falling back to the empty string if nothing was stored. + if ( this.setAttribute ) { + this.setAttribute( "class", + className || value === false ? + "" : + dataPriv.get( this, "__className__" ) || "" + ); + } + } + } ); + }, + + hasClass: function( selector ) { + var className, elem, + i = 0; + + className = " " + selector + " "; + while ( ( elem = this[ i++ ] ) ) { + if ( elem.nodeType === 1 && + ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { + return true; + } + } + + return false; + } +} ); + + + + +var rreturn = /\r/g; + +jQuery.fn.extend( { + val: function( value ) { + var hooks, ret, valueIsFunction, + elem = this[ 0 ]; + + if ( !arguments.length ) { + if ( elem ) { + hooks = jQuery.valHooks[ elem.type ] || + jQuery.valHooks[ elem.nodeName.toLowerCase() ]; + + if ( hooks && + "get" in hooks && + ( ret = hooks.get( elem, "value" ) ) !== undefined + ) { + return ret; + } + + ret = elem.value; + + // Handle most common string cases + if ( typeof ret === "string" ) { + return ret.replace( rreturn, "" ); + } + + // Handle cases where value is null/undef or number + return ret == null ? "" : ret; + } + + return; + } + + valueIsFunction = isFunction( value ); + + return this.each( function( i ) { + var val; + + if ( this.nodeType !== 1 ) { + return; + } + + if ( valueIsFunction ) { + val = value.call( this, i, jQuery( this ).val() ); + } else { + val = value; + } + + // Treat null/undefined as ""; convert numbers to string + if ( val == null ) { + val = ""; + + } else if ( typeof val === "number" ) { + val += ""; + + } else if ( Array.isArray( val ) ) { + val = jQuery.map( val, function( value ) { + return value == null ? "" : value + ""; + } ); + } + + hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; + + // If set returns undefined, fall back to normal setting + if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { + this.value = val; + } + } ); + } +} ); + +jQuery.extend( { + valHooks: { + option: { + get: function( elem ) { + + var val = jQuery.find.attr( elem, "value" ); + return val != null ? + val : + + // Support: IE <=10 - 11 only + // option.text throws exceptions (#14686, #14858) + // Strip and collapse whitespace + // https://html.spec.whatwg.org/#strip-and-collapse-whitespace + stripAndCollapse( jQuery.text( elem ) ); + } + }, + select: { + get: function( elem ) { + var value, option, i, + options = elem.options, + index = elem.selectedIndex, + one = elem.type === "select-one", + values = one ? null : [], + max = one ? index + 1 : options.length; + + if ( index < 0 ) { + i = max; + + } else { + i = one ? index : 0; + } + + // Loop through all the selected options + for ( ; i < max; i++ ) { + option = options[ i ]; + + // Support: IE <=9 only + // IE8-9 doesn't update selected after form reset (#2551) + if ( ( option.selected || i === index ) && + + // Don't return options that are disabled or in a disabled optgroup + !option.disabled && + ( !option.parentNode.disabled || + !nodeName( option.parentNode, "optgroup" ) ) ) { + + // Get the specific value for the option + value = jQuery( option ).val(); + + // We don't need an array for one selects + if ( one ) { + return value; + } + + // Multi-Selects return an array + values.push( value ); + } + } + + return values; + }, + + set: function( elem, value ) { + var optionSet, option, + options = elem.options, + values = jQuery.makeArray( value ), + i = options.length; + + while ( i-- ) { + option = options[ i ]; + + /* eslint-disable no-cond-assign */ + + if ( option.selected = + jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 + ) { + optionSet = true; + } + + /* eslint-enable no-cond-assign */ + } + + // Force browsers to behave consistently when non-matching value is set + if ( !optionSet ) { + elem.selectedIndex = -1; + } + return values; + } + } + } +} ); + +// Radios and checkboxes getter/setter +jQuery.each( [ "radio", "checkbox" ], function() { + jQuery.valHooks[ this ] = { + set: function( elem, value ) { + if ( Array.isArray( value ) ) { + return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); + } + } + }; + if ( !support.checkOn ) { + jQuery.valHooks[ this ].get = function( elem ) { + return elem.getAttribute( "value" ) === null ? "on" : elem.value; + }; + } +} ); + + + + +// Return jQuery for attributes-only inclusion + + +support.focusin = "onfocusin" in window; + + +var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, + stopPropagationCallback = function( e ) { + e.stopPropagation(); + }; + +jQuery.extend( jQuery.event, { + + trigger: function( event, data, elem, onlyHandlers ) { + + var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, + eventPath = [ elem || document ], + type = hasOwn.call( event, "type" ) ? event.type : event, + namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; + + cur = lastElement = tmp = elem = elem || document; + + // Don't do events on text and comment nodes + if ( elem.nodeType === 3 || elem.nodeType === 8 ) { + return; + } + + // focus/blur morphs to focusin/out; ensure we're not firing them right now + if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { + return; + } + + if ( type.indexOf( "." ) > -1 ) { + + // Namespaced trigger; create a regexp to match event type in handle() + namespaces = type.split( "." ); + type = namespaces.shift(); + namespaces.sort(); + } + ontype = type.indexOf( ":" ) < 0 && "on" + type; + + // Caller can pass in a jQuery.Event object, Object, or just an event type string + event = event[ jQuery.expando ] ? + event : + new jQuery.Event( type, typeof event === "object" && event ); + + // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) + event.isTrigger = onlyHandlers ? 2 : 3; + event.namespace = namespaces.join( "." ); + event.rnamespace = event.namespace ? + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : + null; + + // Clean up the event in case it is being reused + event.result = undefined; + if ( !event.target ) { + event.target = elem; + } + + // Clone any incoming data and prepend the event, creating the handler arg list + data = data == null ? + [ event ] : + jQuery.makeArray( data, [ event ] ); + + // Allow special events to draw outside the lines + special = jQuery.event.special[ type ] || {}; + if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { + return; + } + + // Determine event propagation path in advance, per W3C events spec (#9951) + // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) + if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { + + bubbleType = special.delegateType || type; + if ( !rfocusMorph.test( bubbleType + type ) ) { + cur = cur.parentNode; + } + for ( ; cur; cur = cur.parentNode ) { + eventPath.push( cur ); + tmp = cur; + } + + // Only add window if we got to document (e.g., not plain obj or detached DOM) + if ( tmp === ( elem.ownerDocument || document ) ) { + eventPath.push( tmp.defaultView || tmp.parentWindow || window ); + } + } + + // Fire handlers on the event path + i = 0; + while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { + lastElement = cur; + event.type = i > 1 ? + bubbleType : + special.bindType || type; + + // jQuery handler + handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] && + dataPriv.get( cur, "handle" ); + if ( handle ) { + handle.apply( cur, data ); + } + + // Native handler + handle = ontype && cur[ ontype ]; + if ( handle && handle.apply && acceptData( cur ) ) { + event.result = handle.apply( cur, data ); + if ( event.result === false ) { + event.preventDefault(); + } + } + } + event.type = type; + + // If nobody prevented the default action, do it now + if ( !onlyHandlers && !event.isDefaultPrevented() ) { + + if ( ( !special._default || + special._default.apply( eventPath.pop(), data ) === false ) && + acceptData( elem ) ) { + + // Call a native DOM method on the target with the same name as the event. + // Don't do default actions on window, that's where global variables be (#6170) + if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { + + // Don't re-trigger an onFOO event when we call its FOO() method + tmp = elem[ ontype ]; + + if ( tmp ) { + elem[ ontype ] = null; + } + + // Prevent re-triggering of the same event, since we already bubbled it above + jQuery.event.triggered = type; + + if ( event.isPropagationStopped() ) { + lastElement.addEventListener( type, stopPropagationCallback ); + } + + elem[ type ](); + + if ( event.isPropagationStopped() ) { + lastElement.removeEventListener( type, stopPropagationCallback ); + } + + jQuery.event.triggered = undefined; + + if ( tmp ) { + elem[ ontype ] = tmp; + } + } + } + } + + return event.result; + }, + + // Piggyback on a donor event to simulate a different one + // Used only for `focus(in | out)` events + simulate: function( type, elem, event ) { + var e = jQuery.extend( + new jQuery.Event(), + event, + { + type: type, + isSimulated: true + } + ); + + jQuery.event.trigger( e, null, elem ); + } + +} ); + +jQuery.fn.extend( { + + trigger: function( type, data ) { + return this.each( function() { + jQuery.event.trigger( type, data, this ); + } ); + }, + triggerHandler: function( type, data ) { + var elem = this[ 0 ]; + if ( elem ) { + return jQuery.event.trigger( type, data, elem, true ); + } + } +} ); + + +// Support: Firefox <=44 +// Firefox doesn't have focus(in | out) events +// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 +// +// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 +// focus(in | out) events fire after focus & blur events, +// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order +// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 +if ( !support.focusin ) { + jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { + + // Attach a single capturing handler on the document while someone wants focusin/focusout + var handler = function( event ) { + jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); + }; + + jQuery.event.special[ fix ] = { + setup: function() { + + // Handle: regular nodes (via `this.ownerDocument`), window + // (via `this.document`) & document (via `this`). + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ); + + if ( !attaches ) { + doc.addEventListener( orig, handler, true ); + } + dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); + }, + teardown: function() { + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ) - 1; + + if ( !attaches ) { + doc.removeEventListener( orig, handler, true ); + dataPriv.remove( doc, fix ); + + } else { + dataPriv.access( doc, fix, attaches ); + } + } + }; + } ); +} +var location = window.location; + +var nonce = { guid: Date.now() }; + +var rquery = ( /\?/ ); + + + +// Cross-browser xml parsing +jQuery.parseXML = function( data ) { + var xml, parserErrorElem; + if ( !data || typeof data !== "string" ) { + return null; + } + + // Support: IE 9 - 11 only + // IE throws on parseFromString with invalid input. + try { + xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); + } catch ( e ) {} + + parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ]; + if ( !xml || parserErrorElem ) { + jQuery.error( "Invalid XML: " + ( + parserErrorElem ? + jQuery.map( parserErrorElem.childNodes, function( el ) { + return el.textContent; + } ).join( "\n" ) : + data + ) ); + } + return xml; +}; + + +var + rbracket = /\[\]$/, + rCRLF = /\r?\n/g, + rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, + rsubmittable = /^(?:input|select|textarea|keygen)/i; + +function buildParams( prefix, obj, traditional, add ) { + var name; + + if ( Array.isArray( obj ) ) { + + // Serialize array item. + jQuery.each( obj, function( i, v ) { + if ( traditional || rbracket.test( prefix ) ) { + + // Treat each array item as a scalar. + add( prefix, v ); + + } else { + + // Item is non-scalar (array or object), encode its numeric index. + buildParams( + prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", + v, + traditional, + add + ); + } + } ); + + } else if ( !traditional && toType( obj ) === "object" ) { + + // Serialize object item. + for ( name in obj ) { + buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); + } + + } else { + + // Serialize scalar item. + add( prefix, obj ); + } +} + +// Serialize an array of form elements or a set of +// key/values into a query string +jQuery.param = function( a, traditional ) { + var prefix, + s = [], + add = function( key, valueOrFunction ) { + + // If value is a function, invoke it and use its return value + var value = isFunction( valueOrFunction ) ? + valueOrFunction() : + valueOrFunction; + + s[ s.length ] = encodeURIComponent( key ) + "=" + + encodeURIComponent( value == null ? "" : value ); + }; + + if ( a == null ) { + return ""; + } + + // If an array was passed in, assume that it is an array of form elements. + if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { + + // Serialize the form elements + jQuery.each( a, function() { + add( this.name, this.value ); + } ); + + } else { + + // If traditional, encode the "old" way (the way 1.3.2 or older + // did it), otherwise encode params recursively. + for ( prefix in a ) { + buildParams( prefix, a[ prefix ], traditional, add ); + } + } + + // Return the resulting serialization + return s.join( "&" ); +}; + +jQuery.fn.extend( { + serialize: function() { + return jQuery.param( this.serializeArray() ); + }, + serializeArray: function() { + return this.map( function() { + + // Can add propHook for "elements" to filter or add form elements + var elements = jQuery.prop( this, "elements" ); + return elements ? jQuery.makeArray( elements ) : this; + } ).filter( function() { + var type = this.type; + + // Use .is( ":disabled" ) so that fieldset[disabled] works + return this.name && !jQuery( this ).is( ":disabled" ) && + rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && + ( this.checked || !rcheckableType.test( type ) ); + } ).map( function( _i, elem ) { + var val = jQuery( this ).val(); + + if ( val == null ) { + return null; + } + + if ( Array.isArray( val ) ) { + return jQuery.map( val, function( val ) { + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ); + } + + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ).get(); + } +} ); + + +var + r20 = /%20/g, + rhash = /#.*$/, + rantiCache = /([?&])_=[^&]*/, + rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, + + // #7653, #8125, #8152: local protocol detection + rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, + rnoContent = /^(?:GET|HEAD)$/, + rprotocol = /^\/\//, + + /* Prefilters + * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) + * 2) These are called: + * - BEFORE asking for a transport + * - AFTER param serialization (s.data is a string if s.processData is true) + * 3) key is the dataType + * 4) the catchall symbol "*" can be used + * 5) execution will start with transport dataType and THEN continue down to "*" if needed + */ + prefilters = {}, + + /* Transports bindings + * 1) key is the dataType + * 2) the catchall symbol "*" can be used + * 3) selection will start with transport dataType and THEN go to "*" if needed + */ + transports = {}, + + // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression + allTypes = "*/".concat( "*" ), + + // Anchor tag for parsing the document origin + originAnchor = document.createElement( "a" ); + +originAnchor.href = location.href; + +// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport +function addToPrefiltersOrTransports( structure ) { + + // dataTypeExpression is optional and defaults to "*" + return function( dataTypeExpression, func ) { + + if ( typeof dataTypeExpression !== "string" ) { + func = dataTypeExpression; + dataTypeExpression = "*"; + } + + var dataType, + i = 0, + dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; + + if ( isFunction( func ) ) { + + // For each dataType in the dataTypeExpression + while ( ( dataType = dataTypes[ i++ ] ) ) { + + // Prepend if requested + if ( dataType[ 0 ] === "+" ) { + dataType = dataType.slice( 1 ) || "*"; + ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); + + // Otherwise append + } else { + ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); + } + } + } + }; +} + +// Base inspection function for prefilters and transports +function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { + + var inspected = {}, + seekingTransport = ( structure === transports ); + + function inspect( dataType ) { + var selected; + inspected[ dataType ] = true; + jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { + var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); + if ( typeof dataTypeOrTransport === "string" && + !seekingTransport && !inspected[ dataTypeOrTransport ] ) { + + options.dataTypes.unshift( dataTypeOrTransport ); + inspect( dataTypeOrTransport ); + return false; + } else if ( seekingTransport ) { + return !( selected = dataTypeOrTransport ); + } + } ); + return selected; + } + + return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); +} + +// A special extend for ajax options +// that takes "flat" options (not to be deep extended) +// Fixes #9887 +function ajaxExtend( target, src ) { + var key, deep, + flatOptions = jQuery.ajaxSettings.flatOptions || {}; + + for ( key in src ) { + if ( src[ key ] !== undefined ) { + ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; + } + } + if ( deep ) { + jQuery.extend( true, target, deep ); + } + + return target; +} + +/* Handles responses to an ajax request: + * - finds the right dataType (mediates between content-type and expected dataType) + * - returns the corresponding response + */ +function ajaxHandleResponses( s, jqXHR, responses ) { + + var ct, type, finalDataType, firstDataType, + contents = s.contents, + dataTypes = s.dataTypes; + + // Remove auto dataType and get content-type in the process + while ( dataTypes[ 0 ] === "*" ) { + dataTypes.shift(); + if ( ct === undefined ) { + ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); + } + } + + // Check if we're dealing with a known content-type + if ( ct ) { + for ( type in contents ) { + if ( contents[ type ] && contents[ type ].test( ct ) ) { + dataTypes.unshift( type ); + break; + } + } + } + + // Check to see if we have a response for the expected dataType + if ( dataTypes[ 0 ] in responses ) { + finalDataType = dataTypes[ 0 ]; + } else { + + // Try convertible dataTypes + for ( type in responses ) { + if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { + finalDataType = type; + break; + } + if ( !firstDataType ) { + firstDataType = type; + } + } + + // Or just use first one + finalDataType = finalDataType || firstDataType; + } + + // If we found a dataType + // We add the dataType to the list if needed + // and return the corresponding response + if ( finalDataType ) { + if ( finalDataType !== dataTypes[ 0 ] ) { + dataTypes.unshift( finalDataType ); + } + return responses[ finalDataType ]; + } +} + +/* Chain conversions given the request and the original response + * Also sets the responseXXX fields on the jqXHR instance + */ +function ajaxConvert( s, response, jqXHR, isSuccess ) { + var conv2, current, conv, tmp, prev, + converters = {}, + + // Work with a copy of dataTypes in case we need to modify it for conversion + dataTypes = s.dataTypes.slice(); + + // Create converters map with lowercased keys + if ( dataTypes[ 1 ] ) { + for ( conv in s.converters ) { + converters[ conv.toLowerCase() ] = s.converters[ conv ]; + } + } + + current = dataTypes.shift(); + + // Convert to each sequential dataType + while ( current ) { + + if ( s.responseFields[ current ] ) { + jqXHR[ s.responseFields[ current ] ] = response; + } + + // Apply the dataFilter if provided + if ( !prev && isSuccess && s.dataFilter ) { + response = s.dataFilter( response, s.dataType ); + } + + prev = current; + current = dataTypes.shift(); + + if ( current ) { + + // There's only work to do if current dataType is non-auto + if ( current === "*" ) { + + current = prev; + + // Convert response if prev dataType is non-auto and differs from current + } else if ( prev !== "*" && prev !== current ) { + + // Seek a direct converter + conv = converters[ prev + " " + current ] || converters[ "* " + current ]; + + // If none found, seek a pair + if ( !conv ) { + for ( conv2 in converters ) { + + // If conv2 outputs current + tmp = conv2.split( " " ); + if ( tmp[ 1 ] === current ) { + + // If prev can be converted to accepted input + conv = converters[ prev + " " + tmp[ 0 ] ] || + converters[ "* " + tmp[ 0 ] ]; + if ( conv ) { + + // Condense equivalence converters + if ( conv === true ) { + conv = converters[ conv2 ]; + + // Otherwise, insert the intermediate dataType + } else if ( converters[ conv2 ] !== true ) { + current = tmp[ 0 ]; + dataTypes.unshift( tmp[ 1 ] ); + } + break; + } + } + } + } + + // Apply converter (if not an equivalence) + if ( conv !== true ) { + + // Unless errors are allowed to bubble, catch and return them + if ( conv && s.throws ) { + response = conv( response ); + } else { + try { + response = conv( response ); + } catch ( e ) { + return { + state: "parsererror", + error: conv ? e : "No conversion from " + prev + " to " + current + }; + } + } + } + } + } + } + + return { state: "success", data: response }; +} + +jQuery.extend( { + + // Counter for holding the number of active queries + active: 0, + + // Last-Modified header cache for next request + lastModified: {}, + etag: {}, + + ajaxSettings: { + url: location.href, + type: "GET", + isLocal: rlocalProtocol.test( location.protocol ), + global: true, + processData: true, + async: true, + contentType: "application/x-www-form-urlencoded; charset=UTF-8", + + /* + timeout: 0, + data: null, + dataType: null, + username: null, + password: null, + cache: null, + throws: false, + traditional: false, + headers: {}, + */ + + accepts: { + "*": allTypes, + text: "text/plain", + html: "text/html", + xml: "application/xml, text/xml", + json: "application/json, text/javascript" + }, + + contents: { + xml: /\bxml\b/, + html: /\bhtml/, + json: /\bjson\b/ + }, + + responseFields: { + xml: "responseXML", + text: "responseText", + json: "responseJSON" + }, + + // Data converters + // Keys separate source (or catchall "*") and destination types with a single space + converters: { + + // Convert anything to text + "* text": String, + + // Text to html (true = no transformation) + "text html": true, + + // Evaluate text as a json expression + "text json": JSON.parse, + + // Parse text as xml + "text xml": jQuery.parseXML + }, + + // For options that shouldn't be deep extended: + // you can add your own custom options here if + // and when you create one that shouldn't be + // deep extended (see ajaxExtend) + flatOptions: { + url: true, + context: true + } + }, + + // Creates a full fledged settings object into target + // with both ajaxSettings and settings fields. + // If target is omitted, writes into ajaxSettings. + ajaxSetup: function( target, settings ) { + return settings ? + + // Building a settings object + ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : + + // Extending ajaxSettings + ajaxExtend( jQuery.ajaxSettings, target ); + }, + + ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), + ajaxTransport: addToPrefiltersOrTransports( transports ), + + // Main method + ajax: function( url, options ) { + + // If url is an object, simulate pre-1.5 signature + if ( typeof url === "object" ) { + options = url; + url = undefined; + } + + // Force options to be an object + options = options || {}; + + var transport, + + // URL without anti-cache param + cacheURL, + + // Response headers + responseHeadersString, + responseHeaders, + + // timeout handle + timeoutTimer, + + // Url cleanup var + urlAnchor, + + // Request state (becomes false upon send and true upon completion) + completed, + + // To know if global events are to be dispatched + fireGlobals, + + // Loop variable + i, + + // uncached part of the url + uncached, + + // Create the final options object + s = jQuery.ajaxSetup( {}, options ), + + // Callbacks context + callbackContext = s.context || s, + + // Context for global events is callbackContext if it is a DOM node or jQuery collection + globalEventContext = s.context && + ( callbackContext.nodeType || callbackContext.jquery ) ? + jQuery( callbackContext ) : + jQuery.event, + + // Deferreds + deferred = jQuery.Deferred(), + completeDeferred = jQuery.Callbacks( "once memory" ), + + // Status-dependent callbacks + statusCode = s.statusCode || {}, + + // Headers (they are sent all at once) + requestHeaders = {}, + requestHeadersNames = {}, + + // Default abort message + strAbort = "canceled", + + // Fake xhr + jqXHR = { + readyState: 0, + + // Builds headers hashtable if needed + getResponseHeader: function( key ) { + var match; + if ( completed ) { + if ( !responseHeaders ) { + responseHeaders = {}; + while ( ( match = rheaders.exec( responseHeadersString ) ) ) { + responseHeaders[ match[ 1 ].toLowerCase() + " " ] = + ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) + .concat( match[ 2 ] ); + } + } + match = responseHeaders[ key.toLowerCase() + " " ]; + } + return match == null ? null : match.join( ", " ); + }, + + // Raw string + getAllResponseHeaders: function() { + return completed ? responseHeadersString : null; + }, + + // Caches the header + setRequestHeader: function( name, value ) { + if ( completed == null ) { + name = requestHeadersNames[ name.toLowerCase() ] = + requestHeadersNames[ name.toLowerCase() ] || name; + requestHeaders[ name ] = value; + } + return this; + }, + + // Overrides response content-type header + overrideMimeType: function( type ) { + if ( completed == null ) { + s.mimeType = type; + } + return this; + }, + + // Status-dependent callbacks + statusCode: function( map ) { + var code; + if ( map ) { + if ( completed ) { + + // Execute the appropriate callbacks + jqXHR.always( map[ jqXHR.status ] ); + } else { + + // Lazy-add the new callbacks in a way that preserves old ones + for ( code in map ) { + statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; + } + } + } + return this; + }, + + // Cancel the request + abort: function( statusText ) { + var finalText = statusText || strAbort; + if ( transport ) { + transport.abort( finalText ); + } + done( 0, finalText ); + return this; + } + }; + + // Attach deferreds + deferred.promise( jqXHR ); + + // Add protocol if not provided (prefilters might expect it) + // Handle falsy url in the settings object (#10093: consistency with old signature) + // We also use the url parameter if available + s.url = ( ( url || s.url || location.href ) + "" ) + .replace( rprotocol, location.protocol + "//" ); + + // Alias method option to type as per ticket #12004 + s.type = options.method || options.type || s.method || s.type; + + // Extract dataTypes list + s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; + + // A cross-domain request is in order when the origin doesn't match the current origin. + if ( s.crossDomain == null ) { + urlAnchor = document.createElement( "a" ); + + // Support: IE <=8 - 11, Edge 12 - 15 + // IE throws exception on accessing the href property if url is malformed, + // e.g. http://example.com:80x/ + try { + urlAnchor.href = s.url; + + // Support: IE <=8 - 11 only + // Anchor's host property isn't correctly set when s.url is relative + urlAnchor.href = urlAnchor.href; + s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== + urlAnchor.protocol + "//" + urlAnchor.host; + } catch ( e ) { + + // If there is an error parsing the URL, assume it is crossDomain, + // it can be rejected by the transport if it is invalid + s.crossDomain = true; + } + } + + // Convert data if not already a string + if ( s.data && s.processData && typeof s.data !== "string" ) { + s.data = jQuery.param( s.data, s.traditional ); + } + + // Apply prefilters + inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); + + // If request was aborted inside a prefilter, stop there + if ( completed ) { + return jqXHR; + } + + // We can fire global events as of now if asked to + // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) + fireGlobals = jQuery.event && s.global; + + // Watch for a new set of requests + if ( fireGlobals && jQuery.active++ === 0 ) { + jQuery.event.trigger( "ajaxStart" ); + } + + // Uppercase the type + s.type = s.type.toUpperCase(); + + // Determine if request has content + s.hasContent = !rnoContent.test( s.type ); + + // Save the URL in case we're toying with the If-Modified-Since + // and/or If-None-Match header later on + // Remove hash to simplify url manipulation + cacheURL = s.url.replace( rhash, "" ); + + // More options handling for requests with no content + if ( !s.hasContent ) { + + // Remember the hash so we can put it back + uncached = s.url.slice( cacheURL.length ); + + // If data is available and should be processed, append data to url + if ( s.data && ( s.processData || typeof s.data === "string" ) ) { + cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; + + // #9682: remove data so that it's not used in an eventual retry + delete s.data; + } + + // Add or update anti-cache param if needed + if ( s.cache === false ) { + cacheURL = cacheURL.replace( rantiCache, "$1" ); + uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + + uncached; + } + + // Put hash and anti-cache on the URL that will be requested (gh-1732) + s.url = cacheURL + uncached; + + // Change '%20' to '+' if this is encoded form body content (gh-2658) + } else if ( s.data && s.processData && + ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { + s.data = s.data.replace( r20, "+" ); + } + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + if ( jQuery.lastModified[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); + } + if ( jQuery.etag[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); + } + } + + // Set the correct header, if data is being sent + if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { + jqXHR.setRequestHeader( "Content-Type", s.contentType ); + } + + // Set the Accepts header for the server, depending on the dataType + jqXHR.setRequestHeader( + "Accept", + s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? + s.accepts[ s.dataTypes[ 0 ] ] + + ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : + s.accepts[ "*" ] + ); + + // Check for headers option + for ( i in s.headers ) { + jqXHR.setRequestHeader( i, s.headers[ i ] ); + } + + // Allow custom headers/mimetypes and early abort + if ( s.beforeSend && + ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { + + // Abort if not done already and return + return jqXHR.abort(); + } + + // Aborting is no longer a cancellation + strAbort = "abort"; + + // Install callbacks on deferreds + completeDeferred.add( s.complete ); + jqXHR.done( s.success ); + jqXHR.fail( s.error ); + + // Get transport + transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); + + // If no transport, we auto-abort + if ( !transport ) { + done( -1, "No Transport" ); + } else { + jqXHR.readyState = 1; + + // Send global event + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); + } + + // If request was aborted inside ajaxSend, stop there + if ( completed ) { + return jqXHR; + } + + // Timeout + if ( s.async && s.timeout > 0 ) { + timeoutTimer = window.setTimeout( function() { + jqXHR.abort( "timeout" ); + }, s.timeout ); + } + + try { + completed = false; + transport.send( requestHeaders, done ); + } catch ( e ) { + + // Rethrow post-completion exceptions + if ( completed ) { + throw e; + } + + // Propagate others as results + done( -1, e ); + } + } + + // Callback for when everything is done + function done( status, nativeStatusText, responses, headers ) { + var isSuccess, success, error, response, modified, + statusText = nativeStatusText; + + // Ignore repeat invocations + if ( completed ) { + return; + } + + completed = true; + + // Clear timeout if it exists + if ( timeoutTimer ) { + window.clearTimeout( timeoutTimer ); + } + + // Dereference transport for early garbage collection + // (no matter how long the jqXHR object will be used) + transport = undefined; + + // Cache response headers + responseHeadersString = headers || ""; + + // Set readyState + jqXHR.readyState = status > 0 ? 4 : 0; + + // Determine if successful + isSuccess = status >= 200 && status < 300 || status === 304; + + // Get response data + if ( responses ) { + response = ajaxHandleResponses( s, jqXHR, responses ); + } + + // Use a noop converter for missing script but not if jsonp + if ( !isSuccess && + jQuery.inArray( "script", s.dataTypes ) > -1 && + jQuery.inArray( "json", s.dataTypes ) < 0 ) { + s.converters[ "text script" ] = function() {}; + } + + // Convert no matter what (that way responseXXX fields are always set) + response = ajaxConvert( s, response, jqXHR, isSuccess ); + + // If successful, handle type chaining + if ( isSuccess ) { + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + modified = jqXHR.getResponseHeader( "Last-Modified" ); + if ( modified ) { + jQuery.lastModified[ cacheURL ] = modified; + } + modified = jqXHR.getResponseHeader( "etag" ); + if ( modified ) { + jQuery.etag[ cacheURL ] = modified; + } + } + + // if no content + if ( status === 204 || s.type === "HEAD" ) { + statusText = "nocontent"; + + // if not modified + } else if ( status === 304 ) { + statusText = "notmodified"; + + // If we have data, let's convert it + } else { + statusText = response.state; + success = response.data; + error = response.error; + isSuccess = !error; + } + } else { + + // Extract error from statusText and normalize for non-aborts + error = statusText; + if ( status || !statusText ) { + statusText = "error"; + if ( status < 0 ) { + status = 0; + } + } + } + + // Set data for the fake xhr object + jqXHR.status = status; + jqXHR.statusText = ( nativeStatusText || statusText ) + ""; + + // Success/Error + if ( isSuccess ) { + deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); + } else { + deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); + } + + // Status-dependent callbacks + jqXHR.statusCode( statusCode ); + statusCode = undefined; + + if ( fireGlobals ) { + globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", + [ jqXHR, s, isSuccess ? success : error ] ); + } + + // Complete + completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); + + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); + + // Handle the global AJAX counter + if ( !( --jQuery.active ) ) { + jQuery.event.trigger( "ajaxStop" ); + } + } + } + + return jqXHR; + }, + + getJSON: function( url, data, callback ) { + return jQuery.get( url, data, callback, "json" ); + }, + + getScript: function( url, callback ) { + return jQuery.get( url, undefined, callback, "script" ); + } +} ); + +jQuery.each( [ "get", "post" ], function( _i, method ) { + jQuery[ method ] = function( url, data, callback, type ) { + + // Shift arguments if data argument was omitted + if ( isFunction( data ) ) { + type = type || callback; + callback = data; + data = undefined; + } + + // The url can be an options object (which then must have .url) + return jQuery.ajax( jQuery.extend( { + url: url, + type: method, + dataType: type, + data: data, + success: callback + }, jQuery.isPlainObject( url ) && url ) ); + }; +} ); + +jQuery.ajaxPrefilter( function( s ) { + var i; + for ( i in s.headers ) { + if ( i.toLowerCase() === "content-type" ) { + s.contentType = s.headers[ i ] || ""; + } + } +} ); + + +jQuery._evalUrl = function( url, options, doc ) { + return jQuery.ajax( { + url: url, + + // Make this explicit, since user can override this through ajaxSetup (#11264) + type: "GET", + dataType: "script", + cache: true, + async: false, + global: false, + + // Only evaluate the response if it is successful (gh-4126) + // dataFilter is not invoked for failure responses, so using it instead + // of the default converter is kludgy but it works. + converters: { + "text script": function() {} + }, + dataFilter: function( response ) { + jQuery.globalEval( response, options, doc ); + } + } ); +}; + + +jQuery.fn.extend( { + wrapAll: function( html ) { + var wrap; + + if ( this[ 0 ] ) { + if ( isFunction( html ) ) { + html = html.call( this[ 0 ] ); + } + + // The elements to wrap the target around + wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); + + if ( this[ 0 ].parentNode ) { + wrap.insertBefore( this[ 0 ] ); + } + + wrap.map( function() { + var elem = this; + + while ( elem.firstElementChild ) { + elem = elem.firstElementChild; + } + + return elem; + } ).append( this ); + } + + return this; + }, + + wrapInner: function( html ) { + if ( isFunction( html ) ) { + return this.each( function( i ) { + jQuery( this ).wrapInner( html.call( this, i ) ); + } ); + } + + return this.each( function() { + var self = jQuery( this ), + contents = self.contents(); + + if ( contents.length ) { + contents.wrapAll( html ); + + } else { + self.append( html ); + } + } ); + }, + + wrap: function( html ) { + var htmlIsFunction = isFunction( html ); + + return this.each( function( i ) { + jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html ); + } ); + }, + + unwrap: function( selector ) { + this.parent( selector ).not( "body" ).each( function() { + jQuery( this ).replaceWith( this.childNodes ); + } ); + return this; + } +} ); + + +jQuery.expr.pseudos.hidden = function( elem ) { + return !jQuery.expr.pseudos.visible( elem ); +}; +jQuery.expr.pseudos.visible = function( elem ) { + return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length ); +}; + + + + +jQuery.ajaxSettings.xhr = function() { + try { + return new window.XMLHttpRequest(); + } catch ( e ) {} +}; + +var xhrSuccessStatus = { + + // File protocol always yields status code 0, assume 200 + 0: 200, + + // Support: IE <=9 only + // #1450: sometimes IE returns 1223 when it should be 204 + 1223: 204 + }, + xhrSupported = jQuery.ajaxSettings.xhr(); + +support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported ); +support.ajax = xhrSupported = !!xhrSupported; + +jQuery.ajaxTransport( function( options ) { + var callback, errorCallback; + + // Cross domain only allowed if supported through XMLHttpRequest + if ( support.cors || xhrSupported && !options.crossDomain ) { + return { + send: function( headers, complete ) { + var i, + xhr = options.xhr(); + + xhr.open( + options.type, + options.url, + options.async, + options.username, + options.password + ); + + // Apply custom fields if provided + if ( options.xhrFields ) { + for ( i in options.xhrFields ) { + xhr[ i ] = options.xhrFields[ i ]; + } + } + + // Override mime type if needed + if ( options.mimeType && xhr.overrideMimeType ) { + xhr.overrideMimeType( options.mimeType ); + } + + // X-Requested-With header + // For cross-domain requests, seeing as conditions for a preflight are + // akin to a jigsaw puzzle, we simply never set it to be sure. + // (it can always be set on a per-request basis or even using ajaxSetup) + // For same-domain requests, won't change header if already provided. + if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) { + headers[ "X-Requested-With" ] = "XMLHttpRequest"; + } + + // Set headers + for ( i in headers ) { + xhr.setRequestHeader( i, headers[ i ] ); + } + + // Callback + callback = function( type ) { + return function() { + if ( callback ) { + callback = errorCallback = xhr.onload = + xhr.onerror = xhr.onabort = xhr.ontimeout = + xhr.onreadystatechange = null; + + if ( type === "abort" ) { + xhr.abort(); + } else if ( type === "error" ) { + + // Support: IE <=9 only + // On a manual native abort, IE9 throws + // errors on any property access that is not readyState + if ( typeof xhr.status !== "number" ) { + complete( 0, "error" ); + } else { + complete( + + // File: protocol always yields status 0; see #8605, #14207 + xhr.status, + xhr.statusText + ); + } + } else { + complete( + xhrSuccessStatus[ xhr.status ] || xhr.status, + xhr.statusText, + + // Support: IE <=9 only + // IE9 has no XHR2 but throws on binary (trac-11426) + // For XHR2 non-text, let the caller handle it (gh-2498) + ( xhr.responseType || "text" ) !== "text" || + typeof xhr.responseText !== "string" ? + { binary: xhr.response } : + { text: xhr.responseText }, + xhr.getAllResponseHeaders() + ); + } + } + }; + }; + + // Listen to events + xhr.onload = callback(); + errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" ); + + // Support: IE 9 only + // Use onreadystatechange to replace onabort + // to handle uncaught aborts + if ( xhr.onabort !== undefined ) { + xhr.onabort = errorCallback; + } else { + xhr.onreadystatechange = function() { + + // Check readyState before timeout as it changes + if ( xhr.readyState === 4 ) { + + // Allow onerror to be called first, + // but that will not handle a native abort + // Also, save errorCallback to a variable + // as xhr.onerror cannot be accessed + window.setTimeout( function() { + if ( callback ) { + errorCallback(); + } + } ); + } + }; + } + + // Create the abort callback + callback = callback( "abort" ); + + try { + + // Do send the request (this may raise an exception) + xhr.send( options.hasContent && options.data || null ); + } catch ( e ) { + + // #14683: Only rethrow if this hasn't been notified as an error yet + if ( callback ) { + throw e; + } + } + }, + + abort: function() { + if ( callback ) { + callback(); + } + } + }; + } +} ); + + + + +// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432) +jQuery.ajaxPrefilter( function( s ) { + if ( s.crossDomain ) { + s.contents.script = false; + } +} ); + +// Install script dataType +jQuery.ajaxSetup( { + accepts: { + script: "text/javascript, application/javascript, " + + "application/ecmascript, application/x-ecmascript" + }, + contents: { + script: /\b(?:java|ecma)script\b/ + }, + converters: { + "text script": function( text ) { + jQuery.globalEval( text ); + return text; + } + } +} ); + +// Handle cache's special case and crossDomain +jQuery.ajaxPrefilter( "script", function( s ) { + if ( s.cache === undefined ) { + s.cache = false; + } + if ( s.crossDomain ) { + s.type = "GET"; + } +} ); + +// Bind script tag hack transport +jQuery.ajaxTransport( "script", function( s ) { + + // This transport only deals with cross domain or forced-by-attrs requests + if ( s.crossDomain || s.scriptAttrs ) { + var script, callback; + return { + send: function( _, complete ) { + script = jQuery( " + + Examples — cppflow 2.0 documentation + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Examples

+
+

Create and load model

+

To create a model that you can open with cppflow you just need to create a tf.Module or a tf.keras.Model and save it. Using the functional API of keras this is as easy as:

+
import tensorflow as tf
+
+
+input = tf.keras.Input(shape=(5,))
+
+output = tf.keras.layers.Dense(5, activation=tf.nn.relu)(input)
+output = tf.keras.layers.Dense(1, activation=tf.nn.sigmoid)(output)
+model = tf.keras.Model(inputs=input, outputs=output)
+
+model.compile()
+
+# Export the model to a SavedModel
+model.save('model', save_format='tf')
+
+
+

Now a new directory named model is created, and it contains the saved model. You can open it from cppflow using the model class, to then feed it with a tensor to obtain the output.

+
#include <iostream>
+#include "cppflow/cppflow.h"
+
+
+int main() {
+
+    auto input = cppflow::fill({10, 5}, 1.0f);
+    cppflow::model model("../model");
+    auto output = model(input);
+
+    std::cout << output << std::endl;
+
+    return 0;
+}
+
+
+
+
+

Inference on EfficientNet

+

For this example we use a pretrained EfficientNet network that is available in Keras applications. Running the following code will create a model directory with the definition of the EfficientNet and its weights.

+
import tensorflow as tf
+
+model = tf.keras.applications.EfficientNetB0()
+
+# Export the model to a SavedModel
+model.save('model', save_format='tf')
+
+
+

Now we can open the model from cppflow and perform inference with a real image.

+Inference on EfficientNet from c++ with a picture of a cat +

We can load the image using cppflow::read_file and cppflow::decode_jpeg. Then we have to convert it to float and feed it to the network.

+
#include <iostream>
+#include "cppflow/cppflow.h"
+
+
+int main() {
+
+    auto input = cppflow::decode_jpeg(cppflow::read_file(std::string("../my_cat.jpg")));
+    input = cppflow::cast(input, TF_UINT8, TF_FLOAT);
+    input = cppflow::expand_dims(input, 0);
+    cppflow::model model("../model");
+    auto output = model(input);
+
+    std::cout << "It's a tiger cat: " << cppflow::arg_max(output, 1) << std::endl;
+
+    return 0;
+}
+
+
+

To see the prediction of the network we apply cppflow::arg_max to the ouput and it will show the number of the predicted class, which corresponds with a tiger cat.

+
+
+

Multi input/output model

+

For this example we will create a Keras model that takes two inputs and produce two outputs:

+
import tensorflow as tf
+
+input_1 = tf.keras.Input(shape=(5,), name='my_input_1')
+input_2 = tf.keras.Input(shape=(5,), name='my_input_2')
+
+x1 = tf.keras.layers.Dense(5, activation=tf.nn.relu)(input_1)
+x2 = tf.keras.layers.Dense(5, activation=tf.nn.relu)(input_2)
+
+output_1 = tf.keras.layers.Dense(1, activation=tf.nn.sigmoid, name='my_outputs_1')(x1)
+output_2 = tf.keras.layers.Dense(1, activation=tf.nn.sigmoid, name='my_outputs_2')(x2)
+
+model = tf.keras.Model(inputs=[input_1, input_2], outputs=[output_1, output_2])
+
+model.compile()
+
+# Export the model to a SavedModel
+model.save('model', save_format='tf')
+
+
+

Now, we will inspect the model with the saved_model_cli to retrieve the name of the operations, and to know how to call the model.

+
$ saved_model_cli show --dir model
+'serve'
+$ saved_model_cli show --dir model --tag_set serve
+SignatureDef key: "__saved_model_init_op"
+SignatureDef key: "serving_default"
+$ saved_model_cli show --dir model --tag_set serve --signature_def serving_default
+The given SavedModel SignatureDef contains the following input(s):
+  inputs['my_input_1'] tensor_info:
+      dtype: DT_FLOAT
+      shape: (-1, 5)
+      name: serving_default_my_input_1:0
+  inputs['my_input_2'] tensor_info:
+      dtype: DT_FLOAT
+      shape: (-1, 5)
+      name: serving_default_my_input_2:0
+The given SavedModel SignatureDef contains the following output(s):
+  outputs['my_outputs_1'] tensor_info:
+      dtype: DT_FLOAT
+      shape: (-1, 1)
+      name: StatefulPartitionedCall:0
+  outputs['my_outputs_2'] tensor_info:
+      dtype: DT_FLOAT
+      shape: (-1, 1)
+      name: StatefulPartitionedCall:1
+Method name is: tensorflow/serving/predict
+
+
+

From this output we can see that there are two inputs (serving_default_my_input_1:0 and serving_default_my_input_2:0) and two outputs (StatefulPartitionedCall:0 and StatefulPartitionedCall:1). You can run the model specifying multiple inputs as a vector of tuples <name of the input, input tensor and multiple outputs as a vector with the name of the outputs:

+
#include <iostream>
+#include "cppflow/cppflow.h"
+
+int main() {
+
+    auto input_1 = cppflow::fill({10, 5}, 1.0f);
+    auto input_2 = cppflow::fill({10, 5}, -1.0f);
+    cppflow::model model("../model");
+
+    auto output = model({{"serving_default_my_input_1:0", input_1}, {"serving_default_my_input_2:0", input_2}}, {"StatefulPartitionedCall:0", "StatefulPartitionedCall:1"});
+
+    std::cout << "output_1: " << output[0] << std::endl;
+    std::cout << "output_2: " << output[1] << std::endl;
+    return 0;
+}
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt deleted file mode 100644 index cfe90f3..0000000 --- a/examples/CMakeLists.txt +++ /dev/null @@ -1,5 +0,0 @@ -add_subdirectory(eager_op_multithread) -add_subdirectory(efficientnet) -add_subdirectory(load_model) -add_subdirectory(multi_input_output) -add_subdirectory(tensor) diff --git a/examples/eager_op_multithread/CMakeLists.txt b/examples/eager_op_multithread/CMakeLists.txt deleted file mode 100644 index 33cf6e6..0000000 --- a/examples/eager_op_multithread/CMakeLists.txt +++ /dev/null @@ -1,10 +0,0 @@ -cmake_minimum_required(VERSION 3.10) -project(eager_op_multithread) - -find_package(Threads REQUIRED) - -#set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fno-omit-frame-pointer -fsanitize=thread") -#set(CMAKE_LINKER_FLAGS "${CMAKE_LINKER_FLAGS} -fno-omit-frame-pointer -fsanitize=thread") - -add_executable(eager_op_multithread main.cpp) -target_link_libraries(eager_op_multithread Threads::Threads cppflow) diff --git a/examples/eager_op_multithread/main.cpp b/examples/eager_op_multithread/main.cpp deleted file mode 100644 index 013a861..0000000 --- a/examples/eager_op_multithread/main.cpp +++ /dev/null @@ -1,75 +0,0 @@ -// MIT License -// -// Copyright (c) 2020 Jiannan Liu -// Copyright (c) 2022 Sergio Izquierdo -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files (the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions: -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -// SOFTWARE. - -/*! - * @file main.cpp - * @brief Test the behavior of cppflow with multiple threads - * @details Test the behavior of cppflow with multiple threads - * @author Jiannan Liu - * @author Sergio Izquierdo - * @date @showdate "%B %d, %Y" 2020-10-26 - */ - -// CppFlow headers -#include - -// C++ headers -#include -#include -#include -#include - -constexpr size_t num_iter = 10240; -constexpr size_t num_threads = 32; - -void test1(float input1, float input2, float input3) { - float target = (input1+input2)*input3; - for (size_t i = 0; i < num_iter; i++) { - cppflow::tensor t1(input1), t2(input2), t3(input3); - auto result = (t1+t2)*t3; - float result_value = result.get_data()[0]; - if (std::abs(target/result_value-1.0f) > 1e-6) { - std::cout << "error: result_value=" << result_value - << ", target=" << target << std::endl; - } - } -} - -int main() { - std::vector threads; - for (size_t i = 0; i < num_threads; i++) { - if (i % 2 == 0) { - std::thread t1(test1, 3, 10, 100); - threads.push_back(std::move(t1)); - } else { - std::thread t2(test1, 130, 10, 100); - threads.push_back(std::move(t2)); - } - } - - for (auto& t : threads) { - t.join(); - } - - return 0; -} diff --git a/examples/efficientnet/CMakeLists.txt b/examples/efficientnet/CMakeLists.txt deleted file mode 100644 index 18a7a0c..0000000 --- a/examples/efficientnet/CMakeLists.txt +++ /dev/null @@ -1,9 +0,0 @@ -cmake_minimum_required(VERSION 3.10) -project(efficientnet) - -add_executable(efficientnet main.cpp) -target_link_libraries(efficientnet cppflow) -target_compile_definitions(efficientnet PUBLIC - CAT_PATH="${CMAKE_CURRENT_SOURCE_DIR}/my_cat.jpg" - MODEL_PATH="${CMAKE_CURRENT_SOURCE_DIR}/model" -) diff --git a/examples/efficientnet/create_model.py b/examples/efficientnet/create_model.py deleted file mode 100644 index e0b0887..0000000 --- a/examples/efficientnet/create_model.py +++ /dev/null @@ -1,43 +0,0 @@ -#!/usr/bin/env python -""" - Example for create model functionality. -""" - -# MIT License -# -# Copyright (c) 2020 Sergio Izquierdo -# -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in -# all copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -# @file create_model.py -# -# @brief Creates and saves an EfficientNet model. -# -# @section description_create_model Creates and saves an EfficientNet model. -# -# @section author_create_model Author(s) -# - Created by Sergio Izquierdo - -# Imports -import tensorflow as tf - -model = tf.keras.applications.EfficientNetB0() - -# Export the model to a SavedModel -model.save('model', save_format='tf') diff --git a/examples/efficientnet/main.cpp b/examples/efficientnet/main.cpp deleted file mode 100644 index 5f41e48..0000000 --- a/examples/efficientnet/main.cpp +++ /dev/null @@ -1,52 +0,0 @@ -// MIT License -// -// Copyright (c) 2020 Sergio Izquierdo -// Copyright (c) 2021 Florian -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files (the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions: -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -// SOFTWARE. - -/*! - * @file main.cpp - * @brief Run EfficientNet on a cat image as an example. - * @details Run an EfficientNet model on a cat image and print the result. - * The EfficientNet model should be downloaded running create_model.py - * @author Florian - * @author Sergio Izquierdo - * @date @showdate "%B %d, %Y" 2020-09-16 - */ - -// CppFlow headers -#include - -// C++ headers -#include - -int main() { - auto input = cppflow::decode_jpeg( - cppflow::read_file(std::string(CAT_PATH))); - input = cppflow::cast(input, TF_UINT8, TF_FLOAT); - input = cppflow::expand_dims(input, 0); - cppflow::model model(std::string(MODEL_PATH)); - auto output = model(input); - - std::cout << "It's a tiger cat: " << cppflow::arg_max(output, 1) - << std::endl; - - return 0; -} diff --git a/examples/efficientnet/my_cat.jpg b/examples/efficientnet/my_cat.jpg deleted file mode 100644 index 951abe5..0000000 Binary files a/examples/efficientnet/my_cat.jpg and /dev/null differ diff --git a/examples/load_frozen_graph/CMakeLists.txt b/examples/load_frozen_graph/CMakeLists.txt deleted file mode 100644 index 3710e6a..0000000 --- a/examples/load_frozen_graph/CMakeLists.txt +++ /dev/null @@ -1,13 +0,0 @@ -cmake_minimum_required(VERSION 3.10) -project(example) - -find_library(TENSORFLOW_LIB tensorflow HINT $ENV{HOME}/libtensorflow2/lib) - -set(CMAKE_CXX_STANDARD 17) -# set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fno-omit-frame-pointer -fsanitize=address") -set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fno-omit-frame-pointer") -set(CMAKE_LINKER_FLAGS "${CMAKE_LINKER_FLAGS} -lasan") - -add_executable(example main.cpp) -target_include_directories(example PRIVATE ../../include $ENV{HOME}/libtensorflow2/include) -target_link_libraries (example "${TENSORFLOW_LIB}") diff --git a/examples/load_frozen_graph/create_model.py b/examples/load_frozen_graph/create_model.py deleted file mode 100644 index c59a384..0000000 --- a/examples/load_frozen_graph/create_model.py +++ /dev/null @@ -1,63 +0,0 @@ -#!/usr/bin/env python -""" - Example for a load frozen tf graph functionality. -""" - -# MIT License -# -# Copyright (c) 2021 Daisuke Kato -# Copyright (c) 2021 Paul -# Copyright (c) 2022 Sergio Izquierdo -# -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in -# all copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -# @file create_model.py -# -# @brief Creates and saves a simple Keras model as a frozen graph. -# -# @section Creates and saves a simple Keras model as a frozen graph. -# -# @section author_create_model Author(s) -# - Created by Daisuke Kato -# - Created by Paul -# - Modified by Sergio Izquierdo - -# Imports -import tensorflow as tf -from tensorflow.python.framework.convert_to_constants import ( - convert_variables_to_constants_v2, -) - -input_1 = tf.keras.Input(shape=(5,)) -output_1 = tf.keras.layers.Dense(5, activation=tf.nn.relu)(input_1) -output_1 = tf.keras.layers.Dense(1, activation=tf.nn.sigmoid)(output_1) -model = tf.keras.Model(inputs=input_1, outputs=output_1) - -# Create frozen graph -x = tf.TensorSpec(model.input_shape, tf.float32, name="x") -concrete_function = tf.function(lambda x: model(x)).get_concrete_function(x) -frozen_model = convert_variables_to_constants_v2(concrete_function) - -# Check input/output node name -print(f"{frozen_model.inputs=}") -print(f"{frozen_model.outputs=}") - -# Save the graph as protobuf format -directory = "." -tf.io.write_graph(frozen_model.graph, directory, "model.pb", as_text=False) diff --git a/examples/load_frozen_graph/main.cpp b/examples/load_frozen_graph/main.cpp deleted file mode 100644 index 290524d..0000000 --- a/examples/load_frozen_graph/main.cpp +++ /dev/null @@ -1,57 +0,0 @@ -// MIT License -// -// Copyright (c) 2021 Daisuke Kato -// Copyright (c) 2021 Paul -// Copyright (c) 2022 Sergio Izquierdo -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files (the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions: -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -// SOFTWARE. - -/*! - * @file main.cpp - * @brief Loads a frozen graph model and runs it with dummy data - * @details Loads a simple Keras model saved in frozen graph format - * and runs it with dummy data - * @author Daisuke Kato - * @author Paul - * @author Sergio Izquierdo - * @date @showdate "%B %d, %Y" 2021-09-16 - */ - -// CppFlow headers -#include -#include - -// C++ headers -#include - -int main() { - auto input = cppflow::fill({10, 5}, 1.0f); - std::cout << "start" << std::endl; - cppflow::model model("../model.pb", cppflow::model::FROZEN_GRAPH); - auto output = model({{"x:0", input}}, {{"Identity:0"}})[0]; - - std::cout << output << std::endl; - - auto values = output.get_data(); - - for (auto v : values) { - std::cout << v << std::endl; - } - return 0; -} diff --git a/examples/load_frozen_graph/model.pb b/examples/load_frozen_graph/model.pb deleted file mode 100644 index adb2c75..0000000 Binary files a/examples/load_frozen_graph/model.pb and /dev/null differ diff --git a/examples/load_model/CMakeLists.txt b/examples/load_model/CMakeLists.txt deleted file mode 100644 index 2858d3a..0000000 --- a/examples/load_model/CMakeLists.txt +++ /dev/null @@ -1,11 +0,0 @@ -cmake_minimum_required(VERSION 3.10) -project(load_model) - -set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fno-omit-frame-pointer -fsanitize=address") -set(CMAKE_LINKER_FLAGS "${CMAKE_LINKER_FLAGS} -lasan") - -add_executable(load_model main.cpp) -target_link_libraries(load_model cppflow) -target_compile_definitions(load_model PUBLIC - MODEL_PATH="${CMAKE_CURRENT_SOURCE_DIR}/model" -) diff --git a/examples/load_model/create_model.py b/examples/load_model/create_model.py deleted file mode 100644 index 96eb8df..0000000 --- a/examples/load_model/create_model.py +++ /dev/null @@ -1,49 +0,0 @@ -#!/usr/bin/env python -""" - Example for a load model functionality. -""" - -# MIT License -# -# Copyright (c) 2019 Sergio Izquierdo -# -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in -# all copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -# @file create_model.py -# -# @brief Creates and saves a simple Keras model as a saved model. -# -# @section Creates and saves a simple Keras model as a saved model. -# -# @section author_create_model Author(s) -# - Created by Sergio Izquierdo - -# Imports -import tensorflow as tf - -input_1 = tf.keras.Input(shape=(5,)) - -output_1 = tf.keras.layers.Dense(5, activation=tf.nn.relu)(input_1) -output_1 = tf.keras.layers.Dense(1, activation=tf.nn.sigmoid)(output_1) -model = tf.keras.Model(inputs=input_1, outputs=output_1) - -model.compile() - -# Export the model to a SavedModel -model.save('model', save_format='tf') diff --git a/examples/load_model/main.cpp b/examples/load_model/main.cpp deleted file mode 100644 index 81031b5..0000000 --- a/examples/load_model/main.cpp +++ /dev/null @@ -1,55 +0,0 @@ -// MIT License -// -// Copyright (c) 2019 Sergio Izquierdo -// Copyright (c) 2019 Paul Nykiel -// Copyright (c) 2020 Afaq Sabir -// Copyright (c) 2021 Florian -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files (the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions: -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -// SOFTWARE. - -/*! - * @file main.cpp - * @author Afaq Sabir - * @author Florian - * @author Paul Nykiel - * @author Sergio Izquierdo - * @date @showdate "%B %d, %Y" 2019-05-16 - */ - -// CppFlow headers -#include -#include - -// C++ headers -#include - -int main() { - auto input = cppflow::fill({10, 5}, 1.0f); - cppflow::model model(std::string(MODEL_PATH)); - auto output = model(input); - - std::cout << output << std::endl; - - auto values = output.get_data(); - - for (auto v : values) { - std::cout << v << std::endl; - } - return 0; -} diff --git a/examples/load_model/model/saved_model.pb b/examples/load_model/model/saved_model.pb deleted file mode 100644 index 9cd0058..0000000 Binary files a/examples/load_model/model/saved_model.pb and /dev/null differ diff --git a/examples/load_model/model/variables/variables.data-00000-of-00001 b/examples/load_model/model/variables/variables.data-00000-of-00001 deleted file mode 100644 index def833b..0000000 Binary files a/examples/load_model/model/variables/variables.data-00000-of-00001 and /dev/null differ diff --git a/examples/load_model/model/variables/variables.index b/examples/load_model/model/variables/variables.index deleted file mode 100644 index 7b2f01e..0000000 Binary files a/examples/load_model/model/variables/variables.index and /dev/null differ diff --git a/examples/multi_input_output/CMakeLists.txt b/examples/multi_input_output/CMakeLists.txt deleted file mode 100644 index 41101cc..0000000 --- a/examples/multi_input_output/CMakeLists.txt +++ /dev/null @@ -1,8 +0,0 @@ -cmake_minimum_required(VERSION 3.10) -project(multi_input_output) - -add_executable(multi_input_output main.cpp) -target_link_libraries(multi_input_output cppflow) -target_compile_definitions(multi_input_output PUBLIC - MODEL_PATH="${CMAKE_CURRENT_SOURCE_DIR}/model" -) diff --git a/examples/multi_input_output/create_model.py b/examples/multi_input_output/create_model.py deleted file mode 100644 index d93cc8d..0000000 --- a/examples/multi_input_output/create_model.py +++ /dev/null @@ -1,56 +0,0 @@ -#!/usr/bin/env python -""" - Example for a multiple inputs and outputs functionality. -""" - -# MIT License -# -# Copyright (c) 2020 Sergio Izquierdo -# -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in -# all copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -# @file create_model.py -# -# @brief Creates and saves a simple multi input multi output Keras model. -# -# @section Creates and saves a simple multi input multi output Keras model. -# -# @section author_create_model Author(s) -# - Created by Sergio Izquierdo - -# Imports -import tensorflow as tf - -input_1 = tf.keras.Input(shape=(5,), name='my_input_1') -input_2 = tf.keras.Input(shape=(5,), name='my_input_2') - -x1 = tf.keras.layers.Dense(5, activation=tf.nn.relu)(input_1) -x2 = tf.keras.layers.Dense(5, activation=tf.nn.relu)(input_2) - -output_1 = tf.keras.layers.Dense(1, activation=tf.nn.sigmoid, - name='my_outputs_1')(x1) -output_2 = tf.keras.layers.Dense(1, activation=tf.nn.sigmoid, - name='my_outputs_2')(x2) - -model = tf.keras.Model(inputs=[input_1, input_2], outputs=[output_1, output_2]) - -model.compile() - -# Export the model to a SavedModel -model.save('model', save_format='tf') diff --git a/examples/multi_input_output/main.cpp b/examples/multi_input_output/main.cpp deleted file mode 100644 index 198af35..0000000 --- a/examples/multi_input_output/main.cpp +++ /dev/null @@ -1,54 +0,0 @@ -// MIT License -// -// Copyright (c) 2020 Sergio Izquierdo -// Copyright (c) 2021 Florian -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files (the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions: -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -// SOFTWARE. - -/*! - * @file main.cpp - * @brief Loads a saved multi input/output model and runs it with dummy data - * @details Loads a simple Keras model with multiple inputs and outputs saved - * in saved model format and runs it with dummy data - * @author Florian - * @author Sergio Izquierdo - * @date @showdate "%B %d, %Y" 2020-10-15 - */ - -// CppFlow headers -#include -#include - -// C++ headers -#include - -int main() { - auto input_1 = cppflow::fill({10, 5}, 1.0f); - auto input_2 = cppflow::fill({10, 5}, -1.0f); - cppflow::model model(std::string(MODEL_PATH)); - - auto output = model({{"serving_default_my_input_1:0", input_1}, - {"serving_default_my_input_2:0", input_2}}, - {"StatefulPartitionedCall:0", - "StatefulPartitionedCall:1"}); - - std::cout << "output_1: " << output[0] << std::endl; - std::cout << "output_2: " << output[1] << std::endl; - return 0; -} diff --git a/examples/multi_input_output/model/saved_model.pb b/examples/multi_input_output/model/saved_model.pb deleted file mode 100644 index 94ea4ee..0000000 Binary files a/examples/multi_input_output/model/saved_model.pb and /dev/null differ diff --git a/examples/multi_input_output/model/variables/variables.data-00000-of-00001 b/examples/multi_input_output/model/variables/variables.data-00000-of-00001 deleted file mode 100644 index 95973d1..0000000 Binary files a/examples/multi_input_output/model/variables/variables.data-00000-of-00001 and /dev/null differ diff --git a/examples/multi_input_output/model/variables/variables.index b/examples/multi_input_output/model/variables/variables.index deleted file mode 100644 index a26b949..0000000 Binary files a/examples/multi_input_output/model/variables/variables.index and /dev/null differ diff --git a/examples/tensor/CMakeLists.txt b/examples/tensor/CMakeLists.txt deleted file mode 100644 index b62ab7e..0000000 --- a/examples/tensor/CMakeLists.txt +++ /dev/null @@ -1,5 +0,0 @@ -cmake_minimum_required(VERSION 3.10) -project(tensor) - -add_executable(tensor main.cpp odr.cpp) -target_link_libraries(tensor cppflow) diff --git a/examples/tensor/main.cpp b/examples/tensor/main.cpp deleted file mode 100644 index 8c90002..0000000 --- a/examples/tensor/main.cpp +++ /dev/null @@ -1,132 +0,0 @@ -// MIT License -// -// Copyright (c) 2020 Jiannan Liu -// Copyright (c) 2021 Seungtaek Kim -// Copyright (c) 2022 Sergio Izquierdo -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files (the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions: -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -// SOFTWARE. - -/*! - * @file main.cpp - * @brief Tests tensors allocation and deallocation - * @details Tests tensors allocation and deallocation - * @author Jiannan Liu - * @author Seungtaek Kim - * @author Sergio Izquierdo - * @date @showdate "%B %d, %Y" 2020-10-26 - */ - -// CppFlow headers -#include - -// C++ headers -#include -#include -#include -#include - -bool float_equal(const float f1, const float f2) { - return std::abs(f1/f2-1.0f) < 1e-6; -} - -void test1(const bool is_cpu) { - std::cout << "test1 starts: is_cpu=" << is_cpu << std::endl; - float target = 1.0; - int64_t ndim = 2; - cppflow::tensor t1; - - if (is_cpu) { - std::vector _data(ndim, target); - t1 = cppflow::tensor(_data, {ndim}); - } else { - t1 = cppflow::fill({ndim}, target); - } - - std::cout << "tensor::device(true) : " << t1.device(true) << std::endl; - std::cout << "tensor::device(false) : " << t1.device(false) << std::endl; - - auto t1_tensor = t1.get_tensor(); - auto raw_data = static_cast(TF_TensorData(t1_tensor.get())); - float result_value = raw_data[0]; - if (float_equal(result_value, target)) { - std::cout << "tensor::get_tensor() test1-1: pass" << std::endl; - } else { - std::cout << "tensor::get_tensor() test1-1: result_value=" - << result_value << ", target=" << target << std::endl; - throw std::runtime_error("tensor::get_tensor() test1-1: failed"); - } - - // IMPORTANT NOTE: CANNOT modify the returned cache - float target2 = target + 10.0; - raw_data[1] = target2; - result_value = t1.get_data()[0]; - float result_value2 = t1.get_data()[1]; - if (float_equal(result_value, target)) { - std::cout << "tensor::get_tensor() test1-2: pass" << std::endl; - } else { - std::cout << "tensor::get_tensor() test1-2: failed, result_value=" - << result_value << ", target=" << target << std::endl; - throw std::runtime_error("tensor::get_tensor() test1-2: failed"); - } - if (float_equal(result_value2, target2)) { - std::cout << "tensor::get_tensor() test1-3: pass" << std::endl; - } else { - std::cout << "The failure of test1-3 is not considered as a bug." - << std::endl; - std::cout << "tensor::get_tensor() test1-3: failed, result_value=" - << result_value2 << ", target2=" << target2 << std::endl; - } - - auto t2 = t1 + cppflow::tensor(0.f); - std::cout << "Can NOT modify the cache!" << std::endl; - std::cout << "t2: " << t2 << std::endl; - - auto dt = cppflow::to_string(t1.dtype()); - std::string expected_dtype{"TF_FLOAT"}; - if (dt == expected_dtype) { - std::cout << "tensor::get_tensor() test1-4: pass" << std::endl; - } else { - std::cout << "tensor::get_tensor() test1-4: dtype=" << dt - << ", expected_dtype=" << expected_dtype << std::endl; - throw std::runtime_error("tensor::get_tensor() test1-4: failed"); - } - - auto shape_tensor = t1.shape(); - auto shape = shape_tensor.get_data()[0]; - if (shape == ndim) { - std::cout << "tensor::get_tensor() test1-5: pass" << std::endl; - } else { - std::cout << "tensor::get_tensor() test1-5: shape_tensor.dtype()=" - << cppflow::to_string(shape_tensor.dtype()) << std::endl; - std::cout << "tensor::get_tensor() test1-5: shape_tensor=" - << shape_tensor << std::endl; - std::cout << "tensor::get_tensor() test1-5: shape()=" << shape - << ", ndim=" << ndim << std::endl; - throw std::runtime_error("tensor::get_tensor() test1-5: failed"); - } - - std::cout << std::endl; -} - -int main() { - test1(true); - test1(false); - - return 0; -} diff --git a/examples/tensor/odr.cpp b/examples/tensor/odr.cpp deleted file mode 100644 index 2dc7744..0000000 --- a/examples/tensor/odr.cpp +++ /dev/null @@ -1,35 +0,0 @@ -// MIT License -// -// Copyright (c) 2020 Jiannan Liu -// Copyright (c) 2022 Sergio Izquierdo -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files (the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions: -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -// SOFTWARE. - -/*! - * @file odr.cpp - * @author Jiannan Liu - * @author Sergio Izquierdo - * @date @showdate "%B %d, %Y" 2020-10-26 - */ - -// CppFlow headers -#include - -// Do NOT remove this file -// test ODR violation diff --git a/genindex.html b/genindex.html new file mode 100644 index 0000000..9b0bcbf --- /dev/null +++ b/genindex.html @@ -0,0 +1,114 @@ + + + + + + Index — cppflow 2.0 documentation + + + + + + + + + + + + + + + +
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+ + + + \ No newline at end of file diff --git a/include/cppflow/context.h b/include/cppflow/context.h deleted file mode 100644 index ba712d0..0000000 --- a/include/cppflow/context.h +++ /dev/null @@ -1,121 +0,0 @@ -// MIT License -// -// Copyright (c) 2020 Sergio Izquierdo -// Copyright (c) 2020 Jiannan Liu -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files (the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions: -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -// SOFTWARE. - -/*! - * @file context.h - * @author Jiannan Liu - * @author Sergio Izquierdo - * @date @showdate "%B %d, %Y" 2020-06-27 - */ - -#ifndef INCLUDE_CPPFLOW_CONTEXT_H_ -#define INCLUDE_CPPFLOW_CONTEXT_H_ - -// C headers -#include -#include - -// C++ headers -#include -#include -#include - -namespace cppflow { - -inline bool status_check(TF_Status* status) { - if (TF_GetCode(status) != TF_OK) { - throw std::runtime_error(TF_Message(status)); - } - return true; -} - -class context { - public: - explicit context(TFE_ContextOptions* opts = nullptr); - - context(context const&) = delete; - context(context&&) noexcept; - - ~context(); - - context& operator=(context const&) = delete; - context& operator=(context&&) noexcept; - - static TFE_Context* get_context(); - - // only use get_status() for eager ops - static TF_Status* get_status(); - - private: - TFE_Context* tfe_context{nullptr}; -}; // Class context - -// @todo create ContextManager class if needed -// Set new context, thread unsafe, must be called at the beginning. -// TFE_ContextOptions* tfe_opts = ... -// cppflow::get_global_context() = cppflow::context(tfe_opts); -inline context& get_global_context() { - static context global_context; - return global_context; -} -} // namespace cppflow - -namespace cppflow { - -inline TFE_Context* context::get_context() { - return get_global_context().tfe_context; -} - -inline TF_Status* context::get_status() { - thread_local std::unique_ptr - local_tf_status(TF_NewStatus(), &TF_DeleteStatus); - return local_tf_status.get(); -} - -inline context::context(TFE_ContextOptions* opts) { - auto tf_status = context::get_status(); - if (opts == nullptr) { - std::unique_ptr - new_opts(TFE_NewContextOptions(), &TFE_DeleteContextOptions); - this->tfe_context = TFE_NewContext(new_opts.get(), tf_status); - } else { - this->tfe_context = TFE_NewContext(opts, tf_status); - } - status_check(tf_status); -} - -inline context::context(context&& ctx) noexcept - : tfe_context(std::exchange(ctx.tfe_context, nullptr)) {} - -inline context& context::operator=(context&& ctx) noexcept { - tfe_context = std::exchange(ctx.tfe_context, tfe_context); - return *this; -} - -inline context::~context() { - TFE_DeleteContext(this->tfe_context); -} - -} // namespace cppflow - -#endif // INCLUDE_CPPFLOW_CONTEXT_H_ diff --git a/include/cppflow/cppflow.h b/include/cppflow/cppflow.h deleted file mode 100644 index 8b24a2e..0000000 --- a/include/cppflow/cppflow.h +++ /dev/null @@ -1,69 +0,0 @@ -// MIT License -// -// Copyright (c) 2020 Sergio Izquierdo -// Copyright (c) 2020 Jiannan Liu -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files (the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions: -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -// SOFTWARE. - -/*! - * @file cppflow.h - * @author Jiannan Liu - * @author Sergio Izquierdo - * @date @showdate "%B %d, %Y" 2020-09-17 - */ - -#ifndef INCLUDE_CPPFLOW_CPPFLOW_H_ -#define INCLUDE_CPPFLOW_CPPFLOW_H_ - -// C headers -#include - -// C++ headers -#include - -// CppFlow headers -#include "cppflow/datatype.h" -#include "cppflow/model.h" -#include "cppflow/ops.h" -#include "cppflow/raw_ops.h" -#include "cppflow/tensor.h" - -namespace cppflow { - -/** - * Version of TensorFlow and CppFlow - * @return A string containing the version of TensorFow and CppFlow - */ -std::string version(); - -} // namespace cppflow - -/****************************** - * IMPLEMENTATION DETAILS * - ******************************/ - -namespace cppflow { - -inline std::string version() { - return "TensorFlow: " + std::string(TF_Version()) + " CppFlow: 2.0.0"; -} - -} // namespace cppflow - -#endif // INCLUDE_CPPFLOW_CPPFLOW_H_ diff --git a/include/cppflow/datatype.h b/include/cppflow/datatype.h deleted file mode 100644 index 5bc396f..0000000 --- a/include/cppflow/datatype.h +++ /dev/null @@ -1,146 +0,0 @@ -// MIT License -// -// Copyright (c) 2020 Sergio Izquierdo -// Copyright (c) 2020 Jiannan Liu -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files (the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions: -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -// SOFTWARE. - -/*! - * @file datatype.h - * @author Jiannan Liu - * @author Sergio Izquierdo - * @date @showdate "%B %d, %Y" 2020-07-12 - */ - -#ifndef INCLUDE_CPPFLOW_DATATYPE_H_ -#define INCLUDE_CPPFLOW_DATATYPE_H_ - -// C++ headers -#include -#include -#include -#include -#include - -namespace cppflow { - -using datatype = TF_DataType; - -/** - * @return A string representing dt - */ -inline std::string to_string(datatype dt) { - switch (dt) { - case TF_FLOAT: - return "TF_FLOAT"; - case TF_DOUBLE: - return "TF_DOUBLE"; - case TF_INT32: - return "TF_INT32"; - case TF_UINT8: - return "TF_UINT8"; - case TF_INT16: - return "TF_INT16"; - case TF_INT8: - return "TF_INT8"; - case TF_STRING: - return "TF_STRING"; - case TF_COMPLEX64: - return "TF_COMPLEX64"; - case TF_INT64: - return "TF_INT64"; - case TF_BOOL: - return "TF_BOOL"; - case TF_QINT8: - return "TF_QINT8"; - case TF_QUINT8: - return "TF_QUINT8"; - case TF_QINT32: - return "TF_QINT32"; - case TF_BFLOAT16: - return "TF_BFLOAT16"; - case TF_QINT16: - return "TF_QINT16"; - case TF_QUINT16: - return "TF_QUINT16"; - case TF_UINT16: - return "TF_UINT16"; - case TF_COMPLEX128: - return "TF_COMPLEX128"; - case TF_HALF: - return "TF_HALF"; - case TF_RESOURCE: - return "TF_RESOURCE"; - case TF_VARIANT: - return "TF_VARIANT"; - case TF_UINT32: - return "TF_UINT32"; - case TF_UINT64: - return "TF_UINT64"; - default: - return "DATATYPE_NOT_KNOWN"; - } -} - -/** - * - * @tparam T - * @return The TensorFlow type of T - */ -template -TF_DataType deduce_tf_type() { - if (std::is_same::value) - return TF_FLOAT; - if (std::is_same::value) - return TF_DOUBLE; - if (std::is_same::value) - return TF_INT32; - if (std::is_same::value) - return TF_UINT8; - if (std::is_same::value) - return TF_INT16; - if (std::is_same::value) - return TF_INT8; - if (std::is_same::value) - return TF_INT64; - if (std::is_same::value) - return TF_BOOL; - if (std::is_same::value) - return TF_UINT16; - if (std::is_same::value) - return TF_UINT32; - if (std::is_same::value) - return TF_UINT64; - - // decode with `c++filt --type $output` for gcc - throw std::runtime_error{ - "Could not deduce type! type_name: " + std::string(typeid(T).name())}; -} - -/** - * @return The stream os after inserting the string representation of dt - */ -inline std::ostream& operator<<(std::ostream& os, datatype dt) { - os << to_string(dt); - return os; -} - -} // namespace cppflow - -#endif // INCLUDE_CPPFLOW_DATATYPE_H_ diff --git a/include/cppflow/defer.h b/include/cppflow/defer.h deleted file mode 100644 index 96db7b1..0000000 --- a/include/cppflow/defer.h +++ /dev/null @@ -1,56 +0,0 @@ -// MIT License -// -// Copyright (c) 2020 liufeng27 -// Copyright (c) 2022 Sergio Izquierdo -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files (the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions: -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -// SOFTWARE. - -/*! - * @file defer.h - * @author liufeng27 - * @author Sergio Izquierdo - * @date @showdate "%B %d, %Y" 2020-11-07 - */ -#pragma once - -// C++ headers -#include - -namespace cppflow { - -class defer { - public: - typedef std::function Func; - - explicit defer(const Func& func) : _func(func) {} - ~defer() { - _func(); - } - - defer(const defer&) = delete; - defer(defer&&) = delete; - defer& operator=(const defer&) = delete; - void* operator new (size_t) = delete; - void operator delete (void*) = delete; - - private: - Func _func; -}; // Class defer - -} // namespace cppflow diff --git a/include/cppflow/model.h b/include/cppflow/model.h deleted file mode 100644 index 368e145..0000000 --- a/include/cppflow/model.h +++ /dev/null @@ -1,296 +0,0 @@ -// MIT License -// -// Copyright (c) 2020 Sergio Izquierdo -// Copyright (c) 2020 liufeng27 -// Copyright (c) 2020 Jiannan Liu -// Copyright (c) 2021 Paolo Galeone -// Copyright (c) 2021 Paul -// Copyright (c) 2022 Tim Upthegrove -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files (the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions: -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -// SOFTWARE. - -/*! - * @file model.h - * @author Jiannan Liu - * @author liufeng27 - * @author Paul - * @author Paolo Galeone - * @author Sergio Izquierdo - * @author Tim Upthegrove - * @date @showdate "%B %d, %Y" 2020-06-29 - */ - -#ifndef INCLUDE_CPPFLOW_MODEL_H_ -#define INCLUDE_CPPFLOW_MODEL_H_ - -// C headers -#include - -// C++ headers -#include -#include -#include -#include -#include -#include - -// CppFlow headers -#include "cppflow/context.h" -#include "cppflow/defer.h" -#include "cppflow/tensor.h" - -namespace cppflow { - -class model { - public: - enum TYPE { - SAVED_MODEL, - FROZEN_GRAPH, - }; // enum TYPE - - explicit model(const std::string& filename, - const TYPE type = TYPE::SAVED_MODEL); - model(const model &model) = default; - model(model &&model) = default; - - ~model() = default; - - model &operator=(const model &other) = default; - model &operator=(model &&other) = default; - std::vector operator()( - std::vector> inputs, - std::vector outputs); - tensor operator()(const tensor& input); - - std::vector get_operations() const; - std::vector get_operation_shape(const std::string& operation) const; - - private: - TF_Buffer * readGraph(const std::string& filename); - - std::shared_ptr status; - std::shared_ptr graph; - std::shared_ptr session; -}; // Class model - -} // namespace cppflow - -namespace cppflow { - -inline model::model(const std::string &filename, const TYPE type) { - this->status = {TF_NewStatus(), &TF_DeleteStatus}; - this->graph = {TF_NewGraph(), TF_DeleteGraph}; - - // Create the session. - std::unique_ptr - session_options = {TF_NewSessionOptions(), TF_DeleteSessionOptions}; - - auto session_deleter = [this](TF_Session* sess) { - TF_DeleteSession(sess, this->status.get()); - status_check(this->status.get()); - }; - - if (type == TYPE::SAVED_MODEL) { - std::unique_ptr run_options = { - TF_NewBufferFromString("", 0), TF_DeleteBuffer}; - std::unique_ptr meta_graph = { - TF_NewBuffer(), TF_DeleteBuffer}; - - int tag_len = 1; - const char* tag = "serve"; - this->session = { - TF_LoadSessionFromSavedModel(session_options.get(), run_options.get(), - filename.c_str(), &tag, tag_len, - this->graph.get(), meta_graph.get(), - this->status.get()), session_deleter}; - } else if (type == TYPE::FROZEN_GRAPH) { - this->session = {TF_NewSession(this->graph.get(), session_options.get(), - this->status.get()), - session_deleter}; - status_check(this->status.get()); - - // Import the graph definition - TF_Buffer* def = readGraph(filename); - if (def == nullptr) { - throw std::runtime_error("Failed to import graph def from file"); - } - - std::unique_ptr graph_opts = { - TF_NewImportGraphDefOptions(), TF_DeleteImportGraphDefOptions}; - TF_GraphImportGraphDef(this->graph.get(), def, graph_opts.get(), - this->status.get()); - TF_DeleteBuffer(def); - } else { - throw std::runtime_error("Model type unknown"); - } - - status_check(this->status.get()); -} - -inline std::vector model::get_operations() const { - std::vector result; - size_t pos = 0; - TF_Operation* oper; - - // Iterate through the operations of a graph - while ((oper = TF_GraphNextOperation(this->graph.get(), &pos)) != nullptr) { - result.emplace_back(TF_OperationName(oper)); - } - return result; -} - -inline std::vector model::get_operation_shape( - const std::string& operation) const { - // Get operation by the name - TF_Output out_op; - out_op.oper = TF_GraphOperationByName(this->graph.get(), operation.c_str()); - out_op.index = 0; - - std::vector shape; - - // Operation does not exist - if (!out_op.oper) - throw std::runtime_error("No operation named \"" + operation + "\" exists"); - - if (operation == "NoOp") - throw std::runtime_error("NoOp doesn't have a shape"); - - // DIMENSIONS - - // Get number of dimensions - int n_dims = TF_GraphGetTensorNumDims(this->graph.get(), out_op, - this->status.get()); - - // If is not a scalar - if (n_dims > 0) { - // Get dimensions - auto* dims = new int64_t[n_dims]; - TF_GraphGetTensorShape(this->graph.get(), out_op, dims, n_dims, - this->status.get()); - - // Check error on Model Status - status_check(this->status.get()); - - shape = std::vector(dims, dims + n_dims); - - delete[] dims; - } - - return shape; -} - -inline std::tuple parse_name(const std::string& name) { - auto idx = name.find(':'); - return (idx == std::string::npos ? std::make_tuple(name, 0) : - std::make_tuple(name.substr(0, idx), - std::stoi(name.substr(idx + 1)))); -} - -inline std::vector model::operator()( - std::vector> inputs, - std::vector outputs) { - - std::vector inp_ops(inputs.size()); - std::vector inp_val(inputs.size(), nullptr); - - for (decltype(inputs.size()) i=0; i < inputs.size(); i++) { - // Operations - const auto[op_name, op_idx] = parse_name(std::get<0>(inputs[i])); - inp_ops[i].oper = TF_GraphOperationByName(this->graph.get(), - op_name.c_str()); - inp_ops[i].index = op_idx; - - if (!inp_ops[i].oper) - throw std::runtime_error("No operation named \"" + op_name + "\" exists"); - - // Values - inp_val[i] = std::get<1>(inputs[i]).get_tensor().get(); - } - - std::vector out_ops(outputs.size()); - auto out_val = std::make_unique(outputs.size()); - for (decltype(outputs.size()) i=0; i < outputs.size(); i++) { - const auto[op_name, op_idx] = parse_name(outputs[i]); - out_ops[i].oper = TF_GraphOperationByName(this->graph.get(), - op_name.c_str()); - out_ops[i].index = op_idx; - - if (!out_ops[i].oper) - throw std::runtime_error("No operation named \"" + op_name + "\" exists"); - } - - TF_SessionRun(this->session.get(), /*run_options*/ NULL, - inp_ops.data(), inp_val.data(), static_cast(inputs.size()), - out_ops.data(), out_val.get(), static_cast(outputs.size()), - /*targets*/ NULL, /*ntargets*/ 0, /*run_metadata*/ NULL, - this->status.get()); - status_check(this->status.get()); - - std::vector result; - result.reserve(outputs.size()); - for (decltype(outputs.size()) i=0; i < outputs.size(); i++) { - result.emplace_back(tensor(out_val[i])); - } - - return result; -} - -inline tensor model::operator()(const tensor& input) { - return (*this)({{"serving_default_input_1", input}}, - {"StatefulPartitionedCall"})[0]; -} - -inline TF_Buffer * model::readGraph(const std::string& filename) { - std::ifstream file(filename, std::ios::binary | std::ios::ate); - - // Error opening the file - if (!file.is_open()) { - std::cerr << "Unable to open file: " << filename << std::endl; - return nullptr; - } - - // Cursor is at the end to get size - auto size = file.tellg(); - // Move cursor to the beginning - file.seekg(0, std::ios::beg); - - // Read - auto data = std::make_unique(size); - file.seekg(0, std::ios::beg); - file.read(data.get(), size); - - // Error reading the file - if (!file) { - std::cerr << "Unable to read the full file: " << filename << std::endl; - return nullptr; - } - - // Create tensorflow buffer from read data - TF_Buffer* buffer = TF_NewBufferFromString(data.get(), size); - - // Close file and remove data - file.close(); - - return buffer; -} - -} // namespace cppflow - -#endif // INCLUDE_CPPFLOW_MODEL_H_ diff --git a/include/cppflow/ops.h b/include/cppflow/ops.h deleted file mode 100644 index d7fbe95..0000000 --- a/include/cppflow/ops.h +++ /dev/null @@ -1,137 +0,0 @@ -// MIT License -// -// Copyright (c) 2020 Sergio Izquierdo -// Copyright (c) 2020 Jiannan Liu -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files (the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions: -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -// SOFTWARE. - -/*! - * @file ops.h - * @author Jiannan Liu - * @author Sergio Izquierdo - * @date @showdate "%B %d, %Y" 2020-07-31 - */ - -#ifndef INCLUDE_CPPFLOW_OPS_H_ -#define INCLUDE_CPPFLOW_OPS_H_ - -// C++ headers -#include - -// CppFlow headers -#include "cppflow/tensor.h" -#include "cppflow/raw_ops.h" - -namespace cppflow { - -/** - * @name Operators - */ -//@{ - -/** - * @returns x + y elementwise - */ -tensor operator+(const tensor& x, const tensor& y); - -/** - * @returns x - y elementwise - */ -tensor operator-(const tensor& x, const tensor& y); - -/** - * @returns x * y elementwise - */ -tensor operator*(const tensor& x, const tensor& y); - -/** - * @return x / y elementwise - */ -tensor operator/(const tensor& x, const tensor& y); - -std::ostream& operator<<(std::ostream& os, const cppflow::tensor& t); - -//@} - -/** - * @return A string representing t in the form: - * (tensor: shape=?, data= - * ?) - */ -std::string to_string(const tensor& t); -} // namespace cppflow - -/****************************** - * IMPLEMENTATION DETAILS * - ******************************/ - -namespace cppflow { - -// Operators - -inline tensor operator+(const tensor& x, const tensor& y) { - return add(x, y); -} - -inline tensor operator-(const tensor& x, const tensor& y) { - return sub(x, y); -} - -inline tensor operator*(const tensor& x, const tensor& y) { - return mul(x, y); -} - -inline tensor operator/(const tensor& x, const tensor& y) { - return div(x, y); -} - -inline std::ostream& operator<<(std::ostream& os, const cppflow::tensor& t) { - std::string res = to_string(t); - return os << res; -} - - -inline std::string to_string(const tensor &t) { - auto res_tensor = string_format({t.shape(), t}, - "(tensor: shape=%s, dtype="+ to_string(t.dtype()) + ", data=\n%s)"); - auto res_tensor_h = res_tensor.get_tensor(); - -#ifdef TENSORFLOW_C_TF_TSTRING_H_ - // For future version TensorFlow 2.4 - // auto *t_str = reinterpret_cast( - // TF_TensorData(res_tensor_h.get())); - auto *t_str = (TF_TString *)(TF_TensorData(res_tensor_h.get())); - auto result = std::string(TF_TString_GetDataPointer(t_str), - TF_TString_GetSize(t_str)); -#else - const char* dst[1] = {nullptr}; - size_t dst_len[1] = {3}; - TF_StringDecode(static_cast(TF_TensorData(res_tensor_h.get())) + 8, - TF_TensorByteSize(res_tensor_h.get()), dst, dst_len, - context::get_status()); - status_check(context::get_status()); - auto result = std::string(dst[0], *dst_len); -#endif // TENSORFLOW_C_TF_TSTRING_H_ - - return result; -} - -} // namespace cppflow - -#endif // INCLUDE_CPPFLOW_OPS_H_ diff --git a/include/cppflow/ops_generator/generator.py b/include/cppflow/ops_generator/generator.py deleted file mode 100644 index 8d86ff6..0000000 --- a/include/cppflow/ops_generator/generator.py +++ /dev/null @@ -1,385 +0,0 @@ -#!/usr/bin/env python -""" - Generates the raw_ops.h cpp code. -""" - -# MIT License -# -# Copyright (c) 2020 Sergio Izquierdo -# Copyright (c) 2020 Jiannan Liu -# Copyright (c) 2022 Alfredo Rodriguez -# -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in -# all copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -## -# @file generator.py -# @author Alfredo Rodriguez -# @author Jiannan Liu -# @author Sergio Izquierdo -# @date @showdate "%B %d, %Y" 2020-09-16 - -import tensorflow as tf -from tensorflow.core.framework import op_def_pb2 -from google.protobuf import text_format -from termcolor import colored -import re -import textwrap - -ops = op_def_pb2.OpList() -text_format.Merge(open('ops.pbtxt').read(), ops) - - -class Attribute: - """Class that describes the attribute. - - Attributes: - attr: An attribute. - name: The attribute's name. - type: The attribute's type. - islist: Whether a list attributes. - number_attr: Number of attributes - default: The attribute's default value. - """ - - def __init__(self, attr, number_attr_list): - - self.attr = attr - self.name = self.attr.name - - - if self.attr.type == 'func': - raise Exception('Passing functions as arguments is ' - 'not yet supported') - - # List attributes are defined as 'list(attr)'' - self.type, self.islist = ((self.attr.type, False) - if self.attr.type[:4] != 'list' - else (self.attr.type[5:-1], True)) - - self.number_attr = [i for n, i in number_attr_list if self.name == n] - self.number_attr, self.type = ((self.number_attr[0].name, 'n_attr') - if len(self.number_attr) - else (None, self.type)) - - self.default = (bool(len(self.attr.default_value.ListFields())) and - not self.islist and - self.type not in ['shape', 'tensor']) - - def declaration(self): - - # Basic T types attributes are not used - if self.name == 'T': return '' - - # Number attributes are infered from others (no need for an argument) - if self.number_attr is not None: return '' - - # Convert from TF types to C++ types - cpptype = { - 'shape' : 'const std::vector&', - 'int' : 'int64_t', - 'float' : 'float', - 'string': 'const std::string&', - 'type' : 'datatype', # Refers to cppflow::datatype - 'bool' : 'bool', - 'tensor': 'const tensor&' - }[self.type] - - # Warp list attributes in a C++ vector - if self.islist: - cpptype = cpptype.replace('&', '') # Not inner reference types - cpptype = ('const std::vector<{}>&' - .format(cpptype.replace('const', ''))) - - - # Get the default value for the attribute - # Not yet supported for lists - # Not supported for tensors or shape - if (self.default and not self.islist and - self.type not in ['shape', 'tensor']): - cppdefault = '=' + { - 'int' : str(self.attr.default_value.i), - 'bool' : str(self.attr.default_value.b).lower(), - 'string' : '"' + str(self.attr.default_value.s)[2:-1] + '"', - 'float' : ('{:.4e}'.format(self.attr.default_value.f) - .replace('inf', - 'std::numeric_limits::infinity()')), - 'type' : ('static_cast({})' - .format(self.attr.default_value.type)) - }[self.type] - else: - cppdefault = '' - - # datatype name=defaultval - return (cpptype + ' ' + self.name.replace('template', 'template_arg') + - cppdefault) - - def code(self): - - # Basic T types attributes are not used - if self.name == 'T': return '' - - if self.islist: - return textwrap.dedent({ - 'string' : ''' - std::vector {0}_sizes; {0}_sizes.reserve({0}.size()); - std::transform({0}.begin(), {0}.end(), std::back_inserter({0}_sizes), [](const auto& s) {{ return s.size();}}); - TFE_OpSetAttrStringList(op.get(), "{orig:}", reinterpret_cast({0}.data()), {0}_sizes.data(), static_cast({0}.size())); - ''', - 'int' : 'TFE_OpSetAttrIntList(op.get(), "{orig:}", {0}.data(), static_cast({0}.size()));', - 'float' : 'TFE_OpSetAttrFloatList(op.get(), "{orig:}", {0}.data(), static_cast({0}.size()));', - 'bool' : 'TFE_OpSetAttrBoolList(op.get(), "{orig:}", std::vector({0}.begin(), {0}.end()).data(), {0}.size());', - 'type' : 'TFE_OpSetAttrTypeList(op.get(), "{orig:}", reinterpret_cast({0}.data()), static_cast({0}.size()));', - 'shape' : ''' - std::vector {0}_values; {0}_values.reserve({0}.size()); - std::vector {0}_ndims; {0}_ndims.reserve({0}.size()); - std::transform({0}.begin(), {0}.end(), std::back_inserter({0}_values), [](const auto& v) {{ return v.data();}}); - std::transform({0}.begin(), {0}.end(), std::back_inserter({0}_ndims), [](const auto& v) {{ return static_cast(v.size());}}); - TFE_OpSetAttrShapeList(op.get(), "{orig:}", {0}_values.data(), {0}_ndims.data(), static_cast({0}.size()), context::get_status()); - status_check(context::get_status()); - ''', - }[self.type].format(self.name.replace('template', 'template_arg'), - orig=self.name)).replace('\n', '\n ') - - else: - return (textwrap.dedent({ - 'shape' : ''' - TFE_OpSetAttrShape(op.get(), "{orig:}", {0}.data(), static_cast({0}.size()), context::get_status()); - status_check(context::get_status()); - ''', - 'int' : 'TFE_OpSetAttrInt(op.get(), "{orig:}", {0});', - 'float' : 'TFE_OpSetAttrFloat(op.get(), "{orig:}", {0});', - 'string': 'TFE_OpSetAttrString(op.get(), "{orig:}", (void*) {0}.c_str(), {0}.size());', - 'type' : 'TFE_OpSetAttrType(op.get(), "{orig:}", {0});', - 'bool' : 'TFE_OpSetAttrBool(op.get(), "{orig:}", (unsigned char){0});', - 'tensor': ''' - TFE_OpSetAttrTensor(op.get(), "{orig:}", {0}.get_tensor().get(), context::get_status()); - status_check(context::get_status()); - ''', - 'n_attr': 'TFE_OpSetAttrInt(op.get(), "{orig:}", {n_attr:}.size());' - - }[self.type].format(self.name.replace('template', 'template_arg'), - orig=self.name, n_attr=self.number_attr)) - .replace('\n', '\n ')) - - - - - - -class Operation: - """Class that describes the operation. - - Attributes: - op: An operation. - inputs: The operation's inputs. - attr_list: The attribute's list. - """ - - def __init__(self, op): - self.op = op - - # More than one output? - if len(self.op.output_arg) != 1: - raise Exception('More than one or no output not yet supported') - - self.inputs = [inp for inp in op.input_arg] - - # Number attributes define the length of an input list - number_attr = [ - (i.number_attr, i) for i in self.inputs if len(i.number_attr) > 0 - ] - - - # Attributes - self.attr_list = sorted([ - Attribute(a, number_attr) for a in self.op.attr - ], key=lambda a: a.default) - - - def code(self): - - # C++ function body - template = textwrap.dedent(''' - {} - inline {} {}({}{}) {{ - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "{}", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - {} - - // Attributes - {} - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {{nullptr}}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); - }} - ''') - - # Add single input template - add_inputs = textwrap.dedent(''' - TFE_OpAddInput(op.get(), {}.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - ''').replace('\n', '\n ') - - add_inputs_list = textwrap.dedent(''' - std::vector {0}_handles; {0}_handles.reserve({0}.size()); - std::transform({0}.begin(), {0}.end(), std::back_inserter({0}_handles), [](const auto& t) {{ return t.tfe_handle.get();}}); - TFE_OpAddInputList(op.get(), {0}_handles.data(), static_cast({0}.size()), context::get_status()); - status_check(context::get_status()); - ''').replace('\n', '\n ') - - # Return type of the function - out = 'tensor' if len(self.op.output_arg) else 'void' - - # snake_case name of the operation - snk = (re.sub(r'(?&{}'.format(n.name) - if len(n.number_attr) or len(n.type_list_attr) - else 'const tensor& {}'.format(n.name.replace('tensor', - 'input_tensor')) - for i, n in enumerate(self.inputs) - ]) - - # Declaration of attributes - atr = ', '.join(a.declaration() for a in self.attr_list - if len(a.declaration())) - atr = (', ' + atr) if inp != '' and atr != '' else atr - - # Operation original name - opn = self.op.name - - # Code for input arguments - inp_code = '\n '.join(add_inputs_list.format(n.name) - if len(n.number_attr) or len(n.type_list_attr) - else add_inputs.format(n.name.replace('tensor', 'input_tensor')) - for n in self.inputs) - - # Code for attributes - atr_code = '\n '.join(a.code() for a in self.attr_list - if len(a.code())) - - return template.format('', out, snk, inp, atr, opn, inp_code, atr_code) - - - -ops_file = textwrap.dedent(''' -// MIT License -// -// Copyright (c) 2020 Sergio Izquierdo -// Copyright (c) 2020 Jiannan Liu -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files (the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions: -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -// SOFTWARE. - -/** - * @file raw_ops.h - * @brief TensorFlow raw_ops mappings - * THIS FILE IS AUTOGENERATED - TO UPDATE USE "generator.py" - * @author Jiannan Liu - * @author Sergio Izquierdo - */ - -#ifndef INCLUDE_CPPFLOW_RAW_OPS_H_ -#define INCLUDE_CPPFLOW_RAW_OPS_H_ - -// C headers -#include -#include -#include - -// C++ headers -#include -#include -#include -#include - -// CppFlow headers -#include "cppflow/tensor.h" -#include "cppflow/datatype.h" - -namespace cppflow {{ - -{} - -}} // namespace cppflow - -#endif // INCLUDE_CPPFLOW_RAW_OPS_H_ - -''') - - - -ops_code = '' - -num_ops = 0 - -# All TF C API operations correspond with tf.raw_ops -for op_name in sorted(dir(tf.raw_ops)): - if not op_name.startswith('_'): - - num_ops += 1 - #if num_ops == 51: - # break - - try: - - # Grab operation definition - op = [op for op in ops.op if op.name == op_name] - if len(op) == 0: raise Exception('Operation not found') - - op = Operation(op[0]) - - ops_code += op.code() - - - # Everything was ok! - print('{:<50} [{}]'.format(op_name, colored(' Ok ', 'green'))) - except Exception as err: - print('{:<50} [{}]'.format(op_name, colored('Failed', 'red'))) - print(' ', err) - - -with open('../raw_ops.h', 'w') as f: - f.write(ops_file.format(ops_code)) diff --git a/include/cppflow/ops_generator/ops.pbtxt b/include/cppflow/ops_generator/ops.pbtxt deleted file mode 100644 index dec894c..0000000 --- a/include/cppflow/ops_generator/ops.pbtxt +++ /dev/null @@ -1,55295 +0,0 @@ -op { - name: "Abort" - attr { - name: "error_msg" - type: "string" - default_value { - s: "" - } - } - attr { - name: "exit_without_error" - type: "bool" - default_value { - b: false - } - } -} -op { - name: "Abs" - input_arg { - name: "x" - type_attr: "T" - } - output_arg { - name: "y" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_BFLOAT16 - type: DT_HALF - type: DT_FLOAT - type: DT_DOUBLE - type: DT_INT8 - type: DT_INT16 - type: DT_INT32 - type: DT_INT64 - } - } - } -} -op { - name: "AccumulateNV2" - input_arg { - name: "inputs" - type_attr: "T" - number_attr: "N" - } - output_arg { - name: "sum" - type_attr: "T" - } - attr { - name: "N" - type: "int" - has_minimum: true - minimum: 1 - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_FLOAT - type: DT_DOUBLE - type: DT_INT32 - type: DT_UINT8 - type: DT_INT16 - type: DT_INT8 - type: DT_COMPLEX64 - type: DT_INT64 - type: DT_QINT8 - type: DT_QUINT8 - type: DT_QINT32 - type: DT_BFLOAT16 - type: DT_UINT16 - type: DT_COMPLEX128 - type: DT_HALF - type: DT_UINT32 - type: DT_UINT64 - } - } - } - attr { - name: "shape" - type: "shape" - } - is_aggregate: true - is_commutative: true -} -op { - name: "AccumulatorApplyGradient" - input_arg { - name: "handle" - type: DT_STRING - is_ref: true - } - input_arg { - name: "local_step" - type: DT_INT64 - } - input_arg { - name: "gradient" - type_attr: "dtype" - } - attr { - name: "dtype" - type: "type" - allowed_values { - list { - type: DT_FLOAT - type: DT_DOUBLE - type: DT_INT32 - type: DT_UINT8 - type: DT_INT16 - type: DT_INT8 - type: DT_COMPLEX64 - type: DT_INT64 - type: DT_QINT8 - type: DT_QUINT8 - type: DT_QINT32 - type: DT_BFLOAT16 - type: DT_UINT16 - type: DT_COMPLEX128 - type: DT_HALF - type: DT_UINT32 - type: DT_UINT64 - } - } - } -} -op { - name: "AccumulatorNumAccumulated" - input_arg { - name: "handle" - type: DT_STRING - is_ref: true - } - output_arg { - name: "num_accumulated" - type: DT_INT32 - } -} -op { - name: "AccumulatorSetGlobalStep" - input_arg { - name: "handle" - type: DT_STRING - is_ref: true - } - input_arg { - name: "new_global_step" - type: DT_INT64 - } -} -op { - name: "AccumulatorTakeGradient" - input_arg { - name: "handle" - type: DT_STRING - is_ref: true - } - input_arg { - name: "num_required" - type: DT_INT32 - } - output_arg { - name: "average" - type_attr: "dtype" - } - attr { - name: "dtype" - type: "type" - allowed_values { - list { - type: DT_FLOAT - type: DT_DOUBLE - type: DT_INT32 - type: DT_UINT8 - type: DT_INT16 - type: DT_INT8 - type: DT_COMPLEX64 - type: DT_INT64 - type: DT_QINT8 - type: DT_QUINT8 - type: DT_QINT32 - type: DT_BFLOAT16 - type: DT_UINT16 - type: DT_COMPLEX128 - type: DT_HALF - type: DT_UINT32 - type: DT_UINT64 - } - } - } -} -op { - name: "Acos" - input_arg { - name: "x" - type_attr: "T" - } - output_arg { - name: "y" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_BFLOAT16 - type: DT_HALF - type: DT_FLOAT - type: DT_DOUBLE - type: DT_INT8 - type: DT_INT16 - type: DT_INT32 - type: DT_INT64 - type: DT_COMPLEX64 - type: DT_COMPLEX128 - } - } - } -} -op { - name: "Acosh" - input_arg { - name: "x" - type_attr: "T" - } - output_arg { - name: "y" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_BFLOAT16 - type: DT_HALF - type: DT_FLOAT - type: DT_DOUBLE - type: DT_COMPLEX64 - type: DT_COMPLEX128 - } - } - } -} -op { - name: "Add" - input_arg { - name: "x" - type_attr: "T" - } - input_arg { - name: "y" - type_attr: "T" - } - output_arg { - name: "z" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_BFLOAT16 - type: DT_HALF - type: DT_FLOAT - type: DT_DOUBLE - type: DT_UINT8 - type: DT_INT8 - type: DT_INT16 - type: DT_INT32 - type: DT_INT64 - type: DT_COMPLEX64 - type: DT_COMPLEX128 - type: DT_STRING - } - } - } -} -op { - name: "AddManySparseToTensorsMap" - input_arg { - name: "sparse_indices" - type: DT_INT64 - } - input_arg { - name: "sparse_values" - type_attr: "T" - } - input_arg { - name: "sparse_shape" - type: DT_INT64 - } - output_arg { - name: "sparse_handles" - type: DT_INT64 - } - attr { - name: "T" - type: "type" - } - attr { - name: "container" - type: "string" - default_value { - s: "" - } - } - attr { - name: "shared_name" - type: "string" - default_value { - s: "" - } - } - is_stateful: true -} -op { - name: "AddN" - input_arg { - name: "inputs" - type_attr: "T" - number_attr: "N" - } - output_arg { - name: "sum" - type_attr: "T" - } - attr { - name: "N" - type: "int" - has_minimum: true - minimum: 1 - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_FLOAT - type: DT_DOUBLE - type: DT_INT32 - type: DT_UINT8 - type: DT_INT16 - type: DT_INT8 - type: DT_COMPLEX64 - type: DT_INT64 - type: DT_QINT8 - type: DT_QUINT8 - type: DT_QINT32 - type: DT_BFLOAT16 - type: DT_UINT16 - type: DT_COMPLEX128 - type: DT_HALF - type: DT_UINT32 - type: DT_UINT64 - type: DT_VARIANT - } - } - } - is_aggregate: true - is_commutative: true -} -op { - name: "AddSparseToTensorsMap" - input_arg { - name: "sparse_indices" - type: DT_INT64 - } - input_arg { - name: "sparse_values" - type_attr: "T" - } - input_arg { - name: "sparse_shape" - type: DT_INT64 - 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type: DT_FLOAT - } - output_arg { - name: "sampled_expected_count" - type: DT_FLOAT - } - attr { - name: "num_true" - type: "int" - has_minimum: true - minimum: 1 - } - attr { - name: "num_sampled" - type: "int" - has_minimum: true - minimum: 1 - } - attr { - name: "unique" - type: "bool" - } - attr { - name: "seed" - type: "int" - default_value { - i: 0 - } - } - attr { - name: "seed2" - type: "int" - default_value { - i: 0 - } - } - is_stateful: true -} -op { - name: "AllToAll" - input_arg { - name: "input" - type_attr: "T" - } - input_arg { - name: "group_assignment" - type: DT_INT32 - } - output_arg { - name: "output" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_FLOAT - type: DT_DOUBLE - type: DT_INT32 - type: DT_UINT8 - type: DT_INT16 - type: DT_INT8 - type: DT_COMPLEX64 - type: DT_INT64 - type: DT_QINT8 - type: DT_QUINT8 - type: DT_QINT32 - type: DT_BFLOAT16 - type: DT_UINT16 - type: DT_COMPLEX128 - type: DT_HALF - type: DT_UINT32 - 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} - output_arg { - name: "output" - type: DT_STRING - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_INT8 - type: DT_INT16 - type: DT_INT32 - type: DT_INT64 - type: DT_COMPLEX64 - type: DT_COMPLEX128 - type: DT_FLOAT - type: DT_DOUBLE - type: DT_BOOL - } - } - } - attr { - name: "precision" - type: "int" - default_value { - i: -1 - } - } - attr { - name: "scientific" - type: "bool" - default_value { - b: false - } - } - attr { - name: "shortest" - type: "bool" - default_value { - b: false - } - } - attr { - name: "width" - type: "int" - default_value { - i: -1 - } - } - attr { - name: "fill" - type: "string" - default_value { - s: "" - } - } -} -op { - name: "Asin" - input_arg { - name: "x" - type_attr: "T" - } - output_arg { - name: "y" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_BFLOAT16 - type: DT_HALF - type: DT_FLOAT - type: DT_DOUBLE - type: DT_INT8 - type: DT_INT16 - type: DT_INT32 - type: DT_INT64 - type: DT_COMPLEX64 - type: DT_COMPLEX128 - } - } - } -} -op { - name: "Asinh" - input_arg { - name: "x" - type_attr: "T" - } - output_arg { - name: "y" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_BFLOAT16 - type: DT_HALF - type: DT_FLOAT - type: DT_DOUBLE - type: DT_COMPLEX64 - type: DT_COMPLEX128 - } - } - } -} -op { - name: "Assert" - input_arg { - name: "condition" - type: DT_BOOL - } - input_arg { - name: "data" - type_list_attr: "T" - } - attr { - name: "T" - type: "list(type)" - has_minimum: true - minimum: 1 - } - attr { - name: "summarize" - type: "int" - default_value { - i: 3 - } - } - is_stateful: true -} -op { - name: "AssertCardinalityDataset" - input_arg { - name: "input_dataset" - type: DT_VARIANT - } - input_arg { - name: "cardinality" - type: DT_INT64 - } - output_arg { - name: "handle" - type: DT_VARIANT - } - attr { - name: "output_types" - type: "list(type)" - has_minimum: true - minimum: 1 - } - attr { - name: "output_shapes" - type: "list(shape)" - has_minimum: true - minimum: 1 - } -} -op { - name: "AssertNextDataset" - input_arg { - name: "input_dataset" - type: DT_VARIANT - } - input_arg { - name: "transformations" - type: DT_STRING - } - output_arg { - name: "handle" - type: DT_VARIANT - } - attr { - name: "output_types" - type: "list(type)" - has_minimum: true - minimum: 1 - } - attr { - name: "output_shapes" - type: "list(shape)" - has_minimum: true - minimum: 1 - } -} -op { - name: "Assign" - input_arg { - name: "ref" - type_attr: "T" - is_ref: true - } - input_arg { - name: "value" - type_attr: "T" - } - output_arg { - name: "output_ref" - type_attr: "T" - is_ref: true - } - attr { - name: "T" - type: "type" - } - attr { - name: "validate_shape" - type: "bool" - default_value { - b: true - } - } - attr { - name: "use_locking" - type: "bool" - default_value { - b: true - } - } - allows_uninitialized_input: true -} -op { - name: "AssignAdd" - input_arg { - name: "ref" - type_attr: "T" - is_ref: true - } - input_arg { - name: "value" - type_attr: "T" - } - output_arg { - name: "output_ref" - type_attr: "T" - is_ref: true - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_FLOAT - type: DT_DOUBLE - type: DT_INT32 - type: DT_UINT8 - type: DT_INT16 - type: DT_INT8 - type: DT_COMPLEX64 - type: DT_INT64 - type: DT_QINT8 - type: DT_QUINT8 - type: DT_QINT32 - type: DT_BFLOAT16 - type: DT_UINT16 - type: DT_COMPLEX128 - type: DT_HALF - type: DT_UINT32 - type: DT_UINT64 - } - } - } - attr { - name: "use_locking" - type: "bool" - default_value { - b: false - } - } -} -op { - name: "AssignAddVariableOp" - input_arg { - name: "resource" - type: DT_RESOURCE - } - input_arg { - name: "value" - type_attr: "dtype" - } - attr { - name: "dtype" - type: "type" - } - is_stateful: true -} -op { - name: "AssignSub" - input_arg { - name: "ref" - type_attr: "T" - is_ref: true - } - input_arg { - name: "value" - type_attr: "T" - } - output_arg { - name: "output_ref" - type_attr: "T" - is_ref: true - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_FLOAT - type: DT_DOUBLE - type: DT_INT32 - type: DT_UINT8 - type: DT_INT16 - type: DT_INT8 - type: DT_COMPLEX64 - type: DT_INT64 - type: DT_QINT8 - type: DT_QUINT8 - type: DT_QINT32 - type: DT_BFLOAT16 - type: DT_UINT16 - type: DT_COMPLEX128 - type: DT_HALF - type: DT_UINT32 - type: DT_UINT64 - } - } - } - attr { - name: "use_locking" - type: "bool" - default_value { - b: false - } - } -} -op { - name: "AssignSubVariableOp" - input_arg { - name: "resource" - type: DT_RESOURCE - } - input_arg { - name: "value" - type_attr: "dtype" - } - attr { - name: "dtype" - type: "type" - } - is_stateful: true -} -op { - name: "AssignVariableOp" - input_arg { - name: "resource" - type: DT_RESOURCE - } - input_arg { - name: "value" - type_attr: "dtype" - } - attr { - name: "dtype" - type: "type" - } - is_stateful: true -} -op { - name: "Atan" - input_arg { - name: "x" - type_attr: "T" - } - output_arg { - name: "y" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_BFLOAT16 - type: DT_HALF - type: DT_FLOAT - type: DT_DOUBLE - type: DT_INT8 - type: DT_INT16 - type: DT_INT32 - type: DT_INT64 - type: DT_COMPLEX64 - type: DT_COMPLEX128 - } - } - } -} -op { - name: "Atan2" - input_arg { - name: "y" - type_attr: "T" - } - input_arg { - name: "x" - type_attr: "T" - } - output_arg { - name: "z" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_BFLOAT16 - type: DT_HALF - type: DT_FLOAT - type: DT_DOUBLE - } - } - } -} -op { - name: "Atanh" - input_arg { - name: "x" - type_attr: "T" - } - output_arg { - name: "y" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_BFLOAT16 - type: DT_HALF - type: DT_FLOAT - type: DT_DOUBLE - type: DT_COMPLEX64 - type: DT_COMPLEX128 - } - } - } -} -op { - name: "AudioSpectrogram" - input_arg { - name: "input" - type: DT_FLOAT - } - output_arg { - name: "spectrogram" - type: DT_FLOAT - } - attr { - name: "window_size" - type: "int" - } - attr { - name: "stride" - type: "int" - } - attr { - name: "magnitude_squared" - type: "bool" - default_value { - b: false - } - } -} -op { - name: "AudioSummary" - input_arg { - name: "tag" - type: DT_STRING - } - input_arg { - name: "tensor" - type: DT_FLOAT - } - output_arg { - name: "summary" - type: DT_STRING - } - attr { - name: "sample_rate" - type: "float" - } - attr { - name: "max_outputs" - type: "int" - default_value { - i: 3 - } - has_minimum: true - minimum: 1 - } - deprecation { - version: 15 - explanation: "Use AudioSummaryV2." - } -} -op { - name: "AudioSummaryV2" - input_arg { - name: "tag" - type: DT_STRING - } - input_arg { - name: "tensor" - type: DT_FLOAT - } - input_arg { - name: "sample_rate" - type: DT_FLOAT - } - output_arg { - name: "summary" - type: DT_STRING - } - attr { - name: "max_outputs" - type: "int" - default_value { - i: 3 - } - has_minimum: true - minimum: 1 - } -} -op { - name: "AutoShardDataset" - input_arg { - name: "input_dataset" - type: DT_VARIANT - } - input_arg { - name: "num_workers" - type: DT_INT64 - } - input_arg { - name: "index" - type: DT_INT64 - } - output_arg { - name: "handle" - type: DT_VARIANT - } - attr { - name: "auto_shard_policy" - type: "int" - default_value { - i: 0 - } - } - attr { - name: "output_types" - type: "list(type)" - has_minimum: true - minimum: 1 - } - attr { - name: "output_shapes" - type: "list(shape)" - has_minimum: true - minimum: 1 - } -} -op { - name: "AvgPool" - input_arg { - name: "value" - type_attr: "T" - } - output_arg { - name: "output" - type_attr: "T" - } - attr { - name: "ksize" - type: "list(int)" - has_minimum: true - minimum: 4 - } - attr { - name: "strides" - type: "list(int)" - has_minimum: true - minimum: 4 - } - attr { - name: "padding" - type: "string" - allowed_values { - list { - s: "SAME" - s: "VALID" - } - } - } - attr { - name: "data_format" - type: "string" - default_value { - s: "NHWC" - } - allowed_values { - list { - s: "NHWC" - s: "NCHW" - } - } - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_HALF - type: DT_BFLOAT16 - type: DT_FLOAT - type: DT_DOUBLE - } - } - } -} -op { - name: "AvgPool3D" - input_arg { - name: "input" - type_attr: "T" - } - output_arg { - name: "output" - type_attr: "T" - } - attr { - name: "ksize" - type: "list(int)" - has_minimum: true - minimum: 5 - } - attr { - name: "strides" - type: "list(int)" - has_minimum: true - minimum: 5 - } - attr { - name: "padding" - type: "string" - allowed_values { - list { - s: "SAME" - s: "VALID" - } - } - } - attr { - name: "data_format" - type: "string" - default_value { - s: "NDHWC" - } - allowed_values { - list { - s: "NDHWC" - s: "NCDHW" - } - } - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_HALF - type: DT_BFLOAT16 - type: DT_FLOAT - type: DT_DOUBLE - } - } - } -} -op { - name: "AvgPool3DGrad" - input_arg { - name: "orig_input_shape" - type: DT_INT32 - } - input_arg { - name: "grad" - type_attr: "T" - } - output_arg { - name: "output" - type_attr: "T" - } - attr { - name: "ksize" - type: "list(int)" - has_minimum: true - minimum: 5 - } - attr { - name: "strides" - type: "list(int)" - has_minimum: true - minimum: 5 - } - attr { - name: "padding" - type: "string" - allowed_values { - list { - s: "SAME" - s: "VALID" - } - } - } - attr { - name: "data_format" - type: "string" - default_value { - s: "NDHWC" - } - allowed_values { - list { - s: "NDHWC" - s: "NCDHW" - } - } - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_HALF - type: DT_BFLOAT16 - type: DT_FLOAT - type: DT_DOUBLE - } - } - } -} -op { - name: "AvgPoolGrad" - input_arg { - name: "orig_input_shape" - type: DT_INT32 - } - input_arg { - name: "grad" - type_attr: "T" - } - output_arg { - name: "output" - type_attr: "T" - } - attr { - name: "ksize" - type: "list(int)" - has_minimum: true - minimum: 4 - } - attr { - name: "strides" - type: "list(int)" - has_minimum: true - minimum: 4 - } - attr { - name: "padding" - type: "string" - allowed_values { - list { - s: "SAME" - s: "VALID" - } - } - } - attr { - name: "data_format" - type: "string" - default_value { - s: "NHWC" - } - allowed_values { - list { - s: "NHWC" - s: "NCHW" - } - } - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_HALF - type: DT_BFLOAT16 - type: DT_FLOAT - type: DT_DOUBLE - } - } - } -} -op { - name: "BandedTriangularSolve" - input_arg { - name: "matrix" - type_attr: "T" - } - input_arg { - name: "rhs" - type_attr: "T" - } - output_arg { - name: "output" - type_attr: "T" - } - attr { - name: "lower" - type: "bool" - default_value { - b: true - } - } - attr { - name: "adjoint" - type: "bool" - default_value { - b: false - } - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_DOUBLE - type: DT_FLOAT - type: DT_HALF - type: DT_COMPLEX64 - type: DT_COMPLEX128 - } - } - } -} -op { - name: "Barrier" - output_arg { - name: "handle" - type: DT_STRING - is_ref: true - } - attr { - name: "component_types" - type: "list(type)" - has_minimum: true - minimum: 1 - } - attr { - name: "shapes" - type: "list(shape)" - default_value { - list { - } - } - has_minimum: true - } - attr { - name: "capacity" - type: "int" - default_value { - i: -1 - } - } - attr { - name: "container" - type: "string" - default_value { - s: "" - } - } - attr { - name: "shared_name" - type: "string" - default_value { - s: "" - } - } - is_stateful: true -} -op { - name: "BarrierClose" - input_arg { - name: "handle" - type: DT_STRING - is_ref: true - } - attr { - name: "cancel_pending_enqueues" - type: "bool" - default_value { - b: false - } - } -} -op { - name: "BarrierIncompleteSize" - input_arg { - name: "handle" - type: DT_STRING - is_ref: true - } - output_arg { - name: "size" - type: DT_INT32 - } -} -op { - name: "BarrierInsertMany" - input_arg { - name: "handle" - type: DT_STRING - is_ref: true - } - input_arg { - name: "keys" - type: DT_STRING - } - input_arg { - name: "values" - type_attr: "T" - } - attr { - name: "T" - type: "type" - } - attr { - name: "component_index" - type: "int" - } -} -op { - name: "BarrierReadySize" - input_arg { - name: "handle" - type: DT_STRING - is_ref: true - } - output_arg { - name: "size" - type: DT_INT32 - } -} -op { - name: "BarrierTakeMany" - input_arg { - name: "handle" - type: DT_STRING - is_ref: true - } - input_arg { - name: "num_elements" - type: DT_INT32 - } - output_arg { - name: "indices" - type: DT_INT64 - } - output_arg { - name: "keys" - type: DT_STRING - } - output_arg { - name: "values" - type_list_attr: "component_types" - } - attr { - name: "component_types" - type: "list(type)" - has_minimum: true - minimum: 1 - } - attr { - name: "allow_small_batch" - type: "bool" - default_value { - b: false - } - } - attr { - name: "wait_for_incomplete" - type: "bool" - default_value { - b: false - } - } - attr { - name: "timeout_ms" - type: "int" - default_value { - i: -1 - } - } -} -op { - name: "Batch" - input_arg { - name: "in_tensors" - type_list_attr: "T" - } - output_arg { - name: "batched_tensors" - type_list_attr: "T" - } - output_arg { - name: "batch_index" - type: DT_INT64 - } - output_arg { - name: "id" - type: DT_INT64 - } - attr { - name: "num_batch_threads" - type: "int" - } - attr { - name: "max_batch_size" - type: "int" - } - attr { - name: "max_enqueued_batches" - type: "int" - default_value { - i: 10 - } - } - attr { - name: "batch_timeout_micros" - type: "int" - } - attr { - name: "allowed_batch_sizes" - type: "list(int)" - default_value { - list { - } - } - } - attr { - name: "grad_timeout_micros" - type: "int" - } - attr { - name: "container" - type: "string" - default_value { - s: "" - } - } - attr { - name: "shared_name" - type: "string" - default_value { - s: "" - } - } - attr { - name: "batching_queue" - type: "string" - default_value { - s: "" - } - } - attr { - name: "T" - type: "list(type)" - has_minimum: true - minimum: 1 - } -} -op { - name: "BatchCholesky" - input_arg { - name: "input" - type_attr: "T" - } - output_arg { - name: "output" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_DOUBLE - type: DT_FLOAT - } - } - } - deprecation { - version: 13 - explanation: "Use Cholesky instead." - } -} -op { - name: "BatchCholeskyGrad" - input_arg { - name: "l" - type_attr: "T" - } - input_arg { - name: "grad" - type_attr: "T" - } - output_arg { - name: "output" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_FLOAT - type: DT_DOUBLE - } - } - } - deprecation { - version: 13 - explanation: "Use CholeskyGrad instead." - } -} -op { - name: "BatchDataset" - input_arg { - name: "input_dataset" - type: DT_VARIANT - } - input_arg { - name: "batch_size" - type: DT_INT64 - } - output_arg { - name: "handle" - type: DT_VARIANT - } - attr { - name: "output_types" - type: "list(type)" - has_minimum: true - minimum: 1 - } - attr { - name: "output_shapes" - type: "list(shape)" - has_minimum: true - minimum: 1 - } -} -op { - name: "BatchDatasetV2" - input_arg { - name: "input_dataset" - type: DT_VARIANT - } - input_arg { - name: "batch_size" - type: DT_INT64 - } - input_arg { - name: "drop_remainder" - type: DT_BOOL - } - output_arg { - name: "handle" - type: DT_VARIANT - } - attr { - name: "parallel_copy" - type: "bool" - default_value { - b: false - } - } - attr { - name: "output_types" - type: "list(type)" - has_minimum: true - minimum: 1 - } - attr { - name: "output_shapes" - type: "list(shape)" - has_minimum: true - minimum: 1 - } -} -op { - name: "BatchFFT" - input_arg { - name: "input" - type: DT_COMPLEX64 - } - output_arg { - name: "output" - type: DT_COMPLEX64 - } - deprecation { - version: 15 - explanation: "Use FFT" - } -} -op { - name: "BatchFFT2D" - input_arg { - name: "input" - type: DT_COMPLEX64 - } - output_arg { - name: "output" - type: DT_COMPLEX64 - } - deprecation { - version: 15 - explanation: "Use FFT2D" - } -} -op { - name: "BatchFFT3D" - input_arg { - name: "input" - type: DT_COMPLEX64 - } - output_arg { - name: "output" - type: DT_COMPLEX64 - } - deprecation { - version: 15 - explanation: "Use FFT3D" - } -} -op { - name: "BatchFunction" - input_arg { - name: "in_tensors" - type_list_attr: "Tin" - } - input_arg { - name: "captured_tensors" - type_list_attr: "Tcaptured" - } - output_arg { - name: "out_tensors" - type_list_attr: "Tout" - } - attr { - name: "f" - type: "func" - } - attr { - name: "num_batch_threads" - type: "int" - } - attr { - name: "max_batch_size" - type: "int" - } - attr { - name: "batch_timeout_micros" - type: "int" - } - attr { - name: "max_enqueued_batches" - type: "int" - default_value { - i: 10 - } - } - attr { - name: "allowed_batch_sizes" - type: "list(int)" - default_value { - list { - } - } - } - attr { - name: "container" - type: "string" - default_value { - s: "" - } - } - attr { - name: "shared_name" - type: "string" - default_value { - s: "" - } - } - attr { - name: "batching_queue" - type: "string" - default_value { - s: "" - } - } - attr { - name: "Tin" - type: "list(type)" - has_minimum: true - minimum: 1 - } - attr { - name: "Tcaptured" - type: "list(type)" - has_minimum: true - } - attr { - name: "Tout" - type: "list(type)" - has_minimum: true - minimum: 1 - } - attr { - name: "enable_large_batch_splitting" - type: "bool" - default_value { - b: false - } - } -} -op { - name: "BatchIFFT" - input_arg { - name: "input" - type: DT_COMPLEX64 - } - output_arg { - name: "output" - type: DT_COMPLEX64 - } - deprecation { - version: 15 - explanation: "Use IFFT" - } -} -op { - name: "BatchIFFT2D" - input_arg { - name: "input" - type: DT_COMPLEX64 - } - output_arg { - name: "output" - type: DT_COMPLEX64 - } - deprecation { - version: 15 - explanation: "Use IFFT2D" - } -} -op { - name: "BatchIFFT3D" - input_arg { - name: "input" - type: DT_COMPLEX64 - } - output_arg { - name: "output" - type: DT_COMPLEX64 - } - deprecation { - version: 15 - explanation: "Use IFFT3D" - } -} -op { - name: "BatchMatMul" - input_arg { - name: "x" - type_attr: "T" - } - input_arg { - name: "y" - type_attr: "T" - } - output_arg { - name: "output" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_BFLOAT16 - type: DT_HALF - type: DT_FLOAT - type: DT_DOUBLE - type: DT_INT32 - type: DT_INT64 - type: DT_COMPLEX64 - type: DT_COMPLEX128 - } - } - } - attr { - name: "adj_x" - type: "bool" - default_value { - b: false - } - } - attr { - name: "adj_y" - type: "bool" - default_value { - b: false - } - } -} -op { - name: "BatchMatMulV2" - input_arg { - name: "x" - type_attr: "T" - } - input_arg { - name: "y" - type_attr: "T" - } - output_arg { - name: "output" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_BFLOAT16 - type: DT_HALF - type: DT_FLOAT - type: DT_DOUBLE - type: DT_INT32 - type: DT_INT64 - type: DT_COMPLEX64 - type: DT_COMPLEX128 - } - } - } - attr { - name: "adj_x" - type: "bool" - default_value { - b: false - } - } - attr { - name: "adj_y" - type: "bool" - default_value { - b: false - } - } -} -op { - name: "BatchMatrixBandPart" - input_arg { - name: "input" - type_attr: "T" - } - input_arg { - name: "num_lower" - type: DT_INT64 - } - input_arg { - name: "num_upper" - type: DT_INT64 - } - output_arg { - name: "band" - type_attr: "T" - } - attr { - name: "T" - type: "type" - } - deprecation { - version: 14 - explanation: "Use MatrixBandPart" - } -} -op { - name: "BatchMatrixDeterminant" - input_arg { - name: "input" - type_attr: "T" - } - output_arg { - name: "output" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_FLOAT - type: DT_DOUBLE - type: DT_COMPLEX64 - type: DT_COMPLEX128 - } - } - } - deprecation { - version: 13 - explanation: "Use MatrixDeterminant instead." - } -} -op { - name: "BatchMatrixDiag" - input_arg { - name: "diagonal" - type_attr: "T" - } - output_arg { - name: "output" - type_attr: "T" - } - attr { - name: "T" - type: "type" - } - deprecation { - version: 14 - explanation: "Use MatrixDiag" - } -} -op { - name: "BatchMatrixDiagPart" - input_arg { - name: "input" - type_attr: "T" - } - output_arg { - name: "diagonal" - type_attr: "T" - } - attr { - name: "T" - type: "type" - } - deprecation { - version: 14 - explanation: "Use MatrixDiagPart" - } -} -op { - name: "BatchMatrixInverse" - input_arg { - name: "input" - type_attr: "T" - } - output_arg { - name: "output" - type_attr: "T" - } - attr { - name: "adjoint" - type: "bool" - default_value { - b: false - } - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_DOUBLE - type: DT_FLOAT - } - } - } - deprecation { - version: 13 - explanation: "Use MatrixInverse instead." - } -} -op { - name: "BatchMatrixSetDiag" - input_arg { - name: "input" - type_attr: "T" - } - input_arg { - name: "diagonal" - type_attr: "T" - } - output_arg { - name: "output" - type_attr: "T" - } - attr { - name: "T" - type: "type" - } - deprecation { - version: 14 - explanation: "Use MatrixSetDiag" - } -} -op { - name: "BatchMatrixSolve" - input_arg { - name: "matrix" - type_attr: "T" - } - input_arg { - name: "rhs" - type_attr: "T" - } - output_arg { - name: "output" - type_attr: "T" - } - attr { - name: "adjoint" - type: "bool" - default_value { - b: false - } - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_DOUBLE - type: DT_FLOAT - } - } - } - deprecation { - version: 13 - explanation: "Use MatrixSolve instead." - } -} -op { - name: "BatchMatrixSolveLs" - input_arg { - name: "matrix" - type_attr: "T" - } - input_arg { - name: "rhs" - type_attr: "T" - } - input_arg { - name: "l2_regularizer" - type: DT_DOUBLE - } - output_arg { - name: "output" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_DOUBLE - type: DT_FLOAT - } - } - } - attr { - name: "fast" - type: "bool" - default_value { - b: true - } - } - deprecation { - version: 13 - explanation: "Use MatrixSolveLs instead." - } -} -op { - name: "BatchMatrixTriangularSolve" - input_arg { - name: "matrix" - type_attr: "T" - } - input_arg { - name: "rhs" - type_attr: "T" - } - output_arg { - name: "output" - type_attr: "T" - } - attr { - name: "lower" - type: "bool" - default_value { - b: true - } - } - attr { - name: "adjoint" - type: "bool" - default_value { - b: false - } - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_DOUBLE - type: DT_FLOAT - } - } - } - deprecation { - version: 13 - explanation: "Use MatrixTriangularSolve instead." - } -} -op { - name: "BatchNormWithGlobalNormalization" - input_arg { - name: "t" - type_attr: "T" - } - input_arg { - name: "m" - type_attr: "T" - } - input_arg { - name: "v" - type_attr: "T" - } - input_arg { - name: "beta" - type_attr: "T" - } - input_arg { - name: "gamma" - type_attr: "T" - } - output_arg { - name: "result" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_FLOAT - type: DT_DOUBLE - type: DT_INT32 - type: DT_UINT8 - type: DT_INT16 - type: DT_INT8 - type: DT_COMPLEX64 - type: DT_INT64 - type: DT_QINT8 - type: DT_QUINT8 - type: DT_QINT32 - type: DT_BFLOAT16 - type: DT_UINT16 - type: DT_COMPLEX128 - type: DT_HALF - type: DT_UINT32 - type: DT_UINT64 - } - } - } - attr { - name: "variance_epsilon" - type: "float" - } - attr { - name: "scale_after_normalization" - type: "bool" - } - deprecation { - version: 9 - explanation: "Use tf.nn.batch_normalization()" - } -} -op { - name: "BatchNormWithGlobalNormalizationGrad" - input_arg { - name: "t" - type_attr: "T" - } - input_arg { - name: "m" - type_attr: "T" - } - input_arg { - name: "v" - type_attr: "T" - } - input_arg { - name: "gamma" - type_attr: "T" - } - input_arg { - name: "backprop" - type_attr: "T" - } - output_arg { - name: "dx" - type_attr: "T" - } - output_arg { - name: "dm" - type_attr: "T" - } - output_arg { - name: "dv" - type_attr: "T" - } - output_arg { - name: "db" - type_attr: "T" - } - output_arg { - name: "dg" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_FLOAT - type: DT_DOUBLE - type: DT_INT32 - type: DT_UINT8 - type: DT_INT16 - type: DT_INT8 - type: DT_COMPLEX64 - type: DT_INT64 - type: DT_QINT8 - type: DT_QUINT8 - type: DT_QINT32 - type: DT_BFLOAT16 - type: DT_UINT16 - type: DT_COMPLEX128 - type: DT_HALF - type: DT_UINT32 - type: DT_UINT64 - } - } - } - attr { - name: "variance_epsilon" - type: "float" - } - attr { - name: "scale_after_normalization" - type: "bool" - } - deprecation { - version: 9 - explanation: "Use tf.nn.batch_normalization()" - } -} -op { - name: "BatchSelfAdjointEig" - input_arg { - name: "input" - type_attr: "T" - } - output_arg { - name: "output" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_DOUBLE - type: DT_FLOAT - } - } - } - deprecation { - version: 11 - explanation: "Use SelfAdjointEigV2 instead." - } -} -op { - name: "BatchSelfAdjointEigV2" - input_arg { - name: "input" - type_attr: "T" - } - output_arg { - name: "e" - type_attr: "T" - } - output_arg { - name: "v" - type_attr: "T" - } - attr { - name: "compute_v" - type: "bool" - default_value { - b: true - } - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_DOUBLE - type: DT_FLOAT - } - } - } - deprecation { - version: 13 - explanation: "Use SelfAdjointEigV2 instead." - } -} -op { - name: "BatchSvd" - input_arg { - name: "input" - type_attr: "T" - } - output_arg { - name: "s" - type_attr: "T" - } - output_arg { - name: "u" - type_attr: "T" - } - output_arg { - name: "v" - type_attr: "T" - } - attr { - name: "compute_uv" - type: "bool" - default_value { - b: true - } - } - attr { - name: "full_matrices" - type: "bool" - default_value { - b: false - } - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_DOUBLE - type: DT_FLOAT - type: DT_COMPLEX64 - type: DT_COMPLEX128 - } - } - } - deprecation { - version: 13 - explanation: "Use Svd instead." - } -} -op { - name: "BatchToSpace" - input_arg { - name: "input" - type_attr: "T" - } - input_arg { - name: "crops" - type_attr: "Tidx" - } - output_arg { - name: "output" - type_attr: "T" - } - attr { - name: "T" - type: "type" - } - attr { - name: "block_size" - type: "int" - has_minimum: true - minimum: 2 - } - attr { - name: "Tidx" - type: "type" - default_value { - type: DT_INT32 - } - allowed_values { - list { - type: DT_INT32 - type: DT_INT64 - } - } - } -} -op { - name: "BatchToSpaceND" - input_arg { - name: "input" - type_attr: "T" - } - input_arg { - name: "block_shape" - type_attr: "Tblock_shape" - } - input_arg { - name: "crops" - type_attr: "Tcrops" - } - output_arg { - name: "output" - type_attr: "T" - } - attr { - name: "T" - type: "type" - } - attr { - name: "Tblock_shape" - type: "type" - default_value { - type: DT_INT32 - } - allowed_values { - list { - type: DT_INT32 - type: DT_INT64 - } - } - } - attr { - name: "Tcrops" - type: "type" - default_value { - type: DT_INT32 - } - allowed_values { - list { - type: DT_INT32 - type: DT_INT64 - } - } - } -} -op { - name: "BesselI0" - input_arg { - name: "x" - type_attr: "T" - } - output_arg { - name: "y" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_BFLOAT16 - type: DT_HALF - type: DT_FLOAT - type: DT_DOUBLE - } - } - } -} -op { - name: "BesselI0e" - input_arg { - name: "x" - type_attr: "T" - } - output_arg { - name: "y" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_BFLOAT16 - type: DT_HALF - type: DT_FLOAT - type: DT_DOUBLE - } - } - } -} -op { - name: "BesselI1" - input_arg { - name: "x" - type_attr: "T" - } - output_arg { - name: "y" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_BFLOAT16 - type: DT_HALF - type: DT_FLOAT - type: DT_DOUBLE - } - } - } -} -op { - name: "BesselI1e" - input_arg { - name: "x" - type_attr: "T" - } - output_arg { - name: "y" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_BFLOAT16 - type: DT_HALF - type: DT_FLOAT - type: DT_DOUBLE - } - } - } -} -op { - name: "BesselJ0" - input_arg { - name: "x" - type_attr: "T" - } - output_arg { - name: "y" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_BFLOAT16 - type: DT_HALF - type: DT_FLOAT - type: DT_DOUBLE - } - } - } -} -op { - name: "BesselJ1" - input_arg { - name: "x" - type_attr: "T" - } - output_arg { - name: "y" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_BFLOAT16 - type: DT_HALF - type: DT_FLOAT - type: DT_DOUBLE - } - } - } -} -op { - name: "BesselK0" - input_arg { - name: "x" - type_attr: "T" - } - output_arg { - name: "y" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_BFLOAT16 - type: DT_HALF - type: DT_FLOAT - type: DT_DOUBLE - } - } - } -} -op { - name: "BesselK0e" - input_arg { - name: "x" - type_attr: "T" - } - output_arg { - name: "y" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_BFLOAT16 - type: DT_HALF - type: DT_FLOAT - type: DT_DOUBLE - } - } - } -} -op { - name: "BesselK1" - input_arg { - name: "x" - type_attr: "T" - } - output_arg { - name: "y" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_BFLOAT16 - type: DT_HALF - type: DT_FLOAT - type: DT_DOUBLE - } - } - } -} -op { - name: "BesselK1e" - input_arg { - name: "x" - type_attr: "T" - } - output_arg { - name: "y" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_BFLOAT16 - type: DT_HALF - type: DT_FLOAT - type: DT_DOUBLE - } - } - } -} -op { - name: "BesselY0" - input_arg { - name: "x" - type_attr: "T" - } - output_arg { - name: "y" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_BFLOAT16 - type: DT_HALF - type: DT_FLOAT - type: DT_DOUBLE - } - } - } -} -op { - name: "BesselY1" - input_arg { - name: "x" - type_attr: "T" - } - output_arg { - name: "y" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_BFLOAT16 - type: DT_HALF - type: DT_FLOAT - type: DT_DOUBLE - } - } - } -} -op { - name: "Betainc" - input_arg { - name: "a" - type_attr: "T" - } - input_arg { - name: "b" - type_attr: "T" - } - input_arg { - name: "x" - type_attr: "T" - } - output_arg { - name: "z" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_FLOAT - type: DT_DOUBLE - } - } - } -} -op { - name: "BiasAdd" - input_arg { - name: "value" - type_attr: "T" - } - input_arg { - name: "bias" - type_attr: "T" - } - output_arg { - name: "output" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_FLOAT - type: DT_DOUBLE - type: DT_INT32 - type: DT_UINT8 - type: DT_INT16 - type: DT_INT8 - type: DT_COMPLEX64 - type: DT_INT64 - type: DT_QINT8 - type: DT_QUINT8 - type: DT_QINT32 - type: DT_BFLOAT16 - type: DT_UINT16 - type: DT_COMPLEX128 - type: DT_HALF - type: DT_UINT32 - type: DT_UINT64 - } - } - } - attr { - name: "data_format" - type: "string" - default_value { - s: "NHWC" - } - allowed_values { - list { - s: "NHWC" - s: "NCHW" - } - } - } -} -op { - name: "BiasAddGrad" - input_arg { - name: "out_backprop" - type_attr: "T" - } - output_arg { - name: "output" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_FLOAT - type: DT_DOUBLE - type: DT_INT32 - type: DT_UINT8 - type: DT_INT16 - type: DT_INT8 - type: DT_COMPLEX64 - type: DT_INT64 - type: DT_QINT8 - type: DT_QUINT8 - type: DT_QINT32 - type: DT_BFLOAT16 - type: DT_UINT16 - type: DT_COMPLEX128 - type: DT_HALF - type: DT_UINT32 - type: DT_UINT64 - } - } - } - attr { - name: "data_format" - type: "string" - default_value { - s: "NHWC" - } - allowed_values { - list { - s: "NHWC" - s: "NCHW" - } - } - } -} -op { - name: "BiasAddV1" - input_arg { - name: "value" - type_attr: "T" - } - input_arg { - name: "bias" - type_attr: "T" - } - output_arg { - name: "output" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_FLOAT - type: DT_DOUBLE - type: DT_INT32 - type: DT_UINT8 - type: DT_INT16 - type: DT_INT8 - type: DT_COMPLEX64 - type: DT_INT64 - type: DT_QINT8 - type: DT_QUINT8 - type: DT_QINT32 - type: DT_BFLOAT16 - type: DT_UINT16 - type: DT_COMPLEX128 - type: DT_HALF - type: DT_UINT32 - type: DT_UINT64 - } - } - } -} -op { - name: "Bincount" - input_arg { - name: "arr" - type: DT_INT32 - } - input_arg { - name: "size" - type: DT_INT32 - } - input_arg { - name: "weights" - type_attr: "T" - } - output_arg { - name: "bins" - type_attr: "T" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_INT32 - type: DT_INT64 - type: DT_FLOAT - type: DT_DOUBLE - } - } - } -} -op { - name: "Bitcast" - input_arg { - name: "input" - type_attr: "T" - } - output_arg { - name: "output" - type_attr: "type" - } - attr { - name: "T" - type: "type" - allowed_values { - list { - type: DT_BFLOAT16 - type: DT_HALF - type: DT_FLOAT - type: DT_DOUBLE - type: DT_INT64 - type: DT_INT32 - type: DT_UINT8 - type: DT_UINT16 - type: DT_UINT32 - type: DT_UINT64 - type: DT_INT8 - type: DT_INT16 - type: DT_COMPLEX64 - type: DT_COMPLEX128 - type: DT_QINT8 - type: DT_QUINT8 - type: DT_QINT16 - type: DT_QUINT16 - type: DT_QINT32 - } - } - } - attr { - name: "type" - type: "type" - allowed_values { - list { - type: DT_BFLOAT16 - type: DT_HALF - type: DT_FLOAT - type: DT_DOUBLE - type: DT_INT64 - type: DT_INT32 - type: DT_UINT8 - type: DT_UINT16 - type: DT_UINT32 - type: DT_UINT64 - type: DT_INT8 - type: DT_INT16 - type: DT_COMPLEX64 - type: DT_COMPLEX128 - type: DT_QINT8 - type: DT_QUINT8 - type: DT_QINT16 - type: DT_QUINT16 - type: DT_QINT32 - } - } - } -} -op { - name: "BitwiseAnd" - input_arg { - name: "x" - type_attr: "T" - } - input_arg { - name: "y" - type_attr: "T" - 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type: DT_UINT8 - type: DT_UINT16 - type: DT_UINT32 - type: DT_UINT64 - } - } - } - is_commutative: true -} -op { - name: "BlockLSTM" - input_arg { - name: "seq_len_max" - type: DT_INT64 - } - input_arg { - name: "x" - type_attr: "T" - } - input_arg { - name: "cs_prev" - type_attr: "T" - } - input_arg { - name: "h_prev" - type_attr: "T" - } - input_arg { - name: "w" - type_attr: "T" - } - input_arg { - name: "wci" - type_attr: "T" - } - input_arg { - name: "wcf" - type_attr: "T" - } - input_arg { - name: "wco" - type_attr: "T" - } - input_arg { - name: "b" - type_attr: "T" - } - output_arg { - name: "i" - type_attr: "T" - } - output_arg { - name: "cs" - type_attr: "T" - } - output_arg { - name: "f" - type_attr: "T" - } - output_arg { - name: "o" - type_attr: "T" - } - output_arg { - name: "ci" - type_attr: "T" - } - output_arg { - name: "co" - type_attr: "T" - } - output_arg { - name: "h" - type_attr: "T" - } - attr { - name: "forget_bias" - type: "float" - default_value { - f: 1 - } - 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- - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor accumulate_n_v2(const std::vector&inputs, const std::vector& shape) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "AccumulateNV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector inputs_handles; inputs_handles.reserve(inputs.size()); - std::transform(inputs.begin(), inputs.end(), std::back_inserter(inputs_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), inputs_handles.data(), static_cast(inputs.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "N", inputs.size()); - - TFE_OpSetAttrShape(op.get(), "shape", shape.data(), static_cast(shape.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor accumulator_num_accumulated(const tensor& handle) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "AccumulatorNumAccumulated", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor accumulator_take_gradient(const tensor& handle, const tensor& num_required, datatype dtype) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "AccumulatorTakeGradient", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_required.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor acos(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Acos", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor acosh(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Acosh", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor add(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Add", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor add_many_sparse_to_tensors_map(const tensor& sparse_indices, const tensor& sparse_values, const tensor& sparse_shape, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "AddManySparseToTensorsMap", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), sparse_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), sparse_values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), sparse_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor add_n(const std::vector&inputs) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "AddN", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector inputs_handles; inputs_handles.reserve(inputs.size()); - std::transform(inputs.begin(), inputs.end(), std::back_inserter(inputs_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), inputs_handles.data(), static_cast(inputs.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "N", inputs.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor add_sparse_to_tensors_map(const tensor& sparse_indices, const tensor& sparse_values, const tensor& sparse_shape, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "AddSparseToTensorsMap", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), sparse_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), sparse_values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), sparse_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor add_v2(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "AddV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor adjust_contrast(const tensor& images, const tensor& contrast_factor, const tensor& min_value, const tensor& max_value) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "AdjustContrast", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), images.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), contrast_factor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), min_value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), max_value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor adjust_contrastv2(const tensor& images, const tensor& contrast_factor) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "AdjustContrastv2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), images.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), contrast_factor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor adjust_hue(const tensor& images, const tensor& delta) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "AdjustHue", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), images.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), delta.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor adjust_saturation(const tensor& images, const tensor& scale) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "AdjustSaturation", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), images.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), scale.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor all(const tensor& input, const tensor& reduction_indices, bool keep_dims=false, datatype Tidx=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "All", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), reduction_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "keep_dims", (unsigned char)keep_dims); - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor all_to_all(const tensor& input, const tensor& group_assignment, int64_t concat_dimension, int64_t split_dimension, int64_t split_count) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "AllToAll", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), group_assignment.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "concat_dimension", concat_dimension); - TFE_OpSetAttrInt(op.get(), "split_dimension", split_dimension); - TFE_OpSetAttrInt(op.get(), "split_count", split_count); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor angle(const tensor& input, datatype Tout=static_cast(1)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Angle", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tout", Tout); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor anonymous_iterator(const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "AnonymousIterator", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor any(const tensor& input, const tensor& reduction_indices, bool keep_dims=false, datatype Tidx=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Any", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), reduction_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "keep_dims", (unsigned char)keep_dims); - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor apply_ada_max(const tensor& var, const tensor& m, const tensor& v, const tensor& beta1_power, const tensor& lr, const tensor& beta1, const tensor& beta2, const tensor& epsilon, const tensor& grad, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ApplyAdaMax", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), m.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), v.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), beta1_power.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), beta1.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), beta2.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), epsilon.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor apply_adadelta(const tensor& var, const tensor& accum, const tensor& accum_update, const tensor& lr, const tensor& rho, const tensor& epsilon, const tensor& grad, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ApplyAdadelta", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), accum.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), accum_update.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), rho.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), epsilon.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor apply_adagrad(const tensor& var, const tensor& accum, const tensor& lr, const tensor& grad, bool use_locking=false, bool update_slots=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ApplyAdagrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), accum.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - TFE_OpSetAttrBool(op.get(), "update_slots", (unsigned char)update_slots); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor apply_adagrad_d_a(const tensor& var, const tensor& gradient_accumulator, const tensor& gradient_squared_accumulator, const tensor& grad, const tensor& lr, const tensor& l1, const tensor& l2, const tensor& global_step, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ApplyAdagradDA", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), gradient_accumulator.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), gradient_squared_accumulator.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), l1.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), l2.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), global_step.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor apply_adagrad_v2(const tensor& var, const tensor& accum, const tensor& lr, const tensor& epsilon, const tensor& grad, bool use_locking=false, bool update_slots=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ApplyAdagradV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), accum.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), epsilon.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - TFE_OpSetAttrBool(op.get(), "update_slots", (unsigned char)update_slots); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor apply_adam(const tensor& var, const tensor& m, const tensor& v, const tensor& beta1_power, const tensor& beta2_power, const tensor& lr, const tensor& beta1, const tensor& beta2, const tensor& epsilon, const tensor& grad, bool use_locking=false, bool use_nesterov=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ApplyAdam", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), m.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), v.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), beta1_power.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), beta2_power.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), beta1.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), beta2.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), epsilon.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - TFE_OpSetAttrBool(op.get(), "use_nesterov", (unsigned char)use_nesterov); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor apply_add_sign(const tensor& var, const tensor& m, const tensor& lr, const tensor& alpha, const tensor& sign_decay, const tensor& beta, const tensor& grad, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ApplyAddSign", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), m.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), alpha.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), sign_decay.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), beta.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor apply_centered_r_m_s_prop(const tensor& var, const tensor& mg, const tensor& ms, const tensor& mom, const tensor& lr, const tensor& rho, const tensor& momentum, const tensor& epsilon, const tensor& grad, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ApplyCenteredRMSProp", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), mg.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), ms.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), mom.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), rho.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), momentum.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), epsilon.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor apply_ftrl(const tensor& var, const tensor& accum, const tensor& linear, const tensor& grad, const tensor& lr, const tensor& l1, const tensor& l2, const tensor& lr_power, bool use_locking=false, bool multiply_linear_by_lr=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ApplyFtrl", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), accum.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), linear.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), l1.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), l2.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr_power.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - TFE_OpSetAttrBool(op.get(), "multiply_linear_by_lr", (unsigned char)multiply_linear_by_lr); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor apply_ftrl_v2(const tensor& var, const tensor& accum, const tensor& linear, const tensor& grad, const tensor& lr, const tensor& l1, const tensor& l2, const tensor& l2_shrinkage, const tensor& lr_power, bool use_locking=false, bool multiply_linear_by_lr=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ApplyFtrlV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), accum.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), linear.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), l1.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), l2.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), l2_shrinkage.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr_power.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - TFE_OpSetAttrBool(op.get(), "multiply_linear_by_lr", (unsigned char)multiply_linear_by_lr); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor apply_gradient_descent(const tensor& var, const tensor& alpha, const tensor& delta, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ApplyGradientDescent", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), alpha.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), delta.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor apply_momentum(const tensor& var, const tensor& accum, const tensor& lr, const tensor& grad, const tensor& momentum, bool use_locking=false, bool use_nesterov=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ApplyMomentum", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), accum.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), momentum.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - TFE_OpSetAttrBool(op.get(), "use_nesterov", (unsigned char)use_nesterov); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor apply_power_sign(const tensor& var, const tensor& m, const tensor& lr, const tensor& logbase, const tensor& sign_decay, const tensor& beta, const tensor& grad, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ApplyPowerSign", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), m.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), logbase.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), sign_decay.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), beta.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor apply_proximal_adagrad(const tensor& var, const tensor& accum, const tensor& lr, const tensor& l1, const tensor& l2, const tensor& grad, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ApplyProximalAdagrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), accum.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), l1.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), l2.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor apply_proximal_gradient_descent(const tensor& var, const tensor& alpha, const tensor& l1, const tensor& l2, const tensor& delta, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ApplyProximalGradientDescent", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), alpha.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), l1.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), l2.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), delta.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor apply_r_m_s_prop(const tensor& var, const tensor& ms, const tensor& mom, const tensor& lr, const tensor& rho, const tensor& momentum, const tensor& epsilon, const tensor& grad, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ApplyRMSProp", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), ms.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), mom.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), rho.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), momentum.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), epsilon.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor approximate_equal(const tensor& x, const tensor& y, float tolerance=1.0000e-05) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ApproximateEqual", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrFloat(op.get(), "tolerance", tolerance); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor arg_max(const tensor& input, const tensor& dimension, datatype Tidx=static_cast(3), datatype output_type=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ArgMax", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), dimension.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - TFE_OpSetAttrType(op.get(), "output_type", output_type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor arg_min(const tensor& input, const tensor& dimension, datatype Tidx=static_cast(3), datatype output_type=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ArgMin", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), dimension.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - TFE_OpSetAttrType(op.get(), "output_type", output_type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor as_string(const tensor& input, int64_t precision=-1, bool scientific=false, bool shortest=false, int64_t width=-1, const std::string& fill="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "AsString", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "precision", precision); - TFE_OpSetAttrBool(op.get(), "scientific", (unsigned char)scientific); - TFE_OpSetAttrBool(op.get(), "shortest", (unsigned char)shortest); - TFE_OpSetAttrInt(op.get(), "width", width); - TFE_OpSetAttrString(op.get(), "fill", (void*) fill.c_str(), fill.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor asin(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Asin", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor asinh(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Asinh", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor assert_cardinality_dataset(const tensor& input_dataset, const tensor& cardinality, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "AssertCardinalityDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), cardinality.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor assert_next_dataset(const tensor& input_dataset, const tensor& transformations, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "AssertNextDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), transformations.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor assign(const tensor& ref, const tensor& value, bool validate_shape=true, bool use_locking=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Assign", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), ref.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "validate_shape", (unsigned char)validate_shape); - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor assign_add(const tensor& ref, const tensor& value, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "AssignAdd", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), ref.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor assign_sub(const tensor& ref, const tensor& value, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "AssignSub", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), ref.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor atan(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Atan", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor atan2(const tensor& y, const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Atan2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor atanh(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Atanh", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor audio_spectrogram(const tensor& input, int64_t window_size, int64_t stride, bool magnitude_squared=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "AudioSpectrogram", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "window_size", window_size); - TFE_OpSetAttrInt(op.get(), "stride", stride); - TFE_OpSetAttrBool(op.get(), "magnitude_squared", (unsigned char)magnitude_squared); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor audio_summary(const tensor& tag, const tensor& input_tensor, float sample_rate, int64_t max_outputs=3) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "AudioSummary", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), tag.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), input_tensor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrFloat(op.get(), "sample_rate", sample_rate); - TFE_OpSetAttrInt(op.get(), "max_outputs", max_outputs); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor audio_summary_v2(const tensor& tag, const tensor& input_tensor, const tensor& sample_rate, int64_t max_outputs=3) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "AudioSummaryV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), tag.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), input_tensor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), sample_rate.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "max_outputs", max_outputs); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor auto_shard_dataset(const tensor& input_dataset, const tensor& num_workers, const tensor& index, const std::vector& output_types, const std::vector< std::vector>& output_shapes, int64_t auto_shard_policy=0) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "AutoShardDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_workers.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), index.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrInt(op.get(), "auto_shard_policy", auto_shard_policy); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor avg_pool(const tensor& value, const std::vector& ksize, const std::vector& strides, const std::string& padding, const std::string& data_format="NHWC") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "AvgPool", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "ksize", ksize.data(), static_cast(ksize.size())); - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrString(op.get(), "data_format", (void*) data_format.c_str(), data_format.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor avg_pool3_d(const tensor& input, const std::vector& ksize, const std::vector& strides, const std::string& padding, const std::string& data_format="NDHWC") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "AvgPool3D", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "ksize", ksize.data(), static_cast(ksize.size())); - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrString(op.get(), "data_format", (void*) data_format.c_str(), data_format.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor avg_pool3_d_grad(const tensor& orig_input_shape, const tensor& grad, const std::vector& ksize, const std::vector& strides, const std::string& padding, const std::string& data_format="NDHWC") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "AvgPool3DGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), orig_input_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "ksize", ksize.data(), static_cast(ksize.size())); - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrString(op.get(), "data_format", (void*) data_format.c_str(), data_format.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor avg_pool_grad(const tensor& orig_input_shape, const tensor& grad, const std::vector& ksize, const std::vector& strides, const std::string& padding, const std::string& data_format="NHWC") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "AvgPoolGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), orig_input_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "ksize", ksize.data(), static_cast(ksize.size())); - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrString(op.get(), "data_format", (void*) data_format.c_str(), data_format.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor banded_triangular_solve(const tensor& matrix, const tensor& rhs, bool lower=true, bool adjoint=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BandedTriangularSolve", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), matrix.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), rhs.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "lower", (unsigned char)lower); - TFE_OpSetAttrBool(op.get(), "adjoint", (unsigned char)adjoint); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor barrier(const std::vector& component_types, const std::vector< std::vector>& shapes, int64_t capacity=-1, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Barrier", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "component_types", reinterpret_cast(component_types.data()), static_cast(component_types.size())); - - std::vector shapes_values; shapes_values.reserve(shapes.size()); - std::vector shapes_ndims; shapes_ndims.reserve(shapes.size()); - std::transform(shapes.begin(), shapes.end(), std::back_inserter(shapes_values), [](const auto& v) { return v.data();}); - std::transform(shapes.begin(), shapes.end(), std::back_inserter(shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "shapes", shapes_values.data(), shapes_ndims.data(), static_cast(shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrInt(op.get(), "capacity", capacity); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor barrier_incomplete_size(const tensor& handle) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BarrierIncompleteSize", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor barrier_ready_size(const tensor& handle) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BarrierReadySize", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor batch_cholesky(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BatchCholesky", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor batch_cholesky_grad(const tensor& l, const tensor& grad) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BatchCholeskyGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), l.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor batch_dataset(const tensor& input_dataset, const tensor& batch_size, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BatchDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), batch_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor batch_dataset_v2(const tensor& input_dataset, const tensor& batch_size, const tensor& drop_remainder, const std::vector& output_types, const std::vector< std::vector>& output_shapes, bool parallel_copy=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BatchDatasetV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), batch_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), drop_remainder.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrBool(op.get(), "parallel_copy", (unsigned char)parallel_copy); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor batch_f_f_t(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BatchFFT", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor batch_f_f_t2_d(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BatchFFT2D", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor batch_f_f_t3_d(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BatchFFT3D", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor batch_i_f_f_t(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BatchIFFT", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor batch_i_f_f_t2_d(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BatchIFFT2D", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor batch_i_f_f_t3_d(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BatchIFFT3D", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor batch_mat_mul(const tensor& x, const tensor& y, bool adj_x=false, bool adj_y=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BatchMatMul", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "adj_x", (unsigned char)adj_x); - TFE_OpSetAttrBool(op.get(), "adj_y", (unsigned char)adj_y); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor batch_mat_mul_v2(const tensor& x, const tensor& y, bool adj_x=false, bool adj_y=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BatchMatMulV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "adj_x", (unsigned char)adj_x); - TFE_OpSetAttrBool(op.get(), "adj_y", (unsigned char)adj_y); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor batch_matrix_band_part(const tensor& input, const tensor& num_lower, const tensor& num_upper) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BatchMatrixBandPart", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_lower.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_upper.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor batch_matrix_determinant(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BatchMatrixDeterminant", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor batch_matrix_diag(const tensor& diagonal) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BatchMatrixDiag", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), diagonal.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor batch_matrix_diag_part(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BatchMatrixDiagPart", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor batch_matrix_inverse(const tensor& input, bool adjoint=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BatchMatrixInverse", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "adjoint", (unsigned char)adjoint); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor batch_matrix_set_diag(const tensor& input, const tensor& diagonal) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BatchMatrixSetDiag", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), diagonal.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor batch_matrix_solve(const tensor& matrix, const tensor& rhs, bool adjoint=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BatchMatrixSolve", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), matrix.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), rhs.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "adjoint", (unsigned char)adjoint); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor batch_matrix_solve_ls(const tensor& matrix, const tensor& rhs, const tensor& l2_regularizer, bool fast=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BatchMatrixSolveLs", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), matrix.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), rhs.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), l2_regularizer.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "fast", (unsigned char)fast); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor batch_matrix_triangular_solve(const tensor& matrix, const tensor& rhs, bool lower=true, bool adjoint=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BatchMatrixTriangularSolve", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), matrix.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), rhs.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "lower", (unsigned char)lower); - TFE_OpSetAttrBool(op.get(), "adjoint", (unsigned char)adjoint); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor batch_norm_with_global_normalization(const tensor& t, const tensor& m, const tensor& v, const tensor& beta, const tensor& gamma, float variance_epsilon, bool scale_after_normalization) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BatchNormWithGlobalNormalization", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), t.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), m.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), v.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), beta.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), gamma.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrFloat(op.get(), "variance_epsilon", variance_epsilon); - TFE_OpSetAttrBool(op.get(), "scale_after_normalization", (unsigned char)scale_after_normalization); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor batch_self_adjoint_eig(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BatchSelfAdjointEig", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor batch_to_space(const tensor& input, const tensor& crops, int64_t block_size, datatype Tidx=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BatchToSpace", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), crops.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "block_size", block_size); - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor batch_to_space_n_d(const tensor& input, const tensor& block_shape, const tensor& crops, datatype Tblock_shape=static_cast(3), datatype Tcrops=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BatchToSpaceND", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), block_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), crops.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tblock_shape", Tblock_shape); - TFE_OpSetAttrType(op.get(), "Tcrops", Tcrops); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor bessel_i0(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BesselI0", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor bessel_i0e(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BesselI0e", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor bessel_i1(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BesselI1", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor bessel_i1e(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BesselI1e", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor bessel_j0(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BesselJ0", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor bessel_j1(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BesselJ1", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor bessel_k0(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BesselK0", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor bessel_k0e(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BesselK0e", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor bessel_k1(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BesselK1", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor bessel_k1e(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BesselK1e", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor bessel_y0(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BesselY0", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor bessel_y1(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BesselY1", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor betainc(const tensor& a, const tensor& b, const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Betainc", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), a.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), b.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor bias_add(const tensor& value, const tensor& bias, const std::string& data_format="NHWC") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BiasAdd", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), bias.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "data_format", (void*) data_format.c_str(), data_format.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor bias_add_grad(const tensor& out_backprop, const std::string& data_format="NHWC") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BiasAddGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), out_backprop.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "data_format", (void*) data_format.c_str(), data_format.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor bias_add_v1(const tensor& value, const tensor& bias) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BiasAddV1", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), bias.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor bincount(const tensor& arr, const tensor& size, const tensor& weights) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Bincount", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), arr.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), weights.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor bitcast(const tensor& input, datatype type) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Bitcast", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "type", type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor bitwise_and(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BitwiseAnd", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor bitwise_or(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BitwiseOr", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor bitwise_xor(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BitwiseXor", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor boosted_trees_aggregate_stats(const tensor& node_ids, const tensor& gradients, const tensor& hessians, const tensor& feature, int64_t max_splits, int64_t num_buckets) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BoostedTreesAggregateStats", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), node_ids.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), gradients.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), hessians.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), feature.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "max_splits", max_splits); - TFE_OpSetAttrInt(op.get(), "num_buckets", num_buckets); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor boosted_trees_bucketize(const std::vector&float_values, const std::vector&bucket_boundaries) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BoostedTreesBucketize", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector float_values_handles; float_values_handles.reserve(float_values.size()); - std::transform(float_values.begin(), float_values.end(), std::back_inserter(float_values_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), float_values_handles.data(), static_cast(float_values.size()), context::get_status()); - status_check(context::get_status()); - - - std::vector bucket_boundaries_handles; bucket_boundaries_handles.reserve(bucket_boundaries.size()); - std::transform(bucket_boundaries.begin(), bucket_boundaries.end(), std::back_inserter(bucket_boundaries_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), bucket_boundaries_handles.data(), static_cast(bucket_boundaries.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "num_features", float_values.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor boosted_trees_center_bias(const tensor& tree_ensemble_handle, const tensor& mean_gradients, const tensor& mean_hessians, const tensor& l1, const tensor& l2) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BoostedTreesCenterBias", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), tree_ensemble_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), mean_gradients.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), mean_hessians.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), l1.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), l2.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor boosted_trees_ensemble_resource_handle_op(const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BoostedTreesEnsembleResourceHandleOp", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor boosted_trees_example_debug_outputs(const tensor& tree_ensemble_handle, const std::vector&bucketized_features, int64_t logits_dimension) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BoostedTreesExampleDebugOutputs", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), tree_ensemble_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - std::vector bucketized_features_handles; bucketized_features_handles.reserve(bucketized_features.size()); - std::transform(bucketized_features.begin(), bucketized_features.end(), std::back_inserter(bucketized_features_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), bucketized_features_handles.data(), static_cast(bucketized_features.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "num_bucketized_features", bucketized_features.size()); - TFE_OpSetAttrInt(op.get(), "logits_dimension", logits_dimension); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor boosted_trees_flush_quantile_summaries(const tensor& quantile_stream_resource_handle, int64_t num_features) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BoostedTreesFlushQuantileSummaries", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), quantile_stream_resource_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "num_features", num_features); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor boosted_trees_make_quantile_summaries(const std::vector&float_values, const tensor& example_weights, const tensor& epsilon) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BoostedTreesMakeQuantileSummaries", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector float_values_handles; float_values_handles.reserve(float_values.size()); - std::transform(float_values.begin(), float_values.end(), std::back_inserter(float_values_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), float_values_handles.data(), static_cast(float_values.size()), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), example_weights.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), epsilon.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "num_features", float_values.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor boosted_trees_make_stats_summary(const tensor& node_ids, const tensor& gradients, const tensor& hessians, const std::vector&bucketized_features_list, int64_t max_splits, int64_t num_buckets) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BoostedTreesMakeStatsSummary", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), node_ids.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), gradients.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), hessians.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - std::vector bucketized_features_list_handles; bucketized_features_list_handles.reserve(bucketized_features_list.size()); - std::transform(bucketized_features_list.begin(), bucketized_features_list.end(), std::back_inserter(bucketized_features_list_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), bucketized_features_list_handles.data(), static_cast(bucketized_features_list.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "max_splits", max_splits); - TFE_OpSetAttrInt(op.get(), "num_buckets", num_buckets); - TFE_OpSetAttrInt(op.get(), "num_features", bucketized_features_list.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor boosted_trees_predict(const tensor& tree_ensemble_handle, const std::vector&bucketized_features, int64_t logits_dimension) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BoostedTreesPredict", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), tree_ensemble_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - std::vector bucketized_features_handles; bucketized_features_handles.reserve(bucketized_features.size()); - std::transform(bucketized_features.begin(), bucketized_features.end(), std::back_inserter(bucketized_features_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), bucketized_features_handles.data(), static_cast(bucketized_features.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "num_bucketized_features", bucketized_features.size()); - TFE_OpSetAttrInt(op.get(), "logits_dimension", logits_dimension); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor boosted_trees_quantile_stream_resource_get_bucket_boundaries(const tensor& quantile_stream_resource_handle, int64_t num_features) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BoostedTreesQuantileStreamResourceGetBucketBoundaries", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), quantile_stream_resource_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "num_features", num_features); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor boosted_trees_quantile_stream_resource_handle_op(const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BoostedTreesQuantileStreamResourceHandleOp", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor broadcast_args(const tensor& s0, const tensor& s1) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BroadcastArgs", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), s0.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), s1.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor broadcast_to(const tensor& input, const tensor& shape, datatype Tidx=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BroadcastTo", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor bucketize(const tensor& input, const std::vector& boundaries) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Bucketize", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrFloatList(op.get(), "boundaries", boundaries.data(), static_cast(boundaries.size())); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor bytes_produced_stats_dataset(const tensor& input_dataset, const tensor& tag, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "BytesProducedStatsDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), tag.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor c_s_r_sparse_matrix_to_dense(const tensor& sparse_input, datatype type) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "CSRSparseMatrixToDense", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), sparse_input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "type", type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor c_s_v_dataset(const tensor& filenames, const tensor& compression_type, const tensor& buffer_size, const tensor& header, const tensor& field_delim, const tensor& use_quote_delim, const tensor& na_value, const tensor& select_cols, const std::vector&record_defaults, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "CSVDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), filenames.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), compression_type.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), buffer_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), header.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), field_delim.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), use_quote_delim.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), na_value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), select_cols.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - std::vector record_defaults_handles; record_defaults_handles.reserve(record_defaults.size()); - std::transform(record_defaults.begin(), record_defaults.end(), std::back_inserter(record_defaults_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), record_defaults_handles.data(), static_cast(record_defaults.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor cache_dataset(const tensor& input_dataset, const tensor& filename, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "CacheDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), filename.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor cache_dataset_v2(const tensor& input_dataset, const tensor& filename, const tensor& cache, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "CacheDatasetV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), filename.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), cache.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor cast(const tensor& x, datatype SrcT, datatype DstT, bool Truncate=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Cast", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "SrcT", SrcT); - TFE_OpSetAttrType(op.get(), "DstT", DstT); - TFE_OpSetAttrBool(op.get(), "Truncate", (unsigned char)Truncate); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor ceil(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Ceil", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor check_numerics(const tensor& input_tensor, const std::string& message) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "CheckNumerics", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_tensor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "message", (void*) message.c_str(), message.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor check_numerics_v2(const tensor& input_tensor, const std::string& message) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "CheckNumericsV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_tensor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "message", (void*) message.c_str(), message.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor cholesky(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Cholesky", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor cholesky_grad(const tensor& l, const tensor& grad) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "CholeskyGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), l.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor choose_fastest_dataset(const std::vector&input_datasets, int64_t num_experiments, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ChooseFastestDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector input_datasets_handles; input_datasets_handles.reserve(input_datasets.size()); - std::transform(input_datasets.begin(), input_datasets.end(), std::back_inserter(input_datasets_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), input_datasets_handles.data(), static_cast(input_datasets.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "N", input_datasets.size()); - TFE_OpSetAttrInt(op.get(), "num_experiments", num_experiments); - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor clip_by_value(const tensor& t, const tensor& clip_value_min, const tensor& clip_value_max) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ClipByValue", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), t.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), clip_value_min.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), clip_value_max.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor collective_bcast_recv(int64_t group_size, int64_t group_key, int64_t instance_key, const std::vector& shape, const std::string& communication_hint="auto", float timeout_seconds=0.0000e+00) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "CollectiveBcastRecv", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrInt(op.get(), "group_size", group_size); - TFE_OpSetAttrInt(op.get(), "group_key", group_key); - TFE_OpSetAttrInt(op.get(), "instance_key", instance_key); - - TFE_OpSetAttrShape(op.get(), "shape", shape.data(), static_cast(shape.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrString(op.get(), "communication_hint", (void*) communication_hint.c_str(), communication_hint.size()); - TFE_OpSetAttrFloat(op.get(), "timeout_seconds", timeout_seconds); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor collective_bcast_send(const tensor& input, int64_t group_size, int64_t group_key, int64_t instance_key, const std::vector& shape, const std::string& communication_hint="auto", float timeout_seconds=0.0000e+00) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "CollectiveBcastSend", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "group_size", group_size); - TFE_OpSetAttrInt(op.get(), "group_key", group_key); - TFE_OpSetAttrInt(op.get(), "instance_key", instance_key); - - TFE_OpSetAttrShape(op.get(), "shape", shape.data(), static_cast(shape.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrString(op.get(), "communication_hint", (void*) communication_hint.c_str(), communication_hint.size()); - TFE_OpSetAttrFloat(op.get(), "timeout_seconds", timeout_seconds); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor collective_gather(const tensor& input, int64_t group_size, int64_t group_key, int64_t instance_key, const std::vector& shape, const std::string& communication_hint="auto", float timeout_seconds=0.0000e+00) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "CollectiveGather", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "group_size", group_size); - TFE_OpSetAttrInt(op.get(), "group_key", group_key); - TFE_OpSetAttrInt(op.get(), "instance_key", instance_key); - - TFE_OpSetAttrShape(op.get(), "shape", shape.data(), static_cast(shape.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrString(op.get(), "communication_hint", (void*) communication_hint.c_str(), communication_hint.size()); - TFE_OpSetAttrFloat(op.get(), "timeout_seconds", timeout_seconds); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor collective_permute(const tensor& input, const tensor& source_target_pairs) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "CollectivePermute", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), source_target_pairs.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor collective_reduce(const tensor& input, int64_t group_size, int64_t group_key, int64_t instance_key, const std::string& merge_op, const std::string& final_op, const std::vector& subdiv_offsets, const std::vector& wait_for, const std::string& communication_hint="auto", float timeout_seconds=0.0000e+00) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "CollectiveReduce", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "group_size", group_size); - TFE_OpSetAttrInt(op.get(), "group_key", group_key); - TFE_OpSetAttrInt(op.get(), "instance_key", instance_key); - TFE_OpSetAttrString(op.get(), "merge_op", (void*) merge_op.c_str(), merge_op.size()); - TFE_OpSetAttrString(op.get(), "final_op", (void*) final_op.c_str(), final_op.size()); - TFE_OpSetAttrIntList(op.get(), "subdiv_offsets", subdiv_offsets.data(), static_cast(subdiv_offsets.size())); - TFE_OpSetAttrIntList(op.get(), "wait_for", wait_for.data(), static_cast(wait_for.size())); - TFE_OpSetAttrString(op.get(), "communication_hint", (void*) communication_hint.c_str(), communication_hint.size()); - TFE_OpSetAttrFloat(op.get(), "timeout_seconds", timeout_seconds); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor compare_and_bitpack(const tensor& input, const tensor& threshold) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "CompareAndBitpack", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), threshold.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor complex(const tensor& real, const tensor& imag, datatype Tout=static_cast(8)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Complex", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), real.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), imag.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tout", Tout); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor complex_abs(const tensor& x, datatype Tout=static_cast(1)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ComplexAbs", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tout", Tout); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor compress_element(const std::vector&components, const std::vector& input_types) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "CompressElement", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector components_handles; components_handles.reserve(components.size()); - std::transform(components.begin(), components.end(), std::back_inserter(components_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), components_handles.data(), static_cast(components.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "input_types", reinterpret_cast(input_types.data()), static_cast(input_types.size())); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor concat(const tensor& concat_dim, const std::vector&values) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Concat", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), concat_dim.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - std::vector values_handles; values_handles.reserve(values.size()); - std::transform(values.begin(), values.end(), std::back_inserter(values_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), values_handles.data(), static_cast(values.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "N", values.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor concat_offset(const tensor& concat_dim, const std::vector&shape) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ConcatOffset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), concat_dim.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - std::vector shape_handles; shape_handles.reserve(shape.size()); - std::transform(shape.begin(), shape.end(), std::back_inserter(shape_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), shape_handles.data(), static_cast(shape.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "N", shape.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor concat_v2(const std::vector&values, const tensor& axis, datatype Tidx=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ConcatV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector values_handles; values_handles.reserve(values.size()); - std::transform(values.begin(), values.end(), std::back_inserter(values_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), values_handles.data(), static_cast(values.size()), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), axis.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "N", values.size()); - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor concatenate_dataset(const tensor& input_dataset, const tensor& another_dataset, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ConcatenateDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), another_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor conditional_accumulator(datatype dtype, const std::vector& shape, const std::string& container="", const std::string& shared_name="", const std::string& reduction_type="MEAN") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ConditionalAccumulator", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - TFE_OpSetAttrShape(op.get(), "shape", shape.data(), static_cast(shape.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - TFE_OpSetAttrString(op.get(), "reduction_type", (void*) reduction_type.c_str(), reduction_type.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor configure_distributed_t_p_u(const std::string& embedding_config="", const std::string& tpu_embedding_config="", bool is_global_init=false, bool enable_whole_mesh_compilations=false, bool compilation_failure_closes_chips=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ConfigureDistributedTPU", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrString(op.get(), "embedding_config", (void*) embedding_config.c_str(), embedding_config.size()); - TFE_OpSetAttrString(op.get(), "tpu_embedding_config", (void*) tpu_embedding_config.c_str(), tpu_embedding_config.size()); - TFE_OpSetAttrBool(op.get(), "is_global_init", (unsigned char)is_global_init); - TFE_OpSetAttrBool(op.get(), "enable_whole_mesh_compilations", (unsigned char)enable_whole_mesh_compilations); - TFE_OpSetAttrBool(op.get(), "compilation_failure_closes_chips", (unsigned char)compilation_failure_closes_chips); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor conj(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Conj", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor conjugate_transpose(const tensor& x, const tensor& perm, datatype Tperm=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ConjugateTranspose", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), perm.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tperm", Tperm); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor const_tensor(const tensor& value, datatype dtype) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Const", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - - TFE_OpSetAttrTensor(op.get(), "value", value.get_tensor().get(), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor conv2_d(const tensor& input, const tensor& filter, const std::vector& strides, const std::string& padding, const std::vector& explicit_paddings, const std::vector& dilations, bool use_cudnn_on_gpu=true, const std::string& data_format="NHWC") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Conv2D", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), filter.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrIntList(op.get(), "explicit_paddings", explicit_paddings.data(), static_cast(explicit_paddings.size())); - TFE_OpSetAttrIntList(op.get(), "dilations", dilations.data(), static_cast(dilations.size())); - TFE_OpSetAttrBool(op.get(), "use_cudnn_on_gpu", (unsigned char)use_cudnn_on_gpu); - TFE_OpSetAttrString(op.get(), "data_format", (void*) data_format.c_str(), data_format.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor conv2_d_backprop_filter(const tensor& input, const tensor& filter_sizes, const tensor& out_backprop, const std::vector& strides, const std::string& padding, const std::vector& explicit_paddings, const std::vector& dilations, bool use_cudnn_on_gpu=true, const std::string& data_format="NHWC") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Conv2DBackpropFilter", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), filter_sizes.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), out_backprop.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrIntList(op.get(), "explicit_paddings", explicit_paddings.data(), static_cast(explicit_paddings.size())); - TFE_OpSetAttrIntList(op.get(), "dilations", dilations.data(), static_cast(dilations.size())); - TFE_OpSetAttrBool(op.get(), "use_cudnn_on_gpu", (unsigned char)use_cudnn_on_gpu); - TFE_OpSetAttrString(op.get(), "data_format", (void*) data_format.c_str(), data_format.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor conv2_d_backprop_input(const tensor& input_sizes, const tensor& filter, const tensor& out_backprop, const std::vector& strides, const std::string& padding, const std::vector& explicit_paddings, const std::vector& dilations, bool use_cudnn_on_gpu=true, const std::string& data_format="NHWC") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Conv2DBackpropInput", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_sizes.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), filter.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), out_backprop.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrIntList(op.get(), "explicit_paddings", explicit_paddings.data(), static_cast(explicit_paddings.size())); - TFE_OpSetAttrIntList(op.get(), "dilations", dilations.data(), static_cast(dilations.size())); - TFE_OpSetAttrBool(op.get(), "use_cudnn_on_gpu", (unsigned char)use_cudnn_on_gpu); - TFE_OpSetAttrString(op.get(), "data_format", (void*) data_format.c_str(), data_format.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor conv3_d(const tensor& input, const tensor& filter, const std::vector& strides, const std::string& padding, const std::vector& dilations, const std::string& data_format="NDHWC") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Conv3D", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), filter.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrIntList(op.get(), "dilations", dilations.data(), static_cast(dilations.size())); - TFE_OpSetAttrString(op.get(), "data_format", (void*) data_format.c_str(), data_format.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor conv3_d_backprop_filter(const tensor& input, const tensor& filter, const tensor& out_backprop, const std::vector& strides, const std::string& padding, const std::vector& dilations) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Conv3DBackpropFilter", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), filter.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), out_backprop.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrIntList(op.get(), "dilations", dilations.data(), static_cast(dilations.size())); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor conv3_d_backprop_filter_v2(const tensor& input, const tensor& filter_sizes, const tensor& out_backprop, const std::vector& strides, const std::string& padding, const std::vector& dilations, const std::string& data_format="NDHWC") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Conv3DBackpropFilterV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), filter_sizes.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), out_backprop.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrIntList(op.get(), "dilations", dilations.data(), static_cast(dilations.size())); - TFE_OpSetAttrString(op.get(), "data_format", (void*) data_format.c_str(), data_format.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor conv3_d_backprop_input(const tensor& input, const tensor& filter, const tensor& out_backprop, const std::vector& strides, const std::string& padding, const std::vector& dilations) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Conv3DBackpropInput", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), filter.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), out_backprop.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrIntList(op.get(), "dilations", dilations.data(), static_cast(dilations.size())); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor conv3_d_backprop_input_v2(const tensor& input_sizes, const tensor& filter, const tensor& out_backprop, const std::vector& strides, const std::string& padding, const std::vector& dilations, const std::string& data_format="NDHWC", datatype Tshape=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Conv3DBackpropInputV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_sizes.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), filter.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), out_backprop.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrIntList(op.get(), "dilations", dilations.data(), static_cast(dilations.size())); - TFE_OpSetAttrString(op.get(), "data_format", (void*) data_format.c_str(), data_format.size()); - TFE_OpSetAttrType(op.get(), "Tshape", Tshape); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor copy(const tensor& input, const std::vector< std::string>& debug_ops_spec, const std::string& tensor_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Copy", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - std::vector debug_ops_spec_sizes; debug_ops_spec_sizes.reserve(debug_ops_spec.size()); - std::transform(debug_ops_spec.begin(), debug_ops_spec.end(), std::back_inserter(debug_ops_spec_sizes), [](const auto& s) { return s.size();}); - TFE_OpSetAttrStringList(op.get(), "debug_ops_spec", reinterpret_cast(debug_ops_spec.data()), debug_ops_spec_sizes.data(), static_cast(debug_ops_spec.size())); - - TFE_OpSetAttrString(op.get(), "tensor_name", (void*) tensor_name.c_str(), tensor_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor copy_host(const tensor& input, const std::vector< std::string>& debug_ops_spec, const std::string& tensor_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "CopyHost", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - std::vector debug_ops_spec_sizes; debug_ops_spec_sizes.reserve(debug_ops_spec.size()); - std::transform(debug_ops_spec.begin(), debug_ops_spec.end(), std::back_inserter(debug_ops_spec_sizes), [](const auto& s) { return s.size();}); - TFE_OpSetAttrStringList(op.get(), "debug_ops_spec", reinterpret_cast(debug_ops_spec.data()), debug_ops_spec_sizes.data(), static_cast(debug_ops_spec.size())); - - TFE_OpSetAttrString(op.get(), "tensor_name", (void*) tensor_name.c_str(), tensor_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor cos(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Cos", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor cosh(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Cosh", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor count_up_to(const tensor& ref, int64_t limit) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "CountUpTo", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), ref.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "limit", limit); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor crop_and_resize(const tensor& image, const tensor& boxes, const tensor& box_ind, const tensor& crop_size, const std::string& method="bilinear", float extrapolation_value=0.0000e+00) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "CropAndResize", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), image.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), boxes.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), box_ind.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), crop_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "method", (void*) method.c_str(), method.size()); - TFE_OpSetAttrFloat(op.get(), "extrapolation_value", extrapolation_value); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor crop_and_resize_grad_boxes(const tensor& grads, const tensor& image, const tensor& boxes, const tensor& box_ind, const std::string& method="bilinear") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "CropAndResizeGradBoxes", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), grads.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), image.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), boxes.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), box_ind.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "method", (void*) method.c_str(), method.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor crop_and_resize_grad_image(const tensor& grads, const tensor& boxes, const tensor& box_ind, const tensor& image_size, const std::string& method="bilinear") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "CropAndResizeGradImage", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), grads.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), boxes.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), box_ind.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), image_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "method", (void*) method.c_str(), method.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor cross(const tensor& a, const tensor& b) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Cross", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), a.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), b.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor cross_replica_sum(const tensor& input, const tensor& group_assignment) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "CrossReplicaSum", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), group_assignment.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor cudnn_r_n_n_canonical_to_params(const tensor& num_layers, const tensor& num_units, const tensor& input_size, const std::vector&weights, const std::vector&biases, const std::string& rnn_mode="lstm", const std::string& input_mode="linear_input", const std::string& direction="unidirectional", float dropout=0.0000e+00, int64_t seed=0, int64_t seed2=0) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "CudnnRNNCanonicalToParams", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), num_layers.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_units.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), input_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - std::vector weights_handles; weights_handles.reserve(weights.size()); - std::transform(weights.begin(), weights.end(), std::back_inserter(weights_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), weights_handles.data(), static_cast(weights.size()), context::get_status()); - status_check(context::get_status()); - - - std::vector biases_handles; biases_handles.reserve(biases.size()); - std::transform(biases.begin(), biases.end(), std::back_inserter(biases_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), biases_handles.data(), static_cast(biases.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "num_params", weights.size()); - TFE_OpSetAttrString(op.get(), "rnn_mode", (void*) rnn_mode.c_str(), rnn_mode.size()); - TFE_OpSetAttrString(op.get(), "input_mode", (void*) input_mode.c_str(), input_mode.size()); - TFE_OpSetAttrString(op.get(), "direction", (void*) direction.c_str(), direction.size()); - TFE_OpSetAttrFloat(op.get(), "dropout", dropout); - TFE_OpSetAttrInt(op.get(), "seed", seed); - TFE_OpSetAttrInt(op.get(), "seed2", seed2); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor cudnn_r_n_n_canonical_to_params_v2(const tensor& num_layers, const tensor& num_units, const tensor& input_size, const std::vector&weights, const std::vector&biases, const std::string& rnn_mode="lstm", const std::string& input_mode="linear_input", const std::string& direction="unidirectional", float dropout=0.0000e+00, int64_t seed=0, int64_t seed2=0, int64_t num_proj=0) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "CudnnRNNCanonicalToParamsV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), num_layers.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_units.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), input_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - std::vector weights_handles; weights_handles.reserve(weights.size()); - std::transform(weights.begin(), weights.end(), std::back_inserter(weights_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), weights_handles.data(), static_cast(weights.size()), context::get_status()); - status_check(context::get_status()); - - - std::vector biases_handles; biases_handles.reserve(biases.size()); - std::transform(biases.begin(), biases.end(), std::back_inserter(biases_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), biases_handles.data(), static_cast(biases.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "num_params_weights", weights.size()); - TFE_OpSetAttrInt(op.get(), "num_params_biases", biases.size()); - TFE_OpSetAttrString(op.get(), "rnn_mode", (void*) rnn_mode.c_str(), rnn_mode.size()); - TFE_OpSetAttrString(op.get(), "input_mode", (void*) input_mode.c_str(), input_mode.size()); - TFE_OpSetAttrString(op.get(), "direction", (void*) direction.c_str(), direction.size()); - TFE_OpSetAttrFloat(op.get(), "dropout", dropout); - TFE_OpSetAttrInt(op.get(), "seed", seed); - TFE_OpSetAttrInt(op.get(), "seed2", seed2); - TFE_OpSetAttrInt(op.get(), "num_proj", num_proj); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor cudnn_r_n_n_params_size(const tensor& num_layers, const tensor& num_units, const tensor& input_size, datatype S, const std::string& rnn_mode="lstm", const std::string& input_mode="linear_input", const std::string& direction="unidirectional", float dropout=0.0000e+00, int64_t seed=0, int64_t seed2=0, int64_t num_proj=0) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "CudnnRNNParamsSize", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), num_layers.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_units.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), input_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "S", S); - TFE_OpSetAttrString(op.get(), "rnn_mode", (void*) rnn_mode.c_str(), rnn_mode.size()); - TFE_OpSetAttrString(op.get(), "input_mode", (void*) input_mode.c_str(), input_mode.size()); - TFE_OpSetAttrString(op.get(), "direction", (void*) direction.c_str(), direction.size()); - TFE_OpSetAttrFloat(op.get(), "dropout", dropout); - TFE_OpSetAttrInt(op.get(), "seed", seed); - TFE_OpSetAttrInt(op.get(), "seed2", seed2); - TFE_OpSetAttrInt(op.get(), "num_proj", num_proj); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor cumprod(const tensor& x, const tensor& axis, bool exclusive=false, bool reverse=false, datatype Tidx=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Cumprod", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), axis.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "exclusive", (unsigned char)exclusive); - TFE_OpSetAttrBool(op.get(), "reverse", (unsigned char)reverse); - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor cumsum(const tensor& x, const tensor& axis, bool exclusive=false, bool reverse=false, datatype Tidx=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Cumsum", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), axis.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "exclusive", (unsigned char)exclusive); - TFE_OpSetAttrBool(op.get(), "reverse", (unsigned char)reverse); - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor cumulative_logsumexp(const tensor& x, const tensor& axis, bool exclusive=false, bool reverse=false, datatype Tidx=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "CumulativeLogsumexp", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), axis.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "exclusive", (unsigned char)exclusive); - TFE_OpSetAttrBool(op.get(), "reverse", (unsigned char)reverse); - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor data_format_dim_map(const tensor& x, const std::string& src_format="NHWC", const std::string& dst_format="NCHW") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DataFormatDimMap", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "src_format", (void*) src_format.c_str(), src_format.size()); - TFE_OpSetAttrString(op.get(), "dst_format", (void*) dst_format.c_str(), dst_format.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor data_format_vec_permute(const tensor& x, const std::string& src_format="NHWC", const std::string& dst_format="NCHW") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DataFormatVecPermute", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "src_format", (void*) src_format.c_str(), src_format.size()); - TFE_OpSetAttrString(op.get(), "dst_format", (void*) dst_format.c_str(), dst_format.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor data_service_dataset(const tensor& dataset_id, const tensor& processing_mode, const tensor& address, const tensor& protocol, const tensor& job_name, const tensor& max_outstanding_requests, const tensor& iteration_counter, const std::vector& output_types, const std::vector< std::vector>& output_shapes, int64_t task_refresh_interval_hint_ms=-1) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DataServiceDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), dataset_id.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), processing_mode.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), address.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), protocol.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), job_name.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), max_outstanding_requests.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), iteration_counter.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrInt(op.get(), "task_refresh_interval_hint_ms", task_refresh_interval_hint_ms); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor dataset_cardinality(const tensor& input_dataset) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DatasetCardinality", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor dataset_from_graph(const tensor& graph_def) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DatasetFromGraph", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), graph_def.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor dataset_to_graph(const tensor& input_dataset, const std::vector< std::string>& stateful_whitelist, bool allow_stateful=false, bool strip_device_assignment=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DatasetToGraph", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - std::vector stateful_whitelist_sizes; stateful_whitelist_sizes.reserve(stateful_whitelist.size()); - std::transform(stateful_whitelist.begin(), stateful_whitelist.end(), std::back_inserter(stateful_whitelist_sizes), [](const auto& s) { return s.size();}); - TFE_OpSetAttrStringList(op.get(), "stateful_whitelist", reinterpret_cast(stateful_whitelist.data()), stateful_whitelist_sizes.data(), static_cast(stateful_whitelist.size())); - - TFE_OpSetAttrBool(op.get(), "allow_stateful", (unsigned char)allow_stateful); - TFE_OpSetAttrBool(op.get(), "strip_device_assignment", (unsigned char)strip_device_assignment); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor dataset_to_graph_v2(const tensor& input_dataset, int64_t external_state_policy=0, bool strip_device_assignment=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DatasetToGraphV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "external_state_policy", external_state_policy); - TFE_OpSetAttrBool(op.get(), "strip_device_assignment", (unsigned char)strip_device_assignment); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor dataset_to_single_element(const tensor& dataset, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DatasetToSingleElement", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor dawsn(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Dawsn", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor debug_gradient_identity(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DebugGradientIdentity", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor debug_gradient_ref_identity(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DebugGradientRefIdentity", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor debug_identity(const tensor& input, const std::vector< std::string>& debug_urls, const std::string& device_name="", const std::string& tensor_name="", bool gated_grpc=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DebugIdentity", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - std::vector debug_urls_sizes; debug_urls_sizes.reserve(debug_urls.size()); - std::transform(debug_urls.begin(), debug_urls.end(), std::back_inserter(debug_urls_sizes), [](const auto& s) { return s.size();}); - TFE_OpSetAttrStringList(op.get(), "debug_urls", reinterpret_cast(debug_urls.data()), debug_urls_sizes.data(), static_cast(debug_urls.size())); - - TFE_OpSetAttrString(op.get(), "device_name", (void*) device_name.c_str(), device_name.size()); - TFE_OpSetAttrString(op.get(), "tensor_name", (void*) tensor_name.c_str(), tensor_name.size()); - TFE_OpSetAttrBool(op.get(), "gated_grpc", (unsigned char)gated_grpc); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor debug_identity_v2(const tensor& input, const std::vector< std::string>& debug_urls, const std::string& tfdbg_context_id="", const std::string& op_name="", int64_t output_slot=-1, int64_t tensor_debug_mode=-1, int64_t circular_buffer_size=1000, const std::string& tfdbg_run_id="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DebugIdentityV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - std::vector debug_urls_sizes; debug_urls_sizes.reserve(debug_urls.size()); - std::transform(debug_urls.begin(), debug_urls.end(), std::back_inserter(debug_urls_sizes), [](const auto& s) { return s.size();}); - TFE_OpSetAttrStringList(op.get(), "debug_urls", reinterpret_cast(debug_urls.data()), debug_urls_sizes.data(), static_cast(debug_urls.size())); - - TFE_OpSetAttrString(op.get(), "tfdbg_context_id", (void*) tfdbg_context_id.c_str(), tfdbg_context_id.size()); - TFE_OpSetAttrString(op.get(), "op_name", (void*) op_name.c_str(), op_name.size()); - TFE_OpSetAttrInt(op.get(), "output_slot", output_slot); - TFE_OpSetAttrInt(op.get(), "tensor_debug_mode", tensor_debug_mode); - TFE_OpSetAttrInt(op.get(), "circular_buffer_size", circular_buffer_size); - TFE_OpSetAttrString(op.get(), "tfdbg_run_id", (void*) tfdbg_run_id.c_str(), tfdbg_run_id.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor debug_nan_count(const tensor& input, const std::vector< std::string>& debug_urls, const std::string& device_name="", const std::string& tensor_name="", bool gated_grpc=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DebugNanCount", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - std::vector debug_urls_sizes; debug_urls_sizes.reserve(debug_urls.size()); - std::transform(debug_urls.begin(), debug_urls.end(), std::back_inserter(debug_urls_sizes), [](const auto& s) { return s.size();}); - TFE_OpSetAttrStringList(op.get(), "debug_urls", reinterpret_cast(debug_urls.data()), debug_urls_sizes.data(), static_cast(debug_urls.size())); - - TFE_OpSetAttrString(op.get(), "device_name", (void*) device_name.c_str(), device_name.size()); - TFE_OpSetAttrString(op.get(), "tensor_name", (void*) tensor_name.c_str(), tensor_name.size()); - TFE_OpSetAttrBool(op.get(), "gated_grpc", (unsigned char)gated_grpc); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor debug_numeric_summary(const tensor& input, const std::vector< std::string>& debug_urls, const std::string& device_name="", const std::string& tensor_name="", float lower_bound=-std::numeric_limits::infinity(), float upper_bound=std::numeric_limits::infinity(), bool mute_if_healthy=false, bool gated_grpc=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DebugNumericSummary", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - std::vector debug_urls_sizes; debug_urls_sizes.reserve(debug_urls.size()); - std::transform(debug_urls.begin(), debug_urls.end(), std::back_inserter(debug_urls_sizes), [](const auto& s) { return s.size();}); - TFE_OpSetAttrStringList(op.get(), "debug_urls", reinterpret_cast(debug_urls.data()), debug_urls_sizes.data(), static_cast(debug_urls.size())); - - TFE_OpSetAttrString(op.get(), "device_name", (void*) device_name.c_str(), device_name.size()); - TFE_OpSetAttrString(op.get(), "tensor_name", (void*) tensor_name.c_str(), tensor_name.size()); - TFE_OpSetAttrFloat(op.get(), "lower_bound", lower_bound); - TFE_OpSetAttrFloat(op.get(), "upper_bound", upper_bound); - TFE_OpSetAttrBool(op.get(), "mute_if_healthy", (unsigned char)mute_if_healthy); - TFE_OpSetAttrBool(op.get(), "gated_grpc", (unsigned char)gated_grpc); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor debug_numeric_summary_v2(const tensor& input, datatype output_dtype=static_cast(1), int64_t tensor_debug_mode=-1, int64_t tensor_id=-1) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DebugNumericSummaryV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "output_dtype", output_dtype); - TFE_OpSetAttrInt(op.get(), "tensor_debug_mode", tensor_debug_mode); - TFE_OpSetAttrInt(op.get(), "tensor_id", tensor_id); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor decode_and_crop_jpeg(const tensor& contents, const tensor& crop_window, int64_t channels=0, int64_t ratio=1, bool fancy_upscaling=true, bool try_recover_truncated=false, float acceptable_fraction=1.0000e+00, const std::string& dct_method="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DecodeAndCropJpeg", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), contents.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), crop_window.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "channels", channels); - TFE_OpSetAttrInt(op.get(), "ratio", ratio); - TFE_OpSetAttrBool(op.get(), "fancy_upscaling", (unsigned char)fancy_upscaling); - TFE_OpSetAttrBool(op.get(), "try_recover_truncated", (unsigned char)try_recover_truncated); - TFE_OpSetAttrFloat(op.get(), "acceptable_fraction", acceptable_fraction); - TFE_OpSetAttrString(op.get(), "dct_method", (void*) dct_method.c_str(), dct_method.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor decode_base64(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DecodeBase64", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor decode_bmp(const tensor& contents, int64_t channels=0) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DecodeBmp", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), contents.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "channels", channels); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor decode_c_s_v(const tensor& records, const std::vector&record_defaults, const std::vector& OUT_TYPE, const std::vector& select_cols, const std::string& field_delim=",", bool use_quote_delim=true, const std::string& na_value="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DecodeCSV", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), records.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - std::vector record_defaults_handles; record_defaults_handles.reserve(record_defaults.size()); - std::transform(record_defaults.begin(), record_defaults.end(), std::back_inserter(record_defaults_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), record_defaults_handles.data(), static_cast(record_defaults.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "OUT_TYPE", reinterpret_cast(OUT_TYPE.data()), static_cast(OUT_TYPE.size())); - TFE_OpSetAttrIntList(op.get(), "select_cols", select_cols.data(), static_cast(select_cols.size())); - TFE_OpSetAttrString(op.get(), "field_delim", (void*) field_delim.c_str(), field_delim.size()); - TFE_OpSetAttrBool(op.get(), "use_quote_delim", (unsigned char)use_quote_delim); - TFE_OpSetAttrString(op.get(), "na_value", (void*) na_value.c_str(), na_value.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor decode_compressed(const tensor& bytes, const std::string& compression_type="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DecodeCompressed", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), bytes.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "compression_type", (void*) compression_type.c_str(), compression_type.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor decode_gif(const tensor& contents) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DecodeGif", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), contents.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor decode_image(const tensor& contents, int64_t channels=0, datatype dtype=static_cast(4), bool expand_animations=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DecodeImage", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), contents.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "channels", channels); - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrBool(op.get(), "expand_animations", (unsigned char)expand_animations); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor decode_j_s_o_n_example(const tensor& json_examples) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DecodeJSONExample", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), json_examples.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor decode_jpeg(const tensor& contents, int64_t channels=0, int64_t ratio=1, bool fancy_upscaling=true, bool try_recover_truncated=false, float acceptable_fraction=1.0000e+00, const std::string& dct_method="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DecodeJpeg", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), contents.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "channels", channels); - TFE_OpSetAttrInt(op.get(), "ratio", ratio); - TFE_OpSetAttrBool(op.get(), "fancy_upscaling", (unsigned char)fancy_upscaling); - TFE_OpSetAttrBool(op.get(), "try_recover_truncated", (unsigned char)try_recover_truncated); - TFE_OpSetAttrFloat(op.get(), "acceptable_fraction", acceptable_fraction); - TFE_OpSetAttrString(op.get(), "dct_method", (void*) dct_method.c_str(), dct_method.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor decode_padded_raw(const tensor& input_bytes, const tensor& fixed_length, datatype out_type, bool little_endian=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DecodePaddedRaw", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_bytes.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), fixed_length.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "out_type", out_type); - TFE_OpSetAttrBool(op.get(), "little_endian", (unsigned char)little_endian); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor decode_png(const tensor& contents, int64_t channels=0, datatype dtype=static_cast(4)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DecodePng", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), contents.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "channels", channels); - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor decode_raw(const tensor& bytes, datatype out_type, bool little_endian=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DecodeRaw", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), bytes.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "out_type", out_type); - TFE_OpSetAttrBool(op.get(), "little_endian", (unsigned char)little_endian); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor deep_copy(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DeepCopy", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor dense_bincount(const tensor& input, const tensor& size, const tensor& weights, datatype Tidx, bool binary_output=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DenseBincount", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), weights.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - TFE_OpSetAttrBool(op.get(), "binary_output", (unsigned char)binary_output); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor dense_to_c_s_r_sparse_matrix(const tensor& dense_input, const tensor& indices) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DenseToCSRSparseMatrix", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), dense_input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor dense_to_sparse_batch_dataset(const tensor& input_dataset, const tensor& batch_size, const tensor& row_shape, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DenseToSparseBatchDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), batch_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), row_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor depth_to_space(const tensor& input, int64_t block_size, const std::string& data_format="NHWC") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DepthToSpace", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "block_size", block_size); - TFE_OpSetAttrString(op.get(), "data_format", (void*) data_format.c_str(), data_format.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor depthwise_conv2d_native(const tensor& input, const tensor& filter, const std::vector& strides, const std::string& padding, const std::vector& explicit_paddings, const std::vector& dilations, const std::string& data_format="NHWC") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DepthwiseConv2dNative", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), filter.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrIntList(op.get(), "explicit_paddings", explicit_paddings.data(), static_cast(explicit_paddings.size())); - TFE_OpSetAttrIntList(op.get(), "dilations", dilations.data(), static_cast(dilations.size())); - TFE_OpSetAttrString(op.get(), "data_format", (void*) data_format.c_str(), data_format.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor depthwise_conv2d_native_backprop_filter(const tensor& input, const tensor& filter_sizes, const tensor& out_backprop, const std::vector& strides, const std::string& padding, const std::vector& explicit_paddings, const std::vector& dilations, const std::string& data_format="NHWC") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DepthwiseConv2dNativeBackpropFilter", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), filter_sizes.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), out_backprop.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrIntList(op.get(), "explicit_paddings", explicit_paddings.data(), static_cast(explicit_paddings.size())); - TFE_OpSetAttrIntList(op.get(), "dilations", dilations.data(), static_cast(dilations.size())); - TFE_OpSetAttrString(op.get(), "data_format", (void*) data_format.c_str(), data_format.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor depthwise_conv2d_native_backprop_input(const tensor& input_sizes, const tensor& filter, const tensor& out_backprop, const std::vector& strides, const std::string& padding, const std::vector& explicit_paddings, const std::vector& dilations, const std::string& data_format="NHWC") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DepthwiseConv2dNativeBackpropInput", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_sizes.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), filter.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), out_backprop.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrIntList(op.get(), "explicit_paddings", explicit_paddings.data(), static_cast(explicit_paddings.size())); - TFE_OpSetAttrIntList(op.get(), "dilations", dilations.data(), static_cast(dilations.size())); - TFE_OpSetAttrString(op.get(), "data_format", (void*) data_format.c_str(), data_format.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor dequantize(const tensor& input, const tensor& min_range, const tensor& max_range, const std::string& mode="MIN_COMBINED", bool narrow_range=false, int64_t axis=-1, datatype dtype=static_cast(1)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Dequantize", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), min_range.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), max_range.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "mode", (void*) mode.c_str(), mode.size()); - TFE_OpSetAttrBool(op.get(), "narrow_range", (unsigned char)narrow_range); - TFE_OpSetAttrInt(op.get(), "axis", axis); - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor destroy_temporary_variable(const tensor& ref, const std::string& var_name) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DestroyTemporaryVariable", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), ref.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "var_name", (void*) var_name.c_str(), var_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor device_index(const std::vector< std::string>& device_names) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DeviceIndex", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - - std::vector device_names_sizes; device_names_sizes.reserve(device_names.size()); - std::transform(device_names.begin(), device_names.end(), std::back_inserter(device_names_sizes), [](const auto& s) { return s.size();}); - TFE_OpSetAttrStringList(op.get(), "device_names", reinterpret_cast(device_names.data()), device_names_sizes.data(), static_cast(device_names.size())); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor diag(const tensor& diagonal) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Diag", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), diagonal.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor diag_part(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DiagPart", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor digamma(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Digamma", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor dilation2_d(const tensor& input, const tensor& filter, const std::vector& strides, const std::vector& rates, const std::string& padding) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Dilation2D", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), filter.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrIntList(op.get(), "rates", rates.data(), static_cast(rates.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor dilation2_d_backprop_filter(const tensor& input, const tensor& filter, const tensor& out_backprop, const std::vector& strides, const std::vector& rates, const std::string& padding) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Dilation2DBackpropFilter", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), filter.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), out_backprop.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrIntList(op.get(), "rates", rates.data(), static_cast(rates.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor dilation2_d_backprop_input(const tensor& input, const tensor& filter, const tensor& out_backprop, const std::vector& strides, const std::vector& rates, const std::string& padding) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Dilation2DBackpropInput", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), filter.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), out_backprop.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrIntList(op.get(), "rates", rates.data(), static_cast(rates.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor directed_interleave_dataset(const tensor& selector_input_dataset, const std::vector&data_input_datasets, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DirectedInterleaveDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), selector_input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - std::vector data_input_datasets_handles; data_input_datasets_handles.reserve(data_input_datasets.size()); - std::transform(data_input_datasets.begin(), data_input_datasets.end(), std::back_inserter(data_input_datasets_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), data_input_datasets_handles.data(), static_cast(data_input_datasets.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrInt(op.get(), "N", data_input_datasets.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor div(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Div", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor div_no_nan(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DivNoNan", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor draw_bounding_boxes(const tensor& images, const tensor& boxes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DrawBoundingBoxes", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), images.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), boxes.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor draw_bounding_boxes_v2(const tensor& images, const tensor& boxes, const tensor& colors) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DrawBoundingBoxesV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), images.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), boxes.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), colors.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor dummy_iteration_counter() { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DummyIterationCounter", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor dummy_memory_cache() { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DummyMemoryCache", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor dummy_seed_generator() { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DummySeedGenerator", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor dynamic_partition(const tensor& data, const tensor& partitions, int64_t num_partitions) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DynamicPartition", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), data.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), partitions.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "num_partitions", num_partitions); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor dynamic_stitch(const std::vector&indices, const std::vector&data) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "DynamicStitch", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector indices_handles; indices_handles.reserve(indices.size()); - std::transform(indices.begin(), indices.end(), std::back_inserter(indices_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), indices_handles.data(), static_cast(indices.size()), context::get_status()); - status_check(context::get_status()); - - - std::vector data_handles; data_handles.reserve(data.size()); - std::transform(data.begin(), data.end(), std::back_inserter(data_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), data_handles.data(), static_cast(data.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "N", indices.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor eager_py_func(const std::vector&input, const std::string& token, const std::vector& Tin, const std::vector& Tout, bool is_async=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "EagerPyFunc", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector input_handles; input_handles.reserve(input.size()); - std::transform(input.begin(), input.end(), std::back_inserter(input_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), input_handles.data(), static_cast(input.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "token", (void*) token.c_str(), token.size()); - TFE_OpSetAttrTypeList(op.get(), "Tin", reinterpret_cast(Tin.data()), static_cast(Tin.size())); - TFE_OpSetAttrTypeList(op.get(), "Tout", reinterpret_cast(Tout.data()), static_cast(Tout.size())); - TFE_OpSetAttrBool(op.get(), "is_async", (unsigned char)is_async); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor edit_distance(const tensor& hypothesis_indices, const tensor& hypothesis_values, const tensor& hypothesis_shape, const tensor& truth_indices, const tensor& truth_values, const tensor& truth_shape, bool normalize=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "EditDistance", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), hypothesis_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), hypothesis_values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), hypothesis_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), truth_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), truth_values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), truth_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "normalize", (unsigned char)normalize); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor einsum(const std::vector&inputs, const std::string& equation) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Einsum", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector inputs_handles; inputs_handles.reserve(inputs.size()); - std::transform(inputs.begin(), inputs.end(), std::back_inserter(inputs_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), inputs_handles.data(), static_cast(inputs.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "equation", (void*) equation.c_str(), equation.size()); - TFE_OpSetAttrInt(op.get(), "N", inputs.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor elu(const tensor& features) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Elu", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), features.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor elu_grad(const tensor& gradients, const tensor& outputs) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "EluGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), gradients.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), outputs.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor empty(const tensor& shape, datatype dtype, bool init=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Empty", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrBool(op.get(), "init", (unsigned char)init); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor empty_tensor_list(const tensor& element_shape, const tensor& max_num_elements, datatype element_dtype, datatype shape_type) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "EmptyTensorList", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), element_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), max_num_elements.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "element_dtype", element_dtype); - TFE_OpSetAttrType(op.get(), "shape_type", shape_type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor encode_base64(const tensor& input, bool pad=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "EncodeBase64", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "pad", (unsigned char)pad); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor encode_jpeg(const tensor& image, const std::string& format="", int64_t quality=95, bool progressive=false, bool optimize_size=false, bool chroma_downsampling=true, const std::string& density_unit="in", int64_t x_density=300, int64_t y_density=300, const std::string& xmp_metadata="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "EncodeJpeg", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), image.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "format", (void*) format.c_str(), format.size()); - TFE_OpSetAttrInt(op.get(), "quality", quality); - TFE_OpSetAttrBool(op.get(), "progressive", (unsigned char)progressive); - TFE_OpSetAttrBool(op.get(), "optimize_size", (unsigned char)optimize_size); - TFE_OpSetAttrBool(op.get(), "chroma_downsampling", (unsigned char)chroma_downsampling); - TFE_OpSetAttrString(op.get(), "density_unit", (void*) density_unit.c_str(), density_unit.size()); - TFE_OpSetAttrInt(op.get(), "x_density", x_density); - TFE_OpSetAttrInt(op.get(), "y_density", y_density); - TFE_OpSetAttrString(op.get(), "xmp_metadata", (void*) xmp_metadata.c_str(), xmp_metadata.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor encode_jpeg_variable_quality(const tensor& images, const tensor& quality) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "EncodeJpegVariableQuality", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), images.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), quality.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor encode_png(const tensor& image, int64_t compression=-1) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "EncodePng", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), image.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "compression", compression); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor encode_proto(const tensor& sizes, const std::vector&values, const std::vector< std::string>& field_names, const std::string& message_type, const std::vector& Tinput_types, const std::string& descriptor_source="local://") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "EncodeProto", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), sizes.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - std::vector values_handles; values_handles.reserve(values.size()); - std::transform(values.begin(), values.end(), std::back_inserter(values_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), values_handles.data(), static_cast(values.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - std::vector field_names_sizes; field_names_sizes.reserve(field_names.size()); - std::transform(field_names.begin(), field_names.end(), std::back_inserter(field_names_sizes), [](const auto& s) { return s.size();}); - TFE_OpSetAttrStringList(op.get(), "field_names", reinterpret_cast(field_names.data()), field_names_sizes.data(), static_cast(field_names.size())); - - TFE_OpSetAttrString(op.get(), "message_type", (void*) message_type.c_str(), message_type.size()); - TFE_OpSetAttrTypeList(op.get(), "Tinput_types", reinterpret_cast(Tinput_types.data()), static_cast(Tinput_types.size())); - TFE_OpSetAttrString(op.get(), "descriptor_source", (void*) descriptor_source.c_str(), descriptor_source.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor encode_wav(const tensor& audio, const tensor& sample_rate) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "EncodeWav", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), audio.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), sample_rate.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor ensure_shape(const tensor& input, const std::vector& shape) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "EnsureShape", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - TFE_OpSetAttrShape(op.get(), "shape", shape.data(), static_cast(shape.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor enter(const tensor& data, const std::string& frame_name, bool is_constant=false, int64_t parallel_iterations=10) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Enter", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), data.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "frame_name", (void*) frame_name.c_str(), frame_name.size()); - TFE_OpSetAttrBool(op.get(), "is_constant", (unsigned char)is_constant); - TFE_OpSetAttrInt(op.get(), "parallel_iterations", parallel_iterations); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor equal(const tensor& x, const tensor& y, bool incompatible_shape_error=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Equal", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "incompatible_shape_error", (unsigned char)incompatible_shape_error); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor erf(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Erf", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor erfc(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Erfc", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor erfinv(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Erfinv", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor euclidean_norm(const tensor& input, const tensor& reduction_indices, bool keep_dims=false, datatype Tidx=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "EuclideanNorm", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), reduction_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "keep_dims", (unsigned char)keep_dims); - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor exit(const tensor& data) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Exit", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), data.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor exp(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Exp", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor expand_dims(const tensor& input, const tensor& dim, datatype Tdim=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExpandDims", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), dim.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tdim", Tdim); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_assert_next_dataset(const tensor& input_dataset, const tensor& transformations, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalAssertNextDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), transformations.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_auto_shard_dataset(const tensor& input_dataset, const tensor& num_workers, const tensor& index, const std::vector& output_types, const std::vector< std::vector>& output_shapes, int64_t auto_shard_policy=0) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalAutoShardDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_workers.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), index.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrInt(op.get(), "auto_shard_policy", auto_shard_policy); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_bytes_produced_stats_dataset(const tensor& input_dataset, const tensor& tag, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalBytesProducedStatsDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), tag.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_c_s_v_dataset(const tensor& filenames, const tensor& compression_type, const tensor& buffer_size, const tensor& header, const tensor& field_delim, const tensor& use_quote_delim, const tensor& na_value, const tensor& select_cols, const std::vector&record_defaults, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalCSVDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), filenames.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), compression_type.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), buffer_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), header.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), field_delim.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), use_quote_delim.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), na_value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), select_cols.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - std::vector record_defaults_handles; record_defaults_handles.reserve(record_defaults.size()); - std::transform(record_defaults.begin(), record_defaults.end(), std::back_inserter(record_defaults_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), record_defaults_handles.data(), static_cast(record_defaults.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_choose_fastest_dataset(const std::vector&input_datasets, int64_t num_experiments, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalChooseFastestDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector input_datasets_handles; input_datasets_handles.reserve(input_datasets.size()); - std::transform(input_datasets.begin(), input_datasets.end(), std::back_inserter(input_datasets_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), input_datasets_handles.data(), static_cast(input_datasets.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "N", input_datasets.size()); - TFE_OpSetAttrInt(op.get(), "num_experiments", num_experiments); - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_dataset_cardinality(const tensor& input_dataset) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalDatasetCardinality", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_dense_to_sparse_batch_dataset(const tensor& input_dataset, const tensor& batch_size, const tensor& row_shape, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalDenseToSparseBatchDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), batch_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), row_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_directed_interleave_dataset(const tensor& selector_input_dataset, const std::vector&data_input_datasets, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalDirectedInterleaveDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), selector_input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - std::vector data_input_datasets_handles; data_input_datasets_handles.reserve(data_input_datasets.size()); - std::transform(data_input_datasets.begin(), data_input_datasets.end(), std::back_inserter(data_input_datasets_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), data_input_datasets_handles.data(), static_cast(data_input_datasets.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrInt(op.get(), "N", data_input_datasets.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_ignore_errors_dataset(const tensor& input_dataset, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalIgnoreErrorsDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_iterator_get_device(const tensor& resource) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalIteratorGetDevice", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), resource.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_l_m_d_b_dataset(const tensor& filenames, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalLMDBDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), filenames.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_latency_stats_dataset(const tensor& input_dataset, const tensor& tag, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalLatencyStatsDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), tag.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_matching_files_dataset(const tensor& patterns) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalMatchingFilesDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), patterns.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_max_intra_op_parallelism_dataset(const tensor& input_dataset, const tensor& max_intra_op_parallelism, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalMaxIntraOpParallelismDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), max_intra_op_parallelism.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_non_serializable_dataset(const tensor& input_dataset, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalNonSerializableDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_parse_example_dataset(const tensor& input_dataset, const tensor& num_parallel_calls, const std::vector&dense_defaults, const std::vector< std::string>& sparse_keys, const std::vector< std::string>& dense_keys, const std::vector& sparse_types, const std::vector& Tdense, const std::vector< std::vector>& dense_shapes, const std::vector& output_types, const std::vector< std::vector>& output_shapes, bool sloppy=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalParseExampleDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_parallel_calls.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - std::vector dense_defaults_handles; dense_defaults_handles.reserve(dense_defaults.size()); - std::transform(dense_defaults.begin(), dense_defaults.end(), std::back_inserter(dense_defaults_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), dense_defaults_handles.data(), static_cast(dense_defaults.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - std::vector sparse_keys_sizes; sparse_keys_sizes.reserve(sparse_keys.size()); - std::transform(sparse_keys.begin(), sparse_keys.end(), std::back_inserter(sparse_keys_sizes), [](const auto& s) { return s.size();}); - TFE_OpSetAttrStringList(op.get(), "sparse_keys", reinterpret_cast(sparse_keys.data()), sparse_keys_sizes.data(), static_cast(sparse_keys.size())); - - - std::vector dense_keys_sizes; dense_keys_sizes.reserve(dense_keys.size()); - std::transform(dense_keys.begin(), dense_keys.end(), std::back_inserter(dense_keys_sizes), [](const auto& s) { return s.size();}); - TFE_OpSetAttrStringList(op.get(), "dense_keys", reinterpret_cast(dense_keys.data()), dense_keys_sizes.data(), static_cast(dense_keys.size())); - - TFE_OpSetAttrTypeList(op.get(), "sparse_types", reinterpret_cast(sparse_types.data()), static_cast(sparse_types.size())); - TFE_OpSetAttrTypeList(op.get(), "Tdense", reinterpret_cast(Tdense.data()), static_cast(Tdense.size())); - - std::vector dense_shapes_values; dense_shapes_values.reserve(dense_shapes.size()); - std::vector dense_shapes_ndims; dense_shapes_ndims.reserve(dense_shapes.size()); - std::transform(dense_shapes.begin(), dense_shapes.end(), std::back_inserter(dense_shapes_values), [](const auto& v) { return v.data();}); - std::transform(dense_shapes.begin(), dense_shapes.end(), std::back_inserter(dense_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "dense_shapes", dense_shapes_values.data(), dense_shapes_ndims.data(), static_cast(dense_shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrBool(op.get(), "sloppy", (unsigned char)sloppy); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_private_thread_pool_dataset(const tensor& input_dataset, const tensor& num_threads, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalPrivateThreadPoolDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_threads.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_random_dataset(const tensor& seed, const tensor& seed2, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalRandomDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), seed.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), seed2.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_rebatch_dataset(const tensor& input_dataset, const tensor& num_replicas, const std::vector& output_types, const std::vector< std::vector>& output_shapes, bool use_fallback=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalRebatchDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_replicas.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrBool(op.get(), "use_fallback", (unsigned char)use_fallback); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_set_stats_aggregator_dataset(const tensor& input_dataset, const tensor& stats_aggregator, const tensor& tag, const tensor& counter_prefix, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalSetStatsAggregatorDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), stats_aggregator.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), tag.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), counter_prefix.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_sleep_dataset(const tensor& input_dataset, const tensor& sleep_microseconds, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalSleepDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), sleep_microseconds.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_sliding_window_dataset(const tensor& input_dataset, const tensor& window_size, const tensor& window_shift, const tensor& window_stride, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalSlidingWindowDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), window_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), window_shift.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), window_stride.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_sql_dataset(const tensor& driver_name, const tensor& data_source_name, const tensor& query, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalSqlDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), driver_name.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), data_source_name.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), query.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_stats_aggregator_handle(const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalStatsAggregatorHandle", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_stats_aggregator_summary(const tensor& iterator) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalStatsAggregatorSummary", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), iterator.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_thread_pool_dataset(const tensor& input_dataset, const tensor& thread_pool, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalThreadPoolDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), thread_pool.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_thread_pool_handle(int64_t num_threads, const std::string& display_name, int64_t max_intra_op_parallelism=1, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalThreadPoolHandle", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrInt(op.get(), "num_threads", num_threads); - TFE_OpSetAttrString(op.get(), "display_name", (void*) display_name.c_str(), display_name.size()); - TFE_OpSetAttrInt(op.get(), "max_intra_op_parallelism", max_intra_op_parallelism); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_unbatch_dataset(const tensor& input_dataset, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalUnbatchDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor experimental_unique_dataset(const tensor& input_dataset, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExperimentalUniqueDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor expint(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Expint", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor expm1(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Expm1", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor extract_glimpse(const tensor& input, const tensor& size, const tensor& offsets, bool centered=true, bool normalized=true, bool uniform_noise=true, const std::string& noise="uniform") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExtractGlimpse", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), offsets.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "centered", (unsigned char)centered); - TFE_OpSetAttrBool(op.get(), "normalized", (unsigned char)normalized); - TFE_OpSetAttrBool(op.get(), "uniform_noise", (unsigned char)uniform_noise); - TFE_OpSetAttrString(op.get(), "noise", (void*) noise.c_str(), noise.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor extract_glimpse_v2(const tensor& input, const tensor& size, const tensor& offsets, bool centered=true, bool normalized=true, bool uniform_noise=true, const std::string& noise="uniform") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExtractGlimpseV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), offsets.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "centered", (unsigned char)centered); - TFE_OpSetAttrBool(op.get(), "normalized", (unsigned char)normalized); - TFE_OpSetAttrBool(op.get(), "uniform_noise", (unsigned char)uniform_noise); - TFE_OpSetAttrString(op.get(), "noise", (void*) noise.c_str(), noise.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor extract_image_patches(const tensor& images, const std::vector& ksizes, const std::vector& strides, const std::vector& rates, const std::string& padding) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExtractImagePatches", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), images.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "ksizes", ksizes.data(), static_cast(ksizes.size())); - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrIntList(op.get(), "rates", rates.data(), static_cast(rates.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor extract_jpeg_shape(const tensor& contents, datatype output_type=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExtractJpegShape", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), contents.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "output_type", output_type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor extract_volume_patches(const tensor& input, const std::vector& ksizes, const std::vector& strides, const std::string& padding) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ExtractVolumePatches", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "ksizes", ksizes.data(), static_cast(ksizes.size())); - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor f_f_t(const tensor& input, datatype Tcomplex=static_cast(8)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "FFT", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tcomplex", Tcomplex); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor f_f_t2_d(const tensor& input, datatype Tcomplex=static_cast(8)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "FFT2D", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tcomplex", Tcomplex); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor f_f_t3_d(const tensor& input, datatype Tcomplex=static_cast(8)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "FFT3D", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tcomplex", Tcomplex); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor f_i_f_o_queue(const std::vector& component_types, const std::vector< std::vector>& shapes, int64_t capacity=-1, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "FIFOQueue", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "component_types", reinterpret_cast(component_types.data()), static_cast(component_types.size())); - - std::vector shapes_values; shapes_values.reserve(shapes.size()); - std::vector shapes_ndims; shapes_ndims.reserve(shapes.size()); - std::transform(shapes.begin(), shapes.end(), std::back_inserter(shapes_values), [](const auto& v) { return v.data();}); - std::transform(shapes.begin(), shapes.end(), std::back_inserter(shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "shapes", shapes_values.data(), shapes_ndims.data(), static_cast(shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrInt(op.get(), "capacity", capacity); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor f_i_f_o_queue_v2(const std::vector& component_types, const std::vector< std::vector>& shapes, int64_t capacity=-1, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "FIFOQueueV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "component_types", reinterpret_cast(component_types.data()), static_cast(component_types.size())); - - std::vector shapes_values; shapes_values.reserve(shapes.size()); - std::vector shapes_ndims; shapes_ndims.reserve(shapes.size()); - std::transform(shapes.begin(), shapes.end(), std::back_inserter(shapes_values), [](const auto& v) { return v.data();}); - std::transform(shapes.begin(), shapes.end(), std::back_inserter(shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "shapes", shapes_values.data(), shapes_ndims.data(), static_cast(shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrInt(op.get(), "capacity", capacity); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor fact() { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Fact", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor fake_param(datatype dtype, const std::vector& shape) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "FakeParam", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - TFE_OpSetAttrShape(op.get(), "shape", shape.data(), static_cast(shape.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor fake_quant_with_min_max_args(const tensor& inputs, float min=-6.0000e+00, float max=6.0000e+00, int64_t num_bits=8, bool narrow_range=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "FakeQuantWithMinMaxArgs", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), inputs.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrFloat(op.get(), "min", min); - TFE_OpSetAttrFloat(op.get(), "max", max); - TFE_OpSetAttrInt(op.get(), "num_bits", num_bits); - TFE_OpSetAttrBool(op.get(), "narrow_range", (unsigned char)narrow_range); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor fake_quant_with_min_max_args_gradient(const tensor& gradients, const tensor& inputs, float min=-6.0000e+00, float max=6.0000e+00, int64_t num_bits=8, bool narrow_range=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "FakeQuantWithMinMaxArgsGradient", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), gradients.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), inputs.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrFloat(op.get(), "min", min); - TFE_OpSetAttrFloat(op.get(), "max", max); - TFE_OpSetAttrInt(op.get(), "num_bits", num_bits); - TFE_OpSetAttrBool(op.get(), "narrow_range", (unsigned char)narrow_range); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor fake_quant_with_min_max_vars(const tensor& inputs, const tensor& min, const tensor& max, int64_t num_bits=8, bool narrow_range=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "FakeQuantWithMinMaxVars", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), inputs.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), min.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), max.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "num_bits", num_bits); - TFE_OpSetAttrBool(op.get(), "narrow_range", (unsigned char)narrow_range); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor fake_quant_with_min_max_vars_per_channel(const tensor& inputs, const tensor& min, const tensor& max, int64_t num_bits=8, bool narrow_range=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "FakeQuantWithMinMaxVarsPerChannel", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), inputs.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), min.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), max.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "num_bits", num_bits); - TFE_OpSetAttrBool(op.get(), "narrow_range", (unsigned char)narrow_range); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor fake_queue(const tensor& resource) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "FakeQueue", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), resource.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor fill(const tensor& dims, const tensor& value, datatype index_type=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Fill", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), dims.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "index_type", index_type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor filter_by_last_component_dataset(const tensor& input_dataset, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "FilterByLastComponentDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor fingerprint(const tensor& data, const tensor& method) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Fingerprint", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), data.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), method.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor fixed_length_record_dataset(const tensor& filenames, const tensor& header_bytes, const tensor& record_bytes, const tensor& footer_bytes, const tensor& buffer_size) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "FixedLengthRecordDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), filenames.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), header_bytes.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), record_bytes.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), footer_bytes.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), buffer_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor fixed_length_record_dataset_v2(const tensor& filenames, const tensor& header_bytes, const tensor& record_bytes, const tensor& footer_bytes, const tensor& buffer_size, const tensor& compression_type) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "FixedLengthRecordDatasetV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), filenames.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), header_bytes.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), record_bytes.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), footer_bytes.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), buffer_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), compression_type.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor fixed_length_record_reader(int64_t record_bytes, int64_t header_bytes=0, int64_t footer_bytes=0, int64_t hop_bytes=0, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "FixedLengthRecordReader", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrInt(op.get(), "record_bytes", record_bytes); - TFE_OpSetAttrInt(op.get(), "header_bytes", header_bytes); - TFE_OpSetAttrInt(op.get(), "footer_bytes", footer_bytes); - TFE_OpSetAttrInt(op.get(), "hop_bytes", hop_bytes); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor fixed_length_record_reader_v2(int64_t record_bytes, int64_t header_bytes=0, int64_t footer_bytes=0, int64_t hop_bytes=0, const std::string& container="", const std::string& shared_name="", const std::string& encoding="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "FixedLengthRecordReaderV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrInt(op.get(), "record_bytes", record_bytes); - TFE_OpSetAttrInt(op.get(), "header_bytes", header_bytes); - TFE_OpSetAttrInt(op.get(), "footer_bytes", footer_bytes); - TFE_OpSetAttrInt(op.get(), "hop_bytes", hop_bytes); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - TFE_OpSetAttrString(op.get(), "encoding", (void*) encoding.c_str(), encoding.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor floor(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Floor", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor floor_div(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "FloorDiv", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor floor_mod(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "FloorMod", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor fractional_avg_pool_grad(const tensor& orig_input_input_tensor_shape, const tensor& out_backprop, const tensor& row_pooling_sequence, const tensor& col_pooling_sequence, bool overlapping=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "FractionalAvgPoolGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), orig_input_input_tensor_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), out_backprop.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), row_pooling_sequence.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), col_pooling_sequence.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "overlapping", (unsigned char)overlapping); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor fractional_max_pool_grad(const tensor& orig_input, const tensor& orig_output, const tensor& out_backprop, const tensor& row_pooling_sequence, const tensor& col_pooling_sequence, bool overlapping=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "FractionalMaxPoolGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), orig_input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), orig_output.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), out_backprop.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), row_pooling_sequence.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), col_pooling_sequence.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "overlapping", (unsigned char)overlapping); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor fresnel_cos(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "FresnelCos", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor fresnel_sin(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "FresnelSin", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor fused_pad_conv2_d(const tensor& input, const tensor& paddings, const tensor& filter, const std::string& mode, const std::vector& strides, const std::string& padding) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "FusedPadConv2D", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), paddings.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), filter.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "mode", (void*) mode.c_str(), mode.size()); - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor fused_resize_and_pad_conv2_d(const tensor& input, const tensor& size, const tensor& paddings, const tensor& filter, const std::string& mode, const std::vector& strides, const std::string& padding, bool resize_align_corners=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "FusedResizeAndPadConv2D", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), paddings.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), filter.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "mode", (void*) mode.c_str(), mode.size()); - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrBool(op.get(), "resize_align_corners", (unsigned char)resize_align_corners); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor gather(const tensor& params, const tensor& indices, datatype Tparams, datatype Tindices, bool validate_indices=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Gather", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), params.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tparams", Tparams); - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrBool(op.get(), "validate_indices", (unsigned char)validate_indices); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor gather_nd(const tensor& params, const tensor& indices, datatype Tparams, datatype Tindices) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "GatherNd", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), params.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tparams", Tparams); - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor gather_v2(const tensor& params, const tensor& indices, const tensor& axis, datatype Tparams, datatype Tindices, datatype Taxis, int64_t batch_dims=0) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "GatherV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), params.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), axis.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tparams", Tparams); - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrType(op.get(), "Taxis", Taxis); - TFE_OpSetAttrInt(op.get(), "batch_dims", batch_dims); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor get_session_handle(const tensor& value) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "GetSessionHandle", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor get_session_handle_v2(const tensor& value) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "GetSessionHandleV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor get_session_tensor(const tensor& handle, datatype dtype) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "GetSessionTensor", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor greater(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Greater", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor greater_equal(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "GreaterEqual", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor guarantee_const_tensor(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "GuaranteeConst", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor h_s_v_to_r_g_b(const tensor& images) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "HSVToRGB", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), images.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor hash_table(datatype key_dtype, datatype value_dtype, const std::string& container="", const std::string& shared_name="", bool use_node_name_sharing=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "HashTable", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrType(op.get(), "key_dtype", key_dtype); - TFE_OpSetAttrType(op.get(), "value_dtype", value_dtype); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - TFE_OpSetAttrBool(op.get(), "use_node_name_sharing", (unsigned char)use_node_name_sharing); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor hash_table_v2(datatype key_dtype, datatype value_dtype, const std::string& container="", const std::string& shared_name="", bool use_node_name_sharing=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "HashTableV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrType(op.get(), "key_dtype", key_dtype); - TFE_OpSetAttrType(op.get(), "value_dtype", value_dtype); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - TFE_OpSetAttrBool(op.get(), "use_node_name_sharing", (unsigned char)use_node_name_sharing); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor histogram_fixed_width(const tensor& values, const tensor& value_range, const tensor& nbins, datatype dtype=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "HistogramFixedWidth", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), value_range.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), nbins.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor histogram_summary(const tensor& tag, const tensor& values) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "HistogramSummary", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), tag.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor i_f_f_t(const tensor& input, datatype Tcomplex=static_cast(8)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "IFFT", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tcomplex", Tcomplex); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor i_f_f_t2_d(const tensor& input, datatype Tcomplex=static_cast(8)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "IFFT2D", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tcomplex", Tcomplex); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor i_f_f_t3_d(const tensor& input, datatype Tcomplex=static_cast(8)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "IFFT3D", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tcomplex", Tcomplex); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor i_r_f_f_t(const tensor& input, const tensor& fft_length, datatype Treal=static_cast(1), datatype Tcomplex=static_cast(8)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "IRFFT", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), fft_length.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Treal", Treal); - TFE_OpSetAttrType(op.get(), "Tcomplex", Tcomplex); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor i_r_f_f_t2_d(const tensor& input, const tensor& fft_length, datatype Treal=static_cast(1), datatype Tcomplex=static_cast(8)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "IRFFT2D", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), fft_length.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Treal", Treal); - TFE_OpSetAttrType(op.get(), "Tcomplex", Tcomplex); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor i_r_f_f_t3_d(const tensor& input, const tensor& fft_length, datatype Treal=static_cast(1), datatype Tcomplex=static_cast(8)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "IRFFT3D", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), fft_length.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Treal", Treal); - TFE_OpSetAttrType(op.get(), "Tcomplex", Tcomplex); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor identity(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Identity", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor identity_n(const std::vector&input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "IdentityN", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector input_handles; input_handles.reserve(input.size()); - std::transform(input.begin(), input.end(), std::back_inserter(input_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), input_handles.data(), static_cast(input.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor identity_reader(const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "IdentityReader", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor identity_reader_v2(const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "IdentityReaderV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor igamma(const tensor& a, const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Igamma", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), a.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor igamma_grad_a(const tensor& a, const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "IgammaGradA", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), a.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor igammac(const tensor& a, const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Igammac", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), a.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor ignore_errors_dataset(const tensor& input_dataset, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "IgnoreErrorsDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor imag(const tensor& input, datatype Tout=static_cast(1)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Imag", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tout", Tout); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor image_projective_transform_v2(const tensor& images, const tensor& transforms, const tensor& output_shape, datatype dtype, const std::string& interpolation, const std::string& fill_mode="CONSTANT") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ImageProjectiveTransformV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), images.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), transforms.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), output_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrString(op.get(), "interpolation", (void*) interpolation.c_str(), interpolation.size()); - TFE_OpSetAttrString(op.get(), "fill_mode", (void*) fill_mode.c_str(), fill_mode.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor image_summary(const tensor& tag, const tensor& input_tensor, const tensor& bad_color, int64_t max_images=3) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ImageSummary", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), tag.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), input_tensor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - TFE_OpSetAttrTensor(op.get(), "bad_color", bad_color.get_tensor().get(), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrInt(op.get(), "max_images", max_images); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor immutable_const_tensor(datatype dtype, const std::vector& shape, const std::string& memory_region_name) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ImmutableConst", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - TFE_OpSetAttrShape(op.get(), "shape", shape.data(), static_cast(shape.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrString(op.get(), "memory_region_name", (void*) memory_region_name.c_str(), memory_region_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor in_top_k(const tensor& predictions, const tensor& targets, int64_t k) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "InTopK", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), predictions.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), targets.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "k", k); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor in_top_k_v2(const tensor& predictions, const tensor& targets, const tensor& k) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "InTopKV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), predictions.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), targets.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), k.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor infeed_dequeue(datatype dtype, const std::vector& shape) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "InfeedDequeue", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - TFE_OpSetAttrShape(op.get(), "shape", shape.data(), static_cast(shape.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor infeed_dequeue_tuple(const std::vector& dtypes, const std::vector< std::vector>& shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "InfeedDequeueTuple", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "dtypes", reinterpret_cast(dtypes.data()), static_cast(dtypes.size())); - - std::vector shapes_values; shapes_values.reserve(shapes.size()); - std::vector shapes_ndims; shapes_ndims.reserve(shapes.size()); - std::transform(shapes.begin(), shapes.end(), std::back_inserter(shapes_values), [](const auto& v) { return v.data();}); - std::transform(shapes.begin(), shapes.end(), std::back_inserter(shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "shapes", shapes_values.data(), shapes_ndims.data(), static_cast(shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor inplace_add(const tensor& x, const tensor& i, const tensor& v) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "InplaceAdd", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), i.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), v.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor inplace_sub(const tensor& x, const tensor& i, const tensor& v) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "InplaceSub", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), i.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), v.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor inplace_update(const tensor& x, const tensor& i, const tensor& v) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "InplaceUpdate", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), i.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), v.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor inv(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Inv", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor inv_grad(const tensor& y, const tensor& dy) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "InvGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), dy.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor invert(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Invert", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor invert_permutation(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "InvertPermutation", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor is_boosted_trees_ensemble_initialized(const tensor& tree_ensemble_handle) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "IsBoostedTreesEnsembleInitialized", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), tree_ensemble_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor is_boosted_trees_quantile_stream_resource_initialized(const tensor& quantile_stream_resource_handle) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "IsBoostedTreesQuantileStreamResourceInitialized", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), quantile_stream_resource_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor is_finite(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "IsFinite", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor is_inf(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "IsInf", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor is_nan(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "IsNan", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor is_variable_initialized(const tensor& ref, datatype dtype) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "IsVariableInitialized", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), ref.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor iterator(const std::string& shared_name, const std::string& container, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Iterator", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor iterator_from_string_handle(const tensor& string_handle, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "IteratorFromStringHandle", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), string_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor iterator_from_string_handle_v2(const tensor& string_handle, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "IteratorFromStringHandleV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), string_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor iterator_get_device(const tensor& resource) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "IteratorGetDevice", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), resource.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor iterator_get_next(const tensor& iterator, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "IteratorGetNext", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), iterator.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor iterator_get_next_as_optional(const tensor& iterator, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "IteratorGetNextAsOptional", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), iterator.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor iterator_get_next_sync(const tensor& iterator, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "IteratorGetNextSync", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), iterator.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor iterator_to_string_handle(const tensor& resource_handle) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "IteratorToStringHandle", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), resource_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor iterator_v2(const std::string& shared_name, const std::string& container, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "IteratorV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor l2_loss(const tensor& t) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "L2Loss", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), t.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor l_m_d_b_dataset(const tensor& filenames, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "LMDBDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), filenames.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor l_m_d_b_reader(const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "LMDBReader", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor l_r_n(const tensor& input, int64_t depth_radius=5, float bias=1.0000e+00, float alpha=1.0000e+00, float beta=5.0000e-01) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "LRN", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "depth_radius", depth_radius); - TFE_OpSetAttrFloat(op.get(), "bias", bias); - TFE_OpSetAttrFloat(op.get(), "alpha", alpha); - TFE_OpSetAttrFloat(op.get(), "beta", beta); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor l_r_n_grad(const tensor& input_grads, const tensor& input_image, const tensor& output_image, int64_t depth_radius=5, float bias=1.0000e+00, float alpha=1.0000e+00, float beta=5.0000e-01) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "LRNGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_grads.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), input_image.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), output_image.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "depth_radius", depth_radius); - TFE_OpSetAttrFloat(op.get(), "bias", bias); - TFE_OpSetAttrFloat(op.get(), "alpha", alpha); - TFE_OpSetAttrFloat(op.get(), "beta", beta); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor latency_stats_dataset(const tensor& input_dataset, const tensor& tag, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "LatencyStatsDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), tag.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor leaky_relu(const tensor& features, float alpha=2.0000e-01) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "LeakyRelu", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), features.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrFloat(op.get(), "alpha", alpha); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor leaky_relu_grad(const tensor& gradients, const tensor& features, float alpha=2.0000e-01) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "LeakyReluGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), gradients.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), features.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrFloat(op.get(), "alpha", alpha); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor left_shift(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "LeftShift", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor less(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Less", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor less_equal(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "LessEqual", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor lgamma(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Lgamma", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor lin_space(const tensor& start, const tensor& stop, const tensor& num, datatype Tidx=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "LinSpace", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), start.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), stop.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor load_and_remap_matrix(const tensor& ckpt_path, const tensor& old_input_tensor_name, const tensor& row_remapping, const tensor& col_remapping, const tensor& initializing_values, int64_t num_rows, int64_t num_cols, int64_t max_rows_in_memory=-1) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "LoadAndRemapMatrix", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), ckpt_path.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), old_input_tensor_name.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), row_remapping.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), col_remapping.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), initializing_values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "num_rows", num_rows); - TFE_OpSetAttrInt(op.get(), "num_cols", num_cols); - TFE_OpSetAttrInt(op.get(), "max_rows_in_memory", max_rows_in_memory); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor log(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Log", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor log1p(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Log1p", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor log_softmax(const tensor& logits) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "LogSoftmax", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), logits.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor logical_and(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "LogicalAnd", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor logical_not(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "LogicalNot", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor logical_or(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "LogicalOr", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor lookup_table_find(const tensor& table_handle, const tensor& keys, const tensor& default_value, datatype Tin, datatype Tout) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "LookupTableFind", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), table_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), keys.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), default_value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tin", Tin); - TFE_OpSetAttrType(op.get(), "Tout", Tout); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor lookup_table_find_v2(const tensor& table_handle, const tensor& keys, const tensor& default_value, datatype Tin, datatype Tout) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "LookupTableFindV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), table_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), keys.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), default_value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tin", Tin); - TFE_OpSetAttrType(op.get(), "Tout", Tout); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor lookup_table_size(const tensor& table_handle) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "LookupTableSize", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), table_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor lookup_table_size_v2(const tensor& table_handle) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "LookupTableSizeV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), table_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor loop_cond(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "LoopCond", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor lower_bound(const tensor& sorted_inputs, const tensor& values, datatype out_type=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "LowerBound", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), sorted_inputs.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "out_type", out_type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor map_incomplete_size(const std::vector& dtypes, int64_t capacity=0, int64_t memory_limit=0, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MapIncompleteSize", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "dtypes", reinterpret_cast(dtypes.data()), static_cast(dtypes.size())); - TFE_OpSetAttrInt(op.get(), "capacity", capacity); - TFE_OpSetAttrInt(op.get(), "memory_limit", memory_limit); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor map_peek(const tensor& key, const tensor& indices, const std::vector& dtypes, int64_t capacity=0, int64_t memory_limit=0, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MapPeek", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), key.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "dtypes", reinterpret_cast(dtypes.data()), static_cast(dtypes.size())); - TFE_OpSetAttrInt(op.get(), "capacity", capacity); - TFE_OpSetAttrInt(op.get(), "memory_limit", memory_limit); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor map_size(const std::vector& dtypes, int64_t capacity=0, int64_t memory_limit=0, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MapSize", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "dtypes", reinterpret_cast(dtypes.data()), static_cast(dtypes.size())); - TFE_OpSetAttrInt(op.get(), "capacity", capacity); - TFE_OpSetAttrInt(op.get(), "memory_limit", memory_limit); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor map_unstage(const tensor& key, const tensor& indices, const std::vector& dtypes, int64_t capacity=0, int64_t memory_limit=0, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MapUnstage", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), key.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "dtypes", reinterpret_cast(dtypes.data()), static_cast(dtypes.size())); - TFE_OpSetAttrInt(op.get(), "capacity", capacity); - TFE_OpSetAttrInt(op.get(), "memory_limit", memory_limit); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor mat_mul(const tensor& a, const tensor& b, bool transpose_a=false, bool transpose_b=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MatMul", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), a.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), b.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "transpose_a", (unsigned char)transpose_a); - TFE_OpSetAttrBool(op.get(), "transpose_b", (unsigned char)transpose_b); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor matching_files(const tensor& pattern) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MatchingFiles", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), pattern.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor matching_files_dataset(const tensor& patterns) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MatchingFilesDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), patterns.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor matrix_band_part(const tensor& input, const tensor& num_lower, const tensor& num_upper, datatype Tindex=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MatrixBandPart", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_lower.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_upper.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindex", Tindex); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor matrix_determinant(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MatrixDeterminant", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor matrix_diag(const tensor& diagonal) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MatrixDiag", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), diagonal.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor matrix_diag_part(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MatrixDiagPart", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor matrix_diag_part_v2(const tensor& input, const tensor& k, const tensor& padding_value) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MatrixDiagPartV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), k.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), padding_value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor matrix_diag_part_v3(const tensor& input, const tensor& k, const tensor& padding_value, const std::string& align="RIGHT_LEFT") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MatrixDiagPartV3", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), k.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), padding_value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "align", (void*) align.c_str(), align.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor matrix_diag_v2(const tensor& diagonal, const tensor& k, const tensor& num_rows, const tensor& num_cols, const tensor& padding_value) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MatrixDiagV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), diagonal.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), k.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_rows.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_cols.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), padding_value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor matrix_diag_v3(const tensor& diagonal, const tensor& k, const tensor& num_rows, const tensor& num_cols, const tensor& padding_value, const std::string& align="RIGHT_LEFT") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MatrixDiagV3", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), diagonal.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), k.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_rows.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_cols.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), padding_value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "align", (void*) align.c_str(), align.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor matrix_exponential(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MatrixExponential", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor matrix_inverse(const tensor& input, bool adjoint=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MatrixInverse", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "adjoint", (unsigned char)adjoint); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor matrix_logarithm(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MatrixLogarithm", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor matrix_set_diag(const tensor& input, const tensor& diagonal) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MatrixSetDiag", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), diagonal.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor matrix_set_diag_v2(const tensor& input, const tensor& diagonal, const tensor& k) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MatrixSetDiagV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), diagonal.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), k.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor matrix_set_diag_v3(const tensor& input, const tensor& diagonal, const tensor& k, const std::string& align="RIGHT_LEFT") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MatrixSetDiagV3", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), diagonal.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), k.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "align", (void*) align.c_str(), align.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor matrix_solve(const tensor& matrix, const tensor& rhs, bool adjoint=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MatrixSolve", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), matrix.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), rhs.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "adjoint", (unsigned char)adjoint); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor matrix_solve_ls(const tensor& matrix, const tensor& rhs, const tensor& l2_regularizer, bool fast=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MatrixSolveLs", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), matrix.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), rhs.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), l2_regularizer.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "fast", (unsigned char)fast); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor matrix_square_root(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MatrixSquareRoot", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor matrix_triangular_solve(const tensor& matrix, const tensor& rhs, bool lower=true, bool adjoint=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MatrixTriangularSolve", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), matrix.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), rhs.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "lower", (unsigned char)lower); - TFE_OpSetAttrBool(op.get(), "adjoint", (unsigned char)adjoint); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor max(const tensor& input, const tensor& reduction_indices, bool keep_dims=false, datatype Tidx=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Max", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), reduction_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "keep_dims", (unsigned char)keep_dims); - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor max_intra_op_parallelism_dataset(const tensor& input_dataset, const tensor& max_intra_op_parallelism, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MaxIntraOpParallelismDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), max_intra_op_parallelism.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor max_pool(const tensor& input, const std::vector& ksize, const std::vector& strides, const std::string& padding, const std::string& data_format="NHWC") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MaxPool", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "ksize", ksize.data(), static_cast(ksize.size())); - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrString(op.get(), "data_format", (void*) data_format.c_str(), data_format.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor max_pool3_d(const tensor& input, const std::vector& ksize, const std::vector& strides, const std::string& padding, const std::string& data_format="NDHWC") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MaxPool3D", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "ksize", ksize.data(), static_cast(ksize.size())); - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrString(op.get(), "data_format", (void*) data_format.c_str(), data_format.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor max_pool3_d_grad(const tensor& orig_input, const tensor& orig_output, const tensor& grad, const std::vector& ksize, const std::vector& strides, const std::string& padding, const std::string& data_format="NDHWC", datatype TInput=static_cast(1)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MaxPool3DGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), orig_input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), orig_output.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "ksize", ksize.data(), static_cast(ksize.size())); - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrString(op.get(), "data_format", (void*) data_format.c_str(), data_format.size()); - TFE_OpSetAttrType(op.get(), "TInput", TInput); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor max_pool3_d_grad_grad(const tensor& orig_input, const tensor& orig_output, const tensor& grad, const std::vector& ksize, const std::vector& strides, const std::string& padding, const std::string& data_format="NDHWC") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MaxPool3DGradGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), orig_input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), orig_output.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "ksize", ksize.data(), static_cast(ksize.size())); - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrString(op.get(), "data_format", (void*) data_format.c_str(), data_format.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor max_pool_grad(const tensor& orig_input, const tensor& orig_output, const tensor& grad, const std::vector& ksize, const std::vector& strides, const std::string& padding, const std::string& data_format="NHWC") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MaxPoolGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), orig_input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), orig_output.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "ksize", ksize.data(), static_cast(ksize.size())); - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrString(op.get(), "data_format", (void*) data_format.c_str(), data_format.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor max_pool_grad_grad(const tensor& orig_input, const tensor& orig_output, const tensor& grad, const std::vector& ksize, const std::vector& strides, const std::string& padding, const std::string& data_format="NHWC") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MaxPoolGradGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), orig_input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), orig_output.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "ksize", ksize.data(), static_cast(ksize.size())); - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrString(op.get(), "data_format", (void*) data_format.c_str(), data_format.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor max_pool_grad_grad_v2(const tensor& orig_input, const tensor& orig_output, const tensor& grad, const tensor& ksize, const tensor& strides, const std::string& padding, const std::string& data_format="NHWC") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MaxPoolGradGradV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), orig_input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), orig_output.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), ksize.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), strides.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrString(op.get(), "data_format", (void*) data_format.c_str(), data_format.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor max_pool_grad_grad_with_argmax(const tensor& input, const tensor& grad, const tensor& argmax, const std::vector& ksize, const std::vector& strides, const std::string& padding, datatype Targmax, bool include_batch_in_index=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MaxPoolGradGradWithArgmax", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), argmax.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "ksize", ksize.data(), static_cast(ksize.size())); - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrType(op.get(), "Targmax", Targmax); - TFE_OpSetAttrBool(op.get(), "include_batch_in_index", (unsigned char)include_batch_in_index); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor max_pool_grad_v2(const tensor& orig_input, const tensor& orig_output, const tensor& grad, const tensor& ksize, const tensor& strides, const std::string& padding, const std::string& data_format="NHWC") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MaxPoolGradV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), orig_input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), orig_output.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), ksize.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), strides.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrString(op.get(), "data_format", (void*) data_format.c_str(), data_format.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor max_pool_grad_with_argmax(const tensor& input, const tensor& grad, const tensor& argmax, const std::vector& ksize, const std::vector& strides, const std::string& padding, datatype Targmax, bool include_batch_in_index=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MaxPoolGradWithArgmax", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), argmax.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "ksize", ksize.data(), static_cast(ksize.size())); - TFE_OpSetAttrIntList(op.get(), "strides", strides.data(), static_cast(strides.size())); - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrType(op.get(), "Targmax", Targmax); - TFE_OpSetAttrBool(op.get(), "include_batch_in_index", (unsigned char)include_batch_in_index); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor max_pool_v2(const tensor& input, const tensor& ksize, const tensor& strides, const std::string& padding, const std::string& data_format="NHWC") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MaxPoolV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), ksize.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), strides.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "padding", (void*) padding.c_str(), padding.size()); - TFE_OpSetAttrString(op.get(), "data_format", (void*) data_format.c_str(), data_format.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor maximum(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Maximum", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor mean(const tensor& input, const tensor& reduction_indices, bool keep_dims=false, datatype Tidx=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Mean", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), reduction_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "keep_dims", (unsigned char)keep_dims); - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor merge_summary(const std::vector&inputs) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MergeSummary", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector inputs_handles; inputs_handles.reserve(inputs.size()); - std::transform(inputs.begin(), inputs.end(), std::back_inserter(inputs_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), inputs_handles.data(), static_cast(inputs.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "N", inputs.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor mfcc(const tensor& spectrogram, const tensor& sample_rate, float upper_frequency_limit=4.0000e+03, float lower_frequency_limit=2.0000e+01, int64_t filterbank_channel_count=40, int64_t dct_coefficient_count=13) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Mfcc", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), spectrogram.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), sample_rate.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrFloat(op.get(), "upper_frequency_limit", upper_frequency_limit); - TFE_OpSetAttrFloat(op.get(), "lower_frequency_limit", lower_frequency_limit); - TFE_OpSetAttrInt(op.get(), "filterbank_channel_count", filterbank_channel_count); - TFE_OpSetAttrInt(op.get(), "dct_coefficient_count", dct_coefficient_count); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor min(const tensor& input, const tensor& reduction_indices, bool keep_dims=false, datatype Tidx=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Min", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), reduction_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "keep_dims", (unsigned char)keep_dims); - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor minimum(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Minimum", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor mirror_pad(const tensor& input, const tensor& paddings, const std::string& mode, datatype Tpaddings=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MirrorPad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), paddings.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "mode", (void*) mode.c_str(), mode.size()); - TFE_OpSetAttrType(op.get(), "Tpaddings", Tpaddings); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor mirror_pad_grad(const tensor& input, const tensor& paddings, const std::string& mode, datatype Tpaddings=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MirrorPadGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), paddings.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "mode", (void*) mode.c_str(), mode.size()); - TFE_OpSetAttrType(op.get(), "Tpaddings", Tpaddings); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor mod(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Mod", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor model_dataset(const tensor& input_dataset, const std::vector& output_types, const std::vector< std::vector>& output_shapes, int64_t algorithm=0, int64_t cpu_budget=0) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ModelDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrInt(op.get(), "algorithm", algorithm); - TFE_OpSetAttrInt(op.get(), "cpu_budget", cpu_budget); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor mul(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Mul", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor mul_no_nan(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MulNoNan", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor multi_device_iterator(const std::vector< std::string>& devices, const std::string& shared_name, const std::string& container, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MultiDeviceIterator", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - - std::vector devices_sizes; devices_sizes.reserve(devices.size()); - std::transform(devices.begin(), devices.end(), std::back_inserter(devices_sizes), [](const auto& s) { return s.size();}); - TFE_OpSetAttrStringList(op.get(), "devices", reinterpret_cast(devices.data()), devices_sizes.data(), static_cast(devices.size())); - - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor multi_device_iterator_from_string_handle(const tensor& string_handle, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MultiDeviceIteratorFromStringHandle", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), string_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor multi_device_iterator_get_next_from_shard(const tensor& multi_device_iterator, const tensor& shard_num, const tensor& incarnation_id, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MultiDeviceIteratorGetNextFromShard", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), multi_device_iterator.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), shard_num.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), incarnation_id.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor multi_device_iterator_init(const tensor& dataset, const tensor& multi_device_iterator, const tensor& max_buffer_size) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MultiDeviceIteratorInit", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), multi_device_iterator.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), max_buffer_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor multi_device_iterator_to_string_handle(const tensor& multi_device_iterator) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MultiDeviceIteratorToStringHandle", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), multi_device_iterator.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor multinomial(const tensor& logits, const tensor& num_samples, int64_t seed=0, int64_t seed2=0, datatype output_dtype=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Multinomial", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), logits.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_samples.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "seed", seed); - TFE_OpSetAttrInt(op.get(), "seed2", seed2); - TFE_OpSetAttrType(op.get(), "output_dtype", output_dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor mutable_dense_hash_table(const tensor& empty_key, datatype key_dtype, datatype value_dtype, const std::vector& value_shape, const std::string& container="", const std::string& shared_name="", bool use_node_name_sharing=false, int64_t initial_num_buckets=131072, float max_load_factor=8.0000e-01) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MutableDenseHashTable", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), empty_key.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "key_dtype", key_dtype); - TFE_OpSetAttrType(op.get(), "value_dtype", value_dtype); - - TFE_OpSetAttrShape(op.get(), "value_shape", value_shape.data(), static_cast(value_shape.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - TFE_OpSetAttrBool(op.get(), "use_node_name_sharing", (unsigned char)use_node_name_sharing); - TFE_OpSetAttrInt(op.get(), "initial_num_buckets", initial_num_buckets); - TFE_OpSetAttrFloat(op.get(), "max_load_factor", max_load_factor); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor mutable_dense_hash_table_v2(const tensor& empty_key, const tensor& deleted_key, datatype key_dtype, datatype value_dtype, const std::vector& value_shape, const std::string& container="", const std::string& shared_name="", bool use_node_name_sharing=false, int64_t initial_num_buckets=131072, float max_load_factor=8.0000e-01) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MutableDenseHashTableV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), empty_key.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), deleted_key.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "key_dtype", key_dtype); - TFE_OpSetAttrType(op.get(), "value_dtype", value_dtype); - - TFE_OpSetAttrShape(op.get(), "value_shape", value_shape.data(), static_cast(value_shape.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - TFE_OpSetAttrBool(op.get(), "use_node_name_sharing", (unsigned char)use_node_name_sharing); - TFE_OpSetAttrInt(op.get(), "initial_num_buckets", initial_num_buckets); - TFE_OpSetAttrFloat(op.get(), "max_load_factor", max_load_factor); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor mutable_hash_table(datatype key_dtype, datatype value_dtype, const std::string& container="", const std::string& shared_name="", bool use_node_name_sharing=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MutableHashTable", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrType(op.get(), "key_dtype", key_dtype); - TFE_OpSetAttrType(op.get(), "value_dtype", value_dtype); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - TFE_OpSetAttrBool(op.get(), "use_node_name_sharing", (unsigned char)use_node_name_sharing); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor mutable_hash_table_of_tensors(datatype key_dtype, datatype value_dtype, const std::vector& value_shape, const std::string& container="", const std::string& shared_name="", bool use_node_name_sharing=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MutableHashTableOfTensors", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrType(op.get(), "key_dtype", key_dtype); - TFE_OpSetAttrType(op.get(), "value_dtype", value_dtype); - - TFE_OpSetAttrShape(op.get(), "value_shape", value_shape.data(), static_cast(value_shape.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - TFE_OpSetAttrBool(op.get(), "use_node_name_sharing", (unsigned char)use_node_name_sharing); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor mutable_hash_table_of_tensors_v2(datatype key_dtype, datatype value_dtype, const std::vector& value_shape, const std::string& container="", const std::string& shared_name="", bool use_node_name_sharing=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MutableHashTableOfTensorsV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrType(op.get(), "key_dtype", key_dtype); - TFE_OpSetAttrType(op.get(), "value_dtype", value_dtype); - - TFE_OpSetAttrShape(op.get(), "value_shape", value_shape.data(), static_cast(value_shape.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - TFE_OpSetAttrBool(op.get(), "use_node_name_sharing", (unsigned char)use_node_name_sharing); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor mutable_hash_table_v2(datatype key_dtype, datatype value_dtype, const std::string& container="", const std::string& shared_name="", bool use_node_name_sharing=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MutableHashTableV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrType(op.get(), "key_dtype", key_dtype); - TFE_OpSetAttrType(op.get(), "value_dtype", value_dtype); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - TFE_OpSetAttrBool(op.get(), "use_node_name_sharing", (unsigned char)use_node_name_sharing); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor mutex_lock(const tensor& mutex) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MutexLock", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), mutex.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor mutex_v2(const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "MutexV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor nccl_all_reduce(const tensor& input, const std::string& reduction, int64_t num_devices, const std::string& shared_name) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "NcclAllReduce", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "reduction", (void*) reduction.c_str(), reduction.size()); - TFE_OpSetAttrInt(op.get(), "num_devices", num_devices); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor nccl_broadcast(const tensor& input, const std::vector& shape) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "NcclBroadcast", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - TFE_OpSetAttrShape(op.get(), "shape", shape.data(), static_cast(shape.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor nccl_reduce(const std::vector&input, const std::string& reduction) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "NcclReduce", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector input_handles; input_handles.reserve(input.size()); - std::transform(input.begin(), input.end(), std::back_inserter(input_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), input_handles.data(), static_cast(input.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "reduction", (void*) reduction.c_str(), reduction.size()); - TFE_OpSetAttrInt(op.get(), "num_devices", input.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor ndtri(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Ndtri", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor neg(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Neg", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor next_after(const tensor& x1, const tensor& x2) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "NextAfter", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x1.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), x2.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor next_iteration(const tensor& data) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "NextIteration", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), data.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor non_deterministic_ints(const tensor& shape, datatype dtype=static_cast(9), datatype shape_dtype=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "NonDeterministicInts", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrType(op.get(), "shape_dtype", shape_dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor non_max_suppression(const tensor& boxes, const tensor& scores, const tensor& max_output_size, float iou_threshold=5.0000e-01) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "NonMaxSuppression", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), boxes.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), scores.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), max_output_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrFloat(op.get(), "iou_threshold", iou_threshold); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor non_max_suppression_v2(const tensor& boxes, const tensor& scores, const tensor& max_output_size, const tensor& iou_threshold, datatype T_threshold=static_cast(1)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "NonMaxSuppressionV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), boxes.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), scores.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), max_output_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), iou_threshold.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "T_threshold", T_threshold); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor non_max_suppression_v3(const tensor& boxes, const tensor& scores, const tensor& max_output_size, const tensor& iou_threshold, const tensor& score_threshold, datatype T_threshold=static_cast(1)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "NonMaxSuppressionV3", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), boxes.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), scores.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), max_output_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), iou_threshold.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), score_threshold.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "T_threshold", T_threshold); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor non_max_suppression_with_overlaps(const tensor& overlaps, const tensor& scores, const tensor& max_output_size, const tensor& overlap_threshold, const tensor& score_threshold) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "NonMaxSuppressionWithOverlaps", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), overlaps.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), scores.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), max_output_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), overlap_threshold.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), score_threshold.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor non_serializable_dataset(const tensor& input_dataset, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "NonSerializableDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor not_equal(const tensor& x, const tensor& y, bool incompatible_shape_error=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "NotEqual", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "incompatible_shape_error", (unsigned char)incompatible_shape_error); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor nth_element(const tensor& input, const tensor& n, bool reverse=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "NthElement", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), n.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "reverse", (unsigned char)reverse); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor one_hot(const tensor& indices, const tensor& depth, const tensor& on_value, const tensor& off_value, int64_t axis=-1, datatype TI=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "OneHot", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), depth.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), on_value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), off_value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "axis", axis); - TFE_OpSetAttrType(op.get(), "TI", TI); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor ones_like(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "OnesLike", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor optimize_dataset(const tensor& input_dataset, const tensor& optimizations, const std::vector& output_types, const std::vector< std::vector>& output_shapes, const std::vector< std::string>& optimization_configs) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "OptimizeDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), optimizations.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - std::vector optimization_configs_sizes; optimization_configs_sizes.reserve(optimization_configs.size()); - std::transform(optimization_configs.begin(), optimization_configs.end(), std::back_inserter(optimization_configs_sizes), [](const auto& s) { return s.size();}); - TFE_OpSetAttrStringList(op.get(), "optimization_configs", reinterpret_cast(optimization_configs.data()), optimization_configs_sizes.data(), static_cast(optimization_configs.size())); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor optional_from_value(const std::vector&components, const std::vector& Toutput_types) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "OptionalFromValue", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector components_handles; components_handles.reserve(components.size()); - std::transform(components.begin(), components.end(), std::back_inserter(components_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), components_handles.data(), static_cast(components.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "Toutput_types", reinterpret_cast(Toutput_types.data()), static_cast(Toutput_types.size())); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor optional_get_value(const tensor& optional, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "OptionalGetValue", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), optional.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor optional_has_value(const tensor& optional) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "OptionalHasValue", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), optional.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor optional_none() { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "OptionalNone", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor ordered_map_incomplete_size(const std::vector& dtypes, int64_t capacity=0, int64_t memory_limit=0, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "OrderedMapIncompleteSize", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "dtypes", reinterpret_cast(dtypes.data()), static_cast(dtypes.size())); - TFE_OpSetAttrInt(op.get(), "capacity", capacity); - TFE_OpSetAttrInt(op.get(), "memory_limit", memory_limit); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor ordered_map_peek(const tensor& key, const tensor& indices, const std::vector& dtypes, int64_t capacity=0, int64_t memory_limit=0, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "OrderedMapPeek", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), key.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "dtypes", reinterpret_cast(dtypes.data()), static_cast(dtypes.size())); - TFE_OpSetAttrInt(op.get(), "capacity", capacity); - TFE_OpSetAttrInt(op.get(), "memory_limit", memory_limit); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor ordered_map_size(const std::vector& dtypes, int64_t capacity=0, int64_t memory_limit=0, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "OrderedMapSize", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "dtypes", reinterpret_cast(dtypes.data()), static_cast(dtypes.size())); - TFE_OpSetAttrInt(op.get(), "capacity", capacity); - TFE_OpSetAttrInt(op.get(), "memory_limit", memory_limit); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor ordered_map_unstage(const tensor& key, const tensor& indices, const std::vector& dtypes, int64_t capacity=0, int64_t memory_limit=0, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "OrderedMapUnstage", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), key.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "dtypes", reinterpret_cast(dtypes.data()), static_cast(dtypes.size())); - TFE_OpSetAttrInt(op.get(), "capacity", capacity); - TFE_OpSetAttrInt(op.get(), "memory_limit", memory_limit); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor outfeed_dequeue(datatype dtype, const std::vector& shape, int64_t device_ordinal=-1) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "OutfeedDequeue", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - TFE_OpSetAttrShape(op.get(), "shape", shape.data(), static_cast(shape.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrInt(op.get(), "device_ordinal", device_ordinal); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor outfeed_dequeue_tuple(const std::vector& dtypes, const std::vector< std::vector>& shapes, int64_t device_ordinal=-1) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "OutfeedDequeueTuple", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "dtypes", reinterpret_cast(dtypes.data()), static_cast(dtypes.size())); - - std::vector shapes_values; shapes_values.reserve(shapes.size()); - std::vector shapes_ndims; shapes_ndims.reserve(shapes.size()); - std::transform(shapes.begin(), shapes.end(), std::back_inserter(shapes_values), [](const auto& v) { return v.data();}); - std::transform(shapes.begin(), shapes.end(), std::back_inserter(shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "shapes", shapes_values.data(), shapes_ndims.data(), static_cast(shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrInt(op.get(), "device_ordinal", device_ordinal); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor pack(const std::vector&values, int64_t axis=0) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Pack", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector values_handles; values_handles.reserve(values.size()); - std::transform(values.begin(), values.end(), std::back_inserter(values_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), values_handles.data(), static_cast(values.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "N", values.size()); - TFE_OpSetAttrInt(op.get(), "axis", axis); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor pad(const tensor& input, const tensor& paddings, datatype Tpaddings=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Pad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), paddings.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tpaddings", Tpaddings); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor pad_v2(const tensor& input, const tensor& paddings, const tensor& constant_values, datatype Tpaddings=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "PadV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), paddings.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), constant_values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tpaddings", Tpaddings); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor padded_batch_dataset(const tensor& input_dataset, const tensor& batch_size, const std::vector&padded_shapes, const std::vector&padding_values, const std::vector& Toutput_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "PaddedBatchDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), batch_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - std::vector padded_shapes_handles; padded_shapes_handles.reserve(padded_shapes.size()); - std::transform(padded_shapes.begin(), padded_shapes.end(), std::back_inserter(padded_shapes_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), padded_shapes_handles.data(), static_cast(padded_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - std::vector padding_values_handles; padding_values_handles.reserve(padding_values.size()); - std::transform(padding_values.begin(), padding_values.end(), std::back_inserter(padding_values_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), padding_values_handles.data(), static_cast(padding_values.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "Toutput_types", reinterpret_cast(Toutput_types.data()), static_cast(Toutput_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrInt(op.get(), "N", padded_shapes.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor padded_batch_dataset_v2(const tensor& input_dataset, const tensor& batch_size, const std::vector&padded_shapes, const std::vector&padding_values, const tensor& drop_remainder, const std::vector& Toutput_types, const std::vector< std::vector>& output_shapes, bool parallel_copy=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "PaddedBatchDatasetV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), batch_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - std::vector padded_shapes_handles; padded_shapes_handles.reserve(padded_shapes.size()); - std::transform(padded_shapes.begin(), padded_shapes.end(), std::back_inserter(padded_shapes_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), padded_shapes_handles.data(), static_cast(padded_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - std::vector padding_values_handles; padding_values_handles.reserve(padding_values.size()); - std::transform(padding_values.begin(), padding_values.end(), std::back_inserter(padding_values_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), padding_values_handles.data(), static_cast(padding_values.size()), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), drop_remainder.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "Toutput_types", reinterpret_cast(Toutput_types.data()), static_cast(Toutput_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrInt(op.get(), "N", padded_shapes.size()); - TFE_OpSetAttrBool(op.get(), "parallel_copy", (unsigned char)parallel_copy); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor padding_f_i_f_o_queue(const std::vector& component_types, const std::vector< std::vector>& shapes, int64_t capacity=-1, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "PaddingFIFOQueue", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "component_types", reinterpret_cast(component_types.data()), static_cast(component_types.size())); - - std::vector shapes_values; shapes_values.reserve(shapes.size()); - std::vector shapes_ndims; shapes_ndims.reserve(shapes.size()); - std::transform(shapes.begin(), shapes.end(), std::back_inserter(shapes_values), [](const auto& v) { return v.data();}); - std::transform(shapes.begin(), shapes.end(), std::back_inserter(shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "shapes", shapes_values.data(), shapes_ndims.data(), static_cast(shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrInt(op.get(), "capacity", capacity); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor padding_f_i_f_o_queue_v2(const std::vector& component_types, const std::vector< std::vector>& shapes, int64_t capacity=-1, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "PaddingFIFOQueueV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "component_types", reinterpret_cast(component_types.data()), static_cast(component_types.size())); - - std::vector shapes_values; shapes_values.reserve(shapes.size()); - std::vector shapes_ndims; shapes_ndims.reserve(shapes.size()); - std::transform(shapes.begin(), shapes.end(), std::back_inserter(shapes_values), [](const auto& v) { return v.data();}); - std::transform(shapes.begin(), shapes.end(), std::back_inserter(shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "shapes", shapes_values.data(), shapes_ndims.data(), static_cast(shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrInt(op.get(), "capacity", capacity); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor parallel_concat(const std::vector&values, const std::vector& shape) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ParallelConcat", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector values_handles; values_handles.reserve(values.size()); - std::transform(values.begin(), values.end(), std::back_inserter(values_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), values_handles.data(), static_cast(values.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "N", values.size()); - - TFE_OpSetAttrShape(op.get(), "shape", shape.data(), static_cast(shape.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor parallel_dynamic_stitch(const std::vector&indices, const std::vector&data) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ParallelDynamicStitch", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector indices_handles; indices_handles.reserve(indices.size()); - std::transform(indices.begin(), indices.end(), std::back_inserter(indices_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), indices_handles.data(), static_cast(indices.size()), context::get_status()); - status_check(context::get_status()); - - - std::vector data_handles; data_handles.reserve(data.size()); - std::transform(data.begin(), data.end(), std::back_inserter(data_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), data_handles.data(), static_cast(data.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "N", indices.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor parameterized_truncated_normal(const tensor& shape, const tensor& means, const tensor& stdevs, const tensor& minvals, const tensor& maxvals, datatype dtype, int64_t seed=0, int64_t seed2=0) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ParameterizedTruncatedNormal", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), means.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), stdevs.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), minvals.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), maxvals.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrInt(op.get(), "seed", seed); - TFE_OpSetAttrInt(op.get(), "seed2", seed2); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor parse_example_dataset(const tensor& input_dataset, const tensor& num_parallel_calls, const std::vector&dense_defaults, const std::vector< std::string>& sparse_keys, const std::vector< std::string>& dense_keys, const std::vector& sparse_types, const std::vector& Tdense, const std::vector< std::vector>& dense_shapes, const std::vector& output_types, const std::vector< std::vector>& output_shapes, const std::vector< std::string>& ragged_keys, const std::vector& ragged_value_types, const std::vector& ragged_split_types, bool sloppy=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ParseExampleDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_parallel_calls.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - std::vector dense_defaults_handles; dense_defaults_handles.reserve(dense_defaults.size()); - std::transform(dense_defaults.begin(), dense_defaults.end(), std::back_inserter(dense_defaults_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), dense_defaults_handles.data(), static_cast(dense_defaults.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - std::vector sparse_keys_sizes; sparse_keys_sizes.reserve(sparse_keys.size()); - std::transform(sparse_keys.begin(), sparse_keys.end(), std::back_inserter(sparse_keys_sizes), [](const auto& s) { return s.size();}); - TFE_OpSetAttrStringList(op.get(), "sparse_keys", reinterpret_cast(sparse_keys.data()), sparse_keys_sizes.data(), static_cast(sparse_keys.size())); - - - std::vector dense_keys_sizes; dense_keys_sizes.reserve(dense_keys.size()); - std::transform(dense_keys.begin(), dense_keys.end(), std::back_inserter(dense_keys_sizes), [](const auto& s) { return s.size();}); - TFE_OpSetAttrStringList(op.get(), "dense_keys", reinterpret_cast(dense_keys.data()), dense_keys_sizes.data(), static_cast(dense_keys.size())); - - TFE_OpSetAttrTypeList(op.get(), "sparse_types", reinterpret_cast(sparse_types.data()), static_cast(sparse_types.size())); - TFE_OpSetAttrTypeList(op.get(), "Tdense", reinterpret_cast(Tdense.data()), static_cast(Tdense.size())); - - std::vector dense_shapes_values; dense_shapes_values.reserve(dense_shapes.size()); - std::vector dense_shapes_ndims; dense_shapes_ndims.reserve(dense_shapes.size()); - std::transform(dense_shapes.begin(), dense_shapes.end(), std::back_inserter(dense_shapes_values), [](const auto& v) { return v.data();}); - std::transform(dense_shapes.begin(), dense_shapes.end(), std::back_inserter(dense_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "dense_shapes", dense_shapes_values.data(), dense_shapes_ndims.data(), static_cast(dense_shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - std::vector ragged_keys_sizes; ragged_keys_sizes.reserve(ragged_keys.size()); - std::transform(ragged_keys.begin(), ragged_keys.end(), std::back_inserter(ragged_keys_sizes), [](const auto& s) { return s.size();}); - TFE_OpSetAttrStringList(op.get(), "ragged_keys", reinterpret_cast(ragged_keys.data()), ragged_keys_sizes.data(), static_cast(ragged_keys.size())); - - TFE_OpSetAttrTypeList(op.get(), "ragged_value_types", reinterpret_cast(ragged_value_types.data()), static_cast(ragged_value_types.size())); - TFE_OpSetAttrTypeList(op.get(), "ragged_split_types", reinterpret_cast(ragged_split_types.data()), static_cast(ragged_split_types.size())); - TFE_OpSetAttrBool(op.get(), "sloppy", (unsigned char)sloppy); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor parse_example_dataset_v2(const tensor& input_dataset, const tensor& num_parallel_calls, const std::vector&dense_defaults, const std::vector< std::string>& sparse_keys, const std::vector< std::string>& dense_keys, const std::vector& sparse_types, const std::vector& Tdense, const std::vector< std::vector>& dense_shapes, const std::vector& output_types, const std::vector< std::vector>& output_shapes, const std::vector< std::string>& ragged_keys, const std::vector& ragged_value_types, const std::vector& ragged_split_types, const std::string& deterministic="default") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ParseExampleDatasetV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_parallel_calls.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - std::vector dense_defaults_handles; dense_defaults_handles.reserve(dense_defaults.size()); - std::transform(dense_defaults.begin(), dense_defaults.end(), std::back_inserter(dense_defaults_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), dense_defaults_handles.data(), static_cast(dense_defaults.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - std::vector sparse_keys_sizes; sparse_keys_sizes.reserve(sparse_keys.size()); - std::transform(sparse_keys.begin(), sparse_keys.end(), std::back_inserter(sparse_keys_sizes), [](const auto& s) { return s.size();}); - TFE_OpSetAttrStringList(op.get(), "sparse_keys", reinterpret_cast(sparse_keys.data()), sparse_keys_sizes.data(), static_cast(sparse_keys.size())); - - - std::vector dense_keys_sizes; dense_keys_sizes.reserve(dense_keys.size()); - std::transform(dense_keys.begin(), dense_keys.end(), std::back_inserter(dense_keys_sizes), [](const auto& s) { return s.size();}); - TFE_OpSetAttrStringList(op.get(), "dense_keys", reinterpret_cast(dense_keys.data()), dense_keys_sizes.data(), static_cast(dense_keys.size())); - - TFE_OpSetAttrTypeList(op.get(), "sparse_types", reinterpret_cast(sparse_types.data()), static_cast(sparse_types.size())); - TFE_OpSetAttrTypeList(op.get(), "Tdense", reinterpret_cast(Tdense.data()), static_cast(Tdense.size())); - - std::vector dense_shapes_values; dense_shapes_values.reserve(dense_shapes.size()); - std::vector dense_shapes_ndims; dense_shapes_ndims.reserve(dense_shapes.size()); - std::transform(dense_shapes.begin(), dense_shapes.end(), std::back_inserter(dense_shapes_values), [](const auto& v) { return v.data();}); - std::transform(dense_shapes.begin(), dense_shapes.end(), std::back_inserter(dense_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "dense_shapes", dense_shapes_values.data(), dense_shapes_ndims.data(), static_cast(dense_shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - std::vector ragged_keys_sizes; ragged_keys_sizes.reserve(ragged_keys.size()); - std::transform(ragged_keys.begin(), ragged_keys.end(), std::back_inserter(ragged_keys_sizes), [](const auto& s) { return s.size();}); - TFE_OpSetAttrStringList(op.get(), "ragged_keys", reinterpret_cast(ragged_keys.data()), ragged_keys_sizes.data(), static_cast(ragged_keys.size())); - - TFE_OpSetAttrTypeList(op.get(), "ragged_value_types", reinterpret_cast(ragged_value_types.data()), static_cast(ragged_value_types.size())); - TFE_OpSetAttrTypeList(op.get(), "ragged_split_types", reinterpret_cast(ragged_split_types.data()), static_cast(ragged_split_types.size())); - TFE_OpSetAttrString(op.get(), "deterministic", (void*) deterministic.c_str(), deterministic.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor parse_tensor(const tensor& serialized, datatype out_type) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ParseTensor", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), serialized.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "out_type", out_type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor placeholder(datatype dtype, const std::vector& shape) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Placeholder", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - TFE_OpSetAttrShape(op.get(), "shape", shape.data(), static_cast(shape.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor placeholder_v2(datatype dtype, const std::vector& shape) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "PlaceholderV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - TFE_OpSetAttrShape(op.get(), "shape", shape.data(), static_cast(shape.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor placeholder_with_default(const tensor& input, datatype dtype, const std::vector& shape) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "PlaceholderWithDefault", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - TFE_OpSetAttrShape(op.get(), "shape", shape.data(), static_cast(shape.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor polygamma(const tensor& a, const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Polygamma", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), a.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor population_count(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "PopulationCount", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor pow(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Pow", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor prefetch_dataset(const tensor& input_dataset, const tensor& buffer_size, const std::vector& output_types, const std::vector< std::vector>& output_shapes, int64_t slack_period=0, bool legacy_autotune=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "PrefetchDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), buffer_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrInt(op.get(), "slack_period", slack_period); - TFE_OpSetAttrBool(op.get(), "legacy_autotune", (unsigned char)legacy_autotune); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor prelinearize(const tensor& input, datatype dtype, const std::vector& shape, const std::vector& layout) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Prelinearize", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - TFE_OpSetAttrShape(op.get(), "shape", shape.data(), static_cast(shape.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrIntList(op.get(), "layout", layout.data(), static_cast(layout.size())); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor prelinearize_tuple(const std::vector&inputs, const std::vector& dtypes, const std::vector< std::vector>& shapes, const std::vector& layouts) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "PrelinearizeTuple", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector inputs_handles; inputs_handles.reserve(inputs.size()); - std::transform(inputs.begin(), inputs.end(), std::back_inserter(inputs_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), inputs_handles.data(), static_cast(inputs.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "dtypes", reinterpret_cast(dtypes.data()), static_cast(dtypes.size())); - - std::vector shapes_values; shapes_values.reserve(shapes.size()); - std::vector shapes_ndims; shapes_ndims.reserve(shapes.size()); - std::transform(shapes.begin(), shapes.end(), std::back_inserter(shapes_values), [](const auto& v) { return v.data();}); - std::transform(shapes.begin(), shapes.end(), std::back_inserter(shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "shapes", shapes_values.data(), shapes_ndims.data(), static_cast(shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrIntList(op.get(), "layouts", layouts.data(), static_cast(layouts.size())); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor prevent_gradient(const tensor& input, const std::string& message="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "PreventGradient", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "message", (void*) message.c_str(), message.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor print(const tensor& input, const std::vector&data, const std::vector& U, const std::string& message="", int64_t first_n=-1, int64_t summarize=3) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Print", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - std::vector data_handles; data_handles.reserve(data.size()); - std::transform(data.begin(), data.end(), std::back_inserter(data_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), data_handles.data(), static_cast(data.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "U", reinterpret_cast(U.data()), static_cast(U.size())); - TFE_OpSetAttrString(op.get(), "message", (void*) message.c_str(), message.size()); - TFE_OpSetAttrInt(op.get(), "first_n", first_n); - TFE_OpSetAttrInt(op.get(), "summarize", summarize); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor priority_queue(const std::vector& component_types, const std::vector< std::vector>& shapes, int64_t capacity=-1, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "PriorityQueue", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "component_types", reinterpret_cast(component_types.data()), static_cast(component_types.size())); - - std::vector shapes_values; shapes_values.reserve(shapes.size()); - std::vector shapes_ndims; shapes_ndims.reserve(shapes.size()); - std::transform(shapes.begin(), shapes.end(), std::back_inserter(shapes_values), [](const auto& v) { return v.data();}); - std::transform(shapes.begin(), shapes.end(), std::back_inserter(shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "shapes", shapes_values.data(), shapes_ndims.data(), static_cast(shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrInt(op.get(), "capacity", capacity); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor priority_queue_v2(const std::vector& component_types, const std::vector< std::vector>& shapes, int64_t capacity=-1, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "PriorityQueueV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "component_types", reinterpret_cast(component_types.data()), static_cast(component_types.size())); - - std::vector shapes_values; shapes_values.reserve(shapes.size()); - std::vector shapes_ndims; shapes_ndims.reserve(shapes.size()); - std::transform(shapes.begin(), shapes.end(), std::back_inserter(shapes_values), [](const auto& v) { return v.data();}); - std::transform(shapes.begin(), shapes.end(), std::back_inserter(shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "shapes", shapes_values.data(), shapes_ndims.data(), static_cast(shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrInt(op.get(), "capacity", capacity); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor private_thread_pool_dataset(const tensor& input_dataset, const tensor& num_threads, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "PrivateThreadPoolDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_threads.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor prod(const tensor& input, const tensor& reduction_indices, bool keep_dims=false, datatype Tidx=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Prod", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), reduction_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "keep_dims", (unsigned char)keep_dims); - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor py_func(const std::vector&input, const std::string& token, const std::vector& Tin, const std::vector& Tout) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "PyFunc", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector input_handles; input_handles.reserve(input.size()); - std::transform(input.begin(), input.end(), std::back_inserter(input_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), input_handles.data(), static_cast(input.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "token", (void*) token.c_str(), token.size()); - TFE_OpSetAttrTypeList(op.get(), "Tin", reinterpret_cast(Tin.data()), static_cast(Tin.size())); - TFE_OpSetAttrTypeList(op.get(), "Tout", reinterpret_cast(Tout.data()), static_cast(Tout.size())); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor py_func_stateless(const std::vector&input, const std::string& token, const std::vector& Tin, const std::vector& Tout) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "PyFuncStateless", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector input_handles; input_handles.reserve(input.size()); - std::transform(input.begin(), input.end(), std::back_inserter(input_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), input_handles.data(), static_cast(input.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "token", (void*) token.c_str(), token.size()); - TFE_OpSetAttrTypeList(op.get(), "Tin", reinterpret_cast(Tin.data()), static_cast(Tin.size())); - TFE_OpSetAttrTypeList(op.get(), "Tout", reinterpret_cast(Tout.data()), static_cast(Tout.size())); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor quantize_and_dequantize(const tensor& input, bool signed_input=true, int64_t num_bits=8, bool range_given=false, float input_min=0.0000e+00, float input_max=0.0000e+00) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "QuantizeAndDequantize", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "signed_input", (unsigned char)signed_input); - TFE_OpSetAttrInt(op.get(), "num_bits", num_bits); - TFE_OpSetAttrBool(op.get(), "range_given", (unsigned char)range_given); - TFE_OpSetAttrFloat(op.get(), "input_min", input_min); - TFE_OpSetAttrFloat(op.get(), "input_max", input_max); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor quantize_and_dequantize_v2(const tensor& input, const tensor& input_min, const tensor& input_max, bool signed_input=true, int64_t num_bits=8, bool range_given=false, const std::string& round_mode="HALF_TO_EVEN", bool narrow_range=false, int64_t axis=-1) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "QuantizeAndDequantizeV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), input_min.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), input_max.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "signed_input", (unsigned char)signed_input); - TFE_OpSetAttrInt(op.get(), "num_bits", num_bits); - TFE_OpSetAttrBool(op.get(), "range_given", (unsigned char)range_given); - TFE_OpSetAttrString(op.get(), "round_mode", (void*) round_mode.c_str(), round_mode.size()); - TFE_OpSetAttrBool(op.get(), "narrow_range", (unsigned char)narrow_range); - TFE_OpSetAttrInt(op.get(), "axis", axis); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor quantize_and_dequantize_v3(const tensor& input, const tensor& input_min, const tensor& input_max, const tensor& num_bits, bool signed_input=true, bool range_given=true, bool narrow_range=false, int64_t axis=-1) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "QuantizeAndDequantizeV3", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), input_min.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), input_max.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_bits.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "signed_input", (unsigned char)signed_input); - TFE_OpSetAttrBool(op.get(), "range_given", (unsigned char)range_given); - TFE_OpSetAttrBool(op.get(), "narrow_range", (unsigned char)narrow_range); - TFE_OpSetAttrInt(op.get(), "axis", axis); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor quantized_mat_mul_with_bias_and_dequantize(const tensor& a, const tensor& b, const tensor& bias, const tensor& min_a, const tensor& max_a, const tensor& min_b, const tensor& max_b, const tensor& min_freezed_output, const tensor& max_freezed_output, datatype T1, datatype T2, datatype Tbias, datatype Toutput, bool transpose_a=false, bool transpose_b=false, const std::string& input_quant_mode="MIN_FIRST") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "QuantizedMatMulWithBiasAndDequantize", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), a.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), b.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), bias.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), min_a.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), max_a.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), min_b.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), max_b.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), min_freezed_output.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), max_freezed_output.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "T1", T1); - TFE_OpSetAttrType(op.get(), "T2", T2); - TFE_OpSetAttrType(op.get(), "Tbias", Tbias); - TFE_OpSetAttrType(op.get(), "Toutput", Toutput); - TFE_OpSetAttrBool(op.get(), "transpose_a", (unsigned char)transpose_a); - TFE_OpSetAttrBool(op.get(), "transpose_b", (unsigned char)transpose_b); - TFE_OpSetAttrString(op.get(), "input_quant_mode", (void*) input_quant_mode.c_str(), input_quant_mode.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor queue_dequeue(const tensor& handle, const std::vector& component_types, int64_t timeout_ms=-1) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "QueueDequeue", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "component_types", reinterpret_cast(component_types.data()), static_cast(component_types.size())); - TFE_OpSetAttrInt(op.get(), "timeout_ms", timeout_ms); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor queue_dequeue_many(const tensor& handle, const tensor& n, const std::vector& component_types, int64_t timeout_ms=-1) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "QueueDequeueMany", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), n.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "component_types", reinterpret_cast(component_types.data()), static_cast(component_types.size())); - TFE_OpSetAttrInt(op.get(), "timeout_ms", timeout_ms); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor queue_dequeue_many_v2(const tensor& handle, const tensor& n, const std::vector& component_types, int64_t timeout_ms=-1) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "QueueDequeueManyV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), n.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "component_types", reinterpret_cast(component_types.data()), static_cast(component_types.size())); - TFE_OpSetAttrInt(op.get(), "timeout_ms", timeout_ms); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor queue_dequeue_up_to(const tensor& handle, const tensor& n, const std::vector& component_types, int64_t timeout_ms=-1) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "QueueDequeueUpTo", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), n.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "component_types", reinterpret_cast(component_types.data()), static_cast(component_types.size())); - TFE_OpSetAttrInt(op.get(), "timeout_ms", timeout_ms); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor queue_dequeue_up_to_v2(const tensor& handle, const tensor& n, const std::vector& component_types, int64_t timeout_ms=-1) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "QueueDequeueUpToV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), n.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "component_types", reinterpret_cast(component_types.data()), static_cast(component_types.size())); - TFE_OpSetAttrInt(op.get(), "timeout_ms", timeout_ms); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor queue_dequeue_v2(const tensor& handle, const std::vector& component_types, int64_t timeout_ms=-1) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "QueueDequeueV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "component_types", reinterpret_cast(component_types.data()), static_cast(component_types.size())); - TFE_OpSetAttrInt(op.get(), "timeout_ms", timeout_ms); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor queue_is_closed(const tensor& handle) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "QueueIsClosed", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor queue_is_closed_v2(const tensor& handle) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "QueueIsClosedV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor queue_size(const tensor& handle) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "QueueSize", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor queue_size_v2(const tensor& handle) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "QueueSizeV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor r_f_f_t(const tensor& input, const tensor& fft_length, datatype Treal=static_cast(1), datatype Tcomplex=static_cast(8)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RFFT", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), fft_length.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Treal", Treal); - TFE_OpSetAttrType(op.get(), "Tcomplex", Tcomplex); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor r_f_f_t2_d(const tensor& input, const tensor& fft_length, datatype Treal=static_cast(1), datatype Tcomplex=static_cast(8)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RFFT2D", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), fft_length.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Treal", Treal); - TFE_OpSetAttrType(op.get(), "Tcomplex", Tcomplex); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor r_f_f_t3_d(const tensor& input, const tensor& fft_length, datatype Treal=static_cast(1), datatype Tcomplex=static_cast(8)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RFFT3D", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), fft_length.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Treal", Treal); - TFE_OpSetAttrType(op.get(), "Tcomplex", Tcomplex); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor r_g_b_to_h_s_v(const tensor& images) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RGBToHSV", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), images.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor ragged_bincount(const tensor& splits, const tensor& values, const tensor& size, const tensor& weights, datatype Tidx, bool binary_output=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RaggedBincount", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), splits.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), weights.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - TFE_OpSetAttrBool(op.get(), "binary_output", (unsigned char)binary_output); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor ragged_tensor_to_tensor(const tensor& shape, const tensor& values, const tensor& default_value, const std::vector&row_partition_tensors, datatype Tindex, datatype Tshape, const std::vector< std::string>& row_partition_types) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RaggedTensorToTensor", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), default_value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - std::vector row_partition_tensors_handles; row_partition_tensors_handles.reserve(row_partition_tensors.size()); - std::transform(row_partition_tensors.begin(), row_partition_tensors.end(), std::back_inserter(row_partition_tensors_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), row_partition_tensors_handles.data(), static_cast(row_partition_tensors.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindex", Tindex); - TFE_OpSetAttrType(op.get(), "Tshape", Tshape); - TFE_OpSetAttrInt(op.get(), "num_row_partition_tensors", row_partition_tensors.size()); - - std::vector row_partition_types_sizes; row_partition_types_sizes.reserve(row_partition_types.size()); - std::transform(row_partition_types.begin(), row_partition_types.end(), std::back_inserter(row_partition_types_sizes), [](const auto& s) { return s.size();}); - TFE_OpSetAttrStringList(op.get(), "row_partition_types", reinterpret_cast(row_partition_types.data()), row_partition_types_sizes.data(), static_cast(row_partition_types.size())); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor ragged_tensor_to_variant(const std::vector&rt_nested_splits, const tensor& rt_dense_values, datatype Tvalues, bool batched_input, datatype Tsplits=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RaggedTensorToVariant", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector rt_nested_splits_handles; rt_nested_splits_handles.reserve(rt_nested_splits.size()); - std::transform(rt_nested_splits.begin(), rt_nested_splits.end(), std::back_inserter(rt_nested_splits_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), rt_nested_splits_handles.data(), static_cast(rt_nested_splits.size()), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), rt_dense_values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "RAGGED_RANK", rt_nested_splits.size()); - TFE_OpSetAttrType(op.get(), "Tvalues", Tvalues); - TFE_OpSetAttrBool(op.get(), "batched_input", (unsigned char)batched_input); - TFE_OpSetAttrType(op.get(), "Tsplits", Tsplits); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor random_crop(const tensor& image, const tensor& size, int64_t seed=0, int64_t seed2=0) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RandomCrop", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), image.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "seed", seed); - TFE_OpSetAttrInt(op.get(), "seed2", seed2); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor random_dataset(const tensor& seed, const tensor& seed2, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RandomDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), seed.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), seed2.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor random_gamma(const tensor& shape, const tensor& alpha, datatype S, int64_t seed=0, int64_t seed2=0) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RandomGamma", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), alpha.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "S", S); - TFE_OpSetAttrInt(op.get(), "seed", seed); - TFE_OpSetAttrInt(op.get(), "seed2", seed2); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor random_gamma_grad(const tensor& alpha, const tensor& sample) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RandomGammaGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), alpha.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), sample.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor random_poisson(const tensor& shape, const tensor& rate, datatype S, datatype dtype, int64_t seed=0, int64_t seed2=0) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RandomPoisson", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), rate.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "S", S); - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrInt(op.get(), "seed", seed); - TFE_OpSetAttrInt(op.get(), "seed2", seed2); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor random_poisson_v2(const tensor& shape, const tensor& rate, datatype S, int64_t seed=0, int64_t seed2=0, datatype R=static_cast(2), datatype dtype=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RandomPoissonV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), rate.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "S", S); - TFE_OpSetAttrInt(op.get(), "seed", seed); - TFE_OpSetAttrInt(op.get(), "seed2", seed2); - TFE_OpSetAttrType(op.get(), "R", R); - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor random_shuffle(const tensor& value, int64_t seed=0, int64_t seed2=0) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RandomShuffle", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "seed", seed); - TFE_OpSetAttrInt(op.get(), "seed2", seed2); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor random_shuffle_queue(const std::vector& component_types, const std::vector< std::vector>& shapes, int64_t capacity=-1, int64_t min_after_dequeue=0, int64_t seed=0, int64_t seed2=0, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RandomShuffleQueue", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "component_types", reinterpret_cast(component_types.data()), static_cast(component_types.size())); - - std::vector shapes_values; shapes_values.reserve(shapes.size()); - std::vector shapes_ndims; shapes_ndims.reserve(shapes.size()); - std::transform(shapes.begin(), shapes.end(), std::back_inserter(shapes_values), [](const auto& v) { return v.data();}); - std::transform(shapes.begin(), shapes.end(), std::back_inserter(shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "shapes", shapes_values.data(), shapes_ndims.data(), static_cast(shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrInt(op.get(), "capacity", capacity); - TFE_OpSetAttrInt(op.get(), "min_after_dequeue", min_after_dequeue); - TFE_OpSetAttrInt(op.get(), "seed", seed); - TFE_OpSetAttrInt(op.get(), "seed2", seed2); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor random_shuffle_queue_v2(const std::vector& component_types, const std::vector< std::vector>& shapes, int64_t capacity=-1, int64_t min_after_dequeue=0, int64_t seed=0, int64_t seed2=0, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RandomShuffleQueueV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "component_types", reinterpret_cast(component_types.data()), static_cast(component_types.size())); - - std::vector shapes_values; shapes_values.reserve(shapes.size()); - std::vector shapes_ndims; shapes_ndims.reserve(shapes.size()); - std::transform(shapes.begin(), shapes.end(), std::back_inserter(shapes_values), [](const auto& v) { return v.data();}); - std::transform(shapes.begin(), shapes.end(), std::back_inserter(shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "shapes", shapes_values.data(), shapes_ndims.data(), static_cast(shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrInt(op.get(), "capacity", capacity); - TFE_OpSetAttrInt(op.get(), "min_after_dequeue", min_after_dequeue); - TFE_OpSetAttrInt(op.get(), "seed", seed); - TFE_OpSetAttrInt(op.get(), "seed2", seed2); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor random_standard_normal(const tensor& shape, datatype dtype, int64_t seed=0, int64_t seed2=0) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RandomStandardNormal", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrInt(op.get(), "seed", seed); - TFE_OpSetAttrInt(op.get(), "seed2", seed2); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor random_uniform(const tensor& shape, datatype dtype, int64_t seed=0, int64_t seed2=0) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RandomUniform", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrInt(op.get(), "seed", seed); - TFE_OpSetAttrInt(op.get(), "seed2", seed2); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor random_uniform_int(const tensor& shape, const tensor& minval, const tensor& maxval, datatype Tout, int64_t seed=0, int64_t seed2=0) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RandomUniformInt", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), minval.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), maxval.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tout", Tout); - TFE_OpSetAttrInt(op.get(), "seed", seed); - TFE_OpSetAttrInt(op.get(), "seed2", seed2); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor range(const tensor& start, const tensor& limit, const tensor& delta, datatype Tidx=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Range", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), start.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), limit.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), delta.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor range_dataset(const tensor& start, const tensor& stop, const tensor& step, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RangeDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), start.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), stop.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), step.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor rank(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Rank", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor read_file(const tensor& filename) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ReadFile", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), filename.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor read_variable_op(const tensor& resource, datatype dtype) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ReadVariableOp", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), resource.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor reader_num_records_produced(const tensor& reader_handle) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ReaderNumRecordsProduced", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), reader_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor reader_num_records_produced_v2(const tensor& reader_handle) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ReaderNumRecordsProducedV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), reader_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor reader_num_work_units_completed(const tensor& reader_handle) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ReaderNumWorkUnitsCompleted", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), reader_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor reader_num_work_units_completed_v2(const tensor& reader_handle) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ReaderNumWorkUnitsCompletedV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), reader_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor reader_serialize_state(const tensor& reader_handle) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ReaderSerializeState", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), reader_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor reader_serialize_state_v2(const tensor& reader_handle) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ReaderSerializeStateV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), reader_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor real(const tensor& input, datatype Tout=static_cast(1)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Real", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tout", Tout); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor real_div(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RealDiv", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor rebatch_dataset(const tensor& input_dataset, const tensor& num_replicas, const std::vector& output_types, const std::vector< std::vector>& output_shapes, bool use_fallback=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RebatchDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_replicas.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrBool(op.get(), "use_fallback", (unsigned char)use_fallback); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor reciprocal(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Reciprocal", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor reciprocal_grad(const tensor& y, const tensor& dy) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ReciprocalGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), dy.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor record_input(const std::string& file_pattern, int64_t file_random_seed=301, float file_shuffle_shift_ratio=0.0000e+00, int64_t file_buffer_size=10000, int64_t file_parallelism=16, int64_t batch_size=32, const std::string& compression_type="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RecordInput", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrString(op.get(), "file_pattern", (void*) file_pattern.c_str(), file_pattern.size()); - TFE_OpSetAttrInt(op.get(), "file_random_seed", file_random_seed); - TFE_OpSetAttrFloat(op.get(), "file_shuffle_shift_ratio", file_shuffle_shift_ratio); - TFE_OpSetAttrInt(op.get(), "file_buffer_size", file_buffer_size); - TFE_OpSetAttrInt(op.get(), "file_parallelism", file_parallelism); - TFE_OpSetAttrInt(op.get(), "batch_size", batch_size); - TFE_OpSetAttrString(op.get(), "compression_type", (void*) compression_type.c_str(), compression_type.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor recv(datatype tensor_type, const std::string& tensor_name, const std::string& send_device, int64_t send_device_incarnation, const std::string& recv_device, bool client_terminated=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Recv", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrType(op.get(), "tensor_type", tensor_type); - TFE_OpSetAttrString(op.get(), "tensor_name", (void*) tensor_name.c_str(), tensor_name.size()); - TFE_OpSetAttrString(op.get(), "send_device", (void*) send_device.c_str(), send_device.size()); - TFE_OpSetAttrInt(op.get(), "send_device_incarnation", send_device_incarnation); - TFE_OpSetAttrString(op.get(), "recv_device", (void*) recv_device.c_str(), recv_device.size()); - TFE_OpSetAttrBool(op.get(), "client_terminated", (unsigned char)client_terminated); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor recv_t_p_u_embedding_activations(int64_t num_outputs, const std::string& config) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RecvTPUEmbeddingActivations", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrInt(op.get(), "num_outputs", num_outputs); - TFE_OpSetAttrString(op.get(), "config", (void*) config.c_str(), config.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor reduce_join(const tensor& inputs, const tensor& reduction_indices, bool keep_dims=false, const std::string& separator="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ReduceJoin", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), inputs.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), reduction_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "keep_dims", (unsigned char)keep_dims); - TFE_OpSetAttrString(op.get(), "separator", (void*) separator.c_str(), separator.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor ref_enter(const tensor& data, const std::string& frame_name, bool is_constant=false, int64_t parallel_iterations=10) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RefEnter", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), data.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "frame_name", (void*) frame_name.c_str(), frame_name.size()); - TFE_OpSetAttrBool(op.get(), "is_constant", (unsigned char)is_constant); - TFE_OpSetAttrInt(op.get(), "parallel_iterations", parallel_iterations); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor ref_exit(const tensor& data) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RefExit", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), data.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor ref_identity(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RefIdentity", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor ref_next_iteration(const tensor& data) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RefNextIteration", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), data.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor ref_select(const tensor& index, const std::vector&inputs) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RefSelect", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), index.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - std::vector inputs_handles; inputs_handles.reserve(inputs.size()); - std::transform(inputs.begin(), inputs.end(), std::back_inserter(inputs_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), inputs_handles.data(), static_cast(inputs.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "N", inputs.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor regex_full_match(const tensor& input, const tensor& pattern) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RegexFullMatch", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), pattern.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor regex_replace(const tensor& input, const tensor& pattern, const tensor& rewrite, bool replace_global=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RegexReplace", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), pattern.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), rewrite.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "replace_global", (unsigned char)replace_global); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor register_dataset(const tensor& dataset, const tensor& address, const tensor& protocol, int64_t external_state_policy) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RegisterDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), address.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), protocol.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "external_state_policy", external_state_policy); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor relu(const tensor& features) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Relu", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), features.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor relu6(const tensor& features) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Relu6", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), features.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor relu6_grad(const tensor& gradients, const tensor& features) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Relu6Grad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), gradients.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), features.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor relu_grad(const tensor& gradients, const tensor& features) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ReluGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), gradients.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), features.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor repeat_dataset(const tensor& input_dataset, const tensor& count, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RepeatDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), count.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor reshape(const tensor& input_tensor, const tensor& shape, datatype Tshape=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Reshape", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_tensor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tshape", Tshape); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor resize_area(const tensor& images, const tensor& size, bool align_corners=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ResizeArea", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), images.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "align_corners", (unsigned char)align_corners); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor resize_bicubic(const tensor& images, const tensor& size, bool align_corners=false, bool half_pixel_centers=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ResizeBicubic", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), images.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "align_corners", (unsigned char)align_corners); - TFE_OpSetAttrBool(op.get(), "half_pixel_centers", (unsigned char)half_pixel_centers); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor resize_bicubic_grad(const tensor& grads, const tensor& original_image, bool align_corners=false, bool half_pixel_centers=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ResizeBicubicGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), grads.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), original_image.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "align_corners", (unsigned char)align_corners); - TFE_OpSetAttrBool(op.get(), "half_pixel_centers", (unsigned char)half_pixel_centers); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor resize_bilinear(const tensor& images, const tensor& size, bool align_corners=false, bool half_pixel_centers=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ResizeBilinear", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), images.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "align_corners", (unsigned char)align_corners); - TFE_OpSetAttrBool(op.get(), "half_pixel_centers", (unsigned char)half_pixel_centers); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor resize_bilinear_grad(const tensor& grads, const tensor& original_image, bool align_corners=false, bool half_pixel_centers=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ResizeBilinearGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), grads.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), original_image.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "align_corners", (unsigned char)align_corners); - TFE_OpSetAttrBool(op.get(), "half_pixel_centers", (unsigned char)half_pixel_centers); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor resize_nearest_neighbor(const tensor& images, const tensor& size, bool align_corners=false, bool half_pixel_centers=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ResizeNearestNeighbor", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), images.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "align_corners", (unsigned char)align_corners); - TFE_OpSetAttrBool(op.get(), "half_pixel_centers", (unsigned char)half_pixel_centers); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor resize_nearest_neighbor_grad(const tensor& grads, const tensor& size, bool align_corners=false, bool half_pixel_centers=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ResizeNearestNeighborGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), grads.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "align_corners", (unsigned char)align_corners); - TFE_OpSetAttrBool(op.get(), "half_pixel_centers", (unsigned char)half_pixel_centers); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor resource_accumulator_num_accumulated(const tensor& handle) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ResourceAccumulatorNumAccumulated", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor resource_accumulator_take_gradient(const tensor& handle, const tensor& num_required, datatype dtype) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ResourceAccumulatorTakeGradient", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_required.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor resource_conditional_accumulator(datatype dtype, const std::vector& shape, const std::string& container="", const std::string& shared_name="", const std::string& reduction_type="MEAN") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ResourceConditionalAccumulator", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - TFE_OpSetAttrShape(op.get(), "shape", shape.data(), static_cast(shape.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - TFE_OpSetAttrString(op.get(), "reduction_type", (void*) reduction_type.c_str(), reduction_type.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor resource_count_up_to(const tensor& resource, int64_t limit) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ResourceCountUpTo", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), resource.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "limit", limit); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor resource_gather(const tensor& resource, const tensor& indices, datatype dtype, datatype Tindices, int64_t batch_dims=0, bool validate_indices=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ResourceGather", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), resource.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrInt(op.get(), "batch_dims", batch_dims); - TFE_OpSetAttrBool(op.get(), "validate_indices", (unsigned char)validate_indices); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor resource_gather_nd(const tensor& resource, const tensor& indices, datatype dtype, datatype Tindices) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ResourceGatherNd", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), resource.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor restore(const tensor& file_pattern, const tensor& input_tensor_name, datatype dt, int64_t preferred_shard=-1) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Restore", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), file_pattern.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), input_tensor_name.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dt", dt); - TFE_OpSetAttrInt(op.get(), "preferred_shard", preferred_shard); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor restore_slice(const tensor& file_pattern, const tensor& input_tensor_name, const tensor& shape_and_slice, datatype dt, int64_t preferred_shard=-1) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RestoreSlice", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), file_pattern.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), input_tensor_name.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), shape_and_slice.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dt", dt); - TFE_OpSetAttrInt(op.get(), "preferred_shard", preferred_shard); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor restore_v2(const tensor& prefix, const tensor& input_tensor_names, const tensor& shape_and_slices, const std::vector& dtypes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RestoreV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), prefix.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), input_tensor_names.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), shape_and_slices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "dtypes", reinterpret_cast(dtypes.data()), static_cast(dtypes.size())); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor retrieve_t_p_u_embedding_stochastic_gradient_descent_parameters(int64_t num_shards, int64_t shard_id, int64_t table_id=-1, const std::string& table_name="", const std::string& config="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RetrieveTPUEmbeddingStochasticGradientDescentParameters", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrInt(op.get(), "num_shards", num_shards); - TFE_OpSetAttrInt(op.get(), "shard_id", shard_id); - TFE_OpSetAttrInt(op.get(), "table_id", table_id); - TFE_OpSetAttrString(op.get(), "table_name", (void*) table_name.c_str(), table_name.size()); - TFE_OpSetAttrString(op.get(), "config", (void*) config.c_str(), config.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor reverse(const tensor& input_tensor, const tensor& dims) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Reverse", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_tensor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), dims.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor reverse_sequence(const tensor& input, const tensor& seq_lengths, int64_t seq_dim, int64_t batch_dim=0, datatype Tlen=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ReverseSequence", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), seq_lengths.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "seq_dim", seq_dim); - TFE_OpSetAttrInt(op.get(), "batch_dim", batch_dim); - TFE_OpSetAttrType(op.get(), "Tlen", Tlen); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor reverse_v2(const tensor& input_tensor, const tensor& axis, datatype Tidx=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ReverseV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_tensor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), axis.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor right_shift(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RightShift", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor rint(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Rint", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor roll(const tensor& input, const tensor& shift, const tensor& axis, datatype Tshift, datatype Taxis) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Roll", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), shift.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), axis.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tshift", Tshift); - TFE_OpSetAttrType(op.get(), "Taxis", Taxis); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor round(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Round", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor rsqrt(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Rsqrt", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor rsqrt_grad(const tensor& y, const tensor& dy) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "RsqrtGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), dy.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sampling_dataset(const tensor& input_dataset, const tensor& rate, const tensor& seed, const tensor& seed2, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SamplingDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), rate.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), seed.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), seed2.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor scalar_summary(const tensor& tags, const tensor& values) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ScalarSummary", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), tags.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor scale_and_translate(const tensor& images, const tensor& size, const tensor& scale, const tensor& translation, const std::string& kernel_type="lanczos3", bool antialias=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ScaleAndTranslate", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), images.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), scale.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), translation.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "kernel_type", (void*) kernel_type.c_str(), kernel_type.size()); - TFE_OpSetAttrBool(op.get(), "antialias", (unsigned char)antialias); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor scale_and_translate_grad(const tensor& grads, const tensor& original_image, const tensor& scale, const tensor& translation, const std::string& kernel_type="lanczos3", bool antialias=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ScaleAndTranslateGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), grads.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), original_image.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), scale.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), translation.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "kernel_type", (void*) kernel_type.c_str(), kernel_type.size()); - TFE_OpSetAttrBool(op.get(), "antialias", (unsigned char)antialias); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor scatter_add(const tensor& ref, const tensor& indices, const tensor& updates, datatype Tindices, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ScatterAdd", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), ref.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), updates.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor scatter_div(const tensor& ref, const tensor& indices, const tensor& updates, datatype Tindices, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ScatterDiv", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), ref.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), updates.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor scatter_max(const tensor& ref, const tensor& indices, const tensor& updates, datatype Tindices, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ScatterMax", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), ref.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), updates.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor scatter_min(const tensor& ref, const tensor& indices, const tensor& updates, datatype Tindices, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ScatterMin", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), ref.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), updates.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor scatter_mul(const tensor& ref, const tensor& indices, const tensor& updates, datatype Tindices, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ScatterMul", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), ref.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), updates.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor scatter_nd(const tensor& indices, const tensor& updates, const tensor& shape, datatype Tindices) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ScatterNd", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), updates.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor scatter_nd_add(const tensor& ref, const tensor& indices, const tensor& updates, datatype Tindices, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ScatterNdAdd", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), ref.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), updates.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor scatter_nd_max(const tensor& ref, const tensor& indices, const tensor& updates, datatype Tindices, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ScatterNdMax", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), ref.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), updates.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor scatter_nd_min(const tensor& ref, const tensor& indices, const tensor& updates, datatype Tindices, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ScatterNdMin", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), ref.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), updates.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor scatter_nd_non_aliasing_add(const tensor& input, const tensor& indices, const tensor& updates, datatype Tindices) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ScatterNdNonAliasingAdd", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), updates.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor scatter_nd_sub(const tensor& ref, const tensor& indices, const tensor& updates, datatype Tindices, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ScatterNdSub", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), ref.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), updates.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor scatter_nd_update(const tensor& ref, const tensor& indices, const tensor& updates, datatype Tindices, bool use_locking=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ScatterNdUpdate", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), ref.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), updates.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor scatter_sub(const tensor& ref, const tensor& indices, const tensor& updates, datatype Tindices, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ScatterSub", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), ref.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), updates.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor scatter_update(const tensor& ref, const tensor& indices, const tensor& updates, datatype Tindices, bool use_locking=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ScatterUpdate", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), ref.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), updates.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sdca_fprint(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SdcaFprint", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor segment_max(const tensor& data, const tensor& segment_ids, datatype Tindices) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SegmentMax", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), data.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), segment_ids.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor segment_mean(const tensor& data, const tensor& segment_ids, datatype Tindices) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SegmentMean", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), data.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), segment_ids.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor segment_min(const tensor& data, const tensor& segment_ids, datatype Tindices) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SegmentMin", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), data.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), segment_ids.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor segment_prod(const tensor& data, const tensor& segment_ids, datatype Tindices) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SegmentProd", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), data.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), segment_ids.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor segment_sum(const tensor& data, const tensor& segment_ids, datatype Tindices) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SegmentSum", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), data.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), segment_ids.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor select(const tensor& condition, const tensor& t, const tensor& e) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Select", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), condition.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), t.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), e.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor select_v2(const tensor& condition, const tensor& t, const tensor& e) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SelectV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), condition.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), t.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), e.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor self_adjoint_eig(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SelfAdjointEig", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor selu(const tensor& features) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Selu", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), features.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor selu_grad(const tensor& gradients, const tensor& outputs) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SeluGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), gradients.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), outputs.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor serialize_iterator(const tensor& resource_handle, int64_t external_state_policy=0) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SerializeIterator", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), resource_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "external_state_policy", external_state_policy); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor serialize_many_sparse(const tensor& sparse_indices, const tensor& sparse_values, const tensor& sparse_shape, datatype out_type=static_cast(7)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SerializeManySparse", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), sparse_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), sparse_values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), sparse_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "out_type", out_type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor serialize_sparse(const tensor& sparse_indices, const tensor& sparse_values, const tensor& sparse_shape, datatype out_type=static_cast(7)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SerializeSparse", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), sparse_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), sparse_values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), sparse_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "out_type", out_type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor serialize_tensor(const tensor& input_tensor) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SerializeTensor", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_tensor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor set_size(const tensor& set_indices, const tensor& set_values, const tensor& set_shape, bool validate_indices=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SetSize", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), set_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), set_values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), set_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "validate_indices", (unsigned char)validate_indices); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor set_stats_aggregator_dataset(const tensor& input_dataset, const tensor& stats_aggregator, const tensor& tag, const tensor& counter_prefix, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SetStatsAggregatorDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), stats_aggregator.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), tag.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), counter_prefix.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor shape(const tensor& input, datatype out_type=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Shape", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "out_type", out_type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor shape_n(const std::vector&input, datatype out_type=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ShapeN", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector input_handles; input_handles.reserve(input.size()); - std::transform(input.begin(), input.end(), std::back_inserter(input_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), input_handles.data(), static_cast(input.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "N", input.size()); - TFE_OpSetAttrType(op.get(), "out_type", out_type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor shard_dataset(const tensor& input_dataset, const tensor& num_shards, const tensor& index, const std::vector& output_types, const std::vector< std::vector>& output_shapes, bool require_non_empty=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ShardDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_shards.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), index.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrBool(op.get(), "require_non_empty", (unsigned char)require_non_empty); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sharded_filename(const tensor& basename, const tensor& shard, const tensor& num_shards) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ShardedFilename", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), basename.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), shard.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_shards.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sharded_filespec(const tensor& basename, const tensor& num_shards) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ShardedFilespec", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), basename.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_shards.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor shuffle_and_repeat_dataset(const tensor& input_dataset, const tensor& buffer_size, const tensor& seed, const tensor& seed2, const tensor& count, const std::vector& output_types, const std::vector< std::vector>& output_shapes, bool reshuffle_each_iteration=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ShuffleAndRepeatDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), buffer_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), seed.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), seed2.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), count.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrBool(op.get(), "reshuffle_each_iteration", (unsigned char)reshuffle_each_iteration); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor shuffle_and_repeat_dataset_v2(const tensor& input_dataset, const tensor& buffer_size, const tensor& seed, const tensor& seed2, const tensor& count, const tensor& seed_generator, const std::vector& output_types, const std::vector< std::vector>& output_shapes, bool reshuffle_each_iteration=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ShuffleAndRepeatDatasetV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), buffer_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), seed.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), seed2.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), count.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), seed_generator.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrBool(op.get(), "reshuffle_each_iteration", (unsigned char)reshuffle_each_iteration); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor shuffle_dataset(const tensor& input_dataset, const tensor& buffer_size, const tensor& seed, const tensor& seed2, const std::vector& output_types, const std::vector< std::vector>& output_shapes, bool reshuffle_each_iteration=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ShuffleDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), buffer_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), seed.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), seed2.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrBool(op.get(), "reshuffle_each_iteration", (unsigned char)reshuffle_each_iteration); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor shuffle_dataset_v2(const tensor& input_dataset, const tensor& buffer_size, const tensor& seed_generator, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ShuffleDatasetV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), buffer_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), seed_generator.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor shuffle_dataset_v3(const tensor& input_dataset, const tensor& buffer_size, const tensor& seed, const tensor& seed2, const tensor& seed_generator, const std::vector& output_types, const std::vector< std::vector>& output_shapes, bool reshuffle_each_iteration=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ShuffleDatasetV3", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), buffer_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), seed.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), seed2.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), seed_generator.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrBool(op.get(), "reshuffle_each_iteration", (unsigned char)reshuffle_each_iteration); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sigmoid(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Sigmoid", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sigmoid_grad(const tensor& y, const tensor& dy) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SigmoidGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), dy.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sign(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Sign", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sin(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Sin", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sinh(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Sinh", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor size(const tensor& input, datatype out_type=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Size", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "out_type", out_type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor skip_dataset(const tensor& input_dataset, const tensor& count, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SkipDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), count.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sleep_dataset(const tensor& input_dataset, const tensor& sleep_microseconds, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SleepDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), sleep_microseconds.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor slice(const tensor& input, const tensor& begin, const tensor& size, datatype Index) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Slice", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), begin.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Index", Index); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sliding_window_dataset(const tensor& input_dataset, const tensor& window_size, const tensor& window_shift, const tensor& window_stride, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SlidingWindowDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), window_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), window_shift.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), window_stride.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor snapshot(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Snapshot", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor snapshot_dataset(const tensor& input_dataset, const tensor& path, const std::vector& output_types, const std::vector< std::vector>& output_shapes, const std::string& compression="", const std::string& reader_path_prefix="", const std::string& writer_path_prefix="", int64_t shard_size_bytes=10737418240, int64_t pending_snapshot_expiry_seconds=86400, int64_t num_reader_threads=1, int64_t reader_buffer_size=1, int64_t num_writer_threads=1, int64_t writer_buffer_size=1, bool shuffle_on_read=false, int64_t seed=0, int64_t seed2=0, const std::string& mode="auto", const std::string& snapshot_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SnapshotDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), path.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrString(op.get(), "compression", (void*) compression.c_str(), compression.size()); - TFE_OpSetAttrString(op.get(), "reader_path_prefix", (void*) reader_path_prefix.c_str(), reader_path_prefix.size()); - TFE_OpSetAttrString(op.get(), "writer_path_prefix", (void*) writer_path_prefix.c_str(), writer_path_prefix.size()); - TFE_OpSetAttrInt(op.get(), "shard_size_bytes", shard_size_bytes); - TFE_OpSetAttrInt(op.get(), "pending_snapshot_expiry_seconds", pending_snapshot_expiry_seconds); - TFE_OpSetAttrInt(op.get(), "num_reader_threads", num_reader_threads); - TFE_OpSetAttrInt(op.get(), "reader_buffer_size", reader_buffer_size); - TFE_OpSetAttrInt(op.get(), "num_writer_threads", num_writer_threads); - TFE_OpSetAttrInt(op.get(), "writer_buffer_size", writer_buffer_size); - TFE_OpSetAttrBool(op.get(), "shuffle_on_read", (unsigned char)shuffle_on_read); - TFE_OpSetAttrInt(op.get(), "seed", seed); - TFE_OpSetAttrInt(op.get(), "seed2", seed2); - TFE_OpSetAttrString(op.get(), "mode", (void*) mode.c_str(), mode.size()); - TFE_OpSetAttrString(op.get(), "snapshot_name", (void*) snapshot_name.c_str(), snapshot_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sobol_sample(const tensor& dim, const tensor& num_results, const tensor& skip, datatype dtype=static_cast(1)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SobolSample", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), dim.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_results.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), skip.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor softmax(const tensor& logits) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Softmax", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), logits.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor softplus(const tensor& features) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Softplus", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), features.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor softplus_grad(const tensor& gradients, const tensor& features) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SoftplusGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), gradients.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), features.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor softsign(const tensor& features) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Softsign", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), features.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor softsign_grad(const tensor& gradients, const tensor& features) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SoftsignGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), gradients.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), features.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor space_to_batch(const tensor& input, const tensor& paddings, int64_t block_size, datatype Tpaddings=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SpaceToBatch", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), paddings.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "block_size", block_size); - TFE_OpSetAttrType(op.get(), "Tpaddings", Tpaddings); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor space_to_batch_n_d(const tensor& input, const tensor& block_shape, const tensor& paddings, datatype Tblock_shape=static_cast(3), datatype Tpaddings=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SpaceToBatchND", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), block_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), paddings.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tblock_shape", Tblock_shape); - TFE_OpSetAttrType(op.get(), "Tpaddings", Tpaddings); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor space_to_depth(const tensor& input, int64_t block_size, const std::string& data_format="NHWC") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SpaceToDepth", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "block_size", block_size); - TFE_OpSetAttrString(op.get(), "data_format", (void*) data_format.c_str(), data_format.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_apply_adadelta(const tensor& var, const tensor& accum, const tensor& accum_update, const tensor& lr, const tensor& rho, const tensor& epsilon, const tensor& grad, const tensor& indices, datatype Tindices, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseApplyAdadelta", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), accum.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), accum_update.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), rho.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), epsilon.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_apply_adagrad(const tensor& var, const tensor& accum, const tensor& lr, const tensor& grad, const tensor& indices, datatype Tindices, bool use_locking=false, bool update_slots=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseApplyAdagrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), accum.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - TFE_OpSetAttrBool(op.get(), "update_slots", (unsigned char)update_slots); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_apply_adagrad_d_a(const tensor& var, const tensor& gradient_accumulator, const tensor& gradient_squared_accumulator, const tensor& grad, const tensor& indices, const tensor& lr, const tensor& l1, const tensor& l2, const tensor& global_step, datatype Tindices, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseApplyAdagradDA", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), gradient_accumulator.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), gradient_squared_accumulator.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), l1.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), l2.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), global_step.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_apply_adagrad_v2(const tensor& var, const tensor& accum, const tensor& lr, const tensor& epsilon, const tensor& grad, const tensor& indices, datatype Tindices, bool use_locking=false, bool update_slots=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseApplyAdagradV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), accum.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), epsilon.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - TFE_OpSetAttrBool(op.get(), "update_slots", (unsigned char)update_slots); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_apply_centered_r_m_s_prop(const tensor& var, const tensor& mg, const tensor& ms, const tensor& mom, const tensor& lr, const tensor& rho, const tensor& momentum, const tensor& epsilon, const tensor& grad, const tensor& indices, datatype Tindices, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseApplyCenteredRMSProp", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), mg.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), ms.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), mom.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), rho.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), momentum.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), epsilon.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_apply_ftrl(const tensor& var, const tensor& accum, const tensor& linear, const tensor& grad, const tensor& indices, const tensor& lr, const tensor& l1, const tensor& l2, const tensor& lr_power, datatype Tindices, bool use_locking=false, bool multiply_linear_by_lr=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseApplyFtrl", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), accum.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), linear.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), l1.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), l2.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr_power.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - TFE_OpSetAttrBool(op.get(), "multiply_linear_by_lr", (unsigned char)multiply_linear_by_lr); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_apply_ftrl_v2(const tensor& var, const tensor& accum, const tensor& linear, const tensor& grad, const tensor& indices, const tensor& lr, const tensor& l1, const tensor& l2, const tensor& l2_shrinkage, const tensor& lr_power, datatype Tindices, bool use_locking=false, bool multiply_linear_by_lr=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseApplyFtrlV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), accum.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), linear.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), l1.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), l2.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), l2_shrinkage.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr_power.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - TFE_OpSetAttrBool(op.get(), "multiply_linear_by_lr", (unsigned char)multiply_linear_by_lr); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_apply_momentum(const tensor& var, const tensor& accum, const tensor& lr, const tensor& grad, const tensor& indices, const tensor& momentum, datatype Tindices, bool use_locking=false, bool use_nesterov=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseApplyMomentum", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), accum.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), momentum.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - TFE_OpSetAttrBool(op.get(), "use_nesterov", (unsigned char)use_nesterov); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_apply_proximal_adagrad(const tensor& var, const tensor& accum, const tensor& lr, const tensor& l1, const tensor& l2, const tensor& grad, const tensor& indices, datatype Tindices, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseApplyProximalAdagrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), accum.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), l1.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), l2.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_apply_proximal_gradient_descent(const tensor& var, const tensor& alpha, const tensor& l1, const tensor& l2, const tensor& grad, const tensor& indices, datatype Tindices, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseApplyProximalGradientDescent", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), alpha.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), l1.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), l2.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_apply_r_m_s_prop(const tensor& var, const tensor& ms, const tensor& mom, const tensor& lr, const tensor& rho, const tensor& momentum, const tensor& epsilon, const tensor& grad, const tensor& indices, datatype Tindices, bool use_locking=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseApplyRMSProp", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), var.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), ms.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), mom.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lr.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), rho.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), momentum.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), epsilon.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrBool(op.get(), "use_locking", (unsigned char)use_locking); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_bincount(const tensor& indices, const tensor& values, const tensor& dense_shape, const tensor& size, const tensor& weights, datatype Tidx, bool binary_output=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseBincount", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), dense_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), weights.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - TFE_OpSetAttrBool(op.get(), "binary_output", (unsigned char)binary_output); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_conditional_accumulator(datatype dtype, const std::vector& shape, const std::string& container="", const std::string& shared_name="", const std::string& reduction_type="MEAN") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseConditionalAccumulator", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - TFE_OpSetAttrShape(op.get(), "shape", shape.data(), static_cast(shape.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - TFE_OpSetAttrString(op.get(), "reduction_type", (void*) reduction_type.c_str(), reduction_type.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_dense_cwise_add(const tensor& sp_indices, const tensor& sp_values, const tensor& sp_shape, const tensor& dense) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseDenseCwiseAdd", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), sp_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), sp_values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), sp_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), dense.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_dense_cwise_div(const tensor& sp_indices, const tensor& sp_values, const tensor& sp_shape, const tensor& dense) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseDenseCwiseDiv", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), sp_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), sp_values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), sp_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), dense.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_dense_cwise_mul(const tensor& sp_indices, const tensor& sp_values, const tensor& sp_shape, const tensor& dense) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseDenseCwiseMul", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), sp_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), sp_values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), sp_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), dense.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_mat_mul(const tensor& a, const tensor& b, bool transpose_a=false, bool transpose_b=false, bool a_is_sparse=false, bool b_is_sparse=false, datatype Ta=static_cast(1), datatype Tb=static_cast(1)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseMatMul", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), a.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), b.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "transpose_a", (unsigned char)transpose_a); - TFE_OpSetAttrBool(op.get(), "transpose_b", (unsigned char)transpose_b); - TFE_OpSetAttrBool(op.get(), "a_is_sparse", (unsigned char)a_is_sparse); - TFE_OpSetAttrBool(op.get(), "b_is_sparse", (unsigned char)b_is_sparse); - TFE_OpSetAttrType(op.get(), "Ta", Ta); - TFE_OpSetAttrType(op.get(), "Tb", Tb); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_matrix_add(const tensor& a, const tensor& b, const tensor& alpha, const tensor& beta) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseMatrixAdd", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), a.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), b.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), alpha.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), beta.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_matrix_mat_mul(const tensor& a, const tensor& b, bool transpose_a=false, bool transpose_b=false, bool adjoint_a=false, bool adjoint_b=false, bool transpose_output=false, bool conjugate_output=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseMatrixMatMul", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), a.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), b.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "transpose_a", (unsigned char)transpose_a); - TFE_OpSetAttrBool(op.get(), "transpose_b", (unsigned char)transpose_b); - TFE_OpSetAttrBool(op.get(), "adjoint_a", (unsigned char)adjoint_a); - TFE_OpSetAttrBool(op.get(), "adjoint_b", (unsigned char)adjoint_b); - TFE_OpSetAttrBool(op.get(), "transpose_output", (unsigned char)transpose_output); - TFE_OpSetAttrBool(op.get(), "conjugate_output", (unsigned char)conjugate_output); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_matrix_mul(const tensor& a, const tensor& b) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseMatrixMul", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), a.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), b.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_matrix_n_n_z(const tensor& sparse_matrix) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseMatrixNNZ", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), sparse_matrix.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_matrix_ordering_a_m_d(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseMatrixOrderingAMD", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_matrix_softmax(const tensor& logits, datatype type) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseMatrixSoftmax", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), logits.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "type", type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_matrix_softmax_grad(const tensor& softmax, const tensor& grad_softmax, datatype type) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseMatrixSoftmaxGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), softmax.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad_softmax.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "type", type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_matrix_sparse_cholesky(const tensor& input, const tensor& permutation, datatype type) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseMatrixSparseCholesky", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), permutation.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "type", type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_matrix_sparse_mat_mul(const tensor& a, const tensor& b, datatype type, bool transpose_a=false, bool transpose_b=false, bool adjoint_a=false, bool adjoint_b=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseMatrixSparseMatMul", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), a.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), b.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "type", type); - TFE_OpSetAttrBool(op.get(), "transpose_a", (unsigned char)transpose_a); - TFE_OpSetAttrBool(op.get(), "transpose_b", (unsigned char)transpose_b); - TFE_OpSetAttrBool(op.get(), "adjoint_a", (unsigned char)adjoint_a); - TFE_OpSetAttrBool(op.get(), "adjoint_b", (unsigned char)adjoint_b); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_matrix_transpose(const tensor& input, datatype type, bool conjugate=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseMatrixTranspose", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "type", type); - TFE_OpSetAttrBool(op.get(), "conjugate", (unsigned char)conjugate); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_matrix_zeros(const tensor& dense_shape, datatype type) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseMatrixZeros", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), dense_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "type", type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_reduce_max(const tensor& input_indices, const tensor& input_values, const tensor& input_shape, const tensor& reduction_axes, bool keep_dims=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseReduceMax", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), input_values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), input_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), reduction_axes.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "keep_dims", (unsigned char)keep_dims); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_reduce_sum(const tensor& input_indices, const tensor& input_values, const tensor& input_shape, const tensor& reduction_axes, bool keep_dims=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseReduceSum", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), input_values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), input_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), reduction_axes.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "keep_dims", (unsigned char)keep_dims); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_segment_mean(const tensor& data, const tensor& indices, const tensor& segment_ids, datatype Tidx=static_cast(3), datatype Tsegmentids=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseSegmentMean", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), data.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), segment_ids.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - TFE_OpSetAttrType(op.get(), "Tsegmentids", Tsegmentids); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_segment_mean_grad(const tensor& grad, const tensor& indices, const tensor& segment_ids, const tensor& output_dim0, datatype Tidx=static_cast(3), datatype Tsegmentids=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseSegmentMeanGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), segment_ids.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), output_dim0.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - TFE_OpSetAttrType(op.get(), "Tsegmentids", Tsegmentids); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_segment_mean_with_num_segments(const tensor& data, const tensor& indices, const tensor& segment_ids, const tensor& num_segments, datatype Tidx=static_cast(3), datatype Tnumsegments=static_cast(3), datatype Tsegmentids=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseSegmentMeanWithNumSegments", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), data.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), segment_ids.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_segments.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - TFE_OpSetAttrType(op.get(), "Tnumsegments", Tnumsegments); - TFE_OpSetAttrType(op.get(), "Tsegmentids", Tsegmentids); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_segment_sqrt_n(const tensor& data, const tensor& indices, const tensor& segment_ids, datatype Tidx=static_cast(3), datatype Tsegmentids=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseSegmentSqrtN", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), data.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), segment_ids.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - TFE_OpSetAttrType(op.get(), "Tsegmentids", Tsegmentids); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_segment_sqrt_n_grad(const tensor& grad, const tensor& indices, const tensor& segment_ids, const tensor& output_dim0, datatype Tidx=static_cast(3), datatype Tsegmentids=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseSegmentSqrtNGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), segment_ids.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), output_dim0.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - TFE_OpSetAttrType(op.get(), "Tsegmentids", Tsegmentids); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_segment_sqrt_n_with_num_segments(const tensor& data, const tensor& indices, const tensor& segment_ids, const tensor& num_segments, datatype Tidx=static_cast(3), datatype Tnumsegments=static_cast(3), datatype Tsegmentids=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseSegmentSqrtNWithNumSegments", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), data.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), segment_ids.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_segments.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - TFE_OpSetAttrType(op.get(), "Tnumsegments", Tnumsegments); - TFE_OpSetAttrType(op.get(), "Tsegmentids", Tsegmentids); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_segment_sum(const tensor& data, const tensor& indices, const tensor& segment_ids, datatype Tidx=static_cast(3), datatype Tsegmentids=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseSegmentSum", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), data.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), segment_ids.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - TFE_OpSetAttrType(op.get(), "Tsegmentids", Tsegmentids); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_segment_sum_with_num_segments(const tensor& data, const tensor& indices, const tensor& segment_ids, const tensor& num_segments, datatype Tidx=static_cast(3), datatype Tnumsegments=static_cast(3), datatype Tsegmentids=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseSegmentSumWithNumSegments", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), data.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), segment_ids.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_segments.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - TFE_OpSetAttrType(op.get(), "Tnumsegments", Tnumsegments); - TFE_OpSetAttrType(op.get(), "Tsegmentids", Tsegmentids); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_slice_grad(const tensor& backprop_val_grad, const tensor& input_indices, const tensor& input_start, const tensor& output_indices) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseSliceGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), backprop_val_grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), input_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), input_start.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), output_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_softmax(const tensor& sp_indices, const tensor& sp_values, const tensor& sp_shape) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseSoftmax", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), sp_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), sp_values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), sp_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_tensor_dense_add(const tensor& a_indices, const tensor& a_values, const tensor& a_shape, const tensor& b, datatype Tindices) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseTensorDenseAdd", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), a_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), a_values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), a_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), b.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_tensor_dense_mat_mul(const tensor& a_indices, const tensor& a_values, const tensor& a_shape, const tensor& b, datatype Tindices=static_cast(9), bool adjoint_a=false, bool adjoint_b=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseTensorDenseMatMul", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), a_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), a_values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), a_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), b.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrBool(op.get(), "adjoint_a", (unsigned char)adjoint_a); - TFE_OpSetAttrBool(op.get(), "adjoint_b", (unsigned char)adjoint_b); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_tensor_slice_dataset(const tensor& indices, const tensor& values, const tensor& dense_shape, datatype Tvalues) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseTensorSliceDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), dense_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tvalues", Tvalues); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_tensor_to_c_s_r_sparse_matrix(const tensor& indices, const tensor& values, const tensor& dense_shape) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseTensorToCSRSparseMatrix", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), dense_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sparse_to_dense(const tensor& sparse_indices, const tensor& output_shape, const tensor& sparse_values, const tensor& default_value, datatype Tindices, bool validate_indices=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SparseToDense", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), sparse_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), output_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), sparse_values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), default_value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrBool(op.get(), "validate_indices", (unsigned char)validate_indices); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor spence(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Spence", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor split(const tensor& split_dim, const tensor& value, int64_t num_split) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Split", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), split_dim.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "num_split", num_split); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor split_v(const tensor& value, const tensor& size_splits, const tensor& split_dim, int64_t num_split, datatype Tlen=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SplitV", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), size_splits.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), split_dim.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "num_split", num_split); - TFE_OpSetAttrType(op.get(), "Tlen", Tlen); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sql_dataset(const tensor& driver_name, const tensor& data_source_name, const tensor& query, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SqlDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), driver_name.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), data_source_name.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), query.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sqrt(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Sqrt", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sqrt_grad(const tensor& y, const tensor& dy) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SqrtGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), dy.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor square(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Square", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor squared_difference(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SquaredDifference", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor squeeze(const tensor& input, const std::vector& squeeze_dims) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Squeeze", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrIntList(op.get(), "squeeze_dims", squeeze_dims.data(), static_cast(squeeze_dims.size())); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stack(datatype elem_type, const std::string& stack_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Stack", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrType(op.get(), "elem_type", elem_type); - TFE_OpSetAttrString(op.get(), "stack_name", (void*) stack_name.c_str(), stack_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stack_pop(const tensor& handle, datatype elem_type) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StackPop", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "elem_type", elem_type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stack_pop_v2(const tensor& handle, datatype elem_type) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StackPopV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "elem_type", elem_type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stack_push(const tensor& handle, const tensor& elem, bool swap_memory=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StackPush", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), elem.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "swap_memory", (unsigned char)swap_memory); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stack_push_v2(const tensor& handle, const tensor& elem, bool swap_memory=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StackPushV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), elem.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "swap_memory", (unsigned char)swap_memory); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stack_v2(const tensor& max_size, datatype elem_type, const std::string& stack_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StackV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), max_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "elem_type", elem_type); - TFE_OpSetAttrString(op.get(), "stack_name", (void*) stack_name.c_str(), stack_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stage_peek(const tensor& index, const std::vector& dtypes, int64_t capacity=0, int64_t memory_limit=0, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StagePeek", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), index.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "dtypes", reinterpret_cast(dtypes.data()), static_cast(dtypes.size())); - TFE_OpSetAttrInt(op.get(), "capacity", capacity); - TFE_OpSetAttrInt(op.get(), "memory_limit", memory_limit); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stage_size(const std::vector& dtypes, int64_t capacity=0, int64_t memory_limit=0, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StageSize", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "dtypes", reinterpret_cast(dtypes.data()), static_cast(dtypes.size())); - TFE_OpSetAttrInt(op.get(), "capacity", capacity); - TFE_OpSetAttrInt(op.get(), "memory_limit", memory_limit); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stateful_random_binomial(const tensor& resource, const tensor& algorithm, const tensor& shape, const tensor& counts, const tensor& probs, datatype S, datatype dtype=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StatefulRandomBinomial", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), resource.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), algorithm.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), counts.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), probs.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "S", S); - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stateful_standard_normal(const tensor& resource, const tensor& shape, datatype dtype=static_cast(1), datatype shape_dtype=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StatefulStandardNormal", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), resource.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrType(op.get(), "shape_dtype", shape_dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stateful_standard_normal_v2(const tensor& resource, const tensor& algorithm, const tensor& shape, datatype dtype=static_cast(1), datatype shape_dtype=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StatefulStandardNormalV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), resource.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), algorithm.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrType(op.get(), "shape_dtype", shape_dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stateful_truncated_normal(const tensor& resource, const tensor& algorithm, const tensor& shape, datatype dtype=static_cast(1), datatype shape_dtype=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StatefulTruncatedNormal", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), resource.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), algorithm.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrType(op.get(), "shape_dtype", shape_dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stateful_uniform(const tensor& resource, const tensor& algorithm, const tensor& shape, datatype dtype=static_cast(1), datatype shape_dtype=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StatefulUniform", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), resource.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), algorithm.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrType(op.get(), "shape_dtype", shape_dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stateful_uniform_full_int(const tensor& resource, const tensor& algorithm, const tensor& shape, datatype dtype=static_cast(23), datatype shape_dtype=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StatefulUniformFullInt", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), resource.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), algorithm.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrType(op.get(), "shape_dtype", shape_dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stateful_uniform_int(const tensor& resource, const tensor& algorithm, const tensor& shape, const tensor& minval, const tensor& maxval, datatype dtype=static_cast(9), datatype shape_dtype=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StatefulUniformInt", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), resource.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), algorithm.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), minval.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), maxval.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrType(op.get(), "shape_dtype", shape_dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stateless_multinomial(const tensor& logits, const tensor& num_samples, const tensor& seed, datatype Tseed=static_cast(9), datatype output_dtype=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StatelessMultinomial", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), logits.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_samples.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), seed.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tseed", Tseed); - TFE_OpSetAttrType(op.get(), "output_dtype", output_dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stateless_parameterized_truncated_normal(const tensor& shape, const tensor& seed, const tensor& means, const tensor& stddevs, const tensor& minvals, const tensor& maxvals, datatype S, datatype dtype, datatype Tseed=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StatelessParameterizedTruncatedNormal", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), seed.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), means.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), stddevs.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), minvals.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), maxvals.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "S", S); - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrType(op.get(), "Tseed", Tseed); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stateless_random_binomial(const tensor& shape, const tensor& seed, const tensor& counts, const tensor& probs, datatype S, datatype Tseed=static_cast(9), datatype dtype=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StatelessRandomBinomial", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), seed.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), counts.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), probs.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "S", S); - TFE_OpSetAttrType(op.get(), "Tseed", Tseed); - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stateless_random_gamma_v2(const tensor& shape, const tensor& seed, const tensor& alpha, datatype dtype, datatype Tseed=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StatelessRandomGammaV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), seed.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), alpha.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrType(op.get(), "Tseed", Tseed); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stateless_random_normal(const tensor& shape, const tensor& seed, datatype dtype=static_cast(1), datatype Tseed=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StatelessRandomNormal", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), seed.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrType(op.get(), "Tseed", Tseed); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stateless_random_poisson(const tensor& shape, const tensor& seed, const tensor& lam, datatype Rtype, datatype dtype, datatype Tseed=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StatelessRandomPoisson", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), seed.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lam.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Rtype", Rtype); - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrType(op.get(), "Tseed", Tseed); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stateless_random_uniform(const tensor& shape, const tensor& seed, datatype dtype=static_cast(1), datatype Tseed=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StatelessRandomUniform", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), seed.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrType(op.get(), "Tseed", Tseed); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stateless_random_uniform_full_int(const tensor& shape, const tensor& seed, datatype dtype=static_cast(23), datatype Tseed=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StatelessRandomUniformFullInt", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), seed.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrType(op.get(), "Tseed", Tseed); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stateless_random_uniform_int(const tensor& shape, const tensor& seed, const tensor& minval, const tensor& maxval, datatype dtype, datatype Tseed=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StatelessRandomUniformInt", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), seed.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), minval.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), maxval.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrType(op.get(), "Tseed", Tseed); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stateless_truncated_normal(const tensor& shape, const tensor& seed, datatype dtype=static_cast(1), datatype Tseed=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StatelessTruncatedNormal", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), seed.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrType(op.get(), "Tseed", Tseed); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor static_regex_full_match(const tensor& input, const std::string& pattern) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StaticRegexFullMatch", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "pattern", (void*) pattern.c_str(), pattern.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor static_regex_replace(const tensor& input, const std::string& pattern, const std::string& rewrite, bool replace_global=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StaticRegexReplace", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "pattern", (void*) pattern.c_str(), pattern.size()); - TFE_OpSetAttrString(op.get(), "rewrite", (void*) rewrite.c_str(), rewrite.size()); - TFE_OpSetAttrBool(op.get(), "replace_global", (unsigned char)replace_global); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stats_aggregator_handle(const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StatsAggregatorHandle", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stats_aggregator_handle_v2(const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StatsAggregatorHandleV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stats_aggregator_summary(const tensor& iterator) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StatsAggregatorSummary", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), iterator.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor stop_gradient(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StopGradient", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor strided_slice(const tensor& input, const tensor& begin, const tensor& end, const tensor& strides, datatype Index, int64_t begin_mask=0, int64_t end_mask=0, int64_t ellipsis_mask=0, int64_t new_axis_mask=0, int64_t shrink_axis_mask=0) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StridedSlice", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), begin.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), end.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), strides.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Index", Index); - TFE_OpSetAttrInt(op.get(), "begin_mask", begin_mask); - TFE_OpSetAttrInt(op.get(), "end_mask", end_mask); - TFE_OpSetAttrInt(op.get(), "ellipsis_mask", ellipsis_mask); - TFE_OpSetAttrInt(op.get(), "new_axis_mask", new_axis_mask); - TFE_OpSetAttrInt(op.get(), "shrink_axis_mask", shrink_axis_mask); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor strided_slice_assign(const tensor& ref, const tensor& begin, const tensor& end, const tensor& strides, const tensor& value, datatype Index, int64_t begin_mask=0, int64_t end_mask=0, int64_t ellipsis_mask=0, int64_t new_axis_mask=0, int64_t shrink_axis_mask=0) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StridedSliceAssign", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), ref.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), begin.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), end.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), strides.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Index", Index); - TFE_OpSetAttrInt(op.get(), "begin_mask", begin_mask); - TFE_OpSetAttrInt(op.get(), "end_mask", end_mask); - TFE_OpSetAttrInt(op.get(), "ellipsis_mask", ellipsis_mask); - TFE_OpSetAttrInt(op.get(), "new_axis_mask", new_axis_mask); - TFE_OpSetAttrInt(op.get(), "shrink_axis_mask", shrink_axis_mask); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor strided_slice_grad(const tensor& shape, const tensor& begin, const tensor& end, const tensor& strides, const tensor& dy, datatype Index, int64_t begin_mask=0, int64_t end_mask=0, int64_t ellipsis_mask=0, int64_t new_axis_mask=0, int64_t shrink_axis_mask=0) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StridedSliceGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), begin.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), end.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), strides.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), dy.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Index", Index); - TFE_OpSetAttrInt(op.get(), "begin_mask", begin_mask); - TFE_OpSetAttrInt(op.get(), "end_mask", end_mask); - TFE_OpSetAttrInt(op.get(), "ellipsis_mask", ellipsis_mask); - TFE_OpSetAttrInt(op.get(), "new_axis_mask", new_axis_mask); - TFE_OpSetAttrInt(op.get(), "shrink_axis_mask", shrink_axis_mask); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor string_format(const std::vector&inputs, const std::string& template_arg="%s", const std::string& placeholder="%s", int64_t summarize=3) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StringFormat", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector inputs_handles; inputs_handles.reserve(inputs.size()); - std::transform(inputs.begin(), inputs.end(), std::back_inserter(inputs_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), inputs_handles.data(), static_cast(inputs.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "template", (void*) template_arg.c_str(), template_arg.size()); - TFE_OpSetAttrString(op.get(), "placeholder", (void*) placeholder.c_str(), placeholder.size()); - TFE_OpSetAttrInt(op.get(), "summarize", summarize); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor string_join(const std::vector&inputs, const std::string& separator="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StringJoin", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector inputs_handles; inputs_handles.reserve(inputs.size()); - std::transform(inputs.begin(), inputs.end(), std::back_inserter(inputs_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), inputs_handles.data(), static_cast(inputs.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "N", inputs.size()); - TFE_OpSetAttrString(op.get(), "separator", (void*) separator.c_str(), separator.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor string_length(const tensor& input, const std::string& unit="BYTE") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StringLength", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "unit", (void*) unit.c_str(), unit.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor string_lower(const tensor& input, const std::string& encoding="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StringLower", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "encoding", (void*) encoding.c_str(), encoding.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor string_strip(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StringStrip", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor string_to_hash_bucket(const tensor& string_input_tensor, int64_t num_buckets) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StringToHashBucket", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), string_input_tensor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "num_buckets", num_buckets); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor string_to_hash_bucket_fast(const tensor& input, int64_t num_buckets) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StringToHashBucketFast", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "num_buckets", num_buckets); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor string_to_hash_bucket_strong(const tensor& input, int64_t num_buckets, const std::vector& key) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StringToHashBucketStrong", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "num_buckets", num_buckets); - TFE_OpSetAttrIntList(op.get(), "key", key.data(), static_cast(key.size())); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor string_to_number(const tensor& string_input_tensor, datatype out_type=static_cast(1)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StringToNumber", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), string_input_tensor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "out_type", out_type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor string_upper(const tensor& input, const std::string& encoding="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "StringUpper", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "encoding", (void*) encoding.c_str(), encoding.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sub(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Sub", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor substr(const tensor& input, const tensor& pos, const tensor& len, const std::string& unit="BYTE") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Substr", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), pos.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), len.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "unit", (void*) unit.c_str(), unit.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor sum(const tensor& input, const tensor& reduction_indices, bool keep_dims=false, datatype Tidx=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Sum", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), reduction_indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "keep_dims", (unsigned char)keep_dims); - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor summary_writer(const std::string& shared_name="", const std::string& container="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "SummaryWriter", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor t_f_record_dataset(const tensor& filenames, const tensor& compression_type, const tensor& buffer_size) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TFRecordDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), filenames.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), compression_type.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), buffer_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor t_f_record_reader(const std::string& container="", const std::string& shared_name="", const std::string& compression_type="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TFRecordReader", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - TFE_OpSetAttrString(op.get(), "compression_type", (void*) compression_type.c_str(), compression_type.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor t_f_record_reader_v2(const std::string& container="", const std::string& shared_name="", const std::string& compression_type="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TFRecordReaderV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - TFE_OpSetAttrString(op.get(), "compression_type", (void*) compression_type.c_str(), compression_type.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor t_p_u_compilation_result() { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TPUCompilationResult", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor t_p_u_embedding_activations(const tensor& embedding_variable, const tensor& sliced_activations, int64_t table_id, int64_t lookup_id) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TPUEmbeddingActivations", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), embedding_variable.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), sliced_activations.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "table_id", table_id); - TFE_OpSetAttrInt(op.get(), "lookup_id", lookup_id); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor t_p_u_ordinal_selector() { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TPUOrdinalSelector", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor t_p_u_replicated_input(const std::vector&inputs, bool is_mirrored_variable=false, int64_t index=-1, bool is_packed=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TPUReplicatedInput", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector inputs_handles; inputs_handles.reserve(inputs.size()); - std::transform(inputs.begin(), inputs.end(), std::back_inserter(inputs_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), inputs_handles.data(), static_cast(inputs.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "N", inputs.size()); - TFE_OpSetAttrBool(op.get(), "is_mirrored_variable", (unsigned char)is_mirrored_variable); - TFE_OpSetAttrInt(op.get(), "index", index); - TFE_OpSetAttrBool(op.get(), "is_packed", (unsigned char)is_packed); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor t_p_u_replicated_output(const tensor& input, int64_t num_replicas) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TPUReplicatedOutput", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "num_replicas", num_replicas); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor take_dataset(const tensor& input_dataset, const tensor& count, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TakeDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), count.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tan(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Tan", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tanh(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Tanh", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tanh_grad(const tensor& y, const tensor& dy) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TanhGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), dy.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor temporary_variable(const std::vector& shape, datatype dtype, const std::string& var_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TemporaryVariable", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - - TFE_OpSetAttrShape(op.get(), "shape", shape.data(), static_cast(shape.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrString(op.get(), "var_name", (void*) var_name.c_str(), var_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_array(const tensor& size, datatype dtype, const std::vector& element_shape, bool dynamic_size=false, bool clear_after_read=true, const std::string& tensor_array_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorArray", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - TFE_OpSetAttrShape(op.get(), "element_shape", element_shape.data(), static_cast(element_shape.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrBool(op.get(), "dynamic_size", (unsigned char)dynamic_size); - TFE_OpSetAttrBool(op.get(), "clear_after_read", (unsigned char)clear_after_read); - TFE_OpSetAttrString(op.get(), "tensor_array_name", (void*) tensor_array_name.c_str(), tensor_array_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_array_gather(const tensor& handle, const tensor& indices, const tensor& flow_in, datatype dtype, const std::vector& element_shape) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorArrayGather", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), flow_in.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - TFE_OpSetAttrShape(op.get(), "element_shape", element_shape.data(), static_cast(element_shape.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_array_gather_v2(const tensor& handle, const tensor& indices, const tensor& flow_in, datatype dtype, const std::vector& element_shape) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorArrayGatherV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), flow_in.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - TFE_OpSetAttrShape(op.get(), "element_shape", element_shape.data(), static_cast(element_shape.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_array_gather_v3(const tensor& handle, const tensor& indices, const tensor& flow_in, datatype dtype, const std::vector& element_shape) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorArrayGatherV3", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), flow_in.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - TFE_OpSetAttrShape(op.get(), "element_shape", element_shape.data(), static_cast(element_shape.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_array_grad(const tensor& handle, const tensor& flow_in, const std::string& source) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorArrayGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), flow_in.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "source", (void*) source.c_str(), source.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_array_grad_v2(const tensor& handle, const tensor& flow_in, const std::string& source) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorArrayGradV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), flow_in.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "source", (void*) source.c_str(), source.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_array_pack(const tensor& handle, const tensor& flow_in, datatype dtype, const std::vector& element_shape) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorArrayPack", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), flow_in.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - TFE_OpSetAttrShape(op.get(), "element_shape", element_shape.data(), static_cast(element_shape.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_array_read(const tensor& handle, const tensor& index, const tensor& flow_in, datatype dtype) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorArrayRead", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), index.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), flow_in.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_array_read_v2(const tensor& handle, const tensor& index, const tensor& flow_in, datatype dtype) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorArrayReadV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), index.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), flow_in.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_array_read_v3(const tensor& handle, const tensor& index, const tensor& flow_in, datatype dtype) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorArrayReadV3", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), index.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), flow_in.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_array_scatter(const tensor& handle, const tensor& indices, const tensor& value, const tensor& flow_in) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorArrayScatter", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), flow_in.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_array_scatter_v2(const tensor& handle, const tensor& indices, const tensor& value, const tensor& flow_in) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorArrayScatterV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), flow_in.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_array_scatter_v3(const tensor& handle, const tensor& indices, const tensor& value, const tensor& flow_in) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorArrayScatterV3", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), flow_in.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_array_size(const tensor& handle, const tensor& flow_in) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorArraySize", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), flow_in.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_array_size_v2(const tensor& handle, const tensor& flow_in) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorArraySizeV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), flow_in.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_array_size_v3(const tensor& handle, const tensor& flow_in) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorArraySizeV3", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), flow_in.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_array_split(const tensor& handle, const tensor& value, const tensor& lengths, const tensor& flow_in) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorArraySplit", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lengths.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), flow_in.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_array_split_v2(const tensor& handle, const tensor& value, const tensor& lengths, const tensor& flow_in) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorArraySplitV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lengths.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), flow_in.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_array_split_v3(const tensor& handle, const tensor& value, const tensor& lengths, const tensor& flow_in) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorArraySplitV3", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lengths.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), flow_in.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_array_unpack(const tensor& handle, const tensor& value, const tensor& flow_in) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorArrayUnpack", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), flow_in.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_array_v2(const tensor& size, datatype dtype, const std::vector& element_shape, bool dynamic_size=false, bool clear_after_read=true, const std::string& tensor_array_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorArrayV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - TFE_OpSetAttrShape(op.get(), "element_shape", element_shape.data(), static_cast(element_shape.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrBool(op.get(), "dynamic_size", (unsigned char)dynamic_size); - TFE_OpSetAttrBool(op.get(), "clear_after_read", (unsigned char)clear_after_read); - TFE_OpSetAttrString(op.get(), "tensor_array_name", (void*) tensor_array_name.c_str(), tensor_array_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_array_write(const tensor& handle, const tensor& index, const tensor& value, const tensor& flow_in) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorArrayWrite", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), index.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), flow_in.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_array_write_v2(const tensor& handle, const tensor& index, const tensor& value, const tensor& flow_in) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorArrayWriteV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), index.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), flow_in.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_array_write_v3(const tensor& handle, const tensor& index, const tensor& value, const tensor& flow_in) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorArrayWriteV3", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), index.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), flow_in.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_dataset(const std::vector&components, const std::vector& Toutput_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector components_handles; components_handles.reserve(components.size()); - std::transform(components.begin(), components.end(), std::back_inserter(components_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), components_handles.data(), static_cast(components.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "Toutput_types", reinterpret_cast(Toutput_types.data()), static_cast(Toutput_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_list_concat_lists(const tensor& input_a, const tensor& input_b, datatype element_dtype) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorListConcatLists", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_a.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), input_b.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "element_dtype", element_dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_list_element_shape(const tensor& input_handle, datatype shape_type) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorListElementShape", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "shape_type", shape_type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_list_from_tensor(const tensor& input_tensor, const tensor& element_shape, datatype element_dtype, datatype shape_type) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorListFromTensor", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_tensor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), element_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "element_dtype", element_dtype); - TFE_OpSetAttrType(op.get(), "shape_type", shape_type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_list_gather(const tensor& input_handle, const tensor& indices, const tensor& element_shape, datatype element_dtype) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorListGather", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), element_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "element_dtype", element_dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_list_get_item(const tensor& input_handle, const tensor& index, const tensor& element_shape, datatype element_dtype) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorListGetItem", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), index.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), element_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "element_dtype", element_dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_list_length(const tensor& input_handle) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorListLength", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_list_push_back(const tensor& input_handle, const tensor& input_tensor, datatype element_dtype) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorListPushBack", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), input_tensor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "element_dtype", element_dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_list_push_back_batch(const tensor& input_handles, const tensor& input_tensor, datatype element_dtype) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorListPushBackBatch", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_handles.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), input_tensor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "element_dtype", element_dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_list_reserve(const tensor& element_shape, const tensor& num_elements, datatype element_dtype, datatype shape_type) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorListReserve", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), element_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_elements.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "element_dtype", element_dtype); - TFE_OpSetAttrType(op.get(), "shape_type", shape_type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_list_resize(const tensor& input_handle, const tensor& size) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorListResize", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_list_scatter(const tensor& input_tensor, const tensor& indices, const tensor& element_shape, datatype element_dtype, datatype shape_type) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorListScatter", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_tensor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), element_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "element_dtype", element_dtype); - TFE_OpSetAttrType(op.get(), "shape_type", shape_type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_list_scatter_into_existing_list(const tensor& input_handle, const tensor& input_tensor, const tensor& indices, datatype element_dtype) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorListScatterIntoExistingList", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), input_tensor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "element_dtype", element_dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_list_scatter_v2(const tensor& input_tensor, const tensor& indices, const tensor& element_shape, const tensor& num_elements, datatype element_dtype, datatype shape_type) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorListScatterV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_tensor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), element_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_elements.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "element_dtype", element_dtype); - TFE_OpSetAttrType(op.get(), "shape_type", shape_type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_list_set_item(const tensor& input_handle, const tensor& index, const tensor& item, datatype element_dtype) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorListSetItem", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), index.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), item.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "element_dtype", element_dtype); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_list_split(const tensor& input_tensor, const tensor& element_shape, const tensor& lengths, datatype element_dtype, datatype shape_type) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorListSplit", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_tensor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), element_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), lengths.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "element_dtype", element_dtype); - TFE_OpSetAttrType(op.get(), "shape_type", shape_type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_list_stack(const tensor& input_handle, const tensor& element_shape, datatype element_dtype, int64_t num_elements=-1) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorListStack", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), element_shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "element_dtype", element_dtype); - TFE_OpSetAttrInt(op.get(), "num_elements", num_elements); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_scatter_add(const tensor& input_tensor, const tensor& indices, const tensor& updates, datatype Tindices) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorScatterAdd", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_tensor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), updates.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_scatter_max(const tensor& input_tensor, const tensor& indices, const tensor& updates, datatype Tindices) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorScatterMax", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_tensor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), updates.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_scatter_min(const tensor& input_tensor, const tensor& indices, const tensor& updates, datatype Tindices) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorScatterMin", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_tensor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), updates.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_scatter_sub(const tensor& input_tensor, const tensor& indices, const tensor& updates, datatype Tindices) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorScatterSub", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_tensor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), updates.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_scatter_update(const tensor& input_tensor, const tensor& indices, const tensor& updates, datatype Tindices) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorScatterUpdate", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_tensor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), updates.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_slice_dataset(const std::vector&components, const std::vector& Toutput_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorSliceDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector components_handles; components_handles.reserve(components.size()); - std::transform(components.begin(), components.end(), std::back_inserter(components_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), components_handles.data(), static_cast(components.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "Toutput_types", reinterpret_cast(Toutput_types.data()), static_cast(Toutput_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_strided_slice_update(const tensor& input, const tensor& begin, const tensor& end, const tensor& strides, const tensor& value, datatype Index, int64_t begin_mask=0, int64_t end_mask=0, int64_t ellipsis_mask=0, int64_t new_axis_mask=0, int64_t shrink_axis_mask=0) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorStridedSliceUpdate", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), begin.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), end.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), strides.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Index", Index); - TFE_OpSetAttrInt(op.get(), "begin_mask", begin_mask); - TFE_OpSetAttrInt(op.get(), "end_mask", end_mask); - TFE_OpSetAttrInt(op.get(), "ellipsis_mask", ellipsis_mask); - TFE_OpSetAttrInt(op.get(), "new_axis_mask", new_axis_mask); - TFE_OpSetAttrInt(op.get(), "shrink_axis_mask", shrink_axis_mask); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_summary(const tensor& input_tensor, const std::vector< std::string>& labels, const std::string& description="", const std::string& display_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorSummary", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_tensor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - std::vector labels_sizes; labels_sizes.reserve(labels.size()); - std::transform(labels.begin(), labels.end(), std::back_inserter(labels_sizes), [](const auto& s) { return s.size();}); - TFE_OpSetAttrStringList(op.get(), "labels", reinterpret_cast(labels.data()), labels_sizes.data(), static_cast(labels.size())); - - TFE_OpSetAttrString(op.get(), "description", (void*) description.c_str(), description.size()); - TFE_OpSetAttrString(op.get(), "display_name", (void*) display_name.c_str(), display_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tensor_summary_v2(const tensor& tag, const tensor& input_tensor, const tensor& serialized_summary_metadata) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TensorSummaryV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), tag.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), input_tensor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), serialized_summary_metadata.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor text_line_dataset(const tensor& filenames, const tensor& compression_type, const tensor& buffer_size) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TextLineDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), filenames.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), compression_type.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), buffer_size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor text_line_reader(int64_t skip_header_lines=0, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TextLineReader", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrInt(op.get(), "skip_header_lines", skip_header_lines); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor text_line_reader_v2(int64_t skip_header_lines=0, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TextLineReaderV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrInt(op.get(), "skip_header_lines", skip_header_lines); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor thread_pool_dataset(const tensor& input_dataset, const tensor& thread_pool, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ThreadPoolDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), thread_pool.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor thread_pool_handle(int64_t num_threads, const std::string& display_name, int64_t max_intra_op_parallelism=1, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ThreadPoolHandle", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrInt(op.get(), "num_threads", num_threads); - TFE_OpSetAttrString(op.get(), "display_name", (void*) display_name.c_str(), display_name.size()); - TFE_OpSetAttrInt(op.get(), "max_intra_op_parallelism", max_intra_op_parallelism); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tile(const tensor& input, const tensor& multiples, datatype Tmultiples=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Tile", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), multiples.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tmultiples", Tmultiples); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tile_grad(const tensor& input, const tensor& multiples) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TileGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), multiples.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor timestamp() { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Timestamp", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor to_bool(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ToBool", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor transpose(const tensor& x, const tensor& perm, datatype Tperm=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Transpose", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), perm.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tperm", Tperm); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tridiagonal_mat_mul(const tensor& superdiag, const tensor& maindiag, const tensor& subdiag, const tensor& rhs) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TridiagonalMatMul", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), superdiag.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), maindiag.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), subdiag.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), rhs.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor tridiagonal_solve(const tensor& diagonals, const tensor& rhs, bool partial_pivoting=true) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TridiagonalSolve", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), diagonals.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), rhs.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrBool(op.get(), "partial_pivoting", (unsigned char)partial_pivoting); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor truncate_div(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TruncateDiv", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor truncate_mod(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TruncateMod", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor truncated_normal(const tensor& shape, datatype dtype, int64_t seed=0, int64_t seed2=0) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "TruncatedNormal", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), shape.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrInt(op.get(), "seed", seed); - TFE_OpSetAttrInt(op.get(), "seed2", seed2); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor unbatch(const tensor& batched_input_tensor, const tensor& batch_index, const tensor& id, int64_t timeout_micros, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Unbatch", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), batched_input_tensor.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), batch_index.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), id.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "timeout_micros", timeout_micros); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor unbatch_dataset(const tensor& input_dataset, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "UnbatchDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor unbatch_grad(const tensor& original_input, const tensor& batch_index, const tensor& grad, const tensor& id, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "UnbatchGrad", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), original_input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), batch_index.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), grad.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), id.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor uncompress_element(const tensor& compressed, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "UncompressElement", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), compressed.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor unicode_encode(const tensor& input_values, const tensor& input_splits, const std::string& output_encoding, const std::string& errors="replace", int64_t replacement_char=65533, datatype Tsplits=static_cast(9)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "UnicodeEncode", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), input_splits.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "output_encoding", (void*) output_encoding.c_str(), output_encoding.size()); - TFE_OpSetAttrString(op.get(), "errors", (void*) errors.c_str(), errors.size()); - TFE_OpSetAttrInt(op.get(), "replacement_char", replacement_char); - TFE_OpSetAttrType(op.get(), "Tsplits", Tsplits); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor unicode_script(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "UnicodeScript", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor unicode_transcode(const tensor& input, const std::string& input_encoding, const std::string& output_encoding, const std::string& errors="replace", int64_t replacement_char=65533, bool replace_control_characters=false) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "UnicodeTranscode", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrString(op.get(), "input_encoding", (void*) input_encoding.c_str(), input_encoding.size()); - TFE_OpSetAttrString(op.get(), "output_encoding", (void*) output_encoding.c_str(), output_encoding.size()); - TFE_OpSetAttrString(op.get(), "errors", (void*) errors.c_str(), errors.size()); - TFE_OpSetAttrInt(op.get(), "replacement_char", replacement_char); - TFE_OpSetAttrBool(op.get(), "replace_control_characters", (unsigned char)replace_control_characters); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor unique_dataset(const tensor& input_dataset, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "UniqueDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor unpack(const tensor& value, int64_t num, int64_t axis=0) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Unpack", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), value.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrInt(op.get(), "num", num); - TFE_OpSetAttrInt(op.get(), "axis", axis); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor unravel_index(const tensor& indices, const tensor& dims, datatype Tidx=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "UnravelIndex", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), indices.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), dims.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tidx", Tidx); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor unsorted_segment_join(const tensor& inputs, const tensor& segment_ids, const tensor& num_segments, datatype Tindices, const std::string& separator="", datatype Tnumsegments=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "UnsortedSegmentJoin", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), inputs.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), segment_ids.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_segments.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrString(op.get(), "separator", (void*) separator.c_str(), separator.size()); - TFE_OpSetAttrType(op.get(), "Tnumsegments", Tnumsegments); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor unsorted_segment_max(const tensor& data, const tensor& segment_ids, const tensor& num_segments, datatype Tindices, datatype Tnumsegments=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "UnsortedSegmentMax", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), data.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), segment_ids.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_segments.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrType(op.get(), "Tnumsegments", Tnumsegments); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor unsorted_segment_min(const tensor& data, const tensor& segment_ids, const tensor& num_segments, datatype Tindices, datatype Tnumsegments=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "UnsortedSegmentMin", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), data.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), segment_ids.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_segments.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrType(op.get(), "Tnumsegments", Tnumsegments); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor unsorted_segment_prod(const tensor& data, const tensor& segment_ids, const tensor& num_segments, datatype Tindices, datatype Tnumsegments=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "UnsortedSegmentProd", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), data.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), segment_ids.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_segments.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrType(op.get(), "Tnumsegments", Tnumsegments); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor unsorted_segment_sum(const tensor& data, const tensor& segment_ids, const tensor& num_segments, datatype Tindices, datatype Tnumsegments=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "UnsortedSegmentSum", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), data.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), segment_ids.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), num_segments.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "Tindices", Tindices); - TFE_OpSetAttrType(op.get(), "Tnumsegments", Tnumsegments); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor unstage(const std::vector& dtypes, int64_t capacity=0, int64_t memory_limit=0, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Unstage", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "dtypes", reinterpret_cast(dtypes.data()), static_cast(dtypes.size())); - TFE_OpSetAttrInt(op.get(), "capacity", capacity); - TFE_OpSetAttrInt(op.get(), "memory_limit", memory_limit); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor unwrap_dataset_variant(const tensor& input_handle) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "UnwrapDatasetVariant", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor upper_bound(const tensor& sorted_inputs, const tensor& values, datatype out_type=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "UpperBound", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), sorted_inputs.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), values.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "out_type", out_type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor var_handle_op(datatype dtype, const std::vector& shape, const std::vector< std::string>& allowed_devices, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "VarHandleOp", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrType(op.get(), "dtype", dtype); - - TFE_OpSetAttrShape(op.get(), "shape", shape.data(), static_cast(shape.size()), context::get_status()); - status_check(context::get_status()); - - - std::vector allowed_devices_sizes; allowed_devices_sizes.reserve(allowed_devices.size()); - std::transform(allowed_devices.begin(), allowed_devices.end(), std::back_inserter(allowed_devices_sizes), [](const auto& s) { return s.size();}); - TFE_OpSetAttrStringList(op.get(), "allowed_devices", reinterpret_cast(allowed_devices.data()), allowed_devices_sizes.data(), static_cast(allowed_devices.size())); - - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor var_is_initialized_op(const tensor& resource) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "VarIsInitializedOp", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), resource.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor variable(const std::vector& shape, datatype dtype, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Variable", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - - TFE_OpSetAttrShape(op.get(), "shape", shape.data(), static_cast(shape.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor variable_shape(const tensor& input, datatype out_type=static_cast(3)) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "VariableShape", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrType(op.get(), "out_type", out_type); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor variable_v2(const std::vector& shape, datatype dtype, const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "VariableV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - - TFE_OpSetAttrShape(op.get(), "shape", shape.data(), static_cast(shape.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrType(op.get(), "dtype", dtype); - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor where(const tensor& input) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Where", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor whole_file_reader(const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "WholeFileReader", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor whole_file_reader_v2(const std::string& container="", const std::string& shared_name="") { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "WholeFileReaderV2", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - - // Attributes - TFE_OpSetAttrString(op.get(), "container", (void*) container.c_str(), container.size()); - TFE_OpSetAttrString(op.get(), "shared_name", (void*) shared_name.c_str(), shared_name.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor window_dataset(const tensor& input_dataset, const tensor& size, const tensor& shift, const tensor& stride, const tensor& drop_remainder, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "WindowDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_dataset.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), size.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), shift.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), stride.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), drop_remainder.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor worker_heartbeat(const tensor& request) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "WorkerHeartbeat", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), request.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor wrap_dataset_variant(const tensor& input_handle) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "WrapDatasetVariant", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), input_handle.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor xdivy(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Xdivy", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor xlog1py(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Xlog1py", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor xlogy(const tensor& x, const tensor& y) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Xlogy", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), y.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor zeros_like(const tensor& x) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ZerosLike", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor zeta(const tensor& x, const tensor& q) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "Zeta", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - TFE_OpAddInput(op.get(), x.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - TFE_OpAddInput(op.get(), q.tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - - // Attributes - - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -inline tensor zip_dataset(const std::vector&input_datasets, const std::vector& output_types, const std::vector< std::vector>& output_shapes) { - - // Define Op - std::unique_ptr op(TFE_NewOp(context::get_context(), "ZipDataset", context::get_status()), &TFE_DeleteOp); - status_check(context::get_status()); - - // Required input arguments - - std::vector input_datasets_handles; input_datasets_handles.reserve(input_datasets.size()); - std::transform(input_datasets.begin(), input_datasets.end(), std::back_inserter(input_datasets_handles), [](const auto& t) { return t.tfe_handle.get();}); - TFE_OpAddInputList(op.get(), input_datasets_handles.data(), static_cast(input_datasets.size()), context::get_status()); - status_check(context::get_status()); - - - // Attributes - TFE_OpSetAttrTypeList(op.get(), "output_types", reinterpret_cast(output_types.data()), static_cast(output_types.size())); - - std::vector output_shapes_values; output_shapes_values.reserve(output_shapes.size()); - std::vector output_shapes_ndims; output_shapes_ndims.reserve(output_shapes.size()); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_values), [](const auto& v) { return v.data();}); - std::transform(output_shapes.begin(), output_shapes.end(), std::back_inserter(output_shapes_ndims), [](const auto& v) { return static_cast(v.size());}); - TFE_OpSetAttrShapeList(op.get(), "output_shapes", output_shapes_values.data(), output_shapes_ndims.data(), static_cast(output_shapes.size()), context::get_status()); - status_check(context::get_status()); - - TFE_OpSetAttrInt(op.get(), "N", input_datasets.size()); - - // Execute Op - int num_outputs_op = 1; - TFE_TensorHandle* res[1] = {nullptr}; - TFE_Execute(op.get(), res, &num_outputs_op, context::get_status()); - status_check(context::get_status()); - return tensor(res[0]); -} - - -} // cppflow - -#endif - diff --git a/include/cppflow/tensor.h b/include/cppflow/tensor.h deleted file mode 100644 index 51cd940..0000000 --- a/include/cppflow/tensor.h +++ /dev/null @@ -1,324 +0,0 @@ -// MIT License -// -// Copyright (c) 2020 Sergio Izquierdo -// Copyright (c) 2020 CarlPoirier -// Copyright (c) 2020 Jiannan Liu -// Copyright (c) 2020 liufeng27 -// Copyright (c) 2022 Alfredo Rodriguez -// -// Permission is hereby granted, free of charge, to any person obtaining a copy -// of this software and associated documentation files (the "Software"), to deal -// in the Software without restriction, including without limitation the rights -// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -// copies of the Software, and to permit persons to whom the Software is -// furnished to do so, subject to the following conditions: -// -// The above copyright notice and this permission notice shall be included in -// all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -// SOFTWARE. - -/*! - * @file tensor.h - * @author Alfredo Rodriguez - * @author CarlPoirier - * @author Jiannan Liu - * @author liufeng27 - * @author Sergio Izquierdo - * @date @showdate "%B %d, %Y" 2020-06-27 - */ - -#ifndef INCLUDE_CPPFLOW_TENSOR_H_ -#define INCLUDE_CPPFLOW_TENSOR_H_ - -// C headers -#include -#include - -// C++ headers -#include -#include -#include -#include - -// CppFlow headers -#include "cppflow/context.h" -#include "cppflow/datatype.h" - -namespace cppflow { - -/** - * @class tensor - * @brief A TensorFlow eager tensor wrapper - * - */ -class tensor { - public: - tensor()= default; - - /** - * Creates a tensor with the given values and specified shape - * @tparam T A type that can be convertible into a tensor - * @param values The values to be converted (in a flattened version) - * @param shape The shape of the converted tensor - */ - template - tensor(const std::vector& values, const std::vector& shape); - - /** - * Creates a flat tensor with the given values - * @tparam T A type that can be convertible into a tensor - * @param values The values to be converted - */ - template - tensor(const std::initializer_list& values); - - /** - * Creates a tensor with the given value - * @tparam T A type that can be convertible into a tensor - * @param value The value to be converted - */ - template - tensor(const T& value); - tensor(const tensor &tensor) = default; - tensor(tensor &&tensor) = default; - explicit tensor(TFE_TensorHandle* handle); - explicit tensor(TF_Tensor* t); - - ~tensor() = default; - - tensor &operator=(const tensor &other) = default; - tensor &operator=(tensor &&other) = default; - - /** - * @return Shape of the tensor - */ - tensor shape() const; - - /** - * @param on_memory If false, the function will return the name of the device that produced the tensor. - * If true, the function will return the name of the device in whose memory the tensor resides - * @return Returns the name of the device of the tensor - */ - std::string device(bool on_memory = false) const; - - - /** - * @return The tensor datatype - */ - datatype dtype() const; - - /** - * Converts the tensor into a C++ vector - * @tparam T The c++ type (must be equivalent to the tensor type) - * @return A vector representing the flat tensor - */ - template - std::vector get_data() const; - - // NOTE: - // Usually, one should not call get_eager_handle() or get_tensor() below. - // They are designed for implementation details in cppflow. - // If you are calling them directly, it is likely that you are using some - // tenforflow APIs not supported in cppflow. - - // Additional NOTE: - // TF_Tensor is an immutable tensor inside tensorflow. - // TFE_TensorHandle is a TF_Tensor and the associated device, - // plus some data cache - - // @todo Need to determine if we can mark the return value or *this as const - std::shared_ptr get_eager_handle() const { - return tfe_handle; - } - - // Get the TF_Tensor data from the eager handle - // Call `get_data()` instead if possible - // NOTE: - // Changes to the returned TF_Tensor may not be reflected in the - // actual device memory! - // Do *NOT* modify the returned TF_Tensor! - // See comments of `tf_tensor` for more details. - std::shared_ptr get_tensor() const; - - // DO NOT directly access this member, call get_eager_handle() instead - // @todo This is kept as public to be compatible with existing code and - // should be mark as private - std::shared_ptr tfe_handle; - - private: - tensor(enum TF_DataType type, const void* data, size_t len, - const std::vector& shape); - - // This member serves as a local cache of the data in tfe_handle. - // It refers to `local_mirrors_` if on device, or `data_` if on host CPU. - // Changes to this variable may not be reflected in the actual device memory, - // e.g. on GPUs or on remote nodes. - // Access it via get_tensor() if not in constructor - mutable std::shared_ptr tf_tensor; -}; - -} // namespace cppflow - - -/****************************** - * IMPLEMENTATION DETAILS * - ******************************/ - - -namespace cppflow { - -inline tensor::tensor(enum TF_DataType type, const void *data, size_t len, - const std::vector &shape) { - this->tf_tensor = {TF_AllocateTensor(type, shape.data(), - static_cast(shape.size()), len), - TF_DeleteTensor}; - memcpy(TF_TensorData(this->tf_tensor.get()), data, - TF_TensorByteSize(this->tf_tensor.get())); - this->tfe_handle = {TFE_NewTensorHandle(this->tf_tensor.get(), - context::get_status()), - TFE_DeleteTensorHandle}; - status_check(context::get_status()); -} - -template -tensor::tensor(const std::vector& values, const std::vector& shape) - : tensor(deduce_tf_type(), values.data(), values.size() * sizeof(T), - shape) {} - -template -tensor::tensor(const std::initializer_list& values) - : tensor(std::vector(values), {(int64_t) values.size()}) {} - -template -tensor::tensor(const T& value) - : tensor(std::vector({value}), {}) {} - -#ifdef TENSORFLOW_C_TF_TSTRING_H_ - // For future version TensorFlow 2.4 - template<> - inline tensor::tensor(const std::string& value) { - TF_TString tstr[1]; - TF_TString_Init(&tstr[0]); - TF_TString_Copy(&tstr[0], value.c_str(), value.size()); - - // *this = tensor(static_cast(TF_STRING), - // reinterpret_cast(tstr), sizeof(tstr), /*shape*/ {}); - *this = tensor(static_cast(TF_STRING), (void *) tstr, - sizeof(tstr), /*shape*/ {}); - } -#else - template<> - inline tensor::tensor(const std::string& value) { - size_t size = 8 + TF_StringEncodedSize(value.length()); - char* data = new char[value.size() + 8]; - for (int i=0; i < 8; i++) {data[i]=0;} - TF_StringEncode(value.c_str(), value.size(), data + 8, size - 8, - context::get_status()); - status_check(context::get_status()); - - // *this = tensor(static_cast(TF_STRING), - // reinterpret_cast(data), size, /*shape*/ {}); - *this = tensor(static_cast(TF_STRING), (void *) data, - size, /*shape*/ {}); - delete [] data; - } -#endif // TENSORFLOW_C_TF_TSTRING_H_ - -inline tensor::tensor(TFE_TensorHandle* handle) { - this->tfe_handle = {handle, TFE_DeleteTensorHandle}; -} - -inline tensor::tensor(TF_Tensor* t) { - this->tf_tensor = {t, TF_DeleteTensor}; - this->tfe_handle = {TFE_NewTensorHandle(this->tf_tensor.get(), - context::get_status()), - TFE_DeleteTensorHandle}; - status_check(context::get_status()); -} - -inline tensor tensor::shape() const { - auto op = TFE_NewOp(context::get_context(), "Shape", context::get_status()); - status_check(context::get_status()); - - TFE_OpAddInput(op, this->tfe_handle.get(), context::get_status()); - status_check(context::get_status()); - - // Output type should be int64_t - TFE_OpSetAttrType(op, "out_type", cppflow::datatype::TF_INT64); - - // EXECUTE - int n = 1; - TFE_TensorHandle* res[1] = { nullptr }; - TFE_Execute(op, res, &n, context::get_status()); - status_check(context::get_status()); - TFE_DeleteOp(op); - - return tensor(res[0]); -} - -inline std::string tensor::device(bool on_memory) const { - std::string res; - if (on_memory) - res = TFE_TensorHandleBackingDeviceName(this->tfe_handle.get(), - context::get_status()); - else - res = std::string(TFE_TensorHandleDeviceName(this->tfe_handle.get(), - context::get_status())); - - status_check(context::get_status()); - return res; -} - -template -std::vector tensor::get_data() const { - // Check if asked datatype and tensor datatype match - if (this->dtype() != deduce_tf_type()) { - auto type1 = cppflow::to_string(deduce_tf_type()); - auto type2 = cppflow::to_string(this->dtype()); - auto error = "Datatype in function get_data (" + type1 + - ") does not match tensor datatype (" + type2 + ")"; - throw std::runtime_error(error); - } - - auto res_tensor = get_tensor(); - - // Check tensor data is not empty - auto raw_data = TF_TensorData(res_tensor.get()); - // this->error_check(raw_data != nullptr, "Tensor data is empty"); - - size_t size = (TF_TensorByteSize(res_tensor.get()) / - TF_DataTypeSize(TF_TensorType(res_tensor.get()))); - - // Convert to correct type - const auto T_data = static_cast(raw_data); - std::vector r(T_data, T_data + size); - - return r; -} - -inline datatype tensor::dtype() const { - return TFE_TensorHandleDataType(this->tfe_handle.get()); -} - -// NOTE: -// Changes to the returned TF_Tensor are not reflected in -// the actual device memory! -inline std::shared_ptr tensor::get_tensor() const { - if (!tf_tensor) { - tf_tensor = {TFE_TensorHandleResolve(tfe_handle.get(), - context::get_status()), - TF_DeleteTensor}; - status_check(context::get_status()); - } - return tf_tensor; -} -} // namespace cppflow - -#endif // INCLUDE_CPPFLOW_TENSOR_H_ diff --git a/index.html b/index.html new file mode 100644 index 0000000..44485bb --- /dev/null +++ b/index.html @@ -0,0 +1,154 @@ + + + + + + + Easily run TensorFlow models from C++ — cppflow 2.0 documentation + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + cppflow
+

Easily run TensorFlow models from C++

+

With cppflow you can easily run TensorFlow models in C++ without Bazel, without TensorFlow installation and without compiling Tensorflow. Perform tensor manipulation, use eager execution and run saved models directly from C++.

+
+

Guide

+ +
+
+
+

Indices and tables

+ +
+

Remark

+

CppFlow is not related with TensorFlow. The CppFlow icon is a modified version of the TensorFlow logo. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/installation.html b/installation.html new file mode 100644 index 0000000..658b866 --- /dev/null +++ b/installation.html @@ -0,0 +1,163 @@ + + + + + + + Installation — cppflow 2.0 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Installation

+

One of the advantages of cppflow is that you don’t need to compile or install TensorFlow, you just need to download the TF C API. Cppflow is a header-only library, and thus you can just include the cppflow files in your project.

+

To install the C API in your system you have two options:

+
+

Install the TF C API globally

+

You can install the C API in a system directory and do not worry about it again. For this, you just have to download it it and then:

+
sudo tar -C /usr/local -xzf (downloaded file)
+sudo ldconfig
+
+
+
+
+

Install the TF C API in custom directory

+

You can also install the library in a custom directory. In this case, after downloading it and unpacking it you will need to update your PATH or tell CMake where you placed the library with -DCMAKE_PREFIX_PATH=....

+
mkdir -p /path/to/mydir/
+tar -C /path/to/mydir -xzf (downloaded file)
+
+
+

Now, update your path:

+
export LIBRARY_PATH=$LIBRARY_PATH:/path/to/mydir/lib
+export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/path/to/mydir/lib
+
+
+
+
+

Install cppflow

+

Cppflow is just a header-only library, and thus it does not require to build it. To facilitate the installation, we provide a CMake file that will install the library in your system. To install it, you just have to:

+
mkdir build
+cd build
+cmake ..
+make -j
+make install
+
+
+
+

Note

+

If you installed the TF C API in a custom directory, you will need to tell CMake where you placed the library with -DCMAKE_PREFIX_PATH=/path/to/mydir/.

+
+

This will also compile the examples, if you don’t want to compile them, you can use -DBUILD_EXAMPLES=OFF.

+

You are done, now you can proceed to build your first example.

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/objects.inv b/objects.inv new file mode 100644 index 0000000..bcb888b --- /dev/null +++ b/objects.inv @@ -0,0 +1,5 @@ +# Sphinx inventory version 2 +# Project: cppflow +# Version: +# The remainder of this file is compressed using zlib. +xڅQN0+V[P\Q*PTT~$`Ev%wgvfvD#PFG(^$Тn:^XFF~k:afE[JZenIdgBx)'j_ -5wB`7؝4_LAg&L$Bb\ϊseM\rtXF;ևͤKB+"ql!q>$+L'\.afmR)G~F  \ No newline at end of file diff --git a/pylintrc b/pylintrc deleted file mode 100644 index f1fde7c..0000000 --- a/pylintrc +++ /dev/null @@ -1,428 +0,0 @@ -# This Pylint rcfile contains a best-effort configuration to uphold the -# best-practices and style described in the Google Python style guide: -# https://google.github.io/styleguide/pyguide.html -# -# Its canonical open-source location is: -# https://google.github.io/styleguide/pylintrc - -[MASTER] - -# Files or directories to be skipped. They should be base names, not paths. -ignore=third_party - -# Files or directories matching the regex patterns are skipped. The regex -# matches against base names, not paths. -ignore-patterns= - -# Pickle collected data for later comparisons. -persistent=no - -# List of plugins (as comma separated values of python modules names) to load, -# usually to register additional checkers. -load-plugins= - -# Use multiple processes to speed up Pylint. -jobs=4 - -# Allow loading of arbitrary C extensions. Extensions are imported into the -# active Python interpreter and may run arbitrary code. -unsafe-load-any-extension=no - - -[MESSAGES CONTROL] - -# Only show warnings with the listed confidence levels. Leave empty to show -# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED -confidence= - -# Enable the message, report, category or checker with the given id(s). You can -# either give multiple identifier separated by comma (,) or put this option -# multiple time (only on the command line, not in the configuration file where -# it should appear only once). See also the "--disable" option for examples. -#enable= - -# Disable the message, report, category or checker with the given id(s). You -# can either give multiple identifiers separated by comma (,) or put this -# option multiple times (only on the command line, not in the configuration -# file where it should appear only once).You can also use "--disable=all" to -# disable everything first and then reenable specific checks. For example, if -# you want to run only the similarities checker, you can use "--disable=all -# --enable=similarities". If you want to run only the classes checker, but have -# no Warning level messages displayed, use"--disable=all --enable=classes -# --disable=W" -disable=abstract-method, - apply-builtin, - arguments-differ, - attribute-defined-outside-init, - backtick, - bad-option-value, - basestring-builtin, - buffer-builtin, - c-extension-no-member, - consider-using-enumerate, - cmp-builtin, - cmp-method, - coerce-builtin, - coerce-method, - delslice-method, - div-method, - duplicate-code, - eq-without-hash, - execfile-builtin, - file-builtin, - filter-builtin-not-iterating, - fixme, - getslice-method, - global-statement, - hex-method, - idiv-method, - implicit-str-concat, - import-error, - import-self, - import-star-module-level, - inconsistent-return-statements, - input-builtin, - intern-builtin, - invalid-str-codec, - locally-disabled, - long-builtin, - long-suffix, - map-builtin-not-iterating, - misplaced-comparison-constant, - missing-function-docstring, - metaclass-assignment, - next-method-called, - next-method-defined, - no-absolute-import, - no-else-break, - no-else-continue, - no-else-raise, - no-else-return, - no-init, # added - no-member, - no-name-in-module, - no-self-use, - nonzero-method, - oct-method, - old-division, - old-ne-operator, - old-octal-literal, - old-raise-syntax, - parameter-unpacking, - print-statement, - raising-string, - range-builtin-not-iterating, - raw_input-builtin, - rdiv-method, - reduce-builtin, - relative-import, - reload-builtin, - round-builtin, - setslice-method, - signature-differs, - standarderror-builtin, - suppressed-message, - sys-max-int, - too-few-public-methods, - too-many-ancestors, - too-many-arguments, - too-many-boolean-expressions, - too-many-branches, - too-many-instance-attributes, - too-many-locals, - too-many-nested-blocks, - too-many-public-methods, - too-many-return-statements, - too-many-statements, - trailing-newlines, - unichr-builtin, - unicode-builtin, - unnecessary-pass, - unpacking-in-except, - useless-else-on-loop, - useless-object-inheritance, - useless-suppression, - using-cmp-argument, - wrong-import-order, - xrange-builtin, - zip-builtin-not-iterating, - - -[REPORTS] - -# Set the output format. Available formats are text, parseable, colorized, msvs -# (visual studio) and html. You can also give a reporter class, eg -# mypackage.mymodule.MyReporterClass. -output-format=text - -# Tells whether to display a full report or only the messages -reports=no - -# Python expression which should return a note less than 10 (10 is the highest -# note). You have access to the variables errors warning, statement which -# respectively contain the number of errors / warnings messages and the total -# number of statements analyzed. This is used by the global evaluation report -# (RP0004). -evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10) - -# Template used to display messages. This is a python new-style format string -# used to format the message information. See doc for all details -#msg-template= - - -[BASIC] - -# Good variable names which should always be accepted, separated by a comma -good-names=main,_ - -# Bad variable names which should always be refused, separated by a comma -bad-names= - -# Colon-delimited sets of names that determine each other's naming style when -# the name regexes allow several styles. -name-group= - -# Include a hint for the correct naming format with invalid-name -include-naming-hint=no - -# List of decorators that produce properties, such as abc.abstractproperty. Add -# to this list to register other decorators that produce valid properties. -property-classes=abc.abstractproperty,cached_property.cached_property,cached_property.threaded_cached_property,cached_property.cached_property_with_ttl,cached_property.threaded_cached_property_with_ttl - -# Regular expression matching correct function names -function-rgx=^(?:(?PsetUp|tearDown|setUpModule|tearDownModule)|(?P_?[A-Z][a-zA-Z0-9]*)|(?P_?[a-z][a-z0-9_]*))$ - -# Regular expression matching correct variable names -variable-rgx=^[a-z][a-z0-9_]*$ - -# Regular expression matching correct constant names -const-rgx=^(_?[A-Z][A-Z0-9_]*|__[a-z0-9_]+__|_?[a-z][a-z0-9_]*)$ - -# Regular expression matching correct attribute names -attr-rgx=^_{0,2}[a-z][a-z0-9_]*$ - -# Regular expression matching correct argument names -argument-rgx=^[a-z][a-z0-9_]*$ - -# Regular expression matching correct class attribute names -class-attribute-rgx=^(_?[A-Z][A-Z0-9_]*|__[a-z0-9_]+__|_?[a-z][a-z0-9_]*)$ - -# Regular expression matching correct inline iteration names -inlinevar-rgx=^[a-z][a-z0-9_]*$ - -# Regular expression matching correct class names -class-rgx=^_?[A-Z][a-zA-Z0-9]*$ - -# Regular expression matching correct module names -module-rgx=^(_?[a-z][a-z0-9_]*|__init__)$ - -# Regular expression matching correct method names -method-rgx=(?x)^(?:(?P_[a-z0-9_]+__|runTest|setUp|tearDown|setUpTestCase|tearDownTestCase|setupSelf|tearDownClass|setUpClass|(test|assert)_*[A-Z0-9][a-zA-Z0-9_]*|next)|(?P_{0,2}[A-Z][a-zA-Z0-9_]*)|(?P_{0,2}[a-z][a-z0-9_]*))$ - -# Regular expression which should only match function or class names that do -# not require a docstring. -no-docstring-rgx=(__.*__|main|test.*|.*test|.*Test)$ - -# Minimum line length for functions/classes that require docstrings, shorter -# ones are exempt. -docstring-min-length=10 - - -[TYPECHECK] - -# List of decorators that produce context managers, such as -# contextlib.contextmanager. Add to this list to register other decorators that -# produce valid context managers. -contextmanager-decorators=contextlib.contextmanager,contextlib2.contextmanager - -# Tells whether missing members accessed in mixin class should be ignored. A -# mixin class is detected if its name ends with "mixin" (case insensitive). -ignore-mixin-members=yes - -# List of module names for which member attributes should not be checked -# (useful for modules/projects where namespaces are manipulated during runtime -# and thus existing member attributes cannot be deduced by static analysis. It -# supports qualified module names, as well as Unix pattern matching. -ignored-modules= - -# List of class names for which member attributes should not be checked (useful -# for classes with dynamically set attributes). This supports the use of -# qualified names. -ignored-classes=optparse.Values,thread._local,_thread._local - -# List of members which are set dynamically and missed by pylint inference -# system, and so shouldn't trigger E1101 when accessed. Python regular -# expressions are accepted. -generated-members= - - -[FORMAT] - -# Maximum number of characters on a single line. -max-line-length=80 - -# TODO(https://github.com/PyCQA/pylint/issues/3352): Direct pylint to exempt -# lines made too long by directives to pytype. - -# Regexp for a line that is allowed to be longer than the limit. -ignore-long-lines=(?x)( - ^\s*(\#\ )??$| - ^\s*(from\s+\S+\s+)?import\s+.+$) - -# Allow the body of an if to be on the same line as the test if there is no -# else. -single-line-if-stmt=yes - -# Maximum number of lines in a module -max-module-lines=99999 - -# String used as indentation unit. The internal Google style guide mandates 2 -# spaces. Google's externaly-published style guide says 4, consistent with -# PEP 8. -indent-string=' ' - -# Number of spaces of indent required inside a hanging or continued line. -indent-after-paren=4 - -# Expected format of line ending, e.g. empty (any line ending), LF or CRLF. -expected-line-ending-format= - - -[MISCELLANEOUS] - -# List of note tags to take in consideration, separated by a comma. -notes=TODO - - -[STRING] - -# This flag controls whether inconsistent-quotes generates a warning when the -# character used as a quote delimiter is used inconsistently within a module. -check-quote-consistency=yes - - -[VARIABLES] - -# Tells whether we should check for unused import in __init__ files. -init-import=no - -# A regular expression matching the name of dummy variables (i.e. expectedly -# not used). -dummy-variables-rgx=^\*{0,2}(_$|unused_|dummy_) - -# List of additional names supposed to be defined in builtins. Remember that -# you should avoid to define new builtins when possible. -additional-builtins= - -# List of strings which can identify a callback function by name. A callback -# name must start or end with one of those strings. -callbacks=cb_,_cb - -# List of qualified module names which can have objects that can redefine -# builtins. -redefining-builtins-modules=six,six.moves,past.builtins,future.builtins,functools - - -[LOGGING] - -# Logging modules to check that the string format arguments are in logging -# function parameter format -logging-modules=logging,absl.logging,tensorflow.io.logging - - -[SIMILARITIES] - -# Minimum lines number of a similarity. -min-similarity-lines=4 - -# Ignore comments when computing similarities. -ignore-comments=yes - -# Ignore docstrings when computing similarities. -ignore-docstrings=yes - -# Ignore imports when computing similarities. -ignore-imports=no - - -[SPELLING] - -# Spelling dictionary name. Available dictionaries: none. To make it working -# install python-enchant package. -spelling-dict= - -# List of comma separated words that should not be checked. -spelling-ignore-words= - -# A path to a file that contains private dictionary; one word per line. -spelling-private-dict-file= - -# Tells whether to store unknown words to indicated private dictionary in -# --spelling-private-dict-file option instead of raising a message. -spelling-store-unknown-words=no - - -[IMPORTS] - -# Deprecated modules which should not be used, separated by a comma -deprecated-modules=regsub, - TERMIOS, - Bastion, - rexec, - sets - -# Create a graph of every (i.e. internal and external) dependencies in the -# given file (report RP0402 must not be disabled) -import-graph= - -# Create a graph of external dependencies in the given file (report RP0402 must -# not be disabled) -ext-import-graph= - -# Create a graph of internal dependencies in the given file (report RP0402 must -# not be disabled) -int-import-graph= - -# Force import order to recognize a module as part of the standard -# compatibility libraries. -known-standard-library= - -# Force import order to recognize a module as part of a third party library. -known-third-party=enchant, absl - -# Analyse import fallback blocks. This can be used to support both Python 2 and -# 3 compatible code, which means that the block might have code that exists -# only in one or another interpreter, leading to false positives when analysed. -analyse-fallback-blocks=no - - -[CLASSES] - -# List of method names used to declare (i.e. assign) instance attributes. -defining-attr-methods=__init__, - __new__, - setUp - -# List of member names, which should be excluded from the protected access -# warning. -exclude-protected=_asdict, - _fields, - _replace, - _source, - _make - -# List of valid names for the first argument in a class method. -valid-classmethod-first-arg=cls, - class_ - -# List of valid names for the first argument in a metaclass class method. -valid-metaclass-classmethod-first-arg=mcs - - -[EXCEPTIONS] - -# Exceptions that will emit a warning when being caught. Defaults to -# "Exception" -overgeneral-exceptions=StandardError, - Exception, - BaseException diff --git a/quickstart.html b/quickstart.html new file mode 100644 index 0000000..26510fd --- /dev/null +++ b/quickstart.html @@ -0,0 +1,354 @@ + + + + + + + Quickstart — cppflow 2.0 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Quickstart

+
+

First example

+

Once you have downloaded the TF C API and cppflow you can start playing with tensors from C++:

+
#include <iostream>
+#include <cppflow/cppflow.h>
+
+int main() {
+
+    // Create a tensor from a list, a = [1.0, 2.0, 3.0]
+    auto a = cppflow::tensor({1.0, 2.0, 3.0});
+    // Create a tensor of shape 3 filled with 1.0, b = [1.0, 1.0, 1.0]
+    auto b = cppflow::fill({3}, 1.0);
+
+    std::cout << a + b << std::endl;
+
+    return 0;
+}
+
+
+

Easy right?, now you can compile it with the terminal (if you have configured the TF C API and installed cppflow as stated in Installation) using the following command:

+
g++ -std=c++17 -o main.out main.cpp -ltensorflow
+./main.out
+
+
+

You should see the result of a + b:

+
(tensor: shape=[3], data=
+ [2 3 4])
+
+
+
+
+

Using CMake

+

Probably a more convenient way of compiling your code is using CMake.

+
cmake_minimum_required(VERSION 3.10)
+
+project(example)
+
+add_executable(example main.cpp)
+
+find_package(cppflow REQUIRED)
+
+target_include_directories(
+example PUBLIC
+cppflow::cppflow
+)
+
+target_link_libraries(
+example PUBLIC
+cppflow::cppflow
+)
+
+
+

Now you can compile it with:

+
mkdir build
+cd build
+cmake ..
+make
+
+
+
+

Note

+

If you installed the TF C API or cppflow in a custom directory, you will need to tell CMake where you placed them -DCMAKE_PREFIX_PATH=/path/to/mydir/.

+
+
+
+

Load a model

+

You can easily run TensorFlow models with cppflow by loading a saved model. Imagine you have a model saved on the folder coolpredictor that takes an image as an input and produces a vector of probabilities of the class belonging to each possible class. You can load the model, read your image, preprocess it, run the model and get the output just using cppflow:

+
#include <iostream>
+#include <cppflow/cppflow.h>
+
+
+int main() {
+
+    // Load the model
+    cppflow::model model("coolpredictor");
+
+    // Load an image
+    auto input = cppflow::decode_jpeg(cppflow::read_file(std::string("image.jpg")));
+
+    // Cast it to float, normalize to range [0, 1], and add batch_dimension
+    input = cppflow::cast(input, TF_UINT8, TF_FLOAT);
+    input = input / 255.f;
+    input = cppflow::expand_dims(input, 0);
+
+    // Run
+    auto output = model(input);
+
+    // Show the predicted class
+    std::cout << cppflow::arg_max(output, 1) << std::endl;
+}
+
+
+

For a complete (runnable) example you can check this example, which performs inference on an EfficientNet trained on ImageNet.

+
+
+

Complex model call (multi input/output models)

+

By default, calling model(input) will use the input operation named serving_default_input_1 and output operation StatefulPartitionedCall. If you need to use other operations or you need to use multiple inputs or outputs you can directly specify your desired inputs and outputs operations:

+
// Calling model in default mode
+auto output = model(input);
+
+// Calling model as in default mode (but specifying signature)
+auto output = model({{"serving_default_input_1:0", input}},{"StatefulPartitionedCall:0"});
+
+// Calling model with two inputs, named "serving_default_input_1" and "serving_default_input_2"
+auto output = model({{"serving_default_input_1:0", input1}, {"serving_default_input_2:0", input2}},{"StatefulPartitionedCall:0"});
+
+// Calling model with two outputs, named "StatefulPartitionedCall:0" and "StatefulPartitionedCall:1"
+auto output = model({{"serving_default_input_1:0", input}},{"StatefulPartitionedCall:0", "StatefulPartitionedCall:1"});
+
+// Calling model with two inputs and two outputs
+auto output = model({{"serving_default_my_input_1:0", input_1}, {"serving_default_my_input_2:0", input_2}}, {"StatefulPartitionedCall:0", "StatefulPartitionedCall:1"});
+
+
+
+

Note

+

If you don’t know the name of the operations of your model you can use the saved_model_cli to print all the information:

+

saved_model_cli show --dir /path/to/model --all

+

Or you can use model::get_operations() to retrieve the name of the available operations.

+
+

For a complete (runnable) example you can check this example., which uses a toy model with two inputs and two outputs.

+
+
+

GPU Config Options

+

You can specify TensorFlow’s GPU Options to prevent it from reserving all your GPU memory. To do so in cppflow2, you need to change the global context and set your serialized options:

+
// Serialized config options (example of 30% memory fraction)
+// Read more to see how to obtain the serialized options
+std::vector<uint8_t> config{0x32,0xb,0x9,0x34,0x33,0x33,0x33,0x33,0x33,0xd3,0x3f,0x20,0x1};
+// Create new options with your configuration
+TFE_ContextOptions* options = TFE_NewContextOptions();
+TFE_ContextOptionsSetConfig(options, config.data(), config.size(), cppflow::context::get_status());
+// Replace the global context with your options
+cppflow::get_global_context() = cppflow::context(options);
+
+
+

To obtain your desired serialized config options you can just run a small python script to print them:

+
import tensorflow.compat.v1 as tf
+
+def create_serialized_options(fraction, growth):
+    config = tf.ConfigProto()
+    config.gpu_options.per_process_gpu_memory_fraction = fraction
+    config.gpu_options.allow_growth = growth
+    serialized = config.SerializeToString()
+    return '{' + ','.join(list(map(hex, serialized))) + '}'
+
+# Example with 30% and enable memory growth
+# {0x32,0xb,0x9,0x33,0x33,0x33,0x33,0x33,0x33,0xd3,0x3f,0x20,0x1}
+print(create_serialized_options(fraction=0.3, growth=True))
+
+
+

Also, for convenience, here is a list with some precomputed serialized options:

+ +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Memory Fraction

Memory Growth

Serialized Options

10%

True

{0x32,0xb,0x9,0x9a,0x99,0x99,0x99,0x99,0x99,0xb9,0x3f,0x20,0x1}

False

{0x32,0x9,0x9,0x9a,0x99,0x99,0x99,0x99,0x99,0xb9,0x3f}

20%

True

{0x32,0xb,0x9,0x9a,0x99,0x99,0x99,0x99,0x99,0xc9,0x3f,0x20,0x1}

False

{0x32,0x9,0x9,0x9a,0x99,0x99,0x99,0x99,0x99,0xc9,0x3f}

30%

True

{0x32,0xb,0x9,0x34,0x33,0x33,0x33,0x33,0x33,0xd3,0x3f,0x20,0x1}

False

{0x32,0x9,0x9,0x34,0x33,0x33,0x33,0x33,0x33,0xd3,0x3f}

40%

True

{0x32,0xb,0x9,0x9a,0x99,0x99,0x99,0x99,0x99,0xd9,0x3f,0x20,0x1}

False

{0x32,0x9,0x9,0x9a,0x99,0x99,0x99,0x99,0x99,0xd9,0x3f}

50%

True

{0x32,0xb,0x9,0x0,0x0,0x0,0x0,0x0,0x0,0xe0,0x3f,0x20,0x1}

False

{0x32,0x9,0x9,0x0,0x0,0x0,0x0,0x0,0x0,0xe0,0x3f}

60%

True

{0x32,0xb,0x9,0x34,0x33,0x33,0x33,0x33,0x33,0xe3,0x3f,0x20,0x1}

False

{0x32,0x9,0x9,0x34,0x33,0x33,0x33,0x33,0x33,0xe3,0x3f}

70%

True

{0x32,0xb,0x9,0x67,0x66,0x66,0x66,0x66,0x66,0xe6,0x3f,0x20,0x1}

False

{0x32,0x9,0x9,0x67,0x66,0x66,0x66,0x66,0x66,0xe6,0x3f}

80%

True

{0x32,0xb,0x9,0x9a,0x99,0x99,0x99,0x99,0x99,0xe9,0x3f,0x20,0x1}

False

{0x32,0x9,0x9,0x9a,0x99,0x99,0x99,0x99,0x99,0xe9,0x3f}

90%

True

{0x32,0xb,0x9,0xcd,0xcc,0xcc,0xcc,0xcc,0xcc,0xec,0x3f,0x20,0x1}

False

{0x32,0x9,0x9,0xcd,0xcc,0xcc,0xcc,0xcc,0xcc,0xec,0x3f}

+
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