/** * @license * Copyright 2023 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ (function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports, require('@tensorflow/tfjs-core')) : typeof define === 'function' && define.amd ? define(['exports', '@tensorflow/tfjs-core'], factory) : (global = typeof globalThis !== 'undefined' ? globalThis : global || self, factory(global.tf = global.tf || {}, global.tf)); })(this, (function (exports, tfc) { 'use strict'; function _interopNamespaceDefault(e) { var n = Object.create(null); if (e) { Object.keys(e).forEach(function (k) { if (k !== 'default') { var d = Object.getOwnPropertyDescriptor(e, k); Object.defineProperty(n, k, d.get ? d : { enumerable: true, get: function () { return e[k]; } }); } }); } n.default = e; return n; } function _mergeNamespaces(n, m) { m.forEach(function (e) { e && typeof e !== 'string' && !Array.isArray(e) && Object.keys(e).forEach(function (k) { if (k !== 'default' && !(k in n)) { var d = Object.getOwnPropertyDescriptor(e, k); Object.defineProperty(n, k, d.get ? d : { enumerable: true, get: function () { return e[k]; } }); } }); }); return n; } var tfc__namespace = /*#__PURE__*/_interopNamespaceDefault(tfc); /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const ENV$1 = tfc.env(); /** Whether to keep intermediate tensors. */ ENV$1.registerFlag('KEEP_INTERMEDIATE_TENSORS', () => false, debugValue => { if (debugValue) { console.warn('Keep intermediate tensors is ON. This will print the values of all ' + 'intermediate tensors during model inference. Not all models ' + 'support this mode. For details, check e2e/benchmarks/ ' + 'model_config.js. This significantly impacts performance.'); } }); /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * * ============================================================================= */ /** DataType enum. */ var DataType; (function (DataType) { // Not a legal value for DataType. Used to indicate a DataType field // has not been set. DataType[DataType["DT_INVALID"] = 0] = "DT_INVALID"; // Data types that all computation devices are expected to be // capable to support. DataType[DataType["DT_FLOAT"] = 1] = "DT_FLOAT"; DataType[DataType["DT_DOUBLE"] = 2] = "DT_DOUBLE"; DataType[DataType["DT_INT32"] = 3] = "DT_INT32"; DataType[DataType["DT_UINT8"] = 4] = "DT_UINT8"; DataType[DataType["DT_INT16"] = 5] = "DT_INT16"; DataType[DataType["DT_INT8"] = 6] = "DT_INT8"; DataType[DataType["DT_STRING"] = 7] = "DT_STRING"; DataType[DataType["DT_COMPLEX64"] = 8] = "DT_COMPLEX64"; DataType[DataType["DT_INT64"] = 9] = "DT_INT64"; DataType[DataType["DT_BOOL"] = 10] = "DT_BOOL"; DataType[DataType["DT_QINT8"] = 11] = "DT_QINT8"; DataType[DataType["DT_QUINT8"] = 12] = "DT_QUINT8"; DataType[DataType["DT_QINT32"] = 13] = "DT_QINT32"; DataType[DataType["DT_BFLOAT16"] = 14] = "DT_BFLOAT16"; DataType[DataType["DT_QINT16"] = 15] = "DT_QINT16"; DataType[DataType["DT_QUINT16"] = 16] = "DT_QUINT16"; DataType[DataType["DT_UINT16"] = 17] = "DT_UINT16"; DataType[DataType["DT_COMPLEX128"] = 18] = "DT_COMPLEX128"; DataType[DataType["DT_HALF"] = 19] = "DT_HALF"; DataType[DataType["DT_RESOURCE"] = 20] = "DT_RESOURCE"; DataType[DataType["DT_VARIANT"] = 21] = "DT_VARIANT"; DataType[DataType["DT_UINT32"] = 22] = "DT_UINT32"; DataType[DataType["DT_UINT64"] = 23] = "DT_UINT64"; // Do not use! These are only for parameters. Every enum above // should have a corresponding value below (verified by types_test). DataType[DataType["DT_FLOAT_REF"] = 101] = "DT_FLOAT_REF"; DataType[DataType["DT_DOUBLE_REF"] = 102] = "DT_DOUBLE_REF"; DataType[DataType["DT_INT32_REF"] = 103] = "DT_INT32_REF"; DataType[DataType["DT_UINT8_REF"] = 104] = "DT_UINT8_REF"; DataType[DataType["DT_INT16_REF"] = 105] = "DT_INT16_REF"; DataType[DataType["DT_INT8_REF"] = 106] = "DT_INT8_REF"; DataType[DataType["DT_STRING_REF"] = 107] = "DT_STRING_REF"; DataType[DataType["DT_COMPLEX64_REF"] = 108] = "DT_COMPLEX64_REF"; DataType[DataType["DT_INT64_REF"] = 109] = "DT_INT64_REF"; DataType[DataType["DT_BOOL_REF"] = 110] = "DT_BOOL_REF"; DataType[DataType["DT_QINT8_REF"] = 111] = "DT_QINT8_REF"; DataType[DataType["DT_QUINT8_REF"] = 112] = "DT_QUINT8_REF"; DataType[DataType["DT_QINT32_REF"] = 113] = "DT_QINT32_REF"; DataType[DataType["DT_BFLOAT16_REF"] = 114] = "DT_BFLOAT16_REF"; DataType[DataType["DT_QINT16_REF"] = 115] = "DT_QINT16_REF"; DataType[DataType["DT_QUINT16_REF"] = 116] = "DT_QUINT16_REF"; DataType[DataType["DT_UINT16_REF"] = 117] = "DT_UINT16_REF"; DataType[DataType["DT_COMPLEX128_REF"] = 118] = "DT_COMPLEX128_REF"; DataType[DataType["DT_HALF_REF"] = 119] = "DT_HALF_REF"; DataType[DataType["DT_RESOURCE_REF"] = 120] = "DT_RESOURCE_REF"; DataType[DataType["DT_VARIANT_REF"] = 121] = "DT_VARIANT_REF"; DataType[DataType["DT_UINT32_REF"] = 122] = "DT_UINT32_REF"; DataType[DataType["DT_UINT64_REF"] = 123] = "DT_UINT64_REF"; })(DataType || (DataType = {})); var SaverDef; (function (SaverDef) { (function (CheckpointFormatVersion) { CheckpointFormatVersion[CheckpointFormatVersion["LEGACY"] = 0] = "LEGACY"; CheckpointFormatVersion[CheckpointFormatVersion["V1"] = 1] = "V1"; CheckpointFormatVersion[CheckpointFormatVersion["V2"] = 2] = "V2"; })(SaverDef.CheckpointFormatVersion || (SaverDef.CheckpointFormatVersion = {})); })(SaverDef || (SaverDef = {})); /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const CUSTOM_OPS = {}; /** * Register an Op for graph model executor. This allows you to register * TensorFlow custom op or override existing op. * * Here is an example of registering a new MatMul Op. * ```js * const customMatmul = (node) => * tf.matMul( * node.inputs[0], node.inputs[1], * node.attrs['transpose_a'], node.attrs['transpose_b']); * * tf.registerOp('MatMul', customMatmul); * ``` * The inputs and attrs of the node object are based on the TensorFlow op * registry. * * @param name The Tensorflow Op name. * @param opFunc An op function which is called with the current graph node * during execution and needs to return a tensor or a list of tensors. The node * has the following attributes: * - attr: A map from attribute name to its value * - inputs: A list of input tensors * * @doc {heading: 'Models', subheading: 'Op Registry'} */ function registerOp(name, opFunc) { const opMapper = { tfOpName: name, category: 'custom', inputs: [], attrs: [], customExecutor: opFunc }; CUSTOM_OPS[name] = opMapper; } /** * Retrieve the OpMapper object for the registered op. * * @param name The Tensorflow Op name. * * @doc {heading: 'Models', subheading: 'Op Registry'} */ function getRegisteredOp(name) { return CUSTOM_OPS[name]; } /** * Deregister the Op for graph model executor. * * @param name The Tensorflow Op name. * * @doc {heading: 'Models', subheading: 'Op Registry'} */ function deregisterOp(name) { delete CUSTOM_OPS[name]; } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ function getParamValue(paramName, node, tensorMap, context, resourceManager) { const inputParam = node.inputParams[paramName]; if (inputParam && inputParam.inputIndexStart !== undefined) { const start = inputParam.inputIndexStart; const end = inputParam.inputIndexEnd === 0 ? undefined : (inputParam.inputIndexEnd === undefined ? start + 1 : inputParam.inputIndexEnd); const shiftedStart = start < 0 ? node.inputNames.length + start : start; if (inputParam.type === 'tensor') { return getTensor(node.inputNames[shiftedStart], tensorMap, context, resourceManager); } if (inputParam.type === 'tensors') { // TODO(mattSoulanille): This filters out NoOp nodes during execution, but // these should really never be in the execution graph in the first place. // They're necessary for ordering the graph, but should not be visible // during execution. Perhaps have different sets of children, one for // control dependencies and another for real dependencies. const inputs = node.inputs.slice(start, end); const inputNames = node.inputNames.slice(start, end) .filter((_name, index) => { var _a; return ((_a = inputs[index]) === null || _a === void 0 ? void 0 : _a.op) !== 'NoOp'; }); return inputNames.map(name => getTensor(name, tensorMap, context, resourceManager)); } const tensor = getTensor(node.inputNames[shiftedStart], tensorMap, context, resourceManager); const data = tensor.dataSync(); return inputParam.type === 'number' ? data[0] : tfc.util.toNestedArray(tensor.shape, data); } const attrParam = node.attrParams[paramName]; return attrParam && attrParam.value; } /** * Retrieve the tensor from tensorsMap based on input name. * @param name Node input name * @param tensorsMap Tensors map keyed by the node * @param context contains tensors and information for running the current node. * @param resourceManager Optional. Contains global resources of the model. */ function getTensor(name, tensorsMap, context, resourceManager) { const [nodeName, index] = parseNodeName(name, context); if (resourceManager != null) { const tensor = resourceManager.getHashTableHandleByName(nodeName); if (tensor != null) { return tensor; } } const contextId = context.currentContextIds.find(contextId => { return !!tensorsMap[getNodeNameWithContextId(nodeName, contextId)]; }); return contextId !== undefined ? tensorsMap[getNodeNameWithContextId(nodeName, contextId)][index] : undefined; } /** * Retrieve the tensors based on input name for current context. * @param name Node input name * @param tensorsMap Tensors map keyed by the node */ function getTensorsForCurrentContext(name, tensorsMap, context) { return tensorsMap[getNodeNameWithContextId(name, context.currentContextId)]; } /** * Returns the node name, outputName and index from the Node input name. * @param inputName The input name of the node, in format of * node_name:output_index, i.e. MatMul:0, if the output_index is not set, it is * default to 0. * If the input name contains output name i.e. StringSplit:indices:0, it will * return ['StringSplit', 0, 'indices']. */ function getNodeNameAndIndex(inputName, context) { const [nodeName, index, outputName] = parseNodeName(inputName, context); return [ getNodeNameWithContextId(nodeName, context && context.currentContextId), index, outputName ]; } function getNodeNameWithContextId(name, contextId) { return !!contextId ? `${name}-${contextId}` : name; } function parseNodeName(name, context) { if (name === '') { return ['', 0, undefined]; } const isCacheEnabled = context != null && context.parseNodeNameCache != null; if (isCacheEnabled) { const cachedResult = context.parseNodeNameCache.get(name); if (cachedResult != null) { return cachedResult; } } const parts = name.split(':'); let result; if (parts.length === 1) { result = [name, 0, undefined]; } else { const nodeName = parts[0]; const outputName = parts.length === 3 ? parts[1] : undefined; const index = Number(parts[parts.length - 1]); result = [nodeName, index, outputName]; } if (isCacheEnabled) { context.parseNodeNameCache.set(name, result); } return result; } function getPadding(node, tensorMap, context) { let pad = getParamValue('pad', node, tensorMap, context); if (pad === 'explicit') { // This is 1d array, we need to convert it to 2d array pad = getParamValue('explicitPaddings', node, tensorMap, context); const explicitPadding = [[0, 0], [0, 0], [0, 0], [0, 0]]; for (let i = 0; i < 4; i++) { explicitPadding[i][0] = pad[i * 2]; explicitPadding[i][1] = pad[i * 2 + 1]; } return explicitPadding; } return pad; } /** * Reuse the tensor if it is marked as keep, otherwise clone the tensor to * avoid disposal. This is important for TensorArray and TensorList ops, since * internally they use a tensor as the id for TensorArray and TensorList, and * to simplify lookup, they also use Tensor.id as the key to the internal map. * These id tensors have been marked as kept in the backend, we need avoid clone * them in order to create new Tensor.id. * @param tensor */ function cloneTensor(tensor) { return tensor.kept ? tensor : tfc.clone(tensor); } /** * @license * Copyright 2023 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const json$i = [ { 'tfOpName': 'Add', 'category': 'arithmetic', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'AddV2', 'category': 'arithmetic', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'AddN', 'category': 'arithmetic', 'inputs': [ { 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' } ] }, { 'tfOpName': 'BiasAdd', 'category': 'arithmetic', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string', 'notSupported': true } ] }, { 'tfOpName': 'Sub', 'category': 'arithmetic', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'RealDiv', 'category': 'arithmetic', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Div', 'category': 'arithmetic', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'DivNoNan', 'category': 'arithmetic', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'FloorDiv', 'category': 'arithmetic', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Mul', 'category': 'arithmetic', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Maximum', 'category': 'arithmetic', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Minimum', 'category': 'arithmetic', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Pow', 'category': 'arithmetic', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'SquaredDifference', 'category': 'arithmetic', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Mod', 'category': 'arithmetic', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'FloorMod', 'category': 'arithmetic', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] } ]; var arithmetic = { __proto__: null, json: json$i }; /** * @license * Copyright 2023 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const json$h = [ { 'tfOpName': 'Abs', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Acos', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Asin', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Atan', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Atan2', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'y', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Ceil', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'ClipByValue', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'clipValueMin', 'type': 'number' }, { 'start': 2, 'name': 'clipValueMax', 'type': 'number' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Complex', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'real', 'type': 'tensor' }, { 'start': 1, 'name': 'imag', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'ComplexAbs', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Cos', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Cosh', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Elu', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Exp', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Floor', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Log', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Imag', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, { 'tfName': 'Tout', 'name': 'outputType', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Neg', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Real', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, { 'tfName': 'Tout', 'name': 'outputType', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Prelu', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'alpha', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Relu', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Relu6', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Selu', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Sigmoid', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Sin', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Sinh', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Sqrt', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Rsqrt', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Square', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Tan', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Tanh', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Sign', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Round', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Expm1', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Log1p', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Reciprocal', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Softplus', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Asinh', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Acosh', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Atanh', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Erf', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'LeakyRelu', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'alpha', 'name': 'alpha', 'type': 'number', 'defaultValue': 0.2 }, { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'IsNan', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'IsFinite', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'IsInf', 'category': 'basic_math', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] } ]; var basicMath = { __proto__: null, json: json$h }; /** * @license * Copyright 2023 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const json$g = [ { 'tfOpName': 'EmptyTensorList', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'elementShape', 'type': 'shape' }, { 'start': 1, 'name': 'maxNumElements', 'type': 'number' } ], 'attrs': [ { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' } ] }, { 'tfOpName': 'LoopCond', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'pred', 'type': 'tensor' } ] }, { 'tfOpName': 'Switch', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'data', 'type': 'tensor' }, { 'start': 1, 'name': 'pred', 'type': 'tensor' } ] }, { 'tfOpName': 'Merge', 'category': 'control', 'inputs': [ { 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' } ] }, { 'tfOpName': 'Enter', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'tensor', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, { 'tfName': 'frame_name', 'name': 'frameName', 'type': 'string' }, { 'tfName': 'is_constant', 'name': 'isConstant', 'type': 'bool' } ] }, { 'tfOpName': 'Exit', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'tensor', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'NextIteration', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'tensor', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'TensorArrayV3', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'size', 'type': 'number' } ], 'attrs': [ { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }, { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' }, { 'tfName': 'dynamic_size', 'name': 'dynamicSize', 'type': 'bool' }, { 'tfName': 'clear_after_read', 'name': 'clearAfterRead', 'type': 'bool' }, { 'tfName': 'identical_element_shapes', 'name': 'identicalElementShapes', 'type': 'bool' }, { 'tfName': 'tensor_array_name', 'name': 'name', 'type': 'string' } ] }, { 'tfOpName': 'TensorArrayWriteV3', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' }, { 'start': 1, 'name': 'index', 'type': 'number' }, { 'start': 2, 'name': 'tensor', 'type': 'tensor' }, { 'start': 3, 'name': 'flowIn', 'type': 'number' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'TensorArrayReadV3', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' }, { 'start': 1, 'name': 'index', 'type': 'number' }, { 'start': 2, 'name': 'flowIn', 'type': 'number' } ], 'attrs': [ { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'TensorArrayGatherV3', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' }, { 'start': 1, 'name': 'indices', 'type': 'number[]' }, { 'start': 2, 'name': 'flowIn', 'type': 'number' } ], 'attrs': [ { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }, { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' } ] }, { 'tfOpName': 'TensorArrayScatterV3', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' }, { 'start': 1, 'name': 'indices', 'type': 'number[]' }, { 'start': 2, 'name': 'tensor', 'type': 'tensor' }, { 'start': 3, 'name': 'flowIn', 'type': 'number' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' } ] }, { 'tfOpName': 'TensorArrayConcatV3', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' }, { 'start': 1, 'name': 'flowIn', 'type': 'number' } ], 'attrs': [ { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }, { 'tfName': 'element_shape_except0', 'name': 'elementShapeExcept0', 'type': 'shape', 'notSupported': true } ] }, { 'tfOpName': 'TensorArraySplitV3', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' }, { 'start': 1, 'name': 'tensor', 'type': 'tensor' }, { 'start': 2, 'name': 'lengths', 'type': 'number[]' }, { 'start': 3, 'name': 'flowIn', 'type': 'number' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' } ] }, { 'tfOpName': 'TensorArraySizeV3', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' }, { 'start': 1, 'name': 'flowIn', 'type': 'number' } ] }, { 'tfOpName': 'TensorArrayCloseV3', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' } ] }, { 'tfOpName': 'StatelessIf', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'cond', 'type': 'tensor' }, { 'start': 1, 'end': 0, 'name': 'args', 'type': 'tensors' } ], 'attrs': [ { 'tfName': 'then_branch', 'name': 'thenBranch', 'type': 'func' }, { 'tfName': 'else_branch', 'name': 'elseBranch', 'type': 'func' } ] }, { 'tfOpName': 'If', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'cond', 'type': 'tensor' }, { 'start': 1, 'end': 0, 'name': 'args', 'type': 'tensors' } ], 'attrs': [ { 'tfName': 'then_branch', 'name': 'thenBranch', 'type': 'func' }, { 'tfName': 'else_branch', 'name': 'elseBranch', 'type': 'func' } ] }, { 'tfOpName': 'StatelessWhile', 'category': 'control', 'inputs': [ { 'start': 0, 'end': 0, 'name': 'args', 'type': 'tensors' } ], 'attrs': [ { 'tfName': 'cond', 'name': 'cond', 'type': 'func' }, { 'tfName': 'body', 'name': 'body', 'type': 'func' } ] }, { 'tfOpName': 'While', 'category': 'control', 'inputs': [ { 'start': 0, 'end': 0, 'name': 'args', 'type': 'tensors' } ], 'attrs': [ { 'tfName': 'cond', 'name': 'cond', 'type': 'func' }, { 'tfName': 'body', 'name': 'body', 'type': 'func' } ] }, { 'tfOpName': 'TensorListScatter', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'tensor', 'type': 'tensor' }, { 'start': 1, 'name': 'indices', 'type': 'number[]' }, { 'start': 2, 'name': 'elementShape', 'type': 'shape' } ], 'attrs': [ { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' } ] }, { 'tfOpName': 'TensorListScatterV2', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'tensor', 'type': 'tensor' }, { 'start': 1, 'name': 'indices', 'type': 'number[]' }, { 'start': 2, 'name': 'elementShape', 'type': 'shape' }, { 'start': 3, 'name': 'numElements', 'type': 'number' } ], 'attrs': [ { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' } ] }, { 'tfOpName': 'TensorListGather', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' }, { 'start': 1, 'name': 'indices', 'type': 'number[]' }, { 'start': 2, 'name': 'elementShape', 'type': 'shape' } ], 'attrs': [ { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' } ] }, { 'tfOpName': 'TensorListGetItem', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' }, { 'start': 1, 'name': 'index', 'type': 'number' }, { 'start': 2, 'name': 'elementShape', 'type': 'shape' } ], 'attrs': [ { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' } ] }, { 'tfOpName': 'TensorListSetItem', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' }, { 'start': 1, 'name': 'index', 'type': 'number' }, { 'start': 2, 'name': 'tensor', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' } ] }, { 'tfOpName': 'TensorListReserve', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'elementShape', 'type': 'shape' }, { 'start': 1, 'name': 'numElements', 'type': 'number' } ], 'attrs': [ { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' } ] }, { 'tfOpName': 'TensorListFromTensor', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'tensor', 'type': 'tensor' }, { 'start': 1, 'name': 'elementShape', 'type': 'shape' } ], 'attrs': [ { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' } ] }, { 'tfOpName': 'TensorListStack', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' }, { 'start': 1, 'name': 'elementShape', 'type': 'shape' } ], 'attrs': [ { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }, { 'tfName': 'num_elements', 'name': 'numElements', 'type': 'dtype' } ] }, { 'tfOpName': 'TensorListSplit', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'tensor', 'type': 'tensor' }, { 'start': 1, 'name': 'elementShape', 'type': 'shape' }, { 'start': 2, 'name': 'lengths', 'type': 'number[]' } ], 'attrs': [ { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' } ] }, { 'tfOpName': 'TensorListConcat', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' }, { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' } ] }, { 'tfOpName': 'TensorListConcatV2', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' }, { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' } ] }, { 'tfOpName': 'TensorListPopBack', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' }, { 'start': 1, 'name': 'elementShape', 'type': 'shape' } ], 'attrs': [ { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' } ] }, { 'tfOpName': 'TensorListPushBack', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' }, { 'start': 1, 'name': 'tensor', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' } ] }, { 'tfOpName': 'TensorListLength', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' } ] }, { 'tfOpName': 'TensorListResize', 'category': 'control', 'inputs': [ { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' }, { 'start': 1, 'name': 'size', 'type': 'number' } ] } ]; var control = { __proto__: null, json: json$g }; /** * @license * Copyright 2023 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const json$f = [ { 'tfOpName': 'AvgPool', 'category': 'convolution', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' }, { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string', 'notSupported': true }, { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' }, { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'MaxPool', 'category': 'convolution', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' }, { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string', 'notSupported': true }, { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' }, { 'tfName': 'explicit_paddings', 'name': 'explicitPaddings', 'type': 'number[]', 'defaultValue': [], 'notSupported': true }, { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'MaxPoolWithArgmax', 'category': 'convolution', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' }, { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' }, { 'tfName': 'include_batch_in_index', 'name': 'includeBatchInIndex', 'type': 'bool' }, { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'AvgPool3D', 'category': 'convolution', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' }, { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string', 'notSupported': true }, { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' }, { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'MaxPool3D', 'category': 'convolution', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' }, { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string', 'notSupported': true }, { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' }, { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Conv1D', 'category': 'convolution', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'filter', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'stride', 'name': 'stride', 'type': 'number' }, { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string', 'defaultValue': 'NWC' }, { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, { 'tfName': 'dilation', 'name': 'dilation', 'type': 'number', 'defaultValue': 1 } ] }, { 'tfOpName': 'Conv2D', 'category': 'convolution', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'filter', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' }, { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, { 'tfName': 'useCudnnOnGpu', 'name': 'useCudnnOnGpu', 'type': 'bool' }, { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string', 'defaultValue': 'NHWC' }, { 'tfName': 'explicit_paddings', 'name': 'explicitPaddings', 'type': 'number[]', 'defaultValue': [] }, { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' } ] }, { 'tfOpName': '_FusedConv2D', 'category': 'convolution', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'filter', 'type': 'tensor' }, { 'start': 2, 'end': 0, 'name': 'args', 'type': 'tensors' } ], 'attrs': [ { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' }, { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' }, { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, { 'tfName': 'explicit_paddings', 'name': 'explicitPaddings', 'type': 'number[]', 'defaultValue': [] }, { 'tfName': 'use_cudnn_on_gpu', 'name': 'useCudnnOnGpu', 'type': 'bool', 'defaultValue': true }, { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string', 'defaultValue': 'NHWC' }, { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]', 'defaultValue': [ 1, 1, 1, 1 ] }, { 'tfName': 'fused_ops', 'name': 'fusedOps', 'type': 'string[]', 'defaultValue': [] }, { 'tfName': 'epsilon', 'name': 'epsilon', 'type': 'number', 'defaultValue': 0.0001 }, { 'tfName': 'leakyrelu_alpha', 'name': 'leakyreluAlpha', 'type': 'number', 'defaultValue': 0.2 } ] }, { 'tfOpName': 'Conv2DBackpropInput', 'category': 'convolution', 'inputs': [ { 'start': 2, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'filter', 'type': 'tensor' }, { 'start': 0, 'name': 'outputShape', 'type': 'number[]' } ], 'attrs': [ { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' }, { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string', 'notSupported': true }, { 'tfName': 'explicit_paddings', 'name': 'explicitPaddings', 'type': 'number[]', 'defaultValue': [] }, { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]', 'notSupported': true } ] }, { 'tfOpName': 'DepthwiseConv2d', 'category': 'convolution', 'inputs': [ { 'start': 0, 'name': 'input', 'type': 'tensor' }, { 'start': 1, 'name': 'filter', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' }, { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string', 'defaultValue': 'NHWC' }, { 'tfName': 'explicit_paddings', 'name': 'explicitPaddings', 'type': 'number[]', 'defaultValue': [] }, { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' } ] }, { 'tfOpName': 'DepthwiseConv2dNative', 'category': 'convolution', 'inputs': [ { 'start': 0, 'name': 'input', 'type': 'tensor' }, { 'start': 1, 'name': 'filter', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' }, { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string', 'defaultValue': 'NHWC' }, { 'tfName': 'explicit_paddings', 'name': 'explicitPaddings', 'type': 'number[]', 'defaultValue': [] }, { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' } ] }, { 'tfOpName': 'FusedDepthwiseConv2dNative', 'category': 'convolution', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'filter', 'type': 'tensor' }, { 'start': 2, 'end': 0, 'name': 'args', 'type': 'tensors' } ], 'attrs': [ { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' }, { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' }, { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string', 'defaultValue': 'NHWC' }, { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]', 'defaultValue': [ 1, 1, 1, 1 ] }, { 'tfName': 'fused_ops', 'name': 'fusedOps', 'type': 'string[]', 'defaultValue': [] }, { 'tfName': 'explicit_paddings', 'name': 'explicitPaddings', 'type': 'number[]', 'defaultValue': [] } ] }, { 'tfOpName': 'Conv3D', 'category': 'convolution', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'filter', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' }, { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string', 'defaultValue': 'NHWC' }, { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' } ] }, { 'tfOpName': 'Dilation2D', 'category': 'convolution', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'filter', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' }, { 'tfName': 'rates', 'name': 'dilations', 'type': 'number[]' }, { 'tfName': 'padding', 'name': 'pad', 'type': 'string' } ] } ]; var convolution = { __proto__: null, json: json$f }; /** * @license * Copyright 2023 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const json$e = [ { 'tfOpName': 'Fill', 'category': 'creation', 'inputs': [ { 'start': 0, 'name': 'shape', 'type': 'number[]' }, { 'start': 1, 'name': 'value', 'type': 'number' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' } ] }, { 'tfOpName': 'LinSpace', 'category': 'creation', 'inputs': [ { 'start': 0, 'name': 'start', 'type': 'number' }, { 'start': 1, 'name': 'stop', 'type': 'number' }, { 'start': 2, 'name': 'num', 'type': 'number' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'OneHot', 'category': 'creation', 'inputs': [ { 'start': 0, 'name': 'indices', 'type': 'tensor' }, { 'start': 1, 'name': 'depth', 'type': 'number' }, { 'start': 2, 'name': 'onValue', 'type': 'number', 'defaultValue': 1 }, { 'start': 3, 'name': 'offValue', 'type': 'number', 'defaultValue': 0 } ], 'attrs': [ { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'notSupported': true }, { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' } ] }, { 'tfOpName': 'Ones', 'category': 'creation', 'inputs': [ { 'start': 0, 'name': 'shape', 'type': 'number[]' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' } ] }, { 'tfOpName': 'OnesLike', 'category': 'creation', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' } ] }, { 'tfOpName': 'RandomStandardNormal', 'category': 'creation', 'inputs': [ { 'start': 0, 'name': 'shape', 'type': 'number[]' } ], 'attrs': [ { 'tfName': 'seed', 'name': 'seed', 'type': 'number', 'defaultValue': 0 }, { 'tfName': 'seed2', 'name': 'seed2', 'type': 'number', 'defaultValue': 0, 'notSupported': true }, { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }, { 'tfName': 'T', 'name': 'T', 'type': 'number', 'notSupported': true } ] }, { 'tfOpName': 'RandomUniform', 'category': 'creation', 'inputs': [ { 'start': 0, 'name': 'shape', 'type': 'number[]' } ], 'attrs': [ { 'tfName': 'minval', 'name': 'minval', 'type': 'number', 'defaultValue': 0 }, { 'tfName': 'maxval', 'name': 'maxval', 'type': 'number', 'defaultValue': 1 }, { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }, { 'tfName': 'seed', 'name': 'seed', 'type': 'number', 'defaultValue': 0 }, { 'tfName': 'seed2', 'name': 'seed2', 'type': 'number', 'defaultValue': 0, 'notSupported': true }, { 'tfName': 'T', 'name': 'T', 'type': 'number', 'notSupported': true } ] }, { 'tfOpName': 'RandomUniformInt', 'category': 'creation', 'inputs': [ { 'start': 0, 'name': 'shape', 'type': 'number[]' } ], 'attrs': [ { 'tfName': 'minval', 'name': 'minval', 'type': 'number' }, { 'tfName': 'maxval', 'name': 'maxval', 'type': 'number' }, { 'tfName': 'seed', 'name': 'seed', 'type': 'number', 'defaultValue': 0 }, { 'tfName': 'seed2', 'name': 'seed2', 'type': 'number', 'defaultValue': 0, 'notSupported': true } ] }, { 'tfOpName': 'Range', 'category': 'creation', 'inputs': [ { 'start': 0, 'name': 'start', 'type': 'number' }, { 'start': 1, 'name': 'stop', 'type': 'number' }, { 'start': 2, 'name': 'step', 'type': 'number', 'defaultValue': 0 } ], 'attrs': [ { 'tfName': 'Tidx', 'name': 'dtype', 'type': 'dtype' } ] }, { 'tfOpName': 'TruncatedNormal', 'category': 'creation', 'inputs': [ { 'start': 0, 'name': 'shape', 'type': 'number[]' } ], 'attrs': [ { 'tfName': 'means', 'name': 'mean', 'type': 'number', 'defaultValue': 0 }, { 'tfName': 'stddev', 'name': 'stdDev', 'type': 'number', 'defaultValue': 1 }, { 'tfName': 'seed', 'name': 'seed', 'type': 'number' }, { 'tfName': 'seed2', 'name': 'seed2', 'type': 'number', 'defaultValue': 0, 'notSupported': true }, { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }, { 'tfName': 'T', 'name': 'T', 'type': 'number', 'notSupported': true } ] }, { 'tfOpName': 'Zeros', 'category': 'creation', 'inputs': [ { 'start': 0, 'name': 'shape', 'type': 'number[]' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' } ] }, { 'tfOpName': 'ZerosLike', 'category': 'creation', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' } ] }, { 'tfOpName': 'Multinomial', 'category': 'creation', 'inputs': [ { 'start': 0, 'name': 'logits', 'type': 'tensor' }, { 'start': 1, 'name': 'numSamples', 'type': 'number' } ], 'attrs': [ { 'tfName': 'seed', 'name': 'seed', 'type': 'number' }, { 'tfName': 'seed2', 'name': 'seed2', 'type': 'number' }, { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }, { 'tfName': 'output_dtype', 'name': 'output_dtype', 'type': 'dtype' } ] } ]; var creation = { __proto__: null, json: json$e }; /** * @license * Copyright 2023 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const json$d = [ { 'tfOpName': 'NonMaxSuppressionV2', 'category': 'dynamic', 'inputs': [ { 'start': 0, 'name': 'boxes', 'type': 'tensor' }, { 'start': 1, 'name': 'scores', 'type': 'tensor' }, { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' }, { 'start': 3, 'name': 'iouThreshold', 'type': 'number' } ] }, { 'tfOpName': 'NonMaxSuppressionV3', 'category': 'dynamic', 'inputs': [ { 'start': 0, 'name': 'boxes', 'type': 'tensor' }, { 'start': 1, 'name': 'scores', 'type': 'tensor' }, { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' }, { 'start': 3, 'name': 'iouThreshold', 'type': 'number' }, { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' } ] }, { 'tfOpName': 'NonMaxSuppressionV4', 'category': 'dynamic', 'inputs': [ { 'start': 0, 'name': 'boxes', 'type': 'tensor' }, { 'start': 1, 'name': 'scores', 'type': 'tensor' }, { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' }, { 'start': 3, 'name': 'iouThreshold', 'type': 'number' }, { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, { 'tfName': 'T_threshold', 'name': 'threshold', 'type': 'dtype', 'notSupported': true }, { 'tfName': 'pad_to_max_output_size', 'name': 'padToMaxOutputSize', 'type': 'bool' } ] }, { 'tfOpName': 'NonMaxSuppressionV5', 'category': 'dynamic', 'inputs': [ { 'start': 0, 'name': 'boxes', 'type': 'tensor' }, { 'start': 1, 'name': 'scores', 'type': 'tensor' }, { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' }, { 'start': 3, 'name': 'iouThreshold', 'type': 'number' }, { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' }, { 'start': 5, 'name': 'softNmsSigma', 'type': 'number' } ] }, { 'tfOpName': 'Where', 'category': 'dynamic', 'inputs': [ { 'start': 0, 'name': 'condition', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'ListDiff', 'category': 'dynamic', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'y', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] } ]; var dynamic = { __proto__: null, json: json$d }; /** * @license * Copyright 2023 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const json$c = [ { 'tfOpName': 'LowerBound', 'category': 'evaluation', 'inputs': [ { 'start': 0, 'name': 'sortedSequence', 'type': 'tensor' }, { 'start': 1, 'name': 'values', 'type': 'tensor' } ] }, { 'tfOpName': 'TopKV2', 'category': 'evaluation', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'k', 'type': 'number' } ], 'attrs': [ { 'tfName': 'sorted', 'name': 'sorted', 'type': 'bool' } ] }, { 'tfOpName': 'UpperBound', 'category': 'evaluation', 'inputs': [ { 'start': 0, 'name': 'sortedSequence', 'type': 'tensor' }, { 'start': 1, 'name': 'values', 'type': 'tensor' } ] }, { 'tfOpName': 'Unique', 'category': 'evaluation', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ] }, { 'tfOpName': 'UniqueV2', 'category': 'evaluation', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'axis', 'type': 'number' } ] } ]; var evaluation = { __proto__: null, json: json$c }; /** * @license * Copyright 2023 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const json$b = [ { 'tfOpName': 'PlaceholderWithDefault', 'category': 'graph', 'inputs': [ { 'start': 0, 'name': 'default', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'shape', 'name': 'shape', 'type': 'shape' }, { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' } ] }, { 'tfOpName': 'Placeholder', 'category': 'graph', 'attrs': [ { 'tfName': 'shape', 'name': 'shape', 'type': 'shape' }, { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' } ] }, { 'tfOpName': 'Const', 'category': 'graph' }, { 'tfOpName': 'Identity', 'category': 'graph', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ] }, { 'tfOpName': 'IdentityN', 'category': 'graph', 'inputs': [ { 'start': 0, 'end': 0, 'name': 'x', 'type': 'tensors' } ] }, { 'tfOpName': 'Snapshot', 'category': 'graph', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ] }, { 'tfOpName': 'Rank', 'category': 'graph', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ] }, { 'tfOpName': 'Size', 'category': 'graph', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ] }, { 'tfOpName': 'Shape', 'category': 'graph', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ] }, { 'tfOpName': 'ShapeN', 'category': 'graph', 'inputs': [ { 'start': 0, 'end': 0, 'name': 'x', 'type': 'tensors' } ] }, { 'tfOpName': 'Print', 'category': 'graph', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'data', 'type': 'tensors' } ], 'attrs': [ { 'tfName': 'message', 'name': 'message', 'type': 'string' }, { 'tfName': 'first_n', 'name': 'firstN', 'type': 'number', 'notSupported': true }, { 'tfName': 'summarize', 'name': 'summarize', 'type': 'number', 'defaultValue': 3 } ] }, { 'tfOpName': 'NoOp', 'category': 'graph', 'inputs': [] }, { 'tfOpName': 'StopGradient', 'category': 'graph', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ] }, { 'tfOpName': 'FakeQuantWithMinMaxVars', 'category': 'graph', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'min', 'name': 'min', 'type': 'number' }, { 'tfName': 'max', 'name': 'max', 'type': 'number' } ] } ]; var graph = { __proto__: null, json: json$b }; /** * @license * Copyright 2023 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const json$a = [ { 'tfOpName': 'HashTable', 'category': 'hash_table', 'inputs': [], 'attrs': [ { 'tfName': 'shared_name', 'name': 'sharedName', 'type': 'string' }, { 'tfName': 'use_node_name_sharing', 'name': 'useNodeNameSharing', 'type': 'bool' }, { 'tfName': 'key_dtype', 'name': 'keyDType', 'type': 'dtype' }, { 'tfName': 'value_dtype', 'name': 'valueDType', 'type': 'dtype' } ] }, { 'tfOpName': 'HashTableV2', 'category': 'hash_table', 'inputs': [], 'attrs': [ { 'tfName': 'shared_name', 'name': 'sharedName', 'type': 'string' }, { 'tfName': 'use_node_name_sharing', 'name': 'useNodeNameSharing', 'type': 'bool' }, { 'tfName': 'key_dtype', 'name': 'keyDType', 'type': 'dtype' }, { 'tfName': 'value_dtype', 'name': 'valueDType', 'type': 'dtype' } ] }, { 'tfOpName': 'LookupTableImport', 'category': 'hash_table', 'inputs': [ { 'start': 0, 'name': 'tableHandle', 'type': 'tensor' }, { 'start': 1, 'name': 'keys', 'type': 'tensor' }, { 'start': 2, 'name': 'values', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'Tin', 'name': 'tIn', 'type': 'dtype', 'notSupported': true }, { 'tfName': 'Tout', 'name': 'tOut', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'LookupTableImportV2', 'category': 'hash_table', 'inputs': [ { 'start': 0, 'name': 'tableHandle', 'type': 'tensor' }, { 'start': 1, 'name': 'keys', 'type': 'tensor' }, { 'start': 2, 'name': 'values', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'Tin', 'name': 'tIn', 'type': 'dtype', 'notSupported': true }, { 'tfName': 'Tout', 'name': 'tOut', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'LookupTableFind', 'category': 'hash_table', 'inputs': [ { 'start': 0, 'name': 'tableHandle', 'type': 'tensor' }, { 'start': 1, 'name': 'keys', 'type': 'tensor' }, { 'start': 2, 'name': 'defaultValue', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'Tin', 'name': 'tIn', 'type': 'dtype', 'notSupported': true }, { 'tfName': 'Tout', 'name': 'tOut', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'LookupTableFindV2', 'category': 'hash_table', 'inputs': [ { 'start': 0, 'name': 'tableHandle', 'type': 'tensor' }, { 'start': 1, 'name': 'keys', 'type': 'tensor' }, { 'start': 2, 'name': 'defaultValue', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'Tin', 'name': 'tIn', 'type': 'dtype', 'notSupported': true }, { 'tfName': 'Tout', 'name': 'tOut', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'LookupTableSize', 'category': 'hash_table', 'inputs': [ { 'start': 0, 'name': 'tableHandle', 'type': 'tensor' } ] }, { 'tfOpName': 'LookupTableSizeV2', 'category': 'hash_table', 'inputs': [ { 'start': 0, 'name': 'tableHandle', 'type': 'tensor' } ] }, { 'tfOpName': 'InitializeTable', 'category': 'hash_table', 'inputs': [ { 'start': 0, 'name': 'tableHandle', 'type': 'tensor' }, { 'start': 1, 'name': 'keys', 'type': 'tensor' }, { 'start': 2, 'name': 'values', 'type': 'tensor' } ] }, { 'tfOpName': 'InitializeTableV2', 'category': 'hash_table', 'inputs': [ { 'start': 0, 'name': 'tableHandle', 'type': 'tensor' }, { 'start': 1, 'name': 'keys', 'type': 'tensor' }, { 'start': 2, 'name': 'values', 'type': 'tensor' } ] } ]; var hashTable = { __proto__: null, json: json$a }; /** * @license * Copyright 2023 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const json$9 = [ { 'tfOpName': 'ResizeBilinear', 'category': 'image', 'inputs': [ { 'start': 0, 'name': 'images', 'type': 'tensor' }, { 'start': 1, 'name': 'size', 'type': 'number[]' } ], 'attrs': [ { 'tfName': 'align_corners', 'name': 'alignCorners', 'type': 'bool' }, { 'tfName': 'half_pixel_centers', 'name': 'halfPixelCenters', 'type': 'bool' }, { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'ResizeNearestNeighbor', 'category': 'image', 'inputs': [ { 'start': 0, 'name': 'images', 'type': 'tensor' }, { 'start': 1, 'name': 'size', 'type': 'number[]' } ], 'attrs': [ { 'tfName': 'align_corners', 'name': 'alignCorners', 'type': 'bool' }, { 'tfName': 'half_pixel_centers', 'name': 'halfPixelCenters', 'type': 'bool' }, { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'CropAndResize', 'category': 'image', 'inputs': [ { 'start': 0, 'name': 'image', 'type': 'tensor' }, { 'start': 1, 'name': 'boxes', 'type': 'tensor' }, { 'start': 2, 'name': 'boxInd', 'type': 'tensor' }, { 'start': 3, 'name': 'cropSize', 'type': 'number[]' } ], 'attrs': [ { 'tfName': 'method', 'name': 'method', 'type': 'string' }, { 'tfName': 'extrapolation_value', 'name': 'extrapolationValue', 'type': 'number' } ] }, { 'tfOpName': 'ImageProjectiveTransformV3', 'category': 'image', 'inputs': [ { 'start': 0, 'name': 'images', 'type': 'tensor' }, { 'start': 1, 'name': 'transforms', 'type': 'tensor' }, { 'start': 2, 'name': 'outputShape', 'type': 'number[]' }, { 'start': 3, 'name': 'fillValue', 'type': 'number' } ], 'attrs': [ { 'tfName': 'interpolation', 'name': 'interpolation', 'type': 'string' }, { 'tfName': 'fill_mode', 'name': 'fillMode', 'type': 'string' } ] } ]; var image$1 = { __proto__: null, json: json$9 }; /** * @license * Copyright 2023 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const json$8 = [ { 'tfOpName': 'Equal', 'category': 'logical', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'NotEqual', 'category': 'logical', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Greater', 'category': 'logical', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'GreaterEqual', 'category': 'logical', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Less', 'category': 'logical', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'LessEqual', 'category': 'logical', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'LogicalAnd', 'category': 'logical', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'LogicalNot', 'category': 'logical', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'LogicalOr', 'category': 'logical', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Select', 'category': 'logical', 'inputs': [ { 'start': 0, 'name': 'condition', 'type': 'tensor' }, { 'start': 1, 'name': 'a', 'type': 'tensor' }, { 'start': 2, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'SelectV2', 'category': 'logical', 'inputs': [ { 'start': 0, 'name': 'condition', 'type': 'tensor' }, { 'start': 1, 'name': 'a', 'type': 'tensor' }, { 'start': 2, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'BitwiseAnd', 'category': 'logical', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'y', 'type': 'tensor' } ] } ]; var logical = { __proto__: null, json: json$8 }; /** * @license * Copyright 2023 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const json$7 = [ { 'tfOpName': '_FusedMatMul', 'category': 'matrices', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' }, { 'start': 2, 'end': 0, 'name': 'args', 'type': 'tensors' } ], 'attrs': [ { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' }, { 'tfName': 'fused_ops', 'name': 'fusedOps', 'type': 'string[]', 'defaultValue': [] }, { 'tfName': 'epsilon', 'name': 'epsilon', 'type': 'number', 'defaultValue': 0.0001 }, { 'tfName': 'transpose_a', 'name': 'transposeA', 'type': 'bool', 'defaultValue': false }, { 'tfName': 'transpose_b', 'name': 'transposeB', 'type': 'bool', 'defaultValue': false }, { 'tfName': 'leakyrelu_alpha', 'name': 'leakyreluAlpha', 'type': 'number', 'defaultValue': 0.2 }, { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'MatMul', 'category': 'matrices', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'transpose_a', 'name': 'transposeA', 'type': 'bool', 'defaultValue': false }, { 'tfName': 'transpose_b', 'name': 'transposeB', 'type': 'bool', 'defaultValue': false }, { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'BatchMatMul', 'category': 'matrices', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'adj_x', 'name': 'transposeA', 'type': 'bool', 'defaultValue': false }, { 'tfName': 'adj_y', 'name': 'transposeB', 'type': 'bool', 'defaultValue': false }, { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'BatchMatMulV2', 'category': 'matrices', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'b', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'adj_x', 'name': 'transposeA', 'type': 'bool', 'defaultValue': false }, { 'tfName': 'adj_y', 'name': 'transposeB', 'type': 'bool', 'defaultValue': false }, { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Transpose', 'category': 'matrices', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'perm', 'type': 'number[]' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Einsum', 'category': 'matrices', 'inputs': [ { 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' } ], 'attrs': [ { 'tfName': 'equation', 'name': 'equation', 'type': 'string' }, { 'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2 }, { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' } ] }, { 'tfOpName': 'MatrixBandPart', 'category': 'matrices', 'inputs': [ { 'start': 0, 'name': 'a', 'type': 'tensor' }, { 'start': 1, 'name': 'numLower', 'type': 'tensor' }, { 'start': 1, 'name': 'numUpper', 'type': 'tensor' } ] } ]; var matrices = { __proto__: null, json: json$7 }; /** * @license * Copyright 2023 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const json$6 = [ { 'tfOpName': 'EuclideanNorm', 'category': 'normalization', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'axis', 'type': 'number[]' } ], 'attrs': [ { 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool', 'defaultValue': false } ] }, { 'tfOpName': 'FusedBatchNorm', 'category': 'normalization', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'scale', 'type': 'tensor' }, { 'start': 2, 'name': 'offset', 'type': 'tensor' }, { 'start': 3, 'name': 'mean', 'type': 'tensor' }, { 'start': 4, 'name': 'variance', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'epsilon', 'name': 'epsilon', 'type': 'number', 'defaultValue': 0.001 }, { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string', 'notSupported': true } ] }, { 'tfOpName': 'FusedBatchNormV2', 'category': 'normalization', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'scale', 'type': 'tensor' }, { 'start': 2, 'name': 'offset', 'type': 'tensor' }, { 'start': 3, 'name': 'mean', 'type': 'tensor' }, { 'start': 4, 'name': 'variance', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'epsilon', 'name': 'epsilon', 'type': 'number', 'defaultValue': 0.001 }, { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string', 'notSupported': true } ] }, { 'tfOpName': 'FusedBatchNormV3', 'category': 'normalization', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'scale', 'type': 'tensor' }, { 'start': 2, 'name': 'offset', 'type': 'tensor' }, { 'start': 3, 'name': 'mean', 'type': 'tensor' }, { 'start': 4, 'name': 'variance', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'epsilon', 'name': 'epsilon', 'type': 'number', 'defaultValue': 0.001 }, { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string', 'notSupported': true } ] }, { 'tfOpName': 'LRN', 'category': 'normalization', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'depth_radius', 'name': 'radius', 'type': 'number', 'defaultValue': 5 }, { 'tfName': 'bias', 'name': 'bias', 'type': 'number', 'defaultValue': 1 }, { 'tfName': 'alpha', 'name': 'alpha', 'type': 'number', 'defaultValue': 1 }, { 'tfName': 'beta', 'name': 'beta', 'type': 'number', 'defaultValue': 0.5 } ] }, { 'tfOpName': 'Softmax', 'category': 'normalization', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ] }, { 'tfOpName': 'LogSoftmax', 'category': 'normalization', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ] } ]; var normalization = { __proto__: null, json: json$6 }; /** * @license * Copyright 2023 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const json$5 = [ { 'tfOpName': 'Bincount', 'category': 'reduction', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'size', 'type': 'number' }, { 'start': 2, 'name': 'weights', 'type': 'tensor' } ] }, { 'tfOpName': 'DenseBincount', 'category': 'reduction', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'size', 'type': 'number' }, { 'start': 2, 'name': 'weights', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'binary_output', 'name': 'binaryOutput', 'type': 'bool' } ] }, { 'tfOpName': 'Max', 'category': 'reduction', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'axis', 'type': 'number[]' } ], 'attrs': [ { 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' } ] }, { 'tfOpName': 'Mean', 'category': 'reduction', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'axis', 'type': 'number[]' } ], 'attrs': [ { 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' } ] }, { 'tfOpName': 'Min', 'category': 'reduction', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'axis', 'type': 'number[]' } ], 'attrs': [ { 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' } ] }, { 'tfOpName': 'Sum', 'category': 'reduction', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'axis', 'type': 'number[]' } ], 'attrs': [ { 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' } ] }, { 'tfOpName': 'All', 'category': 'reduction', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'axis', 'type': 'number[]' } ], 'attrs': [ { 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' } ] }, { 'tfOpName': 'Any', 'category': 'reduction', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'axis', 'type': 'number[]' } ], 'attrs': [ { 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' } ] }, { 'tfOpName': 'ArgMax', 'category': 'reduction', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'axis', 'type': 'number' } ] }, { 'tfOpName': 'ArgMin', 'category': 'reduction', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'axis', 'type': 'number' } ] }, { 'tfOpName': 'Prod', 'category': 'reduction', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'axis', 'type': 'number[]' } ], 'attrs': [ { 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }, { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'Cumprod', 'category': 'reduction', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'axis', 'type': 'number' } ], 'attrs': [ { 'tfName': 'exclusive', 'name': 'exclusive', 'type': 'bool' }, { 'tfName': 'reverse', 'name': 'reverse', 'type': 'bool' } ] }, { 'tfOpName': 'Cumsum', 'category': 'reduction', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'axis', 'type': 'number' } ], 'attrs': [ { 'tfName': 'exclusive', 'name': 'exclusive', 'type': 'bool' }, { 'tfName': 'reverse', 'name': 'reverse', 'type': 'bool' } ] } ]; var reduction = { __proto__: null, json: json$5 }; /** * @license * Copyright 2023 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const json$4 = [ { 'tfOpName': 'ConcatV2', 'category': 'slice_join', 'inputs': [ { 'start': 0, 'end': -1, 'name': 'tensors', 'type': 'tensors' }, { 'start': -1, 'name': 'axis', 'type': 'number' } ], 'attrs': [ { 'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2 } ] }, { 'tfOpName': 'Concat', 'category': 'slice_join', 'inputs': [ { 'start': 1, 'end': 0, 'name': 'tensors', 'type': 'tensors' }, { 'start': 0, 'name': 'axis', 'type': 'number' } ], 'attrs': [ { 'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2 } ] }, { 'tfOpName': 'GatherV2', 'category': 'slice_join', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'indices', 'type': 'tensor' }, { 'start': 2, 'name': 'axis', 'type': 'number', 'defaultValue': 0 } ], 'attrs': [ { 'tfName': 'batch_dims', 'name': 'batchDims', 'type': 'number', 'defaultValue': 0 } ] }, { 'tfOpName': 'Gather', 'category': 'slice_join', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'indices', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'validate_indices', 'name': 'validateIndices', 'type': 'bool', 'notSupported': true } ] }, { 'tfOpName': 'Reverse', 'category': 'slice_join', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'dims', 'type': 'bool[]' } ] }, { 'tfOpName': 'ReverseV2', 'category': 'slice_join', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'axis', 'type': 'number[]' } ] }, { 'tfOpName': 'Slice', 'category': 'slice_join', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'begin', 'type': 'number[]' }, { 'start': 2, 'name': 'size', 'type': 'number[]' } ] }, { 'tfOpName': 'StridedSlice', 'category': 'slice_join', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'begin', 'type': 'number[]' }, { 'start': 2, 'name': 'end', 'type': 'number[]' }, { 'start': 3, 'name': 'strides', 'type': 'number[]' } ], 'attrs': [ { 'tfName': 'begin_mask', 'name': 'beginMask', 'type': 'number', 'defaultValue': 0 }, { 'tfName': 'end_mask', 'name': 'endMask', 'type': 'number', 'defaultValue': 0 }, { 'tfName': 'new_axis_mask', 'name': 'newAxisMask', 'type': 'number', 'defaultValue': 0 }, { 'tfName': 'ellipsis_mask', 'name': 'ellipsisMask', 'type': 'number', 'defaultValue': 0 }, { 'tfName': 'shrink_axis_mask', 'name': 'shrinkAxisMask', 'type': 'number', 'defaultValue': 0 } ] }, { 'tfOpName': 'Pack', 'category': 'slice_join', 'inputs': [ { 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' } ], 'attrs': [ { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 } ] }, { 'tfOpName': 'Unpack', 'category': 'slice_join', 'inputs': [ { 'start': 0, 'name': 'tensor', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }, { 'tfName': 'num', 'name': 'num', 'type': 'number', 'defaultValue': 0, 'notSupported': true } ] }, { 'tfOpName': 'Tile', 'category': 'slice_join', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'reps', 'type': 'number[]' } ] }, { 'tfOpName': 'Split', 'category': 'slice_join', 'inputs': [ { 'start': 0, 'name': 'axis', 'type': 'number', 'defaultValue': 0 }, { 'start': 1, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'num_split', 'name': 'numOrSizeSplits', 'type': 'number', 'defaultValue': 1 } ] }, { 'tfOpName': 'SplitV', 'category': 'slice_join', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'numOrSizeSplits', 'type': 'number[]' }, { 'start': 2, 'name': 'axis', 'type': 'number', 'defaultValue': 0 } ] }, { 'tfOpName': 'ScatterNd', 'category': 'slice_join', 'inputs': [ { 'start': 0, 'name': 'indices', 'type': 'tensor' }, { 'start': 1, 'name': 'values', 'type': 'tensor' }, { 'start': 2, 'name': 'shape', 'type': 'number[]' } ] }, { 'tfOpName': 'GatherNd', 'category': 'slice_join', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'indices', 'type': 'tensor' } ] }, { 'tfOpName': 'SparseToDense', 'category': 'slice_join', 'inputs': [ { 'start': 0, 'name': 'sparseIndices', 'type': 'tensor' }, { 'start': 1, 'name': 'outputShape', 'type': 'number[]' }, { 'start': 2, 'name': 'sparseValues', 'type': 'tensor' }, { 'start': 3, 'name': 'defaultValue', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'validate_indices', 'name': 'validateIndices', 'type': 'bool', 'defaultValue': false, 'notSupported': true } ] }, { 'tfOpName': 'TensorScatterUpdate', 'category': 'slice_join', 'inputs': [ { 'start': 0, 'name': 'tensor', 'type': 'tensor' }, { 'start': 1, 'name': 'indices', 'type': 'tensor' }, { 'start': 2, 'name': 'values', 'type': 'tensor' } ] } ]; var sliceJoin = { __proto__: null, json: json$4 }; /** * @license * Copyright 2023 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const json$3 = [ { 'tfOpName': 'SparseFillEmptyRows', 'category': 'sparse', 'inputs': [ { 'start': 0, 'name': 'indices', 'type': 'tensor' }, { 'start': 1, 'name': 'values', 'type': 'tensor' }, { 'start': 2, 'name': 'denseShape', 'type': 'tensor' }, { 'start': 3, 'name': 'defaultValue', 'type': 'tensor' } ] }, { 'tfOpName': 'SparseReshape', 'category': 'sparse', 'inputs': [ { 'start': 0, 'name': 'inputIndices', 'type': 'tensor' }, { 'start': 1, 'name': 'inputShape', 'type': 'tensor' }, { 'start': 2, 'name': 'newShape', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true } ] }, { 'tfOpName': 'SparseSegmentMean', 'category': 'sparse', 'inputs': [ { 'start': 0, 'name': 'data', 'type': 'tensor' }, { 'start': 1, 'name': 'indices', 'type': 'tensor' }, { 'start': 2, 'name': 'segmentIds', 'type': 'tensor' } ] }, { 'tfOpName': 'SparseSegmentSum', 'category': 'sparse', 'inputs': [ { 'start': 0, 'name': 'data', 'type': 'tensor' }, { 'start': 1, 'name': 'indices', 'type': 'tensor' }, { 'start': 2, 'name': 'segmentIds', 'type': 'tensor' } ] } ]; var sparse$1 = { __proto__: null, json: json$3 }; /** * @license * Copyright 2023 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const json$2 = [ { 'tfOpName': 'FFT', 'category': 'spectral', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ] }, { 'tfOpName': 'IFFT', 'category': 'spectral', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ] }, { 'tfOpName': 'RFFT', 'category': 'spectral', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'fft_length', 'type': 'number', 'notSupported': true } ] }, { 'tfOpName': 'IRFFT', 'category': 'spectral', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'fft_length', 'type': 'number', 'notSupported': true } ] } ]; var spectral$1 = { __proto__: null, json: json$2 }; /** * @license * Copyright 2023 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const json$1 = [ { 'tfOpName': 'StaticRegexReplace', 'category': 'string', 'inputs': [ { 'start': 0, 'name': 'input', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'pattern', 'name': 'pattern', 'type': 'string' }, { 'tfName': 'rewrite', 'name': 'rewrite', 'type': 'string' }, { 'tfName': 'replace_global', 'name': 'replaceGlobal', 'type': 'bool' } ] }, { 'tfOpName': 'StringNGrams', 'category': 'string', 'inputs': [ { 'start': 0, 'name': 'data', 'type': 'tensor' }, { 'start': 1, 'name': 'dataSplits', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'separator', 'name': 'separator', 'type': 'string' }, { 'tfName': 'ngram_widths', 'name': 'nGramWidths', 'type': 'number[]' }, { 'tfName': 'left_pad', 'name': 'leftPad', 'type': 'string' }, { 'tfName': 'right_pad', 'name': 'rightPad', 'type': 'string' }, { 'tfName': 'pad_width', 'name': 'padWidth', 'type': 'number' }, { 'tfName': 'preserve_short_sequences', 'name': 'preserveShortSequences', 'type': 'bool' } ], 'outputs': [ 'ngrams', 'ngrams_splits' ] }, { 'tfOpName': 'StringSplit', 'category': 'string', 'inputs': [ { 'start': 0, 'name': 'input', 'type': 'tensor' }, { 'start': 1, 'name': 'delimiter', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'skip_empty', 'name': 'skipEmpty', 'type': 'bool' } ], 'outputs': [ 'indices', 'values', 'shape' ] }, { 'tfOpName': 'StringToHashBucketFast', 'category': 'string', 'inputs': [ { 'start': 0, 'name': 'input', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'num_buckets', 'name': 'numBuckets', 'type': 'number' } ] } ]; var string$1 = { __proto__: null, json: json$1 }; /** * @license * Copyright 2023 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const json = [ { 'tfOpName': 'Cast', 'category': 'transformation', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'SrcT', 'name': 'sdtype', 'type': 'dtype', 'notSupported': true }, { 'tfName': 'DstT', 'name': 'dtype', 'type': 'dtype' } ] }, { 'tfOpName': 'ExpandDims', 'category': 'transformation', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'axis', 'type': 'number' } ] }, { 'tfOpName': 'MirrorPad', 'category': 'transformation', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'padding', 'type': 'number[]' } ], 'attrs': [ { 'tfName': 'mode', 'name': 'mode', 'type': 'string' } ] }, { 'tfOpName': 'Pad', 'category': 'transformation', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'padding', 'type': 'number[]' } ], 'attrs': [ { 'tfName': 'constant_value', 'name': 'constantValue', 'type': 'number', 'defaultValue': 0 } ] }, { 'tfOpName': 'PadV2', 'category': 'transformation', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'padding', 'type': 'number[]' }, { 'start': 2, 'name': 'constantValue', 'type': 'number', 'defaultValue': 0 } ] }, { 'tfOpName': 'Reshape', 'category': 'transformation', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'shape', 'type': 'number[]' } ] }, { 'tfOpName': 'EnsureShape', 'category': 'transformation', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'shape', 'type': 'number[]' } ] }, { 'tfOpName': 'Squeeze', 'category': 'transformation', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'axis', 'tfDeprecatedName': 'squeeze_dims', 'name': 'axis', 'type': 'number[]' } ] }, { 'tfOpName': 'SpaceToBatchND', 'category': 'transformation', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'blockShape', 'type': 'number[]' }, { 'start': 2, 'name': 'paddings', 'type': 'number[]' } ] }, { 'tfOpName': 'BatchToSpaceND', 'category': 'transformation', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'blockShape', 'type': 'number[]' }, { 'start': 2, 'name': 'crops', 'type': 'number[]' } ] }, { 'tfOpName': 'DepthToSpace', 'category': 'transformation', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' } ], 'attrs': [ { 'tfName': 'block_size', 'name': 'blockSize', 'type': 'number' }, { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string' } ] }, { 'tfOpName': 'BroadcastTo', 'category': 'transformation', 'inputs': [ { 'start': 0, 'name': 'x', 'type': 'tensor' }, { 'start': 1, 'name': 'shape', 'type': 'number[]' } ], 'attrs': [] }, { 'tfOpName': 'BroadcastArgs', 'category': 'transformation', 'inputs': [ { 'start': 0, 'name': 's0', 'type': 'tensor' }, { 'start': 1, 'name': 's1', 'type': 'tensor' } ], 'attrs': [] } ]; var transformation = { __proto__: null, json: json }; /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ class OperationMapper { // Singleton instance for the mapper static get Instance() { return this._instance || (this._instance = new this()); } // Loads the op mapping from the JSON file. constructor() { const ops = [ arithmetic, basicMath, control, convolution, creation, dynamic, evaluation, graph, hashTable, image$1, logical, matrices, normalization, reduction, sliceJoin, sparse$1, spectral$1, string$1, transformation ]; const mappersJson = [].concat(...ops.map(op => op.json)); this.opMappers = mappersJson.reduce((map, mapper) => { map[mapper.tfOpName] = mapper; return map; }, {}); } // Converts the model inference graph from Tensorflow GraphDef to local // representation for TensorFlow.js API transformGraph(graph, signature = {}) { const tfNodes = graph.node; const placeholders = []; const weights = []; const initNodes = []; const nodes = tfNodes.reduce((map, node) => { map[node.name] = this.mapNode(node); if (node.op.startsWith('Placeholder')) { placeholders.push(map[node.name]); } else if (node.op === 'Const') { weights.push(map[node.name]); } else if (node.input == null || node.input.length === 0) { initNodes.push(map[node.name]); } return map; }, {}); let inputs = []; const outputs = []; let inputNodeNameToKey = {}; let outputNodeNameToKey = {}; if (signature != null) { inputNodeNameToKey = this.mapSignatureEntries(signature.inputs); outputNodeNameToKey = this.mapSignatureEntries(signature.outputs); } const allNodes = Object.keys(nodes); allNodes.forEach(key => { const node = nodes[key]; node.inputNames.forEach((name, index) => { const [nodeName, , outputName] = getNodeNameAndIndex(name); const inputNode = nodes[nodeName]; if (inputNode.outputs != null) { const outputIndex = inputNode.outputs.indexOf(outputName); if (outputIndex !== -1) { const inputName = `${nodeName}:${outputIndex}`; // update the input name to use the mapped output index directly. node.inputNames[index] = inputName; } } node.inputs.push(inputNode); inputNode.children.push(node); }); }); // if signature has not outputs set, add any node that does not have // outputs. if (Object.keys(outputNodeNameToKey).length === 0) { allNodes.forEach(key => { const node = nodes[key]; if (node.children.length === 0) { outputs.push(node); } }); } else { Object.keys(outputNodeNameToKey).forEach(name => { const [nodeName,] = getNodeNameAndIndex(name); const node = nodes[nodeName]; if (node != null) { node.signatureKey = outputNodeNameToKey[name]; outputs.push(node); } }); } if (Object.keys(inputNodeNameToKey).length > 0) { Object.keys(inputNodeNameToKey).forEach(name => { const [nodeName,] = getNodeNameAndIndex(name); const node = nodes[nodeName]; if (node) { node.signatureKey = inputNodeNameToKey[name]; inputs.push(node); } }); } else { inputs = placeholders; } let functions = {}; if (graph.library != null && graph.library.function != null) { functions = graph.library.function.reduce((functions, func) => { functions[func.signature.name] = this.mapFunction(func); return functions; }, {}); } const result = { nodes, inputs, outputs, weights, placeholders, signature, functions }; if (initNodes.length > 0) { result.initNodes = initNodes; } return result; } mapSignatureEntries(entries) { return Object.keys(entries || {}) .reduce((prev, curr) => { prev[entries[curr].name] = curr; return prev; }, {}); } mapNode(node) { // Unsupported ops will cause an error at run-time (not parse time), since // they may not be used by the actual execution subgraph. const mapper = getRegisteredOp(node.op) || this.opMappers[node.op] || {}; if (node.attr == null) { node.attr = {}; } const newNode = { name: node.name, op: node.op, category: mapper.category, inputNames: (node.input || []).map(input => input.startsWith('^') ? input.slice(1) : input), inputs: [], children: [], inputParams: {}, attrParams: {}, rawAttrs: node.attr, outputs: mapper.outputs }; if (mapper.inputs != null) { newNode.inputParams = mapper.inputs.reduce((map, param) => { map[param.name] = { type: param.type, inputIndexStart: param.start, inputIndexEnd: param.end }; return map; }, {}); } if (mapper.attrs != null) { newNode.attrParams = mapper.attrs.reduce((map, param) => { const type = param.type; let value = undefined; switch (param.type) { case 'string': value = getStringParam(node.attr, param.tfName, param.defaultValue); if (value === undefined && !!param.tfDeprecatedName) { value = getStringParam(node.attr, param.tfDeprecatedName, param.defaultValue); } break; case 'string[]': value = getStringArrayParam(node.attr, param.tfName, param.defaultValue); if (value === undefined && !!param.tfDeprecatedName) { value = getStringArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue); } break; case 'number': value = getNumberParam(node.attr, param.tfName, (param.defaultValue || 0)); if (value === undefined && !!param.tfDeprecatedName) { value = getNumberParam(node.attr, param.tfDeprecatedName, param.defaultValue); } break; case 'number[]': value = getNumericArrayParam(node.attr, param.tfName, param.defaultValue); if (value === undefined && !!param.tfDeprecatedName) { value = getNumericArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue); } break; case 'bool': value = getBoolParam(node.attr, param.tfName, param.defaultValue); if (value === undefined && !!param.tfDeprecatedName) { value = getBoolParam(node.attr, param.tfDeprecatedName, param.defaultValue); } break; case 'bool[]': value = getBoolArrayParam(node.attr, param.tfName, param.defaultValue); if (value === undefined && !!param.tfDeprecatedName) { value = getBoolArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue); } break; case 'shape': value = getTensorShapeParam(node.attr, param.tfName, param.defaultValue); if (value === undefined && !!param.tfDeprecatedName) { value = getTensorShapeParam(node.attr, param.tfDeprecatedName, param.defaultValue); } break; case 'shape[]': value = getTensorShapeArrayParam(node.attr, param.tfName, param.defaultValue); if (value === undefined && !!param.tfDeprecatedName) { value = getTensorShapeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue); } break; case 'dtype': value = getDtypeParam(node.attr, param.tfName, param.defaultValue); if (value === undefined && !!param.tfDeprecatedName) { value = getDtypeParam(node.attr, param.tfDeprecatedName, param.defaultValue); } break; case 'dtype[]': value = getDtypeArrayParam(node.attr, param.tfName, param.defaultValue); if (value === undefined && !!param.tfDeprecatedName) { value = getDtypeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue); } break; case 'func': value = getFuncParam(node.attr, param.tfName, param.defaultValue); if (value === undefined && !!param.tfDeprecatedName) { value = getFuncParam(node.attr, param.tfDeprecatedName, param.defaultValue); } break; case 'tensor': case 'tensors': break; default: throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`); } map[param.name] = { value, type }; return map; }, {}); } return newNode; } // map the TFunctionDef to TFJS graph object mapFunction(functionDef) { const tfNodes = functionDef.nodeDef; const placeholders = []; const weights = []; let nodes = {}; if (tfNodes != null) { nodes = tfNodes.reduce((map, node) => { map[node.name] = this.mapNode(node); if (node.op === 'Const') { weights.push(map[node.name]); } return map; }, {}); } const inputs = []; const outputs = []; functionDef.signature.inputArg.forEach(arg => { const [nodeName,] = getNodeNameAndIndex(arg.name); const node = { name: nodeName, op: 'Placeholder', inputs: [], inputNames: [], category: 'graph', inputParams: {}, attrParams: { dtype: { value: parseDtypeParam(arg.type), type: 'dtype' } }, children: [] }; node.signatureKey = arg.name; inputs.push(node); nodes[nodeName] = node; }); const allNodes = Object.keys(nodes); allNodes.forEach(key => { const node = nodes[key]; node.inputNames.forEach((name, index) => { const [nodeName, , outputName] = getNodeNameAndIndex(name); const inputNode = nodes[nodeName]; if (inputNode.outputs != null) { const outputIndex = inputNode.outputs.indexOf(outputName); if (outputIndex !== -1) { const inputName = `${nodeName}:${outputIndex}`; // update the input name to use the mapped output index directly. node.inputNames[index] = inputName; } } node.inputs.push(inputNode); inputNode.children.push(node); }); }); const returnNodeMap = functionDef.ret; functionDef.signature.outputArg.forEach(output => { const [nodeName, index] = getNodeNameAndIndex(returnNodeMap[output.name]); const node = nodes[nodeName]; if (node != null) { node.defaultOutput = index; outputs.push(node); } }); const signature = this.mapArgsToSignature(functionDef); return { nodes, inputs, outputs, weights, placeholders, signature }; } mapArgsToSignature(functionDef) { return { methodName: functionDef.signature.name, inputs: functionDef.signature.inputArg.reduce((map, arg) => { map[arg.name] = this.mapArgToTensorInfo(arg); return map; }, {}), outputs: functionDef.signature.outputArg.reduce((map, arg) => { map[arg.name] = this.mapArgToTensorInfo(arg, functionDef.ret); return map; }, {}), }; } mapArgToTensorInfo(arg, nameMap) { let name = arg.name; if (nameMap != null) { name = nameMap[name]; } return { name, dtype: arg.type }; } } function decodeBase64(text) { const global = tfc.env().global; if (typeof global.atob !== 'undefined') { return global.atob(text); } else if (typeof Buffer !== 'undefined') { return new Buffer(text, 'base64').toString(); } else { throw new Error('Unable to decode base64 in this environment. ' + 'Missing built-in atob() or Buffer()'); } } function parseStringParam(s, keepCase) { const value = Array.isArray(s) ? String.fromCharCode.apply(null, s) : decodeBase64(s); return keepCase ? value : value.toLowerCase(); } function getStringParam(attrs, name, def, keepCase = false) { const param = attrs[name]; if (param != null) { return parseStringParam(param.s, keepCase); } return def; } function getBoolParam(attrs, name, def) { const param = attrs[name]; return param ? param.b : def; } function getNumberParam(attrs, name, def) { const param = attrs[name] || {}; const value = param['i'] != null ? param['i'] : (param['f'] != null ? param['f'] : def); return (typeof value === 'number') ? value : parseInt(value, 10); } function parseDtypeParam(value) { if (typeof (value) === 'string') { // tslint:disable-next-line:no-any value = DataType[value]; } switch (value) { case DataType.DT_FLOAT: case DataType.DT_HALF: return 'float32'; case DataType.DT_INT32: case DataType.DT_INT64: case DataType.DT_INT8: case DataType.DT_UINT8: return 'int32'; case DataType.DT_BOOL: return 'bool'; case DataType.DT_DOUBLE: return 'float32'; case DataType.DT_STRING: return 'string'; default: // Unknown dtype error will happen at runtime (instead of parse time), // since these nodes might not be used by the actual subgraph execution. return null; } } function getFuncParam(attrs, name, def) { const param = attrs[name]; if (param && param.func) { return param.func.name; } return def; } function getDtypeParam(attrs, name, def) { const param = attrs[name]; if (param && param.type) { return parseDtypeParam(param.type); } return def; } function getDtypeArrayParam(attrs, name, def) { const param = attrs[name]; if (param && param.list && param.list.type) { return param.list.type.map(v => parseDtypeParam(v)); } return def; } function parseTensorShapeParam(shape) { if (shape.unknownRank) { return undefined; } if (shape.dim != null) { return shape.dim.map(dim => (typeof dim.size === 'number') ? dim.size : parseInt(dim.size, 10)); } return []; } function getTensorShapeParam(attrs, name, def) { const param = attrs[name]; if (param && param.shape) { return parseTensorShapeParam(param.shape); } return def; } function getNumericArrayParam(attrs, name, def) { const param = attrs[name]; if (param) { return ((param.list.f && param.list.f.length ? param.list.f : param.list.i) || []) .map(v => (typeof v === 'number') ? v : parseInt(v, 10)); } return def; } function getStringArrayParam(attrs, name, def, keepCase = false) { const param = attrs[name]; if (param && param.list && param.list.s) { return param.list.s.map((v) => { return parseStringParam(v, keepCase); }); } return def; } function getTensorShapeArrayParam(attrs, name, def) { const param = attrs[name]; if (param && param.list && param.list.shape) { return param.list.shape.map((v) => { return parseTensorShapeParam(v); }); } return def; } function getBoolArrayParam(attrs, name, def) { const param = attrs[name]; if (param && param.list && param.list.b) { return param.list.b; } return def; } /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Helper class for lookup inputs and params for nodes in the model graph. */ class NodeValueImpl { constructor(node, tensorMap, context) { this.node = node; this.tensorMap = tensorMap; this.context = context; this.inputs = []; this.attrs = {}; this.inputs = node.inputNames.map(name => this.getInput(name)); if (node.rawAttrs != null) { this.attrs = Object.keys(node.rawAttrs) .reduce((attrs, key) => { attrs[key] = this.getAttr(key); return attrs; }, {}); } } /** * Return the value of the attribute or input param. * @param name String: name of attribute or input param. */ getInput(name) { return getTensor(name, this.tensorMap, this.context); } /** * Return the value of the attribute or input param. * @param name String: name of attribute or input param. */ getAttr(name, defaultValue) { const value = this.node.rawAttrs[name]; if (value.tensor != null) { return getTensor(name, this.tensorMap, this.context); } if (value.i != null || value.f != null) { return getNumberParam(this.node.rawAttrs, name, defaultValue); } if (value.s != null) { return getStringParam(this.node.rawAttrs, name, defaultValue); } if (value.b != null) { return getBoolParam(this.node.rawAttrs, name, defaultValue); } if (value.shape != null) { return getTensorShapeParam(this.node.rawAttrs, name, defaultValue); } if (value.type != null) { return getDtypeParam(this.node.rawAttrs, name, defaultValue); } if (value.list != null) { if (value.list.i != null || value.list.f != null) { return getNumericArrayParam(this.node.rawAttrs, name, defaultValue); } if (value.list.s != null) { return getStringArrayParam(this.node.rawAttrs, name, defaultValue); } if (value.list.shape != null) { return getTensorShapeArrayParam(this.node.rawAttrs, name, defaultValue); } if (value.list.b != null) { return getBoolArrayParam(this.node.rawAttrs, name, defaultValue); } if (value.list.type != null) { return getDtypeArrayParam(this.node.rawAttrs, name, defaultValue); } } return defaultValue; } } /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const EPSILON_FLOAT32 = 1e-7; const EPSILON_FLOAT16 = 1e-4; /** * The interface that defines the kernels that should be implemented when * adding a new backend. New backends don't need to implement every one of the * methods, this can be done gradually (throw an error for unimplemented * methods). */ class KernelBackend { refCount(dataId) { return notYetImplemented('refCount'); } incRef(dataId) { return notYetImplemented('incRef'); } timerAvailable() { return true; } time(f) { return notYetImplemented('time'); } read(dataId) { return notYetImplemented('read'); } readSync(dataId) { return notYetImplemented('readSync'); } readToGPU(dataId, options) { return notYetImplemented('readToGPU'); } numDataIds() { return notYetImplemented('numDataIds'); } disposeData(dataId, force) { return notYetImplemented('disposeData'); } write(values, shape, dtype) { return notYetImplemented('write'); } move(dataId, values, shape, dtype, refCount) { return notYetImplemented('move'); } createTensorFromGPUData(values, shape, dtype) { return notYetImplemented('createTensorFromGPUData'); } memory() { return notYetImplemented('memory'); } /** Returns the highest precision for floats in bits (e.g. 16 or 32) */ floatPrecision() { return notYetImplemented('floatPrecision'); } /** Returns the smallest representable number. */ epsilon() { return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16; } dispose() { return notYetImplemented('dispose'); } } function notYetImplemented(kernelName) { throw new Error(`'${kernelName}' not yet implemented or not found in the registry. ` + `This kernel may not be supported by the tfjs backend you have chosen`); } /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Asserts that the expression is true. Otherwise throws an error with the * provided message. * * ```js * const x = 2; * tf.util.assert(x === 2, 'x is not 2'); * ``` * * @param expr The expression to assert (as a boolean). * @param msg A function that returns the message to report when throwing an * error. We use a function for performance reasons. * * @doc {heading: 'Util', namespace: 'util'} */ function assert(expr, msg) { if (!expr) { throw new Error(typeof msg === 'string' ? msg : msg()); } } function assertShapesMatch(shapeA, shapeB, errorMessagePrefix = '') { assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`); } function assertNonNull(a) { assert(a != null, () => `The input to the tensor constructor must be a non-null value.`); } /** * Returns the size (number of elements) of the tensor given its shape. * * ```js * const shape = [3, 4, 2]; * const size = tf.util.sizeFromShape(shape); * console.log(size); * ``` * * @doc {heading: 'Util', namespace: 'util'} */ function sizeFromShape(shape) { if (shape.length === 0) { // Scalar. return 1; } let size = shape[0]; for (let i = 1; i < shape.length; i++) { size *= shape[i]; } return size; } function arraysEqualWithNull(n1, n2) { if (n1 === n2) { return true; } if (n1 == null || n2 == null) { return false; } if (n1.length !== n2.length) { return false; } for (let i = 0; i < n1.length; i++) { if (n1[i] !== null && n2[i] !== null && n1[i] !== n2[i]) { return false; } } return true; } function arraysEqual(n1, n2) { if (n1 === n2) { return true; } if (n1 == null || n2 == null) { return false; } if (n1.length !== n2.length) { return false; } for (let i = 0; i < n1.length; i++) { if (n1[i] !== n2[i]) { return false; } } return true; } function isInt(a) { return a % 1 === 0; } function rightPad(a, size) { if (size <= a.length) { return a; } return a + ' '.repeat(size - a.length); } function parseAxisParam(axis, shape) { const rank = shape.length; // Normalize input axis = axis == null ? shape.map((s, i) => i) : [].concat(axis); // Check for valid range assert(axis.every(ax => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but ` + `got axis ${axis}`); // Check for only integers assert(axis.every(ax => isInt(ax)), () => `All values in axis param must be integers but ` + `got axis ${axis}`); // Handle negative axis. return axis.map(a => a < 0 ? rank + a : a); } /** Reduces the shape by removing all dimensions of shape 1. */ function squeezeShape(shape, axis) { const newShape = []; const keptDims = []; const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0; const axes = (axis == null || isEmptyArray) ? null : parseAxisParam(axis, shape).sort(); let j = 0; for (let i = 0; i < shape.length; ++i) { if (axes != null) { if (axes[j] === i && shape[i] !== 1) { throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`); } if ((axes[j] == null || axes[j] > i) && shape[i] === 1) { newShape.push(shape[i]); keptDims.push(i); } if (axes[j] <= i) { j++; } } if (shape[i] !== 1) { newShape.push(shape[i]); keptDims.push(i); } } return { newShape, keptDims }; } function getTypedArrayFromDType(dtype, size) { return getArrayFromDType(dtype, size); } function getArrayFromDType(dtype, size) { let values = null; if (dtype == null || dtype === 'float32') { values = new Float32Array(size); } else if (dtype === 'int32') { values = new Int32Array(size); } else if (dtype === 'bool') { values = new Uint8Array(size); } else if (dtype === 'string') { values = new Array(size); } else { throw new Error(`Unknown data type ${dtype}`); } return values; } function checkConversionForErrors(vals, dtype) { for (let i = 0; i < vals.length; i++) { const num = vals[i]; if (isNaN(num) || !isFinite(num)) { throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`); } } } /** Returns true if the dtype is valid. */ function isValidDtype(dtype) { return dtype === 'bool' || dtype === 'complex64' || dtype === 'float32' || dtype === 'int32' || dtype === 'string'; } function bytesPerElement(dtype) { if (dtype === 'float32' || dtype === 'int32') { return 4; } else if (dtype === 'complex64') { return 8; } else if (dtype === 'bool') { return 1; } else { throw new Error(`Unknown dtype ${dtype}`); } } /** * Returns the approximate number of bytes allocated in the string array - 2 * bytes per character. Computing the exact bytes for a native string in JS * is not possible since it depends on the encoding of the html page that * serves the website. */ function bytesFromStringArray(arr) { if (arr == null) { return 0; } let bytes = 0; arr.forEach(x => bytes += x.length); return bytes; } /** Returns true if the value is a string. */ function isString(value) { return typeof value === 'string' || value instanceof String; } function isBoolean(value) { return typeof value === 'boolean'; } function isNumber(value) { return typeof value === 'number'; } function inferDtype(values) { if (Array.isArray(values)) { return inferDtype(values[0]); } if (values instanceof Float32Array) { return 'float32'; } else if (values instanceof Int32Array || values instanceof Uint8Array || values instanceof Uint8ClampedArray) { return 'int32'; } else if (isNumber(values)) { return 'float32'; } else if (isString(values)) { return 'string'; } else if (isBoolean(values)) { return 'bool'; } return 'float32'; } function isFunction(f) { return !!(f && f.constructor && f.call && f.apply); } function computeStrides(shape) { const rank = shape.length; if (rank < 2) { return []; } // Last dimension has implicit stride of 1, thus having D-1 (instead of D) // strides. const strides = new Array(rank - 1); strides[rank - 2] = shape[rank - 1]; for (let i = rank - 3; i >= 0; --i) { strides[i] = strides[i + 1] * shape[i + 1]; } return strides; } function createNestedArray(offset, shape, a, isComplex = false) { const ret = new Array(); if (shape.length === 1) { const d = shape[0] * (isComplex ? 2 : 1); for (let i = 0; i < d; i++) { ret[i] = a[offset + i]; } } else { const d = shape[0]; const rest = shape.slice(1); const len = rest.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1); for (let i = 0; i < d; i++) { ret[i] = createNestedArray(offset + i * len, rest, a, isComplex); } } return ret; } // Provide a nested array of TypedArray in given shape. function toNestedArray(shape, a, isComplex = false) { if (shape.length === 0) { // Scalar type should return a single number. return a[0]; } const size = shape.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1); if (size === 0) { // A tensor with shape zero should be turned into empty list. return []; } if (size !== a.length) { throw new Error(`[${shape}] does not match the input size ${a.length}${isComplex ? ' for a complex tensor' : ''}.`); } return createNestedArray(0, shape, a, isComplex); } function makeOnesTypedArray(size, dtype) { const array = makeZerosTypedArray(size, dtype); for (let i = 0; i < array.length; i++) { array[i] = 1; } return array; } function makeZerosTypedArray(size, dtype) { if (dtype == null || dtype === 'float32' || dtype === 'complex64') { return new Float32Array(size); } else if (dtype === 'int32') { return new Int32Array(size); } else if (dtype === 'bool') { return new Uint8Array(size); } else { throw new Error(`Unknown data type ${dtype}`); } } function assertNonNegativeIntegerDimensions(shape) { shape.forEach(dimSize => { assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got ` + `shape [${shape}].`); }); } /** * This method asserts whether an object is a Promise instance. * @param object */ // tslint:disable-next-line: no-any function isPromise(object) { // We chose to not use 'obj instanceOf Promise' for two reasons: // 1. It only reliably works for es6 Promise, not other Promise // implementations. // 2. It doesn't work with framework that uses zone.js. zone.js monkey // patch the async calls, so it is possible the obj (patched) is // comparing to a pre-patched Promise. return object && object.then && typeof object.then === 'function'; } /** * @license * Copyright 2017 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ // Expects flags from URL in the format ?tfjsflags=FLAG1:1,FLAG2:true. const TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags'; /** * The environment contains evaluated flags as well as the registered platform. * This is always used as a global singleton and can be retrieved with * `tf.env()`. * * @doc {heading: 'Environment'} */ class Environment { // tslint:disable-next-line: no-any constructor(global) { this.global = global; this.flags = {}; this.flagRegistry = {}; this.urlFlags = {}; // Jasmine spies on this in 'environment_test.ts' this.getQueryParams = getQueryParams; this.populateURLFlags(); } setPlatform(platformName, platform) { if (this.platform != null) { if (!(env().getBool('IS_TEST') || env().getBool('PROD'))) { console.warn(`Platform ${this.platformName} has already been set. ` + `Overwriting the platform with ${platformName}.`); } } this.platformName = platformName; this.platform = platform; } registerFlag(flagName, evaluationFn, setHook) { this.flagRegistry[flagName] = { evaluationFn, setHook }; // Override the flag value from the URL. This has to happen here because // the environment is initialized before flags get registered. if (this.urlFlags[flagName] != null) { const flagValue = this.urlFlags[flagName]; if (!(env().getBool('IS_TEST') || env().getBool('PROD'))) { console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`); } this.set(flagName, flagValue); } } async getAsync(flagName) { if (flagName in this.flags) { return this.flags[flagName]; } this.flags[flagName] = await this.evaluateFlag(flagName); return this.flags[flagName]; } get(flagName) { if (flagName in this.flags) { return this.flags[flagName]; } const flagValue = this.evaluateFlag(flagName); if (isPromise(flagValue)) { throw new Error(`Flag ${flagName} cannot be synchronously evaluated. ` + `Please use getAsync() instead.`); } this.flags[flagName] = flagValue; return this.flags[flagName]; } getNumber(flagName) { return this.get(flagName); } getBool(flagName) { return this.get(flagName); } getString(flagName) { return this.get(flagName); } getFlags() { return this.flags; } // For backwards compatibility. get features() { return this.flags; } set(flagName, value) { if (this.flagRegistry[flagName] == null) { throw new Error(`Cannot set flag ${flagName} as it has not been registered.`); } this.flags[flagName] = value; if (this.flagRegistry[flagName].setHook != null) { this.flagRegistry[flagName].setHook(value); } } evaluateFlag(flagName) { if (this.flagRegistry[flagName] == null) { throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`); } return this.flagRegistry[flagName].evaluationFn(); } setFlags(flags) { this.flags = Object.assign({}, flags); } reset() { this.flags = {}; this.urlFlags = {}; this.populateURLFlags(); } populateURLFlags() { if (typeof this.global === 'undefined' || typeof this.global.location === 'undefined' || typeof this.global.location.search === 'undefined') { return; } const urlParams = this.getQueryParams(this.global.location.search); if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) { const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(','); keyValues.forEach(keyValue => { const [key, value] = keyValue.split(':'); this.urlFlags[key] = parseValue(key, value); }); } } } function getQueryParams(queryString) { const params = {}; queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => { decodeParam(params, t[0], t[1]); return t.join('='); }); return params; } function decodeParam(params, name, value) { params[decodeURIComponent(name)] = decodeURIComponent(value || ''); } function parseValue(flagName, value) { const lowerCaseValue = value.toLowerCase(); if (lowerCaseValue === 'true' || lowerCaseValue === 'false') { return lowerCaseValue === 'true'; } else if (`${+lowerCaseValue}` === lowerCaseValue) { return +lowerCaseValue; } else { return value; } } /** * Returns the current environment (a global singleton). * * The environment object contains the evaluated feature values as well as the * active platform. * * @doc {heading: 'Environment'} */ function env() { return ENV; } let ENV = null; function setEnvironmentGlobal(environment) { ENV = environment; } /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ // Note that the identifier globalNameSpace is scoped to this module, but will // always resolve to the same global object regardless of how the module is // resolved. // tslint:disable-next-line:no-any let globalNameSpace; // tslint:disable-next-line:no-any function getGlobalNamespace() { if (globalNameSpace == null) { // tslint:disable-next-line:no-any let ns; if (typeof (window) !== 'undefined') { ns = window; } else if (typeof (global) !== 'undefined') { ns = global; } else if (typeof (process) !== 'undefined') { ns = process; } else if (typeof (self) !== 'undefined') { ns = self; } else { throw new Error('Could not find a global object'); } globalNameSpace = ns; } return globalNameSpace; } // tslint:disable-next-line:no-any function getGlobalMap() { const ns = getGlobalNamespace(); if (ns._tfGlobals == null) { ns._tfGlobals = new Map(); } return ns._tfGlobals; } /** * Returns a globally accessible 'singleton' object. * * @param key the name of the object * @param init a function to initialize to initialize this object * the first time it is fetched. */ function getGlobal(key, init) { const globalMap = getGlobalMap(); if (globalMap.has(key)) { return globalMap.get(key); } else { const singleton = init(); globalMap.set(key, singleton); return globalMap.get(key); } } const Abs = 'Abs'; const Acos = 'Acos'; const Acosh = 'Acosh'; const Add = 'Add'; const AddN = 'AddN'; const All = 'All'; const Any = 'Any'; const ArgMax = 'ArgMax'; const ArgMin = 'ArgMin'; const Asin = 'Asin'; const Asinh = 'Asinh'; const Atan = 'Atan'; const Atanh = 'Atanh'; const Atan2 = 'Atan2'; const AvgPool = 'AvgPool'; const AvgPool3D = 'AvgPool3D'; const BatchMatMul = 'BatchMatMul'; const BatchToSpaceND = 'BatchToSpaceND'; const Bincount = 'Bincount'; const BitwiseAnd = 'BitwiseAnd'; const BroadcastArgs = 'BroadcastArgs'; const Cast = 'Cast'; const Ceil = 'Ceil'; const ClipByValue = 'ClipByValue'; const Complex = 'Complex'; const ComplexAbs = 'ComplexAbs'; const Concat = 'Concat'; const Conv2D = 'Conv2D'; const Conv2DBackpropFilter = 'Conv2DBackpropFilter'; const Conv2DBackpropInput = 'Conv2DBackpropInput'; const Conv3D = 'Conv3D'; const Conv3DBackpropInputV2 = 'Conv3DBackpropInputV2'; const Cos = 'Cos'; const Cosh = 'Cosh'; const Cumprod = 'Cumprod'; const Cumsum = 'Cumsum'; const CropAndResize = 'CropAndResize'; const DenseBincount = 'DenseBincount'; const DepthToSpace = 'DepthToSpace'; const DepthwiseConv2dNative = 'DepthwiseConv2dNative'; const DepthwiseConv2dNativeBackpropFilter = 'DepthwiseConv2dNativeBackpropFilter'; const DepthwiseConv2dNativeBackpropInput = 'DepthwiseConv2dNativeBackpropInput'; const Diag = 'Diag'; const Dilation2D = 'Dilation2D'; const RealDiv = 'RealDiv'; const Einsum = 'Einsum'; const Elu = 'Elu'; const Erf = 'Erf'; const Equal = 'Equal'; const Exp = 'Exp'; const ExpandDims = 'ExpandDims'; const Expm1 = 'Expm1'; const FFT = 'FFT'; const Fill = 'Fill'; const FlipLeftRight = 'FlipLeftRight'; const Floor = 'Floor'; const FloorDiv = 'FloorDiv'; const FusedBatchNorm = 'FusedBatchNorm'; const GatherV2 = 'GatherV2'; const GatherNd = 'GatherNd'; const Greater = 'Greater'; const GreaterEqual = 'GreaterEqual'; const Identity = 'Identity'; const IFFT = 'IFFT'; const Imag = 'Imag'; const IsFinite = 'IsFinite'; const IsInf = 'IsInf'; const IsNan = 'IsNan'; const LeakyRelu = 'LeakyRelu'; const Less = 'Less'; const LessEqual = 'LessEqual'; const LinSpace = 'LinSpace'; const Log = 'Log'; const Log1p = 'Log1p'; const LogicalAnd = 'LogicalAnd'; const LogicalNot = 'LogicalNot'; const LogicalOr = 'LogicalOr'; const LRN = 'LRN'; const Max = 'Max'; const Maximum = 'Maximum'; const MaxPool = 'MaxPool'; const MaxPool3D = 'MaxPool3D'; const MaxPoolWithArgmax = 'MaxPoolWithArgmax'; const Mean = 'Mean'; const Min = 'Min'; const Minimum = 'Minimum'; const MirrorPad = 'MirrorPad'; const Mod = 'Mod'; const Multinomial = 'Multinomial'; const Multiply = 'Multiply'; const Neg = 'Neg'; const NotEqual = 'NotEqual'; const NonMaxSuppressionV3 = 'NonMaxSuppressionV3'; const NonMaxSuppressionV4 = 'NonMaxSuppressionV4'; const NonMaxSuppressionV5 = 'NonMaxSuppressionV5'; const OnesLike = 'OnesLike'; const OneHot = 'OneHot'; const Pack = 'Pack'; const PadV2 = 'PadV2'; const Pow = 'Pow'; const Prelu = 'Prelu'; const Prod = 'Prod'; const RaggedGather = 'RaggedGather'; const RaggedRange = 'RaggedRange'; const RaggedTensorToTensor = 'RaggedTensorToTensor'; const Range = 'Range'; const Real = 'Real'; const Reciprocal = 'Reciprocal'; const Relu = 'Relu'; const Reshape = 'Reshape'; const ResizeNearestNeighbor = 'ResizeNearestNeighbor'; const ResizeBilinear = 'ResizeBilinear'; const Relu6 = 'Relu6'; const Reverse = 'Reverse'; const Round = 'Round'; const Rsqrt = 'Rsqrt'; const ScatterNd = 'ScatterNd'; const TensorScatterUpdate = 'TensorScatterUpdate'; const SearchSorted = 'SearchSorted'; const Select = 'Select'; const Selu = 'Selu'; const Slice = 'Slice'; const Sin = 'Sin'; const Sinh = 'Sinh'; const Sign = 'Sign'; const Sigmoid = 'Sigmoid'; const Softplus = 'Softplus'; const Sqrt = 'Sqrt'; const Sum = 'Sum'; const SpaceToBatchND = 'SpaceToBatchND'; const SplitV = 'SplitV'; const Softmax = 'Softmax'; const SparseFillEmptyRows = 'SparseFillEmptyRows'; const SparseReshape = 'SparseReshape'; const SparseSegmentMean = 'SparseSegmentMean'; const SparseSegmentSum = 'SparseSegmentSum'; const SparseToDense = 'SparseToDense'; const SquaredDifference = 'SquaredDifference'; const StaticRegexReplace = 'StaticRegexReplace'; const StridedSlice = 'StridedSlice'; const StringNGrams = 'StringNGrams'; const StringSplit = 'StringSplit'; const StringToHashBucketFast = 'StringToHashBucketFast'; const Sub = 'Sub'; const Tan = 'Tan'; const Tanh = 'Tanh'; const Tile = 'Tile'; const TopK = 'TopK'; const Transform = 'Transform'; const Transpose = 'Transpose'; const Unique = 'Unique'; const Unpack = 'Unpack'; const UnsortedSegmentSum = 'UnsortedSegmentSum'; const ZerosLike = 'ZerosLike'; /** * TensorFlow.js-only kernels */ const Step = 'Step'; const RotateWithOffset = 'RotateWithOffset'; const _FusedMatMul = '_FusedMatMul'; const FusedConv2D = 'FusedConv2D'; const FusedDepthwiseConv2D = 'FusedDepthwiseConv2D'; /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ function warn(...msg) { if (!(env().getBool('IS_TEST') || env().getBool('PROD'))) { console.warn(...msg); } } /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const kernelRegistry = getGlobal('kernelRegistry', () => new Map()); const gradRegistry = getGlobal('gradRegistry', () => new Map()); /** * Returns the kernel function (code) associated with the provided names. * * @param kernelName The official name of the kernel. * @param backendName The official name of the backend. */ function getKernel(kernelName, backendName) { const key = makeKey(kernelName, backendName); return kernelRegistry.get(key); } /** * Returns the registered gradient info associated with the provided kernel. * @param kernelName The official TF kernel name. */ function getGradient(kernelName) { return gradRegistry.get(kernelName); } function getKernelsForBackend(backendName) { const it = kernelRegistry.entries(); const result = []; while (true) { const { done, value } = it.next(); if (done) { break; } const [key, config] = value; const [backend,] = key.split('_'); if (backend === backendName) { result.push(config); } } return result; } function makeKey(kernelName, backendName) { return `${backendName}_${kernelName}`; } /** * @license * Copyright 2023 Google LLC. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ function isTypedArrayBrowser(a) { return a instanceof Float32Array || a instanceof Int32Array || a instanceof Uint8Array || a instanceof Uint8ClampedArray; } var commonjsGlobal = typeof globalThis !== 'undefined' ? globalThis : typeof window !== 'undefined' ? window : typeof global !== 'undefined' ? global : typeof self !== 'undefined' ? self : {}; function getDefaultExportFromCjs (x) { return x && x.__esModule && Object.prototype.hasOwnProperty.call(x, 'default') ? x['default'] : x; } function getAugmentedNamespace(n) { if (n.__esModule) return n; var f = n.default; if (typeof f == "function") { var a = function a () { if (this instanceof a) { var args = [null]; args.push.apply(args, arguments); var Ctor = Function.bind.apply(f, args); return new Ctor(); } return f.apply(this, arguments); }; a.prototype = f.prototype; } else a = {}; Object.defineProperty(a, '__esModule', {value: true}); Object.keys(n).forEach(function (k) { var d = Object.getOwnPropertyDescriptor(n, k); Object.defineProperty(a, k, d.get ? d : { enumerable: true, get: function () { return n[k]; } }); }); return a; } var long = Long$1; /** * wasm optimizations, to do native i64 multiplication and divide */ var wasm = null; try { wasm = new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([ 0, 97, 115, 109, 1, 0, 0, 0, 1, 13, 2, 96, 0, 1, 127, 96, 4, 127, 127, 127, 127, 1, 127, 3, 7, 6, 0, 1, 1, 1, 1, 1, 6, 6, 1, 127, 1, 65, 0, 11, 7, 50, 6, 3, 109, 117, 108, 0, 1, 5, 100, 105, 118, 95, 115, 0, 2, 5, 100, 105, 118, 95, 117, 0, 3, 5, 114, 101, 109, 95, 115, 0, 4, 5, 114, 101, 109, 95, 117, 0, 5, 8, 103, 101, 116, 95, 104, 105, 103, 104, 0, 0, 10, 191, 1, 6, 4, 0, 35, 0, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 126, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 127, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 128, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 129, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 130, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11 ])), {}).exports; } catch (e) { // no wasm support :( } /** * Constructs a 64 bit two's-complement integer, given its low and high 32 bit values as *signed* integers. * See the from* functions below for more convenient ways of constructing Longs. * @exports Long * @class A Long class for representing a 64 bit two's-complement integer value. * @param {number} low The low (signed) 32 bits of the long * @param {number} high The high (signed) 32 bits of the long * @param {boolean=} unsigned Whether unsigned or not, defaults to signed * @constructor */ function Long$1(low, high, unsigned) { /** * The low 32 bits as a signed value. * @type {number} */ this.low = low | 0; /** * The high 32 bits as a signed value. * @type {number} */ this.high = high | 0; /** * Whether unsigned or not. * @type {boolean} */ this.unsigned = !!unsigned; } // The internal representation of a long is the two given signed, 32-bit values. // We use 32-bit pieces because these are the size of integers on which // Javascript performs bit-operations. For operations like addition and // multiplication, we split each number into 16 bit pieces, which can easily be // multiplied within Javascript's floating-point representation without overflow // or change in sign. // // In the algorithms below, we frequently reduce the negative case to the // positive case by negating the input(s) and then post-processing the result. // Note that we must ALWAYS check specially whether those values are MIN_VALUE // (-2^63) because -MIN_VALUE == MIN_VALUE (since 2^63 cannot be represented as // a positive number, it overflows back into a negative). Not handling this // case would often result in infinite recursion. // // Common constant values ZERO, ONE, NEG_ONE, etc. are defined below the from* // methods on which they depend. /** * An indicator used to reliably determine if an object is a Long or not. * @type {boolean} * @const * @private */ Long$1.prototype.__isLong__; Object.defineProperty(Long$1.prototype, "__isLong__", { value: true }); /** * @function * @param {*} obj Object * @returns {boolean} * @inner */ function isLong(obj) { return (obj && obj["__isLong__"]) === true; } /** * Tests if the specified object is a Long. * @function * @param {*} obj Object * @returns {boolean} */ Long$1.isLong = isLong; /** * A cache of the Long representations of small integer values. * @type {!Object} * @inner */ var INT_CACHE = {}; /** * A cache of the Long representations of small unsigned integer values. * @type {!Object} * @inner */ var UINT_CACHE = {}; /** * @param {number} value * @param {boolean=} unsigned * @returns {!Long} * @inner */ function fromInt(value, unsigned) { var obj, cachedObj, cache; if (unsigned) { value >>>= 0; if (cache = (0 <= value && value < 256)) { cachedObj = UINT_CACHE[value]; if (cachedObj) return cachedObj; } obj = fromBits(value, (value | 0) < 0 ? -1 : 0, true); if (cache) UINT_CACHE[value] = obj; return obj; } else { value |= 0; if (cache = (-128 <= value && value < 128)) { cachedObj = INT_CACHE[value]; if (cachedObj) return cachedObj; } obj = fromBits(value, value < 0 ? -1 : 0, false); if (cache) INT_CACHE[value] = obj; return obj; } } /** * Returns a Long representing the given 32 bit integer value. * @function * @param {number} value The 32 bit integer in question * @param {boolean=} unsigned Whether unsigned or not, defaults to signed * @returns {!Long} The corresponding Long value */ Long$1.fromInt = fromInt; /** * @param {number} value * @param {boolean=} unsigned * @returns {!Long} * @inner */ function fromNumber(value, unsigned) { if (isNaN(value)) return unsigned ? UZERO : ZERO; if (unsigned) { if (value < 0) return UZERO; if (value >= TWO_PWR_64_DBL) return MAX_UNSIGNED_VALUE; } else { if (value <= -TWO_PWR_63_DBL) return MIN_VALUE; if (value + 1 >= TWO_PWR_63_DBL) return MAX_VALUE; } if (value < 0) return fromNumber(-value, unsigned).neg(); return fromBits((value % TWO_PWR_32_DBL) | 0, (value / TWO_PWR_32_DBL) | 0, unsigned); } /** * Returns a Long representing the given value, provided that it is a finite number. Otherwise, zero is returned. * @function * @param {number} value The number in question * @param {boolean=} unsigned Whether unsigned or not, defaults to signed * @returns {!Long} The corresponding Long value */ Long$1.fromNumber = fromNumber; /** * @param {number} lowBits * @param {number} highBits * @param {boolean=} unsigned * @returns {!Long} * @inner */ function fromBits(lowBits, highBits, unsigned) { return new Long$1(lowBits, highBits, unsigned); } /** * Returns a Long representing the 64 bit integer that comes by concatenating the given low and high bits. Each is * assumed to use 32 bits. * @function * @param {number} lowBits The low 32 bits * @param {number} highBits The high 32 bits * @param {boolean=} unsigned Whether unsigned or not, defaults to signed * @returns {!Long} The corresponding Long value */ Long$1.fromBits = fromBits; /** * @function * @param {number} base * @param {number} exponent * @returns {number} * @inner */ var pow_dbl = Math.pow; // Used 4 times (4*8 to 15+4) /** * @param {string} str * @param {(boolean|number)=} unsigned * @param {number=} radix * @returns {!Long} * @inner */ function fromString(str, unsigned, radix) { if (str.length === 0) throw Error('empty string'); if (str === "NaN" || str === "Infinity" || str === "+Infinity" || str === "-Infinity") return ZERO; if (typeof unsigned === 'number') { // For goog.math.long compatibility radix = unsigned, unsigned = false; } else { unsigned = !! unsigned; } radix = radix || 10; if (radix < 2 || 36 < radix) throw RangeError('radix'); var p; if ((p = str.indexOf('-')) > 0) throw Error('interior hyphen'); else if (p === 0) { return fromString(str.substring(1), unsigned, radix).neg(); } // Do several (8) digits each time through the loop, so as to // minimize the calls to the very expensive emulated div. var radixToPower = fromNumber(pow_dbl(radix, 8)); var result = ZERO; for (var i = 0; i < str.length; i += 8) { var size = Math.min(8, str.length - i), value = parseInt(str.substring(i, i + size), radix); if (size < 8) { var power = fromNumber(pow_dbl(radix, size)); result = result.mul(power).add(fromNumber(value)); } else { result = result.mul(radixToPower); result = result.add(fromNumber(value)); } } result.unsigned = unsigned; return result; } /** * Returns a Long representation of the given string, written using the specified radix. * @function * @param {string} str The textual representation of the Long * @param {(boolean|number)=} unsigned Whether unsigned or not, defaults to signed * @param {number=} radix The radix in which the text is written (2-36), defaults to 10 * @returns {!Long} The corresponding Long value */ Long$1.fromString = fromString; /** * @function * @param {!Long|number|string|!{low: number, high: number, unsigned: boolean}} val * @param {boolean=} unsigned * @returns {!Long} * @inner */ function fromValue(val, unsigned) { if (typeof val === 'number') return fromNumber(val, unsigned); if (typeof val === 'string') return fromString(val, unsigned); // Throws for non-objects, converts non-instanceof Long: return fromBits(val.low, val.high, typeof unsigned === 'boolean' ? unsigned : val.unsigned); } /** * Converts the specified value to a Long using the appropriate from* function for its type. * @function * @param {!Long|number|string|!{low: number, high: number, unsigned: boolean}} val Value * @param {boolean=} unsigned Whether unsigned or not, defaults to signed * @returns {!Long} */ Long$1.fromValue = fromValue; // NOTE: the compiler should inline these constant values below and then remove these variables, so there should be // no runtime penalty for these. /** * @type {number} * @const * @inner */ var TWO_PWR_16_DBL = 1 << 16; /** * @type {number} * @const * @inner */ var TWO_PWR_24_DBL = 1 << 24; /** * @type {number} * @const * @inner */ var TWO_PWR_32_DBL = TWO_PWR_16_DBL * TWO_PWR_16_DBL; /** * @type {number} * @const * @inner */ var TWO_PWR_64_DBL = TWO_PWR_32_DBL * TWO_PWR_32_DBL; /** * @type {number} * @const * @inner */ var TWO_PWR_63_DBL = TWO_PWR_64_DBL / 2; /** * @type {!Long} * @const * @inner */ var TWO_PWR_24 = fromInt(TWO_PWR_24_DBL); /** * @type {!Long} * @inner */ var ZERO = fromInt(0); /** * Signed zero. * @type {!Long} */ Long$1.ZERO = ZERO; /** * @type {!Long} * @inner */ var UZERO = fromInt(0, true); /** * Unsigned zero. * @type {!Long} */ Long$1.UZERO = UZERO; /** * @type {!Long} * @inner */ var ONE = fromInt(1); /** * Signed one. * @type {!Long} */ Long$1.ONE = ONE; /** * @type {!Long} * @inner */ var UONE = fromInt(1, true); /** * Unsigned one. * @type {!Long} */ Long$1.UONE = UONE; /** * @type {!Long} * @inner */ var NEG_ONE = fromInt(-1); /** * Signed negative one. * @type {!Long} */ Long$1.NEG_ONE = NEG_ONE; /** * @type {!Long} * @inner */ var MAX_VALUE = fromBits(0xFFFFFFFF|0, 0x7FFFFFFF|0, false); /** * Maximum signed value. * @type {!Long} */ Long$1.MAX_VALUE = MAX_VALUE; /** * @type {!Long} * @inner */ var MAX_UNSIGNED_VALUE = fromBits(0xFFFFFFFF|0, 0xFFFFFFFF|0, true); /** * Maximum unsigned value. * @type {!Long} */ Long$1.MAX_UNSIGNED_VALUE = MAX_UNSIGNED_VALUE; /** * @type {!Long} * @inner */ var MIN_VALUE = fromBits(0, 0x80000000|0, false); /** * Minimum signed value. * @type {!Long} */ Long$1.MIN_VALUE = MIN_VALUE; /** * @alias Long.prototype * @inner */ var LongPrototype = Long$1.prototype; /** * Converts the Long to a 32 bit integer, assuming it is a 32 bit integer. * @returns {number} */ LongPrototype.toInt = function toInt() { return this.unsigned ? this.low >>> 0 : this.low; }; /** * Converts the Long to a the nearest floating-point representation of this value (double, 53 bit mantissa). * @returns {number} */ LongPrototype.toNumber = function toNumber() { if (this.unsigned) return ((this.high >>> 0) * TWO_PWR_32_DBL) + (this.low >>> 0); return this.high * TWO_PWR_32_DBL + (this.low >>> 0); }; /** * Converts the Long to a string written in the specified radix. * @param {number=} radix Radix (2-36), defaults to 10 * @returns {string} * @override * @throws {RangeError} If `radix` is out of range */ LongPrototype.toString = function toString(radix) { radix = radix || 10; if (radix < 2 || 36 < radix) throw RangeError('radix'); if (this.isZero()) return '0'; if (this.isNegative()) { // Unsigned Longs are never negative if (this.eq(MIN_VALUE)) { // We need to change the Long value before it can be negated, so we remove // the bottom-most digit in this base and then recurse to do the rest. var radixLong = fromNumber(radix), div = this.div(radixLong), rem1 = div.mul(radixLong).sub(this); return div.toString(radix) + rem1.toInt().toString(radix); } else return '-' + this.neg().toString(radix); } // Do several (6) digits each time through the loop, so as to // minimize the calls to the very expensive emulated div. var radixToPower = fromNumber(pow_dbl(radix, 6), this.unsigned), rem = this; var result = ''; while (true) { var remDiv = rem.div(radixToPower), intval = rem.sub(remDiv.mul(radixToPower)).toInt() >>> 0, digits = intval.toString(radix); rem = remDiv; if (rem.isZero()) return digits + result; else { while (digits.length < 6) digits = '0' + digits; result = '' + digits + result; } } }; /** * Gets the high 32 bits as a signed integer. * @returns {number} Signed high bits */ LongPrototype.getHighBits = function getHighBits() { return this.high; }; /** * Gets the high 32 bits as an unsigned integer. * @returns {number} Unsigned high bits */ LongPrototype.getHighBitsUnsigned = function getHighBitsUnsigned() { return this.high >>> 0; }; /** * Gets the low 32 bits as a signed integer. * @returns {number} Signed low bits */ LongPrototype.getLowBits = function getLowBits() { return this.low; }; /** * Gets the low 32 bits as an unsigned integer. * @returns {number} Unsigned low bits */ LongPrototype.getLowBitsUnsigned = function getLowBitsUnsigned() { return this.low >>> 0; }; /** * Gets the number of bits needed to represent the absolute value of this Long. * @returns {number} */ LongPrototype.getNumBitsAbs = function getNumBitsAbs() { if (this.isNegative()) // Unsigned Longs are never negative return this.eq(MIN_VALUE) ? 64 : this.neg().getNumBitsAbs(); var val = this.high != 0 ? this.high : this.low; for (var bit = 31; bit > 0; bit--) if ((val & (1 << bit)) != 0) break; return this.high != 0 ? bit + 33 : bit + 1; }; /** * Tests if this Long's value equals zero. * @returns {boolean} */ LongPrototype.isZero = function isZero() { return this.high === 0 && this.low === 0; }; /** * Tests if this Long's value equals zero. This is an alias of {@link Long#isZero}. * @returns {boolean} */ LongPrototype.eqz = LongPrototype.isZero; /** * Tests if this Long's value is negative. * @returns {boolean} */ LongPrototype.isNegative = function isNegative() { return !this.unsigned && this.high < 0; }; /** * Tests if this Long's value is positive. * @returns {boolean} */ LongPrototype.isPositive = function isPositive() { return this.unsigned || this.high >= 0; }; /** * Tests if this Long's value is odd. * @returns {boolean} */ LongPrototype.isOdd = function isOdd() { return (this.low & 1) === 1; }; /** * Tests if this Long's value is even. * @returns {boolean} */ LongPrototype.isEven = function isEven() { return (this.low & 1) === 0; }; /** * Tests if this Long's value equals the specified's. * @param {!Long|number|string} other Other value * @returns {boolean} */ LongPrototype.equals = function equals(other) { if (!isLong(other)) other = fromValue(other); if (this.unsigned !== other.unsigned && (this.high >>> 31) === 1 && (other.high >>> 31) === 1) return false; return this.high === other.high && this.low === other.low; }; /** * Tests if this Long's value equals the specified's. This is an alias of {@link Long#equals}. * @function * @param {!Long|number|string} other Other value * @returns {boolean} */ LongPrototype.eq = LongPrototype.equals; /** * Tests if this Long's value differs from the specified's. * @param {!Long|number|string} other Other value * @returns {boolean} */ LongPrototype.notEquals = function notEquals(other) { return !this.eq(/* validates */ other); }; /** * Tests if this Long's value differs from the specified's. This is an alias of {@link Long#notEquals}. * @function * @param {!Long|number|string} other Other value * @returns {boolean} */ LongPrototype.neq = LongPrototype.notEquals; /** * Tests if this Long's value differs from the specified's. This is an alias of {@link Long#notEquals}. * @function * @param {!Long|number|string} other Other value * @returns {boolean} */ LongPrototype.ne = LongPrototype.notEquals; /** * Tests if this Long's value is less than the specified's. * @param {!Long|number|string} other Other value * @returns {boolean} */ LongPrototype.lessThan = function lessThan(other) { return this.comp(/* validates */ other) < 0; }; /** * Tests if this Long's value is less than the specified's. This is an alias of {@link Long#lessThan}. * @function * @param {!Long|number|string} other Other value * @returns {boolean} */ LongPrototype.lt = LongPrototype.lessThan; /** * Tests if this Long's value is less than or equal the specified's. * @param {!Long|number|string} other Other value * @returns {boolean} */ LongPrototype.lessThanOrEqual = function lessThanOrEqual(other) { return this.comp(/* validates */ other) <= 0; }; /** * Tests if this Long's value is less than or equal the specified's. This is an alias of {@link Long#lessThanOrEqual}. * @function * @param {!Long|number|string} other Other value * @returns {boolean} */ LongPrototype.lte = LongPrototype.lessThanOrEqual; /** * Tests if this Long's value is less than or equal the specified's. This is an alias of {@link Long#lessThanOrEqual}. * @function * @param {!Long|number|string} other Other value * @returns {boolean} */ LongPrototype.le = LongPrototype.lessThanOrEqual; /** * Tests if this Long's value is greater than the specified's. * @param {!Long|number|string} other Other value * @returns {boolean} */ LongPrototype.greaterThan = function greaterThan(other) { return this.comp(/* validates */ other) > 0; }; /** * Tests if this Long's value is greater than the specified's. This is an alias of {@link Long#greaterThan}. * @function * @param {!Long|number|string} other Other value * @returns {boolean} */ LongPrototype.gt = LongPrototype.greaterThan; /** * Tests if this Long's value is greater than or equal the specified's. * @param {!Long|number|string} other Other value * @returns {boolean} */ LongPrototype.greaterThanOrEqual = function greaterThanOrEqual(other) { return this.comp(/* validates */ other) >= 0; }; /** * Tests if this Long's value is greater than or equal the specified's. This is an alias of {@link Long#greaterThanOrEqual}. * @function * @param {!Long|number|string} other Other value * @returns {boolean} */ LongPrototype.gte = LongPrototype.greaterThanOrEqual; /** * Tests if this Long's value is greater than or equal the specified's. This is an alias of {@link Long#greaterThanOrEqual}. * @function * @param {!Long|number|string} other Other value * @returns {boolean} */ LongPrototype.ge = LongPrototype.greaterThanOrEqual; /** * Compares this Long's value with the specified's. * @param {!Long|number|string} other Other value * @returns {number} 0 if they are the same, 1 if the this is greater and -1 * if the given one is greater */ LongPrototype.compare = function compare(other) { if (!isLong(other)) other = fromValue(other); if (this.eq(other)) return 0; var thisNeg = this.isNegative(), otherNeg = other.isNegative(); if (thisNeg && !otherNeg) return -1; if (!thisNeg && otherNeg) return 1; // At this point the sign bits are the same if (!this.unsigned) return this.sub(other).isNegative() ? -1 : 1; // Both are positive if at least one is unsigned return (other.high >>> 0) > (this.high >>> 0) || (other.high === this.high && (other.low >>> 0) > (this.low >>> 0)) ? -1 : 1; }; /** * Compares this Long's value with the specified's. This is an alias of {@link Long#compare}. * @function * @param {!Long|number|string} other Other value * @returns {number} 0 if they are the same, 1 if the this is greater and -1 * if the given one is greater */ LongPrototype.comp = LongPrototype.compare; /** * Negates this Long's value. * @returns {!Long} Negated Long */ LongPrototype.negate = function negate() { if (!this.unsigned && this.eq(MIN_VALUE)) return MIN_VALUE; return this.not().add(ONE); }; /** * Negates this Long's value. This is an alias of {@link Long#negate}. * @function * @returns {!Long} Negated Long */ LongPrototype.neg = LongPrototype.negate; /** * Returns the sum of this and the specified Long. * @param {!Long|number|string} addend Addend * @returns {!Long} Sum */ LongPrototype.add = function add(addend) { if (!isLong(addend)) addend = fromValue(addend); // Divide each number into 4 chunks of 16 bits, and then sum the chunks. var a48 = this.high >>> 16; var a32 = this.high & 0xFFFF; var a16 = this.low >>> 16; var a00 = this.low & 0xFFFF; var b48 = addend.high >>> 16; var b32 = addend.high & 0xFFFF; var b16 = addend.low >>> 16; var b00 = addend.low & 0xFFFF; var c48 = 0, c32 = 0, c16 = 0, c00 = 0; c00 += a00 + b00; c16 += c00 >>> 16; c00 &= 0xFFFF; c16 += a16 + b16; c32 += c16 >>> 16; c16 &= 0xFFFF; c32 += a32 + b32; c48 += c32 >>> 16; c32 &= 0xFFFF; c48 += a48 + b48; c48 &= 0xFFFF; return fromBits((c16 << 16) | c00, (c48 << 16) | c32, this.unsigned); }; /** * Returns the difference of this and the specified Long. * @param {!Long|number|string} subtrahend Subtrahend * @returns {!Long} Difference */ LongPrototype.subtract = function subtract(subtrahend) { if (!isLong(subtrahend)) subtrahend = fromValue(subtrahend); return this.add(subtrahend.neg()); }; /** * Returns the difference of this and the specified Long. This is an alias of {@link Long#subtract}. * @function * @param {!Long|number|string} subtrahend Subtrahend * @returns {!Long} Difference */ LongPrototype.sub = LongPrototype.subtract; /** * Returns the product of this and the specified Long. * @param {!Long|number|string} multiplier Multiplier * @returns {!Long} Product */ LongPrototype.multiply = function multiply(multiplier) { if (this.isZero()) return ZERO; if (!isLong(multiplier)) multiplier = fromValue(multiplier); // use wasm support if present if (wasm) { var low = wasm.mul(this.low, this.high, multiplier.low, multiplier.high); return fromBits(low, wasm.get_high(), this.unsigned); } if (multiplier.isZero()) return ZERO; if (this.eq(MIN_VALUE)) return multiplier.isOdd() ? MIN_VALUE : ZERO; if (multiplier.eq(MIN_VALUE)) return this.isOdd() ? MIN_VALUE : ZERO; if (this.isNegative()) { if (multiplier.isNegative()) return this.neg().mul(multiplier.neg()); else return this.neg().mul(multiplier).neg(); } else if (multiplier.isNegative()) return this.mul(multiplier.neg()).neg(); // If both longs are small, use float multiplication if (this.lt(TWO_PWR_24) && multiplier.lt(TWO_PWR_24)) return fromNumber(this.toNumber() * multiplier.toNumber(), this.unsigned); // Divide each long into 4 chunks of 16 bits, and then add up 4x4 products. // We can skip products that would overflow. var a48 = this.high >>> 16; var a32 = this.high & 0xFFFF; var a16 = this.low >>> 16; var a00 = this.low & 0xFFFF; var b48 = multiplier.high >>> 16; var b32 = multiplier.high & 0xFFFF; var b16 = multiplier.low >>> 16; var b00 = multiplier.low & 0xFFFF; var c48 = 0, c32 = 0, c16 = 0, c00 = 0; c00 += a00 * b00; c16 += c00 >>> 16; c00 &= 0xFFFF; c16 += a16 * b00; c32 += c16 >>> 16; c16 &= 0xFFFF; c16 += a00 * b16; c32 += c16 >>> 16; c16 &= 0xFFFF; c32 += a32 * b00; c48 += c32 >>> 16; c32 &= 0xFFFF; c32 += a16 * b16; c48 += c32 >>> 16; c32 &= 0xFFFF; c32 += a00 * b32; c48 += c32 >>> 16; c32 &= 0xFFFF; c48 += a48 * b00 + a32 * b16 + a16 * b32 + a00 * b48; c48 &= 0xFFFF; return fromBits((c16 << 16) | c00, (c48 << 16) | c32, this.unsigned); }; /** * Returns the product of this and the specified Long. This is an alias of {@link Long#multiply}. * @function * @param {!Long|number|string} multiplier Multiplier * @returns {!Long} Product */ LongPrototype.mul = LongPrototype.multiply; /** * Returns this Long divided by the specified. The result is signed if this Long is signed or * unsigned if this Long is unsigned. * @param {!Long|number|string} divisor Divisor * @returns {!Long} Quotient */ LongPrototype.divide = function divide(divisor) { if (!isLong(divisor)) divisor = fromValue(divisor); if (divisor.isZero()) throw Error('division by zero'); // use wasm support if present if (wasm) { // guard against signed division overflow: the largest // negative number / -1 would be 1 larger than the largest // positive number, due to two's complement. if (!this.unsigned && this.high === -0x80000000 && divisor.low === -1 && divisor.high === -1) { // be consistent with non-wasm code path return this; } var low = (this.unsigned ? wasm.div_u : wasm.div_s)( this.low, this.high, divisor.low, divisor.high ); return fromBits(low, wasm.get_high(), this.unsigned); } if (this.isZero()) return this.unsigned ? UZERO : ZERO; var approx, rem, res; if (!this.unsigned) { // This section is only relevant for signed longs and is derived from the // closure library as a whole. if (this.eq(MIN_VALUE)) { if (divisor.eq(ONE) || divisor.eq(NEG_ONE)) return MIN_VALUE; // recall that -MIN_VALUE == MIN_VALUE else if (divisor.eq(MIN_VALUE)) return ONE; else { // At this point, we have |other| >= 2, so |this/other| < |MIN_VALUE|. var halfThis = this.shr(1); approx = halfThis.div(divisor).shl(1); if (approx.eq(ZERO)) { return divisor.isNegative() ? ONE : NEG_ONE; } else { rem = this.sub(divisor.mul(approx)); res = approx.add(rem.div(divisor)); return res; } } } else if (divisor.eq(MIN_VALUE)) return this.unsigned ? UZERO : ZERO; if (this.isNegative()) { if (divisor.isNegative()) return this.neg().div(divisor.neg()); return this.neg().div(divisor).neg(); } else if (divisor.isNegative()) return this.div(divisor.neg()).neg(); res = ZERO; } else { // The algorithm below has not been made for unsigned longs. It's therefore // required to take special care of the MSB prior to running it. if (!divisor.unsigned) divisor = divisor.toUnsigned(); if (divisor.gt(this)) return UZERO; if (divisor.gt(this.shru(1))) // 15 >>> 1 = 7 ; with divisor = 8 ; true return UONE; res = UZERO; } // Repeat the following until the remainder is less than other: find a // floating-point that approximates remainder / other *from below*, add this // into the result, and subtract it from the remainder. It is critical that // the approximate value is less than or equal to the real value so that the // remainder never becomes negative. rem = this; while (rem.gte(divisor)) { // Approximate the result of division. This may be a little greater or // smaller than the actual value. approx = Math.max(1, Math.floor(rem.toNumber() / divisor.toNumber())); // We will tweak the approximate result by changing it in the 48-th digit or // the smallest non-fractional digit, whichever is larger. var log2 = Math.ceil(Math.log(approx) / Math.LN2), delta = (log2 <= 48) ? 1 : pow_dbl(2, log2 - 48), // Decrease the approximation until it is smaller than the remainder. Note // that if it is too large, the product overflows and is negative. approxRes = fromNumber(approx), approxRem = approxRes.mul(divisor); while (approxRem.isNegative() || approxRem.gt(rem)) { approx -= delta; approxRes = fromNumber(approx, this.unsigned); approxRem = approxRes.mul(divisor); } // We know the answer can't be zero... and actually, zero would cause // infinite recursion since we would make no progress. if (approxRes.isZero()) approxRes = ONE; res = res.add(approxRes); rem = rem.sub(approxRem); } return res; }; /** * Returns this Long divided by the specified. This is an alias of {@link Long#divide}. * @function * @param {!Long|number|string} divisor Divisor * @returns {!Long} Quotient */ LongPrototype.div = LongPrototype.divide; /** * Returns this Long modulo the specified. * @param {!Long|number|string} divisor Divisor * @returns {!Long} Remainder */ LongPrototype.modulo = function modulo(divisor) { if (!isLong(divisor)) divisor = fromValue(divisor); // use wasm support if present if (wasm) { var low = (this.unsigned ? wasm.rem_u : wasm.rem_s)( this.low, this.high, divisor.low, divisor.high ); return fromBits(low, wasm.get_high(), this.unsigned); } return this.sub(this.div(divisor).mul(divisor)); }; /** * Returns this Long modulo the specified. This is an alias of {@link Long#modulo}. * @function * @param {!Long|number|string} divisor Divisor * @returns {!Long} Remainder */ LongPrototype.mod = LongPrototype.modulo; /** * Returns this Long modulo the specified. This is an alias of {@link Long#modulo}. * @function * @param {!Long|number|string} divisor Divisor * @returns {!Long} Remainder */ LongPrototype.rem = LongPrototype.modulo; /** * Returns the bitwise NOT of this Long. * @returns {!Long} */ LongPrototype.not = function not() { return fromBits(~this.low, ~this.high, this.unsigned); }; /** * Returns the bitwise AND of this Long and the specified. * @param {!Long|number|string} other Other Long * @returns {!Long} */ LongPrototype.and = function and(other) { if (!isLong(other)) other = fromValue(other); return fromBits(this.low & other.low, this.high & other.high, this.unsigned); }; /** * Returns the bitwise OR of this Long and the specified. * @param {!Long|number|string} other Other Long * @returns {!Long} */ LongPrototype.or = function or(other) { if (!isLong(other)) other = fromValue(other); return fromBits(this.low | other.low, this.high | other.high, this.unsigned); }; /** * Returns the bitwise XOR of this Long and the given one. * @param {!Long|number|string} other Other Long * @returns {!Long} */ LongPrototype.xor = function xor(other) { if (!isLong(other)) other = fromValue(other); return fromBits(this.low ^ other.low, this.high ^ other.high, this.unsigned); }; /** * Returns this Long with bits shifted to the left by the given amount. * @param {number|!Long} numBits Number of bits * @returns {!Long} Shifted Long */ LongPrototype.shiftLeft = function shiftLeft(numBits) { if (isLong(numBits)) numBits = numBits.toInt(); if ((numBits &= 63) === 0) return this; else if (numBits < 32) return fromBits(this.low << numBits, (this.high << numBits) | (this.low >>> (32 - numBits)), this.unsigned); else return fromBits(0, this.low << (numBits - 32), this.unsigned); }; /** * Returns this Long with bits shifted to the left by the given amount. This is an alias of {@link Long#shiftLeft}. * @function * @param {number|!Long} numBits Number of bits * @returns {!Long} Shifted Long */ LongPrototype.shl = LongPrototype.shiftLeft; /** * Returns this Long with bits arithmetically shifted to the right by the given amount. * @param {number|!Long} numBits Number of bits * @returns {!Long} Shifted Long */ LongPrototype.shiftRight = function shiftRight(numBits) { if (isLong(numBits)) numBits = numBits.toInt(); if ((numBits &= 63) === 0) return this; else if (numBits < 32) return fromBits((this.low >>> numBits) | (this.high << (32 - numBits)), this.high >> numBits, this.unsigned); else return fromBits(this.high >> (numBits - 32), this.high >= 0 ? 0 : -1, this.unsigned); }; /** * Returns this Long with bits arithmetically shifted to the right by the given amount. This is an alias of {@link Long#shiftRight}. * @function * @param {number|!Long} numBits Number of bits * @returns {!Long} Shifted Long */ LongPrototype.shr = LongPrototype.shiftRight; /** * Returns this Long with bits logically shifted to the right by the given amount. * @param {number|!Long} numBits Number of bits * @returns {!Long} Shifted Long */ LongPrototype.shiftRightUnsigned = function shiftRightUnsigned(numBits) { if (isLong(numBits)) numBits = numBits.toInt(); numBits &= 63; if (numBits === 0) return this; else { var high = this.high; if (numBits < 32) { var low = this.low; return fromBits((low >>> numBits) | (high << (32 - numBits)), high >>> numBits, this.unsigned); } else if (numBits === 32) return fromBits(high, 0, this.unsigned); else return fromBits(high >>> (numBits - 32), 0, this.unsigned); } }; /** * Returns this Long with bits logically shifted to the right by the given amount. This is an alias of {@link Long#shiftRightUnsigned}. * @function * @param {number|!Long} numBits Number of bits * @returns {!Long} Shifted Long */ LongPrototype.shru = LongPrototype.shiftRightUnsigned; /** * Returns this Long with bits logically shifted to the right by the given amount. This is an alias of {@link Long#shiftRightUnsigned}. * @function * @param {number|!Long} numBits Number of bits * @returns {!Long} Shifted Long */ LongPrototype.shr_u = LongPrototype.shiftRightUnsigned; /** * Converts this Long to signed. * @returns {!Long} Signed long */ LongPrototype.toSigned = function toSigned() { if (!this.unsigned) return this; return fromBits(this.low, this.high, false); }; /** * Converts this Long to unsigned. * @returns {!Long} Unsigned long */ LongPrototype.toUnsigned = function toUnsigned() { if (this.unsigned) return this; return fromBits(this.low, this.high, true); }; /** * Converts this Long to its byte representation. * @param {boolean=} le Whether little or big endian, defaults to big endian * @returns {!Array.} Byte representation */ LongPrototype.toBytes = function toBytes(le) { return le ? this.toBytesLE() : this.toBytesBE(); }; /** * Converts this Long to its little endian byte representation. * @returns {!Array.} Little endian byte representation */ LongPrototype.toBytesLE = function toBytesLE() { var hi = this.high, lo = this.low; return [ lo & 0xff, lo >>> 8 & 0xff, lo >>> 16 & 0xff, lo >>> 24 , hi & 0xff, hi >>> 8 & 0xff, hi >>> 16 & 0xff, hi >>> 24 ]; }; /** * Converts this Long to its big endian byte representation. * @returns {!Array.} Big endian byte representation */ LongPrototype.toBytesBE = function toBytesBE() { var hi = this.high, lo = this.low; return [ hi >>> 24 , hi >>> 16 & 0xff, hi >>> 8 & 0xff, hi & 0xff, lo >>> 24 , lo >>> 16 & 0xff, lo >>> 8 & 0xff, lo & 0xff ]; }; /** * Creates a Long from its byte representation. * @param {!Array.} bytes Byte representation * @param {boolean=} unsigned Whether unsigned or not, defaults to signed * @param {boolean=} le Whether little or big endian, defaults to big endian * @returns {Long} The corresponding Long value */ Long$1.fromBytes = function fromBytes(bytes, unsigned, le) { return le ? Long$1.fromBytesLE(bytes, unsigned) : Long$1.fromBytesBE(bytes, unsigned); }; /** * Creates a Long from its little endian byte representation. * @param {!Array.} bytes Little endian byte representation * @param {boolean=} unsigned Whether unsigned or not, defaults to signed * @returns {Long} The corresponding Long value */ Long$1.fromBytesLE = function fromBytesLE(bytes, unsigned) { return new Long$1( bytes[0] | bytes[1] << 8 | bytes[2] << 16 | bytes[3] << 24, bytes[4] | bytes[5] << 8 | bytes[6] << 16 | bytes[7] << 24, unsigned ); }; /** * Creates a Long from its big endian byte representation. * @param {!Array.} bytes Big endian byte representation * @param {boolean=} unsigned Whether unsigned or not, defaults to signed * @returns {Long} The corresponding Long value */ Long$1.fromBytesBE = function fromBytesBE(bytes, unsigned) { return new Long$1( bytes[4] << 24 | bytes[5] << 16 | bytes[6] << 8 | bytes[7], bytes[0] << 24 | bytes[1] << 16 | bytes[2] << 8 | bytes[3], unsigned ); }; var long$1 = /*@__PURE__*/getDefaultExportFromCjs(long); var LongExports = /*#__PURE__*/_mergeNamespaces({ __proto__: null, default: long$1 }, [long]); /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ // tslint:disable-next-line const Long = // tslint:disable-next-line long$1 || LongExports; function hexToLong(hex) { return Long.fromString(hex, true, 16); } // Some primes between 2^63 and 2^64 for various uses. // Hex 0xc3a5c85c97cb3127 hexToLong('c3a5c85c97cb3127'); // Hex 0xb492b66fbe98f273 hexToLong('b492b66fbe98f273'); // Hex 0x9ae16a3b2f90404f hexToLong('9ae16a3b2f90404f'); /** * @license * Copyright 2017 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ function noConversionNeeded(a, dtype) { return (a instanceof Float32Array && dtype === 'float32') || (a instanceof Int32Array && dtype === 'int32') || (a instanceof Uint8Array && dtype === 'bool'); } function toTypedArray(a, dtype) { if (dtype === 'string') { throw new Error('Cannot convert a string[] to a TypedArray'); } if (Array.isArray(a)) { a = flatten(a); } if (env().getBool('DEBUG')) { checkConversionForErrors(a, dtype); } if (noConversionNeeded(a, dtype)) { return a; } if (dtype == null || dtype === 'float32' || dtype === 'complex64') { return new Float32Array(a); } else if (dtype === 'int32') { return new Int32Array(a); } else if (dtype === 'bool') { const bool = new Uint8Array(a.length); for (let i = 0; i < bool.length; ++i) { if (Math.round(a[i]) !== 0) { bool[i] = 1; } } return bool; } else { throw new Error(`Unknown data type ${dtype}`); } } /** * Returns the current high-resolution time in milliseconds relative to an * arbitrary time in the past. It works across different platforms (node.js, * browsers). * * ```js * console.log(tf.util.now()); * ``` * * @doc {heading: 'Util', namespace: 'util'} */ function now() { return env().platform.now(); } /** * Encodes the provided string into bytes using the provided encoding scheme. * * @param s The string to encode. * @param encoding The encoding scheme. Defaults to utf-8. * * @doc {heading: 'Util'} */ function encodeString(s, encoding = 'utf-8') { encoding = encoding || 'utf-8'; return env().platform.encode(s, encoding); } /** * Decodes the provided bytes into a string using the provided encoding scheme. * @param bytes The bytes to decode. * * @param encoding The encoding scheme. Defaults to utf-8. * * @doc {heading: 'Util'} */ function decodeString(bytes, encoding = 'utf-8') { encoding = encoding || 'utf-8'; return env().platform.decode(bytes, encoding); } function isTypedArray(a) { // TODO(mattsoulanille): Remove this fallback in 5.0.0 if (env().platform.isTypedArray != null) { return env().platform.isTypedArray(a); } else { return isTypedArrayBrowser(a); } } // NOTE: We explicitly type out what T extends instead of any so that // util.flatten on a nested array of number doesn't try to infer T as a // number[][], causing us to explicitly type util.flatten(). /** * Flattens an arbitrarily nested array. * * ```js * const a = [[1, 2], [3, 4], [5, [6, [7]]]]; * const flat = tf.util.flatten(a); * console.log(flat); * ``` * * @param arr The nested array to flatten. * @param result The destination array which holds the elements. * @param skipTypedArray If true, avoids flattening the typed arrays. Defaults * to false. * * @doc {heading: 'Util', namespace: 'util'} */ function flatten(arr, result = [], skipTypedArray = false) { if (result == null) { result = []; } if (typeof arr === 'boolean' || typeof arr === 'number' || typeof arr === 'string' || isPromise(arr) || arr == null || isTypedArray(arr) && skipTypedArray) { result.push(arr); } else if (Array.isArray(arr) || isTypedArray(arr)) { for (let i = 0; i < arr.length; ++i) { flatten(arr[i], result, skipTypedArray); } } else { let maxIndex = -1; for (const key of Object.keys(arr)) { // 0 or positive integer. if (/^([1-9]+[0-9]*|0)$/.test(key)) { maxIndex = Math.max(maxIndex, Number(key)); } } for (let i = 0; i <= maxIndex; i++) { // tslint:disable-next-line: no-unnecessary-type-assertion flatten(arr[i], result, skipTypedArray); } } return result; } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ class Profiler { constructor(backendTimer, logger) { this.backendTimer = backendTimer; this.logger = logger; if (logger == null) { this.logger = new Logger(); } } profileKernel(kernelName, inputs, f) { let outputs; const holdResultWrapperFn = () => { outputs = f(); }; let timer; const start = now(); if (this.backendTimer.timerAvailable()) { timer = this.backendTimer.time(holdResultWrapperFn); } else { holdResultWrapperFn(); for (const output of outputs) { output.dataSync(); } timer = Promise.resolve({ kernelMs: now() - start }); } if (env().getBool('CHECK_COMPUTATION_FOR_ERRORS')) { for (let i = 0; i < outputs.length; i++) { const output = outputs[i]; // Dangling promise here because we don't want to propagate up // asynchronicity. output.data().then(tensorVals => { checkComputationForErrors(tensorVals, output.dtype, kernelName); }); } } const kernelProfile = { kernelName, outputs, inputs, timeMs: timer.then(timing => timing.kernelMs), extraInfo: timer.then(timing => timing.getExtraProfileInfo != null ? timing.getExtraProfileInfo() : '') }; return kernelProfile; } logKernelProfile(kernelProfile) { const { kernelName, outputs, timeMs, inputs, extraInfo } = kernelProfile; outputs.forEach(result => { Promise.all([result.data(), timeMs, extraInfo]).then(valueContainer => { this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]); }); }); } } function checkComputationForErrors(vals, dtype, kernelName) { if (dtype !== 'float32') { // Only floating point computations will generate NaN values return false; } for (let i = 0; i < vals.length; i++) { const num = vals[i]; if (isNaN(num) || !isFinite(num)) { // Throwing custom exception so behavior is testable. console.warn(`Found ${num} in the result of '${kernelName}'`); return true; } } return false; } class Logger { logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) { const time = typeof timeMs === 'number' ? rightPad(`${timeMs}ms`, 9) : timeMs['error']; const paddedName = rightPad(name, 25); const rank = result.rank; const size = result.size; const shape = rightPad(result.shape.toString(), 14); let inputShapesDescription = ''; for (const name in inputs) { const input = inputs[name]; if (input != null) { // The input might be a non-tensor (e.g HTMLImageElement), in which case // we claim the output shape as input shape. const inputShape = input.shape || result.shape; const inputRank = inputShape.length; inputShapesDescription += `${name}: ${inputRank}D ${inputRank > 0 ? inputShape : ''} `; } } console.log(`%c${paddedName}\t%c${time}\t%c${rank}D ${shape}\t%c${size}\t%c${inputShapesDescription}\t%c${extraInfo}`, 'font-weight:bold', 'color:red', 'color:blue', 'color: orange', 'color: green', 'color: steelblue'); } } /** * @license * Copyright 2017 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes a list of TapeNodes that connect x to y, filtering everything else * out and preserving the order of the original tape elements. * * @param tape The tape elements to filter. * @param xs The input Tensors. * @param y The output Tensor. */ function getFilteredNodesXToY(tape, xs, y) { // Forward pass to compute all the nodes and Tensors that are transitively a // function of x. const tensorsFromX = {}; const nodesFromX = {}; for (let i = 0; i < xs.length; i++) { tensorsFromX[xs[i].id] = true; } for (let i = 0; i < tape.length; i++) { const node = tape[i]; const nodeInputs = node.inputs; for (const inputName in nodeInputs) { const input = nodeInputs[inputName]; let anyInputFromX = false; for (let j = 0; j < xs.length; j++) { if (tensorsFromX[input.id]) { node.outputs.forEach(output => tensorsFromX[output.id] = true); anyInputFromX = true; nodesFromX[node.id] = true; break; } } if (anyInputFromX) { break; } } } // Backward pass to find all of the nodes and Tensors that lead to y. const tensorsLeadToY = {}; tensorsLeadToY[y.id] = true; const nodesToY = {}; for (let i = tape.length - 1; i >= 0; i--) { const node = tape[i]; const nodeInputs = node.inputs; // If any of the outputs lead to y, mark all of the inputs as leading to y. for (let j = 0; j < node.outputs.length; j++) { if (tensorsLeadToY[node.outputs[j].id]) { for (const inputName in nodeInputs) { tensorsLeadToY[nodeInputs[inputName].id] = true; nodesToY[node.id] = true; } break; } } } // Return the paths that come from x and lead to y. const filteredTape = []; for (let i = 0; i < tape.length; i++) { const node = tape[i]; if (nodesFromX[node.id] && nodesToY[node.id]) { // Prune the inputs from the node that aren't a function of x. const prunedInputs = {}; for (const inputName in node.inputs) { const nodeInput = node.inputs[inputName]; if (tensorsFromX[nodeInput.id]) { prunedInputs[inputName] = nodeInput; } } // Copy the node and overwrite inputsAndArgs to the pruned version. const prunedNode = Object.assign({}, node); prunedNode.inputs = prunedInputs; prunedNode.outputs = node.outputs; filteredTape.push(prunedNode); } } return filteredTape; } /** * Backpropagate gradients through the filtered TapeNodes. * * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map * is mutated by this method. * @param filteredTape The filtered TapeNodes to backprop through. */ function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy, add) { // Walk the tape backward and keep a map of Tensor to its gradient. for (let i = filteredTape.length - 1; i >= 0; i--) { const node = filteredTape[i]; const dys = []; node.outputs.forEach(o => { const gradTensor = tensorAccumulatedGradientMap[o.id]; if (gradTensor != null) { dys.push(gradTensor); } else { // This particular output is not in the back-propagation subgraph, so it // does not affect the final output, thus we put null for its dy. dys.push(null); } }); if (node.gradient == null) { throw new Error(`Cannot compute gradient: gradient function not found ` + `for ${node.kernelName}.`); } // Backprop dy through this node and accumulate gradients over the inputs. const inputGradients = node.gradient(dys); for (const inputName in node.inputs) { if (!(inputName in inputGradients)) { throw new Error(`Cannot backprop through input ${inputName}. ` + `Available gradients found: ${Object.keys(inputGradients)}.`); } // Call the gradient function. const dx = tidy(() => inputGradients[inputName]()); if (dx.dtype !== 'float32') { throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` + `${inputName} must have 'float32' dtype, but has '${dx.dtype}'`); } const x = node.inputs[inputName]; if (!arraysEqual(dx.shape, x.shape)) { throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` + `'${inputName}' has shape '${dx.shape}', which does not match ` + `the shape of the input '${x.shape}'`); } if (tensorAccumulatedGradientMap[x.id] == null) { tensorAccumulatedGradientMap[x.id] = dx; } else { const curGradient = tensorAccumulatedGradientMap[x.id]; tensorAccumulatedGradientMap[x.id] = add(curGradient, dx); curGradient.dispose(); } } } } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ // Maximum number of values before we decide to show ellipsis. const FORMAT_LIMIT_NUM_VALS = 20; // Number of first and last values to show when displaying a, b,...,y, z. const FORMAT_NUM_FIRST_LAST_VALS = 3; // Number of significant digits to show. const FORMAT_NUM_SIG_DIGITS = 7; function tensorToString(vals, shape, dtype, verbose) { const strides = computeStrides(shape); const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides); const rank = shape.length; const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol); const lines = ['Tensor']; if (verbose) { lines.push(` dtype: ${dtype}`); lines.push(` rank: ${rank}`); lines.push(` shape: [${shape}]`); lines.push(` values:`); } lines.push(valsLines.map(l => ' ' + l).join('\n')); return lines.join('\n'); } function computeMaxSizePerColumn(vals, shape, dtype, strides) { const n = sizeFromShape(shape); const numCols = strides[strides.length - 1]; const padPerCol = new Array(numCols).fill(0); const rank = shape.length; const valuesOrTuples = dtype === 'complex64' ? createComplexTuples(vals) : vals; if (rank > 1) { for (let row = 0; row < n / numCols; row++) { const offset = row * numCols; for (let j = 0; j < numCols; j++) { padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length); } } } return padPerCol; } function valToString(val, pad, dtype) { let valStr; if (Array.isArray(val)) { valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ` + `${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`; } else if (isString(val)) { valStr = `'${val}'`; } else if (dtype === 'bool') { valStr = boolNumToString(val); } else { valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString(); } return rightPad(valStr, pad); } function boolNumToString(v) { return v === 0 ? 'false' : 'true'; } function subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) { const storagePerElement = dtype === 'complex64' ? 2 : 1; const size = shape[0]; const rank = shape.length; if (rank === 0) { if (dtype === 'complex64') { const complexTuple = createComplexTuples(vals); return [valToString(complexTuple[0], 0, dtype)]; } if (dtype === 'bool') { return [boolNumToString(vals[0])]; } return [vals[0].toString()]; } if (rank === 1) { if (size > FORMAT_LIMIT_NUM_VALS) { const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement; let firstVals = Array.from(vals.slice(0, firstValsSize)); let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement)); if (dtype === 'complex64') { firstVals = createComplexTuples(firstVals); lastVals = createComplexTuples(lastVals); } return [ '[' + firstVals.map((x, i) => valToString(x, padPerCol[i], dtype)) .join(', ') + ', ..., ' + lastVals .map((x, i) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype)) .join(', ') + ']' ]; } const displayVals = dtype === 'complex64' ? createComplexTuples(vals) : Array.from(vals); return [ '[' + displayVals.map((x, i) => valToString(x, padPerCol[i], dtype)) .join(', ') + ']' ]; } // The array is rank 2 or more. const subshape = shape.slice(1); const substrides = strides.slice(1); const stride = strides[0] * storagePerElement; const lines = []; if (size > FORMAT_LIMIT_NUM_VALS) { for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) { const start = i * stride; const end = start + stride; lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false /* isLast */)); } lines.push('...'); for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) { const start = i * stride; const end = start + stride; lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */)); } } else { for (let i = 0; i < size; i++) { const start = i * stride; const end = start + stride; lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */)); } } const sep = rank === 2 ? ',' : ''; lines[0] = '[' + (size > 0 ? lines[0] + sep : ''); for (let i = 1; i < lines.length - 1; i++) { lines[i] = ' ' + lines[i] + sep; } let newLineSep = ',\n'; for (let i = 2; i < rank; i++) { newLineSep += '\n'; } lines[lines.length - 1] = ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep); return lines; } function createComplexTuples(vals) { const complexTuples = []; for (let i = 0; i < vals.length; i += 2) { complexTuples.push([vals[i], vals[i + 1]]); } return complexTuples; } /** * @license * Copyright 2017 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * A mutable object, similar to `tf.Tensor`, that allows users to set values * at locations before converting to an immutable `tf.Tensor`. * * See `tf.buffer` for creating a tensor buffer. * * @doc {heading: 'Tensors', subheading: 'Classes'} */ class TensorBuffer { constructor(shape, dtype, values) { this.dtype = dtype; this.shape = shape.slice(); this.size = sizeFromShape(shape); if (values != null) { const n = values.length; assert(n === this.size, () => `Length of values '${n}' does not match the size ` + `inferred by the shape '${this.size}'.`); } if (dtype === 'complex64') { throw new Error(`complex64 dtype TensorBuffers are not supported. Please create ` + `a TensorBuffer for the real and imaginary parts separately and ` + `call tf.complex(real, imag).`); } this.values = values || getArrayFromDType(dtype, this.size); this.strides = computeStrides(shape); } /** * Sets a value in the buffer at a given location. * * @param value The value to set. * @param locs The location indices. * * @doc {heading: 'Tensors', subheading: 'Creation'} */ set(value, ...locs) { if (locs.length === 0) { locs = [0]; } assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must ` + `match the rank (${this.rank})`); const index = this.locToIndex(locs); this.values[index] = value; } /** * Returns the value in the buffer at the provided location. * * @param locs The location indices. * * @doc {heading: 'Tensors', subheading: 'Creation'} */ get(...locs) { if (locs.length === 0) { locs = [0]; } let i = 0; for (const loc of locs) { if (loc < 0 || loc >= this.shape[i]) { const msg = `Requested out of range element at ${locs}. ` + ` Buffer shape=${this.shape}`; throw new Error(msg); } i++; } let index = locs[locs.length - 1]; for (let i = 0; i < locs.length - 1; ++i) { index += this.strides[i] * locs[i]; } return this.values[index]; } locToIndex(locs) { if (this.rank === 0) { return 0; } else if (this.rank === 1) { return locs[0]; } let index = locs[locs.length - 1]; for (let i = 0; i < locs.length - 1; ++i) { index += this.strides[i] * locs[i]; } return index; } indexToLoc(index) { if (this.rank === 0) { return []; } else if (this.rank === 1) { return [index]; } const locs = new Array(this.shape.length); for (let i = 0; i < locs.length - 1; ++i) { locs[i] = Math.floor(index / this.strides[i]); index -= locs[i] * this.strides[i]; } locs[locs.length - 1] = index; return locs; } get rank() { return this.shape.length; } /** * Creates an immutable `tf.Tensor` object from the buffer. * * @doc {heading: 'Tensors', subheading: 'Creation'} */ toTensor() { return trackerFn().makeTensor(this.values, this.shape, this.dtype); } } // For tracking tensor creation and disposal. let trackerFn = null; // Used by chaining methods to call into ops. let opHandler = null; /** * An external consumer can register itself as the tensor tracker. This way * the Tensor class can notify the tracker for every tensor created and * disposed. */ function setTensorTracker(fn) { trackerFn = fn; } /** * A `tf.Tensor` object represents an immutable, multidimensional array of * numbers that has a shape and a data type. * * For performance reasons, functions that create tensors do not necessarily * perform a copy of the data passed to them (e.g. if the data is passed as a * `Float32Array`), and changes to the data will change the tensor. This is not * a feature and is not supported. To avoid this behavior, use the tensor before * changing the input data or create a copy with `copy = tf.add(yourTensor, 0)`. * * See `tf.tensor` for details on how to create a `tf.Tensor`. * * @doc {heading: 'Tensors', subheading: 'Classes'} */ class Tensor { constructor(shape, dtype, dataId, id) { /** Whether this tensor has been globally kept. */ this.kept = false; this.isDisposedInternal = false; this.shape = shape.slice(); this.dtype = dtype || 'float32'; this.size = sizeFromShape(shape); this.strides = computeStrides(shape); this.dataId = dataId; this.id = id; this.rankType = (this.rank < 5 ? this.rank.toString() : 'higher'); } get rank() { return this.shape.length; } /** * Returns a promise of `tf.TensorBuffer` that holds the underlying data. * * @doc {heading: 'Tensors', subheading: 'Classes'} */ async buffer() { const vals = await this.data(); return opHandler.buffer(this.shape, this.dtype, vals); } /** * Returns a `tf.TensorBuffer` that holds the underlying data. * @doc {heading: 'Tensors', subheading: 'Classes'} */ bufferSync() { return opHandler.buffer(this.shape, this.dtype, this.dataSync()); } /** * Returns the tensor data as a nested array. The transfer of data is done * asynchronously. * * @doc {heading: 'Tensors', subheading: 'Classes'} */ async array() { const vals = await this.data(); return toNestedArray(this.shape, vals, this.dtype === 'complex64'); } /** * Returns the tensor data as a nested array. The transfer of data is done * synchronously. * * @doc {heading: 'Tensors', subheading: 'Classes'} */ arraySync() { return toNestedArray(this.shape, this.dataSync(), this.dtype === 'complex64'); } /** * Asynchronously downloads the values from the `tf.Tensor`. Returns a * promise of `TypedArray` that resolves when the computation has finished. * * @doc {heading: 'Tensors', subheading: 'Classes'} */ async data() { this.throwIfDisposed(); const data = trackerFn().read(this.dataId); if (this.dtype === 'string') { const bytes = await data; try { return bytes.map(b => decodeString(b)); } catch (_a) { throw new Error('Failed to decode the string bytes into utf-8. ' + 'To get the original bytes, call tensor.bytes().'); } } return data; } /** * Copy the tensor's data to a new GPU resource. Comparing to the `dataSync()` * and `data()`, this method prevents data from being downloaded to CPU. * * For WebGL backend, the data will be stored on a densely packed texture. * This means that the texture will use the RGBA channels to store value. * * For WebGPU backend, the data will be stored on a buffer. There is no * parameter, so can not use a user-defined size to create the buffer. * * @param options: * For WebGL, * - customTexShape: Optional. If set, will use the user defined * texture shape to create the texture. * * @returns For WebGL backend, a GPUData contains the new texture and * its information. * { * tensorRef: The tensor that is associated with this texture, * texture: WebGLTexture, * texShape: [number, number] // [height, width] * } * * For WebGPU backend, a GPUData contains the new buffer. * { * tensorRef: The tensor that is associated with this buffer, * buffer: GPUBuffer, * } * * Remember to dispose the GPUData after it is used by * `res.tensorRef.dispose()`. * * @doc {heading: 'Tensors', subheading: 'Classes'} */ dataToGPU(options) { this.throwIfDisposed(); return trackerFn().readToGPU(this.dataId, options); } /** * Synchronously downloads the values from the `tf.Tensor`. This blocks the * UI thread until the values are ready, which can cause performance issues. * * @doc {heading: 'Tensors', subheading: 'Classes'} */ dataSync() { this.throwIfDisposed(); const data = trackerFn().readSync(this.dataId); if (this.dtype === 'string') { try { return data.map(b => decodeString(b)); } catch (_a) { throw new Error('Failed to decode the string bytes into utf-8. ' + 'To get the original bytes, call tensor.bytes().'); } } return data; } /** Returns the underlying bytes of the tensor's data. */ async bytes() { this.throwIfDisposed(); const data = await trackerFn().read(this.dataId); if (this.dtype === 'string') { return data; } else { return new Uint8Array(data.buffer); } } /** * Disposes `tf.Tensor` from memory. * * @doc {heading: 'Tensors', subheading: 'Classes'} */ dispose() { if (this.isDisposed) { return; } if (this.kerasMask) { this.kerasMask.dispose(); } trackerFn().disposeTensor(this); this.isDisposedInternal = true; } get isDisposed() { return this.isDisposedInternal; } throwIfDisposed() { if (this.isDisposed) { throw new Error(`Tensor is disposed.`); } } /** * Prints the `tf.Tensor`. See `tf.print` for details. * * @param verbose Whether to print verbose information about the tensor, * including dtype and size. * * @doc {heading: 'Tensors', subheading: 'Classes'} */ print(verbose = false) { return opHandler.print(this, verbose); } /** * Returns a copy of the tensor. See `tf.clone` for details. * @doc {heading: 'Tensors', subheading: 'Classes'} */ clone() { this.throwIfDisposed(); return opHandler.clone(this); } /** * Returns a human-readable description of the tensor. Useful for logging. * * @doc {heading: 'Tensors', subheading: 'Classes'} */ toString(verbose = false) { const vals = this.dataSync(); return tensorToString(vals, this.shape, this.dtype, verbose); } cast(dtype) { this.throwIfDisposed(); return opHandler.cast(this, dtype); } variable(trainable = true, name, dtype) { this.throwIfDisposed(); return trackerFn().makeVariable(this, trainable, name, dtype); } } Object.defineProperty(Tensor, Symbol.hasInstance, { value: (instance) => { // Implementation note: we should use properties of the object that will be // defined before the constructor body has finished executing (methods). // This is because when this code is transpiled by babel, babel will call // classCallCheck before the constructor body is run. // See https://github.com/tensorflow/tfjs/issues/3384 for backstory. return !!instance && instance.data != null && instance.dataSync != null && instance.throwIfDisposed != null; } }); function getGlobalTensorClass() { // Use getGlobal so that we can augment the Tensor class across package // boundaries becase the node resolution alg may result in different modules // being returned for this file depending on the path they are loaded from. return getGlobal('Tensor', () => { return Tensor; }); } // Global side effect. Cache global reference to Tensor class getGlobalTensorClass(); /** * A mutable `tf.Tensor`, useful for persisting state, e.g. for training. * * @doc {heading: 'Tensors', subheading: 'Classes'} */ class Variable extends Tensor { constructor(initialValue, trainable, name, tensorId) { super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId); this.trainable = trainable; this.name = name; } /** * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have * the same shape and dtype as the old `tf.Tensor`. * * @param newValue New tensor to be assigned to this variable. * * @doc {heading: 'Tensors', subheading: 'Classes'} */ assign(newValue) { if (newValue.dtype !== this.dtype) { throw new Error(`dtype of the new value (${newValue.dtype}) and ` + `previous value (${this.dtype}) must match`); } if (!arraysEqual(newValue.shape, this.shape)) { throw new Error(`shape of the new value (${newValue.shape}) and ` + `previous value (${this.shape}) must match`); } trackerFn().disposeTensor(this); this.dataId = newValue.dataId; trackerFn().incRef(this, null /* backend */); } dispose() { trackerFn().disposeVariable(this); this.isDisposedInternal = true; } } Object.defineProperty(Variable, Symbol.hasInstance, { value: (instance) => { return instance instanceof Tensor && instance.assign != null && instance.assign instanceof Function; } }); /** * @license * Copyright 2017 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ var Rank; (function (Rank) { Rank["R0"] = "R0"; Rank["R1"] = "R1"; Rank["R2"] = "R2"; Rank["R3"] = "R3"; Rank["R4"] = "R4"; Rank["R5"] = "R5"; Rank["R6"] = "R6"; })(Rank || (Rank = {})); // Looks for upcasting types. Used, for example, in operations with mixed dtype // inputs. var UpcastInt32AndMap; (function (UpcastInt32AndMap) { UpcastInt32AndMap["float32"] = "float32"; UpcastInt32AndMap["int32"] = "int32"; UpcastInt32AndMap["bool"] = "int32"; UpcastInt32AndMap["complex64"] = "complex64"; })(UpcastInt32AndMap || (UpcastInt32AndMap = {})); var UpcastBoolAndMap; (function (UpcastBoolAndMap) { UpcastBoolAndMap["float32"] = "float32"; UpcastBoolAndMap["int32"] = "int32"; UpcastBoolAndMap["bool"] = "bool"; UpcastBoolAndMap["complex64"] = "complex64"; })(UpcastBoolAndMap || (UpcastBoolAndMap = {})); var UpcastFloat32AndMap; (function (UpcastFloat32AndMap) { UpcastFloat32AndMap["float32"] = "float32"; UpcastFloat32AndMap["int32"] = "float32"; UpcastFloat32AndMap["bool"] = "float32"; UpcastFloat32AndMap["complex64"] = "complex64"; })(UpcastFloat32AndMap || (UpcastFloat32AndMap = {})); var UpcastComplex64AndMap; (function (UpcastComplex64AndMap) { UpcastComplex64AndMap["float32"] = "complex64"; UpcastComplex64AndMap["int32"] = "complex64"; UpcastComplex64AndMap["bool"] = "complex64"; UpcastComplex64AndMap["complex64"] = "complex64"; })(UpcastComplex64AndMap || (UpcastComplex64AndMap = {})); const upcastTypeMap = { 'float32': UpcastFloat32AndMap, 'int32': UpcastInt32AndMap, 'bool': UpcastBoolAndMap, 'complex64': UpcastComplex64AndMap }; function upcastType(typeA, typeB) { if (typeA === 'string' || typeB === 'string') { if (typeA === 'string' && typeB === 'string') { return 'string'; } throw new Error(`Can not upcast ${typeA} with ${typeB}`); } return upcastTypeMap[typeA][typeB]; } function isWebGLData(values) { return values != null && typeof values === 'object' && 'texture' in values && values.texture instanceof WebGLTexture; } function isWebGPUData(values) { return typeof GPUBuffer !== 'undefined' && values != null && typeof values === 'object' && 'buffer' in values && values.buffer instanceof GPUBuffer; } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ function makeTypesMatch(a, b) { if (a.dtype === b.dtype) { return [a, b]; } const dtype = upcastType(a.dtype, b.dtype); return [a.cast(dtype), b.cast(dtype)]; } function assertTypesMatch(a, b) { assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and` + ` second(${b.dtype}) input must match`); } /** * Extracts any `Tensor`s found within the provided object. * * @param container an object that may be a `Tensor` or may directly contain * `Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. In general it * is safe to pass any object here, except that `Promise`s are not * supported. * @returns An array of `Tensors` found within the passed object. If the * argument is simply a `Tensor', a list containing that `Tensor` is * returned. If the object is not a `Tensor` or does not * contain `Tensors`, an empty list is returned. */ function getTensorsInContainer(result) { const list = []; const seen = new Set(); walkTensorContainer(result, list, seen); return list; } function walkTensorContainer(container, list, seen) { if (container == null) { return; } if (container instanceof Tensor) { list.push(container); return; } if (!isIterable(container)) { return; } // Iteration over keys works also for arrays. const iterable = container; for (const k in iterable) { const val = iterable[k]; if (!seen.has(val)) { seen.add(val); walkTensorContainer(val, list, seen); } } } // tslint:disable-next-line:no-any function isIterable(obj) { return Array.isArray(obj) || typeof obj === 'object'; } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ function isRegisteredKernelInvocation(kernelInvocation) { return kernelInvocation.kernelName != null; } class EngineState { constructor() { // Public since optimizers will use it. this.registeredVariables = {}; this.nextTapeNodeId = 0; this.numBytes = 0; this.numTensors = 0; this.numStringTensors = 0; this.numDataBuffers = 0; // Number of nested tf.grad() statements when computing higher-order // gradients. E.g. `1` for first-order gradients and `2` for second-order // gradients. Used to track if the tape should be removed after a backprop. this.gradientDepth = 0; // Number of nested kernel calls. When kernel depth is greater than 1, we turn // off the tape. this.kernelDepth = 0; this.scopeStack = []; /** * Keeps track of the number of data moves during a kernel execution. We * maintain a stack since kernels can call other kernels, recursively. */ this.numDataMovesStack = []; this.nextScopeId = 0; this.tensorInfo = new WeakMap(); this.profiling = false; this.activeProfile = { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null, get kernelNames() { return Array.from(new Set(this.kernels.map(k => k.name))); } }; } dispose() { for (const variableName in this.registeredVariables) { this.registeredVariables[variableName].dispose(); } } } class Engine { constructor(ENV) { this.ENV = ENV; this.registry = {}; this.registryFactory = {}; this.pendingBackendInitId = 0; this.state = new EngineState(); } async ready() { if (this.pendingBackendInit != null) { return this.pendingBackendInit.then(() => { }); } if (this.backendInstance != null) { return; } const sortedBackends = this.getSortedBackends(); for (let i = 0; i < sortedBackends.length; i++) { const backendName = sortedBackends[i]; const success = await this.initializeBackend(backendName).success; if (success) { await this.setBackend(backendName); return; } } throw new Error(`Could not initialize any backends, all backend initializations ` + `failed.`); } get backend() { if (this.pendingBackendInit != null) { throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make ` + `sure to await tf.ready() or await tf.setBackend() before calling ` + `other methods`); } if (this.backendInstance == null) { const { name, asyncInit } = this.initializeBackendsAndReturnBest(); if (asyncInit) { throw new Error(`The highest priority backend '${name}' has not yet been ` + `initialized. Make sure to await tf.ready() or ` + `await tf.setBackend() before calling other methods`); } this.setBackend(name); } return this.backendInstance; } backendNames() { return Object.keys(this.registryFactory); } findBackend(backendName) { if (!(backendName in this.registry)) { // If the backend hasn't been initialized but we have a registry entry for // it, initialize it and return it. if (backendName in this.registryFactory) { const { asyncInit } = this.initializeBackend(backendName); if (asyncInit) { // Backend is not ready yet. return null; } } else { return null; } } return this.registry[backendName]; } findBackendFactory(backendName) { if (!(backendName in this.registryFactory)) { return null; } return this.registryFactory[backendName].factory; } registerBackend(backendName, factory, priority = 1) { if (backendName in this.registryFactory) { warn(`${backendName} backend was already registered. ` + `Reusing existing backend factory.`); return false; } this.registryFactory[backendName] = { factory, priority }; return true; } async setBackend(backendName) { if (this.registryFactory[backendName] == null) { throw new Error(`Backend name '${backendName}' not found in registry`); } this.backendName = backendName; if (this.registry[backendName] == null) { this.backendInstance = null; const { success, asyncInit } = this.initializeBackend(backendName); const result = asyncInit ? await success : success; if (!result) { return false; } } this.backendInstance = this.registry[backendName]; this.setupRegisteredKernels(); // Reset the profiler. this.profiler = new Profiler(this.backendInstance); return true; } setupRegisteredKernels() { const kernels = getKernelsForBackend(this.backendName); kernels.forEach(kernel => { if (kernel.setupFunc != null) { kernel.setupFunc(this.backendInstance); } }); } disposeRegisteredKernels(backendName) { const kernels = getKernelsForBackend(backendName); kernels.forEach(kernel => { if (kernel.disposeFunc != null) { kernel.disposeFunc(this.registry[backendName]); } }); } /** * Initializes a backend by looking up the backend name in the factory * registry and calling the factory method. Returns a boolean representing * whether the initialization of the backend suceeded. Throws an error if * there is no backend in the factory registry. */ initializeBackend(backendName) { const registryFactoryEntry = this.registryFactory[backendName]; if (registryFactoryEntry == null) { throw new Error(`Cannot initialize backend ${backendName}, no registration found.`); } try { const backend = registryFactoryEntry.factory(); /* Test if the factory returns a promise. Done in a more liberal way than previous 'Promise.resolve(backend)===backend' as we needed to account for custom Promise implementations (e.g. Angular) */ if (backend && !(backend instanceof KernelBackend) && typeof backend.then === 'function') { const promiseId = ++this.pendingBackendInitId; const success = backend .then(backendInstance => { // Outdated promise. Another backend was set in the meantime. if (promiseId < this.pendingBackendInitId) { return false; } this.registry[backendName] = backendInstance; this.pendingBackendInit = null; return true; }) .catch(err => { // Outdated promise. Another backend was set in the meantime. if (promiseId < this.pendingBackendInitId) { return false; } this.pendingBackendInit = null; warn(`Initialization of backend ${backendName} failed`); warn(err.stack || err.message); return false; }); this.pendingBackendInit = success; return { success, asyncInit: true }; } else { this.registry[backendName] = backend; return { success: true, asyncInit: false }; } } catch (err) { warn(`Initialization of backend ${backendName} failed`); warn(err.stack || err.message); return { success: false, asyncInit: false }; } } removeBackend(backendName) { if (!(backendName in this.registryFactory)) { throw new Error(`${backendName} backend not found in registry`); } if (this.backendName === backendName && this.pendingBackendInit != null) { // There is a pending promise of the backend we want to remove. Make it // obsolete. this.pendingBackendInitId++; } if (backendName in this.registry) { this.disposeRegisteredKernels(backendName); this.registry[backendName].dispose(); delete this.registry[backendName]; } delete this.registryFactory[backendName]; // Unset the backend if it is active. if (this.backendName === backendName) { this.pendingBackendInit = null; this.backendName = null; this.backendInstance = null; } } getSortedBackends() { if (Object.keys(this.registryFactory).length === 0) { throw new Error('No backend found in registry.'); } return Object.keys(this.registryFactory).sort((a, b) => { // Highest priority comes first. return this.registryFactory[b].priority - this.registryFactory[a].priority; }); } initializeBackendsAndReturnBest() { const sortedBackends = this.getSortedBackends(); for (let i = 0; i < sortedBackends.length; i++) { const backendName = sortedBackends[i]; const { success, asyncInit } = this.initializeBackend(backendName); if (asyncInit || success) { return { name: backendName, asyncInit }; } } throw new Error(`Could not initialize any backends, all backend initializations ` + `failed.`); } moveData(backend, dataId) { const info = this.state.tensorInfo.get(dataId); const srcBackend = info.backend; const values = this.readSync(dataId); const refCount = srcBackend.refCount(dataId); // Delete the tensor from the old backend and move it to the new // backend. srcBackend.disposeData(dataId, true); info.backend = backend; backend.move(dataId, values, info.shape, info.dtype, refCount); if (this.shouldCheckForMemLeaks()) { // Track the number of moves during a kernel execution to correctly // detect memory leaks. this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++; } } tidy(nameOrFn, fn) { let name = null; if (fn == null) { // Called with only 1 argument. if (typeof nameOrFn !== 'function') { throw new Error('Please provide a function to tidy()'); } fn = nameOrFn; } else { // Called with 2 arguments. if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) { throw new Error('When calling with two arguments, the first argument ' + 'to tidy() must be a string'); } if (typeof fn !== 'function') { throw new Error('When calling with two arguments, the 2nd argument ' + 'to tidy() must be a function'); } name = nameOrFn; // TODO(nsthorat,smilkov): Do operation logging and performance // profiling. } let result; return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => { result = fn(); if (result instanceof Promise) { console.error('Cannot return a Promise inside of tidy.'); } return result; }); } scopedRun(start, end, f) { start(); try { const res = f(); end(); return res; } catch (ex) { end(); throw ex; } } nextTensorId() { return Engine.nextTensorId++; } nextVariableId() { return Engine.nextVariableId++; } /** * This method is called instead of the public-facing tensor.clone() when * saving a tensor for backwards pass. It makes sure to add the clone * operation to the tape regardless of being called inside a kernel * execution. */ clone(x) { const y = ENGINE.runKernel(Identity, { x }); const inputs = { x }; const grad = (dy) => ({ x: () => { const dtype = 'float32'; const gradInputs = { x: dy }; const attrs = { dtype }; return ENGINE.runKernel(Cast, gradInputs, // tslint:disable-next-line: no-unnecessary-type-assertion attrs); } }); const saved = []; this.addTapeNode(this.state.activeScope.name, inputs, [y], grad, saved, {}); return y; } /** * Execute a kernel with the given name and return the output tensor. * * @param kernelName The name of the kernel to execute. * @param inputs A map of input names to tensors. * @param attrs A map of attribute names to their values. An attribute is a * primitive (non-tensor) input to the kernel. * @param inputsToSave A list of tensors, inputs to save for the backprop * computation. * @param outputsToSave A list of booleans, specifying which output to save * for the backprop computation. These are booleans since the output * tensors are not visible to the user. */ runKernel(kernelName, inputs, attrs) { if (this.backendName == null) { // backend has not been initialized yet (backend initialization is lazy // can be deferred until an op/ kernel is run). // The below getter has side effects that will try to initialize the // backend and set properties like this.backendName // tslint:disable-next-line: no-unused-expression this.backend; } const hasKernel = getKernel(kernelName, this.backendName) != null; if (!hasKernel) { throw new Error(`Kernel '${kernelName}' not registered for backend '${this.backendName}'`); } return this.runKernelFunc({ kernelName, inputs, attrs }); } shouldCheckForMemLeaks() { return this.ENV.getBool('IS_TEST'); } checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) { const numDataIdsAfter = this.backend.numDataIds(); // Count the number of data ids associated with the result of the kernel. let numOutputDataIds = 0; outInfos.forEach(info => { // Complex numbers allocate 3 data ids, one for 'real', one for // 'imaginary', and one for the container that holds the former two. numOutputDataIds += (info.dtype === 'complex64' ? 3 : 1); }); // Account for the number of moves during kernel execution. A "data move" // can happen in the middle of a kernel execution, placing a new (key,value) // pair in the data storage. Since data moves have net zero effect (we // always remove the data from the old backend), we have to cancel them out // when detecting memory leaks. const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]; const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves; if (dataIdsLeaked > 0) { throw new Error(`Backend '${this.backendName}' has an internal memory leak ` + `(${dataIdsLeaked} data ids) after running '${kernelName}'`); } } /** * Internal helper method to execute a kernel Func * * Use `runKernel` to execute kernels from outside of engine. */ runKernelFunc(kernelParams) { let outputs; let saved = []; const isTapeOn = this.isTapeOn(); const startingBytecount = this.state.numBytes; const startingNumTensors = this.state.numTensors; if (this.shouldCheckForMemLeaks()) { this.state.numDataMovesStack.push(0); } let kernelFunc; if (this.backendName == null) { // backend has not been initialized yet (backend initialization is lazy // can be deferred until an op/ kernel is run). // The below getter has side effects that will try to initialize the // backend and set properties like this.backendName // tslint:disable-next-line: no-unused-expression this.backend; } let out; const kernelOrScopeName = isRegisteredKernelInvocation(kernelParams) ? kernelParams.kernelName : this.state.activeScope != null ? this.state.activeScope.name : ''; // Create the kernelFunc from either a registered kernel OR passed in // forward/backward functions (used by custom grad). In this context a // kernelFunc wraps a kernel implementation with some bookkeeping. if (isRegisteredKernelInvocation(kernelParams)) { const { kernelName, inputs, attrs } = kernelParams; if (this.backendName == null) { // backend has not been initialized yet (backend initialization is lazy // can be deferred until an op/ kernel is run). // The below getter has side effects that will try to initialize the // backend and set properties like this.backendName // tslint:disable-next-line: no-unused-expression this.backend; } const kernel = getKernel(kernelName, this.backendName); assert(kernel != null, () => `Cannot find registered kernel '${kernelName}' for backend '${this.backendName}'`); kernelFunc = () => { const numDataIdsBefore = this.backend.numDataIds(); out = kernel.kernelFunc({ inputs, attrs, backend: this.backend }); const outInfos = Array.isArray(out) ? out : [out]; if (this.shouldCheckForMemLeaks()) { this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos); } const outTensors = outInfos.map((outInfo) => { // todo (yassogba) remove this option (Tensor) when node backend // methods have been modularized and they all return tensorInfo. // TensorInfos do not have a rank attribute. if (outInfo.rank != null) { return outInfo; } return this.makeTensorFromTensorInfo(outInfo); }); // Save any required inputs and outputs. // Do not save unless we are recording to the tape. Otherwise it would // cause a mem leak since there would be no backprop for these tensors // (which would otherwise dispose them). if (isTapeOn) { const tensorsToSave = this.getTensorsForGradient(kernelName, inputs, outTensors); saved = this.saveTensorsForBackwardMode(tensorsToSave); } return outTensors; }; } else { const { forwardFunc } = kernelParams; // Running a customGrad op. const saveFunc = (tensors) => { // Do not save unless we are recording to the tape. Otherwise it would // cause a mem leak since we would never run backprop, which disposes // the kept tensors. if (!isTapeOn) { return; } saved = tensors.map(tensor => this.keep(this.clone(tensor))); }; kernelFunc = () => { const numDataIdsBefore = this.backend.numDataIds(); out = this.tidy(() => forwardFunc(this.backend, saveFunc)); const outs = (Array.isArray(out) ? out : [out]); if (this.shouldCheckForMemLeaks()) { // Scope name is used to print a more helpful error message if needed. this.checkKernelForMemLeak(kernelOrScopeName, numDataIdsBefore, outs); } return outs; }; } // // Run the kernelFunc. Optionally profiling it. // const { inputs, attrs } = kernelParams; const backwardsFunc = isRegisteredKernelInvocation(kernelParams) ? null : kernelParams.backwardsFunc; let kernelProfile; this.scopedRun( // Stop recording to a tape when running a kernel. () => this.state.kernelDepth++, () => this.state.kernelDepth--, () => { if (!this.ENV.getBool('DEBUG') && !this.state.profiling) { outputs = kernelFunc(); } else { kernelProfile = this.profiler.profileKernel(kernelOrScopeName, inputs, () => kernelFunc()); if (this.ENV.getBool('DEBUG')) { this.profiler.logKernelProfile(kernelProfile); } outputs = kernelProfile.outputs; } }); if (isTapeOn) { this.addTapeNode(kernelOrScopeName, inputs, outputs, backwardsFunc, saved, attrs); } if (this.state.profiling) { this.state.activeProfile.kernels.push({ name: kernelOrScopeName, bytesAdded: this.state.numBytes - startingBytecount, totalBytesSnapshot: this.state.numBytes, tensorsAdded: this.state.numTensors - startingNumTensors, totalTensorsSnapshot: this.state.numTensors, inputShapes: Object.keys(inputs).map(key => inputs[key] != null ? inputs[key].shape : null), outputShapes: outputs.map(item => item.shape), kernelTimeMs: kernelProfile.timeMs, extraInfo: kernelProfile.extraInfo }); } return (Array.isArray(out) ? outputs : outputs[0]); } /** * Saves tensors used in forward mode for use in backward mode. * * @param tensors the list of tensors to save. */ saveTensorsForBackwardMode(tensors) { const saved = tensors.map(tensor => this.keep(this.clone(tensor))); return saved; } /** * Returns a list of tensors to save for a given gradient calculation. * * @param kernelName name of kernel to look up gradient for. * @param inputs a map of input tensors. * @param outputs an array of output tensors from forward mode of kernel. */ getTensorsForGradient(kernelName, inputs, outputs) { const gradConfig = getGradient(kernelName); if (gradConfig != null) { const inputsToSave = gradConfig.inputsToSave || []; const outputsToSave = gradConfig.outputsToSave || []; // If saveAllInputs is true, all inputs will be saved. Otherwise, inputs // specified in inputsToSave will be saved. let inputTensorsToSave; if (gradConfig.saveAllInputs) { assert(Array.isArray(inputs), () => 'saveAllInputs is true, expected inputs to be an array.'); inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]); } else { inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]); } const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]); return inputTensorsToSave.concat(outputTensorsToSave); } // We return an empty list rather than throw an error because the kernel we // are looking up may not actually be relevant to backproping through the // overall function // // See 'does not error if irrelevant (pruned) ops are missing grads' test // in gradients_test.ts for an example. return []; } /** * Internal method used by public APIs for tensor creation. Makes a new * tensor with the provided shape, dtype and values. It always * creates a new data id and writes the values to the underlying backend. */ makeTensor(values, shape, dtype, backend) { if (values == null) { throw new Error('Values passed to engine.makeTensor() are null'); } dtype = dtype || 'float32'; backend = backend || this.backend; let backendVals = values; if (dtype === 'string' && isString(values[0])) { backendVals = values.map(d => encodeString(d)); } const dataId = backend.write(backendVals, shape, dtype); const t = new Tensor(shape, dtype, dataId, this.nextTensorId()); this.trackTensor(t, backend); // Count bytes for string tensors. if (dtype === 'string') { const info = this.state.tensorInfo.get(dataId); const newBytes = bytesFromStringArray(backendVals); this.state.numBytes += newBytes - info.bytes; info.bytes = newBytes; } return t; } /** * Internal method used by backends. Makes a new tensor * that is a wrapper around an existing data id. It doesn't create * a new data id, only increments the ref count used in memory tracking. * @deprecated */ makeTensorFromDataId(dataId, shape, dtype, backend) { dtype = dtype || 'float32'; const tensorInfo = { dataId, shape, dtype }; return this.makeTensorFromTensorInfo(tensorInfo, backend); } /** * Internal method used by backends. Makes a new tensor that is a wrapper * around an existing data id in TensorInfo. It doesn't create a new data id, * only increments the ref count used in memory tracking. */ makeTensorFromTensorInfo(tensorInfo, backend) { const { dataId, shape, dtype } = tensorInfo; const t = new Tensor(shape, dtype, dataId, this.nextTensorId()); this.trackTensor(t, backend); return t; } makeVariable(initialValue, trainable = true, name, dtype) { name = name || this.nextVariableId().toString(); if (dtype != null && dtype !== initialValue.dtype) { initialValue = initialValue.cast(dtype); } const v = new Variable(initialValue, trainable, name, this.nextTensorId()); if (this.state.registeredVariables[v.name] != null) { throw new Error(`Variable with name ${v.name} was already registered`); } this.state.registeredVariables[v.name] = v; this.incRef(v, this.backend); return v; } trackTensor(a, backend) { this.state.numTensors++; if (a.dtype === 'string') { this.state.numStringTensors++; } // Bytes for complex numbers are counted by their components. Bytes for // string tensors are counted when writing values. let bytes = 0; if (a.dtype !== 'complex64' && a.dtype !== 'string') { bytes = a.size * bytesPerElement(a.dtype); } this.state.numBytes += bytes; if (!this.state.tensorInfo.has(a.dataId)) { this.state.numDataBuffers++; this.state.tensorInfo.set(a.dataId, { backend: backend || this.backend, dtype: a.dtype, shape: a.shape, bytes }); } if (!(a instanceof Variable)) { this.track(a); } } // Track the tensor by dataId and increase the refCount for the dataId in the // backend. // TODO(pyu10055): This is currently used by makeVariable method, to increase // refCount on the backend for the dataId. It can potentially be replaced with // Identity op indead of calling backend directly. incRef(a, backend) { this.trackTensor(a, backend); this.backend.incRef(a.dataId); } removeDataId(dataId, backend) { if (this.state.tensorInfo.has(dataId) && this.state.tensorInfo.get(dataId).backend === backend) { this.state.tensorInfo.delete(dataId); this.state.numDataBuffers--; } } disposeTensor(a) { if (!this.state.tensorInfo.has(a.dataId)) { return; } const info = this.state.tensorInfo.get(a.dataId); this.state.numTensors--; if (a.dtype === 'string') { this.state.numStringTensors--; this.state.numBytes -= info.bytes; } // Don't count bytes for complex numbers as they are counted by their // components. if (a.dtype !== 'complex64' && a.dtype !== 'string') { const bytes = a.size * bytesPerElement(a.dtype); this.state.numBytes -= bytes; } // Remove the reference to dataId if backend dispose the data successfully if (info.backend.disposeData(a.dataId)) { this.removeDataId(a.dataId, info.backend); } // TODO(nsthorat): Construct an error and save the stack trace for // debugging when in debug mode. Creating a stack trace is too expensive // to do unconditionally. } disposeVariables() { for (const varName in this.state.registeredVariables) { const v = this.state.registeredVariables[varName]; this.disposeVariable(v); } } disposeVariable(v) { this.disposeTensor(v); if (this.state.registeredVariables[v.name] != null) { delete this.state.registeredVariables[v.name]; } } memory() { const info = this.backend.memory(); info.numTensors = this.state.numTensors; info.numDataBuffers = this.state.numDataBuffers; info.numBytes = this.state.numBytes; if (this.state.numStringTensors > 0) { info.unreliable = true; if (info.reasons == null) { info.reasons = []; } info.reasons.push('Memory usage by string tensors is approximate ' + '(2 bytes per character)'); } return info; } async profile(query) { this.state.profiling = true; const startBytes = this.state.numBytes; const startNumTensors = this.state.numTensors; this.state.activeProfile.kernels = []; this.state.activeProfile.result = await query(); this.state.profiling = false; this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map(d => d.totalBytesSnapshot)); this.state.activeProfile.newBytes = this.state.numBytes - startBytes; this.state.activeProfile.newTensors = this.state.numTensors - startNumTensors; for (const kernel of this.state.activeProfile.kernels) { kernel.kernelTimeMs = await kernel.kernelTimeMs; kernel.extraInfo = await kernel.extraInfo; } return this.state.activeProfile; } isTapeOn() { return this.state.gradientDepth > 0 && this.state.kernelDepth === 0; } addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) { const tapeNode = { id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved }; const gradConfig = getGradient(kernelName); if (gradConfig != null) { gradientsFunc = gradConfig.gradFunc; } if (gradientsFunc != null) { tapeNode.gradient = (dys) => { // TODO(smilkov): To optimize back-prop, pass dys that are not used in // the backprop graph to the user as null instead of zeros dys = dys.map((dy, i) => { if (dy == null) { const output = outputs[i]; const vals = makeZerosTypedArray(output.size, output.dtype); return this.makeTensor(vals, output.shape, output.dtype); } return dy; }); // Grad functions of ops with single outputs expect a dy, while ops // with multiple outputs expect dys (array of dy). return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs); }; } this.state.activeTape.push(tapeNode); } keep(result) { result.kept = true; return result; } startTape() { if (this.state.gradientDepth === 0) { this.state.activeTape = []; } this.state.gradientDepth++; } endTape() { this.state.gradientDepth--; } /** * Start a scope. Use this with endScope() to achieve the same functionality * as scope() without the need for a function closure. */ startScope(name) { const scopeInfo = { track: [], name: 'unnamed scope', id: this.state.nextScopeId++ }; if (name) { scopeInfo.name = name; } this.state.scopeStack.push(scopeInfo); this.state.activeScope = scopeInfo; } /** * End a scope. Use this with startScope() to achieve the same functionality * as scope() without the need for a function closure. */ endScope(result) { const tensorsToTrackInParent = getTensorsInContainer(result); const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map(t => t.id)); // Dispose the arrays tracked in this scope. for (let i = 0; i < this.state.activeScope.track.length; i++) { const tensor = this.state.activeScope.track[i]; if (!tensor.kept && !tensorsToTrackInParentSet.has(tensor.id)) { tensor.dispose(); } } const oldScope = this.state.scopeStack.pop(); this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1]; // Track the current result in the parent scope. tensorsToTrackInParent.forEach(tensor => { // Only track the tensor if was allocated in the inner scope and is not // globally kept. if (!tensor.kept && tensor.scopeId === oldScope.id) { this.track(tensor); } }); } /** * Returns gradients of `f` with respect to each of the `xs`. The gradients * returned are of the same length as `xs`, but some might be null if `f` * was not a function of that `x`. It also takes optional dy to multiply the * gradient, which defaults to `1`. */ gradients(f, xs, dy, allowNoGradients = false) { assert(xs.length > 0, () => 'gradients() received an empty list of xs.'); if (dy != null && dy.dtype !== 'float32') { throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`); } const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy('forward', f)); assert(y instanceof Tensor, () => 'The result y returned by f() must be a tensor.'); // Filter out the nodes that don't connect x => y. const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y); if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) { throw new Error('Cannot compute gradient of y=f(x) with respect to x. Make sure ' + 'that the f you passed encloses all operations that lead from x ' + 'to y.'); } return this.tidy('backward', () => { const accumulatedGradientMap = {}; accumulatedGradientMap[y.id] = (dy == null) ? ones$1(y.shape) : dy; // Backprop gradients through the filtered nodes. backpropagateGradients(accumulatedGradientMap, filteredTape, // Pass the tidy function to avoid circular dep with `tape.ts`. f => this.tidy(f), // Pass an add function to avoide a circular dep with `tape.ts`. add$1); const grads = xs.map(x => accumulatedGradientMap[x.id]); if (this.state.gradientDepth === 0) { // This means that we are not computing higher-order gradients // and can clean up the tape. this.state.activeTape.forEach(node => { for (const tensor of node.saved) { tensor.dispose(); } }); this.state.activeTape = null; } return { value: y, grads }; }); } customGrad(f) { assert(isFunction(f), () => 'The f passed in customGrad(f) must be a function.'); return (...inputs) => { assert(inputs.every(t => t instanceof Tensor), () => 'The args passed in customGrad(f)(x1, x2,...) must all be ' + 'tensors'); let res; const inputMap = {}; inputs.forEach((input, i) => { inputMap[i] = input; }); const forwardFunc = (_, save) => { res = f(...[...inputs, save]); assert(res.value instanceof Tensor, () => 'The function f passed in customGrad(f) must return an ' + 'object where `obj.value` is a tensor'); assert(isFunction(res.gradFunc), () => 'The function f passed in customGrad(f) must return an ' + 'object where `obj.gradFunc` is a function.'); return res.value; }; const backwardsFunc = (dy, saved) => { const gradRes = res.gradFunc(dy, saved); const grads = Array.isArray(gradRes) ? gradRes : [gradRes]; assert(grads.length === inputs.length, () => 'The function f passed in customGrad(f) must return an ' + 'object where `obj.gradFunc` is a function that returns ' + 'the same number of tensors as inputs passed to f(...).'); assert(grads.every(t => t instanceof Tensor), () => 'The function f passed in customGrad(f) must return an ' + 'object where `obj.gradFunc` is a function that returns ' + 'a list of only tensors.'); const gradMap = {}; grads.forEach((grad, i) => { gradMap[i] = () => grad; }); return gradMap; }; return this.runKernelFunc({ forwardFunc, backwardsFunc, inputs: inputMap, }); }; } readSync(dataId) { // Route the read to the correct backend. const info = this.state.tensorInfo.get(dataId); return info.backend.readSync(dataId); } read(dataId) { // Route the read to the correct backend. const info = this.state.tensorInfo.get(dataId); return info.backend.read(dataId); } readToGPU(dataId, options) { // Route the read to the correct backend. const info = this.state.tensorInfo.get(dataId); return info.backend.readToGPU(dataId, options); } async time(query) { const start = now(); const timingInfo = await this.backend.time(query); timingInfo.wallMs = now() - start; return timingInfo; } /** * Tracks a Tensor in the current scope to be automatically cleaned up * when the current scope ends, and returns the value. * * @param result The Tensor to track in the current scope. */ track(result) { if (this.state.activeScope != null) { result.scopeId = this.state.activeScope.id; this.state.activeScope.track.push(result); } return result; } get registeredVariables() { return this.state.registeredVariables; } /** * Resets the engine state. Removes all backends but does not remove * registered backend factories. */ reset() { // Make any pending promise obsolete. this.pendingBackendInitId++; this.state.dispose(); this.ENV.reset(); this.state = new EngineState(); for (const backendName in this.registry) { this.disposeRegisteredKernels(backendName); this.registry[backendName].dispose(); delete this.registry[backendName]; } this.backendName = null; this.backendInstance = null; this.pendingBackendInit = null; } } Engine.nextTensorId = 0; Engine.nextVariableId = 0; function ones$1(shape) { const values = makeOnesTypedArray(sizeFromShape(shape), 'float32'); return ENGINE.makeTensor(values, shape, 'float32'); } function getOrMakeEngine() { const ns = getGlobalNamespace(); if (ns._tfengine == null) { const environment = new Environment(ns); ns._tfengine = new Engine(environment); } setEnvironmentGlobal(ns._tfengine.ENV); // Tell the current tensor interface that the global engine is responsible // for tracking. setTensorTracker(() => ns._tfengine); return ns._tfengine; } const ENGINE = getOrMakeEngine(); /** * A implementation of the add op for use within engine and tape. * * This allows us to avoid a circular dependency between add.ts and engine. * It is exported to be available in tape tests. */ function add$1(a, b) { // We duplicate Add here to avoid a circular dependency with add.ts. const inputs = { a, b }; return ENGINE.runKernel(Add, inputs); } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ function inferShape(val, dtype) { let firstElem = val; if (isTypedArray(val)) { return dtype === 'string' ? [] : [val.length]; } if (isWebGLData(val)) { const usedChannels = val.channels || 'RGBA'; return [val.height, val.width * usedChannels.length]; } else if (isWebGPUData(val)) { return [val.buffer.size / (dtype == null ? 4 : bytesPerElement(dtype))]; } if (!Array.isArray(val)) { return []; // Scalar. } const shape = []; while (Array.isArray(firstElem) || isTypedArray(firstElem) && dtype !== 'string') { shape.push(firstElem.length); firstElem = firstElem[0]; } if (Array.isArray(val) && env().getBool('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')) { deepAssertShapeConsistency(val, shape, []); } return shape; } function deepAssertShapeConsistency(val, shape, indices) { indices = indices || []; if (!(Array.isArray(val)) && !isTypedArray(val)) { assert(shape.length === 0, () => `Element arr[${indices.join('][')}] is a primitive, ` + `but should be an array/TypedArray of ${shape[0]} elements`); return; } assert(shape.length > 0, () => `Element arr[${indices.join('][')}] should be a primitive, ` + `but is an array of ${val.length} elements`); assert(val.length === shape[0], () => `Element arr[${indices.join('][')}] should have ${shape[0]} ` + `elements, but has ${val.length} elements`); const subShape = shape.slice(1); for (let i = 0; i < val.length; ++i) { deepAssertShapeConsistency(val[i], subShape, indices.concat(i)); } } function assertDtype(expectedDtype, actualDType, argName, functionName) { if (expectedDtype === 'string_or_numeric') { return; } if (expectedDtype == null) { throw new Error(`Expected dtype cannot be null.`); } if (expectedDtype !== 'numeric' && expectedDtype !== actualDType || expectedDtype === 'numeric' && actualDType === 'string') { throw new Error(`Argument '${argName}' passed to '${functionName}' must ` + `be ${expectedDtype} tensor, but got ${actualDType} tensor`); } } function convertToTensor(x, argName, functionName, parseAsDtype = 'numeric') { if (x instanceof getGlobalTensorClass()) { assertDtype(parseAsDtype, x.dtype, argName, functionName); return x; } let inferredDtype = inferDtype(x); // If the user expects a bool/int/float, use that info to update the // inferredDtype when it is not a string. if (inferredDtype !== 'string' && ['bool', 'int32', 'float32'].indexOf(parseAsDtype) >= 0) { inferredDtype = parseAsDtype; } assertDtype(parseAsDtype, inferredDtype, argName, functionName); if ((x == null) || (!isTypedArray(x) && !Array.isArray(x) && typeof x !== 'number' && typeof x !== 'boolean' && typeof x !== 'string')) { const type = x == null ? 'null' : x.constructor.name; throw new Error(`Argument '${argName}' passed to '${functionName}' must be a ` + `Tensor or TensorLike, but got '${type}'`); } const inferredShape = inferShape(x, inferredDtype); if (!isTypedArray(x) && !Array.isArray(x)) { x = [x]; } const skipTypedArray = true; const values = inferredDtype !== 'string' ? toTypedArray(x, inferredDtype) : flatten(x, [], skipTypedArray); return ENGINE.makeTensor(values, inferredShape, inferredDtype); } function convertToTensorArray(arg, argName, functionName, parseAsDtype = 'numeric') { if (!Array.isArray(arg)) { throw new Error(`Argument ${argName} passed to ${functionName} must be a ` + '`Tensor[]` or `TensorLike[]`'); } const tensors = arg; return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName, parseAsDtype)); } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const OP_SCOPE_SUFFIX = '__op'; /** * Used for wrapping functions that perform math operations on * Tensors. The function will be wrapped in a named scope that cleans all * memory usage after the function is done. */ function op(f) { const keys = Object.keys(f); if (keys.length !== 1) { throw new Error(`Please provide an object with a single key ` + `(operation name) mapping to a function. Got an object with ` + `${keys.length} keys.`); } let opName = keys[0]; const fn = f[opName]; // Strip the underscore from the end of the function name. if (opName.endsWith('_')) { opName = opName.substring(0, opName.length - 1); } // add an __op suffix to distinguish ops from kernels in tf.profile opName = opName + OP_SCOPE_SUFFIX; // tslint:disable-next-line:no-any const f2 = (...args) => { ENGINE.startScope(opName); try { const result = fn(...args); if (isPromise(result)) { console.error('Cannot return a Promise inside of tidy.'); } ENGINE.endScope(result); return result; } catch (ex) { ENGINE.endScope(null); throw ex; } }; Object.defineProperty(f2, 'name', { value: opName, configurable: true }); // tslint:disable-next-line:no-any return f2; } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes absolute value element-wise: `abs(x)` * * ```js * const x = tf.tensor1d([-1, 2, -3, 4]); * * x.abs().print(); // or tf.abs(x) * ``` * @param x The input `tf.Tensor`. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function abs_(x) { const $x = convertToTensor(x, 'x', 'abs'); if ($x.dtype === 'complex64') { const inputs = { x: $x }; return ENGINE.runKernel(ComplexAbs, inputs); } else { const inputs = { x: $x }; return ENGINE.runKernel(Abs, inputs); } } const abs = /* @__PURE__ */ op({ abs_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes acos of the input `tf.Tensor` element-wise: `acos(x)` * * ```js * const x = tf.tensor1d([0, 1, -1, .7]); * * x.acos().print(); // or tf.acos(x) * ``` * @param x The input tensor. * @doc {heading: 'Operations', subheading: 'Basic math'} */ function acos_(x) { const $x = convertToTensor(x, 'x', 'acos'); const inputs = { x: $x }; return ENGINE.runKernel(Acos, inputs); } const acos = /* @__PURE__ */ op({ acos_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the inverse hyperbolic cos of the input `tf.Tensor` element-wise: * `acosh(x)` * * ```js * const x = tf.tensor1d([10, 1, 3, 5.7]); * * x.acosh().print(); // or tf.acosh(x) * ``` * @param x The input tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function acosh_(x) { const $x = convertToTensor(x, 'x', 'acosh'); const inputs = { x: $x }; return ENGINE.runKernel(Acosh, inputs); } const acosh = /* @__PURE__ */ op({ acosh_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting. * * * ```js * const a = tf.tensor1d([1, 2, 3, 4]); * const b = tf.tensor1d([10, 20, 30, 40]); * * a.add(b).print(); // or tf.add(a, b) * ``` * * ```js * // Broadcast add a with b. * const a = tf.scalar(5); * const b = tf.tensor1d([10, 20, 30, 40]); * * a.add(b).print(); // or tf.add(a, b) * ``` * @param a The first `tf.Tensor` to add. * @param b The second `tf.Tensor` to add. Must have the same type as `a`. * * @doc {heading: 'Operations', subheading: 'Arithmetic'} */ function add_(a, b) { let $a = convertToTensor(a, 'a', 'add'); let $b = convertToTensor(b, 'b', 'add'); [$a, $b] = makeTypesMatch($a, $b); const inputs = { a: $a, b: $b }; return ENGINE.runKernel(Add, inputs); } const add = /* @__PURE__ */ op({ add_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype. * * ```js * const a = tf.tensor1d([1, 2]); * const b = tf.tensor1d([3, 4]); * const c = tf.tensor1d([5, 6]); * * tf.addN([a, b, c]).print(); * ``` * @param tensors A list of tensors with the same shape and dtype. * @doc {heading: 'Operations', subheading: 'Arithmetic'} */ function addN_(tensors) { assert(Array.isArray(tensors), () => 'The argument passed to tf.addN() must be a list of tensors'); assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ` + `${tensors.length}`); const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'addN')); const firstTensor = $tensors[0]; $tensors.forEach(t => { if (t.dtype !== firstTensor.dtype) { throw new Error('All tensors passed to tf.addN() must have the same dtype'); } }); $tensors.forEach(t => { if (!arraysEqual(t.shape, firstTensor.shape)) { throw new Error('All tensors passed to tf.addN() must have the same shape'); } }); const inputs = $tensors; return ENGINE.runKernel(AddN, inputs); } const addN = /* @__PURE__ */ op({ addN_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the logical and of elements across dimensions of a `tf.Tensor`. * * Reduces the input along the dimensions given in `axes`. Unless `keepDims` * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in * `axes`. If `keepDims` is true, the reduced dimensions are retained with * length 1. If `axes` has no entries, all dimensions are reduced, and a * `tf.Tensor` with a single element is returned. * * ```js * const x = tf.tensor1d([1, 1, 1], 'bool'); * * x.all().print(); // or tf.all(x) * ``` * * ```js * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool'); * * const axis = 1; * x.all(axis).print(); // or tf.all(x, axis) * ``` * * @param x The input tensor. Must be of dtype bool. * @param axis The dimension(s) to reduce. By default it reduces * all dimensions. * @param keepDims If true, retains reduced dimensions with size 1. * * @doc {heading: 'Operations', subheading: 'Reduction'} */ function all_(x, axis = null, keepDims = false) { const $x = convertToTensor(x, 'x', 'all', 'bool'); const inputs = { x: $x }; const attrs = { axis, keepDims }; return ENGINE.runKernel(All, inputs, attrs); } const all = /* @__PURE__ */ op({ all_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the logical or of elements across dimensions of a `tf.Tensor`. * * Reduces the input along the dimensions given in `axes`. Unless `keepDims` * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in * `axes`. If `keepDims` is true, the reduced dimensions are retained with * length 1. If `axes` has no entries, all dimensions are reduced, and a * `tf.Tensor` with a single element is returned. * * ```js * const x = tf.tensor1d([1, 1, 1], 'bool'); * * x.any().print(); // or tf.any(x) * ``` * * ```js * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool'); * * const axis = 1; * x.any(axis).print(); // or tf.any(x, axis) * ``` * * @param x The input tensor. Must be of dtype bool. * @param axis The dimension(s) to reduce. By default it reduces * all dimensions. * @param keepDims If true, retains reduced dimensions with size 1. * * @doc {heading: 'Operations', subheading: 'Reduction'} */ function any_(x, axis = null, keepDims = false) { const $x = convertToTensor(x, 'x', 'any', 'bool'); const inputs = { x: $x }; const attrs = { axis, keepDims }; return ENGINE.runKernel(Any, inputs, attrs); } // tslint:disable-next-line:variable-name const any = /* @__PURE__ */ op({ any_ }); /** * @license * Copyright 2020 Google Inc. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns the indices of the maximum values along an `axis`. * * The result has the same shape as `input` with the dimension along `axis` * removed. * * ```js * const x = tf.tensor1d([1, 2, 3]); * * x.argMax().print(); // or tf.argMax(x) * ``` * * ```js * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]); * * const axis = 1; * x.argMax(axis).print(); // or tf.argMax(x, axis) * ``` * * @param x The input tensor. * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension). * * @doc {heading: 'Operations', subheading: 'Reduction'} */ function argMax_(x, axis = 0) { const $x = convertToTensor(x, 'x', 'argMax'); const inputs = { x: $x }; const attrs = { axis }; return ENGINE.runKernel(ArgMax, inputs, attrs); } const argMax = /* @__PURE__ */ op({ argMax_ }); /** * @license * Copyright 2020 Google Inc. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns the indices of the minimum values along an `axis`. * * The result has the same shape as `input` with the dimension along `axis` * removed. * * ```js * const x = tf.tensor1d([1, 2, 3]); * * x.argMin().print(); // or tf.argMin(x) * ``` * * ```js * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]); * * const axis = 1; * x.argMin(axis).print(); // or tf.argMin(x, axis) * ``` * * @param x The input tensor. * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension). * * @doc {heading: 'Operations', subheading: 'Reduction'} */ function argMin_(x, axis = 0) { const $x = convertToTensor(x, 'x', 'argMin'); const inputs = { x: $x }; const attrs = { axis }; return ENGINE.runKernel(ArgMin, inputs, attrs); } const argMin = /* @__PURE__ */ op({ argMin_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes asin of the input `tf.Tensor` element-wise: `asin(x)` * * ```js * const x = tf.tensor1d([0, 1, -1, .7]); * * x.asin().print(); // or tf.asin(x) * ``` * @param x The input tensor. * @doc {heading: 'Operations', subheading: 'Basic math'} */ function asin_(x) { const $x = convertToTensor(x, 'x', 'asin'); const inputs = { x: $x }; return ENGINE.runKernel(Asin, inputs); } const asin = /* @__PURE__ */ op({ asin_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes inverse hyperbolic sin of the input `tf.Tensor` element-wise: * `asinh(x)` * * ```js * const x = tf.tensor1d([0, 1, -1, .7]); * * x.asinh().print(); // or tf.asinh(x) * ``` * @param x The input tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function asinh_(x) { const $x = convertToTensor(x, 'x', 'asinh'); const inputs = { x: $x }; return ENGINE.runKernel(Asinh, inputs); } const asinh = /* @__PURE__ */ op({ asinh_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes atan of the input `tf.Tensor` element-wise: `atan(x)` * * ```js * const x = tf.tensor1d([0, 1, -1, .7]); * * x.atan().print(); // or tf.atan(x) * ``` * @param x The input tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function atan_(x) { const $x = convertToTensor(x, 'x', 'atan'); const inputs = { x: $x }; return ENGINE.runKernel(Atan, inputs); } const atan = /* @__PURE__ */ op({ atan_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`. * Supports broadcasting. * * ```js * const a = tf.tensor1d([1.0, 1.0, -1.0, .7]); * const b = tf.tensor1d([2.0, 13.0, 3.5, .21]); * * tf.atan2(a, b).print() * ``` * * @param a The first tensor. * @param b The second tensor. Must have the same dtype as `a`. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function atan2_(a, b) { let $a = convertToTensor(a, 'a', 'atan2'); let $b = convertToTensor(b, 'b', 'atan2'); [$a, $b] = makeTypesMatch($a, $b); const inputs = { a: $a, b: $b }; return ENGINE.runKernel(Atan2, inputs); } const atan2 = /* @__PURE__ */ op({ atan2_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes inverse hyperbolic tan of the input `tf.Tensor` element-wise: * `atanh(x)` * * ```js * const x = tf.tensor1d([0, .1, -.1, .7]); * * x.atanh().print(); // or tf.atanh(x) * ``` * @param x The input tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function atanh_(x) { const $x = convertToTensor(x, 'x', 'atanh'); const inputs = { x: $x }; return ENGINE.runKernel(Atanh, inputs); } const atanh = /* @__PURE__ */ op({ atanh_ }); /** * @license * Copyright 2020 Google Inc. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Casts a `tf.Tensor` to a new dtype. * * ```js * const x = tf.tensor1d([1.5, 2.5, 3]); * tf.cast(x, 'int32').print(); * ``` * @param x The input tensor to be casted. * @param dtype The dtype to cast the input tensor to. * * @doc {heading: 'Tensors', subheading: 'Transformations'} */ function cast_(x, dtype) { const $x = convertToTensor(x, 'x', 'cast'); // Sanity checks. if (!isValidDtype(dtype)) { throw new Error(`Failed to cast to unknown dtype ${dtype}`); } if (dtype === 'string' && $x.dtype !== 'string' || dtype !== 'string' && $x.dtype === 'string') { throw new Error('Only strings can be casted to strings'); } const inputs = { x: $x }; const attrs = { dtype }; return ENGINE.runKernel(Cast, inputs, attrs); } const cast = /* @__PURE__ */ op({ cast_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ function computePool2DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat = 'channelsLast') { const [filterHeight, filterWidth] = parseTupleParam(filterSize); let filterShape; if (dataFormat === 'channelsLast') { filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]]; } else if (dataFormat === 'channelsFirst') { filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]]; } else { throw new Error(`Unknown dataFormat ${dataFormat}`); } return computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, false, dataFormat); } /** * Computes the information for a forward pass of a convolution/pooling * operation. */ function computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, depthwise = false, dataFormat = 'channelsLast') { let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1]; if (dataFormat === 'channelsLast') { [batchSize, inHeight, inWidth, inChannels] = inShape; } else if (dataFormat === 'channelsFirst') { [batchSize, inChannels, inHeight, inWidth] = inShape; } else { throw new Error(`Unknown dataFormat ${dataFormat}`); } const [filterHeight, filterWidth, , filterChannels] = filterShape; const [strideHeight, strideWidth] = parseTupleParam(strides); const [dilationHeight, dilationWidth] = parseTupleParam(dilations); const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight); const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth); const { padInfo, outHeight, outWidth } = getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat); const outChannels = depthwise ? filterChannels * inChannels : filterChannels; let outShape; if (dataFormat === 'channelsFirst') { outShape = [batchSize, outChannels, outHeight, outWidth]; } else if (dataFormat === 'channelsLast') { outShape = [batchSize, outHeight, outWidth, outChannels]; } return { batchSize, dataFormat, inHeight, inWidth, inChannels, outHeight, outWidth, outChannels, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, effectiveFilterHeight, effectiveFilterWidth, dilationHeight, dilationWidth, inShape, outShape, filterShape }; } function computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) { if (zeroPad == null) { zeroPad = computeDefaultPad(inShape, fieldSize, stride); } const inputRows = inShape[0]; const inputCols = inShape[1]; const outputRows = round$1((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode); const outputCols = round$1((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode); return [outputRows, outputCols]; } function computeDefaultPad(inputShape, fieldSize, stride, dilation = 1) { const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation); return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2); } function parseTupleParam(param) { if (typeof param === 'number') { return [param, param, param]; } if (param.length === 2) { return [param[0], param[1], 1]; } return param; } /* See https://www.tensorflow.org/api_docs/python/tf/nn/atrous_conv2d * Atrous convolution is equivalent to standard convolution with upsampled * filters with effective_filter_height = * filter_height + (filter_height - 1) * (dilation - 1) * and effective_filter_width = * filter_width + (filter_width - 1) * (dilation - 1), * produced by inserting dilation - 1 zeros along consecutive elements across * the filters' spatial dimensions. * When there is a dilation, this converts a filter dimension to the * effective filter dimension, so it can be used in a standard convolution. */ function getEffectiveFilterSize(filterSize, dilation) { if (dilation <= 1) { return filterSize; } return filterSize + (filterSize - 1) * (dilation - 1); } function getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) { let padInfo; let outHeight; let outWidth; if (typeof pad === 'number') { const padType = (pad === 0) ? 'VALID' : 'NUMBER'; padInfo = { top: pad, bottom: pad, left: pad, right: pad, type: padType }; const outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad, roundingMode); outHeight = outShape[0]; outWidth = outShape[1]; } else if (pad === 'same') { outHeight = Math.ceil(inHeight / strideHeight); outWidth = Math.ceil(inWidth / strideWidth); const padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight); const padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth); const top = Math.floor(padAlongHeight / 2); const bottom = padAlongHeight - top; const left = Math.floor(padAlongWidth / 2); const right = padAlongWidth - left; padInfo = { top, bottom, left, right, type: 'SAME' }; } else if (pad === 'valid') { padInfo = { top: 0, bottom: 0, left: 0, right: 0, type: 'VALID' }; outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight); outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth); } else if (typeof pad === 'object') { const top = dataFormat === 'channelsLast' ? pad[1][0] : pad[2][0]; const bottom = dataFormat === 'channelsLast' ? pad[1][1] : pad[2][1]; const left = dataFormat === 'channelsLast' ? pad[2][0] : pad[3][0]; const right = dataFormat === 'channelsLast' ? pad[2][1] : pad[3][1]; const padType = (top === 0 && bottom === 0 && left === 0 && right === 0) ? 'VALID' : 'EXPLICIT'; padInfo = { top, bottom, left, right, type: padType }; outHeight = round$1((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode); outWidth = round$1((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode); } else { throw Error(`Unknown padding parameter: ${pad}`); } return { padInfo, outHeight, outWidth }; } /** * Rounds a value depending on the rounding mode * @param value * @param roundingMode A string from: 'ceil', 'round', 'floor'. If none is * provided, it will default to truncate. */ function round$1(value, roundingMode) { if (!roundingMode) { return Math.trunc(value); } switch (roundingMode) { case 'round': // used for Caffe Conv return Math.round(value); case 'ceil': // used for Caffe Pool return Math.ceil(value); case 'floor': return Math.floor(value); default: throw new Error(`Unknown roundingMode ${roundingMode}`); } } function tupleValuesAreOne(param) { const [dimA, dimB, dimC] = parseTupleParam(param); return dimA === 1 && dimB === 1 && dimC === 1; } function eitherStridesOrDilationsAreOne(strides, dilations) { return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations); } function stridesOrDilationsArePositive(values) { return parseTupleParam(values).every(value => value > 0); } /** * Check validity of pad when using dimRoundingMode. * @param opDesc A string of op description * @param pad The type of padding algorithm. * - `same` and stride 1: output will be of same size as input, * regardless of filter size. * - `valid` output will be smaller than input if filter is larger * than 1x1. * - For more info, see this guide: * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is * provided, it will default to truncate. * @throws unknown padding parameter */ function checkPadOnDimRoundingMode(opDesc, pad, dimRoundingMode) { if (dimRoundingMode != null) { if (typeof pad === 'string') { throw Error(`Error in ${opDesc}: pad must be an integer when using ` + `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`); } else if (typeof pad === 'number') { assert(isInt(pad), () => `Error in ${opDesc}: pad must be an integer when using ` + `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`); } else if (typeof pad === 'object') { pad.forEach(p => { p.forEach(v => { assert(isInt(v), () => `Error in ${opDesc}: pad must be an integer when using ` + `dimRoundingMode ${dimRoundingMode} but got pad ${v}.`); }); }); } else { throw Error(`Error in ${opDesc}: Unknown padding parameter: ${pad}`); } } } /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Reshapes a `tf.Tensor` to a given shape. * * Given an input tensor, returns a new tensor with the same values as the * input tensor with shape `shape`. * * If one component of shape is the special value -1, the size of that * dimension is computed so that the total size remains constant. In * particular, a shape of [-1] flattens into 1-D. At most one component of * shape can be -1. * * If shape is 1-D or higher, then the operation returns a tensor with shape * shape filled with the values of tensor. In this case, the number of * elements implied by shape must be the same as the number of elements in * tensor. * * ```js * const x = tf.tensor1d([1, 2, 3, 4]); * x.reshape([2, 2]).print(); * ``` * * @param x The input tensor to be reshaped. * @param shape An array of integers defining the output tensor shape. * * @doc {heading: 'Tensors', subheading: 'Transformations'} */ function reshape_(x, shape) { const $x = convertToTensor(x, 'x', 'reshape', 'string_or_numeric'); const inputs = { x: $x }; const attrs = { shape }; return ENGINE.runKernel(Reshape, inputs, attrs); } const reshape = /* @__PURE__ */ op({ reshape_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the 2D average pooling of an image. * * @param x The input tensor, of rank 4 or rank 3 of shape * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed. * @param filterSize The filter size: `[filterHeight, filterWidth]`. If * `filterSize` is a single number, then `filterHeight == filterWidth`. * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If * `strides` is a single number, then `strideHeight == strideWidth`. * @param pad The type of padding algorithm: * - `same` and stride 1: output will be of same size as input, * regardless of filter size. * - `valid`: output will be smaller than input if filter is larger * than 1x1. * - For more info, see this guide: * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is * provided, it will default to truncate. * * @doc {heading: 'Operations', subheading: 'Convolution'} */ function avgPool_(x, filterSize, strides, pad, dimRoundingMode) { const $x = convertToTensor(x, 'x', 'avgPool', 'float32'); const dilations = 1; assert(eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' + `Got strides ${strides} and dilations '${dilations}'`); let x4D = $x; let reshapedTo4D = false; if ($x.rank === 3) { reshapedTo4D = true; x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); } assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`); checkPadOnDimRoundingMode('avgPool', pad, dimRoundingMode); const inputs = { x: x4D }; const attrs = { filterSize, strides, pad, dimRoundingMode }; // tslint:disable-next-line: no-unnecessary-type-assertion let res = ENGINE.runKernel(AvgPool, inputs, attrs); res = cast(res, $x.dtype); if (reshapedTo4D) { return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); } return res; } const avgPool = /* @__PURE__ */ op({ avgPool_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the 3D average pooling. * * ```js * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]); * const result = tf.avgPool3d(x, 2, 1, 'valid'); * result.print(); * ``` * * @param x The input tensor, of rank 5 or rank 4 of shape * `[batch, depth, height, width, inChannels]`. * @param filterSize The filter size: * `[filterDepth, filterHeight, filterWidth]`. * If `filterSize` is a single number, * then `filterDepth == filterHeight == filterWidth`. * @param strides The strides of the pooling: * `[strideDepth, strideHeight, strideWidth]`. * If `strides` is a single number, * then `strideDepth == strideHeight == strideWidth`. * @param pad The type of padding algorithm. * - `same` and stride 1: output will be of same size as input, * regardless of filter size. * - `valid`: output will be smaller than input if filter is larger * than 1*1x1. * - For more info, see this guide: * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is * provided, it will default to truncate. * @param dataFormat An optional string from: "NDHWC", "NCDHW". Defaults to * "NDHWC". Specify the data format of the input and output data. With the * default format "NDHWC", the data is stored in the order of: [batch, * depth, height, width, channels]. Only "NDHWC" is currently supported. * * @doc {heading: 'Operations', subheading: 'Convolution'} */ function avgPool3d_(x, filterSize, strides, pad, dimRoundingMode, dataFormat = 'NDHWC') { const $x = convertToTensor(x, 'x', 'avgPool3d', 'float32'); let x5D = $x; let reshapedTo5D = false; if ($x.rank === 4) { reshapedTo5D = true; x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]); } assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`); assert(dataFormat === 'NDHWC', () => `Error in avgPool3d: Only NDHWC is currently supported, ` + `but got dataFormat of ${dataFormat}`); assert((typeof strides === 'number' && strides > 0) || (Array.isArray(strides) && strides[0] > 0 && strides[1] > 0 && strides[2] > 0), () => `Error in avgPool3d: Stride must be > 0, but got '${strides}'`); checkPadOnDimRoundingMode('avgPool3d', pad, dimRoundingMode); const inputs = { x: x5D }; const attrs = { filterSize, strides, pad, dimRoundingMode, dataFormat }; // tslint:disable-next-line: no-unnecessary-type-assertion let res = ENGINE.runKernel(AvgPool3D, inputs, attrs); res = cast(res, x5D.dtype); if (reshapedTo5D) { return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]); } return res; } const avgPool3d = /* @__PURE__ */ op({ avgPool3d_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates a new tensor with the same values and shape as the specified * tensor. * * ```js * const x = tf.tensor([1, 2]); * * x.clone().print(); * ``` * * @param x The tensor to clone. * * @doc {heading: 'Tensors', subheading: 'Creation'} */ function clone_(x) { const $x = convertToTensor(x, 'x', 'clone', 'string_or_numeric'); const inputs = { x: $x }; // Note this op is called tf.identity in python. Hence the kernel name used // here. return ENGINE.runKernel(Identity, inputs); } const clone = /* @__PURE__ */ op({ clone_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Concatenates a list of `tf.Tensor`s along a given axis. * * The tensors ranks and types must match, and their sizes must match in all * dimensions except `axis`. * * Also available are stricter rank-specific methods that assert that * `tensors` are of the given rank: * - `tf.concat1d` * - `tf.concat2d` * - `tf.concat3d` * - `tf.concat4d` * * Except `tf.concat1d` (which does not have axis param), all methods have * same signature as this method. * * ```js * const a = tf.tensor1d([1, 2]); * const b = tf.tensor1d([3, 4]); * a.concat(b).print(); // or a.concat(b) * ``` * * ```js * const a = tf.tensor1d([1, 2]); * const b = tf.tensor1d([3, 4]); * const c = tf.tensor1d([5, 6]); * tf.concat([a, b, c]).print(); * ``` * * ```js * const a = tf.tensor2d([[1, 2], [10, 20]]); * const b = tf.tensor2d([[3, 4], [30, 40]]); * const axis = 1; * tf.concat([a, b], axis).print(); * ``` * @param tensors A list of tensors to concatenate. * @param axis The axis to concatenate along. Defaults to 0 (the first dim). * * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */ function concat_(tensors, axis = 0) { assert(tensors.length >= 1, () => 'Pass at least one tensor to concat'); const $tensors = convertToTensorArray(tensors, 'tensors', 'concat', 'string_or_numeric'); if ($tensors[0].dtype === 'complex64') { $tensors.forEach(tensor => { if (tensor.dtype !== 'complex64') { throw new Error(`Cannot concatenate complex64 tensors with a tensor with dtype ${tensor.dtype}. `); } }); } if ($tensors.length === 1) { return clone($tensors[0]); } const inputs = $tensors; const attr = { axis }; return ENGINE.runKernel(Concat, inputs, attr); } const concat = /* @__PURE__ */ op({ concat_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the dot product of two matrices, A * B. These must be matrices. * * ```js * const a = tf.tensor2d([1, 2], [1, 2]); * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]); * * a.matMul(b).print(); // or tf.matMul(a, b) * ``` * @param a First matrix in dot product operation. * @param b Second matrix in dot product operation. * @param transposeA If true, `a` is transposed before multiplication. * @param transposeB If true, `b` is transposed before multiplication. * * @doc {heading: 'Operations', subheading: 'Matrices'} */ function matMul_(a, b, transposeA = false, transposeB = false) { let $a = convertToTensor(a, 'a', 'matMul'); let $b = convertToTensor(b, 'b', 'matMul'); [$a, $b] = makeTypesMatch($a, $b); const inputs = { a: $a, b: $b }; const attrs = { transposeA, transposeB }; return ENGINE.runKernel(BatchMatMul, inputs, attrs); } const matMul$1 = /* @__PURE__ */ op({ matMul_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting. * * We also expose `tf.mulStrict` which has the same signature as this op and * asserts that `a` and `b` are the same shape (does not broadcast). * * ```js * const a = tf.tensor1d([1, 2, 3, 4]); * const b = tf.tensor1d([2, 3, 4, 5]); * * a.mul(b).print(); // or tf.mul(a, b) * ``` * * ```js * // Broadcast mul a with b. * const a = tf.tensor1d([1, 2, 3, 4]); * const b = tf.scalar(5); * * a.mul(b).print(); // or tf.mul(a, b) * ``` * @param a The first tensor to multiply. * @param b The second tensor to multiply. Must have the same dtype as `a`. * * @doc {heading: 'Operations', subheading: 'Arithmetic'} */ function mul_(a, b) { let $a = convertToTensor(a, 'a', 'mul'); let $b = convertToTensor(b, 'b', 'mul'); [$a, $b] = makeTypesMatch($a, $b); const inputs = { a: $a, b: $b }; return ENGINE.runKernel(Multiply, inputs); } const mul = /* @__PURE__ */ op({ mul_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes sigmoid element-wise, `1 / (1 + exp(-x))` * * ```js * const x = tf.tensor1d([0, -1, 2, -3]); * * x.sigmoid().print(); // or tf.sigmoid(x) * ``` * @param x The input tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function sigmoid_(x) { const $x = convertToTensor(x, 'x', 'sigmoid', 'float32'); const inputs = { x: $x }; return ENGINE.runKernel(Sigmoid, inputs); } const sigmoid = /* @__PURE__ */ op({ sigmoid_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Extracts a slice from a `tf.Tensor` starting at coordinates `begin` * and is of size `size`. * * Also available are stricter rank-specific methods with the same signature * as this method that assert that `x` is of the given rank: * - `tf.slice1d` * - `tf.slice2d` * - `tf.slice3d` * - `tf.slice4d` * * ```js * const x = tf.tensor1d([1, 2, 3, 4]); * * x.slice([1], [2]).print(); * ``` * * ```js * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); * * x.slice([1, 0], [1, 2]).print(); * ``` * @param x The input `tf.Tensor` to slice from. * @param begin The coordinates to start the slice from. The length can be * less than the rank of x - the rest of the axes will have implicit 0 as * start. Can also be a single number, in which case it specifies the * first axis. * @param size The size of the slice. The length can be less than the rank of * x - the rest of the axes will have implicit -1. A value of -1 requests * the rest of the dimensions in the axis. Can also be a single number, * in which case it specifies the size of the first axis. * * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */ function slice_(x, begin, size) { const $x = convertToTensor(x, 'x', 'slice', 'string_or_numeric'); if ($x.rank === 0) { throw new Error('Slicing scalar is not possible'); } const inputs = { x: $x }; const attrs = { begin, size }; return ENGINE.runKernel(Slice, inputs, attrs); } const slice = /* @__PURE__ */ op({ slice_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes hyperbolic tangent of the input `tf.Tensor` element-wise: `tanh(x)` * * ```js * const x = tf.tensor1d([0, 1, -1, 70]); * * x.tanh().print(); // or tf.tanh(x) * ``` * @param x The input tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function tanh_(x) { const $x = convertToTensor(x, 'x', 'tanh', 'float32'); const inputs = { x: $x }; return ENGINE.runKernel(Tanh, inputs); } const tanh = /* @__PURE__ */ op({ tanh_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the next state and output of a BasicLSTMCell. * * Returns `[newC, newH]`. * * Derived from tf.contrib.rnn.BasicLSTMCell. * * @param forgetBias Forget bias for the cell. * @param lstmKernel The weights for the cell. * @param lstmBias The bias for the cell. * @param data The input to the cell. * @param c Previous cell state. * @param h Previous cell output. * * @doc {heading: 'Operations', subheading: 'RNN'} */ function basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) { const $forgetBias = convertToTensor(forgetBias, 'forgetBias', 'basicLSTMCell'); const $lstmKernel = convertToTensor(lstmKernel, 'lstmKernel', 'basicLSTMCell'); const $lstmBias = convertToTensor(lstmBias, 'lstmBias', 'basicLSTMCell'); const $data = convertToTensor(data, 'data', 'basicLSTMCell'); const $c = convertToTensor(c, 'c', 'basicLSTMCell'); const $h = convertToTensor(h, 'h', 'basicLSTMCell'); const combined = concat([$data, $h], 1); const weighted = matMul$1(combined, $lstmKernel); const res = add(weighted, $lstmBias); // i = input_gate, j = new_input, f = forget_gate, o = output_gate const batchSize = res.shape[0]; const sliceCols = res.shape[1] / 4; const sliceSize = [batchSize, sliceCols]; const i = slice(res, [0, 0], sliceSize); const j = slice(res, [0, sliceCols], sliceSize); const f = slice(res, [0, sliceCols * 2], sliceSize); const o = slice(res, [0, sliceCols * 3], sliceSize); const newC = add(mul(sigmoid(i), tanh(j)), mul($c, sigmoid(add($forgetBias, f)))); const newH = mul(tanh(newC), sigmoid(o)); return [newC, newH]; } const basicLSTMCell = /* @__PURE__ */ op({ basicLSTMCell_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * This operation reshapes the "batch" dimension 0 into `M + 1` dimensions of * shape `blockShape + [batch]`, interleaves these blocks back into the grid * defined by the spatial dimensions `[1, ..., M]`, to obtain a result with * the same rank as the input. The spatial dimensions of this intermediate * result are then optionally cropped according to `crops` to produce the * output. This is the reverse of `tf.spaceToBatchND`. See below for a precise * description. * * ```js * const x = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]); * const blockShape = [2, 2]; * const crops = [[0, 0], [0, 0]]; * * x.batchToSpaceND(blockShape, crops).print(); * ``` * * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape + * remainingShape`, where spatialShape has `M` dimensions. * @param blockShape A 1-D array. Must have shape `[M]`, all values must * be >= 1. * @param crops A 2-D array. Must have shape `[M, 2]`, all values must be >= 0. * `crops[i] = [cropStart, cropEnd]` specifies the amount to crop from input * dimension `i + 1`, which corresponds to spatial dimension `i`. It is required * that `cropStart[i] + cropEnd[i] <= blockShape[i] * inputShape[i + 1]` * * This operation is equivalent to the following steps: * * 1. Reshape `x` to `reshaped` of shape: `[blockShape[0], ..., * blockShape[M-1], batch / prod(blockShape), x.shape[1], ..., * x.shape[N-1]]` * * 2. Permute dimensions of `reshaped` to produce `permuted` of shape `[batch / * prod(blockShape),x.shape[1], blockShape[0], ..., x.shape[M], * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]` * * 3. Reshape `permuted` to produce `reshapedPermuted` of shape `[batch / * prod(blockShape),x.shape[1] * blockShape[0], ..., x.shape[M] * * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]` * * 4. Crop the start and end of dimensions `[1, ..., M]` of `reshapedPermuted` * according to `crops` to produce the output of shape: `[batch / * prod(blockShape),x.shape[1] * blockShape[0] - crops[0,0] - crops[0,1], * ..., x.shape[M] * blockShape[M-1] - crops[M-1,0] - * crops[M-1,1],x.shape[M+1], ..., x.shape[N-1]]` * * @doc {heading: 'Tensors', subheading: 'Transformations'} */ function batchToSpaceND_(x, blockShape, crops) { const $x = convertToTensor(x, 'x', 'batchToSpaceND'); const prod = blockShape.reduce((a, b) => a * b); assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`); assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length ${blockShape.length}`); assert($x.shape[0] % prod === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of ` + `the elements of blockShape ${blockShape.join(' * ')} === ${prod}`); const inputs = { x: $x }; const attrs = { blockShape, crops }; return ENGINE.runKernel(BatchToSpaceND, inputs, attrs); } const batchToSpaceND = /* @__PURE__ */ op({ batchToSpaceND_ }); function xAs4D(x) { let x4D; if (x.rank === 0 || x.rank === 1) { x4D = reshape(x, [1, 1, 1, x.size]); } else if (x.rank === 2) { x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]); } else if (x.rank === 3) { x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]); } else { x4D = x; } return x4D; } /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Batch normalization. * * As described in * [http://arxiv.org/abs/1502.03167](http://arxiv.org/abs/1502.03167). * * Mean, variance, scale, and offset can be of two shapes: * - The same shape as the input. * - In the common case, the depth dimension is the last dimension of x, so * the values would be a `tf.Tensor1D` of shape [depth]. * * Also available are stricter rank-specific methods with the same signature * as this method that assert that parameters passed are of given rank * - `tf.batchNorm2d` * - `tf.batchNorm3d` * - `tf.batchNorm4d` * * @param x The input Tensor. * @param mean A mean Tensor. * @param variance A variance Tensor. * @param offset An offset Tensor. * @param scale A scale Tensor. * @param varianceEpsilon A small float number to avoid dividing by 0. * * @doc {heading: 'Operations', subheading: 'Normalization'} */ function batchNorm_(x, mean, variance, offset, scale, varianceEpsilon) { if (varianceEpsilon == null) { varianceEpsilon = 0.001; } const $x = convertToTensor(x, 'x', 'batchNorm'); const $mean = convertToTensor(mean, 'mean', 'batchNorm'); const $variance = convertToTensor(variance, 'variance', 'batchNorm'); let $scale; if (scale != null) { $scale = convertToTensor(scale, 'scale', 'batchNorm'); } let $offset; if (offset != null) { $offset = convertToTensor(offset, 'offset', 'batchNorm'); } assert($mean.rank === $variance.rank, () => 'Batch normalization gradient requires mean and variance to have ' + 'equal ranks.'); assert($offset == null || $mean.rank === $offset.rank, () => 'Batch normalization gradient requires mean and offset to have ' + 'equal ranks.'); assert($scale == null || $mean.rank === $scale.rank, () => 'Batch normalization gradient requires mean and scale to have ' + 'equal ranks.'); const x4D = xAs4D($x); const inputs = { x: x4D, scale: $scale, offset: $offset, mean: $mean, variance: $variance }; const attrs = { varianceEpsilon }; // tslint:disable-next-line: no-unnecessary-type-assertion const res = ENGINE.runKernel(FusedBatchNorm, inputs, attrs); return reshape(res, $x.shape); } const batchNorm = /* @__PURE__ */ op({ batchNorm_ }); /** * Batch normalization, strictly for 2D. For the more relaxed version, see * `tf.batchNorm`. * * @param x The input Tensor. * @param mean A mean Tensor. * @param variance A variance Tensor. * @param offset An offset Tensor. * @param scale A scale Tensor. * @param varianceEpsilon A small float number to avoid dividing by 0. */ function batchNorm2d_(x, mean, variance, offset, scale, varianceEpsilon) { const $x = convertToTensor(x, 'x', 'batchNorm'); const $mean = convertToTensor(mean, 'mean', 'batchNorm'); const $variance = convertToTensor(variance, 'variance', 'batchNorm'); let $scale; if (scale != null) { $scale = convertToTensor(scale, 'scale', 'batchNorm'); } let $offset; if (offset != null) { $offset = convertToTensor(offset, 'offset', 'batchNorm'); } assert($x.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ` + `${$x.rank}.`); assert($mean.rank === 2 || $mean.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but ` + `got rank ${$mean.rank}.`); assert($variance.rank === 2 || $variance.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 ` + `but got rank ${$variance.rank}.`); if ($scale != null) { assert($scale.rank === 2 || $scale.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 ` + `but got rank ${$scale.rank}.`); } if ($offset != null) { assert($offset.rank === 2 || $offset.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 ` + `but got rank ${$offset.rank}.`); } return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon); } const batchNorm2d = /* @__PURE__ */ op({ batchNorm2d_ }); /** * Batch normalization, strictly for 3D. For the more relaxed version, see * `tf.batchNorm`. * * @param x The input Tensor. * @param mean A mean Tensor. * @param variance A variance Tensor. * @param offset An offset Tensor. * @param scale A scale Tensor. * @param varianceEpsilon A small float number to avoid dividing by 0. */ function batchNorm3d_(x, mean, variance, offset, scale, varianceEpsilon) { const $x = convertToTensor(x, 'x', 'batchNorm'); const $mean = convertToTensor(mean, 'mean', 'batchNorm'); const $variance = convertToTensor(variance, 'variance', 'batchNorm'); let $scale; if (scale != null) { $scale = convertToTensor(scale, 'scale', 'batchNorm'); } let $offset; if (offset != null) { $offset = convertToTensor(offset, 'offset', 'batchNorm'); } assert($x.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ` + `${$x.rank}.`); assert($mean.rank === 3 || $mean.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but ` + `got rank ${$mean.rank}.`); assert($variance.rank === 3 || $variance.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 ` + `but got rank ${$variance.rank}.`); if ($scale != null) { assert($scale.rank === 3 || $scale.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 ` + `but got rank ${$scale.rank}.`); } if ($offset != null) { assert($offset.rank === 3 || $offset.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 ` + `but got rank ${$offset.rank}.`); } return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon); } const batchNorm3d = /* @__PURE__ */ op({ batchNorm3d_ }); /** * Batch normalization, strictly for 4D. For the more relaxed version, see * `tf.batchNorm`. * * @param x The input Tensor. * @param mean A mean Tensor. * @param variance A variance Tensor. * @param offset An offset Tensor. * @param scale A scale Tensor. * @param varianceEpsilon A small float number to avoid dividing by 0. */ function batchNorm4d_(x, mean, variance, offset, scale, varianceEpsilon) { const $x = convertToTensor(x, 'x', 'batchNorm'); const $mean = convertToTensor(mean, 'mean', 'batchNorm'); const $variance = convertToTensor(variance, 'variance', 'batchNorm'); let $scale; if (scale != null) { $scale = convertToTensor(scale, 'scale', 'batchNorm'); } let $offset; if (offset != null) { $offset = convertToTensor(offset, 'offset', 'batchNorm'); } assert($x.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ` + `${$x.rank}.`); assert($mean.rank === 4 || $mean.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but ` + `got rank ${$mean.rank}.`); assert($variance.rank === 4 || $variance.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 ` + `but got rank ${$variance.rank}.`); if ($scale != null) { assert($scale.rank === 4 || $scale.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 ` + `but got rank ${$scale.rank}.`); } if ($offset != null) { assert($offset.rank === 4 || $offset.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 ` + `but got rank ${$offset.rank}.`); } return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon); } const batchNorm4d = /* @__PURE__ */ op({ batchNorm4d_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Outputs a vector with length `size` and the same dtype as `weights`. * * If `weights` are empty, then index `i` stores the number of times the value * `i` is counted in `x`. If `weights` are non-empty, then index `i` stores the * sum of the value in `weights` at each index where the corresponding value in * `x` is `i`. * * Values in `x` outside of the range [0, size) are ignored. * * @param x The input int tensor, rank 1. * @param weights The weights tensor, must have the same shape as x, or a * length-0 Tensor, in which case it acts as all weights equal to 1. * @param size Non-negative integer. * * @doc {heading: 'Operations', subheading: 'Reduction'} */ function bincount_(x, weights, size) { const $x = convertToTensor(x, 'x', 'bincount'); const $weights = convertToTensor(weights, 'weights', 'bincount'); assert($x.dtype === 'int32', () => `Error in bincount: input ` + `dtype must be int32, but got ${$x.dtype}`); assert(size >= 0, () => `size must be non-negative, but got ${size}.`); assert($weights.size === $x.size || $weights.size === 0, () => `Error in bincount: weights must have the same size as input or` + `0-length, but got input shape: ${$x.shape}, weights shape: ` + `${$weights.shape}.`); const inputs = { x: $x, weights: $weights }; const attrs = { size }; return ENGINE.runKernel(Bincount, inputs, attrs); } const bincount = /* @__PURE__ */ op({ bincount_ }); /** * @license * Copyright 2023 Google LLC. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Bitwise `AND` operation for input tensors. * * Given two input tensors, returns a new tensor * with the `AND` calculated values. * * The method supports int32 values * * * ```js * const x = tf.tensor1d([0, 5, 3, 14], 'int32'); * const y = tf.tensor1d([5, 0, 7, 11], 'int32'); * tf.bitwiseAnd(x, y).print(); * ``` * * @param x The input tensor to be calculated. * @param y The input tensor to be calculated. * * @doc {heading: 'Operations', subheading: 'Logical'} */ function bitwiseAnd_(x, y) { const $x = convertToTensor(x, 'x', 'bitwiseAnd'); const $y = convertToTensor(y, 'y', 'bitwiseAnd'); if (!arraysEqual($x.shape, $y.shape)) { throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${$x.shape}, y: ${$y.shape}`); } if ($x.dtype !== 'int32' || $y.dtype !== 'int32') { throw new Error(`BitwiseAnd: Only supports 'int32' values in tensor, found type of x: ${$x.dtype} and type of y: ${$y.dtype}`); } const inputs = { a: $x, b: $y }; return ENGINE.runKernel(BitwiseAnd, inputs); } const bitwiseAnd = /* @__PURE__ */ op({ bitwiseAnd_ }); /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Return the shape of s0 op s1 with broadcast. * * compute r0, the broadcasted shape as a tensor. * s0, s1 and r0 are all integer vectors. * * This function returns the shape of the result of an operation between * two tensors of size s0 and s1 performed with broadcast. * * @param s0 A tensor representing a shape * @param s1 A tensor representing a shape * * @doc {heading: 'Tensors', subheading: 'Transformations'} */ function broadcastArgs_(s0, s1) { const shape1Input = convertToTensor(s0, 's0', 'broadcastArgs', 'int32'); const shape2Input = convertToTensor(s1, 's1', 'broadcastArgs', 'int32'); if (shape1Input.rank !== 1) { throw new Error('broadcastArgs(): first input must be a vector (rank=1). ' + `Has rank ${shape1Input.rank}`); } if (shape2Input.rank !== 1) { throw new Error('broadcastArgs(): second input must be a vector (rank=1). ' + `Has rank ${shape2Input.rank}`); } const inputs = { s0: shape1Input, s1: shape2Input }; return ENGINE.runKernel(BroadcastArgs, inputs); } const broadcastArgs = /* @__PURE__ */ op({ broadcastArgs_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Broadcast an array to a compatible shape NumPy-style. * * The tensor's shape is compared to the broadcast shape from end to beginning. * Ones are prepended to the tensor's shape until it has the same length as * the broadcast shape. If input.shape[i]==shape[i], the (i+1)-th axis is * already broadcast-compatible. If input.shape[i]==1 and shape[i]==N, then * the input tensor is tiled N times along that axis (using tf.tile). * * @param input The tensor that is to be broadcasted. * @param shape The input is to be broadcast to this shape. * * @doc {heading: 'Tensors', subheading: 'Transformations'} */ function broadcastTo_(x, shape) { let input = convertToTensor(x, 'broadcastTo', 'x'); const xShape = input.shape; assertNonNegativeIntegerDimensions(shape); if (shape.length < input.rank) { throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input.rank}.`); } if (shape.length > input.rank) { const newShape = input.shape.slice(); while (newShape.length < shape.length) { newShape.unshift(1); } input = reshape(input, newShape); } const inputShape = input.shape; const reps = Array.from(shape); for (let i = shape.length - 1; i >= 0; i--) { if (inputShape[i] === shape[i]) { reps[i] = 1; } else if (input.shape[i] !== 1) { throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`); } } const axes = reps.map((n, i) => n > 1 ? i : -1).filter(i => i >= 0); if (axes.length === 0) { return clone(input); } // TODO call broadcastTo kernel directly once backends implement broadcstTo const inputs = { x: input }; const attrs = { reps }; return ENGINE.runKernel(Tile, inputs, attrs); } const broadcastTo = /* @__PURE__ */ op({ broadcastTo_ }); /** * @license * Copyright 2020 Google Inc. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates an empty `tf.TensorBuffer` with the specified `shape` and `dtype`. * * The values are stored in CPU as `TypedArray`. Fill the buffer using * `buffer.set()`, or by modifying directly `buffer.values`. * * When done, call `buffer.toTensor()` to get an immutable `tf.Tensor` with * those values. * * ```js * // Create a buffer and set values at particular indices. * const buffer = tf.buffer([2, 2]); * buffer.set(3, 0, 0); * buffer.set(5, 1, 0); * * // Convert the buffer back to a tensor. * buffer.toTensor().print(); * ``` * * @param shape An array of integers defining the output tensor shape. * @param dtype The dtype of the buffer. Defaults to 'float32'. * @param values The values of the buffer as `TypedArray`. Defaults to * zeros. * * @doc {heading: 'Tensors', subheading: 'Creation'} */ function buffer(shape, dtype = 'float32', values) { dtype = dtype || 'float32'; assertNonNegativeIntegerDimensions(shape); return new TensorBuffer(shape, dtype, values); } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes ceiling of input `tf.Tensor` element-wise: `ceil(x)` * * ```js * const x = tf.tensor1d([.6, 1.1, -3.3]); * * x.ceil().print(); // or tf.ceil(x) * ``` * @param x The input Tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function ceil_(x) { const $x = convertToTensor(x, 'x', 'ceil', 'float32'); const inputs = { x: $x }; return ENGINE.runKernel(Ceil, inputs); } const ceil = /* @__PURE__ */ op({ ceil_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates a `tf.Tensor` filled with a scalar value. * * ```js * tf.fill([2, 2], 4).print(); * ``` * * @param shape An array of integers defining the output tensor shape. * @param value The scalar value to fill the tensor with. * @param dtype The type of an element in the resulting tensor. Defaults to * 'float32' if the given param value is a number, otherwise 'string'. * * @doc {heading: 'Tensors', subheading: 'Creation'} */ function fill(shape, value, dtype) { assertNonNegativeIntegerDimensions(shape); dtype = dtype || inferDtype(value); const attrs = { shape, value, dtype }; return ENGINE.runKernel(Fill, {}, attrs); } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Clips values element-wise. `max(min(x, clipValueMax), clipValueMin)` * * ```js * const x = tf.tensor1d([-1, 2, -3, 4]); * * x.clipByValue(-2, 3).print(); // or tf.clipByValue(x, -2, 3) * ``` * @param x The input tensor. * @param clipValueMin Lower bound of range to be clipped to. * @param clipValueMax Upper bound of range to be clipped to. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function clipByValue_(x, clipValueMin, clipValueMax) { const $x = convertToTensor(x, 'x', 'clipByValue'); assert((clipValueMin <= clipValueMax), () => `Error in clip: min (${clipValueMin}) must be ` + `less than or equal to max (${clipValueMax}).`); if (clipValueMin === clipValueMax) { return fill($x.shape, clipValueMin, $x.dtype); } const inputs = { x: $x }; const attrs = { clipValueMin, clipValueMax }; return ENGINE.runKernel(ClipByValue, inputs, attrs); } const clipByValue = /* @__PURE__ */ op({ clipByValue_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Converts two real numbers to a complex number. * * Given a tensor `real` representing the real part of a complex number, and a * tensor `imag` representing the imaginary part of a complex number, this * operation returns complex numbers elementwise of the form [r0, i0, r1, i1], * where r represents the real part and i represents the imag part. * * The input tensors real and imag must have the same shape. * * ```js * const real = tf.tensor1d([2.25, 3.25]); * const imag = tf.tensor1d([4.75, 5.75]); * const complex = tf.complex(real, imag); * * complex.print(); * ``` * * @doc {heading: 'Tensors', subheading: 'Creation'} */ function complex_(real, imag) { const $real = convertToTensor(real, 'real', 'complex'); const $imag = convertToTensor(imag, 'imag', 'complex'); assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, ` + `must match in call to tf.complex().`); const inputs = { real: $real, imag: $imag }; return ENGINE.runKernel(Complex, inputs); } const complex = /* @__PURE__ */ op({ complex_ }); /** * Concatenates a list of`tf.Tensor1D`s along an axis. See `concat` for details. * * For example, if: * A: shape(3) = |r1, g1, b1| * B: shape(2) = |r2, g2| * C = tf.concat1d([A, B]) == |r1, g1, b1, r2, g2| * * @param tensors A list of`tf.Tensor`s to concatenate. * @return The concatenated array. */ function concat1d_(tensors) { return concat(tensors, 0 /* axis */); } const concat1d = /* @__PURE__ */ op({ concat1d_ }); /** * Concatenates a list of`tf.Tensor2D`s along an axis. See `concat` for details. * * For example, if: * A: shape(2, 3) = | r1, g1, b1 | * | r2, g2, b2 | * * B: shape(2, 3) = | r3, g3, b3 | * | r4, g4, b4 | * * C = tf.concat2d([A, B], axis) * * if axis = 0: * C: shape(4, 3) = | r1, g1, b1 | * | r2, g2, b2 | * | r3, g3, b3 | * | r4, g4, b4 | * * if axis = 1: * C = shape(2, 6) = | r1, g1, b1, r3, g3, b3 | * | r2, g2, b2, r4, g4, b4 | * * * @param tensors A list of `tf.Tensor`s to concatenate. * @param axis The axis to concatenate along. * @return The concatenated array. */ function concat2d_(tensors, axis) { return concat(tensors, axis); } const concat2d = /* @__PURE__ */ op({ concat2d_ }); /** * Concatenates a list of `tf.Tensor3D`s along an axis. * See `concat` for details. * * For example, if: * A: shape(2, 1, 3) = | r1, g1, b1 | * | r2, g2, b2 | * * B: shape(2, 1, 3) = | r3, g3, b3 | * | r4, g4, b4 | * * C = tf.concat3d([A, B], axis) * * if axis = 0: * C: shape(4, 1, 3) = | r1, g1, b1 | * | r2, g2, b2 | * | r3, g3, b3 | * | r4, g4, b4 | * * if axis = 1: * C: shape(2, 2, 3) = | r1, g1, b1, r3, g3, b3 | * | r2, g2, b2, r4, g4, b4 | * * if axis = 2: * C = shape(2, 1, 6) = | r1, g1, b1, r3, g3, b3 | * | r2, g2, b2, r4, g4, b4 | * * @param tensors A list of`tf.Tensor`s to concatenate. * @param axis The axis to concate along. * @return The concatenated array. */ function concat3d_(tensors, axis) { return concat(tensors, axis); } const concat3d = /* @__PURE__ */ op({ concat3d_ }); /** * Concatenates a list of `tf.Tensor4D`s along an axis. * See `concat` for details. * * @param tensors A list of `tf.Tensor`s to concatenate. * @param axis The axis to concate along. * @return The concatenated array. */ function concat4d_(tensors, axis) { return concat(tensors, axis); } const concat4d = /* @__PURE__ */ op({ concat4d_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes a 2D convolution over the input x. * * @param x The input tensor, of rank 4 or rank 3, of shape * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is * assumed. * @param filter The filter, rank 4, of shape * `[filterHeight, filterWidth, inDepth, outDepth]`. * @param strides The strides of the convolution: `[strideHeight, * strideWidth]`. * @param pad The type of padding algorithm. * - `same` and stride 1: output will be of same size as input, * regardless of filter size. * - `valid`: output will be smaller than input if filter is larger * than 1x1. * - For more info, see this guide: * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to * "NHWC". Specify the data format of the input and output data. With the * default format "NHWC", the data is stored in the order of: [batch, * height, width, channels]. * @param dilations The dilation rates: `[dilationHeight, dilationWidth]` * in which we sample input values across the height and width dimensions * in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single * number, then `dilationHeight == dilationWidth`. If it is greater than * 1, then all values of `strides` must be 1. * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is * provided, it will default to truncate. * * @doc {heading: 'Operations', subheading: 'Convolution'} */ function conv2d_(x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode) { const $x = convertToTensor(x, 'x', 'conv2d', 'float32'); const $filter = convertToTensor(filter, 'filter', 'conv2d', 'float32'); let x4D = $x; let reshapedTo4D = false; if ($x.rank === 3) { reshapedTo4D = true; x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); } assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`); assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ` + `${$filter.rank}.`); checkPadOnDimRoundingMode('conv2d', pad, dimRoundingMode); const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1]; assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match ` + `input depth for filter ${$filter.shape[2]}.`); assert(eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv2D: Either strides or dilations must be 1. ' + `Got strides ${strides} and dilations '${dilations}'`); assert(stridesOrDilationsArePositive(dilations), () => 'Error in conv2D: Dilated rates should be larger than 0.'); assert(stridesOrDilationsArePositive(strides), () => 'Error in conv2D: Strides should be larger than 0.'); const inputs = { x: x4D, filter: $filter }; const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode }; // tslint:disable-next-line: no-unnecessary-type-assertion const res = ENGINE.runKernel(Conv2D, inputs, attrs); if (reshapedTo4D) { return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); } return res; } const conv2d$1 = /* @__PURE__ */ op({ conv2d_ }); /** * Computes a 1D convolution over the input x. * * @param x The input tensor, of rank 3 or rank 2, of shape * `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed. * @param filter The filter, rank 3, of shape * `[filterWidth, inDepth, outDepth]`. * @param stride The number of entries by which the filter is moved right at * each step. * @param pad The type of padding algorithm. * - `same` and stride 1: output will be of same size as input, * regardless of filter size. * - `valid`: output will be smaller than input if filter is larger * than 1x1. * - For more info, see this guide: * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) * @param dataFormat An optional string from "NWC", "NCW". Defaults to "NWC", * the data is stored in the order of [batch, in_width, in_channels]. Only * "NWC" is currently supported. * @param dilation The dilation rate in which we sample input values in * atrous convolution. Defaults to `1`. If it is greater than 1, then * stride must be `1`. * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is * provided, it will default to truncate. * * @doc {heading: 'Operations', subheading: 'Convolution'} */ function conv1d_(x, filter, stride, pad, dataFormat = 'NWC', dilation = 1, dimRoundingMode) { const $x = convertToTensor(x, 'x', 'conv1d'); const $filter = convertToTensor(filter, 'filter', 'conv1d'); let x3D = $x; let reshapedTo3D = false; if ($x.rank === 2) { reshapedTo3D = true; x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]); } assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`); assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ` + `${$filter.rank}.`); checkPadOnDimRoundingMode('conv1d', pad, dimRoundingMode); assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match ` + `input depth for filter ${$filter.shape[1]}.`); assert(eitherStridesOrDilationsAreOne(stride, dilation), () => 'Error in conv1D: Either stride or dilation must be 1. ' + `Got stride ${stride} and dilation '${dilation}'`); assert(stridesOrDilationsArePositive(dilation), () => 'Error in conv1D: Dilated rates should be larger than 0.'); assert(stridesOrDilationsArePositive(stride), () => 'Error in conv1D: Stride should be larger than 0.'); assert(dataFormat === 'NWC', () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`); const filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]); const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]); const strides = [1, stride]; const dilations = [1, dilation]; const conv2dDataFormat = 'NHWC'; const res = conv2d$1(input4D, filter4D, strides, pad, conv2dDataFormat, dilations, dimRoundingMode); if (reshapedTo3D) { return reshape(res, [res.shape[2], res.shape[3]]); } return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]); } const conv1d = /* @__PURE__ */ op({ conv1d_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the derivative of the input of a 2D convolution. * * @param xShape The shape of the input: [batch, height, width, inDepth]. * If length of 3, batch of 1 is assumed. * @param dy The derivative of the output, of rank 4 or rank 3 of shape * `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is * assumed. * @param filter The filter, rank 4, of shape * `[filterHeight, filterWidth, inDepth, outDepth]`. * @param strides The strides of the convolution: `[strideHeight, * strideWidth]`. * @param pad The type of padding algorithm used: * - `same` and stride 1: output will be of same size as input, * regardless of filter size. * - `valid`: output will be smaller than input if filter is larger * than 1x1. * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to * "NHWC". Specify the data format of the input and output data. With the * default format "NHWC", the data is stored in the order of: [batch, * height, width, channels]. * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is * provided, it will default to truncate. */ function conv2DBackpropInput_(xShape, dy, filter, strides, pad, dataFormat = 'NHWC', dimRoundingMode) { assert(xShape.length === dy.rank, () => `Length of inShape ` + `(${xShape.length}) and rank of dy (${dy.rank}) must match`); let xShape4D = xShape; let dy4D = dy; let reshapedTo4D = false; if (dy.rank === 3) { reshapedTo4D = true; dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]); xShape4D = [1, xShape[0], xShape[1], xShape[2]]; } assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ` + `${xShape4D.length}.`); assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got ` + `rank ${dy4D.rank}`); assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got ` + `rank ${filter.rank}`); const inDepth = dataFormat === 'NHWC' ? xShape4D[3] : xShape4D[1]; const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1]; assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must ` + `match input depth for filter ${filter.shape[2]}.`); assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must ` + `match output depth for filter ${filter.shape[3]}.`); checkPadOnDimRoundingMode('conv2dDerInput', pad, dimRoundingMode); const inputs = { dy: dy4D, filter }; const attrs = { strides, pad, dataFormat, dimRoundingMode, inputShape: xShape4D }; // tslint:disable-next-line: no-unnecessary-type-assertion const res = ENGINE.runKernel(Conv2DBackpropInput, inputs, attrs); if (reshapedTo4D) { return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); } return res; } const conv2DBackpropInput = /* @__PURE__ */ op({ conv2DBackpropInput_ }); /** * Computes the transposed 2D convolution of an image, also known as a * deconvolution. * * @param x The input image, of rank 4 or rank 3, of shape * `[batch, height, width, inDepth]`. If rank 3, batch of 1 is assumed. * @param filter The filter, rank 4, of shape * `[filterHeight, filterWidth, outDepth, inDepth]`. * `inDepth` must match `inDepth` in `x`. * @param outputShape Output shape, of rank 4 or rank 3: * `[batch, height, width, outDepth]`. If rank 3, batch of 1 is assumed. * @param strides The strides of the original convolution: * `[strideHeight, strideWidth]`. * @param pad The type of padding algorithm used in the non-transpose version * of the op. * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is * provided, it will default to truncate. * * @doc {heading: 'Operations', subheading: 'Convolution'} */ function conv2dTranspose_(x, filter, outputShape, strides, pad, dimRoundingMode) { const $x = convertToTensor(x, 'x', 'conv2dTranspose'); const $filter = convertToTensor(filter, 'filter', 'conv2dTranspose'); return conv2DBackpropInput(outputShape, $x, $filter, strides, pad, 'NHWC', dimRoundingMode); } const conv2dTranspose = /* @__PURE__ */ op({ conv2dTranspose_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes a 3D convolution over the input x. * * @param x The input tensor, of rank 5 or rank 4, of shape * `[batch, depth, height, width, channels]`. If rank 4, * batch of 1 is assumed. * @param filter The filter, rank 5, of shape * `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`. * inChannels must match between input and filter. * @param strides The strides of the convolution: `[strideDepth, strideHeight, * strideWidth]`. * @param pad The type of padding algorithm. * - `same` and stride 1: output will be of same size as input, * regardless of filter size. * - `valid`: output will be smaller than input if filter is larger * than 1x1. * - For more info, see this guide: * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) * @param dataFormat: An optional string from: "NDHWC", "NCDHW". Defaults to * "NDHWC". Specify the data format of the input and output data. With the * default format "NDHWC", the data is stored in the order of: [batch, * depth, height, width, channels]. Only "NDHWC" is currently supported. * @param dilations The dilation rates: `[dilationDepth, dilationHeight, * dilationWidth]` in which we sample input values across the height * and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`. * If `dilations` is a single number, then * `dilationDepth == dilationHeight == dilationWidth`. If it is greater * than 1, then all values of `strides` must be 1. * * @doc {heading: 'Operations', subheading: 'Convolution'} */ function conv3d_(x, filter, strides, pad, dataFormat = 'NDHWC', dilations = [1, 1, 1]) { const $x = convertToTensor(x, 'x', 'conv3d'); const $filter = convertToTensor(filter, 'filter', 'conv3d'); let x5D = $x; let reshapedTo5D = false; if ($x.rank === 4) { reshapedTo5D = true; x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]); } assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`); assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ` + `${$filter.rank}.`); assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match ` + `input depth for filter ${$filter.shape[3]}.`); assert(eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv3D: Either strides or dilations must be 1. ' + `Got strides ${strides} and dilations '${dilations}'`); assert(dataFormat === 'NDHWC', () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`); assert(stridesOrDilationsArePositive(dilations), () => 'Error in conv3D: Dilated rates should be larger than 0.'); assert(stridesOrDilationsArePositive(strides), () => 'Error in conv3D: Strides should be larger than 0.'); const inputs = { x: x5D, filter: $filter }; const attrs = { strides, pad, dataFormat, dilations }; // tslint:disable-next-line: no-unnecessary-type-assertion const res = ENGINE.runKernel(Conv3D, inputs, attrs); if (reshapedTo5D) { return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]); } return res; } const conv3d = /* @__PURE__ */ op({ conv3d_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the derivative of the input of a 3D convolution. * * @param xShape The shape of the input: [batch, depth, height, width, * in_channels]. If length of 4, batch of 1 is assumed. * @param dy The derivative of the output, of rank 5 or rank 4 of shape * `[batch, outDepth, outHeight, outWidth, in_channels]`. * If rank 4, batch of 1 is assumed. * @param filter The filter, rank 5, of shape * `[filterDepth, filterHeight, filterWidth, inDepth, outDepth]`. * @param strides The strides of the convolution: `[strideDepth, strideHeight, * strideWidth]`. * @param pad The type of padding algorithm used: * - `same` and stride 1: output will be of same size as input, * regardless of filter size. * - `valid`: output will be smaller than input if filter is larger * than 1x1. */ function conv3DBackpropInput_(xShape, dy, filter, strides, pad) { assert(xShape.length === dy.rank, () => `Length of inShape ` + `(${xShape.length}) and rank of dy (${dy.rank}) must match`); let xShape5D = xShape; let dy5D = dy; let reshapedTo5D = false; if (dy.rank === 4) { reshapedTo5D = true; dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]); xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]]; } const inDepth = xShape5D[4]; const outDepth = dy5D.shape[4]; assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ` + `${xShape5D.length}.`); assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got ` + `rank ${dy5D.rank}`); assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got ` + `rank ${filter.rank}`); assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must ` + `match input depth for filter ${filter.shape[3]}.`); assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must ` + `match output depth for filter ${filter.shape[4]}.`); const inputs = { dy: dy5D, filter }; const attrs = { pad, strides, inputShape: xShape5D }; // tslint:disable-next-line: no-unnecessary-type-assertion const res = ENGINE.runKernel(Conv3DBackpropInputV2, inputs, attrs); if (reshapedTo5D) { return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]); } return res; } const conv3DBackpropInput = /* @__PURE__ */ op({ conv3DBackpropInput_ }); /** * Computes the transposed 3D convolution of a volume, also known as a * deconvolution. * * @param x The input image, of rank 5 or rank 4, of shape * `[batch, depth, height, width, inDepth]`. If rank 4, batch of 1 is assumed. * @param filter The filter, rank 4, of shape * `[depth, filterHeight, filterWidth, outDepth, inDepth]`. * `inDepth` must match `inDepth` in `x`. * @param outputShape Output shape, of rank 5 or rank 4: * `[batch, depth, height, width, outDepth]`. If rank 3, batch of 1 is * assumed. * @param strides The strides of the original convolution: * `[strideDepth, strideHeight, strideWidth]`. * @param pad The type of padding algorithm used in the non-transpose version * of the op. * * @doc {heading: 'Operations', subheading: 'Convolution'} */ function conv3dTranspose_(x, filter, outputShape, strides, pad) { const $x = convertToTensor(x, 'x', 'conv3dTranspose'); const $filter = convertToTensor(filter, 'filter', 'conv3dTranspose'); return conv3DBackpropInput(outputShape, $x, $filter, strides, pad); } const conv3dTranspose = /* @__PURE__ */ op({ conv3dTranspose_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes cos of the input `tf.Tensor` element-wise: `cos(x)` * * ```js * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]); * * x.cos().print(); // or tf.cos(x) * ``` * @param x The input tensor. Must be float32 type. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function cos_(x) { const $x = convertToTensor(x, 'x', 'cos', 'float32'); const inputs = { x: $x }; return ENGINE.runKernel(Cos, inputs); } const cos = /* @__PURE__ */ op({ cos_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes hyperbolic cos of the input `tf.Tensor` element-wise: `cosh(x)` * * ```js * const x = tf.tensor1d([0, 1, -1, .7]); * * x.cosh().print(); // or tf.cosh(x) * ``` * @param x The input tensor. Must be float32 type. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function cosh_(x) { const $x = convertToTensor(x, 'x', 'cosh', 'float32'); const inputs = { x: $x }; return ENGINE.runKernel(Cosh, inputs); } const cosh = /* @__PURE__ */ op({ cosh_ }); /** * @license * Copyright 2022 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the 'License'); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an 'AS IS' BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the cumulative product of a `tf.Tensor` along `axis`. * * ```js * const x = tf.tensor([1, 2, 3, 4]); * x.cumprod().print(); * ``` * ```js * const x = tf.tensor([[1, 2], [3, 4]]); * x.cumprod().print(); * ``` * * @param x The input tensor to cumulatively multiply. * @param axis The axis along which to multiply. Optional. Defaults to 0. * @param exclusive Whether to perform exclusive cumulative product. Optional. * Defaults to false. If set to true then the product of each tensor entry * does not include its own value, but only the values previous to it * along the specified axis. * @param reverse Whether to multiply in the opposite direction. Optional. * Defaults to false. * * @doc {heading: 'Operations', subheading: 'Scan'} */ function cumprod_(x, axis = 0, exclusive = false, reverse = false) { const $x = convertToTensor(x, 'x', 'cumprod'); const inputs = { x: $x }; const attrs = { axis, exclusive, reverse }; return ENGINE.runKernel(Cumprod, inputs, attrs); } const cumprod = /* @__PURE__ */ op({ cumprod_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the cumulative sum of a `tf.Tensor` along `axis`. * * ```js * const x = tf.tensor([1, 2, 3, 4]); * x.cumsum().print(); * ``` * ```js * const x = tf.tensor([[1, 2], [3, 4]]); * x.cumsum().print(); * ``` * * @param x The input tensor to be summed. * @param axis The axis along which to sum. Optional. Defaults to 0. * @param exclusive Whether to perform exclusive cumulative sum. Optional. * Defaults to false. If set to true then the sum of each tensor entry * does not include its own value, but only the values previous to it * along the specified axis. * @param reverse Whether to sum in the opposite direction. Optional. * Defaults to false. * * @doc {heading: 'Operations', subheading: 'Scan'} */ function cumsum_(x, axis = 0, exclusive = false, reverse = false) { const $x = convertToTensor(x, 'x', 'cumsum'); const inputs = { x: $x }; const attrs = { axis, exclusive, reverse }; return ENGINE.runKernel(Cumsum, inputs, attrs); } const cumsum = /* @__PURE__ */ op({ cumsum_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Outputs a vector with length `size` and the same dtype as `weights`. * * If `weights` are empty, then index `i` stores the number of times the value * `i` is counted in `x`. If `weights` are non-empty, then index `i` stores the * sum of the value in `weights` at each index where the corresponding value in * `x` is `i`. * * Values in `x` outside of the range [0, size) are ignored. * * @param x The input int tensor, rank 1 or rank 2. * @param weights The weights tensor, must have the same shape as x, or a * length-0 Tensor, in which case it acts as all weights equal to 1. * @param size Non-negative integer. * @param binaryOutput Optional. Whether the kernel should count the appearance * or number of occurrences. Defaults to False. * * @doc {heading: 'Operations', subheading: 'Reduction'} */ function denseBincount_(x, weights, size, binaryOutput = false) { const $x = convertToTensor(x, 'x', 'denseBincount'); const $weights = convertToTensor(weights, 'weights', 'denseBincount'); assert($x.dtype === 'int32', () => `Error in denseBincount: input ` + `dtype must be int32, but got ${$x.dtype}`); assert($x.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got ` + `rank ${$x.rank}.`); assert(size >= 0, () => `size must be non-negative, but got ${size}.`); assert($weights.size === $x.size || $weights.size === 0, () => `Error in denseBincount: weights must have the same shape as x or ` + `0-length, but got x shape: ${$x.shape}, weights shape: ` + `${$weights.shape}.`); const inputs = { x: $x, weights: $weights }; const attrs = { size, binaryOutput }; return ENGINE.runKernel(DenseBincount, inputs, attrs); } const denseBincount = /* @__PURE__ */ op({ denseBincount_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Rearranges data from depth into blocks of spatial data. More specifically, * this op outputs a copy of the input tensor where values from the `depth` * dimension are moved in spatial blocks to the `height` and `width` dimensions. * The attr `blockSize` indicates the input block size and how the data is * moved. * * - Chunks of data of size `blockSize * blockSize` from depth are rearranged * into non-overlapping blocks of size `blockSize x blockSize` * * - The width the output tensor is `inputWidth * blockSize`, whereas the * height is `inputHeight * blockSize` * * - The Y, X coordinates within each block of the output image are determined * by the high order component of the input channel index * * - The depth of the input tensor must be divisible by `blockSize * * blockSize` * * The `dataFormat` attr specifies the layout of the input and output tensors * with the following options: "NHWC": [ `batch, height, width, channels` ] * "NCHW": [ `batch, channels, height, width` ] * * ```js * const x = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]); * const blockSize = 2; * const dataFormat = "NHWC"; * * tf.depthToSpace(x, blockSize, dataFormat).print(); * ``` * * @param x The input tensor of rank 4 * @param blockSIze An `int` that is `>= 2`. The size of the spatial block * @param dataFormat An optional string from: "NHWC", "NCHW". Defaults to "NHWC" * * @doc {heading: 'Tensors', subheading: 'Transformations'} */ function depthToSpace_(x, blockSize, dataFormat = 'NHWC') { const $x = convertToTensor(x, 'x', 'depthToSpace', 'float32'); const inputHeight = (dataFormat === 'NHWC') ? $x.shape[1] : $x.shape[2]; const inputWidth = (dataFormat === 'NHWC') ? $x.shape[2] : $x.shape[3]; const inputDepth = (dataFormat === 'NHWC') ? $x.shape[3] : $x.shape[1]; assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`); assert(inputHeight * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying ${inputHeight} and ${blockSize} for depthToSpace with input shape ${$x.shape}`); assert(inputWidth * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying ${inputWidth} and ${blockSize} for depthToSpace with input shape ${$x.shape}`); assert((inputDepth % (blockSize * blockSize) === 0), () => `Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`); const inputs = { x: $x }; const attrs = { blockSize, dataFormat }; return ENGINE.runKernel(DepthToSpace, inputs, attrs); } const depthToSpace = /* @__PURE__ */ op({ depthToSpace_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Depthwise 2D convolution. * * Given a 4D `input` array and a `filter` array of shape * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing * `inChannels` convolutional filters of depth 1, this op applies a * different filter to each input channel (expanding from 1 channel to * `channelMultiplier` channels for each), then concatenates the results * together. The output has `inChannels * channelMultiplier` channels. * * See * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d]( * https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d) * for more details. * * @param x The input tensor, of rank 4 or rank 3, of shape * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is * assumed. * @param filter The filter tensor, rank 4, of shape * `[filterHeight, filterWidth, inChannels, channelMultiplier]`. * @param strides The strides of the convolution: `[strideHeight, * strideWidth]`. If strides is a single number, then `strideHeight == * strideWidth`. * @param pad The type of padding algorithm. * - `same` and stride 1: output will be of same size as input, * regardless of filter size. * - `valid`: output will be smaller than input if filter is larger * than 1x1. * - For more info, see this guide: * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) * @param dilations The dilation rates: `[dilationHeight, dilationWidth]` * in which we sample input values across the height and width dimensions * in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single * number, then `dilationHeight == dilationWidth`. If it is greater than * 1, then all values of `strides` must be 1. * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to * "NHWC". Specify the data format of the input and output data. With the * default format "NHWC", the data is stored in the order of: [batch, * height, width, channels]. Only "NHWC" is currently supported. * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is * provided, it will default to truncate. * * @doc {heading: 'Operations', subheading: 'Convolution'} */ function depthwiseConv2d_(x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode) { const $x = convertToTensor(x, 'x', 'depthwiseConv2d', 'float32'); const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d', 'float32'); let x4D = $x; let reshapedTo4D = false; if ($x.rank === 3) { reshapedTo4D = true; x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); } assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got ` + `rank ${x4D.rank}.`); assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ` + `${$filter.rank}.`); const inChannels = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1]; assert(inChannels === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels ` + `(${inChannels}) must match the inChannels dimension in ` + `filter ${$filter.shape[2]}.`); checkPadOnDimRoundingMode('depthwiseConv2d', pad, dimRoundingMode); const inputs = { x: x4D, filter: $filter }; const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode }; // tslint:disable-next-line: no-unnecessary-type-assertion const res = ENGINE.runKernel(DepthwiseConv2dNative, inputs, attrs); if (reshapedTo4D) { return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); } return res; } const depthwiseConv2d$1 = /* @__PURE__ */ op({ depthwiseConv2d_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns a diagonal tensor with given diagonal values. * * Given a diagonal, this operation returns a tensor with the diagonal and * everything else padded with zeros. * * Assume the input has dimensions `[D1,..., Dk]`, then the output is a tensor * of rank 2k with dimensions `[D1,..., Dk, D1,..., Dk]` * * ```js * const x = tf.tensor1d([1, 2, 3, 4]); * * tf.diag(x).print() * ``` * ```js * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [4, 2]) * * tf.diag(x).print() * ``` * @param x The input tensor. * * @doc {heading: 'Tensors', subheading: 'Creation'} */ function diag_(x) { const $x = convertToTensor(x, 'x', 'diag'); const inputs = { x: $x }; return ENGINE.runKernel(Diag, inputs); } const diag = /* @__PURE__ */ op({ diag_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the grayscale dilation over the input `x`. * * @param x The input tensor, rank 3 or rank 4 of shape * `[batch, height, width, depth]`. If rank 3, batch of 1 is assumed. * @param filter The filter tensor, rank 3, of shape * `[filterHeight, filterWidth, depth]`. * @param strides The strides of the sliding window for each dimension of the * input tensor: `[strideHeight, strideWidth]`. * If `strides` is a single number, * then `strideHeight == strideWidth`. * @param pad The type of padding algorithm. * - `same` and stride 1: output will be of same size as input, * regardless of filter size. * - `valid`: output will be smaller than input if filter is larger * than 1*1x1. * - For more info, see this guide: * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) * @param dataFormat Specify the data format of the input and output data. * Defaults to 'NHWC'. Only 'NHWC' is currently supported. With the * default format "NHWC", the data is stored in the order of: [batch, * height, width, channels]. * @param dilations The dilation rates: `[dilationHeight, dilationWidth]` * in which we sample input values across the height and width dimensions * for atrous morphological dilation. Defaults to `[1, 1]`. If `dilations` * is a single number, then `dilationHeight == dilationWidth`. If it is * greater than 1, then all values of `strides` must be 1. * * @doc {heading: 'Operations', subheading: 'Convolution'} */ function dilation2d_(x, filter, strides, pad, dilations = [1, 1], dataFormat = 'NHWC') { const $x = convertToTensor(x, 'x', 'dilation2d'); const $filter = convertToTensor(filter, 'filter', 'dilation2d'); assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ` + `${$x.rank}.`); assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ` + `${$filter.rank}.`); assert(dataFormat === 'NHWC', () => `Error in dilation2d: Only NHWC is currently supported, ` + `but got dataFormat of ${dataFormat}`); let x4D = $x; let reshapedTo4D = false; if ($x.rank === 3) { x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); reshapedTo4D = true; } assert(x4D.shape[3] === $filter.shape[2], () => `Error in dilation2d: input and filter must have the same depth: ${x4D.shape[3]} vs ${$filter.shape[2]}`); const inputs = { x: x4D, filter: $filter }; const attrs = { strides, pad, dilations }; // tslint:disable-next-line: no-unnecessary-type-assertion const res = ENGINE.runKernel(Dilation2D, inputs, attrs); if (reshapedTo4D) { return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); } return res; } const dilation2d = /* @__PURE__ */ op({ dilation2d_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. * The result is rounded with floor function. * * * ```js * const a = tf.tensor1d([1, 4, 9, 16]); * const b = tf.tensor1d([1, 2, 3, 4]); * * a.floorDiv(b).print(); // or tf.div(a, b) * ``` * * ```js * // Broadcast div a with b. * const a = tf.tensor1d([2, 4, 6, 8]); * const b = tf.scalar(2); * * a.floorDiv(b).print(); // or tf.floorDiv(a, b) * ``` * * @param a The first tensor as the numerator. * @param b The second tensor as the denominator. Must have the same dtype as * `a`. * * @doc {heading: 'Operations', subheading: 'Arithmetic'} */ function floorDiv_(a, b) { let $a = convertToTensor(a, 'a', 'floorDiv'); let $b = convertToTensor(b, 'b', 'floorDiv'); [$a, $b] = makeTypesMatch($a, $b); const inputs = { a: $a, b: $b }; return ENGINE.runKernel(FloorDiv, inputs); } const floorDiv = /* @__PURE__ */ op({ floorDiv_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. * * ```js * const a = tf.tensor1d([1, 4, 9, 16]); * const b = tf.tensor1d([1, 2, 3, 4]); * * a.div(b).print(); // or tf.div(a, b) * ``` * * ```js * // Broadcast div a with b. * const a = tf.tensor1d([2, 4, 6, 8]); * const b = tf.scalar(2); * * a.div(b).print(); // or tf.div(a, b) * ``` * * @param a The first tensor as the numerator. * @param b The second tensor as the denominator. Must have the same dtype as * `a`. * * @doc {heading: 'Operations', subheading: 'Arithmetic'} */ function div_(a, b) { let $a = convertToTensor(a, 'a', 'div'); let $b = convertToTensor(b, 'b', 'div'); [$a, $b] = makeTypesMatch($a, $b); if ($a.dtype === 'int32' && $b.dtype === 'int32') { return floorDiv($a, $b); } const inputs = { a: $a, b: $b }; const attrs = {}; // tslint:disable-next-line: no-unnecessary-type-assertion return ENGINE.runKernel(RealDiv, inputs, attrs); } const div = /* @__PURE__ */ op({ div_ }); /** * @license * Copyright 2017 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns the axes in the output space that should be reduced to produce * the input space. */ function getReductionAxes(inShape, outShape) { const result = []; for (let i = 0; i < outShape.length; i++) { const inDim = inShape[inShape.length - i - 1]; const outAxis = outShape.length - i - 1; const outDim = outShape[outAxis]; if (inDim == null || (inDim === 1 && outDim > 1)) { result.unshift(outAxis); } } return result; } function assertAndGetBroadcastShape(shapeA, shapeB) { const l = Math.max(shapeA.length, shapeB.length); const result = new Array(l); for (let i = 0; i < l; i++) { let a = shapeA[shapeA.length - i - 1]; if (a == null) { a = 1; } let b = shapeB[shapeB.length - i - 1]; if (b == null) { b = 1; } if (a === 1) { result[l - i - 1] = b; } else if (b === 1) { result[l - i - 1] = a; } else if (a !== b) { const errMsg = `Operands could not be broadcast together with shapes ` + `${shapeA} and ${shapeB}.`; throw Error(errMsg); } else { result[l - i - 1] = a; } } return result; } /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns the truth value of (a == b) element-wise. Supports broadcasting. * * ```js * const a = tf.tensor1d([1, 2, 3]); * const b = tf.tensor1d([2, 2, 2]); * * a.equal(b).print(); * ``` * * @param a The first input tensor. * @param b The second input tensor. Must have the same dtype as `a`. * * @doc {heading: 'Operations', subheading: 'Logical'} */ function equal_(a, b) { let $a = convertToTensor(a, 'a', 'equal', 'string_or_numeric'); let $b = convertToTensor(b, 'b', 'equal', 'string_or_numeric'); [$a, $b] = makeTypesMatch($a, $b); assertAndGetBroadcastShape($a.shape, $b.shape); const inputs = { a: $a, b: $b }; return ENGINE.runKernel(Equal, inputs); } const equal = /* @__PURE__ */ op({ equal_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns the elements, either `a` or `b` depending on the `condition`. * * If the condition is true, select from `a`, otherwise select from `b`. * * ```js * const cond = tf.tensor1d([false, false, true], 'bool'); * const a = tf.tensor1d([1 , 2, 3]); * const b = tf.tensor1d([-1, -2, -3]); * * a.where(cond, b).print(); * ``` * * @param condition The input condition. Must be of dtype bool. * @param a If `condition` is rank 1, `a` may have a higher rank but * its first dimension must match the size of `condition`. * @param b A tensor with the same dtype as `a` and with shape that is * compatible with `a`. * @return A tensor with same dtype as `a` and `b`, and shape that is * broadcastable from `a` and `b`. * * @doc {heading: 'Operations', subheading: 'Logical'} */ function where_(condition, a, b) { const $a = convertToTensor(a, 'a', 'where'); const $b = convertToTensor(b, 'b', 'where'); const $condition = convertToTensor(condition, 'condition', 'where', 'bool'); // TODO: move this logic to forward function when the broadcastTo op is // implemented in WASM. // Find the broadcastable shape for $condition, $a, and $b. const broadcastShape = assertAndGetBroadcastShape(assertAndGetBroadcastShape($condition.shape, $a.shape), $b.shape); const $broadcastedCondition = broadcastTo($condition, broadcastShape); const $broadcastedA = broadcastTo($a, broadcastShape); const $broadcastedB = broadcastTo($b, broadcastShape); const inputs = { condition: $broadcastedCondition, t: $broadcastedA, e: $broadcastedB }; return ENGINE.runKernel(Select, inputs); } const where = /* @__PURE__ */ op({ where_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates a `tf.Tensor` with all elements set to 0 with the same shape as the * given tensor. * * ```js * const x = tf.tensor([1, 2]); * tf.zerosLike(x).print(); * ``` * * @param x The tensor of required shape. * * @doc {heading: 'Tensors', subheading: 'Creation'} */ function zerosLike_(x) { const $x = convertToTensor(x, 'x', 'zerosLike'); const inputs = { x: $x }; return ENGINE.runKernel(ZerosLike, inputs); } const zerosLike = /* @__PURE__ */ op({ zerosLike_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. Return 0 * if denominator is 0. * * * ```js * const a = tf.tensor1d([1, 4, 9, 16]); * const b = tf.tensor1d([1, 2, 3, 4]); * const c = tf.tensor1d([0, 0, 0, 0]); * * a.divNoNan(b).print(); // or tf.divNoNan(a, b) * a.divNoNan(c).print(); // or tf.divNoNan(a, c) * ``` * * ```js * // Broadcast div a with b. * const a = tf.tensor1d([2, 4, 6, 8]); * const b = tf.scalar(2); * const c = tf.scalar(0); * * a.divNoNan(b).print(); // or tf.divNoNan(a, b) * a.divNoNan(c).print(); // or tf.divNoNan(a, c) * ``` * * @param a The first tensor as the numerator. * @param b The second tensor as the denominator. Must have the same dtype as * `a`. * * @doc {heading: 'Operations', subheading: 'Arithmetic'} */ function divNoNan_(a, b) { // TODO: Make this into its own kernel. let $a = convertToTensor(a, 'a', 'div'); let $b = convertToTensor(b, 'b', 'div'); [$a, $b] = makeTypesMatch($a, $b); const divResult = div($a, $b); const zeros = zerosLike(divResult); const bEqualsZero = equal($b, zeros); return where(bEqualsZero, zeros, divResult); } const divNoNan = /* @__PURE__ */ op({ divNoNan_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the dot product of two matrices and/or vectors, `t1` and `t2`. * * ```js * const a = tf.tensor1d([1, 2]); * const b = tf.tensor2d([[1, 2], [3, 4]]); * const c = tf.tensor2d([[1, 2, 3], [4, 5, 6]]); * * a.dot(b).print(); // or tf.dot(a, b) * b.dot(a).print(); * b.dot(c).print(); * ``` * @param t1 The first tensor in the dot operation. * @param t2 The second tensor in the dot operation. * * @doc {heading: 'Operations', subheading: 'Matrices'} */ function dot_(t1, t2) { const $t1 = convertToTensor(t1, 't1', 'dot'); const $t2 = convertToTensor(t2, 't2', 'dot'); assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ` + `${$t1.rank} and ${$t2.rank}.`); const t1Inner = ($t1.rank === 1 ? $t1.size : $t1.shape[1]); const t2Inner = ($t2.rank === 1 ? $t2.size : $t2.shape[0]); assert(t1Inner === t2Inner, () => `Error in dot: inner dimensions of inputs must match, but got ` + `${t1Inner} and ${t2Inner}.`); if ($t1.rank === 1 && $t2.rank === 1) { const t12D = reshape($t1, [1, -1]); const t22D = reshape($t2, [-1, 1]); const t1t2 = matMul$1(t12D, t22D); return reshape(t1t2, []); } else if ($t1.rank === 1 && $t2.rank === 2) { const t12D = reshape($t1, [1, -1]); const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]); const t1t2 = matMul$1(t12D, t22D); return reshape(t1t2, [t1t2.size]); } else if ($t1.rank === 2 && $t2.rank === 1) { const t22D = reshape($t2, [-1, 1]); const t1t2 = matMul$1($t1, t22D); return reshape(t1t2, [t1t2.size]); } else { const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]); const t1t2 = matMul$1($t1, t22D); return t1t2; } } const dot = /* @__PURE__ */ op({ dot_ }); /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Tensor contraction over specified indices and outer product. * * `einsum` allows defining Tensors by defining their element-wise computation. * This computation is based on * [Einstein summation](https://en.wikipedia.org/wiki/Einstein_notation). * * Some special cases include: * * Matrix multiplication: * ```js * const x = tf.tensor2d([[1, 2, 3], [4, 5, 6]]); * const y = tf.tensor2d([[0, 1], [2, 3], [4, 5]]); * x.print(); * y.print(); * tf.einsum('ij,jk->ik', x, y).print(); * ``` * * Dot product: * ```js * const x = tf.tensor1d([1, 2, 3]); * const y = tf.tensor1d([0, 1, 2]); * x.print(); * y.print(); * tf.einsum('i,i->', x, y).print(); * ``` * * Batch dot product: * ```js * const x = tf.tensor2d([[1, 2, 3], [4, 5, 6]]); * const y = tf.tensor2d([[0, 1, 2], [3, 4, 5]]); * x.print(); * y.print(); * tf.einsum('bi,bi->b', x, y).print(); * ``` * * Outer prouduct: * ```js * const x = tf.tensor1d([1, 3, 5]); * const y = tf.tensor1d([2, 4, 6]); * x.print(); * y.print(); * tf.einsum('i,j->ij', x, y).print(); * ``` * * Matrix transpose: * ```js * const x = tf.tensor2d([[1, 2], [3, 4]]); * x.print(); * tf.einsum('ij->ji', x).print(); * ``` * * Batch matrix transpose: * ```js * const x = tf.tensor3d([[[1, 2], [3, 4]], [[-1, -2], [-3, -4]]]); * x.print(); * tf.einsum('bij->bji', x).print(); * ``` * * Limitations: * * This implementation of einsum has the following limitations: * * - Does not support >2 input tensors. * - Does not support duplicate axes for any given input tensor. E.g., equation * 'ii->' is not supported. * - The `...` notation is not supported. * * @param equation a string describing the contraction, in the same format as * [numpy.einsum](https://numpy.org/doc/stable/reference/generated/numpy.einsum.html). * @param tensors the input(s) to contract (each one a Tensor), whose shapes * should be consistent with equation. * @returns The output tensor. * * @doc {heading: 'Tensors', subheading: 'Matrices'} */ function einsum_(equation, ...tensors) { const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'einsum')); const attrs = { equation }; return ENGINE.runKernel(Einsum, $tensors, attrs); } const einsum = /* @__PURE__ */ op({ einsum_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes exponential linear element-wise: `x > 0 ? x : (e ^ x) - 1`. * * ```js * const x = tf.tensor1d([-1, 1, -3, 2]); * * x.elu().print(); // or tf.elu(x) * ``` * @param x The input tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function elu_(x) { const $x = convertToTensor(x, 'x', 'elu', 'float32'); const inputs = { x: $x }; return ENGINE.runKernel(Elu, inputs); } const elu = /* @__PURE__ */ op({ elu_ }); /** * @license * Copyright 2023 Google LLC. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Checks the input tensor mathes the given shape. * * Given an input tensor, returns a new tensor with the same values as the * input tensor with shape `shape`. * * The method supports the null value in tensor. It will still check the shapes, * and null is a placeholder. * * * ```js * const x = tf.tensor1d([1, 2, 3, 4]); * const y = tf.tensor1d([1, null, 3, 4]); * const z = tf.tensor2d([1, 2, 3, 4], [2,2]); * tf.ensureShape(x, [4]).print(); * tf.ensureShape(y, [4]).print(); * tf.ensureShape(z, [null, 2]).print(); * ``` * * @param x The input tensor to be ensured. * @param shape A TensorShape representing the shape of this tensor, an array * or null. * * @doc {heading: 'Tensors', subheading: 'Transformations'} */ function ensureShape_(x, shape) { const $x = convertToTensor(x, 'x', 'ensureShape', 'string_or_numeric'); if (!arraysEqualWithNull($x.shape, shape)) { throw new Error(`EnsureShape: Shape of tensor ${$x.shape} is not compatible with expected shape ${shape}`); } return x; } const ensureShape = /* @__PURE__ */ op({ ensureShape_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes Gauss error function of the input `tf.Tensor` element-wise: * `erf(x)` * * ```js * const x = tf.tensor1d([0, .1, -.1, .7]); * * x.erf().print(); // or tf.erf(x); * ``` * @param x The input tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function erf_(x) { let $x = convertToTensor(x, 'x', 'erf'); assert($x.dtype === 'int32' || $x.dtype === 'float32', () => 'Input dtype must be `int32` or `float32`.'); if ($x.dtype === 'int32') { $x = cast($x, 'float32'); } const inputs = { x: $x }; return ENGINE.runKernel(Erf, inputs); } const erf = /* @__PURE__ */ op({ erf_ }); /** * @license * Copyright 2017 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ function combineLocations(outputLoc, reduceLoc, axes) { const rank = outputLoc.length + reduceLoc.length; const loc = []; let outIdx = 0; let reduceIdx = 0; for (let dim = 0; dim < rank; dim++) { if (axes.indexOf(dim) === -1) { loc.push(outputLoc[outIdx++]); } else { loc.push(reduceLoc[reduceIdx++]); } } return loc; } function expandShapeToKeepDim(shape, axes) { const reduceSubShape = axes.map(x => 1); return combineLocations(shape, reduceSubShape, axes); } /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the maximum of elements across dimensions of a `tf.Tensor`. * * Reduces the input along the dimensions given in `axes`. Unless `keepDims` * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in * `axes`. If `keepDims` is true, the reduced dimensions are retained with * length 1. If `axes` has no entries, all dimensions are reduced, and a * `tf.Tensor` with a single element is returned. * * ```js * const x = tf.tensor1d([1, 2, 3]); * * x.max().print(); // or tf.max(x) * ``` * * ```js * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); * * const axis = 1; * x.max(axis).print(); // or tf.max(x, axis) * ``` * * @param x The input tensor. * @param axis The dimension(s) to reduce. By default it reduces * all dimensions. * @param keepDims If true, retains reduced dimensions with size 1. * * @doc {heading: 'Operations', subheading: 'Reduction'} */ function max_(x, axis = null, keepDims = false) { const $x = convertToTensor(x, 'x', 'max'); const inputs = { x: $x }; const attrs = { reductionIndices: axis, keepDims }; return ENGINE.runKernel(Max, inputs, attrs); } const max = /* @__PURE__ */ op({ max_ }); /** * @license * Copyright 2020 Google Inc. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the minimum value from the input. * * Reduces the input along the dimensions given in `axes`. Unless `keepDims` * is true, the rank of the array is reduced by 1 for each entry in `axes`. * If `keepDims` is true, the reduced dimensions are retained with length 1. * If `axes` has no entries, all dimensions are reduced, and an array with a * single element is returned. * * ```js * const x = tf.tensor1d([1, 2, 3]); * * x.min().print(); // or tf.min(x) * ``` * * ```js * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); * * const axis = 1; * x.min(axis).print(); // or tf.min(x, axis) * ``` * * @param x The input Tensor. * @param axis The dimension(s) to reduce. By default it reduces * all dimensions. * @param keepDims If true, retains reduced dimensions with size 1. * * @doc {heading: 'Operations', subheading: 'Reduction'} */ function min_(x, axis = null, keepDims = false) { const $x = convertToTensor(x, 'x', 'min'); const inputs = { x: $x }; const attrs = { axis, keepDims }; // tslint:disable-next-line: no-unnecessary-type-assertion return ENGINE.runKernel(Min, inputs, attrs); } const min = /* @__PURE__ */ op({ min_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the power of one `tf.Tensor` to another. Supports broadcasting. * * Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for * corresponding elements in x and y. The result's dtype will be the upcasted * type of the `base` and `exp` dtypes. * * ```js * const a = tf.tensor([[2, 3], [4, 5]]) * const b = tf.tensor([[1, 2], [3, 0]]).toInt(); * * a.pow(b).print(); // or tf.pow(a, b) * ``` * * ```js * const a = tf.tensor([[1, 2], [3, 4]]) * const b = tf.tensor(2).toInt(); * * a.pow(b).print(); // or tf.pow(a, b) * ``` * We also expose `powStrict` which has the same signature as this op and * asserts that `base` and `exp` are the same shape (does not broadcast). * * @param base The base `tf.Tensor` to pow element-wise. * @param exp The exponent `tf.Tensor` to pow element-wise. * * @doc {heading: 'Operations', subheading: 'Arithmetic'} */ function pow_(base, exp) { let $base = convertToTensor(base, 'base', 'pow'); let $exp = convertToTensor(exp, 'exp', 'pow'); [$base, $exp] = makeTypesMatch($base, $exp); const inputs = { a: $base, b: $exp }; return ENGINE.runKernel(Pow, inputs); } const pow = /* @__PURE__ */ op({ pow_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** This is shared code across all tensor creation methods. */ function makeTensor(values, shape, inferredShape, dtype) { if (dtype == null) { dtype = inferDtype(values); } else if (dtype === 'complex64') { throw new Error(`Cannot construct a complex64 tensor directly. ` + `Please use tf.complex(real, imag).`); } if (isWebGPUData(values) || isWebGLData(values)) { if (dtype !== 'float32' && dtype !== 'int32') { throw new Error(`Creating tensor from GPU data only supports ` + `'float32'|'int32' dtype, while the dtype is ${dtype}.`); } return ENGINE.backend.createTensorFromGPUData(values, shape || inferredShape, dtype); } if (!isTypedArray(values) && !Array.isArray(values) && typeof values !== 'number' && typeof values !== 'boolean' && typeof values !== 'string') { throw new Error('values passed to tensor(values) must be a number/boolean/string or ' + 'an array of numbers/booleans/strings, or a TypedArray'); } // Verify that the shape matches the inferred shape. if (shape != null) { assertNonNegativeIntegerDimensions(shape); const providedSize = sizeFromShape(shape); const inferredSize = sizeFromShape(inferredShape); assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ` + `${providedSize} values but has ${inferredSize}`); for (let i = 0; i < inferredShape.length; ++i) { const inferred = inferredShape[i]; const flatDimsDontMatch = i === inferredShape.length - 1 ? inferred !== sizeFromShape(shape.slice(i)) : true; assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape ` + `(${inferredShape}) does not match the provided ` + `shape (${shape}). `); } } if (!isTypedArray(values) && !Array.isArray(values)) { values = [values]; } shape = shape || inferredShape; values = dtype !== 'string' ? toTypedArray(values, dtype) : flatten(values, [], true); return ENGINE.makeTensor(values, shape, dtype); } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates rank-0 `tf.Tensor` (scalar) with the provided value and dtype. * * The same functionality can be achieved with `tf.tensor`, but in general * we recommend using `tf.scalar` as it makes the code more readable. * * ```js * tf.scalar(3.14).print(); * ``` * * @param value The value of the scalar. * @param dtype The data type. * * @doc {heading: 'Tensors', subheading: 'Creation'} */ function scalar(value, dtype) { if (((isTypedArray(value) && dtype !== 'string') || Array.isArray(value)) && dtype !== 'complex64') { throw new Error('Error creating a new Scalar: value must be a primitive ' + '(number|boolean|string)'); } if (dtype === 'string' && isTypedArray(value) && !(value instanceof Uint8Array)) { throw new Error('When making a scalar from encoded string, ' + 'the value must be `Uint8Array`.'); } const shape = []; const inferredShape = []; return makeTensor(value, shape, inferredShape, dtype); } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes square root of the input `tf.Tensor` element-wise: `y = sqrt(x)` * * ```js * const x = tf.tensor1d([1, 2, 4, -1]); * * x.sqrt().print(); // or tf.sqrt(x) * ``` * @param x The input tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function sqrt_(x) { const $x = convertToTensor(x, 'x', 'sqrt', 'float32'); const inputs = { x: $x }; return ENGINE.runKernel(Sqrt, inputs); } const sqrt = /* @__PURE__ */ op({ sqrt_ }); /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes square of `x` element-wise: `x ^ 2` * * ```js * const x = tf.tensor1d([1, 2, Math.sqrt(2), -1]); * * x.square().print(); // or tf.square(x) * ``` * @param x The input Tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function square_(x) { const $x = convertToTensor(x, 'x', 'square'); const attrs = {}; return ENGINE.runKernel('Square', { x: $x }, attrs); } const square = /* @__PURE__ */ op({ square_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the sum of elements across dimensions of a `tf.Tensor`. * * Reduces the input along the dimensions given in `axes`. Unless `keepDims` * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in * `axes`. If `keepDims` is true, the reduced dimensions are retained with * length 1. If axes has no entries, all dimensions are reduced, and a * `tf.Tensor` with a single element is returned. * * ```js * const x = tf.tensor1d([1, 2, 3]); * * x.sum().print(); // or tf.sum(x) * ``` * * ```js * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); * * const axis = 1; * x.sum(axis).print(); // or tf.sum(x, axis) * ``` * * @param x The input tensor to compute the sum over. If the dtype is `bool` * it will be converted to `int32` and the output dtype will be `int32`. * @param axis The dimension(s) to reduce. By default it reduces * all dimensions. * @param keepDims If true, retains reduced dimensions with size 1. * * @doc {heading: 'Operations', subheading: 'Reduction'} */ function sum_(x, axis = null, keepDims = false) { let $x = convertToTensor(x, 'x', 'sum'); if ($x.dtype === 'bool') { $x = cast($x, 'int32'); } const inputs = { x: $x }; const attrs = { axis, keepDims }; return ENGINE.runKernel(Sum, inputs, attrs); } const sum = /* @__PURE__ */ op({ sum_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the norm of scalar, vectors, and matrices. * This function can compute several different vector norms (the 1-norm, the * Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0) * and matrix norms (Frobenius, 1-norm, and inf-norm). * * ```js * const x = tf.tensor1d([1, 2, 3, 4]); * * x.norm().print(); // or tf.norm(x) * ``` * * @param x The input array. * @param ord Optional. Order of the norm. Supported norm types are * following: * * | ord | norm for matrices | norm for vectors * |------------|---------------------------|--------------------- * |'euclidean' |Frobenius norm |2-norm * |'fro' |Frobenius norm | * |Infinity |max(sum(abs(x), axis=1)) |max(abs(x)) * |-Infinity |min(sum(abs(x), axis=1)) |min(abs(x)) * |1 |max(sum(abs(x), axis=0)) |sum(abs(x)) * |2 | |sum(abs(x)^2)^(1/2) * * @param axis Optional. If axis is null (the default), the input is * considered a vector and a single vector norm is computed over the entire * set of values in the Tensor, i.e. norm(x, ord) is equivalent * to norm(x.reshape([-1]), ord). If axis is an integer, the input * is considered a batch of vectors, and axis determines the axis in x * over which to compute vector norms. If axis is a 2-tuple of integer it is * considered a batch of matrices and axis determines the axes in NDArray * over which to compute a matrix norm. * @param keepDims Optional. If true, the norm has the same dimensionality * as the input. * * @doc {heading: 'Operations', subheading: 'Matrices'} */ function norm_(x, ord = 'euclidean', axis = null, keepDims = false) { x = convertToTensor(x, 'x', 'norm'); const norm = normImpl(x, ord, axis); let keepDimsShape = norm.shape; if (keepDims) { const axes = parseAxisParam(axis, x.shape); keepDimsShape = expandShapeToKeepDim(norm.shape, axes); } return reshape(norm, keepDimsShape); } function normImpl(x, p, axis = null) { if (x.rank === 0) { return abs(x); } // consider vector when no axis is specified if (x.rank !== 1 && axis === null) { return normImpl(reshape(x, [-1]), p, axis); } // vector if (x.rank === 1 || typeof axis === 'number' || Array.isArray(axis) && axis.length === 1) { if (p === 1) { return sum(abs(x), axis); } if (p === Infinity) { return max(abs(x), axis); } if (p === -Infinity) { return min(abs(x), axis); } if (p === 'euclidean' || p === 2) { // norm(x, 2) = sum(abs(xi) ^ 2) ^ 1/2 return sqrt(sum(pow(abs(x), scalar(2, 'int32')), axis)); } throw new Error(`Error in norm: invalid ord value: ${p}`); } // matrix (assumption axis[0] < axis[1]) if (Array.isArray(axis) && axis.length === 2) { if (p === 1) { return max(sum(abs(x), axis[0]), axis[1] - 1); } if (p === Infinity) { return max(sum(abs(x), axis[1]), axis[0]); } if (p === -Infinity) { return min(sum(abs(x), axis[1]), axis[0]); } if (p === 'fro' || p === 'euclidean') { // norm(x) = sqrt(sum(pow(x, 2))) return sqrt(sum(square(x), axis)); } throw new Error(`Error in norm: invalid ord value: ${p}`); } throw new Error(`Error in norm: invalid axis: ${axis}`); } const norm = /* @__PURE__ */ op({ norm_ }); /** * @license * Copyright 2022 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the Euclidean norm of scalar, vectors, and matrices. * * ```js * const x = tf.tensor1d([1, 2, 3, 4]); * * x.euclideanNorm().print(); // or tf.euclideanNorm(x) * ``` * * @param x The input array. * @param axis Optional. If axis is null (the default), the input is * considered a vector and a single vector norm is computed over the entire * set of values in the Tensor, i.e. euclideanNorm(x) is equivalent * to euclideanNorm(x.reshape([-1])). If axis is an integer, the input * is considered a batch of vectors, and axis determines the axis in x * over which to compute vector norms. If axis is a 2-tuple of integer it is * considered a batch of matrices and axis determines the axes in NDArray * over which to compute a matrix norm. * @param keepDims Optional. If true, the norm has the same dimensionality * as the input. * * @doc {heading: 'Operations', subheading: 'Matrices'} */ function euclideanNorm_(x, axis = null, keepDims = false) { return norm(x, 'euclidean', axis, keepDims); } const euclideanNorm = /* @__PURE__ */ op({ euclideanNorm_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes exponential of the input `tf.Tensor` element-wise. `e ^ x` * * ```js * const x = tf.tensor1d([1, 2, -3]); * * x.exp().print(); // or tf.exp(x) * ``` * @param x The input tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function exp_(x) { const $x = convertToTensor(x, 'x', 'exp'); const inputs = { x: $x }; return ENGINE.runKernel(Exp, inputs); } const exp = /* @__PURE__ */ op({ exp_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension * into the tensor's shape. * * ```js * const x = tf.tensor1d([1, 2, 3, 4]); * const axis = 1; * x.expandDims(axis).print(); * ``` * * @param x The input tensor whose dimensions are to be expanded. * @param axis The dimension index at which to insert shape of `1`. Defaults * to 0 (the first dimension). * * @doc {heading: 'Tensors', subheading: 'Transformations'} */ function expandDims_(x, axis = 0) { const $x = convertToTensor(x, 'x', 'expandDims', 'string_or_numeric'); assert(axis <= $x.rank, () => 'Axis must be <= rank of the tensor'); const inputs = { input: $x }; const attrs = { dim: axis }; return ENGINE.runKernel(ExpandDims, inputs, attrs); } const expandDims = /* @__PURE__ */ op({ expandDims_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes exponential of the input `tf.Tensor` minus one element-wise. * `e ^ x - 1` * * ```js * const x = tf.tensor1d([1, 2, -3]); * * x.expm1().print(); // or tf.expm1(x) * ``` * @param x The input tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function expm1_(x) { const $x = convertToTensor(x, 'x', 'expm1'); const inputs = { x: $x }; return ENGINE.runKernel(Expm1, inputs); } const expm1 = /* @__PURE__ */ op({ expm1_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Construct a tensor by repeating it the number of times given by reps. * * This operation creates a new tensor by replicating `input` `reps` * times. The output tensor's `i`th dimension has `input.shape[i] * * reps[i]` elements, and the values of `input` are replicated * `reps[i]` times along the `i`th dimension. For example, tiling * `[a, b, c, d]` by `[2]` produces `[a, b, c, d, a, b, c, d]`. * * ```js * const a = tf.tensor1d([1, 2]); * * a.tile([2]).print(); // or tf.tile(a, [2]) * ``` * * ```js * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]); * * a.tile([1, 2]).print(); // or tf.tile(a, [1,2]) * ``` * @param x The tensor to tile. * @param reps Determines the number of replications per dimension. * * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */ function tile_(x, reps) { const $x = convertToTensor(x, 'x', 'tile', 'string_or_numeric'); assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} ` + `must match length of reps ${reps}.`); const inputs = { x: $x }; const attrs = { reps }; return ENGINE.runKernel(Tile, inputs, attrs); } const tile = /* @__PURE__ */ op({ tile_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Create an identity matrix. * * @param numRows Number of rows. * @param numColumns Number of columns. Defaults to `numRows`. * @param batchShape If provided, will add the batch shape to the beginning * of the shape of the returned `tf.Tensor` by repeating the identity * matrix. * @param dtype Data type. * @returns Identity matrix of the specified size and data type, possibly * with batch repetition if `batchShape` is specified. * * @doc {heading: 'Tensors', subheading: 'Creation'} */ function eye_(numRows, numColumns, batchShape, dtype = 'float32') { if (numColumns == null) { numColumns = numRows; } const buff = buffer([numRows, numColumns], dtype); const n = numRows <= numColumns ? numRows : numColumns; for (let i = 0; i < n; ++i) { buff.set(1, i, i); } const out = reshape(buff.toTensor(), [numRows, numColumns]); if (batchShape == null) { return out; } else { if (batchShape.length === 1) { return tile(expandDims(out, 0), [batchShape[0], 1, 1]); } else if (batchShape.length === 2) { // tslint:disable-next-line:no-unnecessary-type-assertion return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]); } else if (batchShape.length === 3) { // tslint:disable-next-line:no-unnecessary-type-assertion return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [ batchShape[0], batchShape[1], batchShape[2], 1, 1 ]); } else { throw new Error(`eye() currently supports only 1D and 2D ` + // tslint:disable-next-line:no-any `batchShapes, but received ${batchShape.length}D.`); } } } const eye = /* @__PURE__ */ op({ eye_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes floor of input `tf.Tensor` element-wise: `floor(x)`. * * ```js * const x = tf.tensor1d([.6, 1.1, -3.3]); * * x.floor().print(); // or tf.floor(x) * ``` * @param x The input tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function floor_(x) { const $x = convertToTensor(x, 'x', 'floor', 'float32'); const inputs = { x: $x }; return ENGINE.runKernel(Floor, inputs); } const floor = /* @__PURE__ */ op({ floor_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Gather slices from tensor `x`'s axis `axis` according to `indices`. * * ```js * const x = tf.tensor1d([1, 2, 3, 4]); * const indices = tf.tensor1d([1, 3, 3], 'int32'); * * x.gather(indices).print(); * ``` * * ```js * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); * const indices = tf.tensor1d([1, 1, 0], 'int32'); * * x.gather(indices).print(); * ``` * @param x The input tensor whose slices are to be gathered. * @param indices The indices of the values to extract. * @param axis The axis over which to select values. Defaults to 0. * @param batchDims Optional. The number of batch dimensions. It must be less * than or equal to rank(indices). Defaults to 0. * The output tensor will have shape of * `x.shape[:axis] + indices.shape[batchDims:] + x.shape[axis + 1:]` * * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */ function gather_(x, indices, axis = 0, batchDims = 0) { const $x = convertToTensor(x, 'x', 'gather'); const $indices = convertToTensor(indices, 'indices', 'gather', 'int32'); const inputs = { x: $x, indices: $indices }; const attrs = { axis, batchDims }; return ENGINE.runKernel(GatherV2, inputs, attrs); } const gather = /* @__PURE__ */ op({ gather_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns the truth value of (a > b) element-wise. Supports broadcasting. * * ```js * const a = tf.tensor1d([1, 2, 3]); * const b = tf.tensor1d([2, 2, 2]); * * a.greater(b).print(); * ``` * * @param a The first input tensor. * @param b The second input tensor. Must have the same dtype as `a`. * * @doc {heading: 'Operations', subheading: 'Logical'} */ function greater_(a, b) { let $a = convertToTensor(a, 'a', 'greater', 'string_or_numeric'); let $b = convertToTensor(b, 'b', 'greater', 'string_or_numeric'); [$a, $b] = makeTypesMatch($a, $b); assertAndGetBroadcastShape($a.shape, $b.shape); const inputs = { a: $a, b: $b }; return ENGINE.runKernel(Greater, inputs); } const greater = /* @__PURE__ */ op({ greater_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns the truth value of (a >= b) element-wise. Supports broadcasting. * * ```js * const a = tf.tensor1d([1, 2, 3]); * const b = tf.tensor1d([2, 2, 2]); * * a.greaterEqual(b).print(); * ``` * * @param a The first input tensor. * @param b The second input tensor. Must have the same dtype as `a`. * * @doc {heading: 'Operations', subheading: 'Logical'} */ function greaterEqual_(a, b) { let $a = convertToTensor(a, 'a', 'greaterEqual', 'string_or_numeric'); let $b = convertToTensor(b, 'b', 'greaterEqual', 'string_or_numeric'); [$a, $b] = makeTypesMatch($a, $b); assertAndGetBroadcastShape($a.shape, $b.shape); const inputs = { a: $a, b: $b }; return ENGINE.runKernel(GreaterEqual, inputs); } const greaterEqual = /* @__PURE__ */ op({ greaterEqual_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns the imaginary part of a complex (or real) tensor. * * Given a tensor input, this operation returns a tensor of type float that is * the imaginary part of each element in input considered as a complex number. * If input is real, a tensor of all zeros is returned. * * ```js * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]); * tf.imag(x).print(); * ``` * * @doc {heading: 'Tensors', subheading: 'Creation'} */ function imag_(input) { const $input = convertToTensor(input, 'input', 'imag'); const inputs = { input: $input }; return ENGINE.runKernel(Imag, inputs); } const imag = /* @__PURE__ */ op({ imag_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns which elements of x are finite. * * ```js * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]); * * x.isFinite().print(); // or tf.isNaN(x) * ``` * @param x The input Tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function isFinite_(x) { const $x = convertToTensor(x, 'x', 'isFinite'); const inputs = { x: $x }; return ENGINE.runKernel(IsFinite, inputs); } const isFinite$1 = /* @__PURE__ */ op({ isFinite_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns which elements of x are Infinity or -Infinity. * * ```js * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]); * * x.isInf().print(); // or tf.isNaN(x) * ``` * @param x The input Tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function isInf_(x) { const $x = convertToTensor(x, 'x', 'isInf'); const inputs = { x: $x }; return ENGINE.runKernel(IsInf, inputs); } const isInf = /* @__PURE__ */ op({ isInf_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns which elements of x are NaN. * * ```js * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]); * * x.isNaN().print(); // or tf.isNaN(x) * ``` * @param x The input Tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function isNaN_(x) { const $x = convertToTensor(x, 'x', 'isNaN'); const inputs = { x: $x }; return ENGINE.runKernel(IsNan, inputs); } const isNaN$1 = /* @__PURE__ */ op({ isNaN_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes leaky rectified linear element-wise. * * See * [http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf]( * http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf) * * ```js * const x = tf.tensor1d([-1, 2, -3, 4]); * * x.leakyRelu(0.1).print(); // or tf.leakyRelu(x, 0.1) * ``` * @param x The input tensor. * @param alpha The scaling factor for negative values, defaults to 0.2. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function leakyRelu_(x, alpha = 0.2) { const $x = convertToTensor(x, 'x', 'leakyRelu'); const inputs = { x: $x }; const attrs = { alpha }; return ENGINE.runKernel(LeakyRelu, inputs, attrs); } const leakyRelu = /* @__PURE__ */ op({ leakyRelu_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns the truth value of (a < b) element-wise. Supports broadcasting. * * ```js * const a = tf.tensor1d([1, 2, 3]); * const b = tf.tensor1d([2, 2, 2]); * * a.less(b).print(); * ``` * @param a The first input tensor. * @param b The second input tensor. Must have the same dtype as `a`. * * @doc {heading: 'Operations', subheading: 'Logical'} */ function less_(a, b) { let $a = convertToTensor(a, 'a', 'less', 'string_or_numeric'); let $b = convertToTensor(b, 'b', 'less', 'string_or_numeric'); [$a, $b] = makeTypesMatch($a, $b); assertAndGetBroadcastShape($a.shape, $b.shape); const inputs = { a: $a, b: $b }; return ENGINE.runKernel(Less, inputs); } const less = /* @__PURE__ */ op({ less_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns the truth value of (a <= b) element-wise. Supports broadcasting. * * ```js * const a = tf.tensor1d([1, 2, 3]); * const b = tf.tensor1d([2, 2, 2]); * * a.lessEqual(b).print(); * ``` * * @param a The first input tensor. * @param b The second input tensor. Must have the same dtype as `a`. * * @doc {heading: 'Operations', subheading: 'Logical'} */ function lessEqual_(a, b) { let $a = convertToTensor(a, 'a', 'lessEqual', 'string_or_numeric'); let $b = convertToTensor(b, 'b', 'lessEqual', 'string_or_numeric'); [$a, $b] = makeTypesMatch($a, $b); assertAndGetBroadcastShape($a.shape, $b.shape); const inputs = { a: $a, b: $b }; return ENGINE.runKernel(LessEqual, inputs); } const lessEqual = /* @__PURE__ */ op({ lessEqual_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Return an evenly spaced sequence of numbers over the given interval. * * ```js * tf.linspace(0, 9, 10).print(); * ``` * @param start The start value of the sequence. * @param stop The end value of the sequence. * @param num The number of values to generate. * * @doc {heading: 'Tensors', subheading: 'Creation'} */ function linspace(start, stop, num) { if (num <= 0) { throw new Error('The number of values should be positive.'); } const attrs = { start, stop, num }; return ENGINE.runKernel(LinSpace, {}, attrs); } /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Normalizes the activation of a local neighborhood across or within * channels. * * @param x The input tensor. The 4-D input tensor is treated as a 3-D array * of 1D vectors (along the last dimension), and each vector is * normalized independently. * @param depthRadius The number of adjacent channels in the 1D normalization * window. * @param bias A constant bias term for the basis. * @param alpha A scale factor, usually positive. * @param beta An exponent. * * @doc {heading: 'Operations', subheading: 'Normalization'} */ function localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) { const $x = convertToTensor(x, 'x', 'localResponseNormalization'); assert($x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got rank ${$x.rank}.`); assert(isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an ` + `integer but got depthRadius ${depthRadius}.`); let x4D = $x; let reshapedTo4D = false; if ($x.rank === 3) { reshapedTo4D = true; x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); } const inputs = { x: x4D }; const attrs = { depthRadius, bias, alpha, beta }; // tslint:disable-next-line: no-unnecessary-type-assertion const res = ENGINE.runKernel(LRN, inputs, attrs); if (reshapedTo4D) { return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); } else { return res; } } const localResponseNormalization = /* @__PURE__ */ op({ localResponseNormalization_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes natural logarithm of the input `tf.Tensor` element-wise: `ln(x)` * * ```js * const x = tf.tensor1d([1, 2, Math.E]); * * x.log().print(); // or tf.log(x) * ``` * @param x The input tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function log_(x) { const $x = convertToTensor(x, 'x', 'log', 'float32'); const inputs = { x: $x }; return ENGINE.runKernel(Log, inputs); } const log = /* @__PURE__ */ op({ log_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes natural logarithm of the input `tf.Tensor` plus one * element-wise: `ln(1 + x)` * * ```js * const x = tf.tensor1d([1, 2, Math.E - 1]); * * x.log1p().print(); // or tf.log1p(x) * ``` * @param x The input tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function log1p_(x) { const $x = convertToTensor(x, 'x', 'log1p'); const inputs = { x: $x }; return ENGINE.runKernel(Log1p, inputs); } const log1p = /* @__PURE__ */ op({ log1p_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Overrides the gradient computation of a function `f`. * * Takes a function * `f(...inputs, save) => {value: Tensor, gradFunc: (dy, saved) => Tensor[]}` * and returns another function `g(...inputs)` which takes the same inputs as * `f`. When called, `g` returns `f().value`. In backward mode, custom gradients * with respect to each input of `f` are computed using `f().gradFunc`. * * The `save` function passed to `f` should be used for saving tensors needed * in the gradient. And the `saved` passed to the `gradFunc` is a * `NamedTensorMap`, which contains those saved tensors. * * ```js * const customOp = tf.customGrad((x, save) => { * // Save x to make sure it's available later for the gradient. * save([x]); * // Override gradient of our custom x ^ 2 op to be dy * abs(x); * return { * value: x.square(), * // Note `saved.x` which points to the `x` we saved earlier. * gradFunc: (dy, saved) => [dy.mul(saved[0].abs())] * }; * }); * * const x = tf.tensor1d([-1, -2, 3]); * const dx = tf.grad(x => customOp(x)); * * console.log(`f(x):`); * customOp(x).print(); * console.log(`f'(x):`); * dx(x).print(); * ``` * * @param f The function to evaluate in forward mode, which should return * `{value: Tensor, gradFunc: (dy, saved) => Tensor[]}`, where `gradFunc` * returns the custom gradients of `f` with respect to its inputs. * * @doc {heading: 'Training', subheading: 'Gradients'} */ function customGrad(f) { return ENGINE.customGrad(f); } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes `-1 * x` element-wise. * * ```js * const x = tf.tensor2d([1, 2, -2, 0], [2, 2]); * * x.neg().print(); // or tf.neg(x) * ``` * * @param x The input tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function neg_(x) { const $x = convertToTensor(x, 'x', 'neg'); const inputs = { x: $x }; return ENGINE.runKernel(Neg, inputs); } const neg = /* @__PURE__ */ op({ neg_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes softplus of the input `tf.Tensor` element-wise: `log(exp(x) + 1)` * * ```js * const x = tf.tensor1d([0, 1, -1, .7]); * * x.softplus().print(); // or tf.softplus(x) * ``` * @param x The input tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function softplus_(x) { const $x = convertToTensor(x, 'x', 'softplus'); const inputs = { x: $x }; return ENGINE.runKernel(Softplus, inputs); } const softplus = /* @__PURE__ */ op({ softplus_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes log sigmoid of the input `tf.Tensor` element-wise: * `logSigmoid(x)`. For numerical stability, we use `-tf.softplus(-x)`. * * ```js * const x = tf.tensor1d([0, 1, -1, .7]); * * x.logSigmoid().print(); // or tf.logSigmoid(x) * ``` * @param x The input tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function logSigmoid_(x) { const $x = convertToTensor(x, 'x', 'logSigmoid'); // Use a custom gradient to maintain previous implementation. // There is no LogSigmoid kernel in TF so we can't use engine.runKernel // directly const customOp = customGrad((x) => { // TODO(yassogba) we can remove the chained softplus call here only // after backends have modualrized softplus at which point we can call // engine runKernel(..., Sotfplus, ...) directly. const value = neg(softplus(neg(x))); const gradFunc = (dy) => { const derX = mul(dy, sigmoid(neg(x))); return derX; }; return { value, gradFunc }; }); return customOp($x); } const logSigmoid = /* @__PURE__ */ op({ logSigmoid_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Subtracts two `tf.Tensor`s element-wise, A - B. Supports broadcasting. * * ```js * const a = tf.tensor1d([10, 20, 30, 40]); * const b = tf.tensor1d([1, 2, 3, 4]); * * a.sub(b).print(); // or tf.sub(a, b) * ``` * * ```js * // Broadcast subtract a with b. * const a = tf.tensor1d([10, 20, 30, 40]); * const b = tf.scalar(5); * * a.sub(b).print(); // or tf.sub(a, b) * ``` * @param a The first `tf.Tensor` to subtract from. * @param b The second `tf.Tensor` to be subtracted. Must have the same dtype as * `a`. * * @doc {heading: 'Operations', subheading: 'Arithmetic'} */ function sub_(a, b) { let $a = convertToTensor(a, 'a', 'sub'); let $b = convertToTensor(b, 'b', 'sub'); [$a, $b] = makeTypesMatch($a, $b); const inputs = { a: $a, b: $b }; return ENGINE.runKernel(Sub, inputs); } const sub = /* @__PURE__ */ op({ sub_ }); /** * @license * Copyright 2020 Google Inc. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the log softmax. * * ```js * const a = tf.tensor1d([1, 2, 3]); * * a.logSoftmax().print(); // or tf.logSoftmax(a) * ``` * * ```js * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]); * * a.logSoftmax().print(); // or tf.logSoftmax(a) * ``` * * @param logits The logits array. * @param axis The dimension softmax would be performed on. Defaults to `-1` * which indicates the last dimension. * * @doc {heading: 'Operations', subheading: 'Normalization'} */ function logSoftmax_(logits, axis = -1) { const $logits = convertToTensor(logits, 'logits', 'logSoftmax'); if (axis === -1) { axis = $logits.rank - 1; } if (axis !== $logits.rank - 1) { throw Error('Log Softmax along a non-last dimension is not yet supported. ' + `Logits was rank ${$logits.rank} and axis was ${axis}`); } // const forward: ForwardFunc = (backend, save) => { // const keepDims = true; // const xMax = max(logits, axis, true); // const shifted = sub(logits, xMax); // const value = // sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis, // keepDims))); // save([value]); // return value; // }; // Use a custom gradient for numerical stability. const customOp = customGrad((logits, save) => { const keepDims = true; const xMax = max(logits, axis, true); const shifted = sub(logits, xMax); const value = sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis, keepDims))); save([value]); const gradFunc = (dy, saved) => { const [value] = saved; const keepDims = true; const softmax = exp(value); return sub(dy, mul(sum(dy, axis, keepDims), softmax)); }; return { value, gradFunc }; }); return customOp($logits); // TODO Use Engine.runKernel when CPU/WebGL/WASM backends implement this. // const inputs: LogSoftmaxInputs = {logits: $logits}; // const attrs: LogSoftmaxAttrs = {axis}; // return ENGINE.runKernel( // LogSoftmax, inputs as unknown as NamedTensorMap, // attrs as unknown as NamedAttrMap); } const logSoftmax = /* @__PURE__ */ op({ logSoftmax_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the log(sum(exp(elements across the reduction dimensions))). * * Reduces the input along the dimensions given in `axis`. Unless `keepDims` * is true, the rank of the array is reduced by 1 for each entry in `axis`. * If `keepDims` is true, the reduced dimensions are retained with length 1. * If `axis` has no entries, all dimensions are reduced, and an array with a * single element is returned. * * ```js * const x = tf.tensor1d([1, 2, 3]); * * x.logSumExp().print(); // or tf.logSumExp(x) * ``` * * ```js * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); * * const axis = 1; * x.logSumExp(axis).print(); // or tf.logSumExp(a, axis) * ``` * @param x The input tensor. * @param axis The dimension(s) to reduce. If null (the default), * reduces all dimensions. * @param keepDims If true, retains reduced dimensions with length * of 1. Defaults to false. * * @doc {heading: 'Operations', subheading: 'Reduction'} */ function logSumExp_(x, axis = null, keepDims = false) { const $x = convertToTensor(x, 'x', 'logSumExp'); const axes = parseAxisParam(axis, $x.shape); const xMax = max($x, axes, true /* keepDims */); const a = sub($x, xMax); const b = exp(a); const c = sum(b, axes); const d = log(c); const res = add(reshape(xMax, d.shape), d); if (keepDims) { const newShape = expandShapeToKeepDim(res.shape, axes); return reshape(res, newShape); } return res; } const logSumExp = /* @__PURE__ */ op({ logSumExp_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns the truth value of `a AND b` element-wise. Supports broadcasting. * * ```js * const a = tf.tensor1d([false, false, true, true], 'bool'); * const b = tf.tensor1d([false, true, false, true], 'bool'); * * a.logicalAnd(b).print(); * ``` * * @param a The first input tensor. Must be of dtype bool. * @param b The second input tensor. Must be of dtype bool. * * @doc {heading: 'Operations', subheading: 'Logical'} */ function logicalAnd_(a, b) { const $a = convertToTensor(a, 'a', 'logicalAnd', 'bool'); const $b = convertToTensor(b, 'b', 'logicalAnd', 'bool'); assertAndGetBroadcastShape($a.shape, $b.shape); const inputs = { a: $a, b: $b }; return ENGINE.runKernel(LogicalAnd, inputs); } const logicalAnd = /* @__PURE__ */ op({ logicalAnd_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns the truth value of `NOT x` element-wise. * * ```js * const a = tf.tensor1d([false, true], 'bool'); * * a.logicalNot().print(); * ``` * * @param x The input tensor. Must be of dtype 'bool'. * * @doc {heading: 'Operations', subheading: 'Logical'} */ function logicalNot_(x) { const $x = convertToTensor(x, 'x', 'logicalNot', 'bool'); const inputs = { x: $x }; return ENGINE.runKernel(LogicalNot, inputs); } const logicalNot = /* @__PURE__ */ op({ logicalNot_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns the truth value of `a OR b` element-wise. Supports broadcasting. * * ```js * const a = tf.tensor1d([false, false, true, true], 'bool'); * const b = tf.tensor1d([false, true, false, true], 'bool'); * * a.logicalOr(b).print(); * ``` * @param a The first input tensor. Must be of dtype bool. * @param b The second input tensor. Must be of dtype bool. * * @doc {heading: 'Operations', subheading: 'Logical'} */ function logicalOr_(a, b) { const $a = convertToTensor(a, 'a', 'logicalOr', 'bool'); const $b = convertToTensor(b, 'b', 'logicalOr', 'bool'); assertAndGetBroadcastShape($a.shape, $b.shape); const inputs = { a: $a, b: $b }; return ENGINE.runKernel(LogicalOr, inputs); } const logicalOr = /* @__PURE__ */ op({ logicalOr_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns the truth value of `a XOR b` element-wise. Supports broadcasting. * * ```js * const a = tf.tensor1d([false, false, true, true], 'bool'); * const b = tf.tensor1d([false, true, false, true], 'bool'); * * a.logicalXor(b).print(); * ``` * * @param a The first input tensor. Must be of dtype bool. * @param b The second input tensor. Must be of dtype bool. * * @doc {heading: 'Operations', subheading: 'Logical'} */ function logicalXor_(a, b) { const $a = convertToTensor(a, 'a', 'logicalXor', 'bool'); const $b = convertToTensor(b, 'b', 'logicalXor', 'bool'); assertAndGetBroadcastShape($a.shape, $b.shape); // x ^ y = (x | y) & ~(x & y) return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b))); } const logicalXor = /* @__PURE__ */ op({ logicalXor_ }); /** * @license * Copyright 2022 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const INT32_MAX = 2147483648; /** * Searches for where a value would go in a sorted sequence. * * This is not a method for checking containment (like javascript in). * * The typical use case for this operation is "binning", "bucketing", or * "discretizing". The values are assigned to bucket-indices based on the edges * listed in 'sortedSequence'. This operation returns the bucket-index for each * value. * * The side argument controls which index is returned if a value lands exactly * on an edge. * * The axis is not settable for this operation. It always operates on the * innermost dimension (axis=-1). The operation will accept any number of outer * dimensions. * * Note: This operation assumes that 'sortedSequence' is sorted along the * innermost axis, maybe using 'sort(..., axis=-1)'. If the sequence is not * sorted no error is raised and the content of the returned tensor is not well * defined. * * ```js * const edges = tf.tensor1d([-1, 3.3, 9.1, 10.0]); * let values = tf.tensor1d([0.0, 4.1, 12.0]); * const result1 = tf.searchSorted(edges, values, 'left'); * result1.print(); // [1, 2, 4] * * const seq = tf.tensor1d([0, 3, 9, 10, 10]); * values = tf.tensor1d([0, 4, 10]); * const result2 = tf.searchSorted(seq, values, 'left'); * result2.print(); // [0, 2, 3] * const result3 = tf.searchSorted(seq, values, 'right'); * result3.print(); // [1, 2, 5] * * const sortedSequence = tf.tensor2d([[0., 3., 8., 9., 10.], * [1., 2., 3., 4., 5.]]); * values = tf.tensor2d([[9.8, 2.1, 4.3], * [0.1, 6.6, 4.5, ]]); * const result4 = tf.searchSorted(sortedSequence, values, 'left'); * result4.print(); // [[4, 1, 2], [0, 5, 4]] * ``` * @param sortedSequence: N-D. Sorted sequence. * @param values: N-D. Search values. * @param side: 'left'|'right'. Defaults to 'left'. 'left' corresponds to lower * bound and 'right' to upper bound. * @return An N-D int32 tensor the size of values containing the result of * applying either lower bound or upper bound (depending on side) to each * value. The result is not a global index to the entire Tensor, but the * index in the last dimension. * @doc {heading: 'Operations', subheading: 'Evaluation'} */ function searchSorted_(sortedSequence, values, side = 'left') { const $sortedSequence = convertToTensor(sortedSequence, 'sortedSequence', 'searchSorted'); const $values = convertToTensor(values, 'values', 'searchSorted'); const sequenceSize = $sortedSequence.shape[$sortedSequence.shape.length - 1]; const valuesSize = $values.shape[$values.shape.length - 1]; const $sortedSequence2D = reshape($sortedSequence, [-1, sequenceSize]); const $values2D = reshape($values, [-1, valuesSize]); if ($sortedSequence2D.rank < 2) { throw new Error(`Sorted input argument must be at least 2-dimensional`); } if ($sortedSequence2D.shape[0] !== $values2D.shape[0]) { throw new Error(`Leading dimension of 'sortedSequence' and 'values' must match.`); } if (sizeFromShape($values2D.shape) >= INT32_MAX) { throw new Error(`values tensor size must less than ${INT32_MAX}`); } if ($sortedSequence2D.shape[1] >= INT32_MAX) { throw new Error(`trailing dim_size must less than ${INT32_MAX} for int32 output type, was ${$sortedSequence2D.shape[1]}`); } const inputs = { sortedSequence: $sortedSequence2D, values: $values2D, }; const attrs = { side }; return ENGINE.runKernel(SearchSorted, inputs, attrs); } const searchSorted = /* @__PURE__ */ op({ searchSorted_ }); /** * @license * Copyright 2022 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Searches for where a value would go in a sorted sequence. * * This is not a method for checking containment (like javascript in). * * The typical use case for this operation is "binning", "bucketing", or * "discretizing". The values are assigned to bucket-indices based on the edges * listed in 'sortedSequence'. This operation returns the bucket-index for each * value. * * The index returned corresponds to the first edge greater than or equal to the * value. * * The axis is not settable for this operation. It always operates on the * innermost dimension (axis=-1). The operation will accept any number of outer * dimensions. * * Note: This operation assumes that 'lowerBound' is sorted along the * innermost axis, maybe using 'sort(..., axis=-1)'. If the sequence is not * sorted no error is raised and the content of the returned tensor is not well * defined. * * ```js * const edges = tf.tensor1d([-1, 3.3, 9.1, 10.0]); * let values = tf.tensor1d([0.0, 4.1, 12.0]); * const result1 = tf.lowerBound(edges, values); * result1.print(); // [1, 2, 4] * * const seq = tf.tensor1d([0, 3, 9, 10, 10]); * values = tf.tensor1d([0, 4, 10]); * const result2 = tf.lowerBound(seq, values); * result2.print(); // [0, 2, 3] * * const sortedSequence = tf.tensor2d([[0., 3., 8., 9., 10.], * [1., 2., 3., 4., 5.]]); * values = tf.tensor2d([[9.8, 2.1, 4.3], * [0.1, 6.6, 4.5, ]]); * const result3 = tf.lowerBound(sortedSequence, values); * result3.print(); // [[4, 1, 2], [0, 5, 4]] * ``` * @param sortedSequence: N-D. Sorted sequence. * @param values: N-D. Search values. * @return An N-D int32 tensor the size of values containing the result of * applying lower bound to each value. The result is not a global index to * the entire Tensor, but the index in the last dimension. * @doc {heading: 'Operations', subheading: 'Evaluation'} */ function lowerBound(sortedSequence, values) { return searchSorted(sortedSequence, values, 'left'); } /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the 2D max pooling of an image. * * @param x The input tensor, of rank 4 or rank 3 of shape * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed. * @param filterSize The filter size: `[filterHeight, filterWidth]`. If * `filterSize` is a single number, then `filterHeight == filterWidth`. * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If * `strides` is a single number, then `strideHeight == strideWidth`. * @param dilations The dilation rates: `[dilationHeight, dilationWidth]` * in which we sample input values across the height and width dimensions * in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single * number, then `dilationHeight == dilationWidth`. If it is greater than * 1, then all values of `strides` must be 1. * @param pad The type of padding algorithm. * - `same` and stride 1: output will be of same size as input, * regardless of filter size. * - `valid`: output will be smaller than input if filter is larger * than 1x1. * - For more info, see this guide: * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is * provided, it will default to truncate. */ function maxPool_(x, filterSize, strides, pad, dimRoundingMode) { const $x = convertToTensor(x, 'x', 'maxPool'); const dilations = 1; let x4D = $x; let reshapedTo4D = false; if ($x.rank === 3) { reshapedTo4D = true; x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); } assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`); assert(eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' + `Got strides ${strides} and dilations '${dilations}'`); checkPadOnDimRoundingMode('maxPool', pad, dimRoundingMode); const inputs = { x: x4D }; const attrs = { filterSize, strides, pad, dimRoundingMode }; // tslint:disable-next-line: no-unnecessary-type-assertion const res = ENGINE.runKernel(MaxPool, inputs, attrs); if (reshapedTo4D) { return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); } return res; } const maxPool = /* @__PURE__ */ op({ maxPool_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the 3D max pooling. * * ```js * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]); * const result = tf.maxPool3d(x, 2, 1, 'valid'); * result.print(); * ``` * * @param x The input tensor, of rank 5 or rank 4 of shape * `[batch, depth, height, width, inChannels]`. * @param filterSize The filter size: * `[filterDepth, filterHeight, filterWidth]`. * If `filterSize` is a single number, * then `filterDepth == filterHeight == filterWidth`. * @param strides The strides of the pooling: * `[strideDepth, strideHeight, strideWidth]`. * If `strides` is a single number, * then `strideDepth == strideHeight == strideWidth`. * @param pad The type of padding algorithm. * - `same` and stride 1: output will be of same size as input, * regardless of filter size. * - `valid`: output will be smaller than input if filter is larger * than 1*1x1. * - For more info, see this guide: * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is * provided, it will default to truncate. * @param dataFormat An optional string from: "NDHWC", "NCDHW". Defaults to * "NDHWC". Specify the data format of the input and output data. With the * default format "NDHWC", the data is stored in the order of: [batch, * depth, height, width, channels]. Only "NDHWC" is currently supported. * @doc {heading: 'Operations', subheading: 'Convolution'} */ function maxPool3d_(x, filterSize = [1, 1, 1], strides, pad, dimRoundingMode, dataFormat = 'NDHWC') { const $x = convertToTensor(x, 'x', 'maxPool3d'); let x5D = $x; let reshapedTo5D = false; if ($x.rank === 4) { reshapedTo5D = true; x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]); } assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`); assert(dataFormat === 'NDHWC', () => `Error in maxPool3d: Only NDHWC is currently supported, ` + `but got dataFormat of ${dataFormat}`); checkPadOnDimRoundingMode('maxPool3d', pad, dimRoundingMode); const inputs = { x: x5D }; const attrs = { filterSize, strides, pad, dimRoundingMode, dataFormat }; // tslint:disable-next-line: no-unnecessary-type-assertion const res = ENGINE.runKernel(MaxPool3D, inputs, attrs); if (reshapedTo5D) { return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]); } return res; } const maxPool3d = /* @__PURE__ */ op({ maxPool3d_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the 2D max pooling of an image with Argmax index. * The indices in argmax are flattened, so that a maximum value at position `[b, * y, x, c]` becomes flattened index: `(y * width + x) * channels + c` if * include_batch_in_index is False; `((b * height + y) * width + x) * channels * +c` if include_batch_in_index is True. * * The indices returned are always in `[0, height) x [0, width)` before * flattening. * * @param x The input tensor, of rank 4 or rank 3 of shape * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed. * @param filterSize The filter size: `[filterHeight, filterWidth]`. If * `filterSize` is a single number, then `filterHeight == filterWidth`. * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If * `strides` is a single number, then `strideHeight == strideWidth`. * @param dataFormat An optional string from: "NDHWC", "NCDHW". Defaults to * "NDHWC". Specify the data format of the input and output data. With the * default format "NDHWC", the data is stored in the order of: [batch, * depth, height, width, channels]. Only "NDHWC" is currently supported. * @param pad The type of padding algorithm. * - `same` and stride 1: output will be of same size as input, * regardless of filter size. * - `valid`: output will be smaller than input if filter is larger * than 1x1. * - For more info, see this guide: * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) * @param includeBatchIndex Defaults to False. Whether to include batch * dimension in flattened index of argmax. * * @doc {heading: 'Operations', subheading: 'Convolution'} */ function maxPoolWithArgmax_(x, filterSize, strides, pad, includeBatchInIndex = false) { const $x = convertToTensor(x, 'x', 'maxPoolWithArgmax'); const inputs = { x: $x }; const attrs = { filterSize, strides, pad, includeBatchInIndex }; // tslint:disable-next-line: no-unnecessary-type-assertion const result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs); return { result: result[0], indexes: result[1] }; } const maxPoolWithArgmax = /* @__PURE__ */ op({ maxPoolWithArgmax_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns the max of a and b (`a > b ? a : b`) element-wise. * Supports broadcasting. * * We also expose `tf.maximumStrict` which has the same signature as this op and * asserts that `a` and `b` are the same shape (does not broadcast). * * ```js * const a = tf.tensor1d([1, 4, 3, 16]); * const b = tf.tensor1d([1, 2, 9, 4]); * * a.maximum(b).print(); // or tf.maximum(a, b) * ``` * * ```js * // Broadcast maximum a with b. * const a = tf.tensor1d([2, 4, 6, 8]); * const b = tf.scalar(5); * * a.maximum(b).print(); // or tf.maximum(a, b) * ``` * * @param a The first tensor. * @param b The second tensor. Must have the same type as `a`. * * @doc {heading: 'Operations', subheading: 'Arithmetic'} */ function maximum_(a, b) { let $a = convertToTensor(a, 'a', 'maximum'); let $b = convertToTensor(b, 'b', 'maximum'); [$a, $b] = makeTypesMatch($a, $b); if ($a.dtype === 'bool') { $a = cast($a, 'int32'); $b = cast($b, 'int32'); } assertAndGetBroadcastShape($a.shape, $b.shape); const inputs = { a: $a, b: $b }; return ENGINE.runKernel(Maximum, inputs); } const maximum = /* @__PURE__ */ op({ maximum_ }); /** * @license * Copyright 2020 Google Inc. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the mean of elements across dimensions of a `tf.Tensor`. * * Reduces `x` along the dimensions given in `axis`. Unless `keepDims` is * true, the rank of the `tf.Tensor` is reduced by 1 for each entry in `axis`. * If `keepDims` is true, the reduced dimensions are retained with length 1. * If `axis` has no entries, all dimensions are reduced, and a `tf.Tensor` with * a single element is returned. * * ```js * const x = tf.tensor1d([1, 2, 3]); * * x.mean().print(); // or tf.mean(a) * ``` * * ```js * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); * * const axis = 1; * x.mean(axis).print(); // or tf.mean(x, axis) * ``` * * @param x The input tensor. * @param axis The dimension(s) to reduce. By default it reduces * all dimensions. * @param keepDims If true, retains reduced dimensions with size 1. * * @doc {heading: 'Operations', subheading: 'Reduction'} */ function mean_(x, axis = null, keepDims = false) { const $x = convertToTensor(x, 'x', 'mean'); const inputs = { x: $x }; const attrs = { axis, keepDims }; return ENGINE.runKernel(Mean, inputs, attrs); } const mean = /* @__PURE__ */ op({ mean_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates a `tf.Tensor` with all elements set to 0. * * ```js * tf.zeros([2, 2]).print(); * ``` * * @param shape An array of integers defining the output tensor shape. * @param dtype The type of an element in the resulting tensor. Can * be 'float32', 'int32' or 'bool'. Defaults to 'float'. * * @doc {heading: 'Tensors', subheading: 'Creation'} */ function zeros(shape, dtype = 'float32') { assertNonNegativeIntegerDimensions(shape); if (dtype === 'complex64') { const real = zeros(shape, 'float32'); const imag = zeros(shape, 'float32'); return complex(real, imag); } const values = makeZerosTypedArray(sizeFromShape(shape), dtype); return ENGINE.makeTensor(values, shape, dtype); } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates a `tf.Tensor` with all elements set to 1. * * ```js * tf.ones([2, 2]).print(); * ``` * * @param shape An array of integers defining the output tensor shape. * @param dtype The type of an element in the resulting tensor. Defaults to * 'float'. * * @doc {heading: 'Tensors', subheading: 'Creation'} */ function ones(shape, dtype = 'float32') { assertNonNegativeIntegerDimensions(shape); if (dtype === 'complex64') { const real = ones(shape, 'float32'); const imag = zeros(shape, 'float32'); return complex(real, imag); } const values = makeOnesTypedArray(sizeFromShape(shape), dtype); return ENGINE.makeTensor(values, shape, dtype); } /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Broadcasts parameters for evaluation on an N-D grid. * * Given N one-dimensional coordinate arrays `*args`, returns a list `outputs` * of N-D coordinate arrays for evaluating expressions on an N-D grid. * * Notes: * `meshgrid` supports cartesian ('xy') and matrix ('ij') indexing conventions. * When the `indexing` argument is set to 'xy' (the default), the broadcasting * instructions for the first two dimensions are swapped. * Examples: * Calling `const [X, Y] = meshgrid(x, y)` with the tensors * * ```javascript * const x = [1, 2, 3]; * const y = [4, 5, 6]; * const [X, Y] = tf.meshgrid(x, y); * // X = [[1, 2, 3], * // [1, 2, 3], * // [1, 2, 3]] * // Y = [[4, 4, 4], * // [5, 5, 5], * // [6, 6, 6]] * ``` * * @param x Tensor with rank geq 1. * @param y Tensor with rank geq 1. * @param indexing * * @doc {heading: 'Operations', subheading: 'Slicing and Joining'} */ function meshgrid(x, y, { indexing = 'xy' } = {}) { if (indexing !== 'xy' && indexing !== 'ij') { throw new TypeError(`${indexing} is not a valid third argument to meshgrid`); } if (x === undefined) { return []; } let $x = convertToTensor(x, 'x', 'meshgrid', x instanceof Tensor ? x.dtype : 'float32'); if (y === undefined) { return [$x]; } let $y = convertToTensor(y, 'y', 'meshgrid', y instanceof Tensor ? y.dtype : 'float32'); const w = sizeFromShape($x.shape); const h = sizeFromShape($y.shape); if (indexing === 'xy') { $x = reshape($x, [1, -1]); $y = reshape($y, [-1, 1]); return [ matMul$1(ones([h, 1], $x.dtype), $x), matMul$1($y, ones([1, w], $y.dtype)), ]; } $x = reshape($x, [-1, 1]); $y = reshape($y, [1, -1]); return [ matMul$1($x, ones([1, h], $x.dtype)), matMul$1(ones([w, 1], $y.dtype), $y), ]; } /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns the min of a and b (`a < b ? a : b`) element-wise. * Supports broadcasting. * * We also expose `minimumStrict` which has the same signature as this op and * asserts that `a` and `b` are the same shape (does not broadcast). * * ```js * const a = tf.tensor1d([1, 4, 3, 16]); * const b = tf.tensor1d([1, 2, 9, 4]); * * a.minimum(b).print(); // or tf.minimum(a, b) * ``` * * ```js * // Broadcast minimum a with b. * const a = tf.tensor1d([2, 4, 6, 8]); * const b = tf.scalar(5); * * a.minimum(b).print(); // or tf.minimum(a, b) * ``` * * @param a The first tensor. * @param b The second tensor. Must have the same type as `a`. * * @doc {heading: 'Operations', subheading: 'Arithmetic'} */ function minimum_(a, b) { let $a = convertToTensor(a, 'a', 'minimum'); let $b = convertToTensor(b, 'b', 'minimum'); [$a, $b] = makeTypesMatch($a, $b); if ($a.dtype === 'bool') { $a = cast($a, 'int32'); $b = cast($b, 'int32'); } assertAndGetBroadcastShape($a.shape, $b.shape); const inputs = { a: $a, b: $b }; return ENGINE.runKernel(Minimum, inputs); } const minimum = /* @__PURE__ */ op({ minimum_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Pads a `tf.Tensor` using mirror padding. * * This operation implements the `REFLECT` and `SYMMETRIC` modes of pad. * * ```js * const x = tf.range(0, 9).reshape([1, 1, 3, 3]); * x.mirrorPad([[0, 0], [0, 0], [2, 2], [2, 2]], 'reflect').print(); * ``` * @param x The tensor to pad. * @param paddings An array of length `R` (the rank of the tensor), where * each element is a length-2 tuple of ints `[padBefore, padAfter]`, * specifying how much to pad along each dimension of the tensor. * In "reflect" mode, the padded regions do not include the borders, * while in "symmetric" mode the padded regions do include the borders. * For example, if the input is `[1, 2, 3]` and paddings is `[0, 2]`, * then the output is `[1, 2, 3, 2, 1]` in "reflect" mode, and * `[1, 2, 3, 3, 2]` in "symmetric" mode. * If `mode` is "reflect" then both `paddings[D, 0]` and `paddings[D, 1]` * must be no greater than `x.shape[D] - 1`. If mode is "symmetric" * then both `paddings[D, 0]` and `paddings[D, 1]` must be no greater than * `x.shape[D]` * @param mode String to specify padding mode. Can be `'reflect' | 'symmetric'` */ /** @doc {heading: 'Tensors', subheading: 'Transformations'} */ function mirrorPad_(x, paddings, mode) { assert(mode === 'reflect' || mode === 'symmetric', () => `Invalid mode. Mode must be either reflect or symmetric. ` + `Got ${mode}.`); const $x = convertToTensor(x, 'x', 'mirrorPad'); if ($x.rank === 0) { throw new Error('mirrorPad(scalar) is not defined. ' + 'Pass non-scalar to mirrorPad'); } assert(paddings.length === $x.rank, () => `Padding doesn't match input. Must be ${$x.rank}. ` + `Got ${paddings.length}.`); const shapeOffset = mode === 'reflect' ? 1 : 0; for (let i = 0; i < $x.rank; i++) { assert(paddings[i].length === 2, () => `Invalid number of paddings. Must be length of 2 each.`); assert(paddings[i][0] >= 0 && paddings[i][0] <= $x.shape[i] - shapeOffset && paddings[i][1] >= 0 && paddings[i][1] <= $x.shape[i] - shapeOffset, () => `Padding in dimension ${i} cannot be greater than or equal ` + `to ${$x.shape[i] - shapeOffset} or less than 0 for input of ` + `shape ${$x.shape}`); } const attrs = { paddings, mode }; const inputs = { x: $x }; return ENGINE.runKernel(MirrorPad, inputs, attrs); } const mirrorPad = /* @__PURE__ */ op({ mirrorPad_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns the mod of a and b element-wise. * `floor(x / y) * y + mod(x, y) = x` * Supports broadcasting. * * We also expose `tf.modStrict` which has the same signature as this op and * asserts that `a` and `b` are the same shape (does not broadcast). * * ```js * const a = tf.tensor1d([1, 4, 3, 16]); * const b = tf.tensor1d([1, 2, 9, 4]); * * a.mod(b).print(); // or tf.mod(a, b) * ``` * * ```js * // Broadcast a mod b. * const a = tf.tensor1d([2, 4, 6, 8]); * const b = tf.scalar(5); * * a.mod(b).print(); // or tf.mod(a, b) * ``` * * @param a The first tensor. * @param b The second tensor. Must have the same type as `a`. * * @doc {heading: 'Operations', subheading: 'Arithmetic'} */ function mod_(a, b) { let $a = convertToTensor(a, 'a', 'mod'); let $b = convertToTensor(b, 'b', 'mod'); [$a, $b] = makeTypesMatch($a, $b); const inputs = { a: $a, b: $b }; return ENGINE.runKernel(Mod, inputs); } const mod = /* @__PURE__ */ op({ mod_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Calculates the mean and variance of `x`. The mean and variance are * calculated by aggregating the contents of `x` across `axes`. If `x` is * 1-D and `axes = [0]` this is just the mean and variance of a vector. * * @param x The input tensor. * @param axis The dimension(s) along with to compute mean and * variance. By default it reduces all dimensions. * @param keepDims If true, the moments have the same dimensionality as the * input. * @return An object with two keys: `mean` and `variance`. * * @doc {heading: 'Operations', subheading: 'Normalization'} */ function moments_(x, axis = null, keepDims = false) { x = convertToTensor(x, 'x', 'moments'); const axes = parseAxisParam(axis, x.shape); const xMean = mean(x, axes, keepDims); let keepDimsShape = xMean.shape; if (!keepDims) { keepDimsShape = expandShapeToKeepDim(xMean.shape, axes); } const devSquared = square(sub(cast(x, 'float32'), reshape(xMean, keepDimsShape))); const variance = mean(devSquared, axes, keepDims); return { mean: xMean, variance }; } const moments = /* @__PURE__ */ op({ moments_ }); /** * Computes the next states and outputs of a stack of LSTMCells. * * Each cell output is used as input to the next cell. * * Returns `[cellState, cellOutput]`. * * Derived from tf.contrib.rn.MultiRNNCell. * * @param lstmCells Array of LSTMCell functions. * @param data The input to the cell. * @param c Array of previous cell states. * @param h Array of previous cell outputs. * * @doc {heading: 'Operations', subheading: 'RNN'} */ function multiRNNCell_(lstmCells, data, c, h) { const $data = convertToTensor(data, 'data', 'multiRNNCell'); const $c = convertToTensorArray(c, 'c', 'multiRNNCell'); const $h = convertToTensorArray(h, 'h', 'multiRNNCell'); let input = $data; const newStates = []; for (let i = 0; i < lstmCells.length; i++) { const output = lstmCells[i](input, $c[i], $h[i]); newStates.push(output[0]); newStates.push(output[1]); input = output[1]; } const newC = []; const newH = []; for (let i = 0; i < newStates.length; i += 2) { newC.push(newStates[i]); newH.push(newStates[i + 1]); } return [newC, newH]; } const multiRNNCell = /* @__PURE__ */ op({ multiRNNCell_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates a `tf.Tensor` with values drawn from a multinomial distribution. * * ```js * const probs = tf.tensor([.75, .25]); * tf.multinomial(probs, 3).print(); * ``` * * @param logits 1D array with unnormalized log-probabilities, or * 2D array of shape `[batchSize, numOutcomes]`. See the `normalized` * parameter. * @param numSamples Number of samples to draw for each row slice. * @param seed The seed number. * @param normalized Whether the provided `logits` are normalized true * probabilities (sum to 1). Defaults to false. * @return 1D array of shape `[numSamples]`, or 2D array of shape * `[batchSize, numSamples]`, depending on the rank of the input. * * @doc {heading: 'Tensors', subheading: 'Random'} */ function multinomial_(logits, numSamples, seed, normalized = false) { const $logits = convertToTensor(logits, 'logits', 'multinomial'); const numOutcomes = $logits.size; const origRank = $logits.rank; if (numOutcomes < 2) { throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ` + `${numOutcomes}.`); } if (origRank > 2) { throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`); } // TODO(lina128): Investigate correct seed behavior. The code seems not allow // setting see to 0. seed = seed || Math.random(); // The kernel only accepts (and returns) rank 2 tensors. const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits; const inputs = { logits: logits2D }; const attrs = { numSamples, seed, normalized }; // tslint:disable-next-line: no-unnecessary-type-assertion const res = ENGINE.runKernel(Multinomial, inputs, attrs); // tslint:disable-next-line:no-unnecessary-type-assertion return origRank === 1 ? reshape(res, [res.size]) : res; } const multinomial = /* @__PURE__ */ op({ multinomial_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns the truth value of (a != b) element-wise. Supports broadcasting. * * ```js * const a = tf.tensor1d([1, 2, 3]); * const b = tf.tensor1d([0, 2, 3]); * * a.notEqual(b).print(); * ``` * @param a The first input tensor. * @param b The second input tensor. Must have the same dtype as `a`. * * @doc {heading: 'Operations', subheading: 'Logical'} */ function notEqual_(a, b) { let $a = convertToTensor(a, 'a', 'notEqual', 'string_or_numeric'); let $b = convertToTensor(b, 'b', 'notEqual', 'string_or_numeric'); [$a, $b] = makeTypesMatch($a, $b); assertAndGetBroadcastShape($a.shape, $b.shape); const inputs = { a: $a, b: $b }; return ENGINE.runKernel(NotEqual, inputs); } const notEqual = /* @__PURE__ */ op({ notEqual_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates a one-hot `tf.Tensor`. The locations represented by `indices` take * value `onValue` (defaults to 1), while all other locations take value * `offValue` (defaults to 0). If `indices` is rank `R`, the output has rank * `R+1` with the last axis of size `depth`. * `indices` used to encode prediction class must start from 0. For example, * if you have 3 classes of data, class 1 should be encoded as 0, class 2 * should be 1, and class 3 should be 2. * * ```js * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print(); * ``` * * @param indices `tf.Tensor` of indices with dtype `int32`. Indices must * start from 0. * @param depth The depth of the one hot dimension. * @param onValue A number used to fill in the output when the index matches * the location. * @param offValue A number used to fill in the output when the index does * not match the location. * @param dtype The dtype of the output tensor, default to 'int32'. * * @doc {heading: 'Tensors', subheading: 'Creation'} */ function oneHot_(indices, depth, onValue = 1, offValue = 0, dtype = 'int32') { if (depth < 2) { throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`); } const $indices = convertToTensor(indices, 'indices', 'oneHot', 'int32'); const inputs = { indices: $indices }; const attrs = { dtype, depth, onValue, offValue }; return ENGINE.runKernel(OneHot, inputs, attrs); } const oneHot = /* @__PURE__ */ op({ oneHot_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates a `tf.Tensor` with all elements set to 1 with the same shape as the * given tensor. * * ```js * const x = tf.tensor([1, 2]); * tf.onesLike(x).print(); * ``` * @param x A tensor. * * @doc {heading: 'Tensors', subheading: 'Creation'} */ function onesLike_(x) { const $x = convertToTensor(x, 'x', 'onesLike'); const inputs = { x: $x }; return ENGINE.runKernel(OnesLike, inputs); } const onesLike = /* @__PURE__ */ op({ onesLike_ }); /** * Computes the outer product of two vectors, `v1` and `v2`. * * ```js * const a = tf.tensor1d([1, 2, 3]); * const b = tf.tensor1d([3, 4, 5]); * * tf.outerProduct(a, b).print(); * ``` * @param v1 The first vector in the outer product operation. * @param v2 The second vector in the outer product operation. * * @doc {heading: 'Operations', subheading: 'Matrices'} */ function outerProduct_(v1, v2) { const $v1 = convertToTensor(v1, 'v1', 'outerProduct'); const $v2 = convertToTensor(v2, 'v2', 'outerProduct'); assert($v1.rank === 1 && $v2.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ` + `${$v1.rank} and ${$v2.rank}.`); const v12D = reshape($v1, [-1, 1]); const v22D = reshape($v2, [1, -1]); return matMul$1(v12D, v22D); } const outerProduct = /* @__PURE__ */ op({ outerProduct_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Pads a `tf.Tensor` with a given value and paddings. * * This operation implements `CONSTANT` mode. For `REFLECT` and `SYMMETRIC`, * refer to `tf.mirrorPad`. * * Also available are stricter rank-specific methods with the same signature * as this method that assert that `paddings` is of given length. * - `tf.pad1d` * - `tf.pad2d` * - `tf.pad3d` * - `tf.pad4d` * * ```js * const x = tf.tensor1d([1, 2, 3, 4]); * x.pad([[1, 2]]).print(); * ``` * @param x The tensor to pad. * @param paddings An array of length `R` (the rank of the tensor), where * each element is a length-2 tuple of ints `[padBefore, padAfter]`, * specifying how much to pad along each dimension of the tensor. * @param constantValue The pad value to use. Defaults to 0. * * @doc {heading: 'Tensors', subheading: 'Transformations'} */ function pad_(x, paddings, constantValue = 0) { const $x = convertToTensor(x, 'x', 'pad'); if ($x.rank === 0) { throw new Error('pad(scalar) is not defined. Pass non-scalar to pad'); } const attrs = { paddings, constantValue }; const inputs = { x: $x }; return ENGINE.runKernel(PadV2, inputs, attrs); } const pad = /* @__PURE__ */ op({ pad_ }); /** * Pads a `tf.Tensor1D` with a given value and paddings. See `pad` for details. */ function pad1d_(x, paddings, constantValue = 0) { assert(paddings.length === 2, () => 'Invalid number of paddings. Must be length of 2.'); return pad(x, [paddings], constantValue); } const pad1d = /* @__PURE__ */ op({ pad1d_ }); /** * Pads a `tf.Tensor2D` with a given value and paddings. See `pad` for details. */ function pad2d_(x, paddings, constantValue = 0) { assert(paddings.length === 2 && paddings[0].length === 2 && paddings[1].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.'); return pad(x, paddings, constantValue); } const pad2d = /* @__PURE__ */ op({ pad2d_ }); /** * Pads a `tf.Tensor3D` with a given value and paddings. See `pad` for details. */ function pad3d_(x, paddings, constantValue = 0) { assert(paddings.length === 3 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.'); return pad(x, paddings, constantValue); } const pad3d = /* @__PURE__ */ op({ pad3d_ }); /** * Pads a `tf.Tensor4D` with a given value and paddings. See `pad` for details. */ function pad4d_(x, paddings, constantValue = 0) { assert(paddings.length === 4 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2 && paddings[3].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.'); return pad(x, paddings, constantValue); } const pad4d = /* @__PURE__ */ op({ pad4d_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * This operation divides "spatial" dimensions `[1, ..., M]` of the input into * a grid of blocks of shape `blockShape`, and interleaves these blocks with * the "batch" dimension (0) such that in the output, the spatial * dimensions `[1, ..., M]` correspond to the position within the grid, * and the batch dimension combines both the position within a spatial block * and the original batch position. Prior to division into blocks, * the spatial dimensions of the input are optionally zero padded * according to `paddings`. See below for a precise description. * * ```js * const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]); * const blockShape = [2, 2]; * const paddings = [[0, 0], [0, 0]]; * * x.spaceToBatchND(blockShape, paddings).print(); * ``` * * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape + * remainingShape`, where spatialShape has `M` dimensions. * @param blockShape A 1-D array. Must have shape `[M]`, all values must * be >= 1. * @param paddings A 2-D array. Must have shape `[M, 2]`, all values must be >= * 0. `paddings[i] = [padStart, padEnd]` specifies the amount to zero-pad * from input dimension `i + 1`, which corresponds to spatial dimension `i`. It * is required that * `(inputShape[i + 1] + padStart + padEnd) % blockShape[i] === 0` * * This operation is equivalent to the following steps: * * 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the input * according to `paddings` to produce `padded` of shape paddedShape. * * 2. Reshape `padded` to `reshapedPadded` of shape: * `[batch] + [paddedShape[1] / blockShape[0], blockShape[0], ..., * paddedShape[M] / blockShape[M-1], blockShape[M-1]] + remainingShape` * * 3. Permute dimensions of `reshapedPadded` to produce `permutedReshapedPadded` * of shape: `blockShape + [batch] + [paddedShape[1] / blockShape[0], ..., * paddedShape[M] / blockShape[M-1]] + remainingShape` * * 4. Reshape `permutedReshapedPadded` to flatten `blockShape` into the * batch dimension, producing an output tensor of shape: * `[batch * prod(blockShape)] + [paddedShape[1] / blockShape[0], ..., * paddedShape[M] / blockShape[M-1]] + remainingShape` * * @doc {heading: 'Tensors', subheading: 'Transformations'} */ function spaceToBatchND_(x, blockShape, paddings) { const $x = convertToTensor(x, 'x', 'spaceToBatchND'); assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`); assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`); assert($x.shape.reduce((a, b, i) => { if (i > 0 && i <= blockShape.length) { return a && ((b + paddings[i - 1][0] + paddings[i - 1][1]) % blockShape[i - 1] === 0); } return a; }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`); const inputs = { x: $x }; const attrs = { blockShape, paddings }; return ENGINE.runKernel(SpaceToBatchND, inputs, attrs); } const spaceToBatchND = /* @__PURE__ */ op({ spaceToBatchND_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Performs an N-D pooling operation * * @param input The input tensor, of rank 4 or rank 3 of shape * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed. * @param windowShape The filter size: `[filterHeight, filterWidth]`. If * `filterSize` is a single number, then `filterHeight == filterWidth`. * @param poolingType The type of pooling, either 'max' or 'avg'. * @param pad The type of padding algorithm: * - `same` and stride 1: output will be of same size as input, * regardless of filter size. * - `valid`: output will be smaller than input if filter is larger * than 1x1. * - For more info, see this guide: * [https://www.tensorflow.org/api_guides/python/nn#Convolution]( * https://www.tensorflow.org/api_guides/python/nn#Convolution) * @param dilations The dilation rates: `[dilationHeight, dilationWidth]` * in which we sample input values across the height and width dimensions * in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single * number, then `dilationHeight == dilationWidth`. If it is greater than * 1, then all values of `strides` must be 1. * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If * `strides` is a single number, then `strideHeight == strideWidth`. * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is * provided, it will default to truncate. * * @doc {heading: 'Operations', subheading: 'Convolution'} */ function pool_(input, windowShape, poolingType, pad, dilations, strides, dimRoundingMode) { if (dilations == null) { dilations = [1, 1]; } if (strides == null) { strides = 1; } if (pad === 0) { pad = 'valid'; } const $x = convertToTensor(input, 'x', 'maxPool'); let x4D = $x; let reshapedTo4D = false; if ($x.rank === 3) { reshapedTo4D = true; x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); } assert(eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in pool: Either strides or dilations must be 1. ' + `Got strides ${strides} and dilations '${dilations}'`); const convInfo = computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad); const dilation = [convInfo.dilationHeight, convInfo.dilationWidth]; // The following implementation does batchToSpace(pool(spaceToBatch(x))) // whenever dilation > 1 since the TF kernels do not support dilation > 1. // tslint:disable-next-line:max-line-length // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L1037 let basePadding; if (pad === 'same') { basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation); } else { basePadding = [[0, 0], [0, 0]]; } const isDilationOne = dilation[0] === 1 && dilation[1] === 1; const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding); const convertedPad = isDilationOne ? pad : 'valid'; const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding); const forwardOp = poolingType === 'avg' ? () => avgPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode) : () => maxPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode); const y = forwardOp(); const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops); if (reshapedTo4D) { return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); } return res; } // Helper function to compute crops and paddings for pool with dilation > 1. // tslint:disable-next-line:max-line-length // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/array_ops.py#L2184 function requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) { const padStart = basePadding.map(b => b[0]); const origPadEnd = basePadding.map(b => b[1]); const fullInputShape = inputShape.concat(padStart, origPadEnd); const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b); const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]); const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]); const crops = blockShape.map((_, i) => [0, padEndExtra[i]]); return [paddings, crops]; } // Helper function to compute base paddings for pool with dilation > 1. // tslint:disable-next-line:max-line-length // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L524 function withSpaceToBatchBasePaddings(filterShape, dilation) { // Spatial dimensions of the filters and the upsampled filters in which we // introduce (rate - 1) zeros between consecutive filter values. const dilatedFilterShape = filterShape.map((s, i) => { return s + (s - 1) * (dilation[i] - 1); }); const padExtraShape = dilatedFilterShape.map(s => s - 1); // When padding is odd, we pad more at end, following the same // convention as conv2d. const padExtraStart = padExtraShape.map(s => Math.floor(s / 2)); const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]); return padExtraShape.map((_, i) => { return [padExtraStart[i], padExtraEnd[i]]; }); } const pool = /* @__PURE__ */ op({ pool_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes leaky rectified linear element-wise with parametric alphas. * * `x < 0 ? alpha * x : f(x) = x` * * ```js * const x = tf.tensor1d([-1, 2, -3, 4]); * const alpha = tf.scalar(0.1); * * x.prelu(alpha).print(); // or tf.prelu(x, alpha) * ``` * @param x The input tensor. * @param alpha Scaling factor for negative values. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function prelu_(x, alpha) { const $x = convertToTensor(x, 'x', 'prelu'); const $alpha = convertToTensor(alpha, 'alpha', 'prelu'); const inputs = { x: $x, alpha: $alpha }; return ENGINE.runKernel(Prelu, inputs); } const prelu = /* @__PURE__ */ op({ prelu_ }); /** * @license * Copyright 2020 Google Inc. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Prints information about the `tf.Tensor` including its data. * * ```js * const verbose = true; * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose); * ``` * @param x The tensor to be printed. * @param verbose Whether to print verbose information about the ` Tensor`, * including dtype and size. * * @doc {heading: 'Tensors', subheading: 'Creation'} */ function print(x, verbose = false) { console.log(x.toString(verbose)); } /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the product of elements across dimensions of a `tf.Tensor`. * * Reduces the input along the dimensions given in `axes`. Unless `keepDims` * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in * `axes`. If `keepDims` is true, the reduced dimensions are retained with * length 1. If `axes` has no entries, all dimensions are reduced, and a * `tf.Tensor` with a single element is returned. * * ```js * const x = tf.tensor1d([1, 2, 3]); * * x.prod().print(); // or tf.prod(x) * ``` * * ```js * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); * * const axis = 1; * x.prod(axis).print(); // or tf.prod(x, axis) * ``` * * @param x The input tensor to compute the product over. If the dtype is `bool` * it will be converted to `int32` and the output dtype will be `int32`. * @param axis The dimension(s) to reduce. By default it reduces * all dimensions. * @param keepDims If true, retains reduced dimensions with size 1. * * @doc {heading: 'Operations', subheading: 'Reduction'} */ function prod_(x, axis = null, keepDims = false) { let $x = convertToTensor(x, 'x', 'prod'); if ($x.dtype === 'bool') { // bool is not an allowed type for the underlying kernel. $x = cast($x, 'int32'); } const inputs = { x: $x }; const attrs = { axis, keepDims }; return ENGINE.runKernel(Prod, inputs, attrs); } const prod = /* @__PURE__ */ op({ prod_ }); /** * @license * Copyright 2022 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ function raggedGather_(paramsNestedSplits, paramsDenseValues, indices, outputRaggedRank) { const $paramsNestedSplits = paramsNestedSplits.map((t, i) => convertToTensor(t, `tensors${i}`, 'raggedGather', 'int32')); const $paramsDenseValues = convertToTensor(paramsDenseValues, 'paramsDenseValues', 'raggedGather'); const $indices = convertToTensor(indices, 'indices', 'raggedGather', 'int32'); const inputs = { paramsNestedSplits: $paramsNestedSplits, paramsDenseValues: $paramsDenseValues, indices: $indices, }; const attrs = { outputRaggedRank }; const result = ENGINE.runKernel(RaggedGather, inputs, attrs); return { outputNestedSplits: result.slice(0, result.length - 1), outputDenseValues: result[result.length - 1], }; } const raggedGather = /* @__PURE__ */ op({ raggedGather_ }); /** * @license * Copyright 2022 Google LLC. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns a RaggedTensor result composed from rtDenseValues and rtNestedSplits, * such that result[i] = [starts[i], starts[i] + deltas[i], ..., limits[i]]). * * @param starts: A Tensor. Must be one of the following types: * 'float32', 'int32'. The starts of each range. * @param limits: A Tensor. Must have the same type as starts. The limits of * each range. * @param deltas: A Tensor. Must have the same type as starts. The deltas of * each range. * @return A map with the following properties: * - rtNestedSplits: A Tensor of type 'int32'. * - rtDenseValues: A Tensor. Has the same type as starts. */ function raggedRange_(starts, limits, deltas) { const $starts = convertToTensor(starts, 'starts', 'raggedRange'); const $limits = convertToTensor(limits, 'limits', 'raggedRange', $starts.dtype); const $deltas = convertToTensor(deltas, 'deltas', 'raggedRange', $starts.dtype); const inputs = { starts: $starts, limits: $limits, deltas: $deltas, }; const result = ENGINE.runKernel(RaggedRange, inputs); return { rtNestedSplits: result[0], rtDenseValues: result[1], }; } const raggedRange = /* @__PURE__ */ op({ raggedRange_ }); /** * @license * Copyright 2022 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Create a dense tensor from a ragged tensor, possibly altering its shape. * * The raggedTensorToTensor op creates a dense tensor from am array of row * partition tensors, a value vector, and default values. If the shape is * unspecified, the minimal shape required to contain all the elements in the * ragged tensor (the natural shape) will be used. If some dimensions are left * unspecified, then the size of the natural shape is used in that dimension. * * The defaultValue will be broadcast to the output shape. After that, the * values from the ragged tensor overwrite the default values. Note that the * defaultValue must have less dimensions than the value. * * The row partition tensors are in the order of the dimensions. At present, the * types can be: "ROW_SPLITS": the row_splits tensor from the ragged tensor. * "VALUE_ROWIDS": the value_rowids tensor from the ragged tensor. * "FIRST_DIM_SIZE": if value_rowids is used for the first dimension, then it * is preceded by "FIRST_DIM_SIZE". * ``` * @param shape: A Tensor. Must be one of the following types: 'int32'. The * desired shape of the output tensor. If left unspecified (empty), the * minimal shape required to contain all the elements in the ragged tensor * (the natural shape) will be used. If some dimensions are left * unspecified, then the size of the natural shape is used in that * dimension. * * Note that dense dimensions cannot be modified by the shape argument. * Trying to change the size of a dense dimension will cause the op to fail. * Examples: natural shape: [4, 5, 6] shape: -1 output shape: [4, 5, 6] * * natural shape: [4, 5, 6] shape: [3, -1, 2] output shape: [3, 5, 2] * * natural shape: [4, 5, 6] shape: [3, 7, 2] output shape: [3, 7, 2] * @param values: A Tensor. A 1D tensor representing the values of the ragged * tensor. * @param defaultValue: A Tensor. Must have the same type as values. The * defaultValue when the shape is larger than the ragged tensor. The * defaultValue is broadcast until it is the shape of the output tensor, * and then overwritten by values in the ragged tensor. The default value * must be compatible with this broadcast operation, and must have fewer * dimensions than the value tensor. * @param rowPartitionTensors: A list of at least 1 Tensor objects with the same * type in: 'int32'. * @param rowPartitionTypes: A list of strings. The types of the row partition * tensors. At present, these can be: * "ROW_SPLITS": the row_splits tensor from the ragged tensor. * "VALUE_ROWIDS": the value_rowids tensor from the ragged tensor. * "FIRST_DIM_SIZE": if value_rowids is used for the first dimension, then * it is preceeded by "FIRST_DIM_SIZE". The tensors are in the order of * the dimensions. * @return A Tensor. Has the same type as values. * @doc {heading: 'Operations', subheading: 'Ragged'} */ function raggedTensorToTensor_(shape, values, defaultValue, rowPartitionTensors, rowPartitionTypes) { const $shape = convertToTensor(shape, 'shape', 'raggedTensorToTensor', 'int32'); const $values = convertToTensor(values, 'values', 'raggedTensorToTensor'); const $defaultValue = convertToTensor(defaultValue, 'defaultValue', 'raggedTensorToTensor', $values.dtype); const $rowPartitionTensors = rowPartitionTensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'raggedTensorToTensor', 'int32')); const inputs = { shape: $shape, values: $values, defaultValue: $defaultValue, rowPartitionTensors: $rowPartitionTensors }; const attrs = { rowPartitionTypes }; return ENGINE.runKernel(RaggedTensorToTensor, inputs, attrs); } const raggedTensorToTensor = /* @__PURE__ */ op({ raggedTensorToTensor_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates a `tf.Tensor` with values sampled from a random number generator * function defined by the user. * * @param shape An array of integers defining the output tensor shape. * @param randFunction A random number generator function which is called * for each element in the output tensor. * @param dtype The data type of the output tensor. Defaults to 'float32'. * * @doc {heading: 'Tensors', subheading: 'Random'} */ function rand_(shape, randFunction, dtype) { assertNonNegativeIntegerDimensions(shape); const size = sizeFromShape(shape); let values = null; if (dtype == null || dtype === 'float32') { values = new Float32Array(size); } else if (dtype === 'int32') { values = new Int32Array(size); } else if (dtype === 'bool') { values = new Uint8Array(size); } else { throw new Error(`Unknown data type ${dtype}`); } for (let i = 0; i < size; i++) { values[i] = randFunction(); } return ENGINE.makeTensor(values, shape, dtype); } const rand = /* @__PURE__ */ op({ rand_ }); var alea$1 = {exports: {}}; (function (module) { // A port of an algorithm by Johannes Baagøe , 2010 // http://baagoe.com/en/RandomMusings/javascript/ // https://github.com/nquinlan/better-random-numbers-for-javascript-mirror // Original work is under MIT license - // Copyright (C) 2010 by Johannes Baagøe // // 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. (function(global, module, define) { function Alea(seed) { var me = this, mash = Mash(); me.next = function() { var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32 me.s0 = me.s1; me.s1 = me.s2; return me.s2 = t - (me.c = t | 0); }; // Apply the seeding algorithm from Baagoe. me.c = 1; me.s0 = mash(' '); me.s1 = mash(' '); me.s2 = mash(' '); me.s0 -= mash(seed); if (me.s0 < 0) { me.s0 += 1; } me.s1 -= mash(seed); if (me.s1 < 0) { me.s1 += 1; } me.s2 -= mash(seed); if (me.s2 < 0) { me.s2 += 1; } mash = null; } function copy(f, t) { t.c = f.c; t.s0 = f.s0; t.s1 = f.s1; t.s2 = f.s2; return t; } function impl(seed, opts) { var xg = new Alea(seed), state = opts && opts.state, prng = xg.next; prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }; prng.double = function() { return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53 }; prng.quick = prng; if (state) { if (typeof(state) == 'object') copy(state, xg); prng.state = function() { return copy(xg, {}); }; } return prng; } function Mash() { var n = 0xefc8249d; var mash = function(data) { data = String(data); for (var i = 0; i < data.length; i++) { n += data.charCodeAt(i); var h = 0.02519603282416938 * n; n = h >>> 0; h -= n; h *= n; n = h >>> 0; h -= n; n += h * 0x100000000; // 2^32 } return (n >>> 0) * 2.3283064365386963e-10; // 2^-32 }; return mash; } if (module && module.exports) { module.exports = impl; } else if (define && define.amd) { define(function() { return impl; }); } else { this.alea = impl; } })( commonjsGlobal, module, // present in node.js (typeof undefined) == 'function' // present with an AMD loader ); } (alea$1)); var aleaExports = alea$1.exports; var xor128$1 = {exports: {}}; (function (module) { // A Javascript implementaion of the "xor128" prng algorithm by // George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper (function(global, module, define) { function XorGen(seed) { var me = this, strseed = ''; me.x = 0; me.y = 0; me.z = 0; me.w = 0; // Set up generator function. me.next = function() { var t = me.x ^ (me.x << 11); me.x = me.y; me.y = me.z; me.z = me.w; return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8); }; if (seed === (seed | 0)) { // Integer seed. me.x = seed; } else { // String seed. strseed += seed; } // Mix in string seed, then discard an initial batch of 64 values. for (var k = 0; k < strseed.length + 64; k++) { me.x ^= strseed.charCodeAt(k) | 0; me.next(); } } function copy(f, t) { t.x = f.x; t.y = f.y; t.z = f.z; t.w = f.w; return t; } function impl(seed, opts) { var xg = new XorGen(seed), state = opts && opts.state, prng = function() { return (xg.next() >>> 0) / 0x100000000; }; prng.double = function() { do { var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 0x100000000, result = (top + bot) / (1 << 21); } while (result === 0); return result; }; prng.int32 = xg.next; prng.quick = prng; if (state) { if (typeof(state) == 'object') copy(state, xg); prng.state = function() { return copy(xg, {}); }; } return prng; } if (module && module.exports) { module.exports = impl; } else if (define && define.amd) { define(function() { return impl; }); } else { this.xor128 = impl; } })( commonjsGlobal, module, // present in node.js (typeof undefined) == 'function' // present with an AMD loader ); } (xor128$1)); var xor128Exports = xor128$1.exports; var xorwow$1 = {exports: {}}; (function (module) { // A Javascript implementaion of the "xorwow" prng algorithm by // George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper (function(global, module, define) { function XorGen(seed) { var me = this, strseed = ''; // Set up generator function. me.next = function() { var t = (me.x ^ (me.x >>> 2)); me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v; return (me.d = (me.d + 362437 | 0)) + (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0; }; me.x = 0; me.y = 0; me.z = 0; me.w = 0; me.v = 0; if (seed === (seed | 0)) { // Integer seed. me.x = seed; } else { // String seed. strseed += seed; } // Mix in string seed, then discard an initial batch of 64 values. for (var k = 0; k < strseed.length + 64; k++) { me.x ^= strseed.charCodeAt(k) | 0; if (k == strseed.length) { me.d = me.x << 10 ^ me.x >>> 4; } me.next(); } } function copy(f, t) { t.x = f.x; t.y = f.y; t.z = f.z; t.w = f.w; t.v = f.v; t.d = f.d; return t; } function impl(seed, opts) { var xg = new XorGen(seed), state = opts && opts.state, prng = function() { return (xg.next() >>> 0) / 0x100000000; }; prng.double = function() { do { var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 0x100000000, result = (top + bot) / (1 << 21); } while (result === 0); return result; }; prng.int32 = xg.next; prng.quick = prng; if (state) { if (typeof(state) == 'object') copy(state, xg); prng.state = function() { return copy(xg, {}); }; } return prng; } if (module && module.exports) { module.exports = impl; } else if (define && define.amd) { define(function() { return impl; }); } else { this.xorwow = impl; } })( commonjsGlobal, module, // present in node.js (typeof undefined) == 'function' // present with an AMD loader ); } (xorwow$1)); var xorwowExports = xorwow$1.exports; var xorshift7$1 = {exports: {}}; (function (module) { // A Javascript implementaion of the "xorshift7" algorithm by // François Panneton and Pierre L'ecuyer: // "On the Xorgshift Random Number Generators" // http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf (function(global, module, define) { function XorGen(seed) { var me = this; // Set up generator function. me.next = function() { // Update xor generator. var X = me.x, i = me.i, t, v; t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24); t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10); t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3); t = X[(i + 4) & 7]; v ^= t ^ (t << 7); t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9); X[i] = v; me.i = (i + 1) & 7; return v; }; function init(me, seed) { var j, X = []; if (seed === (seed | 0)) { // Seed state array using a 32-bit integer. X[0] = seed; } else { // Seed state using a string. seed = '' + seed; for (j = 0; j < seed.length; ++j) { X[j & 7] = (X[j & 7] << 15) ^ (seed.charCodeAt(j) + X[(j + 1) & 7] << 13); } } // Enforce an array length of 8, not all zeroes. while (X.length < 8) X.push(0); for (j = 0; j < 8 && X[j] === 0; ++j); if (j == 8) X[7] = -1; else X[j]; me.x = X; me.i = 0; // Discard an initial 256 values. for (j = 256; j > 0; --j) { me.next(); } } init(me, seed); } function copy(f, t) { t.x = f.x.slice(); t.i = f.i; return t; } function impl(seed, opts) { if (seed == null) seed = +(new Date); var xg = new XorGen(seed), state = opts && opts.state, prng = function() { return (xg.next() >>> 0) / 0x100000000; }; prng.double = function() { do { var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 0x100000000, result = (top + bot) / (1 << 21); } while (result === 0); return result; }; prng.int32 = xg.next; prng.quick = prng; if (state) { if (state.x) copy(state, xg); prng.state = function() { return copy(xg, {}); }; } return prng; } if (module && module.exports) { module.exports = impl; } else if (define && define.amd) { define(function() { return impl; }); } else { this.xorshift7 = impl; } })( commonjsGlobal, module, // present in node.js (typeof undefined) == 'function' // present with an AMD loader ); } (xorshift7$1)); var xorshift7Exports = xorshift7$1.exports; var xor4096$1 = {exports: {}}; (function (module) { // A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm. // // This fast non-cryptographic random number generator is designed for // use in Monte-Carlo algorithms. It combines a long-period xorshift // generator with a Weyl generator, and it passes all common batteries // of stasticial tests for randomness while consuming only a few nanoseconds // for each prng generated. For background on the generator, see Brent's // paper: "Some long-period random number generators using shifts and xors." // http://arxiv.org/pdf/1004.3115v1.pdf // // Usage: // // var xor4096 = require('xor4096'); // random = xor4096(1); // Seed with int32 or string. // assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits. // assert.equal(random.int32(), 1806534897); // signed int32, 32 bits. // // For nonzero numeric keys, this impelementation provides a sequence // identical to that by Brent's xorgens 3 implementaion in C. This // implementation also provides for initalizing the generator with // string seeds, or for saving and restoring the state of the generator. // // On Chrome, this prng benchmarks about 2.1 times slower than // Javascript's built-in Math.random(). (function(global, module, define) { function XorGen(seed) { var me = this; // Set up generator function. me.next = function() { var w = me.w, X = me.X, i = me.i, t, v; // Update Weyl generator. me.w = w = (w + 0x61c88647) | 0; // Update xor generator. v = X[(i + 34) & 127]; t = X[i = ((i + 1) & 127)]; v ^= v << 13; t ^= t << 17; v ^= v >>> 15; t ^= t >>> 12; // Update Xor generator array state. v = X[i] = v ^ t; me.i = i; // Result is the combination. return (v + (w ^ (w >>> 16))) | 0; }; function init(me, seed) { var t, v, i, j, w, X = [], limit = 128; if (seed === (seed | 0)) { // Numeric seeds initialize v, which is used to generates X. v = seed; seed = null; } else { // String seeds are mixed into v and X one character at a time. seed = seed + '\0'; v = 0; limit = Math.max(limit, seed.length); } // Initialize circular array and weyl value. for (i = 0, j = -32; j < limit; ++j) { // Put the unicode characters into the array, and shuffle them. if (seed) v ^= seed.charCodeAt((j + 32) % seed.length); // After 32 shuffles, take v as the starting w value. if (j === 0) w = v; v ^= v << 10; v ^= v >>> 15; v ^= v << 4; v ^= v >>> 13; if (j >= 0) { w = (w + 0x61c88647) | 0; // Weyl. t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array. i = (0 == t) ? i + 1 : 0; // Count zeroes. } } // We have detected all zeroes; make the key nonzero. if (i >= 128) { X[(seed && seed.length || 0) & 127] = -1; } // Run the generator 512 times to further mix the state before using it. // Factoring this as a function slows the main generator, so it is just // unrolled here. The weyl generator is not advanced while warming up. i = 127; for (j = 4 * 128; j > 0; --j) { v = X[(i + 34) & 127]; t = X[i = ((i + 1) & 127)]; v ^= v << 13; t ^= t << 17; v ^= v >>> 15; t ^= t >>> 12; X[i] = v ^ t; } // Storing state as object members is faster than using closure variables. me.w = w; me.X = X; me.i = i; } init(me, seed); } function copy(f, t) { t.i = f.i; t.w = f.w; t.X = f.X.slice(); return t; } function impl(seed, opts) { if (seed == null) seed = +(new Date); var xg = new XorGen(seed), state = opts && opts.state, prng = function() { return (xg.next() >>> 0) / 0x100000000; }; prng.double = function() { do { var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 0x100000000, result = (top + bot) / (1 << 21); } while (result === 0); return result; }; prng.int32 = xg.next; prng.quick = prng; if (state) { if (state.X) copy(state, xg); prng.state = function() { return copy(xg, {}); }; } return prng; } if (module && module.exports) { module.exports = impl; } else if (define && define.amd) { define(function() { return impl; }); } else { this.xor4096 = impl; } })( commonjsGlobal, // window object or global module, // present in node.js (typeof undefined) == 'function' // present with an AMD loader ); } (xor4096$1)); var xor4096Exports = xor4096$1.exports; var tychei$1 = {exports: {}}; (function (module) { // A Javascript implementaion of the "Tyche-i" prng algorithm by // Samuel Neves and Filipe Araujo. // See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf (function(global, module, define) { function XorGen(seed) { var me = this, strseed = ''; // Set up generator function. me.next = function() { var b = me.b, c = me.c, d = me.d, a = me.a; b = (b << 25) ^ (b >>> 7) ^ c; c = (c - d) | 0; d = (d << 24) ^ (d >>> 8) ^ a; a = (a - b) | 0; me.b = b = (b << 20) ^ (b >>> 12) ^ c; me.c = c = (c - d) | 0; me.d = (d << 16) ^ (c >>> 16) ^ a; return me.a = (a - b) | 0; }; /* The following is non-inverted tyche, which has better internal * bit diffusion, but which is about 25% slower than tyche-i in JS. me.next = function() { var a = me.a, b = me.b, c = me.c, d = me.d; a = (me.a + me.b | 0) >>> 0; d = me.d ^ a; d = d << 16 ^ d >>> 16; c = me.c + d | 0; b = me.b ^ c; b = b << 12 ^ d >>> 20; me.a = a = a + b | 0; d = d ^ a; me.d = d = d << 8 ^ d >>> 24; me.c = c = c + d | 0; b = b ^ c; return me.b = (b << 7 ^ b >>> 25); } */ me.a = 0; me.b = 0; me.c = 2654435769 | 0; me.d = 1367130551; if (seed === Math.floor(seed)) { // Integer seed. me.a = (seed / 0x100000000) | 0; me.b = seed | 0; } else { // String seed. strseed += seed; } // Mix in string seed, then discard an initial batch of 64 values. for (var k = 0; k < strseed.length + 20; k++) { me.b ^= strseed.charCodeAt(k) | 0; me.next(); } } function copy(f, t) { t.a = f.a; t.b = f.b; t.c = f.c; t.d = f.d; return t; } function impl(seed, opts) { var xg = new XorGen(seed), state = opts && opts.state, prng = function() { return (xg.next() >>> 0) / 0x100000000; }; prng.double = function() { do { var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 0x100000000, result = (top + bot) / (1 << 21); } while (result === 0); return result; }; prng.int32 = xg.next; prng.quick = prng; if (state) { if (typeof(state) == 'object') copy(state, xg); prng.state = function() { return copy(xg, {}); }; } return prng; } if (module && module.exports) { module.exports = impl; } else if (define && define.amd) { define(function() { return impl; }); } else { this.tychei = impl; } })( commonjsGlobal, module, // present in node.js (typeof undefined) == 'function' // present with an AMD loader ); } (tychei$1)); var tycheiExports = tychei$1.exports; var seedrandom$1 = {exports: {}}; var _nodeResolve_empty = {}; var _nodeResolve_empty$1 = { __proto__: null, default: _nodeResolve_empty }; var require$$0 = /*@__PURE__*/getAugmentedNamespace(_nodeResolve_empty$1); /* Copyright 2019 David Bau. 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. */ (function (module) { (function (global, pool, math) { // // The following constants are related to IEEE 754 limits. // var width = 256, // each RC4 output is 0 <= x < 256 chunks = 6, // at least six RC4 outputs for each double digits = 52, // there are 52 significant digits in a double rngname = 'random', // rngname: name for Math.random and Math.seedrandom startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask = width - 1, nodecrypto; // node.js crypto module, initialized at the bottom. // // seedrandom() // This is the seedrandom function described above. // function seedrandom(seed, options, callback) { var key = []; options = (options == true) ? { entropy: true } : (options || {}); // Flatten the seed string or build one from local entropy if needed. var shortseed = mixkey(flatten( options.entropy ? [seed, tostring(pool)] : (seed == null) ? autoseed() : seed, 3), key); // Use the seed to initialize an ARC4 generator. var arc4 = new ARC4(key); // This function returns a random double in [0, 1) that contains // randomness in every bit of the mantissa of the IEEE 754 value. var prng = function() { var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48 d = startdenom, // and denominator d = 2 ^ 48. x = 0; // and no 'extra last byte'. while (n < significance) { // Fill up all significant digits by n = (n + x) * width; // shifting numerator and d *= width; // denominator and generating a x = arc4.g(1); // new least-significant-byte. } while (n >= overflow) { // To avoid rounding up, before adding n /= 2; // last byte, shift everything d /= 2; // right using integer math until x >>>= 1; // we have exactly the desired bits. } return (n + x) / d; // Form the number within [0, 1). }; prng.int32 = function() { return arc4.g(4) | 0; }; prng.quick = function() { return arc4.g(4) / 0x100000000; }; prng.double = prng; // Mix the randomness into accumulated entropy. mixkey(tostring(arc4.S), pool); // Calling convention: what to return as a function of prng, seed, is_math. return (options.pass || callback || function(prng, seed, is_math_call, state) { if (state) { // Load the arc4 state from the given state if it has an S array. if (state.S) { copy(state, arc4); } // Only provide the .state method if requested via options.state. prng.state = function() { return copy(arc4, {}); }; } // If called as a method of Math (Math.seedrandom()), mutate // Math.random because that is how seedrandom.js has worked since v1.0. if (is_math_call) { math[rngname] = prng; return seed; } // Otherwise, it is a newer calling convention, so return the // prng directly. else return prng; })( prng, shortseed, 'global' in options ? options.global : (this == math), options.state); } // // ARC4 // // An ARC4 implementation. The constructor takes a key in the form of // an array of at most (width) integers that should be 0 <= x < (width). // // The g(count) method returns a pseudorandom integer that concatenates // the next (count) outputs from ARC4. Its return value is a number x // that is in the range 0 <= x < (width ^ count). // function ARC4(key) { var t, keylen = key.length, me = this, i = 0, j = me.i = me.j = 0, s = me.S = []; // The empty key [] is treated as [0]. if (!keylen) { key = [keylen++]; } // Set up S using the standard key scheduling algorithm. while (i < width) { s[i] = i++; } for (i = 0; i < width; i++) { s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))]; s[j] = t; } // The "g" method returns the next (count) outputs as one number. (me.g = function(count) { // Using instance members instead of closure state nearly doubles speed. var t, r = 0, i = me.i, j = me.j, s = me.S; while (count--) { t = s[i = mask & (i + 1)]; r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))]; } me.i = i; me.j = j; return r; // For robust unpredictability, the function call below automatically // discards an initial batch of values. This is called RC4-drop[256]. // See http://google.com/search?q=rsa+fluhrer+response&btnI })(width); } // // copy() // Copies internal state of ARC4 to or from a plain object. // function copy(f, t) { t.i = f.i; t.j = f.j; t.S = f.S.slice(); return t; } // // flatten() // Converts an object tree to nested arrays of strings. // function flatten(obj, depth) { var result = [], typ = (typeof obj), prop; if (depth && typ == 'object') { for (prop in obj) { try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {} } } return (result.length ? result : typ == 'string' ? obj : obj + '\0'); } // // mixkey() // Mixes a string seed into a key that is an array of integers, and // returns a shortened string seed that is equivalent to the result key. // function mixkey(seed, key) { var stringseed = seed + '', smear, j = 0; while (j < stringseed.length) { key[mask & j] = mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++)); } return tostring(key); } // // autoseed() // Returns an object for autoseeding, using window.crypto and Node crypto // module if available. // function autoseed() { try { var out; if (nodecrypto && (out = nodecrypto.randomBytes)) { // The use of 'out' to remember randomBytes makes tight minified code. out = out(width); } else { out = new Uint8Array(width); (global.crypto || global.msCrypto).getRandomValues(out); } return tostring(out); } catch (e) { var browser = global.navigator, plugins = browser && browser.plugins; return [+new Date, global, plugins, global.screen, tostring(pool)]; } } // // tostring() // Converts an array of charcodes to a string // function tostring(a) { return String.fromCharCode.apply(0, a); } // // When seedrandom.js is loaded, we immediately mix a few bits // from the built-in RNG into the entropy pool. Because we do // not want to interfere with deterministic PRNG state later, // seedrandom will not call math.random on its own again after // initialization. // mixkey(math.random(), pool); // // Nodejs and AMD support: export the implementation as a module using // either convention. // if (module.exports) { module.exports = seedrandom; // When in node.js, try using crypto package for autoseeding. try { nodecrypto = require$$0; } catch (ex) {} } else { // When included as a plain script, set up Math.seedrandom global. math['seed' + rngname] = seedrandom; } // End anonymous scope, and pass initial values. })( // global: `self` in browsers (including strict mode and web workers), // otherwise `this` in Node and other environments (typeof self !== 'undefined') ? self : commonjsGlobal, [], // pool: entropy pool starts empty Math // math: package containing random, pow, and seedrandom ); } (seedrandom$1)); var seedrandomExports = seedrandom$1.exports; // A library of seedable RNGs implemented in Javascript. // // Usage: // // var seedrandom = require('seedrandom'); // var random = seedrandom(1); // or any seed. // var x = random(); // 0 <= x < 1. Every bit is random. // var x = random.quick(); // 0 <= x < 1. 32 bits of randomness. // alea, a 53-bit multiply-with-carry generator by Johannes Baagøe. // Period: ~2^116 // Reported to pass all BigCrush tests. var alea = aleaExports; // xor128, a pure xor-shift generator by George Marsaglia. // Period: 2^128-1. // Reported to fail: MatrixRank and LinearComp. var xor128 = xor128Exports; // xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl. // Period: 2^192-2^32 // Reported to fail: CollisionOver, SimpPoker, and LinearComp. var xorwow = xorwowExports; // xorshift7, by François Panneton and Pierre L'ecuyer, takes // a different approach: it adds robustness by allowing more shifts // than Marsaglia's original three. It is a 7-shift generator // with 256 bits, that passes BigCrush with no systmatic failures. // Period 2^256-1. // No systematic BigCrush failures reported. var xorshift7 = xorshift7Exports; // xor4096, by Richard Brent, is a 4096-bit xor-shift with a // very long period that also adds a Weyl generator. It also passes // BigCrush with no systematic failures. Its long period may // be useful if you have many generators and need to avoid // collisions. // Period: 2^4128-2^32. // No systematic BigCrush failures reported. var xor4096 = xor4096Exports; // Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random // number generator derived from ChaCha, a modern stream cipher. // https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf // Period: ~2^127 // No systematic BigCrush failures reported. var tychei = tycheiExports; // The original ARC4-based prng included in this library. // Period: ~2^1600 var sr = seedrandomExports; sr.alea = alea; sr.xor128 = xor128; sr.xorwow = xorwow; sr.xorshift7 = xorshift7; sr.xor4096 = xor4096; sr.tychei = tychei; var seedrandom = sr; /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ // https://en.wikipedia.org/wiki/Marsaglia_polar_method class MPRandGauss { constructor(mean, stdDeviation, dtype, truncated, seed) { this.mean = mean; this.stdDev = stdDeviation; this.dtype = dtype; this.nextVal = NaN; this.truncated = truncated; if (this.truncated) { this.upper = this.mean + this.stdDev * 2; this.lower = this.mean - this.stdDev * 2; } const seedValue = seed ? seed : Math.random(); this.random = seedrandom.alea(seedValue.toString()); } /** Returns next sample from a Gaussian distribution. */ nextValue() { if (!isNaN(this.nextVal)) { const value = this.nextVal; this.nextVal = NaN; return value; } let resultX, resultY; let isValid = false; while (!isValid) { let v1, v2, s; do { v1 = 2 * this.random() - 1; v2 = 2 * this.random() - 1; s = v1 * v1 + v2 * v2; } while (s >= 1 || s === 0); const mul = Math.sqrt(-2.0 * Math.log(s) / s); resultX = this.mean + this.stdDev * v1 * mul; resultY = this.mean + this.stdDev * v2 * mul; if (!this.truncated || this.isValidTruncated(resultX)) { isValid = true; } } if (!this.truncated || this.isValidTruncated(resultY)) { this.nextVal = this.convertValue(resultY); } return this.convertValue(resultX); } /** Handles proper rounding for non-floating-point numbers. */ convertValue(value) { if (this.dtype == null || this.dtype === 'float32') { return value; } return Math.round(value); } /** Returns true if less than 2-standard-deviations from the mean. */ isValidTruncated(value) { return value <= this.upper && value >= this.lower; } } // Marsaglia, George, and Wai Wan Tsang. 2000. "A Simple Method for Generating // Gamma Variables." class RandGamma { constructor(alpha, beta, dtype, seed) { this.alpha = alpha; this.beta = 1 / beta; // convert rate to scale parameter this.dtype = dtype; const seedValue = seed ? seed : Math.random(); this.randu = seedrandom.alea(seedValue.toString()); this.randn = new MPRandGauss(0, 1, dtype, false, this.randu()); if (alpha < 1) { this.d = alpha + (2 / 3); } else { this.d = alpha - (1 / 3); } this.c = 1 / Math.sqrt(9 * this.d); } /** Returns next sample from a gamma distribution. */ nextValue() { let x2, v0, v1, x, u, v; while (true) { do { x = this.randn.nextValue(); v = 1 + (this.c * x); } while (v <= 0); v *= v * v; x2 = x * x; v0 = 1 - (0.331 * x2 * x2); v1 = (0.5 * x2) + (this.d * (1 - v + Math.log(v))); u = this.randu(); if (u < v0 || Math.log(u) < v1) { break; } } v = (1 / this.beta) * this.d * v; if (this.alpha < 1) { v *= Math.pow(this.randu(), 1 / this.alpha); } return this.convertValue(v); } /** Handles proper rounding for non-floating-point numbers. */ convertValue(value) { if (this.dtype === 'float32') { return value; } return Math.round(value); } } class UniformRandom { constructor(min = 0, max = 1, dtype, seed) { /** Handles proper rounding for non floating point numbers. */ this.canReturnFloat = () => (this.dtype == null || this.dtype === 'float32'); this.min = min; this.range = max - min; this.dtype = dtype; if (seed == null) { seed = Math.random(); } if (typeof seed === 'number') { seed = seed.toString(); } if (!this.canReturnFloat() && this.range <= 1) { throw new Error(`The difference between ${min} - ${max} <= 1 and dtype is not float`); } this.random = seedrandom.alea(seed); } convertValue(value) { if (this.canReturnFloat()) { return value; } return Math.round(value); } nextValue() { return this.convertValue(this.min + this.range * this.random()); } } /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates a `tf.Tensor` with values sampled from a gamma distribution. * * ```js * tf.randomGamma([2, 2], 1).print(); * ``` * * @param shape An array of integers defining the output tensor shape. * @param alpha The shape parameter of the gamma distribution. * @param beta The inverse scale parameter of the gamma distribution. Defaults * to 1. * @param dtype The data type of the output. Defaults to float32. * @param seed The seed for the random number generator. * * @doc {heading: 'Tensors', subheading: 'Random'} */ function randomGamma_(shape, alpha, beta = 1, dtype = 'float32', seed) { assertNonNegativeIntegerDimensions(shape); if (beta == null) { beta = 1; } if (dtype == null) { dtype = 'float32'; } if (dtype !== 'float32' && dtype !== 'int32') { throw new Error(`Unsupported data type ${dtype}`); } const rgamma = new RandGamma(alpha, beta, dtype, seed); const res = buffer(shape, dtype); for (let i = 0; i < res.values.length; i++) { res.values[i] = rgamma.nextValue(); } return res.toTensor(); } const randomGamma = /* @__PURE__ */ op({ randomGamma_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates a `tf.Tensor` with values sampled from a normal distribution. * * ```js * tf.randomNormal([2, 2]).print(); * ``` * * @param shape An array of integers defining the output tensor shape. * @param mean The mean of the normal distribution. * @param stdDev The standard deviation of the normal distribution. * @param dtype The data type of the output. * @param seed The seed for the random number generator. * * @doc {heading: 'Tensors', subheading: 'Random'} */ function randomNormal_(shape, mean = 0, stdDev = 1, dtype, seed) { assertNonNegativeIntegerDimensions(shape); if (dtype != null && dtype === 'bool') { throw new Error(`Unsupported data type ${dtype}`); } const randGauss = new MPRandGauss(mean, stdDev, dtype, false /* truncated */, seed); const res = buffer(shape, dtype); for (let i = 0; i < res.values.length; i++) { res.values[i] = randGauss.nextValue(); } return res.toTensor(); } const randomNormal = /* @__PURE__ */ op({ randomNormal_ }); /** * @license * Copyright 2022 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates a `tf.Tensor` with values sampled from a normal distribution. * * The generated values will have mean 0 and standard deviation 1. * * ```js * tf.randomStandardNormal([2, 2]).print(); * ``` * * @param shape An array of integers defining the output tensor shape. * @param dtype The data type of the output. * @param seed The seed for the random number generator. * * @doc {heading: 'Tensors', subheading: 'Random'} */ function randomStandardNormal_(shape, dtype, seed) { if (dtype != null && dtype === 'bool') { throw new Error(`Unsupported data type ${dtype}`); } return randomNormal(shape, 0, 1, dtype, seed); } const randomStandardNormal = /* @__PURE__ */ op({ randomStandardNormal_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates a `tf.Tensor` with values sampled from a uniform distribution. * * The generated values follow a uniform distribution in the range [minval, * maxval). The lower bound minval is included in the range, while the upper * bound maxval is excluded. * * ```js * tf.randomUniform([2, 2]).print(); * ``` * * @param shape An array of integers defining the output tensor shape. * @param minval The lower bound on the range of random values to generate. * Defaults to 0. * @param maxval The upper bound on the range of random values to generate. * Defaults to 1. * @param dtype The data type of the output tensor. Defaults to 'float32'. * @param seed An optional int. Defaults to 0. If seed is set to be non-zero, * the random number generator is seeded by the given seed. Otherwise, it is * seeded by a random seed. * * @doc {heading: 'Tensors', subheading: 'Random'} */ function randomUniform_(shape, minval = 0, maxval = 1, dtype = 'float32', seed) { assertNonNegativeIntegerDimensions(shape); const res = buffer(shape, dtype); const random = new UniformRandom(minval, maxval, null, seed); for (let i = 0; i < res.values.length; i++) { res.values[i] = random.nextValue(); } return res.toTensor(); } const randomUniform = /* @__PURE__ */ op({ randomUniform_ }); /** * @license * Copyright 2023 Google LLC. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates a `tf.Tensor` with integers sampled from a uniform distribution. * * The generated values are uniform integers in the range [minval, maxval). The * lower bound minval is included in the range, while the upper bound maxval is * excluded. * * ```js * tf.randomUniformInt([2, 2], 0, 10).print(); * ``` * * @param shape An array of integers defining the output tensor shape. * @param minval Inclusive lower bound on the generated integers. * @param maxval Exclusive upper bound on the generated integers. * @param seed An optional int. Defaults to 0. If seed is set to be non-zero, * the random number generator is seeded by the given seed. Otherwise, it is * seeded by a random seed. * * @doc {heading: 'Tensors', subheading: 'Random'} */ function randomUniformInt_(shape, minval, maxval, seed) { // TODO(mattsoulanille): Handle optional seed2 input. return randomUniform(shape, minval, maxval, 'int32', seed); } const randomUniformInt = /* @__PURE__ */ op({ randomUniformInt_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates a new `tf.Tensor1D` filled with the numbers in the range provided. * * The tensor is a half-open interval meaning it includes start, but * excludes stop. Decrementing ranges and negative step values are also * supported. * * * ```js * tf.range(0, 9, 2).print(); * ``` * * @param start An integer start value * @param stop An integer stop value * @param step An integer increment (will default to 1 or -1) * @param dtype The data type of the output tensor. Defaults to 'float32'. * * @doc {heading: 'Tensors', subheading: 'Creation'} */ function range(start, stop, step = 1, dtype = 'float32') { if (step === 0) { throw new Error('Cannot have a step of zero'); } const attrs = { start, stop, step, dtype }; return ENGINE.runKernel(Range, {} /* inputs */, attrs); } /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns the real part of a complex (or real) tensor. * * Given a tensor input, this operation returns a tensor of type float that is * the real part of each element in input considered as a complex number. * * If the input is real, it simply makes a clone. * * ```js * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]); * tf.real(x).print(); * ``` * * @doc {heading: 'Tensors', subheading: 'Creation'} */ function real_(input) { const $input = convertToTensor(input, 'input', 'real'); const inputs = { input: $input }; return ENGINE.runKernel(Real, inputs); } const real = /* @__PURE__ */ op({ real_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes reciprocal of x element-wise: `1 / x` * * ```js * const x = tf.tensor1d([0, 1, 2]); * * x.reciprocal().print(); // or tf.reciprocal(x) * ``` * @param x The input tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function reciprocal_(x) { const $x = convertToTensor(x, 'x', 'reciprocal'); const inputs = { x: $x }; return ENGINE.runKernel(Reciprocal, inputs); } const reciprocal = /* @__PURE__ */ op({ reciprocal_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes rectified linear element-wise: `max(x, 0)`. * * ```js * const x = tf.tensor1d([-1, 2, -3, 4]); * * x.relu().print(); // or tf.relu(x) * ``` * @param x The input tensor. If the dtype is `bool`, the output dtype will be * `int32`. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function relu_(x) { const $x = convertToTensor(x, 'x', 'relu'); const inputs = { x: $x }; return ENGINE.runKernel(Relu, inputs); } const relu = /* @__PURE__ */ op({ relu_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes rectified linear 6 element-wise: `min(max(x, 0), 6)`. * * ```js * const x = tf.tensor1d([-1, 2, -3, 8]); * * x.relu6().print(); // or tf.relu6(x) * ``` * @param x The input tensor. If the dtype is `bool`, the output dtype will be * `int32`. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function relu6_(x) { const $x = convertToTensor(x, 'x', 'relu6'); const inputs = { x: $x }; return ENGINE.runKernel(Relu6, inputs); } const relu6 = /* @__PURE__ */ op({ relu6_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Reverses a `tf.Tensor` along a specified axis. * * Also available are stricter rank-specific methods that assert that `x` is * of the given rank: * - `tf.reverse1d` * - `tf.reverse2d` * - `tf.reverse3d` * - `tf.reverse4d` * * Except `tf.reverse1d` (which does not have axis param), all methods have * same signature as this method. * * ```js * const x = tf.tensor1d([1, 2, 3, 4]); * * x.reverse().print(); * ``` * * ```js * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); * * const axis = 1; * x.reverse(axis).print(); * ``` * @param x The input tensor to be reversed. * @param axis The set of dimensions to reverse. Must be in the * range [-rank(x), rank(x)). Defaults to all axes. * * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */ function reverse_(x, axis) { const $x = convertToTensor(x, 'x', 'reverse'); const inputs = { x: $x }; const attrs = { dims: axis }; return ENGINE.runKernel(Reverse, inputs, attrs); } const reverse = /* @__PURE__ */ op({ reverse_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Reverses a `tf.Tensor1D`. * * @param x The input tensor. */ function reverse1d_(x) { const $x = convertToTensor(x, 'x', 'reverse'); assert($x.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`); return reverse($x, 0); } const reverse1d = /* @__PURE__ */ op({ reverse1d_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Reverses a `tf.Tensor2D` along a specified axis. * * @param x The input tensor. * @param axis The set of dimensions to reverse. Must be in the * range [-rank(x), rank(x)). Defaults to all axes. */ function reverse2d_(x, axis) { const $x = convertToTensor(x, 'x', 'reverse'); assert($x.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`); return reverse($x, axis); } const reverse2d = /* @__PURE__ */ op({ reverse2d_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Reverses a `tf.Tensor3D` along a specified axis. * * @param x The input tensor. * @param axis The set of dimensions to reverse. Must be in the * range [-rank(x), rank(x)). Defaults to all axes. */ function reverse3d_(x, axis) { const $x = convertToTensor(x, 'x', 'reverse'); assert($x.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`); return reverse($x, axis); } const reverse3d = /* @__PURE__ */ op({ reverse3d_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Reverses a `tf.Tensor4D` along a specified axis. * * @param x The input tensor. * @param axis The set of dimensions to reverse. Must be in the * range [-rank(x), rank(x)). Defaults to all axes. */ function reverse4d_(x, axis) { const $x = convertToTensor(x, 'x', 'reverse'); assert($x.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`); return reverse($x, axis); } const reverse4d = /* @__PURE__ */ op({ reverse4d_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes round of input `tf.Tensor` element-wise: `round(x)`. * It implements banker's rounding. * * ```js * const x = tf.tensor1d([.6, 1.1, -3.3]); * * x.round().print(); // or tf.round(x) * ``` * @param x The input tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function round_(x) { const $x = convertToTensor(x, 'x', 'round'); const inputs = { x: $x }; return ENGINE.runKernel(Round, inputs); } const round = /* @__PURE__ */ op({ round_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes reciprocal of square root of the input `tf.Tensor` element-wise: * `y = 1 / sqrt(x)` * * ```js * const x = tf.tensor1d([1, 2, 4, -1]); * * x.rsqrt().print(); // or tf.rsqrt(x) * ``` * @param x The input tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function rsqrt_(x) { const $x = convertToTensor(x, 'x', 'rsqrt', 'float32'); const inputs = { x: $x }; return ENGINE.runKernel(Rsqrt, inputs); } const rsqrt = /* @__PURE__ */ op({ rsqrt_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes scaled exponential linear element-wise. * * `x < 0 ? scale * alpha * (exp(x) - 1) : scale * x` * * ```js * const x = tf.tensor1d([-1, 2, -3, 4]); * * x.selu().print(); // or tf.selu(x) * ``` * @param x The input tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function selu_(x) { const $x = convertToTensor(x, 'x', 'selu'); const inputs = { x: $x }; return ENGINE.runKernel(Selu, inputs); } const selu = /* @__PURE__ */ op({ selu_ }); /** * 2-D convolution with separable filters. * * Performs a depthwise convolution that acts separately on channels followed * by a pointwise convolution that mixes channels. Note that this is * separability between dimensions [1, 2] and 3, not spatial separability * between dimensions 1 and 2. * * See * [https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d]( * https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d) * for more details. * * @param x The input tensor, of rank 4 or rank 3, of shape * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is * assumed. * @param depthwiseFilter The depthwise filter tensor, rank 4, of shape * `[filterHeight, filterWidth, inChannels, channelMultiplier]`. This is * the filter used in the first step. * @param pointwiseFilter The pointwise filter tensor, rank 4, of shape * `[1, 1, inChannels * channelMultiplier, outChannels]`. This is * the filter used in the second step. * @param strides The strides of the convolution: `[strideHeight, * strideWidth]`. If strides is a single number, then `strideHeight == * strideWidth`. * @param pad The type of padding algorithm. * - `same` and stride 1: output will be of same size as input, * regardless of filter size. * - `valid`: output will be smaller than input if filter is larger * than 1x1. * - For more info, see this guide: * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) * @param dilations The dilation rates: `[dilationHeight, dilationWidth]` * in which we sample input values across the height and width dimensions * in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single * number, then `dilationHeight == dilationWidth`. If it is greater than * 1, then all values of `strides` must be 1. * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to * "NHWC". Specify the data format of the input and output data. With the * default format "NHWC", the data is stored in the order of: [batch, * height, width, channels]. Only "NHWC" is currently supported. * * @doc {heading: 'Operations', subheading: 'Convolution'} */ function separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad, dilation = [1, 1], dataFormat = 'NHWC') { const $x = convertToTensor(x, 'x', 'separableConv2d'); const $depthwiseFilter = convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d'); const $pointwiseFilter = convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d'); let x4D = $x; let reshapedTo4D = false; if ($x.rank === 3) { reshapedTo4D = true; x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); } if (dataFormat === 'NCHW') { throw new Error('separableConv2d currently does not support dataFormat NCHW; only ' + 'NHWC is supported'); } assert(x4D.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got ` + `rank ${x4D.rank}.`); assert($depthwiseFilter.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but ` + `got rank ${$depthwiseFilter.rank}.`); assert($pointwiseFilter.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but ` + `got rank ${$depthwiseFilter.rank}.`); assert($pointwiseFilter.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter ` + ` must be 1, but got ${$pointwiseFilter.shape[0]}.`); assert($pointwiseFilter.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise ` + `filter must be 1, but got ${$pointwiseFilter.shape[1]}.`); const inChannels = $depthwiseFilter.shape[2]; const channelMultiplier = $depthwiseFilter.shape[3]; assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, () => `Error in separableConv2d: the third dimension of pointwise filter ` + `must be ${inChannels * channelMultiplier}, ` + `but got ${$pointwiseFilter.shape[2]}.`); const depthwise = depthwiseConv2d$1(x4D, $depthwiseFilter, strides, pad, dataFormat, dilation); const pointwiseStride = 1; const res = conv2d$1(depthwise, $pointwiseFilter, pointwiseStride, 'valid', dataFormat); if (reshapedTo4D) { return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); } return res; } const separableConv2d = /* @__PURE__ */ op({ separableConv2d_ }); /** * @license * Copyright 2020 Google Inc. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the difference between two lists of numbers. * * Given a Tensor `x` and a Tensor `y`, this operation returns a Tensor `out` * that represents all values that are in `x` but not in `y`. The returned * Tensor `out` is sorted in the same order that the numbers appear in `x` * (duplicates are preserved). This operation also returns a Tensor indices that * represents the position of each out element in `x`. In other words: * * `out[i] = x[idx[i]] for i in [0, 1, ..., out.length - 1]` * * ```js * const x = [1, 2, 3, 4, 5, 6]; * const y = [1, 3, 5]; * * const [out, indices] = await tf.setdiff1dAsync(x, y); * out.print(); // [2, 4, 6] * indices.print(); // [1, 3, 5] * ``` * * @param x 1-D Tensor. Values to keep. * @param y 1-D Tensor. Must have the same type as x. Values to exclude in the * output. * @returns Promise of Tensor tuple [out, indices]. * out: Tensor with the same type as x. * indices: A Tensor of type int32. * * @doc {heading: 'Tensors', subheading: 'Transformations'} */ async function setdiff1dAsync_(x, y) { const $x = convertToTensor(x, 'x', 'setdiff1d'); const $y = convertToTensor(y, 'y', 'setdiff1d'); assert($x.dtype === $y.dtype, () => `x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`); assert($x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`); assert($y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`); const xVals = await $x.data(); const yVals = await $y.data(); const ySet = new Set(yVals); let outputSize = 0; for (let i = 0; i < xVals.length; i++) { if (!ySet.has(xVals[i])) { outputSize++; } } const buffer = new TensorBuffer([outputSize], $x.dtype); const indices = new TensorBuffer([outputSize], 'int32'); for (let i = 0, p = 0; i < xVals.length; i++) { if (!ySet.has(xVals[i])) { buffer.values[p] = xVals[i]; indices.values[p] = i; p++; } } return [buffer.toTensor(), indices.toTensor()]; } const setdiff1dAsync = setdiff1dAsync_; /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns an element-wise indication of the sign of a number. * * ```js * const x = tf.tensor1d([.6, 1.1, -3.3, NaN, 0]); * * x.sign().print(); // or tf.sign(x) * ``` * @param x The input Tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function sign_(x) { const $x = convertToTensor(x, 'x', 'sign'); const inputs = { x: $x }; return ENGINE.runKernel(Sign, inputs); } const sign = /* @__PURE__ */ op({ sign_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes sin of the input Tensor element-wise: `sin(x)` * * ```js * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]); * * x.sin().print(); // or tf.sin(x) * ``` * @param x The input tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function sin_(x) { const $x = convertToTensor(x, 'x', 'sin', 'float32'); const inputs = { x: $x }; return ENGINE.runKernel(Sin, inputs); } const sin = /* @__PURE__ */ op({ sin_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes hyperbolic sin of the input `tf.Tensor` element-wise: `sinh(x)` * * ```js * const x = tf.tensor1d([0, 1, -1, .7]); * * x.sinh().print(); // or tf.sinh(x) * ``` * @param x The input tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function sinh_(x) { const $x = convertToTensor(x, 'x', 'sinh'); const inputs = { x: $x }; return ENGINE.runKernel(Sinh, inputs); } const sinh = /* @__PURE__ */ op({ sinh_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Extracts a 1D slice from 1D array starting at coordinates `begin` and is * of length `size`. See `slice` for details. */ function slice1d_(x, begin, size) { const $x = convertToTensor(x, 'x', 'slice1d'); assert($x.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`); return slice($x, [begin], [size]); } const slice1d = /* @__PURE__ */ op({ slice1d_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Extracts a 2D slice from a 2D array starting at coordinates `begin` and * is of size `size`. See `slice` for details. */ function slice2d_(x, begin, size) { const $x = convertToTensor(x, 'x', 'slice2d'); assert($x.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`); return slice($x, begin, size); } const slice2d = /* @__PURE__ */ op({ slice2d_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Extracts a 3D slice from a 3D array starting at coordinates `begin` and * is of size `size`. See `slice` for details. */ function slice3d_(x, begin, size) { const $x = convertToTensor(x, 'x', 'slice3d'); assert($x.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`); return slice($x, begin, size); } const slice3d = /* @__PURE__ */ op({ slice3d_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Extracts a 4D slice from a 4D array starting at coordinates `begin` and * is of size `size`. See `slice` for details. */ function slice4d_(x, begin, size) { const $x = convertToTensor(x, 'x', 'slice4d'); assert($x.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`); return slice($x, begin, size); } const slice4d = /* @__PURE__ */ op({ slice4d_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the softmax normalized vector given the logits. * * ```js * const a = tf.tensor1d([1, 2, 3]); * * a.softmax().print(); // or tf.softmax(a) * ``` * * ```js * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]); * * a.softmax().print(); // or tf.softmax(a) * ``` * * @param logits The logits array. * @param dim The dimension softmax would be performed on. Defaults to `-1` * which indicates the last dimension. * * @doc {heading: 'Operations', subheading: 'Normalization'} */ function softmax_(logits, dim = -1) { const $logits = convertToTensor(logits, 'logits', 'softmax', 'float32'); if (dim === -1) { dim = $logits.rank - 1; } if (dim !== $logits.rank - 1) { throw Error('Softmax along a non-last dimension is not yet supported. ' + `Logits was rank ${$logits.rank} and dim was ${dim}`); } const inputs = { logits: $logits }; const attrs = { dim }; return ENGINE.runKernel(Softmax, inputs, attrs); } const softmax = /* @__PURE__ */ op({ softmax_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Fast Fourier transform. * * Computes the 1-dimensional discrete Fourier transform over the inner-most * dimension of input. * * ```js * const real = tf.tensor1d([1, 2, 3]); * const imag = tf.tensor1d([1, 2, 3]); * const x = tf.complex(real, imag); * * x.fft().print(); // tf.spectral.fft(x).print(); * ``` * @param input The complex input to compute an fft over. * * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'} */ function fft_(input) { assert(input.dtype === 'complex64', () => `The dtype for tf.spectral.fft() must be complex64 ` + `but got ${input.dtype}.`); const inputs = { input }; return ENGINE.runKernel(FFT, inputs); } const fft = /* @__PURE__ */ op({ fft_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Inverse fast Fourier transform. * * Computes the inverse 1-dimensional discrete Fourier transform over the * inner-most dimension of input. * * ```js * const real = tf.tensor1d([1, 2, 3]); * const imag = tf.tensor1d([1, 2, 3]); * const x = tf.complex(real, imag); * * x.ifft().print(); // tf.spectral.ifft(x).print(); * ``` * @param input The complex input to compute an ifft over. * * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'} */ function ifft_(input) { assert(input.dtype === 'complex64', () => `The dtype for tf.spectral.ifft() must be complex64 ` + `but got ${input.dtype}.`); const inputs = { input }; return ENGINE.runKernel(IFFT, inputs); } const ifft = /* @__PURE__ */ op({ ifft_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Inversed real value input fast Fourier transform. * * Computes the 1-dimensional inversed discrete Fourier transform over the * inner-most dimension of the real input. * * ```js * const real = tf.tensor1d([1, 2, 3]); * const imag = tf.tensor1d([0, 0, 0]); * const x = tf.complex(real, imag); * * x.irfft().print(); * ``` * @param input The real value input to compute an irfft over. * * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'} */ function irfft_(input) { const innerDimensionSize = input.shape[input.shape.length - 1]; const batch = input.size / innerDimensionSize; let ret; if (innerDimensionSize <= 2) { const complexInput = reshape(input, [batch, innerDimensionSize]); ret = ifft(complexInput); } else { // The length of unique components of the DFT of a real-valued signal // is 2 * (input_len - 1) const outputShape = [batch, 2 * (innerDimensionSize - 1)]; const realInput = reshape(real(input), [batch, innerDimensionSize]); const imagInput = reshape(imag(input), [batch, innerDimensionSize]); const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1); const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1)); const r = concat([realInput, realConjugate], 1); const i = concat([imagInput, imagConjugate], 1); const complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]); ret = ifft(complexInput); } ret = real(ret); // reshape the result if the input is 3D tensor. if (input.rank === 3 && input.shape[0] !== 0) { const temp = ret; const batch = input.shape[0]; ret = reshape(ret, [batch, ret.shape[0] / batch, ret.shape[1]]); temp.dispose(); } return ret; } const irfft = /* @__PURE__ */ op({ irfft_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Splits a `tf.Tensor` into sub tensors. * * If `numOrSizeSplits` is a number, splits `x` along dimension `axis` * into `numOrSizeSplits` smaller tensors. * Requires that `numOrSizeSplits` evenly divides `x.shape[axis]`. * * If `numOrSizeSplits` is a number array, splits `x` into * `numOrSizeSplits.length` pieces. The shape of the `i`-th piece has the * same size as `x` except along dimension `axis` where the size is * `numOrSizeSplits[i]`. * * ```js * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]); * const [a, b] = tf.split(x, 2, 1); * a.print(); * b.print(); * * const [c, d, e] = tf.split(x, [1, 2, 1], 1); * c.print(); * d.print(); * e.print(); * ``` * * @param x The input tensor to split. * @param numOrSizeSplits Either an integer indicating the number of * splits along the axis or an array of integers containing the sizes of * each output tensor along the axis. If a number then it must evenly divide * `x.shape[axis]`; otherwise the sum of sizes must match `x.shape[axis]`. * Can contain one -1 indicating that dimension is to be inferred. * @param axis The dimension along which to split. Defaults to 0 (the first * dim). * * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */ function split_(x, numOrSizeSplits, axis = 0) { const $x = convertToTensor(x, 'x', 'split'); const inputs = { x: $x }; const attr = { numOrSizeSplits, axis }; return ENGINE.runKernel(SplitV, inputs, attr); } const split$1 = /* @__PURE__ */ op({ split_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Real value input fast Fourier transform. * * Computes the 1-dimensional discrete Fourier transform over the * inner-most dimension of the real input. * * ```js * const real = tf.tensor1d([1, 2, 3]); * * real.rfft().print(); * ``` * @param input The real value input to compute an rfft over. * * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'} */ function rfft_(input, fftLength) { assert(input.dtype === 'float32', () => `The dtype for rfft() must be real value but got ${input.dtype}`); let innerDimensionSize = input.shape[input.shape.length - 1]; const batch = input.size / innerDimensionSize; let adjustedInput; if (fftLength != null && fftLength < innerDimensionSize) { // Need to crop const begin = input.shape.map(v => 0); const size = input.shape.map(v => v); size[input.shape.length - 1] = fftLength; adjustedInput = slice(input, begin, size); innerDimensionSize = fftLength; } else if (fftLength != null && fftLength > innerDimensionSize) { // Need to pad with zeros const zerosShape = input.shape.map(v => v); zerosShape[input.shape.length - 1] = fftLength - innerDimensionSize; adjustedInput = concat([input, zeros(zerosShape)], input.shape.length - 1); innerDimensionSize = fftLength; } else { adjustedInput = input; } // Complement the input with zero imaginary numbers. const zerosInput = zerosLike(adjustedInput); const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]); const ret = fft(complexInput); // Exclude complex conjugations. These conjugations are put symmetrically. const half = Math.floor(innerDimensionSize / 2) + 1; const realValues = real(ret); const imagValues = imag(ret); const realComplexConjugate = split$1(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1); const imagComplexConjugate = split$1(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1); const outputShape = adjustedInput.shape.slice(); outputShape[adjustedInput.shape.length - 1] = half; return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape); } const rfft = /* @__PURE__ */ op({ rfft_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns (a - b) * (a - b) element-wise. * Supports broadcasting. * * ```js * const a = tf.tensor1d([1, 4, 3, 16]); * const b = tf.tensor1d([1, 2, 9, 4]); * * a.squaredDifference(b).print(); // or tf.squaredDifference(a, b) * ``` * * ```js * // Broadcast squared difference a with b. * const a = tf.tensor1d([2, 4, 6, 8]); * const b = tf.scalar(5); * * a.squaredDifference(b).print(); // or tf.squaredDifference(a, b) * ``` * * @param a The first tensor. * @param b The second tensor. Must have the same type as `a`. * * @doc {heading: 'Operations', subheading: 'Arithmetic'} */ function squaredDifference_(a, b) { let $a = convertToTensor(a, 'a', 'squaredDifference'); let $b = convertToTensor(b, 'b', 'squaredDifference'); [$a, $b] = makeTypesMatch($a, $b); assertAndGetBroadcastShape($a.shape, $b.shape); const inputs = { a: $a, b: $b }; const attrs = {}; return ENGINE.runKernel(SquaredDifference, inputs, attrs); } const squaredDifference = /* @__PURE__ */ op({ squaredDifference_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Removes dimensions of size 1 from the shape of a `tf.Tensor`. * * ```js * const x = tf.tensor([1, 2, 3, 4], [1, 1, 4]); * x.squeeze().print(); * ``` * * @param x The input tensor to be squeezed. * @param axis An optional list of numbers. If specified, only * squeezes the dimensions listed. The dimension index starts at 0. It * is an error to squeeze a dimension that is not 1. * * @doc {heading: 'Tensors', subheading: 'Transformations'} */ function squeeze_(x, axis) { const $x = convertToTensor(x, 'x', 'squeeze', 'string_or_numeric'); return reshape($x, squeezeShape($x.shape, axis).newShape); } const squeeze = /* @__PURE__ */ op({ squeeze_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Stacks a list of rank-`R` `tf.Tensor`s into one rank-`(R+1)` `tf.Tensor`. * * ```js * const a = tf.tensor1d([1, 2]); * const b = tf.tensor1d([3, 4]); * const c = tf.tensor1d([5, 6]); * tf.stack([a, b, c]).print(); * ``` * * @param tensors A list of tensor objects with the same shape and dtype. * @param axis The axis to stack along. Defaults to 0 (the first dim). * * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */ function stack_(tensors, axis = 0) { const $tensors = convertToTensorArray(tensors, 'tensors', 'stack', 'string_or_numeric'); assert($tensors.length >= 1, () => 'Pass at least one tensor to tf.stack'); if ($tensors.length > 0) { assert(axis <= $tensors[0].rank, () => 'Axis must be <= rank of the tensor'); } const inputs = $tensors; const attrs = { axis }; return ENGINE.runKernel(Pack, inputs, attrs); } const stack = /* @__PURE__ */ op({ stack_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes step of the input `tf.Tensor` element-wise: `x > 0 ? 1 : alpha` * * ```js * const x = tf.tensor1d([0, 2, -1, -3]); * * x.step(.5).print(); // or tf.step(x, .5) * ``` * @param x The input tensor. * @param alpha The gradient when input is negative. Defaults to 0. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function step_(x, alpha = 0.0) { const $x = convertToTensor(x, 'x', 'step'); const inputs = { x: $x }; const attrs = { alpha }; return ENGINE.runKernel(Step, inputs, attrs); } const step = /* @__PURE__ */ op({ step_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Extracts a strided slice of a tensor. * * Roughly speaking, this op extracts a slice of size (end-begin)/stride from * the given input tensor (x). Starting at the location specified by begin the * slice continues by adding stride to the index until all dimensions are not * less than end. Note that a stride can be negative, which causes a reverse * slice. * * ```js * const t = tf.tensor3d([1, 1, 1 ,2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6], * [3, 2, 3]); * t.stridedSlice([1, 0, 0], [2, 1, 3], [1, 1, 1]).print() // [[[3, 3, 3]]] * t.stridedSlice([1, 0, 0], [2, 2, 3], [1, 1, 1]).print() // [[[3, 3, 3], * // [4, 4, 4]]] * t.stridedSlice([1, -1, 0], [2, -3, 3], [1, -1, 1]).print() // [[[4, 4, 4], * // [3, 3, 3]]] * ``` * * @param x The tensor to stride slice. * @param begin The coordinates to start the slice from. * @param end: The coordinates to end the slice at. * @param strides: The size of the slice. * @param beginMask: If the ith bit of beginMask is set, begin[i] is ignored * and the fullest possible range in that dimension is used instead. * @param endMask: If the ith bit of endMask is set, end[i] is ignored * and the fullest possible range in that dimension is used instead. * @param shrinkAxisMask: a bitmask where bit i implies that * the ith specification should shrink the dimensionality. begin and end must * imply a slice of size 1 in the dimension. * * @doc {heading: 'Operations', subheading: 'Slicing and Joining'} */ function stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) { const $x = convertToTensor(x, 'x', 'stridedSlice', 'string_or_numeric'); const inputs = { x: $x }; const attrs = { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask }; return ENGINE.runKernel(StridedSlice, inputs, attrs); } const stridedSlice = /* @__PURE__ */ op({ stridedSlice_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes tan of the input `tf.Tensor` element-wise, `tan(x)` * * ```js * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]); * * x.tan().print(); // or tf.tan(x) * ``` * @param x The input tensor. * * @doc {heading: 'Operations', subheading: 'Basic math'} */ function tan_(x) { const $x = convertToTensor(x, 'x', 'tan', 'float32'); const inputs = { x: $x }; return ENGINE.runKernel(Tan, inputs); } const tan = /* @__PURE__ */ op({ tan_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates a `tf.Tensor` with the provided values, shape and dtype. * * ```js * // Pass an array of values to create a vector. * tf.tensor([1, 2, 3, 4]).print(); * ``` * * ```js * // Pass a nested array of values to make a matrix or a higher * // dimensional tensor. * tf.tensor([[1, 2], [3, 4]]).print(); * ``` * * ```js * // Pass a flat array and specify a shape yourself. * tf.tensor([1, 2, 3, 4], [2, 2]).print(); * ``` * * ```js * // Pass a `WebGLData` object and specify a shape yourself. * * // This makes it possible for TF.js applications to avoid GPU / CPU sync. * // For example, if your application includes a preprocessing step on the GPU, * // you could upload the GPU output directly to TF.js, rather than first * // downloading the values. * * // Example for WebGL2: * if (tf.findBackend('custom-webgl') == null) { * const customCanvas = document.createElement('canvas'); * const customBackend = new tf.MathBackendWebGL(customCanvas); * tf.registerBackend('custom-webgl', () => customBackend); * } * const savedBackend = tf.getBackend(); * await tf.setBackend('custom-webgl'); * const gl = tf.backend().gpgpu.gl; * const texture = gl.createTexture(); * const tex2d = gl.TEXTURE_2D; * const width = 2; * const height = 2; * * gl.bindTexture(tex2d, texture); * gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); * gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); * gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST); * gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST); * gl.texImage2D( * tex2d, 0, gl.RGBA32F, // internalFormat * width, height, 0, * gl.RGBA, // textureFormat * gl.FLOAT, // textureType * new Float32Array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]) * ); * * // Currently, the `texture` has 4 pixels: * // Pixel0 is {R:0, G:1, B:2, A:3} * // Pixel1 is {R:4, G:5, B:6, A:7} * // Pixel2 is {R:8, G:9, B:10, A:11} * // Pixel3 is {R:12, G:13, B:14, A:15} * * const logicalShape = [height * width * 2]; * const a = tf.tensor({texture, height, width, channels: 'BR'}, logicalShape); * a.print(); * // Tensor value will be [2, 0, 6, 4, 10, 8, 14, 12], since [2, 0] is the * // values of 'B' and 'R' channels of Pixel0, [6, 4] is the values of 'B' and * 'R' * // channels of Pixel1... * * // For postprocessing on the GPU, it's possible to retrieve the texture * // backing any tensor by calling the tensor's `dataToGPU` method like * // so: * * const tex = a.dataToGPU(); * await tf.setBackend(savedBackend); * ``` * * ```js * // Pass a `WebGPUData` object and specify a shape yourself. * * // This makes it possible for TF.js applications to avoid GPU / CPU sync. * // For example, if your application includes a preprocessing step on the GPU, * // you could upload the GPU output directly to TF.js, rather than first * // downloading the values. Unlike WebGL, this optionally supports zero copy * // by WebGPUData.zeroCopy. When zeroCopy is false or undefined(default), this * // passing GPUBuffer can be destroyed after tensor is created. When zeroCopy * // is true, this GPUBuffer is bound directly by the tensor, so do not destroy * // this GPUBuffer until all access is done. * * // Example for WebGPU: * function createGPUBufferFromData(device, data, dtype) { * const bytesPerElement = 4; * const sizeInBytes = data.length * bytesPerElement; * * const gpuWriteBuffer = device.createBuffer({ * mappedAtCreation: true, * size: sizeInBytes, * usage: GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC * }); * const arrayBuffer = gpuWriteBuffer.getMappedRange(); * if (dtype === 'float32') { * new Float32Array(arrayBuffer).set(data); * } else if (dtype === 'int32') { * new Int32Array(arrayBuffer).set(data); * } else { * throw new Error( * `Creating tensor from GPUBuffer only supports` + * `'float32'|'int32' dtype, while the dtype is ${dtype}.`); * } * gpuWriteBuffer.unmap(); * * const gpuReadBuffer = device.createBuffer({ * mappedAtCreation: false, * size: sizeInBytes, * usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.STORAGE | * GPUBufferUsage.COPY_SRC * }); * * const copyEncoder = device.createCommandEncoder(); * copyEncoder.copyBufferToBuffer( * gpuWriteBuffer, 0, gpuReadBuffer, 0, sizeInBytes); * const copyCommands = copyEncoder.finish(); * device.queue.submit([copyCommands]); * gpuWriteBuffer.destroy(); * return gpuReadBuffer; * } * * const savedBackend = tf.getBackend(); * await tf.setBackend('webgpu').catch( * () => {throw new Error( * 'Failed to use WebGPU backend. Please use Chrome Canary to run.')}); * const dtype = 'float32'; * const device = tf.backend().device; * const aData = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]; * const bData = [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4]; * const expected = [2, 4, 6, 8, 6, 8, 10, 12, 10, 12, 14, 16, 14, 16, 18, 20]; * const aBuffer = createGPUBufferFromData(device, aData, dtype); * const shape = [aData.length]; * // To use zeroCopy, use {buffer: aBuffer, zeroCopy: true} instead and destroy * // aBuffer untill all access is done. * const a = tf.tensor({buffer: aBuffer}, shape, dtype); * const b = tf.tensor(bData, shape, dtype); * const result = tf.add(a, b); * result.print(); * a.dispose(); * b.dispose(); * result.dispose(); * aBuffer.destroy(); * await tf.setBackend(savedBackend); * ``` * @param values The values of the tensor. Can be nested array of numbers, * or a flat array, or a `TypedArray`, or a `WebGLData` object, or a * `WebGPUData` object. If the values are strings, they will be encoded as utf-8 * and kept as `Uint8Array[]`. If the values is a `WebGLData` object, the dtype * could only be 'float32' or 'int32' and the object has to have: 1. texture, a * `WebGLTexture`, the texture must share the same `WebGLRenderingContext` with * TFJS's WebGL backend (you could create a custom WebGL backend from your * texture's canvas) and the internal texture format for the input texture must * be floating point or normalized integer; 2. height, the height of the * texture; 3. width, the width of the texture; 4. channels, a non-empty subset * of 'RGBA', indicating the values of which channels will be passed to the * tensor, such as 'R' or 'BR' (The order of the channels affect the order of * tensor values. ). (If the values passed from texture is less than the tensor * size, zeros will be padded at the rear.). If the values is a `WebGPUData` * object, the dtype could only be 'float32' or 'int32 and the object has to * have: buffer, a `GPUBuffer`. The buffer must: 1. share the same `GPUDevice` * with TFJS's WebGPU backend; 2. buffer.usage should at least support * GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC; 3. buffer.size should not * be smaller than the byte size of tensor shape. WebGPUData optionally supports * zero copy by flag zeroCopy. When zeroCopy is false or undefined(default), * this passing GPUBuffer can be destroyed after tensor is created. When * zeroCopy is true, this GPUBuffer is bound directly by the tensor, so do not * destroy this GPUBuffer until all access is done. * @param shape The shape of the tensor. Optional. If not provided, * it is inferred from `values`. * @param dtype The data type. * * @doc {heading: 'Tensors', subheading: 'Creation'} */ function tensor(values, shape, dtype) { const inferredShape = inferShape(values, dtype); return makeTensor(values, shape, inferredShape, dtype); } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates rank-1 `tf.Tensor` with the provided values, shape and dtype. * * The same functionality can be achieved with `tf.tensor`, but in general * we recommend using `tf.tensor1d` as it makes the code more readable. * * ```js * tf.tensor1d([1, 2, 3]).print(); * ``` * * @param values The values of the tensor. Can be array of numbers, * or a `TypedArray`. * @param dtype The data type. * * @doc {heading: 'Tensors', subheading: 'Creation'} */ function tensor1d(values, dtype) { assertNonNull(values); const inferredShape = inferShape(values, dtype); if (inferredShape.length !== 1) { throw new Error('tensor1d() requires values to be a flat/TypedArray'); } const shape = null; return makeTensor(values, shape, inferredShape, dtype); } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates rank-2 `tf.Tensor` with the provided values, shape and dtype. * * The same functionality can be achieved with `tf.tensor`, but in general * we recommend using `tf.tensor2d` as it makes the code more readable. * * ```js * // Pass a nested array. * tf.tensor2d([[1, 2], [3, 4]]).print(); * ``` * ```js * // Pass a flat array and specify a shape. * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(); * ``` * * @param values The values of the tensor. Can be nested array of numbers, * or a flat array, or a `TypedArray`. * @param shape The shape of the tensor. If not provided, it is inferred from * `values`. * @param dtype The data type. * * @doc {heading: 'Tensors', subheading: 'Creation'} */ function tensor2d(values, shape, dtype) { assertNonNull(values); if (shape != null && shape.length !== 2) { throw new Error('tensor2d() requires shape to have two numbers'); } const inferredShape = inferShape(values, dtype); if (inferredShape.length !== 2 && inferredShape.length !== 1) { throw new Error('tensor2d() requires values to be number[][] or flat/TypedArray'); } if (inferredShape.length === 1 && shape == null) { throw new Error('tensor2d() requires shape to be provided when `values` ' + 'are a flat/TypedArray'); } return makeTensor(values, shape, inferredShape, dtype); } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates rank-3 `tf.Tensor` with the provided values, shape and dtype. * * The same functionality can be achieved with `tf.tensor`, but in general * we recommend using `tf.tensor3d` as it makes the code more readable. * * ```js * // Pass a nested array. * tf.tensor3d([[[1], [2]], [[3], [4]]]).print(); * ``` * ```js * // Pass a flat array and specify a shape. * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print(); * ``` * * @param values The values of the tensor. Can be nested array of numbers, * or a flat array, or a `TypedArray`. * @param shape The shape of the tensor. If not provided, it is inferred from * `values`. * @param dtype The data type. * * @doc {heading: 'Tensors', subheading: 'Creation'} */ function tensor3d(values, shape, dtype) { assertNonNull(values); if (shape != null && shape.length !== 3) { throw new Error('tensor3d() requires shape to have three numbers'); } const inferredShape = inferShape(values, dtype); if (inferredShape.length !== 3 && inferredShape.length !== 1) { throw new Error('tensor3d() requires values to be number[][][] or flat/TypedArray'); } if (inferredShape.length === 1 && shape == null) { throw new Error('tensor3d() requires shape to be provided when `values` ' + 'are a flat array'); } return makeTensor(values, shape, inferredShape, dtype); } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates rank-4 `tf.Tensor` with the provided values, shape and dtype. * * The same functionality can be achieved with `tf.tensor`, but in general * we recommend using `tf.tensor4d` as it makes the code more readable. * * ```js * // Pass a nested array. * tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print(); * ``` * ```js * // Pass a flat array and specify a shape. * tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print(); * ``` * * @param values The values of the tensor. Can be nested array of numbers, * or a flat array, or a `TypedArray`. * @param shape The shape of the tensor. Optional. If not provided, * it is inferred from `values`. * @param dtype The data type. * * @doc {heading: 'Tensors', subheading: 'Creation'} */ function tensor4d(values, shape, dtype) { assertNonNull(values); if (shape != null && shape.length !== 4) { throw new Error('tensor4d() requires shape to have four numbers'); } const inferredShape = inferShape(values, dtype); if (inferredShape.length !== 4 && inferredShape.length !== 1) { throw new Error('tensor4d() requires values to be number[][][][] or flat/TypedArray'); } if (inferredShape.length === 1 && shape == null) { throw new Error('tensor4d() requires shape to be provided when `values` ' + 'are a flat array'); } return makeTensor(values, shape, inferredShape, dtype); } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates rank-5 `tf.Tensor` with the provided values, shape and dtype. * * The same functionality can be achieved with `tf.tensor`, but in general * we recommend using `tf.tensor5d` as it makes the code more readable. * * ```js * // Pass a nested array. * tf.tensor5d([[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]).print(); * ``` * ```js * // Pass a flat array and specify a shape. * tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]).print(); * ``` * * @param values The values of the tensor. Can be nested array of numbers, * or a flat array, or a `TypedArray`. * @param shape The shape of the tensor. Optional. If not provided, * it is inferred from `values`. * @param dtype The data type. * * @doc {heading: 'Tensors', subheading: 'Creation'} */ function tensor5d(values, shape, dtype) { assertNonNull(values); if (shape != null && shape.length !== 5) { throw new Error('tensor5d() requires shape to have five numbers'); } const inferredShape = inferShape(values, dtype); if (inferredShape.length !== 5 && inferredShape.length !== 1) { throw new Error('tensor5d() requires values to be ' + 'number[][][][][] or flat/TypedArray'); } if (inferredShape.length === 1 && shape == null) { throw new Error('tensor5d() requires shape to be provided when `values` ' + 'are a flat array'); } return makeTensor(values, shape, inferredShape, dtype); } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates rank-6 `tf.Tensor` with the provided values, shape and dtype. * * The same functionality can be achieved with `tf.tensor`, but in general * we recommend using `tf.tensor6d` as it makes the code more readable. * * ```js * // Pass a nested array. * tf.tensor6d([[[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]]).print(); * ``` * ```js * // Pass a flat array and specify a shape. * tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 2, 2, 2, 1]).print(); * ``` * * @param values The values of the tensor. Can be nested array of numbers, * or a flat array, or a `TypedArray`. * @param shape The shape of the tensor. Optional. If not provided, * it is inferred from `values`. * @param dtype The data type. * * @doc {heading: 'Tensors', subheading: 'Creation'} */ function tensor6d(values, shape, dtype) { assertNonNull(values); if (shape != null && shape.length !== 6) { throw new Error('tensor6d() requires shape to have six numbers'); } const inferredShape = inferShape(values, dtype); if (inferredShape.length !== 6 && inferredShape.length !== 1) { throw new Error('tensor6d() requires values to be number[][][][][][] or ' + 'flat/TypedArray'); } if (inferredShape.length === 1 && shape == null) { throw new Error('tensor6d() requires shape to be provided when `values` ' + 'are a flat array'); } shape = shape || inferredShape; return makeTensor(values, shape, inferredShape, dtype); } /** * Check whether updates.shape = indices.shape[:batchDim] + * shape[sliceDim:] * * @param x The input tensor. */ function validateUpdateShape(shape, indices, updates) { const sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1; const batchDim = (indices.rank > 1) ? indices.rank - 1 : 1; const shapeError = 'Must have updates.shape = indices.shape[:batchDim] + ' + `shape[sliceDim:], got updates.shape: ${updates.shape}` + `, indices.shape: ${indices.shape}, shape: ${shape}` + `, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`; if (updates.rank < batchDim) { throw new Error(shapeError + ` update.rank < ${batchDim}. `); } if (shape.length < sliceDim + (updates.rank - batchDim)) { throw new Error(shapeError + ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`); } if (updates.rank !== batchDim + shape.length - sliceDim) { throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`); } for (let d = 0; d < batchDim; ++d) { if (updates.shape[d] !== indices.shape[d]) { throw new Error(shapeError + ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`); } } for (let d = 0; d < updates.rank - batchDim; ++d) { if (updates.shape[d + batchDim] !== shape[d + sliceDim]) { throw new Error(shapeError + ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`); } } } /** * Validate scatter nd inputs. * * @param update The tensor contains the update values. * @param indices The tensor contains the indices for the update values. * @param shape The shape of the output tensor. */ function validateInput$1(updates, indices, shape) { if (indices.rank < 1) { throw new Error('tf.scatterND() expects the indices to be rank 1 or higher,' + ` but the rank was ${indices.rank}.`); } if (updates.rank < 1) { throw new Error('tf.scatterND() expects the updates to be rank 1 or higher,' + ` but the rank was ${updates.rank}.`); } if (indices.dtype !== 'int32') { throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`); } if (shape.length < 1) { throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`); } if (shape.length === 0) { if (indices.size === 0) { throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`); } if (updates.size === 0) { throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`); } } validateUpdateShape(shape, indices, updates); } /** * @license * Copyright 2022 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates a new tensor by applying sparse updates to individual * values or slices to the passed in tensor according to * indices. This operator is the similar to scatterNd op, except that the * udpates are scattered on an existing tensor (as opposed to a zero-tensor). * * If indices contains duplicates, then we pick the last update for the index. * * If an out of bound index is found on CPU, an error is returned. * * Warning: There are some GPU specific semantics for this operation. * - If an out of bound index is found, the index is ignored. * - The order in which updates are applied is nondeterministic, so the output * will be nondeterministic if indices contains duplicates. * ```js * const shape = [8]; * const tensor = tf.ones(shape); * const indices = tf.tensor2d([4, 3, 1, 7], [4, 1], 'int32'); * const updates = tf.tensor1d([9, 10, 11, 12]); * * tf.tensorScatterUpdate(tensor, indices, updates).print(); * //[1, 11, 1, 10, 9, 1, 1, 12] * ``` * * @param tensor A Tensor. Tensor to copy/update. * @param indices The tensor contains the indices into the output tensor, must * have at least 2 axes: (num_updates, index_depth). * @param updates The tensor contains the value for the indices. * * @doc {heading: 'Operations', subheading: 'Slicing and Joining'} */ function tensorScatterUpdate_(tensor, indices, updates) { const $tensor = convertToTensor(tensor, 'tensor', 'tensorScatterupdate'); const $indices = convertToTensor(indices, 'indices', 'tensorScatterupdate', 'int32'); const $updates = convertToTensor(updates, 'updates', 'tensorScatterupdate'); validateInput$1($updates, $indices, $tensor.shape); if ($tensor.dtype !== $updates.dtype) { throw new Error(`tensor and updates must have the same dtype, instead they are ${$tensor.dtype} and ${$updates.dtype}.`); } const inputs = { tensor: $tensor, indices: $indices, updates: $updates }; const attrs = {}; // tslint:disable-next-line: no-unnecessary-type-assertion return ENGINE.runKernel(TensorScatterUpdate, inputs, attrs); } const tensorScatterUpdate = op({ tensorScatterUpdate_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Finds the values and indices of the `k` largest entries along the last * dimension. * * If the input is a vector (rank=1), finds the k largest entries in the vector * and outputs their values and indices as vectors. Thus values[j] is the j-th * largest entry in input, and its index is indices[j]. * For higher rank inputs, computes the top k entries along the last dimension. * * If two elements are equal, the lower-index element appears first. * * ```js * const a = tf.tensor2d([[1, 5], [4, 3]]); * const {values, indices} = tf.topk(a); * values.print(); * indices.print(); * ``` * @param x 1-D or higher `tf.Tensor` with last dimension being at least `k`. * @param k Number of top elements to look for along the last dimension. * @param sorted If true, the resulting `k` elements will be sorted by the * values in descending order. * * @doc {heading: 'Operations', subheading: 'Evaluation'} */ function topk_(x, k = 1, sorted = true) { const $x = convertToTensor(x, 'x', 'topk'); if ($x.rank === 0) { throw new Error('topk() expects the input to be of rank 1 or higher'); } const lastDim = $x.shape[$x.shape.length - 1]; if (k < 0) { throw new Error(`'k' passed to topk() must be >= 0 but got ${k}`); } if (k > lastDim) { throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) ` + `but got ${k}`); } const inputs = { x: $x }; const attrs = { k, sorted }; const [values, indices] = ENGINE.runKernel(TopK, inputs, attrs); return { values, indices }; } const topk = /* @__PURE__ */ op({ topk_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates a `tf.Tensor` with values sampled from a truncated normal * distribution. * * ```js * tf.truncatedNormal([2, 2]).print(); * ``` * * The generated values follow a normal distribution with specified mean and * standard deviation, except that values whose magnitude is more than 2 * standard deviations from the mean are dropped and re-picked. * * @param shape An array of integers defining the output tensor shape. * @param mean The mean of the normal distribution. * @param stdDev The standard deviation of the normal distribution. * @param dtype The data type of the output tensor. * @param seed The seed for the random number generator. * * @doc {heading: 'Tensors', subheading: 'Creation'} */ function truncatedNormal_(shape, mean = 0, stdDev = 1, dtype, seed) { assertNonNegativeIntegerDimensions(shape); if (dtype != null && dtype === 'bool') { throw new Error(`Unsupported data type $ { dtype }`); } const randGauss = new MPRandGauss(mean, stdDev, dtype, true /* truncated */, seed); const res = buffer(shape, dtype); for (let i = 0; i < res.values.length; i++) { res.values[i] = randGauss.nextValue(); } return res.toTensor(); } const truncatedNormal = /* @__PURE__ */ op({ truncatedNormal_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Finds unique elements along an axis of a tensor. * * It returns a tensor `values` containing all of the unique elements along the * `axis` of the given tensor `x` in the same order that they occur along the * `axis` in `x`; `x` does not need to be sorted. It also returns a tensor * `indices` the same size as the number of the elements in `x` along the `axis` * dimension. It contains the index in the unique output `values`. * * ```js * // A 1-D tensor * const a = tf.tensor1d([1, 1, 2, 4, 4, 4, 7, 8, 8]); * const {values, indices} = tf.unique(a); * values.print(); // [1, 2, 4, 7, 8,] * indices.print(); // [0, 0, 1, 2, 2, 2, 3, 4, 4] * ``` * * ```js * // A 2-D tensor with axis=0 * // * // 'a' is: [[1, 0, 0], * // [1, 0, 0], * // [2, 0, 0]] * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]); * const {values, indices} = tf.unique(a, 0) * values.print(); // [[1, 0, 0], * // [2, 0, 0]] * indices.print(); // [0, 0, 1] * ``` * * ```js * // A 2-D tensor with axis=1 * // * // 'a' is: [[1, 0, 0], * // [1, 0, 0], * // [2, 0, 0]] * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]); * const {values, indices} = tf.unique(a, 1) * values.print(); // [[1, 0], * // [1, 0], * // [2, 0]] * indices.print(); // [0, 1, 1] * ``` * @param x A tensor (int32, string, bool). * @param axis The axis of the tensor to find the unique elements. * @returns [uniqueElements, indices] (see above for details) * * @doc {heading: 'Operations', subheading: 'Evaluation'} */ function unique_(x, axis = 0) { const $x = convertToTensor(x, 'x', 'unique', 'string_or_numeric'); assert($x.rank > 0, () => 'The input tensor must be at least 1D'); const inputs = { x: $x }; const attrs = { axis }; const [values, indices] = ENGINE.runKernel(Unique, inputs, attrs); return { values, indices }; } const unique = /* @__PURE__ */ op({ unique_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the sum along segments of a `tf.Tensor`. * * ```js * const x = tf.tensor1d([1, 2, 3, 4]); * const segmentIds = tf.tensor1d([1, 2, 0, 1], 'int32'); * const numSegments = 3; * * x.unsortedSegmentSum(segmentIds, numSegments).print() * //or tf.unsortedSegmentSum(x, segmentIds, numSegments) * ``` * @param x The `tf.Tensor` that will be summed along its segments. * @param segmentIds A `tf.Tensor1D` whose rank is equal to the rank of `x`'s * dimension along the `axis`. Maps each element of `x` to a segment. * @param numSegments The number of distinct `segmentIds`. * * @doc {heading: 'Operations', subheading: 'Segment'} */ function unsortedSegmentSum_(x, segmentIds, numSegments) { const $x = convertToTensor(x, 'x', 'unsortedSegmentSum'); const $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'unsortedSegmentSum', 'int32'); assert(isInt(numSegments), () => 'numSegments must be of dtype int'); const inputs = { x: $x, segmentIds: $segmentIds }; const attrs = { numSegments }; return ENGINE.runKernel(UnsortedSegmentSum, inputs, attrs); } const unsortedSegmentSum = /* @__PURE__ */ op({ unsortedSegmentSum_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Unstacks a `tf.Tensor` of rank-`R` into a list of rank-`(R-1)` `tf.Tensor`s. * * ```js * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]); * * tf.unstack(a).forEach(tensor => tensor.print()); * ``` * * @param x A tensor object. * @param axis The axis to unstack along. Defaults to 0 (the first dim). * * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */ function unstack_(x, axis = 0) { const $x = convertToTensor(x, 'x', 'unstack', 'string_or_numeric'); assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`); const inputs = { value: $x }; const attrs = { axis }; return ENGINE.runKernel(Unpack, inputs, attrs); } const unstack = /* @__PURE__ */ op({ unstack_ }); /** * @license * Copyright 2022 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Searches for where a value would go in a sorted sequence. * * This is not a method for checking containment (like javascript in). * * The typical use case for this operation is "binning", "bucketing", or * "discretizing". The values are assigned to bucket-indices based on the edges * listed in 'sortedSequence'. This operation returns the bucket-index for each * value. * * The index returned corresponds to the first edge greater than the value. * * The axis is not settable for this operation. It always operates on the * innermost dimension (axis=-1). The operation will accept any number of outer * dimensions. * * Note: This operation assumes that 'upperBound' is sorted along the * innermost axis, maybe using 'sort(..., axis=-1)'. If the sequence is not * sorted no error is raised and the content of the returned tensor is not well * defined. * * ```js * const seq = tf.tensor1d([0, 3, 9, 10, 10]); * const values = tf.tensor1d([0, 4, 10]); * const result = tf.upperBound(seq, values); * result.print(); // [1, 2, 5] * ``` * @param sortedSequence: N-D. Sorted sequence. * @param values: N-D. Search values. * @return An N-D int32 tensor the size of values containing the result of * applying upper bound to each value. The result is not a global index to * the entire Tensor, but the index in the last dimension. * @doc {heading: 'Operations', subheading: 'Evaluation'} */ function upperBound(sortedSequence, values) { return searchSorted(sortedSequence, values, 'right'); } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates a new variable with the provided initial value. * ```js * const x = tf.variable(tf.tensor([1, 2, 3])); * x.assign(tf.tensor([4, 5, 6])); * * x.print(); * ``` * * @param initialValue Initial value for the tensor. * @param trainable If true, optimizers are allowed to update it. * @param name Name of the variable. Defaults to a unique id. * @param dtype If set, initialValue will be converted to the given type. * * @doc {heading: 'Tensors', subheading: 'Creation'} */ function variable(initialValue, trainable = true, name, dtype) { return ENGINE.makeVariable(initialValue, trainable, name, dtype); } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ function whereImpl(condShape, condVals) { const indices = []; for (let i = 0; i < condVals.length; i++) { if (condVals[i]) { indices.push(i); } } const inBuffer = buffer(condShape, 'int32'); const out = buffer([indices.length, condShape.length], 'int32'); for (let i = 0; i < indices.length; i++) { const loc = inBuffer.indexToLoc(indices[i]); const offset = i * condShape.length; out.values.set(loc, offset); } return out.toTensor(); } /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns the coordinates of true elements of condition. * * The coordinates are returned in a 2-D tensor where the first dimension (rows) * represents the number of true elements, and the second dimension (columns) * represents the coordinates of the true elements. Keep in mind, the shape of * the output tensor can vary depending on how many true values there are in * input. Indices are output in row-major order. The resulting tensor has the * shape `[numTrueElems, condition.rank]`. * * This is analogous to calling the python `tf.where(cond)` without an x or y. * * ```js * const cond = tf.tensor1d([false, false, true], 'bool'); * const result = await tf.whereAsync(cond); * result.print(); * ``` * * @doc {heading: 'Operations', subheading: 'Logical'} */ async function whereAsync_(condition) { const $condition = convertToTensor(condition, 'condition', 'whereAsync', 'bool'); const vals = await $condition.data(); const res = whereImpl($condition.shape, vals); if (condition !== $condition) { $condition.dispose(); } return res; } const whereAsync = whereAsync_; /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Apply boolean mask to tensor. * * ```js * const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]); * const mask = tf.tensor1d([1, 0, 1], 'bool'); * const result = await tf.booleanMaskAsync(tensor, mask); * result.print(); * ``` * * @param tensor N-D tensor. * @param mask K-D boolean tensor, K <= N and K must be known statically. * @param axis A 0-D int Tensor representing the axis in tensor to mask from. * By default, axis is 0 which will mask from the first dimension. * Otherwise K + axis <= N. * * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */ async function booleanMaskAsync_(tensor, mask, axis) { const $tensor = convertToTensor(tensor, 'tensor', 'boolMask'); const $mask = convertToTensor(mask, 'mask', 'boolMask', 'bool'); const axisFrom = axis == null ? 0 : axis; const maskDim = $mask.rank; const tensorShape = $tensor.shape; assert(maskDim > 0, () => 'mask cannot be scalar'); assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`); let leadingSize = 1; for (let i = axisFrom; i < axisFrom + maskDim; i++) { leadingSize *= tensorShape[i]; } const targetTensorShape = tensorShape.slice(0, axisFrom) .concat([leadingSize], tensorShape.slice(axisFrom + maskDim)); const reshapedTensor = reshape($tensor, targetTensorShape); const reshapedMask = reshape($mask, [-1]); const positivePositions = await whereAsync(reshapedMask); const indices = squeeze(positivePositions, [1]); const res = gather(reshapedTensor, indices, axisFrom); // Ensure no memory leak. if (tensor !== $tensor) { $tensor.dispose(); } if (mask !== $mask) { $mask.dispose(); } indices.dispose(); reshapedTensor.dispose(); reshapedMask.dispose(); positivePositions.dispose(); return res; } const booleanMaskAsync = booleanMaskAsync_; /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Executes the provided function `fn` and after it is executed, cleans up all * intermediate tensors allocated by `fn` except those returned by `fn`. * `fn` must not return a Promise (async functions not allowed). The returned * result can be a complex object. * * Using this method helps avoid memory leaks. In general, wrap calls to * operations in `tf.tidy` for automatic memory cleanup. * * NOTE: Variables do *not* get cleaned up when inside a tidy(). If you want to * dispose variables, please use `tf.disposeVariables` or call dispose() * directly on variables. * * ```js * // y = 2 ^ 2 + 1 * const y = tf.tidy(() => { * // a, b, and one will be cleaned up when the tidy ends. * const one = tf.scalar(1); * const a = tf.scalar(2); * const b = a.square(); * * console.log('numTensors (in tidy): ' + tf.memory().numTensors); * * // The value returned inside the tidy function will return * // through the tidy, in this case to the variable y. * return b.add(one); * }); * * console.log('numTensors (outside tidy): ' + tf.memory().numTensors); * y.print(); * ``` * * @param nameOrFn The name of the closure, or the function to execute. * If a name is provided, the 2nd argument should be the function. * If debug mode is on, the timing and the memory usage of the function * will be tracked and displayed on the console using the provided name. * @param fn The function to execute. * * @doc {heading: 'Performance', subheading: 'Memory'} */ function tidy(nameOrFn, fn) { return ENGINE.tidy(nameOrFn, fn); } /** * Disposes any `tf.Tensor`s found within the provided object. * * @param container an object that may be a `tf.Tensor` or may directly * contain `tf.Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. If * the object is not a `tf.Tensor` or does not contain `Tensors`, nothing * happens. In general it is safe to pass any object here, except that * `Promise`s are not supported. * * @doc {heading: 'Performance', subheading: 'Memory'} */ function dispose(container) { const tensors = getTensorsInContainer(container); tensors.forEach(tensor => tensor.dispose()); } /** * Returns the current backend name (cpu, webgl, etc). The backend is * responsible for creating tensors and executing operations on those tensors. * * @doc {heading: 'Backends'} */ function getBackend() { return ENGINE.backendName; } /** * Gets the current backend. If no backends have been initialized, this will * attempt to initialize the best backend. Will throw an error if the highest * priority backend has async initialization, in which case you should call * 'await tf.ready()' before running other code. * * @doc {heading: 'Backends'} */ function backend() { return ENGINE.backend; } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Transposes the `tf.Tensor`. Permutes the dimensions according to `perm`. * * The returned `tf.Tensor`'s dimension `i` will correspond to the input * dimension `perm[i]`. If `perm` is not given, it is set to `[n-1...0]`, * where `n` is the rank of the input `tf.Tensor`. Hence by default, this * operation performs a regular matrix transpose on 2-D input `tf.Tensor`s. * * ```js * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]); * * a.transpose().print(); // or tf.transpose(a) * ``` * * @param x The tensor to transpose. * @param perm The permutation of the dimensions of a. * @param conjugate Will conjugate complex input if true. * * @doc {heading: 'Operations', subheading: 'Matrices'} */ function transpose_(x, perm, conjugate) { const $x = convertToTensor(x, 'x', 'transpose'); if (perm == null) { perm = $x.shape.map((s, i) => i).reverse(); } assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} ` + `must match length of perm ${perm}.`); perm.forEach(axis => { assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1}` + ` but got ${perm}`); }); if ($x.rank <= 1) { return $x.clone(); } const inputs = { x: $x }; const attrs = { perm }; if ($x.dtype === 'complex64') { return tidy(() => { let $real = real($x); let $imag = imag($x); $real = ENGINE.runKernel(Transpose, { x: $real }, attrs); $imag = ENGINE.runKernel(Transpose, { x: $imag }, attrs); if (conjugate) { $imag = neg($imag); } return complex($real, $imag); }); } return ENGINE.runKernel(Transpose, inputs, attrs); } const transpose = /* @__PURE__ */ op({ transpose_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Compute the moving average of a variable. * * Without zeroDebias, the moving average operation is defined by: * `v += delta` * where * `delta = (1 - decay) * (x - v)` * * With zeroDebias (default), the `delta` term is scaled to debias the * effect of the (assumed) zero-initialization of `v`. * `delta /= (1 - decay ^ step)` * * For more details on the zero-debiasing algorithm, see: * https://arxiv.org/abs/1412.6980 * * Note that this function is completely stateless and does not keep track of * step count. The step count needs to be maintained by the caller and passed * in as `step`. * * @param v The current moving average value. * @param x New input value, must have the same shape and dtype as `v`. * @param decay The decay factor. Typical values are 0.95 and 0.99. * @param step Step count. * @param zeroDebias: Whether zeroDebias is to be performed (default: `true`). * @returns The new moving average value. * * @doc {heading: 'Operations', subheading: 'Moving Average'} */ function movingAverage_(v, x, decay, step, zeroDebias = true) { const $v = convertToTensor(v, 'v', 'movingAverage'); const $x = convertToTensor(x, 'x', 'movingAverage'); const $decay = convertToTensor(decay, 'decay', 'movingAverage'); assertTypesMatch($v, $x); assert(arraysEqual($v.shape, $x.shape), () => 'Shape mismatch in v and x'); const one = scalar(1); const oneMinusDecay = sub(one, $decay); let update = mul(sub($x, $v), oneMinusDecay); if (zeroDebias) { assert(step != null, () => 'When using zeroDebias: true, step is required.'); const $step = convertToTensor(step, 'step', 'movingAverage'); update = div(update, sub(one, pow($decay, $step))); } return add($v, update); } const movingAverage = /* @__PURE__ */ op({ movingAverage_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates a new tensor by applying sparse updates to individual * values or slices within a zero tensor of the given shape tensor according to * indices. This operator is the inverse of the `tf.gatherND` operator which * extracts values or slices from a given tensor. * * ```js * const indices = tf.tensor2d([4, 3, 1, 7], [4, 1], 'int32'); * const updates = tf.tensor1d([9, 10, 11, 12]); * const shape = [8]; * tf.scatterND(indices, updates, shape).print() //[0, 11, 0, 10, 9, 0, 0, 12] * ``` * * @param indices The tensor contains the indices into the output tensor. * @param updates The tensor contains the value for the indices. * @param shape: The shape of the output tensor. * * @doc {heading: 'Operations', subheading: 'Slicing and Joining'} */ function scatterND_(indices, updates, shape) { assertNonNegativeIntegerDimensions(shape); const $indices = convertToTensor(indices, 'indices', 'scatterND', 'int32'); const $updates = convertToTensor(updates, 'updates', 'scatterND'); validateInput$1($updates, $indices, shape); const inputs = { indices: $indices, updates: $updates }; const attrs = { shape }; // tslint:disable-next-line: no-unnecessary-type-assertion return ENGINE.runKernel(ScatterNd, inputs, attrs); } const scatterND = /* @__PURE__ */ op({ scatterND_ }); /** * Validate sparseToDense inputs. * * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32. * sparseIndices[i] contains the complete index where sparseValues[i] will be * placed. * @param sparseValues A 0-D or 1-D Tensor. Values * corresponding to each row of sparseIndices, or a scalar value to be used for * all sparse indices. * @param outputShape number[]. Shape of the dense output tensor. * @param validateIndices boolean. indice validation is not supported, error * will be thrown if it is set. */ function validateInput(sparseIndices, sparseValues, outputShape, defaultValues) { if (sparseIndices.dtype !== 'int32') { throw new Error('tf.sparseToDense() expects the indices to be int32 type,' + ` but the dtype was ${sparseIndices.dtype}.`); } if (sparseIndices.rank > 2) { throw new Error('sparseIndices should be a scalar, vector, or matrix,' + ` but got shape ${sparseIndices.shape}.`); } const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1; const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1; if (outputShape.length !== numDims) { throw new Error('outputShape has incorrect number of elements:,' + ` ${outputShape.length}, should be: ${numDims}.`); } const numValues = sparseValues.size; if (!(sparseValues.rank === 0 || sparseValues.rank === 1 && numValues === numElems)) { throw new Error('sparseValues has incorrect shape ' + `${sparseValues.shape}, should be [] or [${numElems}]`); } if (sparseValues.dtype !== defaultValues.dtype) { throw new Error('sparseValues.dtype must match defaultValues.dtype'); } } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Converts a sparse representation into a dense tensor. * * Builds an array dense with shape outputShape such that: * * // If sparseIndices is scalar * dense[i] = (i == sparseIndices ? sparseValues : defaultValue) * * // If sparseIndices is a vector, then for each i * dense[sparseIndices[i]] = sparseValues[i] * * // If sparseIndices is an n by d matrix, then for each i in [0, n) * dense[sparseIndices[i][0], ..., sparseIndices[i][d-1]] = sparseValues[i] * All other values in dense are set to defaultValue. If sparseValues is a * scalar, all sparse indices are set to this single value. * * If indices are repeated the final value is summed over all values for those * indices. * * ```js * const indices = tf.tensor1d([4, 5, 6, 1, 2, 3], 'int32'); * const values = tf.tensor1d([10, 11, 12, 13, 14, 15], 'float32'); * const shape = [8]; * tf.sparseToDense(indices, values, shape).print(); * ``` * * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32. * sparseIndices[i] contains the complete index where sparseValues[i] will be * placed. * @param sparseValues A 0-D or 1-D Tensor. Values * corresponding to each row of sparseIndices, or a scalar value to be used for * all sparse indices. * @param outputShape Shape of the dense output tensor. The type is inferred. * @param defaultValue Scalar. Value to set for indices not specified in * sparseIndices. Defaults to zero. * * @doc {heading: 'Operations', subheading: 'Normalization'} */ function sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) { assertNonNegativeIntegerDimensions(outputShape); const $sparseIndices = convertToTensor(sparseIndices, 'sparseIndices', 'sparseToDense', 'int32'); const $sparseValues = convertToTensor(sparseValues, 'sparseValues', 'sparseToDense', 'string_or_numeric'); const $defaultValue = convertToTensor(defaultValue, 'defaultValue', 'sparseToDense', $sparseValues.dtype); validateInput($sparseIndices, $sparseValues, outputShape, $defaultValue); const inputs = { sparseIndices: $sparseIndices, sparseValues: $sparseValues, defaultValue: $defaultValue }; const attrs = { outputShape }; return ENGINE.runKernel(SparseToDense, inputs, attrs); } const sparseToDense = /* @__PURE__ */ op({ sparseToDense_ }); /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Gather slices from input tensor into a Tensor with shape specified by * `indices`. * * `indices` is a K-dimensional integer tensor, best thought of as a * (K-1)-dimensional tensor of indices into input, where each element defines a * slice of input: * output[\\(i_0, ..., i_{K-2}\\)] = input[indices[\\(i_0, ..., i_{K-2}\\)]] * * Whereas in `tf.gather`, `indices` defines slices into the first dimension of * input, in `tf.gatherND`, `indices` defines slices into the first N dimensions * of input, where N = indices.shape[-1]. * * The last dimension of indices can be at most the rank of input: * indices.shape[-1] <= input.rank * * The last dimension of `indices` corresponds to elements * (if indices.shape[-1] == input.rank) or slices * (if indices.shape[-1] < input.rank) along dimension indices.shape[-1] of * input. * The output tensor has shape * indices.shape[:-1] + input.shape[indices.shape[-1]:] * * Note that on CPU, if an out of bound index is found, an error is returned. On * GPU, if an out of bound index is found, a 0 is stored in the corresponding * output value. * * ```js * const indices = tf.tensor2d([0, 1, 1, 0], [2,2], 'int32'); * const input = tf.tensor2d([9, 10, 11, 12], [2, 2]); * tf.gatherND(input, indices).print() // [10, 11] * ``` * * @param x The tensor from which to gather values. * @param indices Index tensor, must be of type int32. * * @doc {heading: 'Operations', subheading: 'Slicing and Joining'} */ function gatherND_(x, indices) { const $indices = convertToTensor(indices, 'indices', 'gatherND', 'int32'); const $x = convertToTensor(x, 'x', 'gatherND', 'string_or_numeric'); const inputs = { params: $x, indices: $indices }; return ENGINE.runKernel(GatherNd, inputs); } const gatherND = /* @__PURE__ */ op({ gatherND_ }); /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Normalize noise shape based on provided tensor and noise shape. * * @param x Tensor. * @param noiseShape The shape for the randomly generated keep/drop flags, as * an array of numbers. Optional. * @returns Normalized noise shape. */ function getNoiseShape(x, noiseShape) { if (noiseShape == null) { return x.shape.slice(); } if (arraysEqual(x.shape, noiseShape)) { return noiseShape; } if (x.shape.length === noiseShape.length) { const newDimension = []; for (let i = 0; i < x.shape.length; i++) { if (noiseShape[i] == null && x.shape[i] != null) { newDimension.push(x.shape[i]); } else { newDimension.push(noiseShape[i]); } } return newDimension; } return noiseShape; } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes dropout. * * ```js * const x = tf.tensor1d([1, 2, 2, 1]); * const rate = 0.75; * const output = tf.dropout(x, rate); * output.print(); * ``` * * @param x A floating point Tensor or TensorLike. * @param rate A float in the range [0, 1). The probability that each element * of x is discarded. * @param noiseShape An array of numbers of type int32, representing the * shape for randomly generated keep/drop flags. If the noiseShape has null * value, it will be automatically replaced with the x's relative dimension * size. Optional. * @param seed Used to create random seeds. Optional. * @returns A Tensor of the same shape of x. * * @doc {heading: 'Operations', subheading: 'Dropout'} */ function dropout_(x, rate, noiseShape, seed) { const $x = convertToTensor(x, 'x', 'dropout'); assert($x.dtype === 'float32', () => `x has to be a floating point tensor since it's going to be ` + `scaled, but got a ${$x.dtype} tensor instead.`); assert(rate >= 0 && rate < 1, () => `rate must be a float in the range [0, 1), but got ${rate}.`); if (rate === 0) { return x instanceof Tensor ? $x.clone() : $x; } const $noiseShape = getNoiseShape($x, noiseShape); const keepProb = 1 - rate; const multiplier = div(floor(add(randomUniform($noiseShape, 0, 1, 'float32', seed), keepProb)), keepProb); return mul($x, multiplier); } const dropout = /* @__PURE__ */ op({ dropout_ }); /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ function enclosingPowerOfTwo(value) { // Return 2**N for integer N such that 2**N >= value. return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2.0)))); } function cosineWindow(windowLength, a, b) { const even = 1 - windowLength % 2; const newValues = new Float32Array(windowLength); for (let i = 0; i < windowLength; ++i) { const cosArg = (2.0 * Math.PI * i) / (windowLength + even - 1); newValues[i] = a - b * Math.cos(cosArg); } return tensor1d(newValues, 'float32'); } /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Returns whether the targets are in the top K predictions. * * ```js * const predictions = tf.tensor2d([[20, 10, 40, 30], [30, 50, -20, 10]]); * const targets = tf.tensor1d([2, 0]); * const precision = await tf.inTopKAsync(predictions, targets); * precision.print(); * ``` * @param predictions 2-D or higher `tf.Tensor` with last dimension being * at least `k`. * @param targets 1-D or higher `tf.Tensor`. * @param k Optional Number of top elements to look at for computing precision, * default to 1. * * @doc {heading: 'Operations', subheading: 'Evaluation'} */ async function inTopKAsync_(predictions, targets, k = 1) { const $predictions = convertToTensor(predictions, 'predictions', 'inTopK'); const $targets = convertToTensor(targets, 'targets', 'inTopK'); assert($predictions.rank > 1, () => 'inTopK() expects the predictions to be of rank 2 or higher, ' + `but got ${$predictions.rank}`); assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than ` + `targets rank, but got predictions rank ` + `${$predictions.rank} and targets rank ${$targets.rank}`); assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, ` + 'except the last dimension.'); const lastDim = $predictions.shape[$predictions.shape.length - 1]; assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last ` + `dimension (${lastDim}), but got ${k}`); const predictionsVals = await $predictions.data(); const targetsVals = await $targets.data(); // Reshape predictionsVals into a 2d tensor [batch, lastDim] // and look up topK along lastDim. const [batch, size] = [predictionsVals.length / lastDim, lastDim]; const precision = getTypedArrayFromDType('bool', batch); for (let b = 0; b < batch; b++) { const offset = b * size; const vals = predictionsVals.subarray(offset, offset + size); const valAndInd = []; for (let i = 0; i < vals.length; i++) { valAndInd.push({ value: vals[i], index: i }); } valAndInd.sort((a, b) => b.value - a.value); precision[b] = 0; for (let i = 0; i < k; i++) { if (valAndInd[i].index === targetsVals[b]) { precision[b] = 1; break; } } } if (predictions !== $predictions) { $predictions.dispose(); } if (targets !== $targets) { $targets.dispose(); } // Output precision has the same shape as targets. return tensor(precision, $targets.shape, 'bool'); } const inTopKAsync = inTopKAsync_; /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the derivative of the filter of a 2D convolution. * * @param x The input tensor, of rank 4 or rank 3 of shape * [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed. * @param dy The dy image, of rank 4 or rank 3, of shape * [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed. * @param filterShape The shape of the filter, length 4, * [filterHeight, filterWidth, inDepth, outDepth]. * @param strides The strides of the convolution: [strideHeight, * strideWidth]. * @param pad A string from: 'same', 'valid'. The type of padding algorithm * used in the forward prop of the op. * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to * "NHWC". Specify the data format of the input and output data. With the * default format "NHWC", the data is stored in the order of: [batch, * height, width, channels]. * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is * provided, it will default to truncate. */ function conv2DBackpropFilter_(x, dy, filterShape, strides, pad, dataFormat = 'NHWC', dimRoundingMode) { let x4D = x; if (x.rank === 3) { x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]); } let dy4D = dy; if (dy4D.rank === 3) { dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]); } assert(x4D.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ` + `${x4D.shape}.`); assert(dy4D.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ` + `${dy4D.shape}.`); assert(filterShape.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ` + `${filterShape}.`); const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1]; const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1]; assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must ` + `match input depth in filter (${filterShape[2]}.`); assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must ` + `match output depth for filter (${filterShape[3]}).`); checkPadOnDimRoundingMode('conv2dDerFilter', pad, dimRoundingMode); const inputs = { x: x4D, dy: dy4D }; const attrs = { strides, pad, dataFormat, dimRoundingMode, filterShape }; // tslint:disable-next-line: no-unnecessary-type-assertion return ENGINE.runKernel(Conv2DBackpropFilter, inputs, attrs); } const conv2DBackpropFilter = /* @__PURE__ */ op({ conv2DBackpropFilter_ }); /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ // Returns gradient for fused activation. function getFusedDyActivation(dy, y, activation) { if (activation == null || activation === 'linear') { return dy; } if (activation === 'relu') { return mul(dy, step(y)); } throw new Error(`Cannot compute gradient for fused activation ${activation}.`); } // Returns gradient for fused bias. function getFusedBiasGradient(bias, dyActivation) { let res = dyActivation; const reduceAxes = getReductionAxes(bias.shape, dyActivation.shape); if (reduceAxes.length > 0) { res = sum(res, reduceAxes); } return reshape(res, bias.shape); } function applyActivation(x, activation, preluActivationWeights, leakyreluAlpha) { if (activation === 'linear') { return x; } else if (activation === 'relu') { return relu(x); } else if (activation === 'elu') { return elu(x); } else if (activation === 'relu6') { return relu6(x); } else if (activation === 'prelu') { return prelu(x, preluActivationWeights); } else if (activation === 'leakyrelu') { return leakyRelu(x, leakyreluAlpha); } else if (activation === 'sigmoid') { return sigmoid(x); } throw new Error(`Unknown fused activation ${activation}.`); } // Whether we should call fused ops. const shouldFuse = (gradientDepth, activation) => { const gradientMode = gradientDepth > 0; return !gradientMode || activation === 'linear'; }; /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes a 2D convolution over the input x, optionally fused with adding a * bias and applying an activation. * * ```js * const inputDepth = 2; * const inShape = [2, 2, 2, inputDepth]; * const outputDepth = 2; * const fSize = 1; * const pad = 0; * const strides = 1; * * const x = tf.tensor4d( [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, * 16], inShape); * const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, * outputDepth]); * * tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat: 'NHWC', * dilations: [1, 1], bias: tf.scalar(5), activation: 'relu' }).print(); * ``` * * @param obj An object with the following properties: * @param x The input tensor, of rank 4 or rank 3, of shape * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is * assumed. * @param filter The filter, rank 4, of shape * `[filterHeight, filterWidth, inDepth, outDepth]`. * @param strides The strides of the convolution: `[strideHeight, * strideWidth]`. * @param pad The type of padding algorithm. * - `same` and stride 1: output will be of same size as input, * regardless of filter size. * - `valid` output will be smaller than input if filter is larger * than 1x1. * - For more info, see this guide: * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) * @param dataFormat An optional string from: "NHWC", "NCHW". Defaults to * "NHWC". Specify the data format of the input and output data. With the * default format "NHWC", the data is stored in the order of: [batch, * height, width, channels]. Only "NHWC" is currently supported. * @param dilations The dilation rates: `[dilationHeight, dilationWidth]` * in which we sample input values across the height and width dimensions * in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single * number, then `dilationHeight == dilationWidth`. If it is greater than * 1, then all values of `strides` must be 1. * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is * provided, it will default to truncate. * @param bias Tensor to be added to the result. * @param activation Name of activation kernel (defaults to `linear`) to be * applied * after biasAdd. * @param preluActivationWeights Tensor of prelu weights to be applied as part * of a `prelu` activation, typically the same shape as `x`. * @param leakyreluAlpha Optional. Alpha to be applied as part of a `leakyrelu` * activation. */ function fusedConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights, leakyreluAlpha }) { activation = activation || 'linear'; if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) { // TODO: Transpose bias and preluActivationWeights properly for NCHW // format before computation. assert(dataFormat === 'NHWC', () => `Error in fused conv2d: got dataFormat of ${dataFormat} but ` + `only NHWC is currently supported for the case of gradient depth ` + `is 0 and the activation is not linear.`); let result = conv2d$1(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode); if (bias != null) { result = add(result, bias); } return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha); } const $x = convertToTensor(x, 'x', 'conv2d', 'float32'); const $filter = convertToTensor(filter, 'filter', 'conv2d', 'float32'); let x4D = $x; let reshapedTo4D = false; if ($x.rank === 3) { reshapedTo4D = true; x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); } assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ` + `${x4D.rank}.`); assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ` + `${$filter.rank}.`); checkPadOnDimRoundingMode('fused conv2d', pad, dimRoundingMode); const inputChannels = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1]; assert($filter.shape[2] === inputChannels, () => `Error in conv2d: depth of input (${inputChannels}) must match ` + `input depth for filter ${$filter.shape[2]}.`); assert(eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv2D: Either strides or dilations must be 1. ' + `Got strides ${strides} and dilations '${dilations}'`); const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode); let $bias; if (bias != null) { $bias = convertToTensor(bias, 'bias', 'fused conv2d'); [$bias] = makeTypesMatch($bias, $x); // According to TensorFlow, the bias is supposed be a 1-D tensor or a // scalar. // // 3-D or 4-D bias is not disabled for NHWC format, because they are // currently being used in some cases. For examplem in our code base, // https://github.com/tensorflow/tfjs/blob/b53bd47e880367ae57493f0ea628abaf08db2d5d/tfjs-core/src/ops/fused/fused_conv2d_test.ts#L1972. if (dataFormat === 'NHWC') { assertAndGetBroadcastShape(convInfo.outShape, $bias.shape); } else { assert($bias.shape.length <= 1, () => `Error in fused conv2d: only supports scalar or 1-D Tensor ` + `bias for NCHW format but got the bias of ` + `rank-${$bias.shape.length}.`); assert($bias.shape.length === 0 || $bias.shape[0] === convInfo.outChannels || $bias.shape[0] === 1, () => `Error in fused conv2d: bias shape (${$bias.shape}) is not ` + `compatible with the number of output channels ` + `(${convInfo.outChannels})`); } } let $preluActivationWeights; if (preluActivationWeights != null) { // PReLU's activation weights could be a scalar, a 1-D tensor or a 3-D // tensor. const alphaShape = preluActivationWeights.shape; assert(alphaShape.length <= 1 || alphaShape.length === 3, () => `Error in fused conv2d: only supports scalar, 1-D Tensor or ` + `3-D Tensor PReLU activation weights but got a tensor of ` + `rank-${alphaShape.length}.`); if (alphaShape.length === 1) { // Whether the data format is NCHW or NHWC, the 1-D PReLU activation // weights tensor should be aligned with the output channels of conv2d // result. assert(alphaShape[0] === 1 || alphaShape[0] === convInfo.outChannels, () => `Error in fused conv2d: PReLU activation weights ` + `(${alphaShape}) is not compatible with the number of output ` + `channels (${convInfo.outChannels}).`); } else if (alphaShape.length === 3) { // Whether the data format is NCHW or NHWC, the PReLU activation weights // tensor should has the compatible shape with the result of conv2d. try { assertAndGetBroadcastShape(alphaShape, convInfo.outShape); } catch (e) { const errMsg = `Error in fused conv2d: PReLU activation weights (${alphaShape}) ` + `is not compatible with the output shape of the conv2d ` + `(${convInfo.outShape}).`; throw Error(errMsg); } } $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused conv2d'); } const grad = (dy, saved) => { assert(dataFormat === 'NHWC', () => `Error in gradient of fused conv2D: got dataFormat of ${dataFormat} but only NHWC is currently supported.`); const [$filter, x4D, y, $bias] = saved; const dyActivation = getFusedDyActivation(dy, y, activation); assert(tupleValuesAreOne(dilations), () => 'Error in gradient of fused conv2D: ' + `dilation rates greater than 1 ` + `are not yet supported in gradients. Got dilations '${dilations}'`); const xDer = conv2DBackpropInput(x4D.shape, dyActivation, $filter, strides, pad); const filterDer = conv2DBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad); const der = [xDer, filterDer]; if ($bias != null) { const biasDer = getFusedBiasGradient($bias, dyActivation); der.push(biasDer); } return der; }; const inputs = { x: x4D, filter: $filter, bias: $bias, preluActivationWeights: $preluActivationWeights }; const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode, activation, leakyreluAlpha }; // Depending on the the params passed in we will have different number of // inputs and thus a a different number of elements in the gradient. if (bias == null) { const customOp = customGrad((x4D, filter, save) => { let res = // tslint:disable-next-line: no-unnecessary-type-assertion ENGINE.runKernel(FusedConv2D, inputs, attrs); save([filter, x4D, res]); if (reshapedTo4D) { // tslint:disable-next-line: no-unnecessary-type-assertion res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); } return { value: res, gradFunc: grad }; }); return customOp(x4D, $filter); } else { const customOpWithBias = customGrad((x4D, filter, bias, save) => { let res = ENGINE.runKernel(FusedConv2D, inputs, attrs); save([filter, x4D, res, bias]); if (reshapedTo4D) { // tslint:disable-next-line: no-unnecessary-type-assertion res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); } return { value: res, gradFunc: grad }; }); return customOpWithBias(x4D, $filter, $bias); } } const conv2d = /* @__PURE__ */ op({ fusedConv2d_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ function depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, strides, pad, dilations = [1, 1], dimRoundingMode) { let x4D = x; if (x.rank === 3) { x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]); } let dy4D = dy; if (dy4D.rank === 3) { dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]); } const inputs = { x: x4D, dy: dy4D }; const attrs = { strides, pad, dimRoundingMode, dilations, filterShape }; // tslint:disable-next-line: no-unnecessary-type-assertion return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter, inputs, attrs); } const depthwiseConv2dNativeBackpropFilter = op({ depthwiseConv2dNativeBackpropFilter_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ function depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, strides, pad, dilations = [1, 1], dimRoundingMode) { let dy4D = dy; let reshapedTo4D = false; if (dy.rank === 3) { reshapedTo4D = true; dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]); } const inputs = { dy: dy4D, filter }; const attrs = { strides, pad, dimRoundingMode, dilations, inputShape: xShape }; const res = // tslint:disable-next-line: no-unnecessary-type-assertion ENGINE.runKernel(DepthwiseConv2dNativeBackpropInput, inputs, attrs); if (reshapedTo4D) { return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); } return res; } const depthwiseConv2dNativeBackpropInput = op({ depthwiseConv2dNativeBackpropInput_ }); /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes depthwise 2D convolution, optionally fused with adding a * bias and applying an activation. * * Given a 4D `input` array and a `filter` array of shape * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing * `inChannels` convolutional filters of depth 1, this op applies a * different filter to each input channel (expanding from 1 channel to * `channelMultiplier` channels for each), then concatenates the results * together. The output has `inChannels * channelMultiplier` channels. * * See * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d]( * https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d) * for more details. * * @param obj An object with the following properties: * @param x The input tensor, of rank 4 or rank 3, of shape * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is * assumed. * @param filter The filter tensor, rank 4, of shape * `[filterHeight, filterWidth, inChannels, channelMultiplier]`. * @param strides The strides of the convolution: `[strideHeight, * strideWidth]`. If strides is a single number, then `strideHeight == * strideWidth`. * @param pad The type of padding algorithm. * - `same` and stride 1: output will be of same size as input, * regardless of filter size. * - `valid`: output will be smaller than input if filter is larger * than 1x1. * - For more info, see this guide: * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) * @param dilations The dilation rates: `[dilationHeight, dilationWidth]` * in which we sample input values across the height and width dimensions * in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single * number, then `dilationHeight == dilationWidth`. If it is greater than * 1, then all values of `strides` must be 1. * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to * "NHWC". Specify the data format of the input and output data. With the * default format "NHWC", the data is stored in the order of: [batch, * height, width, channels]. Only "NHWC" is currently supported. * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is * provided, it will default to truncate. * @param bias Tensor to be added to the result. * @param activation Name of activation kernel (defaults to `linear`). * @param preluActivationWeights Tensor of prelu weights to be applied as part * of a `prelu` activation, typically the same shape as `x`. * @param leakyreluAlpha Optional. Alpha to be applied as part of a `leakyrelu` * activation. */ function fusedDepthwiseConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights, leakyreluAlpha }) { if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) { let result = depthwiseConv2d$1(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode); if (bias != null) { result = add(result, bias); } return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha); } const $x = convertToTensor(x, 'x', 'depthwiseConv2d', 'float32'); const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d', 'float32'); let x4D = $x; let reshapedTo4D = false; if ($x.rank === 3) { reshapedTo4D = true; x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); } assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got ` + `rank ${x4D.rank}.`); assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, ` + `but got rank ${$filter.rank}.`); assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels ` + `(${x4D.shape[3]}) must match the inChannels dimension in ` + `filter ${$filter.shape[2]}.`); if (dilations == null) { dilations = [1, 1]; } assert(eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in fused depthwiseConv2d: Either strides or dilations must ' + `be 1. Got strides ${strides} and dilations '${dilations}'`); checkPadOnDimRoundingMode('fused depthwiseConv2d', pad, dimRoundingMode); const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */); let $bias; if (bias != null) { $bias = convertToTensor(bias, 'bias', 'fused conv2d'); [$bias] = makeTypesMatch($bias, $x); assertAndGetBroadcastShape(convInfo.outShape, $bias.shape); } let $preluActivationWeights; if (preluActivationWeights != null) { $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused depthwiseConv2d'); } const grad = (dy, saved) => { assert(tupleValuesAreOne(dilations), () => 'Error in gradient of fused depthwiseConv2d: dilation rates ' + `greater than 1 are not yet supported. Got dilations ` + `'${dilations}'`); const [$filter, x4D, y, bias] = saved; const dyActivation = getFusedDyActivation(dy, y, activation); const xDer = depthwiseConv2dNativeBackpropInput(x4D.shape, dyActivation, $filter, strides, pad, dilations, dimRoundingMode); const filterDer = depthwiseConv2dNativeBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad, dilations, dimRoundingMode); if (bias != null) { const biasDer = getFusedBiasGradient($bias, dyActivation); return [xDer, filterDer, biasDer]; } return [xDer, filterDer]; }; const inputs = { x: x4D, filter: $filter, bias: $bias, preluActivationWeights: $preluActivationWeights }; const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode, activation, leakyreluAlpha }; // Depending on the the params passed in we will have different number of // inputs and thus a a different number of elements in the gradient. if (bias == null) { const customOp = customGrad((x4D, filter, save) => { // tslint:disable-next-line: no-unnecessary-type-assertion let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs); save([filter, x4D, res]); if (reshapedTo4D) { // tslint:disable-next-line: no-unnecessary-type-assertion res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); } return { value: res, gradFunc: grad }; }); return customOp(x4D, $filter); } else { const customOpWithBias = customGrad((x4D, filter, bias, save) => { // tslint:disable-next-line: no-unnecessary-type-assertion let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs); save([filter, x4D, res, bias]); if (reshapedTo4D) { // tslint:disable-next-line: no-unnecessary-type-assertion res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); } return { value: res, gradFunc: grad }; }); return customOpWithBias(x4D, $filter, $bias); } } const depthwiseConv2d = /* @__PURE__ */ op({ fusedDepthwiseConv2d_ }); /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the dot product of two matrices with optional activation and bias. * * ```js * const a = tf.tensor2d([-1, -2], [1, 2]); * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]); * const bias = tf.tensor2d([1, 2], [1, 2]); * * tf.fused.matMul({a, b, bias, activation: 'relu'}).print(); * ``` * * @param obj An object with the following properties: * - `a` First matrix in dot product operation. * - `b` Second matrix in dot product operation. * - `transposeA` If true, `a` is transposed before multiplication. * - `transposeB` If true, `b` is transposed before multiplication. * - `bias` Matrix to be added to the result. * - `activation` Name of activation kernel (defaults to `linear`). * - `preluActivationWeights` Tensor of prelu weights. * - `leakyreluAlpha` Alpha of leakyrelu. */ function fusedMatMul_({ a, b, transposeA = false, transposeB = false, bias, activation = 'linear', preluActivationWeights, leakyreluAlpha = 0.2, }) { if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) { let result = matMul$1(a, b, transposeA, transposeB); if (bias != null) { result = add(result, bias); } return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha); } let $a = convertToTensor(a, 'a', 'fused matMul'); let $b = convertToTensor(b, 'b', 'fused matMul'); [$a, $b] = makeTypesMatch($a, $b); const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1]; const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2]; const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2]; const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1]; const outerDimsA = $a.shape.slice(0, -2); const outerDimsB = $b.shape.slice(0, -2); const batchDimA = sizeFromShape(outerDimsA); const batchDimB = sizeFromShape(outerDimsB); assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (` + `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` + `${$b.shape} and transposeA=${transposeA}` + ` and transposeB=${transposeB} must match.`); const outShapeOuterDims = assertAndGetBroadcastShape($a.shape.slice(0, -2), $b.shape.slice(0, -2)); const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]); const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) : reshape($a, [batchDimA, outerShapeA, innerShapeA]); const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) : reshape($b, [batchDimB, innerShapeB, outerShapeB]); let $bias; if (bias != null) { $bias = convertToTensor(bias, 'bias', 'fused matMul'); [$bias] = makeTypesMatch($bias, $a); assertAndGetBroadcastShape(outShape, $bias.shape); } let $preluActivationWeights; if (preluActivationWeights != null) { $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused matMul'); } const grad = (dy, saved) => { const [a3D, b3D, y, $bias] = saved; // we reshape dy because the result of the forward is not // necessarily going to be a 3d tensor due to a reshape done at the end of // the customOp. const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation); let aDer; let bDer; if (!transposeA && !transposeB) { aDer = matMul$1(dyActivation, b3D, false, true); bDer = matMul$1(a3D, dyActivation, true, false); } else if (!transposeA && transposeB) { aDer = matMul$1(dyActivation, b3D, false, false); bDer = matMul$1(dyActivation, a3D, true, false); } else if (transposeA && !transposeB) { aDer = matMul$1(b3D, dyActivation, false, true); bDer = matMul$1(a3D, dyActivation, false, false); } else { aDer = matMul$1(b3D, dyActivation, true, true); bDer = matMul$1(dyActivation, a3D, true, true); } if (bias != null) { const biasDer = getFusedBiasGradient($bias, dyActivation); return [aDer, bDer, biasDer]; } else { return [aDer, bDer]; } }; const inputs = { a: a3D, b: b3D, bias: $bias, preluActivationWeights: $preluActivationWeights }; const attrs = { transposeA, transposeB, activation, leakyreluAlpha }; // Depending on the the params passed in we will have different number of // inputs and thus a a different number of elements in the gradient. if (bias == null) { const customOp = customGrad((a3D, b3D, save) => { const res = // tslint:disable-next-line: no-unnecessary-type-assertion ENGINE.runKernel(_FusedMatMul, inputs, attrs); save([a3D, b3D, res]); return { value: reshape(res, outShape), gradFunc: grad }; }); return customOp(a3D, b3D); } else { const customOpWithBias = customGrad((a3D, b3D, $bias, save) => { const res = // tslint:disable-next-line: no-unnecessary-type-assertion ENGINE.runKernel(_FusedMatMul, inputs, attrs); save([a3D, b3D, res, $bias]); return { value: reshape(res, outShape), gradFunc: grad }; }); return customOpWithBias(a3D, b3D, $bias); } } const matMul = /* @__PURE__ */ op({ fusedMatMul_ }); /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ var fused_ops = { __proto__: null, conv2d: conv2d, depthwiseConv2d: depthwiseConv2d, matMul: matMul }; /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Generate a hamming window. * * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows * * ```js * tf.signal.hammingWindow(10).print(); * ``` * @param The length of window * * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'} */ function hammingWindow_(windowLength) { return cosineWindow(windowLength, 0.54, 0.46); } const hammingWindow = /* @__PURE__ */ op({ hammingWindow_ }); /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Generate a Hann window. * * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows * * ```js * tf.signal.hannWindow(10).print(); * ``` * @param The length of window * * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'} */ function hannWindow_(windowLength) { return cosineWindow(windowLength, 0.5, 0.5); } const hannWindow = /* @__PURE__ */ op({ hannWindow_ }); /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Expands input into frames of frameLength. * Slides a window size with frameStep. * * ```js * tf.signal.frame([1, 2, 3], 2, 1).print(); * ``` * @param signal The input tensor to be expanded * @param frameLength Length of each frame * @param frameStep The frame hop size in samples. * @param padEnd Whether to pad the end of signal with padValue. * @param padValue A number to use where the input signal does * not exist when padEnd is True. * * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'} */ function frame_(signal, frameLength, frameStep, padEnd = false, padValue = 0) { let start = 0; const output = []; while (start + frameLength <= signal.size) { output.push(slice(signal, start, frameLength)); start += frameStep; } if (padEnd) { while (start < signal.size) { const padLen = (start + frameLength) - signal.size; const pad = concat([ slice(signal, start, frameLength - padLen), fill([padLen], padValue) ]); output.push(pad); start += frameStep; } } if (output.length === 0) { return tensor2d([], [0, frameLength]); } return reshape(concat(output), [output.length, frameLength]); } const frame = /* @__PURE__ */ op({ frame_ }); /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the Short-time Fourier Transform of signals * See: https://en.wikipedia.org/wiki/Short-time_Fourier_transform * * ```js * const input = tf.tensor1d([1, 1, 1, 1, 1]) * tf.signal.stft(input, 3, 1).print(); * ``` * @param signal 1-dimensional real value tensor. * @param frameLength The window length of samples. * @param frameStep The number of samples to step. * @param fftLength The size of the FFT to apply. * @param windowFn A callable that takes a window length and returns 1-d tensor. * * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'} */ function stft_(signal, frameLength, frameStep, fftLength, windowFn = hannWindow) { if (fftLength == null) { fftLength = enclosingPowerOfTwo(frameLength); } const framedSignal = frame(signal, frameLength, frameStep); const windowedSignal = mul(framedSignal, windowFn(frameLength)); return rfft(windowedSignal, fftLength); } const stft = /* @__PURE__ */ op({ stft_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Extracts crops from the input image tensor and resizes them using bilinear * sampling or nearest neighbor sampling (possibly with aspect ratio change) * to a common output size specified by cropSize. * * @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`, * where imageHeight and imageWidth must be positive, specifying the * batch of images from which to take crops * @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized * coordinates of the box in the `boxInd[i]`th image in the batch * @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range * `[0, batch)` that specifies the image that the `i`-th box refers to. * @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]` * specifying the size to which all crops are resized to. * @param method Optional string from `'bilinear' | 'nearest'`, * defaults to bilinear, which specifies the sampling method for resizing * @param extrapolationValue A threshold for deciding when to remove boxes based * on score. Defaults to 0. * @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]` * * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */ function cropAndResize_(image, boxes, boxInd, cropSize, method = 'bilinear', extrapolationValue = 0) { const $image = convertToTensor(image, 'image', 'cropAndResize'); const $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32'); const $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32'); const numBoxes = $boxes.shape[0]; assert($image.rank === 4, () => 'Error in cropAndResize: image must be rank 4,' + `but got rank ${$image.rank}.`); assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] ` + `but had shape ${$boxes.shape}.`); assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] ` + `but had shape ${$boxes.shape}.`); assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got ` + `length ${cropSize.length}.`); assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`); assert(method === 'bilinear' || method === 'nearest', () => `method must be bilinear or nearest, but was ${method}`); const inputs = { image: $image, boxes: $boxes, boxInd: $boxInd }; const attrs = { method, extrapolationValue, cropSize }; const res = ENGINE.runKernel(CropAndResize, inputs, attrs); return res; } const cropAndResize = /* @__PURE__ */ op({ cropAndResize_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Flips the image left to right. Currently available in the CPU, WebGL, and * WASM backends. * * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`. */ /** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */ function flipLeftRight_(image) { const $image = convertToTensor(image, 'image', 'flipLeftRight', 'float32'); assert($image.rank === 4, () => 'Error in flipLeftRight: image must be rank 4,' + `but got rank ${$image.rank}.`); const inputs = { image: $image }; const res = ENGINE.runKernel(FlipLeftRight, inputs, {}); return res; } const flipLeftRight = /* @__PURE__ */ op({ flipLeftRight_ }); /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Converts images from grayscale to RGB format. * * @param image A grayscale tensor to convert. The `image`'s last dimension must * be size 1 with at least a two-dimensional shape. * * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */ function grayscaleToRGB_(image) { const $image = convertToTensor(image, 'image', 'grayscaleToRGB'); const lastDimsIdx = $image.rank - 1; const lastDims = $image.shape[lastDimsIdx]; assert($image.rank >= 2, () => 'Error in grayscaleToRGB: images must be at least rank 2, ' + `but got rank ${$image.rank}.`); assert(lastDims === 1, () => 'Error in grayscaleToRGB: last dimension of a grayscale image ' + `should be size 1, but got size ${lastDims}.`); const reps = new Array($image.rank); reps.fill(1, 0, lastDimsIdx); reps[lastDimsIdx] = 3; return tile($image, reps); } const grayscaleToRGB = /* @__PURE__ */ op({ grayscaleToRGB_ }); /** * @license * Copyright 2023 Google LLC. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Converts images from RGB format to grayscale. * * @param image A RGB tensor to convert. The `image`'s last dimension must * be size 3 with at least a two-dimensional shape. * * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */ function rgbToGrayscale_(image) { const $image = convertToTensor(image, 'image', 'RGBToGrayscale'); const lastDimsIdx = $image.rank - 1; const lastDims = $image.shape[lastDimsIdx]; assert($image.rank >= 2, () => 'Error in RGBToGrayscale: images must be at least rank 2, ' + `but got rank ${$image.rank}.`); assert(lastDims === 3, () => 'Error in RGBToGrayscale: last dimension of an RGB image ' + `should be size 3, but got size ${lastDims}.`); // Remember original dtype so we can convert back if needed const origDtype = $image.dtype; const fltImage = cast($image, 'float32'); const rgbWeights = tensor1d([0.2989, 0.5870, 0.1140]); let grayFloat; switch ($image.rank) { case 2: grayFloat = einsum('ij,j->i', fltImage, rgbWeights); break; case 3: grayFloat = einsum('ijk,k->ij', fltImage, rgbWeights); break; case 4: grayFloat = einsum('ijkl,l->ijk', fltImage, rgbWeights); break; case 5: grayFloat = einsum('ijklm,m->ijkl', fltImage, rgbWeights); break; case 6: grayFloat = einsum('ijklmn,n->ijklm', fltImage, rgbWeights); break; default: throw new Error('Not a valid tensor rank.'); } grayFloat = expandDims(grayFloat, -1); return cast(grayFloat, origDtype); } const rgbToGrayscale = /* @__PURE__ */ op({ rgbToGrayscale_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Rotates the input image tensor counter-clockwise with an optional offset * center of rotation. Currently available in the CPU, WebGL, and WASM backends. * * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`. * @param radians The amount of rotation. * @param fillValue The value to fill in the empty space leftover * after rotation. Can be either a single grayscale value (0-255), or an * array of three numbers `[red, green, blue]` specifying the red, green, * and blue channels. Defaults to `0` (black). * @param center The center of rotation. Can be either a single value (0-1), or * an array of two numbers `[centerX, centerY]`. Defaults to `0.5` (rotates * the image around its center). * * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */ function rotateWithOffset_(image, radians, fillValue = 0, center = 0.5) { const $image = convertToTensor(image, 'image', 'rotateWithOffset', 'float32'); assert($image.rank === 4, () => 'Error in rotateWithOffset: image must be rank 4,' + `but got rank ${$image.rank}.`); const inputs = { image: $image }; const attrs = { radians, fillValue, center }; const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs); return res; } const rotateWithOffset = /* @__PURE__ */ op({ rotateWithOffset_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ function nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) { if (iouThreshold == null) { iouThreshold = 0.5; } if (scoreThreshold == null) { scoreThreshold = Number.NEGATIVE_INFINITY; } if (softNmsSigma == null) { softNmsSigma = 0.0; } const numBoxes = boxes.shape[0]; maxOutputSize = Math.min(maxOutputSize, numBoxes); assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`); assert(boxes.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`); assert(boxes.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`); assert(scores.rank === 1, () => 'scores must be a 1D tensor'); assert(scores.shape[0] === numBoxes, () => `scores has incompatible shape with boxes. Expected ${numBoxes}, ` + `but was ${scores.shape[0]}`); assert(0 <= softNmsSigma && softNmsSigma <= 1, () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`); return { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma }; } /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Performs non maximum suppression of bounding boxes based on * iou (intersection over union). * * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of * the bounding box. * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`. * @param maxOutputSize The maximum number of boxes to be selected. * @param iouThreshold A float representing the threshold for deciding whether * boxes overlap too much with respect to IOU. Must be between [0, 1]. * Defaults to 0.5 (50% box overlap). * @param scoreThreshold A threshold for deciding when to remove boxes based * on score. Defaults to -inf, which means any score is accepted. * @return A 1D tensor with the selected box indices. * * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */ function nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) { const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression', 'float32'); const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression', 'float32'); const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold); maxOutputSize = inputs.maxOutputSize; iouThreshold = inputs.iouThreshold; scoreThreshold = inputs.scoreThreshold; const attrs = { maxOutputSize, iouThreshold, scoreThreshold }; return ENGINE.runKernel(NonMaxSuppressionV3, { boxes: $boxes, scores: $scores }, attrs); } const nonMaxSuppression = /* @__PURE__ */ op({ nonMaxSuppression_ }); /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Inserts a value into a sorted array. This method allows duplicate, meaning it * allows inserting duplicate value, in which case, the element will be inserted * at the lowest index of the value. * @param arr The array to modify. * @param element The element to insert. * @param comparator Optional. If no comparator is specified, elements are * compared using array_util.defaultComparator, which is suitable for Strings * and Numbers in ascending arrays. If the array contains multiple instances of * the target value, the left-most instance will be returned. To provide a * comparator, it should take 2 arguments to compare and return a negative, * zero, or a positive number. */ function binaryInsert(arr, element, comparator) { const index = binarySearch(arr, element, comparator); const insertionPoint = index < 0 ? -(index + 1) : index; arr.splice(insertionPoint, 0, element); } /** * Searches the array for the target using binary search, returns the index * of the found element, or position to insert if element not found. If no * comparator is specified, elements are compared using array_ * util.defaultComparator, which is suitable for Strings and Numbers in * ascending arrays. If the array contains multiple instances of the target * value, the left-most instance will be returned. * @param arr The array to be searched in. * @param target The target to be searched for. * @param comparator Should take 2 arguments to compare and return a negative, * zero, or a positive number. * @return Lowest index of the target value if found, otherwise the insertion * point where the target should be inserted, in the form of * (-insertionPoint - 1). */ function binarySearch(arr, target, comparator) { return binarySearch_(arr, target, comparator || defaultComparator); } /** * Compares its two arguments for order. * @param a The first element to be compared. * @param b The second element to be compared. * @return A negative number, zero, or a positive number as the first * argument is less than, equal to, or greater than the second. */ function defaultComparator(a, b) { return a > b ? 1 : a < b ? -1 : 0; } function binarySearch_(arr, target, comparator) { let left = 0; let right = arr.length; let middle = 0; let found = false; while (left < right) { middle = left + ((right - left) >>> 1); const compareResult = comparator(target, arr[middle]); if (compareResult > 0) { left = middle + 1; } else { right = middle; // If compareResult is 0, the value is found. We record it is found, // and then keep looking because there may be duplicate. found = !compareResult; } } return found ? left : -left - 1; } /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ function nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) { return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */); } function nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) { return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */, false /* returnScoresTensor */, padToMaxOutputSize /* padToMaxOutputSize */, true /* returnValidOutputs */ ); } function nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) { return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, true /* returnScoresTensor */); } function nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) { // The list is sorted in ascending order, so that we can always pop the // candidate with the largest score in O(1) time. const candidates = []; for (let i = 0; i < scores.length; i++) { if (scores[i] > scoreThreshold) { candidates.push({ score: scores[i], boxIndex: i, suppressBeginIndex: 0 }); } } candidates.sort(ascendingComparator); // If softNmsSigma is 0, the outcome of this algorithm is exactly same as // before. const scale = softNmsSigma > 0 ? (-0.5 / softNmsSigma) : 0.0; const selectedIndices = []; const selectedScores = []; while (selectedIndices.length < maxOutputSize && candidates.length > 0) { const candidate = candidates.pop(); const { score: originalScore, boxIndex, suppressBeginIndex } = candidate; if (originalScore < scoreThreshold) { break; } // Overlapping boxes are likely to have similar scores, therefore we // iterate through the previously selected boxes backwards in order to // see if candidate's score should be suppressed. We use // suppressBeginIndex to track and ensure a candidate can be suppressed // by a selected box no more than once. Also, if the overlap exceeds // iouThreshold, we simply ignore the candidate. let ignoreCandidate = false; for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) { const iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]); if (iou >= iouThreshold) { ignoreCandidate = true; break; } candidate.score = candidate.score * suppressWeight(iouThreshold, scale, iou); if (candidate.score <= scoreThreshold) { break; } } // At this point, if `candidate.score` has not dropped below // `scoreThreshold`, then we know that we went through all of the // previous selections and can safely update `suppressBeginIndex` to the // end of the selected array. Then we can re-insert the candidate with // the updated score and suppressBeginIndex back in the candidate list. // If on the other hand, `candidate.score` has dropped below the score // threshold, we will not add it back to the candidates list. candidate.suppressBeginIndex = selectedIndices.length; if (!ignoreCandidate) { // Candidate has passed all the tests, and is not suppressed, so // select the candidate. if (candidate.score === originalScore) { selectedIndices.push(boxIndex); selectedScores.push(candidate.score); } else if (candidate.score > scoreThreshold) { // Candidate's score is suppressed but is still high enough to be // considered, so add back to the candidates list. binaryInsert(candidates, candidate, ascendingComparator); } } } // NonMaxSuppressionV4 feature: padding output to maxOutputSize. const validOutputs = selectedIndices.length; const elemsToPad = maxOutputSize - validOutputs; if (padToMaxOutputSize && elemsToPad > 0) { selectedIndices.push(...new Array(elemsToPad).fill(0)); selectedScores.push(...new Array(elemsToPad).fill(0.0)); } const result = { selectedIndices }; if (returnScoresTensor) { result['selectedScores'] = selectedScores; } if (returnValidOutputs) { result['validOutputs'] = validOutputs; } return result; } function intersectionOverUnion(boxes, i, j) { const iCoord = boxes.subarray(i * 4, i * 4 + 4); const jCoord = boxes.subarray(j * 4, j * 4 + 4); const yminI = Math.min(iCoord[0], iCoord[2]); const xminI = Math.min(iCoord[1], iCoord[3]); const ymaxI = Math.max(iCoord[0], iCoord[2]); const xmaxI = Math.max(iCoord[1], iCoord[3]); const yminJ = Math.min(jCoord[0], jCoord[2]); const xminJ = Math.min(jCoord[1], jCoord[3]); const ymaxJ = Math.max(jCoord[0], jCoord[2]); const xmaxJ = Math.max(jCoord[1], jCoord[3]); const areaI = (ymaxI - yminI) * (xmaxI - xminI); const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ); if (areaI <= 0 || areaJ <= 0) { return 0.0; } const intersectionYmin = Math.max(yminI, yminJ); const intersectionXmin = Math.max(xminI, xminJ); const intersectionYmax = Math.min(ymaxI, ymaxJ); const intersectionXmax = Math.min(xmaxI, xmaxJ); const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) * Math.max(intersectionXmax - intersectionXmin, 0.0); return intersectionArea / (areaI + areaJ - intersectionArea); } // A Gaussian penalty function, this method always returns values in [0, 1]. // The weight is a function of similarity, the more overlap two boxes are, the // smaller the weight is, meaning highly overlapping boxe will be significantly // penalized. On the other hand, a non-overlapping box will not be penalized. function suppressWeight(iouThreshold, scale, iou) { const weight = Math.exp(scale * iou * iou); return iou <= iouThreshold ? weight : 0.0; } function ascendingComparator(c1, c2) { // For objects with same scores, we make the object with the larger index go // first. In an array that pops from the end, this means that the object with // the smaller index will be popped first. This ensures the same output as // the TensorFlow python version. return (c1.score - c2.score) || ((c1.score === c2.score) && (c2.boxIndex - c1.boxIndex)); } /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Performs non maximum suppression of bounding boxes based on * iou (intersection over union). * * This is the async version of `nonMaxSuppression` * * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of * the bounding box. * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`. * @param maxOutputSize The maximum number of boxes to be selected. * @param iouThreshold A float representing the threshold for deciding whether * boxes overlap too much with respect to IOU. Must be between [0, 1]. * Defaults to 0.5 (50% box overlap). * @param scoreThreshold A threshold for deciding when to remove boxes based * on score. Defaults to -inf, which means any score is accepted. * @return A 1D tensor with the selected box indices. * * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */ async function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) { const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync'); const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync'); const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold); maxOutputSize = inputs.maxOutputSize; iouThreshold = inputs.iouThreshold; scoreThreshold = inputs.scoreThreshold; const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]); const boxesVals = boxesAndScores[0]; const scoresVals = boxesAndScores[1]; // We call a cpu based impl directly with the typedarray data here rather // than a kernel because all kernels are synchronous (and thus cannot await // .data()). const { selectedIndices } = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold); if ($boxes !== boxes) { $boxes.dispose(); } if ($scores !== scores) { $scores.dispose(); } return tensor1d(selectedIndices, 'int32'); } const nonMaxSuppressionAsync = nonMaxSuppressionAsync_; /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Performs non maximum suppression of bounding boxes based on * iou (intersection over union). * * This op also supports a Soft-NMS mode (cf. * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score * of other overlapping boxes, therefore favoring different regions of the image * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma` * parameter to be larger than 0. * * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of * the bounding box. * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`. * @param maxOutputSize The maximum number of boxes to be selected. * @param iouThreshold A float representing the threshold for deciding whether * boxes overlap too much with respect to IOU. Must be between [0, 1]. * Defaults to 0.5 (50% box overlap). * @param scoreThreshold A threshold for deciding when to remove boxes based * on score. Defaults to -inf, which means any score is accepted. * @param softNmsSigma A float representing the sigma parameter for Soft NMS. * When sigma is 0, it falls back to nonMaxSuppression. * @return A map with the following properties: * - selectedIndices: A 1D tensor with the selected box indices. * - selectedScores: A 1D tensor with the corresponding scores for each * selected box. * * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */ function nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0.0) { const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression'); const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression'); const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma); maxOutputSize = params.maxOutputSize; iouThreshold = params.iouThreshold; scoreThreshold = params.scoreThreshold; softNmsSigma = params.softNmsSigma; const inputs = { boxes: $boxes, scores: $scores }; const attrs = { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma }; // tslint:disable-next-line: no-unnecessary-type-assertion const result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs); return { selectedIndices: result[0], selectedScores: result[1] }; } const nonMaxSuppressionWithScore = /* @__PURE__ */ op({ nonMaxSuppressionWithScore_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Asynchronously performs non maximum suppression of bounding boxes based on * iou (intersection over union). * * This op also supports a Soft-NMS mode (cf. * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score * of other overlapping boxes, therefore favoring different regions of the image * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma` * parameter to be larger than 0. * * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of * the bounding box. * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`. * @param maxOutputSize The maximum number of boxes to be selected. * @param iouThreshold A float representing the threshold for deciding whether * boxes overlap too much with respect to IOU. Must be between [0, 1]. * Defaults to 0.5 (50% box overlap). * @param scoreThreshold A threshold for deciding when to remove boxes based * on score. Defaults to -inf, which means any score is accepted. * @param softNmsSigma A float representing the sigma parameter for Soft NMS. * When sigma is 0, it falls back to nonMaxSuppression. * @return A map with the following properties: * - selectedIndices: A 1D tensor with the selected box indices. * - selectedScores: A 1D tensor with the corresponding scores for each * selected box. * * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */ async function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0.0) { const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync'); const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync'); const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma); maxOutputSize = params.maxOutputSize; iouThreshold = params.iouThreshold; scoreThreshold = params.scoreThreshold; softNmsSigma = params.softNmsSigma; const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]); const boxesVals = boxesAndScores[0]; const scoresVals = boxesAndScores[1]; // We call a cpu based impl directly with the typedarray data here rather // than a kernel because all kernels are synchronous (and thus cannot await // .data()). const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma); if ($boxes !== boxes) { $boxes.dispose(); } if ($scores !== scores) { $scores.dispose(); } return { selectedIndices: tensor1d(selectedIndices, 'int32'), selectedScores: tensor1d(selectedScores) }; } const nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_; /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Asynchronously performs non maximum suppression of bounding boxes based on * iou (intersection over union), with an option to pad results. * * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of * the bounding box. * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`. * @param maxOutputSize The maximum number of boxes to be selected. * @param iouThreshold A float representing the threshold for deciding whether * boxes overlap too much with respect to IOU. Must be between [0, 1]. * Defaults to 0.5 (50% box overlap). * @param scoreThreshold A threshold for deciding when to remove boxes based * on score. Defaults to -inf, which means any score is accepted. * @param padToMaxOutputSize Defaults to false. If true, size of output * `selectedIndices` is padded to maxOutputSize. * @return A map with the following properties: * - selectedIndices: A 1D tensor with the selected box indices. * - validOutputs: A scalar denoting how many elements in `selectedIndices` * are valid. Valid elements occur first, then padding. * * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */ function nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) { const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression'); const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression'); const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */); const $maxOutputSize = params.maxOutputSize; const $iouThreshold = params.iouThreshold; const $scoreThreshold = params.scoreThreshold; const inputs = { boxes: $boxes, scores: $scores }; const attrs = { maxOutputSize: $maxOutputSize, iouThreshold: $iouThreshold, scoreThreshold: $scoreThreshold, padToMaxOutputSize }; // tslint:disable-next-line: no-unnecessary-type-assertion const result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs); return { selectedIndices: result[0], validOutputs: result[1] }; } const nonMaxSuppressionPadded = /* @__PURE__ */ op({ nonMaxSuppressionPadded_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Asynchronously performs non maximum suppression of bounding boxes based on * iou (intersection over union), with an option to pad results. * * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of * the bounding box. * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`. * @param maxOutputSize The maximum number of boxes to be selected. * @param iouThreshold A float representing the threshold for deciding whether * boxes overlap too much with respect to IOU. Must be between [0, 1]. * Defaults to 0.5 (50% box overlap). * @param scoreThreshold A threshold for deciding when to remove boxes based * on score. Defaults to -inf, which means any score is accepted. * @param padToMaxOutputSize Defaults to false. If true, size of output * `selectedIndices` is padded to maxOutputSize. * @return A map with the following properties: * - selectedIndices: A 1D tensor with the selected box indices. * - validOutputs: A scalar denoting how many elements in `selectedIndices` * are valid. Valid elements occur first, then padding. * * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */ async function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) { const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync'); const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync'); const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */); const $maxOutputSize = params.maxOutputSize; const $iouThreshold = params.iouThreshold; const $scoreThreshold = params.scoreThreshold; const [boxesVals, scoresVals] = await Promise.all([$boxes.data(), $scores.data()]); // We call a cpu based impl directly with the typedarray data here rather // than a kernel because all kernels are synchronous (and thus cannot await // .data()). const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize); if ($boxes !== boxes) { $boxes.dispose(); } if ($scores !== scores) { $scores.dispose(); } return { selectedIndices: tensor1d(selectedIndices, 'int32'), validOutputs: scalar(validOutputs, 'int32') }; } const nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_; /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Bilinear resize a single 3D image or a batch of 3D images to a new shape. * * @param images The images, of rank 4 or rank 3, of shape * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed. * @param size The new shape `[newHeight, newWidth]` to resize the * images to. Each channel is resized individually. * @param alignCorners Defaults to `false`. If true, rescale * input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4 * corners of images and resized images. If false, rescale by * `new_height / height`. Treat similarly the width dimension. * @param halfPixelCenters Defaults to `false`. Whether to assume pixel centers * are at 0.5, which would make the floating point coordinates of the top * left pixel 0.5, 0.5. * * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */ function resizeBilinear_(images, size, alignCorners = false, halfPixelCenters = false) { const $images = convertToTensor(images, 'images', 'resizeBilinear'); assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got ` + `rank ${$images.rank}.`); assert(size.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ` + `${size}.`); assert(halfPixelCenters === false || alignCorners === false, () => `Error in resizeBilinear: If halfPixelCenters is true, ` + `alignCorners must be false.`); let batchImages = $images; let reshapedTo4D = false; if ($images.rank === 3) { reshapedTo4D = true; batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]); } const inputs = { images: batchImages }; const attrs = { alignCorners, halfPixelCenters, size }; // tslint:disable-next-line: no-unnecessary-type-assertion const res = ENGINE.runKernel(ResizeBilinear, inputs, attrs); if (reshapedTo4D) { return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); } return res; } const resizeBilinear = /* @__PURE__ */ op({ resizeBilinear_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * NearestNeighbor resize a batch of 3D images to a new shape. * * @param images The images, of rank 4 or rank 3, of shape * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed. * @param size The new shape `[newHeight, newWidth]` to resize the * images to. Each channel is resized individually. * @param alignCorners Defaults to False. If true, rescale * input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4 * corners of images and resized images. If false, rescale by * `new_height / height`. Treat similarly the width dimension. * @param halfPixelCenters Defaults to `false`. Whether to assume pixels are of * half the actual dimensions, and yield more accurate resizes. This flag * would also make the floating point coordinates of the top left pixel * 0.5, 0.5. * * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */ function resizeNearestNeighbor_(images, size, alignCorners = false, halfPixelCenters = false) { const $images = convertToTensor(images, 'images', 'resizeNearestNeighbor'); assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got ` + `rank ${$images.rank}.`); assert(size.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ` + `${size}.`); assert($images.dtype === 'float32' || $images.dtype === 'int32', () => '`images` must have `int32` or `float32` as dtype'); assert(halfPixelCenters === false || alignCorners === false, () => `Error in resizeNearestNeighbor: If halfPixelCenters is true, ` + `alignCorners must be false.`); let batchImages = $images; let reshapedTo4D = false; if ($images.rank === 3) { reshapedTo4D = true; batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]); } const inputs = { images: batchImages }; const attrs = { alignCorners, halfPixelCenters, size }; // tslint:disable-next-line: no-unnecessary-type-assertion const res = ENGINE.runKernel(ResizeNearestNeighbor, inputs, attrs); if (reshapedTo4D) { return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); } return res; } const resizeNearestNeighbor = /* @__PURE__ */ op({ resizeNearestNeighbor_ }); /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * https://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Performs image binarization with corresponding threshold * (depends on the method)value, which creates a binary image from a grayscale. * @param image 3d tensor of shape [imageHeight,imageWidth, depth], * where imageHeight and imageWidth must be positive.The image color * range should be [0, 255]. * @param method Optional string from `'binary' | 'otsu'` * which specifies the method for thresholding. Defaults to 'binary'. * @param inverted Optional boolean whichspecifies * if colours should be inverted. Defaults to false. * @param threshValue Optional number which defines threshold value from 0 to 1. * Defaults to 0.5. * @return A 3d tensor of shape [imageHeight,imageWidth, depth], which * contains binarized image. */ function threshold_(image, method = 'binary', inverted = false, threshValue = 0.5) { const $image = convertToTensor(image, 'image', 'threshold'); /* 0.2989, 0.5870, 0.1140 are represent luma coefficients in CCIR601. Reference for converting between RGB and grayscale: https://en.wikipedia.org/wiki/Luma_%28video%29 */ const RED_INTENCITY_COEF = 0.2989; const GREEN_INTENCITY_COEF = 0.5870; const BLUE_INTENCITY_COEF = 0.1140; const totalPixelsInImage = $image.shape[0] * $image.shape[1]; let $threshold = mul(tensor1d([threshValue]), 255); let r, g, b, grayscale; assert($image.rank === 3, () => 'Error in threshold: image must be rank 3,' + `but got rank ${$image.rank}.`); assert($image.shape[2] === 3 || $image.shape[2] === 1, () => 'Error in threshold: ' + 'image color channel must be equal to 3 or 1' + `but got ${$image.shape[2]}.`); assert($image.dtype === 'int32' || $image.dtype === 'float32', () => 'Error in dtype: image dtype must be int32 or float32,' + `but got dtype ${$image.dtype}.`); assert(method === 'otsu' || method === 'binary', () => `Method must be binary or otsu, but was ${method}`); if ($image.shape[2] === 3) { [r, g, b] = split$1($image, [1, 1, 1], -1); const $r = mul(r, RED_INTENCITY_COEF); const $g = mul(g, GREEN_INTENCITY_COEF); const $b = mul(b, BLUE_INTENCITY_COEF); grayscale = add(add($r, $g), $b); } else { grayscale = image; } if (method === 'otsu') { const $histogram = bincount(cast(round(grayscale), 'int32'), tensor([]), 256); $threshold = otsu($histogram, totalPixelsInImage); } const invCondition = inverted ? lessEqual(grayscale, $threshold) : greater(grayscale, $threshold); const result = cast(mul(invCondition, 255), 'int32'); return result; } function otsu(histogram, total) { let bestThresh = tensor1d([-1]); let bestInBetVar = tensor1d([0]); let cInBetVar = tensor1d([0]); let classFirst, classSecond, meanFirst, meanSec, weightForeground, weightBack; for (let index = 0; index < histogram.size - 1; index++) { classFirst = slice(histogram, 0, index + 1); classSecond = slice(histogram, index + 1); weightForeground = div(sum(classFirst), total); weightBack = div(sum(classSecond), total); const meanFirstDivA = sum(mul(classFirst, range(0, classFirst.size))); meanFirst = div(meanFirstDivA, sum(classFirst)); const meanSecFill = fill(classSecond.shape, classFirst.size); const meanSecAdd = add(range(0, classSecond.size), meanSecFill); const meanSecMul = mul(classSecond, (meanSecAdd)); meanSec = div(sum(meanSecMul), sum(classSecond)); const cInBetVarSubA = sub(meanFirst, meanSec); const cInBetVarSubB = sub(meanFirst, meanSec); const cInBetVarMul = mul(weightForeground, weightBack); cInBetVar = mul(mul(cInBetVarMul, cInBetVarSubA), cInBetVarSubB); const condition = greater(cInBetVar, bestInBetVar); bestInBetVar = where(condition, cInBetVar, bestInBetVar); bestThresh = where(condition, tensor1d([index]), bestThresh); } return bestThresh; } const threshold = /* @__PURE__ */ op({ threshold_ }); /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Applies the given transform(s) to the image(s). * * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`. * @param transforms Projective transform matrix/matrices. A tensor1d of length * 8 or tensor of size N x 8. If one row of transforms is [a0, a1, a2, b0, * b1, b2, c0, c1], then it maps the output point (x, y) to a transformed * input point (x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k), * where k = c0 x + c1 y + 1. The transforms are inverted compared to the * transform mapping input points to output points. * @param interpolation Interpolation mode. * Supported values: 'nearest', 'bilinear'. Default to 'nearest'. * @param fillMode Points outside the boundaries of the input are filled * according to the given mode, one of 'constant', 'reflect', 'wrap', * 'nearest'. Default to 'constant'. * 'reflect': (d c b a | a b c d | d c b a ) The input is extended by * reflecting about the edge of the last pixel. * 'constant': (k k k k | a b c d | k k k k) The input is extended by * filling all values beyond the edge with the same constant value k. * 'wrap': (a b c d | a b c d | a b c d) The input is extended by * wrapping around to the opposite edge. * 'nearest': (a a a a | a b c d | d d d d) The input is extended by * the nearest pixel. * @param fillValue A float represents the value to be filled outside the * boundaries when fillMode is 'constant'. * @param Output dimension after the transform, [height, width]. If undefined, * output is the same size as input image. * * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */ function transform_(image, transforms, interpolation = 'nearest', fillMode = 'constant', fillValue = 0, outputShape) { const $image = convertToTensor(image, 'image', 'transform', 'float32'); const $transforms = convertToTensor(transforms, 'transforms', 'transform', 'float32'); assert($image.rank === 4, () => 'Error in transform: image must be rank 4,' + `but got rank ${$image.rank}.`); assert($transforms.rank === 2 && ($transforms.shape[0] === $image.shape[0] || $transforms.shape[0] === 1) && $transforms.shape[1] === 8, () => `Error in transform: Input transform should be batch x 8 or 1 x 8`); assert(outputShape == null || outputShape.length === 2, () => 'Error in transform: outputShape must be [height, width] or null, ' + `but got ${outputShape}.`); const inputs = { image: $image, transforms: $transforms }; const attrs = { interpolation, fillMode, fillValue, outputShape }; return ENGINE.runKernel(Transform, inputs, attrs); } const transform = /* @__PURE__ */ op({ transform_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Copy a tensor setting everything outside a central band in each innermost * matrix to zero. * * The band part is computed as follows: Assume input has `k` dimensions * `[I, J, K, ..., M, N]`, then the output is a tensor with the same shape where * `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`. * The indicator function * `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower)` * `&& (num_upper < 0 || (n-m) <= num_upper)` * * ```js * const x = tf.tensor2d([[ 0, 1, 2, 3], * [-1, 0, 1, 2], * [-2, -1, 0, 1], * [-3, -2, -1, 0]]); * let y = tf.linalg.bandPart(x, 1, -1); * y.print(); // [[ 0, 1, 2, 3], * // [-1, 0, 1, 2], * // [ 0, -1, 0, 1], * // [ 0, 0 , -1, 0]] * let z = tf.linalg.bandPart(x, 2, 1); * z.print(); // [[ 0, 1, 0, 0], * // [-1, 0, 1, 0], * // [-2, -1, 0, 1], * // [ 0, -2, -1, 0]] * ``` * * @param x Rank `k` tensor * @param numLower Number of subdiagonals to keep. * If negative, keep entire lower triangle. * @param numUpper Number of subdiagonals to keep. * If negative, keep entire upper triangle. * @returns Rank `k` tensor of the same shape as input. * The extracted banded tensor. * * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'} */ function bandPart_(a, numLower, numUpper) { const $a = convertToTensor(a, 'a', 'bandPart'); assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`); const shape = $a.shape; const [M, N] = $a.shape.slice(-2); let $numLower; let $numUpper; if (typeof numLower === 'number') { assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`); assert(numLower <= M, () => `bandPart(): numLower (${numLower})` + ` must not be greater than the number of rows (${M}).`); $numLower = convertToTensor(numLower < 0 ? M : numLower, 'numLower', 'bandPart'); } else { assert(numLower.dtype === 'int32', () => `bandPart(): numLower's dtype must be an int32.`); // If numLower is a Scalar, checking `numLower <= M` could hurt performance, // but minimum(numLower, M) could avoid unexpected results. $numLower = where(less(numLower, 0), M, minimum(numLower, M)); } if (typeof numUpper === 'number') { assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`); assert(numUpper <= N, () => `bandPart(): numUpper (${numUpper})` + ` must not be greater than the number of columns (${N}).`); $numUpper = convertToTensor(numUpper < 0 ? N : numUpper, 'numUpper', 'bandPart'); } else { assert(numUpper.dtype === 'int32', () => `bandPart(): numUpper's dtype must be an int32.`); $numUpper = where(less(numUpper, 0), N, minimum(numUpper, N)); } const i = reshape(range(0, M, 1, 'int32'), [-1, 1]); const j = range(0, N, 1, 'int32'); const ij = sub(i, j); const inBand = logicalAnd(lessEqual(ij, $numLower), greaterEqual(ij, neg($numUpper))); const zero = zeros([M, N], $a.dtype); return reshape(stack(unstack(reshape($a, [-1, M, N])) .map(mat => where(inBand, mat, zero))), shape); } const bandPart = /* @__PURE__ */ op({ bandPart_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Gram-Schmidt orthogonalization. * * ```js * const x = tf.tensor2d([[1, 2], [3, 4]]); * let y = tf.linalg.gramSchmidt(x); * y.print(); * console.log('Orthogonalized:'); * y.dot(y.transpose()).print(); // should be nearly the identity matrix. * console.log('First row direction maintained:'); * const data = await y.array(); * console.log(data[0][1] / data[0][0]); // should be nearly 2. * ``` * * @param xs The vectors to be orthogonalized, in one of the two following * formats: * - An Array of `tf.Tensor1D`. * - A `tf.Tensor2D`, i.e., a matrix, in which case the vectors are the rows * of `xs`. * In each case, all the vectors must have the same length and the length * must be greater than or equal to the number of vectors. * @returns The orthogonalized and normalized vectors or matrix. * Orthogonalization means that the vectors or the rows of the matrix * are orthogonal (zero inner products). Normalization means that each * vector or each row of the matrix has an L2 norm that equals `1`. * * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'} */ function gramSchmidt_(xs) { let inputIsTensor2D; if (Array.isArray(xs)) { inputIsTensor2D = false; assert(xs != null && xs.length > 0, () => 'Gram-Schmidt process: input must not be null, undefined, or ' + 'empty'); const dim = xs[0].shape[0]; for (let i = 1; i < xs.length; ++i) { assert(xs[i].shape[0] === dim, () => 'Gram-Schmidt: Non-unique lengths found in the input vectors: ' + `(${xs[i].shape[0]} vs. ${dim})`); } } else { inputIsTensor2D = true; xs = split$1(xs, xs.shape[0], 0).map(x => squeeze(x, [0])); } assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds ` + `number of dimensions (${xs[0].shape[0]}).`); const ys = []; const xs1d = xs; for (let i = 0; i < xs.length; ++i) { ys.push(ENGINE.tidy(() => { let x = xs1d[i]; if (i > 0) { for (let j = 0; j < i; ++j) { const proj = mul(sum(mul(ys[j], x)), ys[j]); x = sub(x, proj); } } return div(x, norm(x, 'euclidean')); })); } if (inputIsTensor2D) { return stack(ys, 0); } else { return ys; } } const gramSchmidt = /* @__PURE__ */ op({ gramSchmidt_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Compute QR decomposition of m-by-n matrix using Householder transformation. * * Implementation based on * [http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf] * (http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf) * * ```js * const a = tf.tensor2d([[1, 2], [3, 4]]); * let [q, r] = tf.linalg.qr(a); * console.log('Q'); * q.print(); * console.log('R'); * r.print(); * console.log('Orthogonalized'); * q.dot(q.transpose()).print() // should be nearly the identity matrix. * console.log('Reconstructed'); * q.dot(r).print(); // should be nearly [[1, 2], [3, 4]]; * ``` * * @param x The `tf.Tensor` to be QR-decomposed. Must have rank >= 2. Suppose * it has the shape `[..., M, N]`. * @param fullMatrices An optional boolean parameter. Defaults to `false`. * If `true`, compute full-sized `Q`. If `false` (the default), * compute only the leading N columns of `Q` and `R`. * @returns An `Array` of two `tf.Tensor`s: `[Q, R]`. `Q` is a unitary matrix, * i.e., its columns all have unit norm and are mutually orthogonal. * If `M >= N`, * If `fullMatrices` is `false` (default), * - `Q` has a shape of `[..., M, N]`, * - `R` has a shape of `[..., N, N]`. * If `fullMatrices` is `true` (default), * - `Q` has a shape of `[..., M, M]`, * - `R` has a shape of `[..., M, N]`. * If `M < N`, * - `Q` has a shape of `[..., M, M]`, * - `R` has a shape of `[..., M, N]`. * @throws If the rank of `x` is less than 2. * * @doc {heading:'Operations', * subheading:'Linear Algebra', * namespace:'linalg'} */ function qr_(x, fullMatrices = false) { assert(x.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`); if (x.rank === 2) { return qr2d(x, fullMatrices); } else { // Rank > 2. // TODO(cais): Below we split the input into individual 2D tensors, // perform QR decomposition on them and then stack the results back // together. We should explore whether this can be parallelized. const outerDimsProd = x.shape.slice(0, x.shape.length - 2) .reduce((value, prev) => value * prev); const x2ds = unstack(reshape(x, [ outerDimsProd, x.shape[x.shape.length - 2], x.shape[x.shape.length - 1] ]), 0); const q2ds = []; const r2ds = []; x2ds.forEach(x2d => { const [q2d, r2d] = qr2d(x2d, fullMatrices); q2ds.push(q2d); r2ds.push(r2d); }); const q = reshape(stack(q2ds, 0), x.shape); const r = reshape(stack(r2ds, 0), x.shape); return [q, r]; } } function qr2d(x, fullMatrices = false) { return ENGINE.tidy(() => { assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`); const m = x.shape[0]; const n = x.shape[1]; let q = eye(m); // Orthogonal transform so far. let r = clone(x); // Transformed matrix so far. const one2D = tensor2d([[1]], [1, 1]); let w = clone(one2D); const iters = m >= n ? n : m; for (let j = 0; j < iters; ++j) { // This tidy within the for-loop ensures we clean up temporary // tensors as soon as they are no longer needed. const rTemp = r; const wTemp = w; const qTemp = q; [w, r, q] = ENGINE.tidy(() => { // Find H = I - tau * w * w', to put zeros below R(j, j). const rjEnd1 = slice(r, [j, j], [m - j, 1]); const normX = norm(rjEnd1); const rjj = slice(r, [j, j], [1, 1]); // The sign() function returns 0 on 0, which causes division by zero. const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]])); const u1 = sub(rjj, mul(s, normX)); const wPre = div(rjEnd1, u1); if (wPre.shape[0] === 1) { w = clone(one2D); } else { w = concat([ one2D, slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]]) ], 0); } const tau = neg(div(matMul$1(s, u1), normX)); // -- R := HR, Q := QH. const rjEndAll = slice(r, [j, 0], [m - j, n]); const tauTimesW = mul(tau, w); const wT = transpose(w); if (j === 0) { r = sub(rjEndAll, matMul$1(tauTimesW, matMul$1(wT, rjEndAll))); } else { const rTimesTau = sub(rjEndAll, matMul$1(tauTimesW, matMul$1(wT, rjEndAll))); r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0); } const tawTimesWT = transpose(tauTimesW); const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]); if (j === 0) { q = sub(qAllJEnd, matMul$1(matMul$1(qAllJEnd, w), tawTimesWT)); } else { const qTimesTau = sub(qAllJEnd, matMul$1(matMul$1(qAllJEnd, w), tawTimesWT)); q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1); } return [w, r, q]; }); dispose([rTemp, wTemp, qTemp]); } if (!fullMatrices && m > n) { q = slice(q, [0, 0], [m, n]); r = slice(r, [0, 0], [n, n]); } return [q, r]; }); } const qr = /* @__PURE__ */ op({ qr_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ var Reduction; (function (Reduction) { Reduction[Reduction["NONE"] = 0] = "NONE"; Reduction[Reduction["MEAN"] = 1] = "MEAN"; Reduction[Reduction["SUM"] = 2] = "SUM"; Reduction[Reduction["SUM_BY_NONZERO_WEIGHTS"] = 3] = "SUM_BY_NONZERO_WEIGHTS"; })(Reduction || (Reduction = {})); /** * Computes the weighted loss between two tensors. * * @param losses Tensor of shape `[batch_size, d1, ..., dN]`. * @param weights Tensor whose rank is either 0, or the same rank as * `losses`, and must be broadcastable to `losses` (i.e., all * dimensions must be either `1`, or the same as the corresponding * `losses` dimension). * * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */ function computeWeightedLoss_(losses, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { const $losses = convertToTensor(losses, 'losses', 'computeWeightedLoss'); let $weights = null; if (weights != null) { $weights = convertToTensor(weights, 'weights', 'computeWeightedLoss'); } const weightedLoss = ($weights == null) ? $losses : mul($losses, $weights); if (reduction === Reduction.NONE) { return weightedLoss; } if (reduction === Reduction.SUM) { return sum(weightedLoss); } if (reduction === Reduction.MEAN) { if ($weights == null) { return mean(weightedLoss); } else { const broadcastFactor = $losses.size / $weights.size; const result = div(sum(weightedLoss), sum($weights)); return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) : result; } } if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) { if ($weights == null) { return div(sum(weightedLoss), scalar($losses.size)); } else { const broadcastedWeights = mul($weights, ones($losses.shape)); const numNonZeros = cast(sum(notEqual(broadcastedWeights, scalar(0))), 'float32'); return div(sum(weightedLoss), numNonZeros); } } throw Error(`Unknown reduction: ${reduction}`); } const computeWeightedLoss = /* @__PURE__ */ op({ computeWeightedLoss_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the absolute difference loss between two tensors. * * @param labels The ground truth output tensor, same dimensions as * 'predictions'. * @param predictions The predicted outputs. * @param weights Tensor whose rank is either 0, or the same rank as * `labels`, and must be broadcastable to `labels` (i.e., all dimensions * must be either `1`, or the same as the corresponding `losses` * dimension). * @param reduction Type of reduction to apply to loss. Should be of type * `Reduction` * * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */ function absoluteDifference_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { const $labels = convertToTensor(labels, 'labels', 'absoluteDifference'); const $predictions = convertToTensor(predictions, 'predictions', 'absoluteDifference'); let $weights = null; if (weights != null) { $weights = convertToTensor(weights, 'weights', 'absoluteDifference'); } assertShapesMatch($labels.shape, $predictions.shape, 'Error in absoluteDifference: '); const losses = abs(sub($labels, $predictions)); return computeWeightedLoss(losses, $weights, reduction); } const absoluteDifference = /* @__PURE__ */ op({ absoluteDifference_ }); /** * Computes the cosine distance loss between two tensors. * * @param labels The ground truth output tensor, same dimensions as * 'predictions'. * @param predictions The predicted outputs. * @param axis The dimension along which the cosine distance is computed. * @param weights Tensor whose rank is either 0, or the same rank as * `labels`, and must be broadcastable to `labels` (i.e., all dimensions * must be either `1`, or the same as the corresponding `losses` * dimension). * @param reduction Type of reduction to apply to loss. Should be of type * `Reduction` * * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */ function cosineDistance_(labels, predictions, axis, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { const $labels = convertToTensor(labels, 'labels', 'cosineDistance'); const $predictions = convertToTensor(predictions, 'predictions', 'cosineDistance'); let $weights = null; if (weights != null) { $weights = convertToTensor(weights, 'weights', 'cosineDistance'); } assertShapesMatch($labels.shape, $predictions.shape, 'Error in cosineDistance: '); const one = scalar(1); const losses = sub(one, sum(mul($labels, $predictions), axis, true)); return computeWeightedLoss(losses, $weights, reduction); } const cosineDistance = /* @__PURE__ */ op({ cosineDistance_ }); /** * Computes the Hinge loss between two tensors. * * @param labels The ground truth output tensor, same dimensions as * 'predictions'. * @param predictions The predicted outputs. * @param weights Tensor whose rank is either 0, or the same rank as * `labels`, and must be broadcastable to `labels` (i.e., all dimensions * must be either `1`, or the same as the corresponding `losses` * dimension). * @param reduction Type of reduction to apply to loss. Should be of type * `Reduction` * * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */ function hingeLoss_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { let $labels = convertToTensor(labels, 'labels', 'hingeLoss'); const $predictions = convertToTensor(predictions, 'predictions', 'hingeLoss'); let $weights = null; if (weights != null) { $weights = convertToTensor(weights, 'weights', 'hingeLoss'); } assertShapesMatch($labels.shape, $predictions.shape, 'Error in hingeLoss: '); const one = scalar(1); // Convert binary labels to (-1, 1) $labels = sub(mul(scalar(2), $labels), one); const losses = relu(sub(one, mul($labels, $predictions))); return computeWeightedLoss(losses, $weights, reduction); } const hingeLoss = /* @__PURE__ */ op({ hingeLoss_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the Huber loss between two tensors. * * @param labels The ground truth output tensor, same dimensions as * 'predictions'. * @param predictions The predicted outputs. * @param weights Tensor whose rank is either 0, or the same rank as * `labels`, and must be broadcastable to `labels` (i.e., all dimensions * must be either `1`, or the same as the corresponding `losses` * dimension). * @param delta Point where Huber loss changes from quadratic to linear. * @param reduction Type of reduction to apply to loss. Should be of type * `Reduction`. * * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */ function huberLoss_(labels, predictions, weights, delta = 1.0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { const $labels = convertToTensor(labels, 'labels', 'huberLoss'); const $predictions = convertToTensor(predictions, 'predictions', 'huberLoss'); let $weights = null; if (weights != null) { $weights = convertToTensor(weights, 'weights', 'huberLoss'); } assertShapesMatch($labels.shape, $predictions.shape, 'Error in huberLoss: '); const deltaScalar = scalar(delta); const error = abs(sub($predictions, $labels)); const quadratic = minimum(error, deltaScalar); const linear = sub(error, quadratic); const losses = add(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear)); return computeWeightedLoss(losses, $weights, reduction); } const huberLoss = /* @__PURE__ */ op({ huberLoss_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the log loss between two tensors. * * @param labels The ground truth output tensor, same dimensions as * 'predictions'. * @param predictions The predicted outputs. * @param weights Tensor whose rank is either 0, or the same rank as * `labels`, and must be broadcastable to `labels` (i.e., all dimensions * must be either `1`, or the same as the corresponding `losses` * dimension). * @param epsilon A small increment to avoid taking log of zero * @param reduction Type of reduction to apply to loss. Should be of type * `Reduction` * * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */ function logLoss_(labels, predictions, weights, epsilon = 1e-7, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { const $labels = convertToTensor(labels, 'labels', 'logLoss'); const $predictions = convertToTensor(predictions, 'predictions', 'logLoss'); let $weights = null; if (weights != null) { $weights = convertToTensor(weights, 'weights', 'logLoss'); } assertShapesMatch($labels.shape, $predictions.shape, 'Error in logLoss: '); const one = scalar(1); const epsilonScalar = scalar(epsilon); const l1 = neg(mul($labels, log(add($predictions, epsilonScalar)))); const l2 = mul(sub(one, $labels), log(add(sub(one, $predictions), epsilonScalar))); const losses = sub(l1, l2); return computeWeightedLoss(losses, $weights, reduction); } const logLoss = /* @__PURE__ */ op({ logLoss_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the mean squared error between two tensors. * * @param labels The ground truth output tensor, same dimensions as * 'predictions'. * @param predictions The predicted outputs. * @param weights Tensor whose rank is either 0, or the same rank as * `labels`, and must be broadcastable to `labels` (i.e., all dimensions * must be either `1`, or the same as the corresponding `losses` * dimension). * @param reduction Type of reduction to apply to loss. Should be of type * `Reduction` * * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */ function meanSquaredError_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { const $labels = convertToTensor(labels, 'labels', 'meanSquaredError'); const $predictions = convertToTensor(predictions, 'predictions', 'meanSquaredError'); let $weights = null; if (weights != null) { $weights = convertToTensor(weights, 'weights', 'meanSquaredError'); } assertShapesMatch($labels.shape, $predictions.shape, 'Error in meanSquaredError: '); const losses = squaredDifference($labels, $predictions); return computeWeightedLoss(losses, $weights, reduction); } const meanSquaredError = /* @__PURE__ */ op({ meanSquaredError_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ function sigmoidCrossEntropyWithLogits_(labels, logits) { const $labels = convertToTensor(labels, 'labels', 'sigmoidCrossEntropyWithLogits'); const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropyWithLogits'); assertShapesMatch($labels.shape, $logits.shape, 'Error in sigmoidCrossEntropyWithLogits: '); /** * Implementation Details: * * For brevity, let `x = logits`, `z = labels`. The logistic loss is * z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x)) * = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x))) * = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x))) * = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x)) * = (1 - z) * x + log(1 + exp(-x)) * = x - x * z + log(1 + exp(-x)) * * For x < 0, to avoid overflow in exp(-x), we reformulate the above * x - x * z + log(1 + exp(-x)) * = log(exp(x)) - x * z + log(1 + exp(-x)) * = - x * z + log(1 + exp(x)) * * Hence, to ensure stability and avoid overflow, the implementation uses * this equivalent formulation: * max(x, 0) - x * z + log(1 + exp(-abs(x))) */ const maxOutput = relu($logits); const outputXTarget = mul($logits, $labels); const sigmoidOutput = log1p(exp(neg(abs($logits)))); return add(sub(maxOutput, outputXTarget), sigmoidOutput); } /** * Computes the sigmoid cross entropy loss between two tensors. * * If labelSmoothing is nonzero, smooth the labels towards 1/2: * * newMulticlassLabels = multiclassLabels * (1 - labelSmoothing) * + 0.5 * labelSmoothing * * @param multiClassLabels The ground truth output tensor of shape * [batch_size, num_classes], same dimensions as 'predictions'. * @param logits The predicted outputs. * @param weights Tensor whose rank is either 0, or the same rank as * `labels`, and must be broadcastable to `labels` (i.e., all dimensions * must be either `1`, or the same as the corresponding `losses` * dimension). * @param labelSmoothing If greater than 0, then smooth the labels. * @param reduction Type of reduction to apply to loss. Should be of type * `Reduction` * * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' } */ function sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { let $multiClassLabels = convertToTensor(multiClassLabels, 'multiClassLabels', 'sigmoidCrossEntropy'); const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropy'); let $weights = null; if (weights != null) { $weights = convertToTensor(weights, 'weights', 'sigmoidCrossEntropy'); } assertShapesMatch($multiClassLabels.shape, $logits.shape, 'Error in sigmoidCrossEntropy: '); if (labelSmoothing > 0) { const labelSmoothingScalar = scalar(labelSmoothing); const one = scalar(1); const half = scalar(0.5); $multiClassLabels = add(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar)); } const losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits); return computeWeightedLoss(losses, $weights, reduction); } const sigmoidCrossEntropy = /* @__PURE__ */ op({ sigmoidCrossEntropy_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes softmax cross entropy between logits and labels. * * Measures the probability error in discrete classification tasks in which * the classes are mutually exclusive (each entry is in exactly one class). * For example, each CIFAR-10 image is labeled with one and only one label: an * image can be a dog or a truck, but not both. * * `NOTE`: While the classes are mutually exclusive, their probabilities need * not be. All that is required is that each row of labels is a valid * probability distribution. If they are not, the computation of the gradient * will be incorrect. * * `WARNING`: This op expects unscaled logits, since it performs a softmax on * logits internally for efficiency. Do not call this op with the output of * softmax, as it will produce incorrect results. * * logits and labels must have the same shape, e.g. [batch_size, num_classes] * and the same dtype. * @param labels The labels array. * @param logits The logits array. * @param dim The dimension softmax would be performed on. Defaults to `-1` * which indicates the last dimension. */ function softmaxCrossEntropyWithLogits_(labels, logits, dim = -1) { if (dim === -1) { dim = logits.rank - 1; } if (dim !== logits.rank - 1) { throw Error(`Softmax cross entropy along a non-last dimension is not yet ` + `supported. Labels / logits was rank ${logits.rank} ` + `and dim was ${dim}`); } // Use a custom gradient for numerical stability. const customOp = customGrad((labels, logits, save) => { // Reference: // 1. http://cs231n.github.io/linear-classify/#softmax // 2. https://blog.feedly.com/tricks-of-the-trade-logsumexp/ const keepDims = true; const lse = logSumExp(logits, [dim], keepDims); const logResult = sub(cast(logits, 'float32'), lse); save([labels, logResult]); const costVector = neg(mul(logResult, labels)); const value = sum(costVector, [dim]); const gradFunc = (dy, saved) => { const [labels, logResult] = saved; const dyShape = expandShapeToKeepDim(dy.shape, [dim]); return [ mul(reshape(dy, dyShape), sub(cast(labels, 'float32'), exp(logResult))), mul(reshape(dy, dyShape), sub(exp(logResult), cast(labels, 'float32'))), ]; }; return { value, gradFunc }; }); return customOp(labels, logits); } /** * Computes the softmax cross entropy loss between two tensors. * * If labelSmoothing is nonzero, smooth the labels towards 1/2: * * newOnehotLabels = onehotLabels * (1 - labelSmoothing) * + labelSmoothing / numClasses * * @param onehotLabels One hot encoded labels * [batch_size, num_classes], same dimensions as 'predictions'. * @param logits The predicted outputs. * @param weights Tensor whose rank is either 0, or 1, and must be * broadcastable to `loss` of shape [batch_size] * @param labelSmoothing If greater than 0, then smooth the labels. * @param reduction Type of reduction to apply to loss. Should be of type * `Reduction` * * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' } */ function softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { let $onehotLabels = convertToTensor(onehotLabels, 'onehotLabels', 'softmaxCrossEntropy'); const $logits = convertToTensor(logits, 'logits', 'softmaxCrossEntropy'); let $weights = null; if (weights != null) { $weights = convertToTensor(weights, 'weights', 'softmaxCrossEntropy'); } assertShapesMatch($onehotLabels.shape, $logits.shape, 'Error in softmaxCrossEntropy: '); if (labelSmoothing > 0) { const labelSmoothingScalar = scalar(labelSmoothing); const one = scalar(1); const numClasses = scalar($onehotLabels.shape[1]); $onehotLabels = add(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses)); } const losses = softmaxCrossEntropyWithLogits_($onehotLabels, $logits); return computeWeightedLoss(losses, $weights, reduction); } const softmaxCrossEntropy = /* @__PURE__ */ op({ softmaxCrossEntropy_ }); /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * The input SparseTensor is represented via the map of inputs {`indices`, * `values`, `denseShape`}. The output SparseTensor has the same `denseShape` * but with indices `outputIndices` and values `outputValues`. This op inserts a * single entry for every row that doesn't have any values. The index is created * as `[row, 0, ..., 0]` and the inserted value is `defaultValue`. * * For example, suppose `spInput` has shape [5, 6] and non-empty values: * [0, 1]: a * [0, 3]: b * [2, 0]: c * [3, 1]: d * * Rows 1 and 4 are empty, so the output will be of shape [5, 6] with values: * [0, 1]: a * [0, 3]: b * [1, 0]: `defaultValue` * [2, 0]: c * [3, 1]: d * [4, 0]: `defaultValue` * * The output SparseTensor will be in row-major order and will have the same * shape as the input. * * This op also returns an indicator vector shaped [dense_shape[0]] such that * emptyRowIndicator[i] = True iff row i was an empty row. * * And a reverse index map vector shaped [indices.shape[0]] that is used during * backpropagation, reverseIndexMap[i] = outi s.t. indices[i, j] == * outputIndices[outi, j] for all j * * ```js * const result = tf.sparse.sparseFillEmptyRows( * [[0, 0], [1, 0], [1, 3], [1, 4], [3, 2], [3, 3]], * [0, 10, 13, 14, 32, 33], [5, 6], -1); * console.log(result); * result['outputIndices'].print(); // [[0, 0], [1, 0], [1, 3], [1, 4], * // [2, 0], [3, 2], [3, 3], [4, 0]] * result['outputValues'].print(); // [0, 10, 13, 14,-1, 32, 33, -1] * result['emptyRowIndicator'].print(); // [false, false, true, false, true] * result['reverseIndexMap'].print(); // [0, 1, 2, 3, 5, 6] * ``` * @param indices: 2-D. The indices of the sparse tensor. * @param values: 1-D. The values of the sparse tensor. * @param denseShape: 1-D. The shape of the sparse tensor. * @param defaultValue: 0-D. Default value to insert into location [row, 0, ..., * 0] for rows missing from the input sparse tensor. * @return A map with the following properties: * - outputIndices * - outputValues: 1-D. The values of the filled sparse tensor. * - emptyRowIndicator: 1-D. Whether the dense row was missing in the input * sparse tensor. * - reverseIndexMap: 1-D. A map from the input indices to the output * indices. * @doc {heading: 'Operations', subheading: 'Sparse'} */ function sparseFillEmptyRows_(indices, values, denseShape, defaultValue) { const $indices = convertToTensor(indices, 'indices', 'sparseFillEmptyRows', 'int32'); const $values = convertToTensor(values, 'values', 'sparseFillEmptyRows'); const $denseShape = convertToTensor(denseShape, 'denseShape', 'sparseFillEmptyRows', 'int32'); const $defaultValue = convertToTensor(defaultValue, 'defaultValue', 'sparseFillEmptyRows', $values.dtype); if ($indices.rank !== 2) { throw new Error(`Indices should be Tensor2D but received shape ${$indices.shape}`); } if ($values.rank !== 1) { throw new Error(`Values should be Tensor1D but received shape ${$values.shape}`); } if ($denseShape.rank !== 1) { throw new Error(`Dense shape should be Tensor1D but received shape ${$denseShape.shape}`); } if ($defaultValue.rank !== 0) { throw new Error(`Default value should be a scalar but received shape ${$defaultValue.shape}`); } const inputs = { indices: $indices, values: $values, denseShape: $denseShape, defaultValue: $defaultValue }; const result = ENGINE.runKernel(SparseFillEmptyRows, inputs); return { outputIndices: result[0], outputValues: result[1], emptyRowIndicator: result[2], reverseIndexMap: result[3] }; } const sparseFillEmptyRows = /* @__PURE__ */ op({ sparseFillEmptyRows_ }); /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * This operation has the same semantics as reshape on the represented dense * tensor. The `inputIndices` are recomputed based on the requested `newShape`. * If one component of `newShape` is the special value -1, the size of that * dimension is computed so that the total dense size remains constant. At most * one component of `newShape` can be -1. The number of dense elements implied * by `newShape` must be the same as the number of dense elements originally * implied by `inputShape`. Reshaping does not affect the order of values in the * SparseTensor. If the input tensor has rank R_in and N non-empty values, and * `newShape` has length R_out, then `inputIndices` has shape [N, R_in], * `inputShape` has length R_in, `outputIndices` has shape [N, R_out], and * `outputShape` has length R_out. * * ```js * const result = tf.sparse.sparseReshape( * [[0, 0, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0], [1, 2, 3]], * [2, 3, 6], [9, -1]); * console.log(result); * result['outputIndices'].print(); //[[0, 0], [0, 1], [1, 2], [4, 2], [8, 1]] * result['outputShape'].print(); // [9, 4] * ``` * @param inputIndices: 2-D. N x R_in matrix with the indices of non-empty * values in a SparseTensor. * @param inputShape: 1-D. R_in Tensor1D with the input SparseTensor's dense * shape. * @param newShape: 1-D. R_out Tensor1D with the requested new dense shape. * @return A map with the following properties: * - outputIndices: 2-D. N x R_out matrix with the updated indices of * non-empty values in the output SparseTensor. * - outputShape: 1-D. R_out vector with the full dense shape of the output * SparseTensor. This is the same as newShape but with any -1 dimensions * filled in. * @doc {heading: 'Operations', subheading: 'Sparse'} */ function sparseReshape_(inputIndices, inputShape, newShape) { const $inputIndices = convertToTensor(inputIndices, 'inputIndices', 'sparseReshape', 'int32'); const $inputShape = convertToTensor(inputShape, 'inputShape', 'sparseReshape', 'int32'); const $newShape = convertToTensor(newShape, 'newShape', 'sparseReshape', 'int32'); if ($inputIndices.rank !== 2) { throw new Error(`Input indices should be Tensor2D but received shape ${$inputIndices.shape}`); } if ($inputShape.rank !== 1) { throw new Error(`Input shape should be Tensor1D but received shape ${$inputShape.shape}`); } if ($newShape.rank !== 1) { throw new Error(`New shape should be Tensor1D but received shape ${$newShape.shape}`); } const inputs = { inputIndices: $inputIndices, inputShape: $inputShape, newShape: $newShape }; const result = ENGINE.runKernel(SparseReshape, inputs); return { outputIndices: result[0], outputShape: result[1] }; } const sparseReshape = /* @__PURE__ */ op({ sparseReshape_ }); /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the mean along sparse segments of a tensor. * * ```js * const c = tf.tensor2d([[1,2,3,4], [-1,-2,-3,-4], [6,7,8,9]]); * // Select two rows, one segment. * const result1 = tf.sparse.sparseSegmentMean(c, * tf.tensor1d([0, 1], 'int32'), * tf.tensor1d([0, 0], 'int32')); * result1.print(); // [[0, 0, 0, 0]] * * // Select two rows, two segments. * const result2 = tf.sparse.sparseSegmentMean(c, * tf.tensor1d([0, 1], 'int32'), * tf.tensor1d([0, 1], 'int32')); * result2.print(); // [[1, 2, 3, 4], [-1, -2, -3, -4]] * * // Select all rows, two segments. * const result3 = tf.sparse.sparseSegmentMean(c, * tf.tensor1d([0, 1, 2], 'int32'), * tf.tensor1d([0, 1, 1], 'int32')); * result3.print(); // [[1.0, 2.0, 3.0, 4.0], [2.5, 2.5, 2.5, 2.5]] * ``` * @param data: A Tensor of at least one dimension with data that will be * assembled in the output. * @param indices: A 1-D Tensor with indices into data. Has same rank as * segmentIds. * @param segmentIds: A 1-D Tensor with indices into the output Tensor. Values * should be sorted and can be repeated. * @return Has same shape as data, except for dimension 0 which has equal to * the number of segments. * * @doc {heading: 'Operations', subheading: 'Sparse'} */ function sparseSegmentMean_(data, indices, segmentIds) { const $data = convertToTensor(data, 'data', 'sparseSegmentMean'); const $indices = convertToTensor(indices, 'indices', 'sparseSegmentMean', 'int32'); const $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'sparseSegmentMean', 'int32'); if ($data.rank < 1) { throw new Error(`Data should be at least 1 dimensional but received scalar`); } if ($indices.rank !== 1) { throw new Error(`Indices should be Tensor1D but received shape ${$indices.shape}`); } if ($segmentIds.rank !== 1) { throw new Error(`Segment ids should be Tensor1D but received shape ${$segmentIds.shape}`); } const inputs = { data: $data, indices: $indices, segmentIds: $segmentIds }; return ENGINE.runKernel(SparseSegmentMean, inputs); } const sparseSegmentMean = /* @__PURE__ */ op({ sparseSegmentMean_ }); /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Computes the sum along sparse segments of a tensor. * * ```js * const c = tf.tensor2d([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]]); * // Select two rows, one segment. * const result1 = tf.sparse.sparseSegmentSum(c, * tf.tensor1d([0, 1], 'int32'), * tf.tensor1d([0, 0], 'int32')); * result1.print(); // [[0, 0, 0, 0]] * * // Select two rows, two segments. * const result2 = tf.sparse.sparseSegmentSum(c, * tf.tensor1d([0, 1], 'int32'), * tf.tensor1d([0, 1], 'int32')); * result2.print(); // [[1, 2, 3, 4], [-1, -2, -3, -4]] * * // Select all rows, two segments. * const result3 = tf.sparse.sparseSegmentSum(c, * tf.tensor1d([0, 1, 2], 'int32'), * tf.tensor1d([0, 0, 1], 'int32')); * result3.print(); // [[0, 0, 0, 0], [5, 6, 7, 8]] * ``` * @param data: A Tensor of at least one dimension with data that will be * assembled in the output. * @param indices: A 1-D Tensor with indices into data. Has same rank as * segmentIds. * @param segmentIds: A 1-D Tensor with indices into the output Tensor. Values * should be sorted and can be repeated. * @return Has same shape as data, except for dimension 0 which has equal to * the number of segments. * * @doc {heading: 'Operations', subheading: 'Sparse'} */ function sparseSegmentSum_(data, indices, segmentIds) { const $data = convertToTensor(data, 'data', 'sparseSegmentSum'); const $indices = convertToTensor(indices, 'indices', 'sparseSegmentSum', 'int32'); const $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'sparseSegmentSum', 'int32'); if ($data.rank < 1) { throw new Error(`Data should be at least 1 dimensional but received scalar`); } if ($indices.rank !== 1) { throw new Error(`Indices should be Tensor1D but received shape ${$indices.shape}`); } if ($segmentIds.rank !== 1) { throw new Error(`Segment ids should be Tensor1D but received shape ${$segmentIds.shape}`); } const inputs = { data: $data, indices: $indices, segmentIds: $segmentIds }; return ENGINE.runKernel(SparseSegmentSum, inputs); } const sparseSegmentSum = /* @__PURE__ */ op({ sparseSegmentSum_ }); /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Creates ngrams from ragged string data. * * This op accepts a ragged tensor with 1 ragged dimension containing only * strings and outputs a ragged tensor with 1 ragged dimension containing ngrams * of that string, joined along the innermost axis. * * ```js * const result = tf.string.stringNGrams( * ['a', 'b', 'c', 'd'], tf.tensor1d([0, 2, 4], 'int32'), * '|', [1, 2], 'LP', 'RP', -1, false); * result['nGrams'].print(); // ['a', 'b', 'LP|a', 'a|b', 'b|RP', * // 'c', 'd', 'LP|c', 'c|d', 'd|RP'] * result['nGramsSplits'].print(); // [0, 5, 10] * ``` * @param data: The values tensor of the ragged string tensor to make ngrams out * of. Must be a 1D string tensor. * @param dataSplits: The splits tensor of the ragged string tensor to make * ngrams out of. * @param separator: The string to append between elements of the token. Use "" * for no separator. * @param nGramWidths: The sizes of the ngrams to create. * @param leftPad: The string to use to pad the left side of the ngram sequence. * Only used if pad_width !== 0. * @param rightPad: The string to use to pad the right side of the ngram * sequence. Only used if pad_width !== 0. * @param padWidth: The number of padding elements to add to each side of each * sequence. Note that padding will never be greater than `nGramWidths`-1 * regardless of this value. If `padWidth`=-1, then add max(`nGramWidths`)-1 * elements. * @param preserveShortSequences: If true, then ensure that at least one ngram * is generated for each input sequence. In particular, if an input sequence * is shorter than min(ngramWidth) + 2*padWidth, then generate a single * ngram containing the entire sequence. If false, then no ngrams are * generated for these short input sequences. * @return A map with the following properties: * - nGrams: The values tensor of the output ngrams ragged tensor. * - nGramsSplits: The splits tensor of the output ngrams ragged tensor. * * @doc {heading: 'Operations', subheading: 'String'} */ function stringNGrams_(data, dataSplits, separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences) { const $data = convertToTensor(data, 'data', 'stringNGrams', 'string'); if ($data.dtype !== 'string') { throw new Error('Data must be of datatype string'); } if ($data.shape.length !== 1) { throw new Error(`Data must be a vector, saw: ${$data.shape}`); } const $dataSplits = convertToTensor(dataSplits, 'dataSplits', 'stringNGrams'); if ($dataSplits.dtype !== 'int32') { throw new Error('Data splits must be of datatype int32'); } const attrs = { separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences }; const inputs = { data: $data, dataSplits: $dataSplits }; const result = ENGINE.runKernel(StringNGrams, inputs, attrs); return { nGrams: result[0], nGramsSplits: result[1] }; } const stringNGrams = /* @__PURE__ */ op({ stringNGrams_ }); /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Split elements of `input` based on `delimiter` into a SparseTensor . * * Let N be the size of source (typically N will be the batch size). Split each * element of `input` based on `delimiter` and return a SparseTensor containing * the splitted tokens. Empty tokens are ignored if `skipEmpty` is set to True. * * `delimiter` can be empty, or a string of split characters. If `delimiter` is * an empty string, each element of `input` is split into individual * character strings. Otherwise every character of `delimiter` is a potential * split point. * * ```js * const result = tf.string.stringSplit(['hello world', 'a b c'], ' '); * result['indices'].print(); // [[0, 0], [0, 1], [1, 0], [1, 1], [1, 2]] * result['values'].print(); // ['hello', 'world', 'a', 'b', 'c'] * result['shape'].print(); // [2, 3] * ``` * @param input: 1-D. Strings to split. * @param delimiter: 0-D. Delimiter characters, or empty string. * @param skipEmpty: Optional. If true, skip the empty strings from the result. * Defaults to true. * @return A map with the following properties: * - indices: A dense matrix of int32 representing the indices of the sparse * tensor. * - values: A vector of strings corresponding to the splited values. * - shape: a length-2 vector of int32 representing the shape of the sparse * tensor, where the first value is N and the second value is the maximum number * of tokens in a single input entry. * * @doc {heading: 'Operations', subheading: 'String'} */ function stringSplit_(input, delimiter, skipEmpty = true) { const $input = convertToTensor(input, 'input', 'stringSplit', 'string'); const $delimiter = convertToTensor(delimiter, 'delimiter', 'stringSplit', 'string'); if ($input.rank !== 1) { throw new Error(`Input should be Tensor1D but received shape ${$input.shape}`); } if ($delimiter.rank !== 0) { throw new Error(`Delimiter should be a scalar but received shape ${$delimiter.shape}`); } const attrs = { skipEmpty }; const inputs = { input: $input, delimiter: $delimiter }; const result = ENGINE.runKernel(StringSplit, inputs, attrs); return { indices: result[0], values: result[1], shape: result[2] }; } const stringSplit = /* @__PURE__ */ op({ stringSplit_ }); /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Converts each string in the input Tensor to its hash mod by a number of * buckets. * * The hash function is deterministic on the content of the string within the * process and will never change. However, it is not suitable for cryptography. * This function may be used when CPU time is scarce and inputs are trusted or * unimportant. There is a risk of adversaries constructing inputs that all hash * to the same bucket. * * ```js * const result = tf.string.stringToHashBucketFast( * ['Hello', 'TensorFlow', '2.x'], 3); * result.print(); // [0, 2, 2] * ``` * @param input: The strings to assign a hash bucket. * @param numBuckets: The number of buckets. * @return A Tensor of the same shape as the input tensor. * * @doc {heading: 'Operations', subheading: 'String'} */ function stringToHashBucketFast_(input, numBuckets) { const $input = convertToTensor(input, 'input', 'stringToHashBucketFast', 'string'); const attrs = { numBuckets }; if (numBuckets <= 0) { throw new Error(`Number of buckets must be at least 1`); } const inputs = { input: $input }; return ENGINE.runKernel(StringToHashBucketFast, inputs, attrs); } const stringToHashBucketFast = /* @__PURE__ */ op({ stringToHashBucketFast_ }); /** * @license * Copyright 2023 Google LLC. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Replace the match of a `pattern` in `input` with `rewrite`. * * ```js * const result = tf.string.staticRegexReplace( * ['format this spacing better'], ' +', ' '); * result.print(); // ['format this spacing better'] * ``` * @param input: A Tensor of type string. The text to be processed. * @param pattern: A string. The regular expression to match the input. * @param rewrite: A string. The rewrite to be applied to the matched * expression. * @param replaceGlobal: An optional bool. Defaults to True. If True, the * replacement is global, otherwise the replacement is done only on the * first match. * @return A Tensor of type string. * * @doc {heading: 'Operations', subheading: 'String'} */ function staticRegexReplace_(input, pattern, rewrite, replaceGlobal = true) { const $input = convertToTensor(input, 'input', 'staticRegexReplace', 'string'); const attrs = { pattern, rewrite, replaceGlobal }; return ENGINE.runKernel(StaticRegexReplace, { x: $input }, attrs); } const staticRegexReplace = /* @__PURE__ */ op({ staticRegexReplace_ }); /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const spectral = { fft, ifft, rfft, irfft }; const signal = { hammingWindow, hannWindow, frame, stft, }; const image = { flipLeftRight, grayscaleToRGB, resizeNearestNeighbor, resizeBilinear, rgbToGrayscale, rotateWithOffset, cropAndResize, nonMaxSuppression, nonMaxSuppressionAsync, nonMaxSuppressionWithScore, nonMaxSuppressionWithScoreAsync, nonMaxSuppressionPadded, nonMaxSuppressionPaddedAsync, threshold, transform }; const linalg = { bandPart, gramSchmidt, qr }; const losses = { absoluteDifference, computeWeightedLoss, cosineDistance, hingeLoss, huberLoss, logLoss, meanSquaredError, sigmoidCrossEntropy, softmaxCrossEntropy }; const sparse = { sparseFillEmptyRows, sparseReshape, sparseSegmentMean, sparseSegmentSum }; // tslint:disable-next-line:variable-name const string = { stringNGrams, stringSplit, stringToHashBucketFast, staticRegexReplace, }; /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ var tfOps = { __proto__: null, OP_SCOPE_SUFFIX: OP_SCOPE_SUFFIX, abs: abs, acos: acos, acosh: acosh, add: add, addN: addN, all: all, any: any, argMax: argMax, argMin: argMin, asin: asin, asinh: asinh, atan: atan, atan2: atan2, atanh: atanh, avgPool: avgPool, avgPool3d: avgPool3d, basicLSTMCell: basicLSTMCell, batchNorm: batchNorm, batchNorm2d: batchNorm2d, batchNorm3d: batchNorm3d, batchNorm4d: batchNorm4d, batchToSpaceND: batchToSpaceND, bincount: bincount, bitwiseAnd: bitwiseAnd, booleanMaskAsync: booleanMaskAsync, broadcastArgs: broadcastArgs, broadcastTo: broadcastTo, buffer: buffer, cast: cast, ceil: ceil, clipByValue: clipByValue, clone: clone, complex: complex, concat: concat, concat1d: concat1d, concat2d: concat2d, concat3d: concat3d, concat4d: concat4d, conv1d: conv1d, conv2d: conv2d$1, conv2dTranspose: conv2dTranspose, conv3d: conv3d, conv3dTranspose: conv3dTranspose, cos: cos, cosh: cosh, cosineWindow: cosineWindow, cumprod: cumprod, cumsum: cumsum, denseBincount: denseBincount, depthToSpace: depthToSpace, depthwiseConv2d: depthwiseConv2d$1, diag: diag, dilation2d: dilation2d, div: div, divNoNan: divNoNan, dot: dot, dropout: dropout, einsum: einsum, elu: elu, enclosingPowerOfTwo: enclosingPowerOfTwo, ensureShape: ensureShape, equal: equal, erf: erf, euclideanNorm: euclideanNorm, exp: exp, expandDims: expandDims, expm1: expm1, eye: eye, fft: fft, fill: fill, floor: floor, floorDiv: floorDiv, fused: fused_ops, gather: gather, gatherND: gatherND, greater: greater, greaterEqual: greaterEqual, ifft: ifft, imag: imag, image: image, inTopKAsync: inTopKAsync, irfft: irfft, isFinite: isFinite$1, isInf: isInf, isNaN: isNaN$1, leakyRelu: leakyRelu, less: less, lessEqual: lessEqual, linalg: linalg, linspace: linspace, localResponseNormalization: localResponseNormalization, log: log, log1p: log1p, logSigmoid: logSigmoid, logSoftmax: logSoftmax, logSumExp: logSumExp, logicalAnd: logicalAnd, logicalNot: logicalNot, logicalOr: logicalOr, logicalXor: logicalXor, losses: losses, lowerBound: lowerBound, matMul: matMul$1, max: max, maxPool: maxPool, maxPool3d: maxPool3d, maxPoolWithArgmax: maxPoolWithArgmax, maximum: maximum, mean: mean, meshgrid: meshgrid, min: min, minimum: minimum, mirrorPad: mirrorPad, mod: mod, moments: moments, movingAverage: movingAverage, mul: mul, multiRNNCell: multiRNNCell, multinomial: multinomial, neg: neg, norm: norm, notEqual: notEqual, oneHot: oneHot, ones: ones, onesLike: onesLike, op: op, outerProduct: outerProduct, pad: pad, pad1d: pad1d, pad2d: pad2d, pad3d: pad3d, pad4d: pad4d, pool: pool, pow: pow, prelu: prelu, print: print, prod: prod, raggedGather: raggedGather, raggedRange: raggedRange, raggedTensorToTensor: raggedTensorToTensor, rand: rand, randomGamma: randomGamma, randomNormal: randomNormal, randomStandardNormal: randomStandardNormal, randomUniform: randomUniform, randomUniformInt: randomUniformInt, range: range, real: real, reciprocal: reciprocal, relu: relu, relu6: relu6, reshape: reshape, reverse: reverse, reverse1d: reverse1d, reverse2d: reverse2d, reverse3d: reverse3d, reverse4d: reverse4d, rfft: rfft, round: round, rsqrt: rsqrt, scalar: scalar, scatterND: scatterND, searchSorted: searchSorted, selu: selu, separableConv2d: separableConv2d, setdiff1dAsync: setdiff1dAsync, sigmoid: sigmoid, sign: sign, signal: signal, sin: sin, sinh: sinh, slice: slice, slice1d: slice1d, slice2d: slice2d, slice3d: slice3d, slice4d: slice4d, softmax: softmax, softplus: softplus, spaceToBatchND: spaceToBatchND, sparse: sparse, sparseToDense: sparseToDense, spectral: spectral, split: split$1, sqrt: sqrt, square: square, squaredDifference: squaredDifference, squeeze: squeeze, stack: stack, step: step, stridedSlice: stridedSlice, string: string, sub: sub, sum: sum, tan: tan, tanh: tanh, tensor: tensor, tensor1d: tensor1d, tensor2d: tensor2d, tensor3d: tensor3d, tensor4d: tensor4d, tensor5d: tensor5d, tensor6d: tensor6d, tensorScatterUpdate: tensorScatterUpdate, tile: tile, topk: topk, transpose: transpose, truncatedNormal: truncatedNormal, unique: unique, unsortedSegmentSum: unsortedSegmentSum, unstack: unstack, upperBound: upperBound, variable: variable, where: where, whereAsync: whereAsync, zeros: zeros, zerosLike: zerosLike }; /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const executeOp$k = (node, tensorMap, context, ops = tfOps) => { switch (node.op) { case 'BiasAdd': case 'AddV2': case 'Add': { return [ops.add(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; } case 'AddN': { return [ops.addN(getParamValue('tensors', node, tensorMap, context))]; } case 'FloorMod': case 'Mod': return [ops.mod(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; case 'Mul': return [ops.mul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; case 'RealDiv': case 'Div': { return [ops.div(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; } case 'DivNoNan': { return [ops.divNoNan(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; } case 'FloorDiv': { return [ops.floorDiv(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; } case 'Sub': { return [ops.sub(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; } case 'Minimum': { return [ops.minimum(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; } case 'Maximum': { return [ops.maximum(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; } case 'Pow': { return [ops.pow(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; } case 'SquaredDifference': { return [ops.squaredDifference(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; } default: throw TypeError(`Node type ${node.op} is not implemented`); } }; /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const executeOp$j = (node, tensorMap, context, ops = tfOps) => { switch (node.op) { case 'Abs': case 'ComplexAbs': return [ops.abs(getParamValue('x', node, tensorMap, context))]; case 'Acos': return [ops.acos(getParamValue('x', node, tensorMap, context))]; case 'Acosh': return [ops.acosh(getParamValue('x', node, tensorMap, context))]; case 'Asin': return [ops.asin(getParamValue('x', node, tensorMap, context))]; case 'Asinh': return [ops.asinh(getParamValue('x', node, tensorMap, context))]; case 'Atan': return [ops.atan(getParamValue('x', node, tensorMap, context))]; case 'Atan2': return [ops.atan2(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context))]; case 'Atanh': return [ops.atanh(getParamValue('x', node, tensorMap, context))]; case 'Ceil': return [ops.ceil(getParamValue('x', node, tensorMap, context))]; case 'Complex': return [ops.complex(getParamValue('real', node, tensorMap, context), getParamValue('imag', node, tensorMap, context))]; case 'Cos': return [ops.cos(getParamValue('x', node, tensorMap, context))]; case 'Cosh': return [ops.cosh(getParamValue('x', node, tensorMap, context))]; case 'Elu': return [ops.elu(getParamValue('x', node, tensorMap, context))]; case 'Erf': return [ops.erf(getParamValue('x', node, tensorMap, context))]; case 'Exp': return [ops.exp(getParamValue('x', node, tensorMap, context))]; case 'Expm1': { return [ops.expm1(getParamValue('x', node, tensorMap, context))]; } case 'Floor': return [ops.floor(getParamValue('x', node, tensorMap, context))]; case 'Log': return [ops.log(getParamValue('x', node, tensorMap, context))]; case 'Log1p': { return [ops.log1p(getParamValue('x', node, tensorMap, context))]; } case 'Imag': return [ops.imag(getParamValue('x', node, tensorMap, context))]; case 'Neg': return [ops.neg(getParamValue('x', node, tensorMap, context))]; case 'Reciprocal': { return [ops.reciprocal(getParamValue('x', node, tensorMap, context))]; } case 'Real': return [ops.real(getParamValue('x', node, tensorMap, context))]; case 'Relu': return [ops.relu(getParamValue('x', node, tensorMap, context))]; case 'Round': { return [ops.round(getParamValue('x', node, tensorMap, context))]; } case 'Selu': return [ops.selu(getParamValue('x', node, tensorMap, context))]; case 'Sigmoid': return [ops.sigmoid(getParamValue('x', node, tensorMap, context))]; case 'Sin': return [ops.sin(getParamValue('x', node, tensorMap, context))]; case 'Sign': { return [ops.sign(getParamValue('x', node, tensorMap, context))]; } case 'Sinh': { return [ops.sinh(getParamValue('x', node, tensorMap, context))]; } case 'Softplus': { return [ops.softplus(getParamValue('x', node, tensorMap, context))]; } case 'Sqrt': { return [ops.sqrt(getParamValue('x', node, tensorMap, context))]; } case 'Square': { return [ops.square(getParamValue('x', node, tensorMap, context))]; } case 'Tanh': { return [ops.tanh(getParamValue('x', node, tensorMap, context))]; } case 'Tan': return [ops.tan(getParamValue('x', node, tensorMap, context))]; case 'ClipByValue': return [ops.clipByValue(getParamValue('x', node, tensorMap, context), getParamValue('clipValueMin', node, tensorMap, context), getParamValue('clipValueMax', node, tensorMap, context))]; case 'Relu6': return [ops.relu6(getParamValue('x', node, tensorMap, context))]; case 'Rsqrt': return [ops.rsqrt(getTensor(node.inputNames[0], tensorMap, context))]; case 'LeakyRelu': return [ops.leakyRelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))]; case 'Prelu': return [ops.prelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))]; case 'IsNan': return [ops.isNaN(getTensor(node.inputNames[0], tensorMap, context))]; case 'IsInf': return [ops.isInf(getTensor(node.inputNames[0], tensorMap, context))]; case 'IsFinite': return [ops.isFinite(getTensor(node.inputNames[0], tensorMap, context))]; default: throw TypeError(`Node type ${node.op} is not implemented`); } }; /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Used by TensorList and TensorArray to verify if elementShape matches, support * negative value as the dim shape. * @param shapeA * @param shapeB * @param errorMessagePrefix */ function assertShapesMatchAllowUndefinedSize(shapeA, shapeB, errorMessagePrefix = '') { // constant shape means unknown rank if (typeof shapeA === 'number' || typeof shapeB === 'number') { return; } tfc.util.assert(shapeA.length === shapeB.length, () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`); for (let i = 0; i < shapeA.length; i++) { const dim0 = shapeA[i]; const dim1 = shapeB[i]; tfc.util.assert(dim0 < 0 || dim1 < 0 || dim0 === dim1, () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`); } } function fullDefinedShape(elementShape) { if (typeof elementShape === 'number' || elementShape.some(dim => dim < 0)) { return false; } return true; } /** * Generate the output element shape from the list elementShape, list tensors * and input param. * @param listElementShape * @param tensors * @param elementShape */ function inferElementShape(listElementShape, tensors, elementShape) { let partialShape = mergeElementShape(listElementShape, elementShape); const notfullDefinedShape = !fullDefinedShape(partialShape); if (notfullDefinedShape && tensors.length === 0) { throw new Error(`Tried to calculate elements of an empty list` + ` with non-fully-defined elementShape: ${partialShape}`); } if (notfullDefinedShape) { tensors.forEach(tensor => { partialShape = mergeElementShape(tensor.shape, partialShape); }); } if (!fullDefinedShape(partialShape)) { throw new Error(`Non-fully-defined elementShape: ${partialShape}`); } return partialShape; } function mergeElementShape(elementShapeA, elementShapeB) { if (typeof elementShapeA === 'number') { return elementShapeB; } if (typeof elementShapeB === 'number') { return elementShapeA; } if (elementShapeA.length !== elementShapeB.length) { throw new Error(`Incompatible ranks during merge: ${elementShapeA} vs. ${elementShapeB}`); } const result = []; for (let i = 0; i < elementShapeA.length; ++i) { const dim0 = elementShapeA[i]; const dim1 = elementShapeB[i]; if (dim0 >= 0 && dim1 >= 0 && dim0 !== dim1) { throw new Error(`Incompatible shape during merge: ${elementShapeA} vs. ${elementShapeB}`); } result[i] = dim0 >= 0 ? dim0 : dim1; } return result; } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * The TensorArray object keeps an array of Tensors. It * allows reading from the array and writing to the array. */ class TensorArray { constructor(name, dtype, maxSize, elementShape, identicalElementShapes, dynamicSize, clearAfterRead) { this.name = name; this.dtype = dtype; this.maxSize = maxSize; this.elementShape = elementShape; this.identicalElementShapes = identicalElementShapes; this.dynamicSize = dynamicSize; this.clearAfterRead = clearAfterRead; this.tensors = []; this.closed_ = false; this.idTensor = tfc.scalar(0); tfc.keep(this.idTensor); } get id() { return this.idTensor.id; } get closed() { return this.closed_; } /** * Dispose the tensors and idTensor and mark the TensoryArray as closed. */ clearAndClose(keepIds) { this.tensors.forEach(tensor => { if (keepIds == null || !keepIds.has(tensor.tensor.id)) { tensor.tensor.dispose(); } }); this.tensors = []; this.closed_ = true; this.idTensor.dispose(); } size() { return this.tensors.length; } /** * Read the value at location index in the TensorArray. * @param index Number the index to read from. */ read(index) { if (this.closed_) { throw new Error(`TensorArray ${this.name} has already been closed.`); } if (index < 0 || index >= this.size()) { throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`); } const tensorWithState = this.tensors[index]; if (tensorWithState.cleared) { throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read ` + `(perhaps try setting clear_after_read = false?).`); } if (this.clearAfterRead) { tensorWithState.cleared = true; } tensorWithState.read = true; return tensorWithState.tensor; } /** * Helper method to read multiple tensors from the specified indices. */ readMany(indices) { return indices.map(index => this.read(index)); } /** * Write value into the index of the TensorArray. * @param index number the index to write to. * @param tensor */ write(index, tensor) { if (this.closed_) { throw new Error(`TensorArray ${this.name} has already been closed.`); } if (index < 0 || !this.dynamicSize && index >= this.maxSize) { throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`); } const t = this.tensors[index] || {}; if (tensor.dtype !== this.dtype) { throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because the value dtype is ${tensor.dtype}, but TensorArray dtype is ${this.dtype}.`); } // Set the shape for the first time write to unknow shape tensor array if (this.size() === 0 && (this.elementShape == null || this.elementShape.length === 0)) { this.elementShape = tensor.shape; } assertShapesMatchAllowUndefinedSize(this.elementShape, tensor.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${index}.`); if (t.read) { throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`); } if (t.written) { throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`); } t.tensor = tensor; tfc.keep(tensor); t.written = true; this.tensors[index] = t; } /** * Helper method to write multiple tensors to the specified indices. */ writeMany(indices, tensors) { if (indices.length !== tensors.length) { throw new Error(`TensorArray ${this.name}: could not write multiple tensors,` + `because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`); } indices.forEach((i, index) => this.write(i, tensors[index])); } /** * Return selected values in the TensorArray as a packed Tensor. All of * selected values must have been written and their shapes must all match. * @param [indices] number[] Optional. Taking values in [0, max_value). If the * TensorArray is not dynamic, max_value=size(). If not specified returns * all tensors in the original order. * @param [dtype] */ gather(indices, dtype) { if (!!dtype && dtype !== this.dtype) { throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`); } if (!indices) { indices = []; for (let i = 0; i < this.size(); i++) { indices.push(i); } } else { indices = indices.slice(0, this.size()); } if (indices.length === 0) { return tfc.tensor([], [0].concat(this.elementShape)); } // Read all the PersistentTensors into a vector to keep track of // their memory. const tensors = this.readMany(indices); assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, 'TensorArray shape mismatch: '); return tfc.stack(tensors, 0); } /** * Return the values in the TensorArray as a concatenated Tensor. */ concat(dtype) { if (!!dtype && dtype !== this.dtype) { throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`); } if (this.size() === 0) { return tfc.tensor([], [0].concat(this.elementShape)); } const indices = []; for (let i = 0; i < this.size(); i++) { indices.push(i); } // Collect all the tensors from the tensors array. const tensors = this.readMany(indices); assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`); return tfc.concat(tensors, 0); } /** * Scatter the values of a Tensor in specific indices of a TensorArray. * @param indices nummber[] values in [0, max_value). If the * TensorArray is not dynamic, max_value=size(). * @param tensor Tensor input tensor. */ scatter(indices, tensor) { if (tensor.dtype !== this.dtype) { throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor.dtype}`); } if (indices.length !== tensor.shape[0]) { throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor.shape[0]}`); } const maxIndex = Math.max(...indices); if (!this.dynamicSize && maxIndex >= this.maxSize) { throw new Error(`Max index must be < array size (${maxIndex} vs. ${this.maxSize})`); } this.writeMany(indices, tfc.unstack(tensor, 0)); } /** * Split the values of a Tensor into the TensorArray. * @param length number[] with the lengths to use when splitting value along * its first dimension. * @param tensor Tensor, the tensor to split. */ split(length, tensor) { if (tensor.dtype !== this.dtype) { throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor.dtype}`); } let totalLength = 0; const cumulativeLengths = length.map(len => { totalLength += len; return totalLength; }); if (totalLength !== tensor.shape[0]) { throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is ${totalLength}, and tensor's shape is: ${tensor.shape}`); } if (!this.dynamicSize && length.length !== this.maxSize) { throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), ` + 'and the TensorArray is not marked as dynamically resizeable'); } const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength; const tensors = []; tfc.tidy(() => { tensor = tfc.reshape(tensor, [1, totalLength, elementPerRow]); for (let i = 0; i < length.length; ++i) { const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1]; const indices = [0, previousLength, 0]; const sizes = [1, length[i], elementPerRow]; tensors[i] = tfc.reshape(tfc.slice(tensor, indices, sizes), this.elementShape); } return tensors; }); const indices = []; for (let i = 0; i < length.length; i++) { indices[i] = i; } this.writeMany(indices, tensors); } } /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * TensorList stores a container of `tf.Tensor` objects, which are accessible * via tensors field. * * In order to get a copy of the underlying list, use the copy method: * ``` * TensorList b = a.copy(); * b.tensors().pushBack(t); // This does not modify a.tensors(). * ``` * * Note that this is not a deep copy: the memory locations of the underlying * tensors will still point to the same locations of the corresponding tensors * in the original. */ class TensorList { get id() { return this.idTensor.id; } /** * * @param tensors list of tensors * @param elementShape shape of each tensor, this can be a single number (any * shape is allowed) or partial shape (dim = -1). * @param elementDtype data type of each tensor * @param maxNumElements The maximum allowed size of `tensors`. Defaults to -1 * meaning that the size of `tensors` is unbounded. */ constructor(tensors, elementShape, elementDtype, maxNumElements = -1) { this.tensors = tensors; this.elementShape = elementShape; this.elementDtype = elementDtype; if (tensors != null) { tensors.forEach(tensor => { if (elementDtype !== tensor.dtype) { throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor.dtype}`); } assertShapesMatchAllowUndefinedSize(elementShape, tensor.shape, 'TensorList shape mismatch: '); tfc.keep(tensor); }); } this.idTensor = tfc.scalar(0); this.maxNumElements = maxNumElements; tfc.keep(this.idTensor); } /** * Get a new TensorList containing a copy of the underlying tensor container. */ copy() { return new TensorList([...this.tensors], this.elementShape, this.elementDtype); } /** * Dispose the tensors and idTensor and clear the tensor list. */ clearAndClose(keepIds) { this.tensors.forEach(tensor => { if (keepIds == null || !keepIds.has(tensor.id)) { tensor.dispose(); } }); this.tensors.length = 0; this.idTensor.dispose(); } /** * The size of the tensors in the tensor list. */ size() { return this.tensors.length; } /** * Return a tensor that stacks a list of rank-R tf.Tensors into one rank-(R+1) * tf.Tensor. * @param elementShape shape of each tensor * @param elementDtype data type of each tensor * @param numElements the number of elements to stack */ stack(elementShape, elementDtype, numElements = -1) { if (elementDtype !== this.elementDtype) { throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`); } if (numElements !== -1 && this.tensors.length !== numElements) { throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`); } assertShapesMatchAllowUndefinedSize(elementShape, this.elementShape, 'TensorList shape mismatch: '); const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape); return tfc.tidy(() => { const reshapedTensors = this.tensors.map(tensor => tfc.reshape(tensor, outputElementShape)); return tfc.stack(reshapedTensors, 0); }); } /** * Pop a tensor from the end of the list. * @param elementShape shape of the tensor * @param elementDtype data type of the tensor */ popBack(elementShape, elementDtype) { if (elementDtype !== this.elementDtype) { throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`); } if (this.size() === 0) { throw new Error('Trying to pop from an empty list.'); } const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape); const tensor = this.tensors.pop(); tensor.kept = false; assertShapesMatchAllowUndefinedSize(tensor.shape, elementShape, 'TensorList shape mismatch: '); return tfc.reshape(tensor, outputElementShape); } /** * Push a tensor to the end of the list. * @param tensor Tensor to be pushed. */ pushBack(tensor) { if (tensor.dtype !== this.elementDtype) { throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${this.elementDtype}`); } assertShapesMatchAllowUndefinedSize(tensor.shape, this.elementShape, 'TensorList shape mismatch: '); if (this.maxNumElements === this.size()) { throw new Error(`Trying to push element into a full list.`); } tfc.keep(tensor); this.tensors.push(tensor); } /** * Update the size of the list. * @param size the new size of the list. */ resize(size) { if (size < 0) { throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`); } if (this.maxNumElements !== -1 && size > this.maxNumElements) { throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`); } const destTensorList = new TensorList([], this.elementShape, this.elementDtype, this.maxNumElements); destTensorList.tensors.length = size; for (let i = 0; i < Math.min(this.tensors.length, size); ++i) { destTensorList.tensors[i] = this.tensors[i]; } return destTensorList; } /** * Retrieve the element at the provided index * @param elementShape shape of the tensor * @param elementDtype dtype of the tensor * @param elementIndex index of the tensor */ getItem(elementIndex, elementShape, elementDtype) { if (elementDtype !== this.elementDtype) { throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`); } if (elementIndex < 0 || elementIndex > this.tensors.length) { throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`); } if (this.tensors[elementIndex] == null) { throw new Error(`element at index ${elementIndex} is null.`); } assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape, elementShape, 'TensorList shape mismatch: '); const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape); return tfc.reshape(this.tensors[elementIndex], outputElementShape); } /** * Set the tensor at the index * @param elementIndex index of the tensor * @param tensor the tensor to be inserted into the list */ setItem(elementIndex, tensor) { if (tensor.dtype !== this.elementDtype) { throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${this.elementDtype}`); } if (elementIndex < 0 || this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) { throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`); } assertShapesMatchAllowUndefinedSize(this.elementShape, tensor.shape, 'TensorList shape mismatch: '); tfc.keep(tensor); // dispose the previous value if it is replacing. if (this.tensors[elementIndex] != null) { this.tensors[elementIndex].kept = false; } this.tensors[elementIndex] = tensor; } /** * Return selected values in the TensorList as a stacked Tensor. All of * selected values must have been written and their shapes must all match. * @param indices indices of tensors to gather * @param elementDtype output tensor dtype * @param elementShape output tensor element shape */ gather(indices, elementDtype, elementShape) { if (elementDtype !== this.elementDtype) { throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`); } assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, 'TensorList shape mismatch: '); // When indices is greater than the size of the list, indices beyond the // size of the list are ignored. indices = indices.slice(0, this.size()); const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape); if (indices.length === 0) { return tfc.tensor([], [0].concat(outputElementShape)); } return tfc.tidy(() => { const tensors = indices.map(i => tfc.reshape(this.tensors[i], outputElementShape)); return tfc.stack(tensors, 0); }); } /** * Return the values in the TensorList as a concatenated Tensor. * @param elementDtype output tensor dtype * @param elementShape output tensor element shape */ concat(elementDtype, elementShape) { if (!!elementDtype && elementDtype !== this.elementDtype) { throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`); } assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, 'TensorList shape mismatch: '); const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape); if (this.size() === 0) { return tfc.tensor([], [0].concat(outputElementShape)); } return tfc.tidy(() => { const tensors = this.tensors.map(t => tfc.reshape(t, outputElementShape)); return tfc.concat(tensors, 0); }); } } /** * Creates a TensorList which, when stacked, has the value of tensor. * @param tensor from tensor * @param elementShape output tensor element shape */ function fromTensor(tensor, elementShape, elementDtype) { const dtype = tensor.dtype; if (tensor.shape.length < 1) { throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor.shape}`); } if (tensor.dtype !== elementDtype) { throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${elementDtype}`); } const tensorElementShape = tensor.shape.slice(1); assertShapesMatchAllowUndefinedSize(tensorElementShape, elementShape, 'TensorList shape mismatch: '); const tensorList = tfc.unstack(tensor); return new TensorList(tensorList, elementShape, dtype); } /** * Return a TensorList of the given size with empty elements. * @param elementShape the shape of the future elements of the list * @param elementDtype the desired type of elements in the list * @param numElements the number of elements to reserve * @param maxNumElements the maximum number of elements in th list */ function reserve(elementShape, elementDtype, numElements, maxNumElements) { return new TensorList([], elementShape, elementDtype, maxNumElements); } /** * Put tensors at specific indices of a stacked tensor into a TensorList. * @param indices list of indices on how to scatter the tensor. * @param tensor input tensor. * @param elementShape the shape of the future elements of the list * @param numElements the number of elements to scatter */ function scatter(tensor, indices, elementShape, numElements) { if (indices.length !== tensor.shape[0]) { throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor.shape[0]}`); } const maxIndex = Math.max(...indices); if (numElements != null && numElements !== -1 && maxIndex >= numElements) { throw new Error(`Max index must be < array size (${maxIndex} vs. ${numElements})`); } const list = new TensorList([], elementShape, tensor.dtype, numElements); const tensors = tfc.unstack(tensor, 0); indices.forEach((value, index) => { list.setItem(value, tensors[index]); }); return list; } /** * Split the values of a Tensor into a TensorList. * @param length the lengths to use when splitting value along * its first dimension. * @param tensor the tensor to split. * @param elementShape the shape of the future elements of the list */ function split(tensor, length, elementShape) { let totalLength = 0; const cumulativeLengths = length.map(len => { totalLength += len; return totalLength; }); if (totalLength !== tensor.shape[0]) { throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is ${totalLength}, and tensor's shape is: ${tensor.shape}`); } const shapeWithoutFirstDim = tensor.shape.slice(1); const outputElementShape = mergeElementShape(shapeWithoutFirstDim, elementShape); const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength; const tensors = tfc.tidy(() => { const tensors = []; tensor = tfc.reshape(tensor, [1, totalLength, elementPerRow]); for (let i = 0; i < length.length; ++i) { const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1]; const indices = [0, previousLength, 0]; const sizes = [1, length[i], elementPerRow]; tensors[i] = tfc.reshape(tfc.slice(tensor, indices, sizes), outputElementShape); } tensor.dispose(); return tensors; }); const list = new TensorList([], elementShape, tensor.dtype, length.length); for (let i = 0; i < tensors.length; i++) { list.setItem(i, tensors[i]); } return list; } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const executeOp$i = async (node, tensorMap, context) => { switch (node.op) { case 'If': case 'StatelessIf': { const thenFunc = getParamValue('thenBranch', node, tensorMap, context); const elseFunc = getParamValue('elseBranch', node, tensorMap, context); const cond = getParamValue('cond', node, tensorMap, context); const args = getParamValue('args', node, tensorMap, context); const condValue = await cond.data(); if (condValue[0]) { return context.functionMap[thenFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap); } else { return context.functionMap[elseFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap); } } case 'While': case 'StatelessWhile': { const bodyFunc = getParamValue('body', node, tensorMap, context); const condFunc = getParamValue('cond', node, tensorMap, context); const args = getParamValue('args', node, tensorMap, context); // Calculate the condition of the loop const condResult = (await context.functionMap[condFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap)); const argIds = args.map(tensor => tensor.id); let condValue = await condResult[0].data(); // Dispose the intermediate tensors for condition function condResult.forEach(tensor => { if (!tensor.kept && argIds.indexOf(tensor.id) === -1) { tensor.dispose(); } }); let result = args; while (condValue[0]) { // Record the previous result for intermediate tensor tracking const origResult = result; // Execution the body of the loop result = await context.functionMap[bodyFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap); const resultIds = result.map(tensor => tensor.id); // Dispose the intermediate tensor for body function that is not global // kept, not input/output of the body function origResult.forEach(tensor => { if (!tensor.kept && argIds.indexOf(tensor.id) === -1 && resultIds.indexOf(tensor.id) === -1) { tensor.dispose(); } }); // Recalcuate the condition of the loop using the latest results. const condResult = (await context.functionMap[condFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap)); condValue = await condResult[0].data(); // Dispose the intermediate tensors for condition function condResult.forEach(tensor => { if (!tensor.kept && argIds.indexOf(tensor.id) === -1 && resultIds.indexOf(tensor.id) === -1) { tensor.dispose(); } }); } return result; } case 'LoopCond': { const pred = getParamValue('pred', node, tensorMap, context); return [cloneTensor(pred)]; } case 'Switch': { const pred = getParamValue('pred', node, tensorMap, context); let data = getParamValue('data', node, tensorMap, context); if (!data.kept) { data = cloneTensor(data); } // Outputs nodes :0 => false, :1 => true return (await pred.data())[0] ? [undefined, data] : [data, undefined]; } case 'Merge': { const inputName = node.inputNames.find(name => getTensor(name, tensorMap, context) !== undefined); if (inputName) { const data = getTensor(inputName, tensorMap, context); return [cloneTensor(data)]; } return undefined; } case 'Enter': { const frameId = getParamValue('frameName', node, tensorMap, context); const data = getParamValue('tensor', node, tensorMap, context); context.enterFrame(frameId); return [cloneTensor(data)]; } case 'Exit': { const data = getParamValue('tensor', node, tensorMap, context); context.exitFrame(); return [cloneTensor(data)]; } case 'NextIteration': { const data = getParamValue('tensor', node, tensorMap, context); context.nextIteration(); return [cloneTensor(data)]; } case 'TensorArrayV3': { const size = getParamValue('size', node, tensorMap, context); const dtype = getParamValue('dtype', node, tensorMap, context); const elementShape = getParamValue('elementShape', node, tensorMap, context); const dynamicSize = getParamValue('dynamicSize', node, tensorMap, context); const clearAfterRead = getParamValue('clearAfterRead', node, tensorMap, context); const identicalElementShapes = getParamValue('identicalElementShapes', node, tensorMap, context); const name = getParamValue('name', node, tensorMap, context); const tensorArray = new TensorArray(name, dtype, size, elementShape, identicalElementShapes, dynamicSize, clearAfterRead); context.addTensorArray(tensorArray); return [tensorArray.idTensor, tfc.scalar(1.0)]; } case 'TensorArrayWriteV3': { const id = getParamValue('tensorArrayId', node, tensorMap, context); const index = getParamValue('index', node, tensorMap, context); const writeTensor = getParamValue('tensor', node, tensorMap, context); const writeTensorArray = context.getTensorArray(id.id); writeTensorArray.write(index, writeTensor); return [writeTensorArray.idTensor]; } case 'TensorArrayReadV3': { const readId = getParamValue('tensorArrayId', node, tensorMap, context); const readIndex = getParamValue('index', node, tensorMap, context); const readTensorArray = context.getTensorArray(readId.id); return [readTensorArray.read(readIndex)]; } case 'TensorArrayGatherV3': { const gatherId = getParamValue('tensorArrayId', node, tensorMap, context); const gatherIndices = getParamValue('indices', node, tensorMap, context); const gatherDtype = getParamValue('dtype', node, tensorMap, context); const gatherTensorArray = context.getTensorArray(gatherId.id); return [gatherTensorArray.gather(gatherIndices, gatherDtype)]; } case 'TensorArrayScatterV3': { const scatterId = getParamValue('tensorArrayId', node, tensorMap, context); const scatterIndices = getParamValue('indices', node, tensorMap, context); const scatterTensor = getParamValue('tensor', node, tensorMap, context); const scatterTensorArray = context.getTensorArray(scatterId.id); scatterTensorArray.scatter(scatterIndices, scatterTensor); return [scatterTensorArray.idTensor]; } case 'TensorArrayConcatV3': { const concatId = getParamValue('tensorArrayId', node, tensorMap, context); const concatTensorArray = context.getTensorArray(concatId.id); const concatDtype = getParamValue('dtype', node, tensorMap, context); return [concatTensorArray.concat(concatDtype)]; } case 'TensorArraySplitV3': { const splitId = getParamValue('tensorArrayId', node, tensorMap, context); const splitTensor = getParamValue('tensor', node, tensorMap, context); const lengths = getParamValue('lengths', node, tensorMap, context); const splitTensorArray = context.getTensorArray(splitId.id); splitTensorArray.split(lengths, splitTensor); return [splitTensorArray.idTensor]; } case 'TensorArraySizeV3': { const sizeId = getParamValue('tensorArrayId', node, tensorMap, context); const sizeTensorArray = context.getTensorArray(sizeId.id); return [tfc.scalar(sizeTensorArray.size(), 'int32')]; } case 'TensorArrayCloseV3': { const closeId = getParamValue('tensorArrayId', node, tensorMap, context); const closeTensorArray = context.getTensorArray(closeId.id); closeTensorArray.clearAndClose(); return [closeTensorArray.idTensor]; } case 'TensorListSetItem': { const idTensor = getParamValue('tensorListId', node, tensorMap, context); const index = getParamValue('index', node, tensorMap, context); const writeTensor = getParamValue('tensor', node, tensorMap, context); const tensorList = context.getTensorList(idTensor.id); tensorList.setItem(index, writeTensor); return [tensorList.idTensor]; } case 'TensorListGetItem': { const idTensor = getParamValue('tensorListId', node, tensorMap, context); const readIndex = getParamValue('index', node, tensorMap, context); const elementShape = getParamValue('elementShape', node, tensorMap, context); const elementDType = getParamValue('elementDType', node, tensorMap, context); const tensorList = context.getTensorList(idTensor.id); return [tensorList.getItem(readIndex, elementShape, elementDType)]; } case 'TensorListScatterV2': case 'TensorListScatter': { const scatterIndices = getParamValue('indices', node, tensorMap, context); const scatterTensor = getParamValue('tensor', node, tensorMap, context); const elementShape = getParamValue('elementShape', node, tensorMap, context); const numElements = getParamValue('numElements', node, tensorMap, context); const tensorList = scatter(scatterTensor, scatterIndices, elementShape, numElements); context.addTensorList(tensorList); return [tensorList.idTensor]; } case 'TensorListReserve': case 'EmptyTensorList': { const elementShape = getParamValue('elementShape', node, tensorMap, context); const elementDtype = getParamValue('elementDType', node, tensorMap, context); let numElementsParam; if (node.op === 'TensorListReserve') { numElementsParam = 'numElements'; } else { numElementsParam = 'maxNumElements'; } const numElements = getParamValue(numElementsParam, node, tensorMap, context); const maxNumElements = node.op === 'TensorListReserve' ? -1 : numElements; const tensorList = reserve(elementShape, elementDtype, numElements, maxNumElements); context.addTensorList(tensorList); return [tensorList.idTensor]; } case 'TensorListGather': { const gatherId = getParamValue('tensorListId', node, tensorMap, context); const gatherIndices = getParamValue('indices', node, tensorMap, context); const elementShape = getParamValue('elementShape', node, tensorMap, context); const elementDtype = getParamValue('elementDType', node, tensorMap, context); const tensorList = context.getTensorList(gatherId.id); return [tensorList.gather(gatherIndices, elementDtype, elementShape)]; } case 'TensorListStack': { const idTensor = getParamValue('tensorListId', node, tensorMap, context); const elementShape = getParamValue('elementShape', node, tensorMap, context); const elementDtype = getParamValue('elementDType', node, tensorMap, context); const numElements = getParamValue('numElements', node, tensorMap, context); const tensorList = context.getTensorList(idTensor.id); return [tensorList.stack(elementShape, elementDtype, numElements)]; } case 'TensorListFromTensor': { const tensor = getParamValue('tensor', node, tensorMap, context); const elementShape = getParamValue('elementShape', node, tensorMap, context); const elementDtype = getParamValue('elementDType', node, tensorMap, context); const tensorList = fromTensor(tensor, elementShape, elementDtype); context.addTensorList(tensorList); return [tensorList.idTensor]; } case 'TensorListConcat': case 'TensorListConcatV2': { const concatId = getParamValue('tensorListId', node, tensorMap, context); const tensorList = context.getTensorList(concatId.id); const concatDtype = getParamValue('dtype', node, tensorMap, context); const elementShape = getParamValue('elementShape', node, tensorMap, context); return [tensorList.concat(concatDtype, elementShape)]; } case 'TensorListPushBack': { const idTensor = getParamValue('tensorListId', node, tensorMap, context); const writeTensor = getParamValue('tensor', node, tensorMap, context); const tensorList = context.getTensorList(idTensor.id); tensorList.pushBack(writeTensor); return [tensorList.idTensor]; } case 'TensorListPopBack': { const idTensor = getParamValue('tensorListId', node, tensorMap, context); const elementShape = getParamValue('elementShape', node, tensorMap, context); const elementDType = getParamValue('elementDType', node, tensorMap, context); const tensorList = context.getTensorList(idTensor.id); return [tensorList.popBack(elementShape, elementDType)]; } case 'TensorListSplit': { const splitTensor = getParamValue('tensor', node, tensorMap, context); const elementShape = getParamValue('elementShape', node, tensorMap, context); const lengths = getParamValue('lengths', node, tensorMap, context); const tensorList = split(splitTensor, lengths, elementShape); context.addTensorList(tensorList); return [tensorList.idTensor]; } case 'TensorListLength': { const idTensor = getParamValue('tensorListId', node, tensorMap, context); const tensorList = context.getTensorList(idTensor.id); return [tfc.scalar(tensorList.size(), 'int32')]; } case 'TensorListResize': { const idTensor = getParamValue('tensorListId', node, tensorMap, context); const size = getParamValue('size', node, tensorMap, context); const srcTensorList = context.getTensorList(idTensor.id); const destTensorList = srcTensorList.resize(size); context.addTensorList(destTensorList); return [destTensorList.idTensor]; } default: throw TypeError(`Node type ${node.op} is not implemented`); } }; /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ function fusedConvAndDepthWiseParams(node, tensorMap, context) { const [extraOp, activationFunc] = getParamValue('fusedOps', node, tensorMap, context); const isBiasAdd = extraOp === 'biasadd'; const noBiasAdd = !isBiasAdd; const isPrelu = activationFunc === 'prelu'; const isBatchNorm = extraOp === 'fusedbatchnorm'; const numArgs = getParamValue('numArgs', node, tensorMap, context); if (isBiasAdd) { if (isPrelu && numArgs !== 2) { throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu ' + 'must have two extra arguments: bias and alpha.'); } if (!isPrelu && isBiasAdd && numArgs !== 1) { throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd must have ' + 'one extra argument: bias.'); } } if (isBatchNorm) { throw new Error('FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported'); } const stride = getParamValue('strides', node, tensorMap, context); const pad = getPadding(node, tensorMap, context); const dataFormat = getParamValue('dataFormat', node, tensorMap, context) .toUpperCase(); const dilations = getParamValue('dilations', node, tensorMap, context); let [biasArg, preluArg] = getParamValue('args', node, tensorMap, context); if (noBiasAdd) { preluArg = biasArg; biasArg = undefined; } const leakyreluAlpha = getParamValue('leakyreluAlpha', node, tensorMap, context); return { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha }; } const executeOp$h = (node, tensorMap, context, ops = tfOps) => { switch (node.op) { case 'Conv1D': { const stride = getParamValue('stride', node, tensorMap, context); const pad = getParamValue('pad', node, tensorMap, context); const dataFormat = getParamValue('dataFormat', node, tensorMap, context) .toUpperCase(); const dilation = getParamValue('dilation', node, tensorMap, context); return [ops.conv1d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), stride, pad, dataFormat, dilation)]; } case 'Conv2D': { const stride = getParamValue('strides', node, tensorMap, context); const pad = getPadding(node, tensorMap, context); const dataFormat = getParamValue('dataFormat', node, tensorMap, context) .toUpperCase(); const dilations = getParamValue('dilations', node, tensorMap, context); return [ops.conv2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])]; } case '_FusedConv2D': { const { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha } = fusedConvAndDepthWiseParams(node, tensorMap, context); return [ops.fused.conv2d({ x: getParamValue('x', node, tensorMap, context), filter: getParamValue('filter', node, tensorMap, context), strides: [stride[1], stride[2]], pad: pad, dataFormat: dataFormat, dilations: [dilations[1], dilations[2]], bias: biasArg, activation: activationFunc, preluActivationWeights: preluArg, leakyreluAlpha })]; } case 'FusedDepthwiseConv2dNative': { const { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha, } = fusedConvAndDepthWiseParams(node, tensorMap, context); return [ops.fused.depthwiseConv2d({ x: getParamValue('x', node, tensorMap, context), filter: getParamValue('filter', node, tensorMap, context), strides: [stride[1], stride[2]], pad: pad, dataFormat: dataFormat, dilations: [dilations[1], dilations[2]], bias: biasArg, activation: activationFunc, preluActivationWeights: preluArg, leakyreluAlpha })]; } case 'Conv2DBackpropInput': case 'Conv2dTranspose': { const shape = getParamValue('outputShape', node, tensorMap, context); const stride = getParamValue('strides', node, tensorMap, context); const pad = getPadding(node, tensorMap, context); return [ops.conv2dTranspose(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), shape, [stride[1], stride[2]], pad)]; } case 'DepthwiseConv2dNative': case 'DepthwiseConv2d': { const stride = getParamValue('strides', node, tensorMap, context); const pad = getPadding(node, tensorMap, context); const dilations = getParamValue('dilations', node, tensorMap, context); const dataFormat = getParamValue('dataFormat', node, tensorMap, context) .toUpperCase(); return [ops.depthwiseConv2d(getParamValue('input', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])]; } case 'Conv3D': { const stride = getParamValue('strides', node, tensorMap, context); const pad = getParamValue('pad', node, tensorMap, context); const dataFormat = getParamValue('dataFormat', node, tensorMap, context) .toUpperCase(); const dilations = getParamValue('dilations', node, tensorMap, context); return [ops.conv3d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2], stride[3]], pad, dataFormat, [dilations[1], dilations[2], dilations[3]])]; } case 'AvgPool': { const stride = getParamValue('strides', node, tensorMap, context); const pad = getParamValue('pad', node, tensorMap, context); const kernelSize = getParamValue('kernelSize', node, tensorMap, context); return [ops.avgPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)]; } case 'MaxPool': { const stride = getParamValue('strides', node, tensorMap, context); const pad = getParamValue('pad', node, tensorMap, context); const kernelSize = getParamValue('kernelSize', node, tensorMap, context); return [ops.maxPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)]; } case 'MaxPoolWithArgmax': { const stride = getParamValue('strides', node, tensorMap, context); const pad = getParamValue('pad', node, tensorMap, context); const kernelSize = getParamValue('kernelSize', node, tensorMap, context); const includeBatchInIndex = getParamValue('includeBatchInIndex', node, tensorMap, context); const { result, indexes } = ops.maxPoolWithArgmax(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad, includeBatchInIndex); return [result, indexes]; } case 'AvgPool3D': { const stride = getParamValue('strides', node, tensorMap, context); const pad = getParamValue('pad', node, tensorMap, context); const kernelSize = getParamValue('kernelSize', node, tensorMap, context); return [ops.avgPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)]; } case 'MaxPool3D': { const stride = getParamValue('strides', node, tensorMap, context); const pad = getParamValue('pad', node, tensorMap, context); const kernelSize = getParamValue('kernelSize', node, tensorMap, context); return [ops.maxPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)]; } case 'Dilation2D': { const strides = getParamValue('strides', node, tensorMap, context); const pad = getParamValue('pad', node, tensorMap, context); const dilations = getParamValue('dilations', node, tensorMap, context); // strides: [1, stride_height, stride_width, 1]. const strideHeight = strides[1]; const strideWidth = strides[2]; // dilations: [1, dilation_height, dilation_width, 1]. const dilationHeight = dilations[1]; const dilationWidth = dilations[2]; return [ops.dilation2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [strideHeight, strideWidth], pad, [dilationHeight, dilationWidth], 'NHWC' /* dataFormat */)]; } default: throw TypeError(`Node type ${node.op} is not implemented`); } }; /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const executeOp$g = (node, tensorMap, context, ops = tfOps) => { switch (node.op) { case 'Fill': { const shape = getParamValue('shape', node, tensorMap, context); const dtype = getParamValue('dtype', node, tensorMap, context); const value = getParamValue('value', node, tensorMap, context); return [ops.fill(shape, value, dtype)]; } case 'LinSpace': { const start = getParamValue('start', node, tensorMap, context); const stop = getParamValue('stop', node, tensorMap, context); const num = getParamValue('num', node, tensorMap, context); return [ops.linspace(start, stop, num)]; } case 'Multinomial': { const logits = getParamValue('logits', node, tensorMap, context); const numSamples = getParamValue('numSamples', node, tensorMap, context); const seed = getParamValue('seed', node, tensorMap, context); return [ops.multinomial(logits, numSamples, seed)]; } case 'OneHot': { const indices = getParamValue('indices', node, tensorMap, context); const depth = getParamValue('depth', node, tensorMap, context); const onValue = getParamValue('onValue', node, tensorMap, context); const offValue = getParamValue('offValue', node, tensorMap, context); const dtype = getParamValue('dtype', node, tensorMap, context); return [ops.oneHot(indices, depth, onValue, offValue, dtype)]; } case 'Ones': { return [ops.ones(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))]; } case 'OnesLike': { return [ops.onesLike(getParamValue('x', node, tensorMap, context))]; } case 'RandomStandardNormal': { return [ops.randomStandardNormal(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context), getParamValue('seed', node, tensorMap, context))]; } case 'RandomUniform': { return [ops.randomUniform( // tslint:disable-next-line:no-any getParamValue('shape', node, tensorMap, context), getParamValue('minval', node, tensorMap, context), getParamValue('maxval', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))]; } case 'RandomUniformInt': { return [ops.randomUniformInt(getParamValue('shape', node, tensorMap, context), getParamValue('minval', node, tensorMap, context), getParamValue('maxval', node, tensorMap, context), getParamValue('seed', node, tensorMap, context))]; } case 'Range': { const start = getParamValue('start', node, tensorMap, context); const stop = getParamValue('stop', node, tensorMap, context); const step = getParamValue('step', node, tensorMap, context); return [ops.range(start, stop, step, getParamValue('dtype', node, tensorMap, context))]; } case 'TruncatedNormal': { const shape = getParamValue('shape', node, tensorMap, context); const mean = getParamValue('mean', node, tensorMap, context); const stdDev = getParamValue('stdDev', node, tensorMap, context); const seed = getParamValue('seed', node, tensorMap, context); return [ops.truncatedNormal(shape, mean, stdDev, getParamValue('dtype', node, tensorMap, context), seed)]; } case 'Zeros': { return [ops.zeros(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))]; } case 'ZerosLike': { return [ops.zerosLike(getParamValue('x', node, tensorMap, context))]; } default: throw TypeError(`Node type ${node.op} is not implemented`); } }; /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ function nmsParams(node, tensorMap, context) { const boxes = getParamValue('boxes', node, tensorMap, context); const scores = getParamValue('scores', node, tensorMap, context); const maxOutputSize = getParamValue('maxOutputSize', node, tensorMap, context); const iouThreshold = getParamValue('iouThreshold', node, tensorMap, context); const scoreThreshold = getParamValue('scoreThreshold', node, tensorMap, context); const softNmsSigma = getParamValue('softNmsSigma', node, tensorMap, context); return { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma }; } const executeOp$f = async (node, tensorMap, context, resourceManager, ops = tfOps) => { switch (node.op) { case 'NonMaxSuppressionV5': { const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = nmsParams(node, tensorMap, context); const result = await ops.image.nonMaxSuppressionWithScoreAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma); return [result.selectedIndices, result.selectedScores]; } case 'NonMaxSuppressionV4': { const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context); const padToMaxOutputSize = getParamValue('padToMaxOutputSize', node, tensorMap, context); const result = await ops.image.nonMaxSuppressionPaddedAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize); return [result.selectedIndices, result.validOutputs]; } case 'NonMaxSuppressionV3': case 'NonMaxSuppressionV2': { const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context); return [await ops.image.nonMaxSuppressionAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold)]; } case 'Where': { const condition = ops.cast(getParamValue('condition', node, tensorMap, context), 'bool'); const result = [await ops.whereAsync(condition)]; condition.dispose(); return result; } case 'ListDiff': { return ops.setdiff1dAsync(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context)); } default: throw TypeError(`Node type ${node.op} is not implemented`); } }; /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const executeOp$e = (node, tensorMap, context, ops = tfOps) => { switch (node.op) { case 'LowerBound': { const sortedSequence = getParamValue('sortedSequence', node, tensorMap, context); const values = getParamValue('values', node, tensorMap, context); return [ops.lowerBound(sortedSequence, values)]; } case 'TopKV2': { const x = getParamValue('x', node, tensorMap, context); const k = getParamValue('k', node, tensorMap, context); const sorted = getParamValue('sorted', node, tensorMap, context); const result = ops.topk(x, k, sorted); return [result.values, result.indices]; } case 'UpperBound': { const sortedSequence = getParamValue('sortedSequence', node, tensorMap, context); const values = getParamValue('values', node, tensorMap, context); return [ops.upperBound(sortedSequence, values)]; } case 'Unique': { const x = getParamValue('x', node, tensorMap, context); const result = ops.unique(x); return [result.values, result.indices]; } case 'UniqueV2': { const x = getParamValue('x', node, tensorMap, context); const axis = getParamValue('axis', node, tensorMap, context); const result = ops.unique(x, axis); return [result.values, result.indices]; } default: throw TypeError(`Node type ${node.op} is not implemented`); } }; /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const executeOp$d = (node, tensorMap, context, ops = tfOps) => { switch (node.op) { case 'Const': { return tensorMap[node.name]; } case 'PlaceholderWithDefault': const def = getParamValue('default', node, tensorMap, context); return [getTensor(node.name, tensorMap, context) || def]; case 'Placeholder': return [getTensor(node.name, tensorMap, context)]; case 'Identity': case 'StopGradient': case 'FakeQuantWithMinMaxVars': { // This op is currently ignored. const data = getParamValue('x', node, tensorMap, context); return [cloneTensor(data)]; } case 'IdentityN': return getParamValue('x', node, tensorMap, context) .map((t) => cloneTensor(t)); case 'Snapshot': const snapshot = getParamValue('x', node, tensorMap, context); return [cloneTensor(snapshot)]; case 'Shape': return [ops.tensor1d(getParamValue('x', node, tensorMap, context).shape, 'int32')]; case 'ShapeN': return getParamValue('x', node, tensorMap, context) .map((t) => ops.tensor1d(t.shape)); case 'Size': return [ops.scalar(getParamValue('x', node, tensorMap, context).size, 'int32')]; case 'Rank': return [ops.scalar(getParamValue('x', node, tensorMap, context).rank, 'int32')]; case 'NoOp': return [ops.scalar(1)]; case 'Print': const input = getParamValue('x', node, tensorMap, context); const data = getParamValue('data', node, tensorMap, context); const message = getParamValue('message', node, tensorMap, context); const summarize = getParamValue('summarize', node, tensorMap, context); console.warn('The graph has a tf.print() operation,' + 'usually used for debugging, which slows down performance.'); console.log(message); for (let i = 0; i < data.length; i++) { console.log(Array.prototype.slice.call(data[i].dataSync()) .slice(0, summarize)); } return [input]; default: throw TypeError(`Node type ${node.op} is not implemented`); } }; /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Hashtable contains a set of tensors, which can be accessed by key. */ class HashTable { get id() { return this.handle.id; } /** * Constructor of HashTable. Creates a hash table. * * @param keyDType `dtype` of the table keys. * @param valueDType `dtype` of the table values. */ constructor(keyDType, valueDType) { this.keyDType = keyDType; this.valueDType = valueDType; this.handle = tfc.scalar(0); // tslint:disable-next-line: no-any this.tensorMap = new Map(); tfc.keep(this.handle); } /** * Dispose the tensors and handle and clear the hashtable. */ clearAndClose() { this.tensorMap.forEach(value => value.dispose()); this.tensorMap.clear(); this.handle.dispose(); } /** * The number of items in the hash table. */ size() { return this.tensorMap.size; } /** * The number of items in the hash table as a rank-0 tensor. */ tensorSize() { return scalar(this.size(), 'int32'); } /** * Replaces the contents of the table with the specified keys and values. * @param keys Keys to store in the hashtable. * @param values Values to store in the hashtable. */ async import(keys, values) { this.checkKeyAndValueTensor(keys, values); // We only store the primitive values of the keys, this allows lookup // to be O(1). const $keys = await keys.data(); // Clear the hashTable before inserting new values. this.tensorMap.forEach(value => value.dispose()); this.tensorMap.clear(); return tfc.tidy(() => { const $values = tfc.unstack(values); const keysLength = $keys.length; const valuesLength = $values.length; tfc.util.assert(keysLength === valuesLength, () => `The number of elements doesn't match, keys has ` + `${keysLength} elements, the values has ${valuesLength} ` + `elements.`); for (let i = 0; i < keysLength; i++) { const key = $keys[i]; const value = $values[i]; tfc.keep(value); this.tensorMap.set(key, value); } return this.handle; }); } /** * Looks up keys in a hash table, outputs the corresponding values. * * Performs batch lookups, for every element in the key tensor, `find` * stacks the corresponding value into the return tensor. * * If an element is not present in the table, the given `defaultValue` is * used. * * @param keys Keys to look up. Must have the same type as the keys of the * table. * @param defaultValue The scalar `defaultValue` is the value output for keys * not present in the table. It must also be of the same type as the * table values. */ async find(keys, defaultValue) { this.checkKeyAndValueTensor(keys, defaultValue); const $keys = await keys.data(); return tfc.tidy(() => { const result = []; for (let i = 0; i < $keys.length; i++) { const key = $keys[i]; const value = this.findWithDefault(key, defaultValue); result.push(value); } return tfc.stack(result); }); } // tslint:disable-next-line: no-any findWithDefault(key, defaultValue) { const result = this.tensorMap.get(key); return result != null ? result : defaultValue; } checkKeyAndValueTensor(key, value) { if (key.dtype !== this.keyDType) { throw new Error(`Expect key dtype ${this.keyDType}, but got ` + `${key.dtype}`); } if (value.dtype !== this.valueDType) { throw new Error(`Expect value dtype ${this.valueDType}, but got ` + `${value.dtype}`); } } } /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const executeOp$c = async (node, tensorMap, context, resourceManager) => { switch (node.op) { case 'HashTable': case 'HashTableV2': { const existingTableHandle = resourceManager.getHashTableHandleByName(node.name); // Table is shared with initializer. if (existingTableHandle != null) { return [existingTableHandle]; } else { const keyDType = getParamValue('keyDType', node, tensorMap, context); const valueDType = getParamValue('valueDType', node, tensorMap, context); const hashTable = new HashTable(keyDType, valueDType); resourceManager.addHashTable(node.name, hashTable); return [hashTable.handle]; } } case 'InitializeTable': case 'InitializeTableV2': case 'LookupTableImport': case 'LookupTableImportV2': { const handle = getParamValue('tableHandle', node, tensorMap, context, resourceManager); const keys = getParamValue('keys', node, tensorMap, context); const values = getParamValue('values', node, tensorMap, context); const hashTable = resourceManager.getHashTableById(handle.id); return [await hashTable.import(keys, values)]; } case 'LookupTableFind': case 'LookupTableFindV2': { const handle = getParamValue('tableHandle', node, tensorMap, context, resourceManager); const keys = getParamValue('keys', node, tensorMap, context); const defaultValue = getParamValue('defaultValue', node, tensorMap, context); const hashTable = resourceManager.getHashTableById(handle.id); return [await hashTable.find(keys, defaultValue)]; } case 'LookupTableSize': case 'LookupTableSizeV2': { const handle = getParamValue('tableHandle', node, tensorMap, context, resourceManager); const hashTable = resourceManager.getHashTableById(handle.id); return [hashTable.tensorSize()]; } default: throw TypeError(`Node type ${node.op} is not implemented`); } }; /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const executeOp$b = (node, tensorMap, context, ops = tfOps) => { switch (node.op) { case 'ResizeBilinear': { const images = getParamValue('images', node, tensorMap, context); const size = getParamValue('size', node, tensorMap, context); const alignCorners = getParamValue('alignCorners', node, tensorMap, context); const halfPixelCenters = getParamValue('halfPixelCenters', node, tensorMap, context); return [ops.image.resizeBilinear(images, [size[0], size[1]], alignCorners, halfPixelCenters)]; } case 'ResizeNearestNeighbor': { const images = getParamValue('images', node, tensorMap, context); const size = getParamValue('size', node, tensorMap, context); const alignCorners = getParamValue('alignCorners', node, tensorMap, context); const halfPixelCenters = getParamValue('halfPixelCenters', node, tensorMap, context); return [ops.image.resizeNearestNeighbor(images, [size[0], size[1]], alignCorners, halfPixelCenters)]; } case 'CropAndResize': { const image = getParamValue('image', node, tensorMap, context); const boxes = getParamValue('boxes', node, tensorMap, context); const boxInd = getParamValue('boxInd', node, tensorMap, context); const cropSize = getParamValue('cropSize', node, tensorMap, context); const method = getParamValue('method', node, tensorMap, context); const extrapolationValue = getParamValue('extrapolationValue', node, tensorMap, context); return [ops.image.cropAndResize(image, boxes, boxInd, cropSize, method, extrapolationValue)]; } case 'ImageProjectiveTransformV3': { const images = getParamValue('images', node, tensorMap, context); const transforms = getParamValue('transforms', node, tensorMap, context); const outputShape = getParamValue('outputShape', node, tensorMap, context); const fillValue = getParamValue('fillValue', node, tensorMap, context); const interpolation = getParamValue('interpolation', node, tensorMap, context); const fillMode = getParamValue('fillMode', node, tensorMap, context); return [ops.image.transform(images, transforms, interpolation.toLowerCase(), fillMode.toLowerCase(), fillValue, outputShape)]; } default: throw TypeError(`Node type ${node.op} is not implemented`); } }; /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const executeOp$a = (node, tensorMap, context, ops = tfOps) => { switch (node.op) { case 'Equal': { return [ops.equal(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; } case 'NotEqual': { return [ops.notEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; } case 'Greater': { return [ops.greater(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; } case 'GreaterEqual': { return [ops.greaterEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; } case 'Less': { return [ops.less(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; } case 'LessEqual': { return [ops.lessEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; } case 'LogicalAnd': { return [ops.logicalAnd(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; } case 'LogicalNot': { return [ops.logicalNot(getParamValue('a', node, tensorMap, context))]; } case 'LogicalOr': { return [ops.logicalOr(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; } case 'Select': case 'SelectV2': { return [ops.where(getParamValue('condition', node, tensorMap, context), getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; } case 'BitwiseAnd': { return [ops.bitwiseAnd(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; } default: throw TypeError(`Node type ${node.op} is not implemented`); } }; /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const executeOp$9 = (node, tensorMap, context, ops = tfOps) => { switch (node.op) { case 'BatchMatMul': case 'BatchMatMulV2': case 'MatMul': return [ops.matMul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context), getParamValue('transposeA', node, tensorMap, context), getParamValue('transposeB', node, tensorMap, context))]; case 'Einsum': return [ops.einsum(getParamValue('equation', node, tensorMap, context), ...getParamValue('tensors', node, tensorMap, context))]; case 'Transpose': return [ops.transpose(getParamValue('x', node, tensorMap, context), getParamValue('perm', node, tensorMap, context))]; case '_FusedMatMul': const [extraOp, activationFunc] = getParamValue('fusedOps', node, tensorMap, context); const isBiasAdd = extraOp === 'biasadd'; const isPrelu = activationFunc === 'prelu'; const numArgs = getParamValue('numArgs', node, tensorMap, context); const leakyreluAlpha = getParamValue('leakyreluAlpha', node, tensorMap, context); if (isBiasAdd) { if (isPrelu && numArgs !== 2) { throw new Error('Fused MatMul with BiasAdd and Prelu must have two ' + 'extra arguments: bias and alpha.'); } if (!isPrelu && numArgs !== 1) { throw new Error('Fused MatMul with BiasAdd must have one extra argument: bias.'); } } const [biasArg, preluArg] = getParamValue('args', node, tensorMap, context); return [ops.fused.matMul({ a: getParamValue('a', node, tensorMap, context), b: getParamValue('b', node, tensorMap, context), transposeA: getParamValue('transposeA', node, tensorMap, context), transposeB: getParamValue('transposeB', node, tensorMap, context), bias: biasArg, activation: activationFunc, preluActivationWeights: preluArg, leakyreluAlpha })]; case 'MatrixBandPart': return [ops.linalg.bandPart(getParamValue('a', node, tensorMap, context), getParamValue('numLower', node, tensorMap, context), getParamValue('numUpper', node, tensorMap, context))]; default: throw TypeError(`Node type ${node.op} is not implemented`); } }; /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const executeOp$8 = (node, tensorMap, context, ops = tfOps) => { switch (node.op) { case 'EuclideanNorm': return [ops.euclideanNorm(getParamValue('x', node, tensorMap, context), getParamValue('axis', node, tensorMap, context), getParamValue('keepDims', node, tensorMap, context))]; case 'FusedBatchNorm': case 'FusedBatchNormV2': { return [ops.batchNorm(getParamValue('x', node, tensorMap, context), getParamValue('mean', node, tensorMap, context), getParamValue('variance', node, tensorMap, context), getParamValue('offset', node, tensorMap, context), getParamValue('scale', node, tensorMap, context), getParamValue('epsilon', node, tensorMap, context))]; } case 'FusedBatchNormV3': { return [ops.batchNorm(getParamValue('x', node, tensorMap, context), getParamValue('mean', node, tensorMap, context), getParamValue('variance', node, tensorMap, context), getParamValue('offset', node, tensorMap, context), getParamValue('scale', node, tensorMap, context), getParamValue('epsilon', node, tensorMap, context))]; } case 'LRN': { return [ops.localResponseNormalization(getParamValue('x', node, tensorMap, context), getParamValue('radius', node, tensorMap, context), getParamValue('bias', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context), getParamValue('beta', node, tensorMap, context))]; } case 'Softmax': { return [ops.softmax(getParamValue('x', node, tensorMap, context))]; } case 'LogSoftmax': { return [ops.logSoftmax(getParamValue('x', node, tensorMap, context))]; } default: throw TypeError(`Node type ${node.op} is not implemented`); } }; /** * @license * Copyright 2022 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const executeOp$7 = (node, tensorMap, context, ops = tfOps) => { switch (node.op) { case 'RaggedGather': { const { outputNestedSplits, outputDenseValues, } = ops.raggedGather(getParamValue('paramsNestedSplits', node, tensorMap, context), getParamValue('paramsDenseValues', node, tensorMap, context), getParamValue('indices', node, tensorMap, context), getParamValue('outputRaggedRank', node, tensorMap, context)); return outputNestedSplits.concat(outputDenseValues); } case 'RaggedRange': { const { rtNestedSplits, rtDenseValues } = ops.raggedRange(getParamValue('starts', node, tensorMap, context), getParamValue('limits', node, tensorMap, context), getParamValue('splits', node, tensorMap, context)); return [rtNestedSplits, rtDenseValues]; } case 'RaggedTensorToTensor': { return [ops.raggedTensorToTensor(getParamValue('shape', node, tensorMap, context), getParamValue('values', node, tensorMap, context), getParamValue('defaultValue', node, tensorMap, context), getParamValue('rowPartitionTensors', node, tensorMap, context), getParamValue('rowPartitionTypes', node, tensorMap, context))]; } default: throw TypeError(`Node type ${node.op} is not implemented`); } }; /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const executeOp$6 = (node, tensorMap, context, ops = tfOps) => { switch (node.op) { case 'Max': { const axis = getParamValue('axis', node, tensorMap, context); const keepDims = getParamValue('keepDims', node, tensorMap, context); return [ops.max(getParamValue('x', node, tensorMap, context), axis, keepDims)]; } case 'Mean': { const axis = getParamValue('axis', node, tensorMap, context); const keepDims = getParamValue('keepDims', node, tensorMap, context); return [ops.mean(getParamValue('x', node, tensorMap, context), axis, keepDims)]; } case 'Min': { const axis = getParamValue('axis', node, tensorMap, context); const keepDims = getParamValue('keepDims', node, tensorMap, context); return [ops.min(getParamValue('x', node, tensorMap, context), axis, keepDims)]; } case 'Sum': { const axis = getParamValue('axis', node, tensorMap, context); const keepDims = getParamValue('keepDims', node, tensorMap, context); return [ops.sum(getParamValue('x', node, tensorMap, context), axis, keepDims)]; } case 'All': { const axis = getParamValue('axis', node, tensorMap, context); const keepDims = getParamValue('keepDims', node, tensorMap, context); return [ops.all(getParamValue('x', node, tensorMap, context), axis, keepDims)]; } case 'Any': { const axis = getParamValue('axis', node, tensorMap, context); const keepDims = getParamValue('keepDims', node, tensorMap, context); return [ops.any(getParamValue('x', node, tensorMap, context), axis, keepDims)]; } case 'ArgMax': { const axis = getParamValue('axis', node, tensorMap, context); return [ops.argMax(getParamValue('x', node, tensorMap, context), axis)]; } case 'ArgMin': { const axis = getParamValue('axis', node, tensorMap, context); return [ops.argMin(getParamValue('x', node, tensorMap, context), axis)]; } case 'Prod': { const axis = getParamValue('axis', node, tensorMap, context); const keepDims = getParamValue('keepDims', node, tensorMap, context); return [ops.prod(getParamValue('x', node, tensorMap, context), axis, keepDims)]; } case 'Cumprod': { const axis = getParamValue('axis', node, tensorMap, context); const exclusive = getParamValue('exclusive', node, tensorMap, context); const reverse = getParamValue('reverse', node, tensorMap, context); return [ops.cumprod(getParamValue('x', node, tensorMap, context), axis, exclusive, reverse)]; } case 'Cumsum': { const axis = getParamValue('axis', node, tensorMap, context); const exclusive = getParamValue('exclusive', node, tensorMap, context); const reverse = getParamValue('reverse', node, tensorMap, context); return [ops.cumsum(getParamValue('x', node, tensorMap, context), axis, exclusive, reverse)]; } case 'Bincount': const x = getParamValue('x', node, tensorMap, context); const weights = getParamValue('weights', node, tensorMap, context); const size = getParamValue('size', node, tensorMap, context); return [ops.bincount(x, weights, size)]; case 'DenseBincount': { const x = getParamValue('x', node, tensorMap, context); const weights = getParamValue('weights', node, tensorMap, context); const size = getParamValue('size', node, tensorMap, context); const binaryOutput = getParamValue('binaryOutput', node, tensorMap, context); return [ops.denseBincount(x, weights, size, binaryOutput)]; } default: throw TypeError(`Node type ${node.op} is not implemented`); } }; /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const executeOp$5 = (node, tensorMap, context, ops = tfOps) => { switch (node.op) { case 'ConcatV2': case 'Concat': { const n = getParamValue('n', node, tensorMap, context); const axis = getParamValue('axis', node, tensorMap, context); let inputs = getParamValue('tensors', node, tensorMap, context); inputs = inputs.slice(0, n); return [ops.concat(inputs, axis)]; } case 'Gather': { const input = getParamValue('x', node, tensorMap, context); const indices = getParamValue('indices', node, tensorMap, context); return [ops.gather(input, ops.cast(indices, 'int32'), 0)]; } case 'GatherV2': { const axis = getParamValue('axis', node, tensorMap, context); const batchDims = getParamValue('batchDims', node, tensorMap, context); const input = getParamValue('x', node, tensorMap, context); const indices = getParamValue('indices', node, tensorMap, context); return [ops.gather(input, ops.cast(indices, 'int32'), axis, batchDims)]; } case 'Reverse': { const dims = getParamValue('dims', node, tensorMap, context); const axis = []; for (let i = 0; i < dims.length; i++) { if (dims[i]) { axis.push(i); } } const input = getParamValue('x', node, tensorMap, context); return [ops.reverse(input, axis)]; } case 'ReverseV2': { const axis = getParamValue('axis', node, tensorMap, context); const input = getParamValue('x', node, tensorMap, context); return [ops.reverse(input, axis)]; } case 'Slice': { // tslint:disable-next-line:no-any const begin = getParamValue('begin', node, tensorMap, context); // tslint:disable-next-line:no-any const size = getParamValue('size', node, tensorMap, context); return [ops.slice(getParamValue('x', node, tensorMap, context), begin, size)]; } case 'StridedSlice': { const begin = getParamValue('begin', node, tensorMap, context); const end = getParamValue('end', node, tensorMap, context); const strides = getParamValue('strides', node, tensorMap, context); const beginMask = getParamValue('beginMask', node, tensorMap, context); const endMask = getParamValue('endMask', node, tensorMap, context); const ellipsisMask = getParamValue('ellipsisMask', node, tensorMap, context); const newAxisMask = getParamValue('newAxisMask', node, tensorMap, context); const shrinkAxisMask = getParamValue('shrinkAxisMask', node, tensorMap, context); const tensor = getParamValue('x', node, tensorMap, context); return [ops.stridedSlice(tensor, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask)]; } case 'Pack': { return tfc.tidy(() => { const axis = getParamValue('axis', node, tensorMap, context); const tensors = getParamValue('tensors', node, tensorMap, context); // Reshape the tensors to the first tensor's shape if they don't // match. const shape = tensors[0].shape; const squeezedShape = ops.squeeze(tensors[0]).shape; const mapped = tensors.map(tensor => { const sameShape = tfc.util.arraysEqual(tensor.shape, shape); if (!sameShape && !tfc.util.arraysEqual(ops.squeeze(tensor).shape, squeezedShape)) { throw new Error('the input tensors shape does not match'); } return sameShape ? tensor : ops.reshape(tensor, shape); }); return [ops.stack(mapped, axis)]; }); } case 'Unpack': { const axis = getParamValue('axis', node, tensorMap, context); const tensor = getParamValue('tensor', node, tensorMap, context); return ops.unstack(tensor, axis); } case 'Tile': { const reps = getParamValue('reps', node, tensorMap, context); return [ops.tile(getParamValue('x', node, tensorMap, context), reps)]; } case 'Split': case 'SplitV': { const axis = getParamValue('axis', node, tensorMap, context); const numOrSizeSplits = getParamValue('numOrSizeSplits', node, tensorMap, context); const tensor = getParamValue('x', node, tensorMap, context); return ops.split(tensor, numOrSizeSplits, axis); } case 'ScatterNd': { const indices = getParamValue('indices', node, tensorMap, context); const values = getParamValue('values', node, tensorMap, context); const shape = getParamValue('shape', node, tensorMap, context); return [ops.scatterND(indices, values, shape)]; } case 'GatherNd': { const x = getParamValue('x', node, tensorMap, context); const indices = getParamValue('indices', node, tensorMap, context); return [ops.gatherND(x, indices)]; } case 'SparseToDense': { const indices = getParamValue('sparseIndices', node, tensorMap, context); const shape = getParamValue('outputShape', node, tensorMap, context); const sparseValues = getParamValue('sparseValues', node, tensorMap, context); const defaultValue = getParamValue('defaultValue', node, tensorMap, context); return [ops.sparseToDense(indices, sparseValues, shape, sparseValues.dtype === defaultValue.dtype ? defaultValue : ops.cast(defaultValue, sparseValues.dtype))]; } case 'TensorScatterUpdate': { const indices = getParamValue('indices', node, tensorMap, context); const values = getParamValue('values', node, tensorMap, context); const tensor = getParamValue('tensor', node, tensorMap, context); return [ops.tensorScatterUpdate(tensor, indices, values)]; } default: throw TypeError(`Node type ${node.op} is not implemented`); } }; /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const executeOp$4 = (node, tensorMap, context, ops = tfOps) => { switch (node.op) { case 'SparseFillEmptyRows': { const { outputIndices, outputValues, emptyRowIndicator, reverseIndexMap } = ops.sparse.sparseFillEmptyRows(getParamValue('indices', node, tensorMap, context), getParamValue('values', node, tensorMap, context), getParamValue('denseShape', node, tensorMap, context), getParamValue('defaultValue', node, tensorMap, context)); return [ outputIndices, outputValues, emptyRowIndicator, reverseIndexMap ]; } case 'SparseReshape': { const { outputIndices, outputShape } = ops.sparse.sparseReshape(getParamValue('inputIndices', node, tensorMap, context), getParamValue('inputShape', node, tensorMap, context), getParamValue('newShape', node, tensorMap, context)); return [outputIndices, outputShape]; } case 'SparseSegmentMean': { const outputData = ops.sparse.sparseSegmentMean(getParamValue('data', node, tensorMap, context), getParamValue('indices', node, tensorMap, context), getParamValue('segmentIds', node, tensorMap, context)); return [outputData]; } case 'SparseSegmentSum': { const outputData = ops.sparse.sparseSegmentSum(getParamValue('data', node, tensorMap, context), getParamValue('indices', node, tensorMap, context), getParamValue('segmentIds', node, tensorMap, context)); return [outputData]; } default: throw TypeError(`Node type ${node.op} is not implemented`); } }; /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const executeOp$3 = (node, tensorMap, context, ops = tfOps) => { switch (node.op) { case 'FFT': { return [ops.fft(getParamValue('x', node, tensorMap, context))]; } case 'IFFT': { return [ops.ifft(getParamValue('x', node, tensorMap, context))]; } case 'RFFT': { return [ops.rfft(getParamValue('x', node, tensorMap, context))]; } case 'IRFFT': { return [ops.irfft(getParamValue('x', node, tensorMap, context))]; } default: throw TypeError(`Node type ${node.op} is not implemented`); } }; /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const executeOp$2 = (node, tensorMap, context, ops = tfOps) => { switch (node.op) { case 'StaticRegexReplace': { return [ops.string.staticRegexReplace(getParamValue('input', node, tensorMap, context), getParamValue('pattern', node, tensorMap, context), getParamValue('rewrite', node, tensorMap, context), getParamValue('replaceGlobal', node, tensorMap, context))]; } case 'StringNGrams': { const { nGrams, nGramsSplits } = ops.string.stringNGrams(getParamValue('data', node, tensorMap, context), getParamValue('dataSplits', node, tensorMap, context), getParamValue('separator', node, tensorMap, context), getParamValue('nGramWidths', node, tensorMap, context), getParamValue('leftPad', node, tensorMap, context), getParamValue('rightPad', node, tensorMap, context), getParamValue('padWidth', node, tensorMap, context), getParamValue('preserveShortSequences', node, tensorMap, context)); return [nGrams, nGramsSplits]; } case 'StringSplit': { const { indices, values, shape } = ops.string.stringSplit(getParamValue('input', node, tensorMap, context), getParamValue('delimiter', node, tensorMap, context), getParamValue('skipEmpty', node, tensorMap, context)); return [indices, values, shape]; } case 'StringToHashBucketFast': { const output = ops.string.stringToHashBucketFast(getParamValue('input', node, tensorMap, context), getParamValue('numBuckets', node, tensorMap, context)); return [output]; } default: throw TypeError(`Node type ${node.op} is not implemented`); } }; /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const executeOp$1 = (node, tensorMap, context, ops = tfOps) => { switch (node.op) { case 'Cast': { return [ops.cast(getParamValue('x', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))]; } case 'ExpandDims': { const axis = getParamValue('axis', node, tensorMap, context); return [ops.expandDims(getParamValue('x', node, tensorMap, context), axis)]; } case 'Squeeze': { const axis = getParamValue('axis', node, tensorMap, context); return [ops.squeeze(getParamValue('x', node, tensorMap, context), axis)]; } case 'Reshape': { return [ops.reshape(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))]; } case 'EnsureShape': { return [ops.ensureShape(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))]; } case 'MirrorPad': { return [ops.mirrorPad(getParamValue('x', node, tensorMap, context), getParamValue('padding', node, tensorMap, context), getParamValue('mode', node, tensorMap, context))]; } case 'PadV2': case 'Pad': { return [ops.pad(getParamValue('x', node, tensorMap, context), getParamValue('padding', node, tensorMap, context), getParamValue('constantValue', node, tensorMap, context))]; } case 'SpaceToBatchND': { const blockShape = getParamValue('blockShape', node, tensorMap, context); const paddings = getParamValue('paddings', node, tensorMap, context); return [ops.spaceToBatchND(getParamValue('x', node, tensorMap, context), blockShape, paddings)]; } case 'BatchToSpaceND': { const blockShape = getParamValue('blockShape', node, tensorMap, context); const crops = getParamValue('crops', node, tensorMap, context); return [ops.batchToSpaceND(getParamValue('x', node, tensorMap, context), blockShape, crops)]; } case 'DepthToSpace': { const blockSize = getParamValue('blockSize', node, tensorMap, context); const dataFormat = getParamValue('dataFormat', node, tensorMap, context).toUpperCase(); return [ops.depthToSpace(getParamValue('x', node, tensorMap, context), blockSize, dataFormat)]; } case 'BroadcastTo': { return [ops.broadcastTo(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))]; } case 'BroadcastArgs': { return [ops.broadcastArgs(getParamValue('s0', node, tensorMap, context), getParamValue('s1', node, tensorMap, context))]; } default: throw TypeError(`Node type ${node.op} is not implemented`); } }; /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Executes the op defined by the node object. * @param node * @param tensorMap contains tensors for executed nodes and weights * @param context contains tensors and information for running the current node. * @param resourceManager Optional. Contains global resources of the model. */ function executeOp(node, tensorMap, context, resourceManager, tidy = tfc__namespace.tidy) { const value = ((node, tensorMap, context) => { switch (node.category) { case 'arithmetic': return tidy(() => executeOp$k(node, tensorMap, context)); case 'basic_math': return tidy(() => executeOp$j(node, tensorMap, context)); case 'control': return executeOp$i(node, tensorMap, context); case 'convolution': return tidy(() => executeOp$h(node, tensorMap, context)); case 'creation': return tidy(() => executeOp$g(node, tensorMap, context)); case 'dynamic': return executeOp$f(node, tensorMap, context); case 'evaluation': return tidy(() => executeOp$e(node, tensorMap, context)); case 'image': return tidy(() => executeOp$b(node, tensorMap, context)); case 'graph': return tidy(() => executeOp$d(node, tensorMap, context)); case 'logical': return tidy(() => executeOp$a(node, tensorMap, context)); case 'matrices': return tidy(() => executeOp$9(node, tensorMap, context)); case 'normalization': return tidy(() => executeOp$8(node, tensorMap, context)); case 'ragged': return tidy(() => executeOp$7(node, tensorMap, context)); case 'reduction': return tidy(() => executeOp$6(node, tensorMap, context)); case 'slice_join': return tidy(() => executeOp$5(node, tensorMap, context)); case 'sparse': return tidy(() => executeOp$4(node, tensorMap, context)); case 'spectral': return tidy(() => executeOp$3(node, tensorMap, context)); case 'string': return tidy(() => executeOp$2(node, tensorMap, context)); case 'transformation': return tidy(() => executeOp$1(node, tensorMap, context)); case 'hash_table': return executeOp$c(node, tensorMap, context, resourceManager); case 'custom': const opMapper = getRegisteredOp(node.op); if (opMapper && opMapper.customExecutor) { return opMapper.customExecutor(new NodeValueImpl(node, tensorMap, context)); } else { throw TypeError(`Custom op ${node.op} is not registered.`); } default: throw TypeError(`Unknown op '${node.op}'. File an issue at ` + `https://github.com/tensorflow/tfjs/issues so we can add it` + `, or register a custom execution with tf.registerOp()`); } })(node, tensorMap, context); if (tfc__namespace.util.isPromise(value)) { return value.then((data) => [].concat(data)); } return [].concat(value); } /** * ExecutionContext captures the runtime environment of the node. It keeps * track of the current frame and iteration for the control flow ops. * * For example, typical Dynamic RNN model may contain loops, for which * TensorFlow will generate graphs with Enter/Exit nodes to control the * current execution frame, and NextIteration Nodes for iteration id increment. * For model with branch logic, TensorFLow will generate Switch/Merge ops. */ class ExecutionContext { constructor(weightMap = {}, tensorArrayMap = {}, tensorListMap = {}, functionMap = {}, parseNodeNameCache) { this.weightMap = weightMap; this.tensorArrayMap = tensorArrayMap; this.tensorListMap = tensorListMap; this.functionMap = functionMap; this.parseNodeNameCache = parseNodeNameCache; this.rootContext = { id: 0, frameName: '', iterationId: 0 }; this.contexts = [this.rootContext]; this.lastId = 0; this.generateCurrentContextIds(); } newFrame(id, frameName) { return { id, frameName, iterationId: 0 }; } /** * Set the current context * @param contexts: ExecutionContextInfo[] the current path of execution * frames */ set currentContext(contexts) { if (this.contexts !== contexts) { this.contexts = contexts; this.generateCurrentContextIds(); } } get currentContext() { return this.contexts; } /** * Returns the current context in string format. */ get currentContextId() { return this._currentContextIds[0]; } /** * Returns the current context and all parent contexts in string format. * This allow access to the nodes in the current and parent frames. */ get currentContextIds() { return this._currentContextIds; } generateCurrentContextIds() { const names = []; for (let i = 0; i < this.contexts.length - 1; i++) { const contexts = this.contexts.slice(0, this.contexts.length - i); names.push(this.contextIdforContexts(contexts)); } names.push(''); this._currentContextIds = names; } contextIdforContexts(contexts) { return contexts ? contexts .map(context => (context.id === 0 && context.iterationId === 0) ? '' : `${context.frameName}-${context.iterationId}`) .join('/') : ''; } /** * Enter a new frame, a new context is pushed on the current context list. * @param frameId new frame id */ enterFrame(frameId) { if (this.contexts) { this.lastId++; this.contexts = this.contexts.slice(); this.contexts.push(this.newFrame(this.lastId, frameId)); this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)); } } /** * Exit the current frame, the last context is removed from the current * context list. */ exitFrame() { if (this.contexts && this.contexts.length > 1) { this.contexts = this.contexts.slice(); this.contexts.splice(-1); this.currentContextIds.shift(); } else { throw new Error('Cannot exit frame, the context is empty'); } } /** * Enter the next iteration of a loop, the iteration id of last context is * increased. */ nextIteration() { if (this.contexts && this.contexts.length > 0) { this.contexts = this.contexts.slice(); this.lastId++; const context = Object.assign({}, this.contexts[this.contexts.length - 1]); context.iterationId += 1; context.id = this.lastId; this.contexts.splice(-1, 1, context); this._currentContextIds.splice(0, 1, this.contextIdforContexts(this.contexts)); } else { throw new Error('Cannot increase frame iteration, the context is empty'); } } getWeight(name) { return this.weightMap[name]; } addTensorArray(tensorArray) { this.tensorArrayMap[tensorArray.id] = tensorArray; } getTensorArray(id) { return this.tensorArrayMap[id]; } addTensorList(tensorList) { this.tensorListMap[tensorList.id] = tensorList; } getTensorList(id) { return this.tensorListMap[id]; } dispose(keepIds) { for (const key in this.tensorArrayMap) { this.tensorArrayMap[key].clearAndClose(keepIds); } for (const key in this.tensorListMap) { this.tensorListMap[key].clearAndClose(keepIds); } } } /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * Given graph inputs and desired outputs, find the minimal set of nodes * to execute in order to compute the outputs. In addition return other useful * info such: * - Missing inputs needed to compute the output. * - Whether the subgraph contains dynamic ops (control flow, dynamic shape). * - Alternative inputs in order to avoid async (dynamic op) execution. */ function getExecutionSubgraph(inputs, outputs, weightMap, initNodes) { const usedNodes = new Set(); const missingInputs = []; let dynamicNode = null; let syncInputs = null; // Start with the outputs, going backwards and find all the nodes that are // needed to compute those outputs. const seen = new Set(); const inputNodeNames = new Set(Object.keys(inputs).map((name) => parseNodeName(name)[0])); initNodes = initNodes || []; const initNodeNames = new Set(initNodes.map((node) => parseNodeName(node.name)[0])); const frontier = [...outputs]; while (frontier.length > 0) { const node = frontier.pop(); if (isControlFlow(node) || isDynamicShape(node) || isHashTable(node)) { if (dynamicNode == null) { dynamicNode = node; syncInputs = dynamicNode.children.map(child => child.name) .filter(name => usedNodes.has(name)); } } usedNodes.add(node.name); // Weights are dead end since we already have their values. if (weightMap[node.name] != null) { continue; } // This node is a dead end since it's one of the user-provided inputs. if (inputNodeNames.has(node.name)) { continue; } // This node is a dead end since it doesn't have any inputs. if (initNodeNames.has(node.name)) { continue; } if (node.inputs.length === 0) { missingInputs.push(node.name); continue; } node.inputs.forEach(input => { // Don't add to the frontier if it is already there. if (seen.has(input.name)) { return; } seen.add(input.name); frontier.push(input); }); } return { inputs, outputs, usedNodes, missingInputs, dynamicNode, syncInputs }; } /** * Given the execution info, return a list of nodes in topological order that * need to be executed to compute the output. */ function getNodesInTopologicalOrder(graph, executionInfo) { const { usedNodes, inputs } = executionInfo; const inputNodes = Object.keys(inputs) .map(name => parseNodeName(name)[0]) .map(name => graph.nodes[name]); const initNodes = graph.initNodes || []; const isUsed = (node) => usedNodes.has(typeof node === 'string' ? node : node.name); function unique(nodes) { return [...new Map(nodes.map((node) => [node.name, node])).values()]; } const predefinedNodes = unique([ ...inputNodes, ...graph.weights, ...initNodes, ]).filter(isUsed); const allNodes = unique([ ...predefinedNodes, ...Object.values(graph.nodes), ]).filter(isUsed); const nameToNode = new Map(allNodes.map((node) => [node.name, node])); const inCounts = {}; for (const node of allNodes) { inCounts[node.name] = inCounts[node.name] || 0; for (const child of node.children) { // When the child is unused, set in counts to infinity so that it will // never be decreased to 0 and added to the execution list. if (!isUsed(child)) { inCounts[child.name] = Number.POSITIVE_INFINITY; } inCounts[child.name] = (inCounts[child.name] || 0) + 1; } } // Build execution order for all used nodes regardless whether they are // predefined or not. const frontier = Object.entries(inCounts) .filter(([, inCount]) => inCount === 0) .map(([name]) => name); const orderedNodeNames = [...frontier]; while (frontier.length > 0) { const nodeName = frontier.pop(); const node = nameToNode.get(nodeName); for (const child of node.children.filter(isUsed)) { if (--inCounts[child.name] === 0) { orderedNodeNames.push(child.name); frontier.push(child.name); } } } const orderedNodes = orderedNodeNames.map((name) => nameToNode.get(name)); const filteredOrderedNodes = filterPredefinedReachableNodes(orderedNodes, predefinedNodes); // TODO: Turn validation on/off with tf env flag. validateNodesExecutionOrder(filteredOrderedNodes, predefinedNodes); return filteredOrderedNodes; } /** * This is a helper function of `getNodesInTopologicalOrder`. * Returns ordered nodes reachable by at least one predefined node. * This can help us filter out redundant nodes from the returned node list. * For example: * If we have four nodes with dependencies like this: * a --> b --> c --> d * when node `c` is predefined (e.g. given as an input tensor), we can * skip node `a` and `b` since their outputs will never be used. * * @param orderedNodes Graph nodes in execution order. * @param predefinedNodes Graph inputs, weights, and init nodes. Nodes in this * list must have distinct names. */ function filterPredefinedReachableNodes(orderedNodes, predefinedNodes) { const nameToNode = new Map(orderedNodes.map((node) => [node.name, node])); // TODO: Filter out more nodes when >=2 nodes are predefined in a path. const stack = predefinedNodes.map((node) => node.name); const predefinedReachableNodeNames = new Set(stack); // Perform a DFS starting from the set of all predefined nodes // to find the set of all nodes reachable from the predefined nodes. while (stack.length > 0) { const nodeName = stack.pop(); const node = nameToNode.get(nodeName); for (const child of node.children) { if (!nameToNode.has(child.name) || predefinedReachableNodeNames.has(child.name)) { continue; } predefinedReachableNodeNames.add(child.name); stack.push(child.name); } } // Filter out unreachable nodes and build the ordered node list. const filteredOrderedNodes = orderedNodes.filter((node) => predefinedReachableNodeNames.has(node.name)); return filteredOrderedNodes; } class NodesExecutionOrderError extends Error { constructor(message) { super(`NodesExecutionOrderError: ${message}`); } } /** * This is a helper function of `getNodesInTopologicalOrder`. * Validates property: given nodes `a` and `b`, Order(a) > Order(b) if `a` * is a child of `b`. This function throws an error if validation fails. * * @param orderedNodes Graph nodes in execution order. * @param predefinedNodes Graph inputs, weights, and init nodes. Nodes in this * list must have distinct names. */ function validateNodesExecutionOrder(orderedNodes, predefinedNodes) { const nodeNameToOrder = new Map(orderedNodes.map((node, order) => [node.name, order])); const predefinedNodeNames = new Set(predefinedNodes.map((node) => node.name)); const isPredefined = (node) => predefinedNodeNames.has(typeof node === 'string' ? node : node.name); const willBeExecutedNodeNames = new Set(orderedNodes.map((node) => node.name)); const willBeExecuted = (node) => willBeExecutedNodeNames.has(typeof node === 'string' ? node : node.name); for (const node of orderedNodes) { for (const child of node.children.filter(willBeExecuted)) { if (!nodeNameToOrder.has(child.name)) { throw new NodesExecutionOrderError(`Child ${child.name} of node ${node.name} is unreachable.`); } if (nodeNameToOrder.get(node.name) > nodeNameToOrder.get(child.name)) { throw new NodesExecutionOrderError(`Node ${node.name} is scheduled to run after its child ${child.name}.`); } } if (!isPredefined(node)) { for (const input of node.inputs) { if (!nodeNameToOrder.has(input.name)) { throw new NodesExecutionOrderError(`Input ${input.name} of node ${node.name} is unreachable.`); } if (nodeNameToOrder.get(input.name) > nodeNameToOrder.get(node.name)) { throw new NodesExecutionOrderError(`Node ${node.name} is scheduled to run before its input ${input.name}.`); } } } } } /** * Given the execution info, return a map from node name to the disposable * node name list after its execution. * * @returns A map from node name to disposable nodes after its * execution. That is, for a node `x`, `nodeLiveUntilMap[x]` indicates * all nodes which their intermediate tensors should be disposed after `x` * being executed. */ function getNodeLiveUntilMap(orderedNodes) { const nodeNameToOrder = new Map(orderedNodes.map((node, order) => [node.name, order])); const INF_LIFE = Number.MAX_SAFE_INTEGER; // Make control flow nodes (and consequently their direct parents) // live forever since they're tricky to track correctly. const selfLifespans = orderedNodes.map((node, nodeOrder) => isControlFlow(node) ? INF_LIFE : nodeOrder); const getSelfLifeSpan = (node) => { const selfLife = selfLifespans[nodeNameToOrder.get(node.name)]; if (selfLife == null) { // If nodeToOrder does not contain the node, it is unused or // unreachable in graph. return -1; } return selfLife; }; // `liveUntil[i]` points to the last node in the `orderedNodes` array that // may depend on tensors from node `i`. It indicates that all the // intermediate tensors from `orderedNodes[i]` should be disposed after // `orderedNodes[liveUntil[i]]` is executed. // A node lives long enough to pass on its tensors to its children. // It lives until at least `max(node's position, children's positions)`. const liveUntilOrders = orderedNodes.map((node, nodeOrder) => { return node.children.map(getSelfLifeSpan) .reduce((a, b) => Math.max(a, b), selfLifespans[nodeOrder]); }); // liveUntilMap: // - Key: Name of a node `x` // - Values: All nodes whose intermediate tensors should be disposed // after `x` is executed. const liveUntilMap = new Map(); for (let nodeOrder = 0; nodeOrder < orderedNodes.length; ++nodeOrder) { const liveUntilOrder = liveUntilOrders[nodeOrder]; if (liveUntilOrder === INF_LIFE) { continue; } const node = orderedNodes[nodeOrder]; const liveUntilNode = orderedNodes[liveUntilOrder]; if (!liveUntilMap.has(liveUntilNode.name)) { liveUntilMap.set(liveUntilNode.name, []); } liveUntilMap.get(liveUntilNode.name).push(node); } return liveUntilMap; } const CONTROL_FLOW_OPS = new Set([ 'Switch', 'Merge', 'Enter', 'Exit', 'NextIteration', 'StatelessIf', 'StatelessWhile', 'if', 'While' ]); const DYNAMIC_SHAPE_OPS = new Set([ 'NonMaxSuppressionV2', 'NonMaxSuppressionV3', 'NonMaxSuppressionV5', 'Where' ]); const HASH_TABLE_OPS = new Set([ 'HashTable', 'HashTableV2', 'LookupTableImport', 'LookupTableImportV2', 'LookupTableFind', 'LookupTableFindV2', 'LookupTableSize', 'LookupTableSizeV2' ]); function isControlFlow(node) { return CONTROL_FLOW_OPS.has(node.op); } function isDynamicShape(node) { return DYNAMIC_SHAPE_OPS.has(node.op); } function isHashTable(node) { return HASH_TABLE_OPS.has(node.op); } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ class GraphExecutor { get weightIds() { return this.parent ? this.parent.weightIds : this._weightIds; } get functionExecutorMap() { return this.parent ? this.parent.functionExecutorMap : this._functionExecutorMap; } get weightMap() { return this.parent ? this.parent.weightMap : this._weightMap; } set weightMap(weightMap) { const weightIds = Object.keys(weightMap).map(key => weightMap[key].map(tensor => tensor.id)); this._weightIds = [].concat(...weightIds); this._weightMap = weightMap; } /** * Set `ResourceManager` shared by executors of a model. * @param resourceManager: `ResourceManager` of the `GraphModel`. */ set resourceManager(resourceManager) { this._resourceManager = resourceManager; } get inputs() { return this._inputs.map(node => { return { name: node.name, shape: node.attrParams['shape'] ? node.attrParams['shape'].value : undefined, dtype: node.attrParams['dtype'] ? node.attrParams['dtype'].value : undefined }; }); } get outputs() { return this._outputs.map(node => { return { name: node.name, shape: node.attrParams['shape'] ? node.attrParams['shape'].value : undefined, dtype: node.attrParams['dtype'] ? node.attrParams['dtype'].value : undefined }; }); } get inputNodes() { return this._inputs.map(node => node.signatureKey || node.name); } get outputNodes() { return this._outputs.map((node) => { const name = node.signatureKey || node.name; return node.defaultOutput ? (`${name}:${node.defaultOutput}`) : name; }); } get functions() { return Object.keys(this._functions).reduce((map, key) => { map[key] = this._functions[key].signature; return map; }, {}); } /** * * @param graph Graph the model or function graph to be executed. * @param parent When building function exector you need to set the parent * executor. Since the weights and function executor maps are set at parant * level, that function executor can access the function maps and weight maps * through the parent. */ constructor(graph, parent) { this.graph = graph; this.parent = parent; this.compiledMap = new Map(); this.parseNodeNameCache = new Map(); this._weightMap = {}; this.SEPARATOR = ','; this._functions = {}; this._functionExecutorMap = {}; this.keepIntermediateTensors = false; this._outputs = graph.outputs; this._inputs = graph.inputs; this._initNodes = graph.initNodes; this._signature = graph.signature; this._functions = graph.functions; // create sub-graph executors if (graph.functions != null) { Object.keys(graph.functions).forEach(name => { this._functionExecutorMap[name] = new GraphExecutor(graph.functions[name], this); }); } } getCompilationKey(inputs, outputs) { const sortedInputs = inputs.map(node => node.name).sort(); const sortedOutputs = outputs.map(node => node.name).sort(); return sortedInputs.join(this.SEPARATOR) + '--' + sortedOutputs.join(this.SEPARATOR); } /** * Compiles the inference graph and returns the minimal set of nodes that are * required for execution, in the correct execution order. * @returns {Object} compilation The compile result. * @returns {Node[]} compilation.orderedNodes Nodes in the correct execution * order. * @returns {Map} compilation.nodeLiveUntilMap A map from node * to disposable nodes after its execution. That is, for a node `x`, * `nodeLiveUntilMap[x]` indicates all nodes whose intermediate * tensors should be disposed after `x` is executed. */ compile(inputs, outputs) { const executionInfo = getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes); const { missingInputs, dynamicNode, syncInputs } = executionInfo; if (dynamicNode != null) { throw new Error(`This execution contains the node '${dynamicNode.name}', which has ` + `the dynamic op '${dynamicNode.op}'. Please use ` + `model.executeAsync() instead. Alternatively, to avoid the ` + `dynamic ops, specify the inputs [${syncInputs}]`); } if (missingInputs.length > 0) { const outNames = outputs.map(n => n.name); const inNames = Object.keys(inputs); throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs ` + `[${inNames}]. Missing the following inputs: [${missingInputs}]`); } const orderedNodes = getNodesInTopologicalOrder(this.graph, executionInfo); const nodeLiveUntilMap = getNodeLiveUntilMap(orderedNodes); return { orderedNodes, nodeLiveUntilMap }; } cloneAndKeepTensor(tensor) { if (tensor == null) { return null; } const clone = tensor.clone(); // Keep the clone because`model.execute()` may be called within // a `tidy()`, but the user may inspect these tensors after the // tidy. tfc.keep(clone); return clone; } cloneTensorList(tensors) { if (!tensors) { return null; } const clonedTensor = tensors.map(tensor => { return this.cloneAndKeepTensor(tensor); }); return clonedTensor; } cloneTensorMap(tensorsMap) { return Object.fromEntries(Object.entries(tensorsMap).map(([name, tensorsList]) => { return [name, this.cloneTensorList(tensorsList)]; })); } /** * Executes the inference for given input tensors. * @param inputs Tensor map for the model inputs, keyed by the input node * names. * @param outputs Optional. output node name from the Tensorflow model, if * no outputs are specified, the default outputs of the model would be used. * You can inspect intermediate nodes of the model by adding them to the * outputs array. */ execute(inputs, outputs) { // Dispose any tensors from a prior run to avoid leaking them. this.disposeIntermediateTensors(); inputs = this.mapInputs(inputs); const names = Object.keys(inputs).sort(); this.checkInputs(inputs); this.checkInputShapeAndType(inputs); outputs = this.mapOutputs(outputs); this.checkOutputs(outputs); const inputNodes = names.map(name => this.graph.nodes[parseNodeName(name)[0]]); const outputNodeNames = outputs.map(name => parseNodeName(name)[0]); const outputNodeNameSet = new Set(outputNodeNames); let outputNodes = outputNodeNames.map(name => this.graph.nodes[name]); // If no outputs are specified, then use the default outputs of the model. if (outputNodes.length === 0) { outputNodes = this._outputs; } const compilationKey = this.getCompilationKey(inputNodes, outputNodes); // Do nothing if the compiled graph cache contains the input. let compilation = this.compiledMap.get(compilationKey); if (compilation == null) { compilation = this.compile(inputs, outputNodes); this.compiledMap.set(compilationKey, compilation); } // Keep tensors if KEEP_INTERMEDIATE_TENSORS is on. try { this.keepIntermediateTensors = tfc.env().getBool('KEEP_INTERMEDIATE_TENSORS'); } catch (e) { this.keepIntermediateTensors = false; console.warn(e.message); } const tensorArrayMap = {}; const tensorListMap = {}; return tfc.tidy(() => { const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap, this.parseNodeNameCache); const tensorsMap = Object.assign({}, this.weightMap); if (this.keepIntermediateTensors) { this.clonedTensorsMap = this.cloneTensorMap(this.weightMap); } Object.keys(inputs).forEach(name => { const [nodeName, index] = parseNodeName(name, context); const tensors = []; tensors[index] = inputs[name]; tensorsMap[nodeName] = tensors; if (this.keepIntermediateTensors) { this.clonedTensorsMap[nodeName] = this.cloneTensorList(tensors); } }); const tensorsToKeep = this.getFrozenTensorIds(tensorsMap); const { orderedNodes, nodeLiveUntilMap } = compilation; for (const node of orderedNodes) { if (tensorsMap[node.name]) { continue; } const tensors = executeOp(node, tensorsMap, context, this._resourceManager); if (tfc.util.isPromise(tensors)) { throw new Error(`The execution of the op '${node.op}' returned a promise. ` + `Please use model.executeAsync() instead.`); } tensorsMap[node.name] = tensors; if (this.keepIntermediateTensors) { this.clonedTensorsMap[node.name] = this.cloneTensorList(tensors); } this.checkTensorForDisposalWithNodeLiveUntilInfo(node, tensorsMap, context, tensorsToKeep, outputNodeNameSet, nodeLiveUntilMap.get(node.name)); } // dispose the context for the root executor if (this.parent == null) { context.dispose(tensorsToKeep); } return outputs.map(name => getTensor(name, tensorsMap, context)); }); } getFrozenTensorIds(tensorMap) { const ids = [].concat.apply([], Object.keys(tensorMap) .map(key => tensorMap[key]) .map(tensors => tensors.map(tensor => tensor.id))); return new Set(ids); } checkTensorForDisposal(nodeName, node, tensorMap, context, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount) { // Skip output nodes and any control flow nodes, since its dependency is // tricky to track correctly. if (isControlFlow(node) || outputNodeNameSet.has(nodeName)) { return; } for (const tensor of tensorMap[nodeName]) { if (tensor == null) { continue; } intermediateTensorConsumerCount[tensor.id] = (intermediateTensorConsumerCount[tensor.id] || 0) + node.children.length; } for (const input of node.inputs) { // Skip any control flow nodes, since its dependency is tricky to track // correctly. if (isControlFlow(input)) { continue; } const tensors = getTensorsForCurrentContext(input.name, tensorMap, context); if (tensors == null) { continue; } for (const tensor of tensors) { if (!tensor || tensor.kept || tensorsToKeep.has(tensor.id)) { continue; } // Only intermediate nodes' tensors have counts set, not marked as // kept, and not in `tensorsToKeep`. // Input and weight nodes' tensors should exist in `tensorsToKeep`. // Output and control flow nodes' tensors should never have count set. const count = intermediateTensorConsumerCount[tensor.id]; if (count === 1) { tensor.dispose(); delete intermediateTensorConsumerCount[tensor.id]; } else if (count != null) { intermediateTensorConsumerCount[tensor.id]--; } } } } checkTensorForDisposalWithNodeLiveUntilInfo(node, tensorMap, context, tensorsToKeep, outputNodeNameSet, liveUntilNodes) { function isNonDisposableNode(node) { // Skip output nodes and any control flow nodes, since its dependency is // tricky to track correctly. return isControlFlow(node) || outputNodeNameSet.has(node.name); } if (isControlFlow(node) || liveUntilNodes == null) { return; } for (const nodeToDispose of liveUntilNodes) { if (isNonDisposableNode(nodeToDispose)) { continue; } const tensors = getTensorsForCurrentContext(nodeToDispose.name, tensorMap, context); for (const tensor of tensors) { if (!tensor || tensor.kept || tensorsToKeep.has(tensor.id)) { continue; } tensor.dispose(); } } } /** * Executes the inference for given input tensors in Async fashion. * @param inputs Tensor map for the model inputs, keyed by the input node * names. * @param outputs output node name from the Tensorflow model, if no outputs * are specified, the default outputs of the model would be used. You can * inspect intermediate nodes of the model by adding them to the outputs * array. */ async executeAsync(inputs, outputs) { return this._executeAsync(inputs, outputs); } disposeIntermediateTensors() { if (!this.clonedTensorsMap) { return; } Object.values(this.clonedTensorsMap).forEach(tensorsList => { for (const tensor of tensorsList) { if (tensor && !tensor.isDisposed) { tensor.dispose(); } } }); this.clonedTensorsMap = null; } getIntermediateTensors() { return this.clonedTensorsMap; } /** * Executes the inference for given input tensors in Async fashion. * @param inputs Tensor map for the model inputs, keyed by the input node * names. * @param outputs Optional. output node name from the Tensorflow model, * if no outputs are specified, the default outputs of the model would be * used. You can inspect intermediate nodes of the model by adding them to * the outputs array. * @param isFunctionExecution Optional. Flag for executing a function. * @param tensorArrayMap Optional, global TensorArray map by id. Used for * function execution. * @param tensorArrayMap Optinal global TensorList map by id. Used for * function execution. */ async _executeAsync(inputs, outputs, isFunctionExecution = false, tensorArrayMap = {}, tensorListMap = {}) { // Dispose any tensors from a prior run to avoid leaking them. this.disposeIntermediateTensors(); if (!isFunctionExecution) { inputs = this.mapInputs(inputs); this.checkInputs(inputs); this.checkInputShapeAndType(inputs); outputs = this.mapOutputs(outputs); this.checkOutputs(outputs); } // Keep tensors if KEEP_INTERMEDIATE_TENSORS is on. try { this.keepIntermediateTensors = tfc.env().getBool('KEEP_INTERMEDIATE_TENSORS'); } catch (e) { this.keepIntermediateTensors = false; console.warn(e.message); } const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap, this.parseNodeNameCache); if (this.keepIntermediateTensors) { this.clonedTensorsMap = this.cloneTensorMap(this.weightMap); } // Graph with control flow op requires runtime evaluation of the execution // order, while without control flow the execution order is pre-determined // in the compile method. const tensorsMap = await this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution); const results = outputs.map(name => getTensor(name, tensorsMap, context)); // dispose all the intermediate tensors const outputIds = results.map(t => t.id); const inputIds = Object.keys(inputs).map(name => inputs[name].id); const keepIds = new Set([...outputIds, ...inputIds, ...this.weightIds]); Object.values(tensorsMap).forEach(tensorsList => { tensorsList.forEach(tensor => { if (tensor && !tensor.isDisposed && !keepIds.has(tensor.id)) { tensor.dispose(); } }); }); // dispose the context for the root executor if (this.parent == null) { context.dispose(keepIds); } return results; } async executeFunctionAsync(inputs, tensorArrayMap, tensorListMap) { const mappedInputs = inputs.reduce((map, tensor, index) => { map[this.inputs[index].name] = tensor; return map; }, {}); return this._executeAsync(mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap); } /** * When there are control flow nodes in the graph, the graph execution use * ExecutionContext to keep track of the frames and loop iterators. * @param inputs placeholder tensors for the graph. * @param context the execution context object for current execution. * @param outputNames Optional. output node name from the Tensorflow model, * if no outputs are specified, the default outputs of the model would be * used. You can inspect intermediate nodes of the model by adding them to * the outputs array. * @param isFunctionExecution Flag for executing a function. */ async executeWithControlFlow(inputs, context, outputNames, isFunctionExecution) { const names = Object.keys(inputs); const inputNodes = names.map(name => this.graph.nodes[parseNodeName(name)[0]]); const outputNodeNames = outputNames.map(name => parseNodeName(name)[0]); const outputNodeNameSet = new Set(outputNodeNames); let outputNodes = outputNodeNames.map(name => this.graph.nodes[name]); // If no outputs are specified, then use the default outputs of the model. if (outputNodes.length === 0) { outputNodes = this._outputs; } const { usedNodes, missingInputs, dynamicNode, syncInputs } = getExecutionSubgraph(inputs, outputNodes, this.weightMap, this._initNodes); // First nodes to execute include inputNodes, weights, and initNodes. const stack = [ ...inputNodes, ...this.graph.weights, ...(this._initNodes || []) ].map(node => { return { node, contexts: context.currentContext }; }); const tensorsMap = Object.assign({}, this.weightMap); Object.keys(inputs).forEach(name => { const [nodeName, index] = parseNodeName(name); const tensors = []; tensors[index] = inputs[name]; tensorsMap[nodeName] = tensors; }); const intermediateTensorConsumerCount = {}; const tensorsToKeep = this.getFrozenTensorIds(tensorsMap); const added = {}; while (stack.length > 0) { const promises = this.processStack(inputNodes, stack, context, tensorsMap, added, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount, usedNodes); await Promise.all(promises); } if (dynamicNode == null && !isFunctionExecution) { console.warn(`This model execution did not contain any nodes with control flow ` + `or dynamic output shapes. You can use model.execute() instead.`); } const missingOutputs = outputNodes .filter(node => !isControlFlow(node) && !getTensor(node.name, tensorsMap, context)) .map(node => node.name); if (missingOutputs.length > 0) { let alternativeMsg = ''; if (dynamicNode != null) { alternativeMsg = `Alternatively, to avoid the dynamic ops, use model.execute() ` + `and specify the inputs [${syncInputs}]`; } throw new Error(`Cannot compute the outputs [${missingOutputs}] from the provided ` + `inputs [${names}]. Consider providing the following inputs: ` + `[${missingInputs}]. ${alternativeMsg}`); } return tensorsMap; } processStack(inputNodes, stack, context, tensorMap, added, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount, usedNodes) { const promises = []; while (stack.length > 0) { const item = stack.pop(); context.currentContext = item.contexts; let nodeName = ''; // The tensor of the Enter op with isConstant set should be set // in the parent scope, so it will be available as constant for the // whole loop. if (item.node.op === 'Enter' && getParamValue('isConstant', item.node, tensorMap, context)) { [nodeName] = getNodeNameAndIndex(item.node.name, context); } // only process nodes that are not in the tensorMap yet, this include // inputNodes and internal initNodes. if (tensorMap[item.node.name] == null) { const tensors = executeOp(item.node, tensorMap, context, this._resourceManager); if (!nodeName) { [nodeName] = getNodeNameAndIndex(item.node.name, context); } const currentContext = context.currentContext; if (tfc.util.isPromise(tensors)) { promises.push(tensors.then(t => { tensorMap[nodeName] = t; if (this.keepIntermediateTensors) { this.clonedTensorsMap[nodeName] = this.cloneTensorList(t); } context.currentContext = currentContext; this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount); this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes); return t; })); } else { tensorMap[nodeName] = tensors; if (this.keepIntermediateTensors) { this.clonedTensorsMap[nodeName] = this.cloneTensorList(tensors); } this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount); this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes); } } else { this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes); } } return promises; } processChildNodes(node, stack, context, tensorMap, added, usedNodes) { node.children.forEach((childNode) => { const [nodeName,] = getNodeNameAndIndex(childNode.name, context); if (added[nodeName] || !usedNodes.has(childNode.name)) { return; } // Merge op can be pushed if any of its inputs has value. if (childNode.op === 'Merge') { if (childNode.inputNames.some(name => { return !!getTensor(name, tensorMap, context); })) { added[nodeName] = true; stack.push({ contexts: context.currentContext, node: childNode }); } } else // Otherwise all inputs must to have value. if (childNode.inputNames.every(name => { return !!getTensor(name, tensorMap, context); })) { added[nodeName] = true; stack.push({ contexts: context.currentContext, node: childNode }); } }); } /** * Releases the memory used by the weight tensors. */ dispose() { Object.keys(this.weightMap) .forEach(key => this.weightMap[key].forEach(tensor => tensor.dispose())); } checkInputShapeAndType(inputs) { Object.keys(inputs).forEach(name => { const input = inputs[name]; const [nodeName,] = parseNodeName(name); const node = this.graph.nodes[nodeName]; if (node.attrParams['shape'] && node.attrParams['shape'].value) { const shape = node.attrParams['shape'].value; const match = shape.length === input.shape.length && input.shape.every((dim, index) => shape[index] === -1 || shape[index] === dim); tfc.util.assert(match, () => `The shape of dict['${node.name}'] provided in ` + `model.execute(dict) must be [${shape}], but was ` + `[${input.shape}]`); } if (node.attrParams['dtype'] && node.attrParams['dtype'].value) { tfc.util.assert(input.dtype === node.attrParams['dtype'].value, () => `The dtype of dict['${node.name}'] provided in ` + `model.execute(dict) must be ` + `${node.attrParams['dtype'].value}, but was ${input.dtype}`); } }); } mapInputs(inputs) { var _a, _b; const result = {}; for (const inputName in inputs) { const tensor = (_b = (_a = this._signature) === null || _a === void 0 ? void 0 : _a.inputs) === null || _b === void 0 ? void 0 : _b[inputName]; if (tensor != null) { result[tensor.name] = inputs[inputName]; } else { result[inputName] = inputs[inputName]; } } return result; } checkInputs(inputs) { const notInGraph = Object.keys(inputs).filter(name => { const [nodeName] = parseNodeName(name); return this.graph.nodes[nodeName] == null; }); if (notInGraph.length > 0) { throw new Error(`The dict provided in model.execute(dict) has ` + `keys: [${notInGraph}] that are not part of graph`); } } mapOutputs(outputs) { return outputs.map(name => { var _a, _b; const tensor = (_b = (_a = this._signature) === null || _a === void 0 ? void 0 : _a.outputs) === null || _b === void 0 ? void 0 : _b[name]; if (tensor != null) { return tensor.name; } return name; }, {}); } checkOutputs(outputs) { outputs.forEach(name => { const [normalizedName] = parseNodeName(name); if (!this.graph.nodes[normalizedName]) { throw new Error(`The output '${name}' is not found in the graph`); } }); } } /** * Contains global resources of a model. */ class ResourceManager { constructor(hashTableNameToHandle = {}, hashTableMap = {}) { this.hashTableNameToHandle = hashTableNameToHandle; this.hashTableMap = hashTableMap; } /** * Register a `HashTable` in the resource manager. * * The `HashTable` can be retrieved by `resourceManager.getHashTableById`, * where id is the table handle tensor's id. * * @param name Op node name that creates the `HashTable`. * @param hashTable The `HashTable` to be added to resource manager. */ addHashTable(name, hashTable) { this.hashTableNameToHandle[name] = hashTable.handle; this.hashTableMap[hashTable.id] = hashTable; } /** * Get the table handle by node name. * @param name Op node name that creates the `HashTable`. This name is also * used in the inputs list of lookup and import `HashTable` ops. */ getHashTableHandleByName(name) { return this.hashTableNameToHandle[name]; } /** * Get the actual `HashTable` by its handle tensor's id. * @param id The id of the handle tensor. */ getHashTableById(id) { return this.hashTableMap[id]; } /** * Dispose `ResourceManager`, including its hashTables and tensors in them. */ dispose() { for (const key in this.hashTableMap) { this.hashTableMap[key].clearAndClose(); delete this.hashTableMap[key]; } for (const name in this.hashTableNameToHandle) { this.hashTableNameToHandle[name].dispose(); delete this.hashTableNameToHandle[name]; } } } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /* Type definitions for exporting and importing of models. */ /** * A map from Tensor dtype to number of bytes per element of the Tensor. */ const DTYPE_VALUE_SIZE_MAP = { 'float32': 4, 'float16': 2, 'int32': 4, 'uint16': 2, 'uint8': 1, 'bool': 1, 'complex64': 8 }; /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** Number of bytes reserved for the length of the string. (32bit integer). */ const NUM_BYTES_STRING_LENGTH = 4; async function getWeightBytelengthAsync(spec, slice) { const size = sizeFromShape(spec.shape); let bytesPerValue; if ('quantization' in spec) { const quantization = spec.quantization; bytesPerValue = DTYPE_VALUE_SIZE_MAP[quantization.dtype]; } else if (spec.dtype === 'string') { // Can not statically determine string length. let byteLength = 0; for (let i = 0; i < size; i++) { byteLength += NUM_BYTES_STRING_LENGTH + new Uint32Array(await slice(byteLength, byteLength + NUM_BYTES_STRING_LENGTH))[0]; } return byteLength; } else { bytesPerValue = DTYPE_VALUE_SIZE_MAP[spec.dtype]; } return size * bytesPerValue; } function decodeWeight(spec, byteBuffer) { const name = spec.name; const dtype = spec.dtype; const shape = spec.shape; const size = sizeFromShape(shape); let values; let offset = 0; if ('quantization' in spec) { const quantization = spec.quantization; if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') { if (!('min' in quantization && 'scale' in quantization)) { throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} ` + `doesn't have corresponding metadata min and scale.`); } } else if (quantization.dtype === 'float16') { if (dtype !== 'float32') { throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} ` + `which only supports weights of type float32 not ${dtype}.`); } } else { throw new Error(`Weight ${spec.name} has unknown ` + `quantization dtype ${quantization.dtype}. ` + `Supported quantization dtypes are: ` + `'uint8', 'uint16', and 'float16'.`); } const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype]; const quantizedArray = (quantization.dtype === 'uint8') ? new Uint8Array(byteBuffer) : new Uint16Array(byteBuffer); if (dtype === 'float32') { if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') { values = new Float32Array(quantizedArray.length); for (let i = 0; i < quantizedArray.length; i++) { const v = quantizedArray[i]; values[i] = v * quantization.scale + quantization.min; } } else if (quantization.dtype === 'float16') { // TODO: This is inefficient. Make getFloat16Decoder efficient. const float16Decode = getFloat16Decoder(); values = float16Decode(quantizedArray); } else { throw new Error(`Unsupported quantization type ${quantization.dtype} ` + `for weight type float32.`); } } else if (dtype === 'int32') { if (quantization.dtype !== 'uint8' && quantization.dtype !== 'uint16') { throw new Error(`Unsupported quantization type ${quantization.dtype} ` + `for weight type int32.`); } values = new Int32Array(quantizedArray.length); for (let i = 0; i < quantizedArray.length; i++) { const v = quantizedArray[i]; values[i] = Math.round(v * quantization.scale + quantization.min); } } else { throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`); } offset += size * quantizationSizeFactor; } else if (dtype === 'string') { const size = sizeFromShape(spec.shape); values = []; for (let i = 0; i < size; i++) { const byteLength = new Uint32Array(byteBuffer.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0]; offset += NUM_BYTES_STRING_LENGTH; const bytes = new Uint8Array(byteBuffer.slice(offset, offset + byteLength)); values.push(bytes); offset += byteLength; } } else { const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype]; if (dtype === 'float32') { values = new Float32Array(byteBuffer); } else if (dtype === 'int32') { values = new Int32Array(byteBuffer); } else if (dtype === 'bool') { values = new Uint8Array(byteBuffer); } else if (dtype === 'complex64') { values = new Float32Array(byteBuffer); const real = new Float32Array(values.length / 2); const image = new Float32Array(values.length / 2); for (let i = 0; i < real.length; i++) { real[i] = values[i * 2]; image[i] = values[i * 2 + 1]; } const realTensor = tensor(real, shape, 'float32'); const imageTensor = tensor(image, shape, 'float32'); const complexTensor = complex(realTensor, imageTensor); realTensor.dispose(); imageTensor.dispose(); return complexTensor; } else { throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`); } offset += size * dtypeFactor; } return tensor(values, shape, dtype); } async function readToLength(reader, initialData, length) { let data = new Uint8Array(initialData); while (data.byteLength < length) { const { done, value } = await reader.read(); if (done && value == null) { const missing = length - data.byteLength; throw new Error(`Reader is done but ${missing} bytes are still expected`); } // TODO: Don't create a new array every loop. const newData = new Uint8Array(data.length + value.byteLength); newData.set(data, 0); newData.set(new Uint8Array(value), data.length); data = newData; } return data.buffer; } async function decodeWeightsStream(weightStream, specs) { const tensors = {}; const reader = weightStream.getReader(); let data = new ArrayBuffer(0); for (const spec of specs) { const byteLength = await getWeightBytelengthAsync(spec, async (start, end) => { data = await readToLength(reader, data, end); return data.slice(start, end); }); data = await readToLength(reader, data, byteLength); // Slice the tensor out const tensorData = data.slice(0, byteLength); data = data.slice(byteLength); const weightTensor = decodeWeight(spec, tensorData); tensors[spec.name] = weightTensor; // TODO(mattsoulanille): Better way to call uploadToGPU. // TODO(mattsoulanille): Make this work for webgl too. if (getBackend() === 'webgpu') { const b = backend(); if ('uploadToGPU' in b && sizeFromShape(weightTensor.shape) >= env() .get('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD')) { b.uploadToGPU(weightTensor.dataId); } } } return tensors; } /** * Computes mantisa table for casting Float16 to Float32 * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf * * @returns Uint32Array, 2048 mantissa lookup values. */ function computeFloat16MantisaTable() { const convertMantissa = (i) => { let m = i << 13; let e = 0; while ((m & 0x00800000) === 0) { e -= 0x00800000; m <<= 1; } m &= ~0x00800000; e += 0x38800000; return m | e; }; const mantisaTable = new Uint32Array(2048); mantisaTable[0] = 0; for (let i = 1; i < 1024; i++) { mantisaTable[i] = convertMantissa(i); } for (let i = 1024; i < 2048; i++) { mantisaTable[i] = 0x38000000 + ((i - 1024) << 13); } return mantisaTable; } /** * Computes exponent table for casting Float16 to Float32 * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf * * @returns Uint32Array, 64 exponent lookup values. */ function computeFloat16ExponentTable() { const exponentTable = new Uint32Array(64); exponentTable[0] = 0; exponentTable[31] = 0x47800000; exponentTable[32] = 0x80000000; exponentTable[63] = 0xc7800000; for (let i = 1; i < 31; i++) { exponentTable[i] = i << 23; } for (let i = 33; i < 63; i++) { exponentTable[i] = 0x80000000 + ((i - 32) << 23); } return exponentTable; } /** * Computes offset table for casting Float16 to Float32 * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf * * @returns Uint32Array, 6d offset values. */ function computeFloat16OffsetTable() { const offsetTable = new Uint32Array(64); for (let i = 0; i < 64; i++) { offsetTable[i] = 1024; } offsetTable[0] = offsetTable[32] = 0; return offsetTable; } /** * Retrieve a Float16 decoder which will decode a ByteArray of Float16 values * to a Float32Array. * * @returns Function (buffer: Uint16Array) => Float32Array which decodes * the Uint16Array of Float16 bytes to a Float32Array. */ function getFloat16Decoder() { // Algorithm is based off of // http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf // Cache lookup tables const mantisaTable = computeFloat16MantisaTable(); const exponentTable = computeFloat16ExponentTable(); const offsetTable = computeFloat16OffsetTable(); return (quantizedArray) => { const buffer = new ArrayBuffer(4 * quantizedArray.length); const bufferUint32View = new Uint32Array(buffer); for (let index = 0; index < quantizedArray.length; index++) { const float16Bits = quantizedArray[index]; const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 0x3ff)] + exponentTable[float16Bits >> 10]; bufferUint32View[index] = float32Bits; } return new Float32Array(buffer); }; } /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ const TFHUB_SEARCH_PARAM = '?tfjs-format=file'; const DEFAULT_MODEL_NAME = 'model.json'; /** * A `tf.GraphModel` is a directed, acyclic graph built from a * SavedModel GraphDef and allows inference execution. * * A `tf.GraphModel` can only be created by loading from a model converted from * a [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) using * the command line converter tool and loaded via `tf.loadGraphModel`. * * @doc {heading: 'Models', subheading: 'Classes'} */ class GraphModel { // Returns the version information for the tensorflow model GraphDef. get modelVersion() { return this.version; } get inputNodes() { return this.executor.inputNodes; } get outputNodes() { return this.executor.outputNodes; } get inputs() { return this.executor.inputs; } get outputs() { return this.executor.outputs; } get weights() { return this.executor.weightMap; } get metadata() { return this.artifacts.userDefinedMetadata; } get modelSignature() { return this.signature; } get modelStructuredOutputKeys() { return this.structuredOutputKeys; } /** * @param modelUrl url for the model, or an `io.IOHandler`. * @param weightManifestUrl url for the weight file generated by * scripts/convert.py script. * @param requestOption options for Request, which allows to send credentials * and custom headers. * @param onProgress Optional, progress callback function, fired periodically * before the load is completed. */ constructor(modelUrl, loadOptions = {}, tfio = tfc.io) { this.modelUrl = modelUrl; this.loadOptions = loadOptions; this.version = 'n/a'; this.io = tfio; if (loadOptions == null) { this.loadOptions = {}; } this.resourceManager = new ResourceManager(); } findIOHandler() { const path = this.modelUrl; if (path.load != null) { // Path is an IO Handler. this.handler = path; } else if (this.loadOptions.requestInit != null) { this.handler = this.io.browserHTTPRequest(path, this.loadOptions); } else { const handlers = this.io.getLoadHandlers(path, this.loadOptions); if (handlers.length === 0) { // For backward compatibility: if no load handler can be found, // assume it is a relative http path. handlers.push(this.io.browserHTTPRequest(path, this.loadOptions)); } else if (handlers.length > 1) { throw new Error(`Found more than one (${handlers.length}) load handlers for ` + `URL '${[path]}'`); } this.handler = handlers[0]; } } /** * Loads the model and weight files, construct the in memory weight map and * compile the inference graph. */ load() { this.findIOHandler(); if (this.handler.load == null) { throw new Error('Cannot proceed with model loading because the IOHandler provided ' + 'does not have the `load` method implemented.'); } const loadResult = this.handler.load(); if (tfc.util.isPromise(loadResult)) { return loadResult.then(artifacts => { if (artifacts.getWeightStream == null) { return this.loadSync(artifacts); } return this.loadStreaming(artifacts); }); } return this.loadSync(loadResult); } /** * Synchronously construct the in memory weight map and * compile the inference graph. * * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true} */ loadSync(artifacts) { const weightMap = this.io.decodeWeights(artifacts.weightData, artifacts.weightSpecs); return this.loadWithWeightMap(artifacts, weightMap); } async loadStreaming(artifacts) { if (artifacts.getWeightStream == null) { throw new Error('Model artifacts missing streamWeights function'); } const weightMap = await decodeWeightsStream(artifacts.getWeightStream(), artifacts.weightSpecs); return this.loadWithWeightMap(artifacts, weightMap); } loadWithWeightMap(artifacts, weightMap) { this.artifacts = artifacts; const graph = this.artifacts.modelTopology; let signature = this.artifacts.signature; if (this.artifacts.userDefinedMetadata != null) { const metadata = this.artifacts.userDefinedMetadata; if (metadata.signature != null) { signature = metadata.signature; } if (metadata.structuredOutputKeys != null) { this.structuredOutputKeys = metadata.structuredOutputKeys; } } this.signature = signature; this.version = `${graph.versions.producer}.${graph.versions.minConsumer}`; this.executor = new GraphExecutor(OperationMapper.Instance.transformGraph(graph, this.signature)); this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap); // Attach a model-level resourceManager to each executor to share resources, // such as `HashTable`. this.executor.resourceManager = this.resourceManager; if (artifacts.modelInitializer != null && artifacts.modelInitializer.node != null) { const initializer = OperationMapper.Instance.transformGraph(artifacts.modelInitializer); this.initializer = new GraphExecutor(initializer); this.initializer.weightMap = this.executor.weightMap; // Attach a model-level resourceManager to the initializer, the // hashTables created from when executing the initializer will be stored // in the resourceManager. this.initializer.resourceManager = this.resourceManager; this.initializerSignature = artifacts.initializerSignature; } return true; } /** * Save the configuration and/or weights of the GraphModel. * * An `IOHandler` is an object that has a `save` method of the proper * signature defined. The `save` method manages the storing or * transmission of serialized data ("artifacts") that represent the * model's topology and weights onto or via a specific medium, such as * file downloads, local storage, IndexedDB in the web browser and HTTP * requests to a server. TensorFlow.js provides `IOHandler` * implementations for a number of frequently used saving mediums, such as * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io` * for more details. * * This method also allows you to refer to certain types of `IOHandler`s * as URL-like string shortcuts, such as 'localstorage://' and * 'indexeddb://'. * * Example 1: Save `model`'s topology and weights to browser [local * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage); * then load it back. * * ```js * const modelUrl = * 'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json'; * const model = await tf.loadGraphModel(modelUrl); * const zeros = tf.zeros([1, 224, 224, 3]); * model.predict(zeros).print(); * * const saveResults = await model.save('localstorage://my-model-1'); * * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1'); * console.log('Prediction from loaded model:'); * model.predict(zeros).print(); * ``` * * @param handlerOrURL An instance of `IOHandler` or a URL-like, * scheme-based string shortcut for `IOHandler`. * @param config Options for saving the model. * @returns A `Promise` of `SaveResult`, which summarizes the result of * the saving, such as byte sizes of the saved artifacts for the model's * topology and weight values. * * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true} */ async save(handlerOrURL, config) { if (typeof handlerOrURL === 'string') { const handlers = this.io.getSaveHandlers(handlerOrURL); if (handlers.length === 0) { throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`); } else if (handlers.length > 1) { throw new Error(`Found more than one (${handlers.length}) save handlers for ` + `URL '${handlerOrURL}'`); } handlerOrURL = handlers[0]; } if (handlerOrURL.save == null) { throw new Error('GraphModel.save() cannot proceed because the IOHandler ' + 'provided does not have the `save` attribute defined.'); } return handlerOrURL.save(this.artifacts); } addStructuredOutputNames(outputTensors) { if (this.structuredOutputKeys) { const outputTensorsArray = outputTensors instanceof tfc.Tensor ? [outputTensors] : outputTensors; const outputTensorMap = {}; outputTensorsArray.forEach((outputTensor, i) => outputTensorMap[this.structuredOutputKeys[i]] = outputTensor); return outputTensorMap; } return outputTensors; } /** * Execute the inference for the input tensors. * * @param input The input tensors, when there is single input for the model, * inputs param should be a `tf.Tensor`. For models with mutliple inputs, * inputs params should be in either `tf.Tensor`[] if the input order is * fixed, or otherwise NamedTensorMap format. * * For model with multiple inputs, we recommend you use NamedTensorMap as the * input type, if you use `tf.Tensor`[], the order of the array needs to * follow the * order of inputNodes array. @see {@link GraphModel.inputNodes} * * You can also feed any intermediate nodes using the NamedTensorMap as the * input type. For example, given the graph * InputNode => Intermediate => OutputNode, * you can execute the subgraph Intermediate => OutputNode by calling * model.execute('IntermediateNode' : tf.tensor(...)); * * This is useful for models that uses tf.dynamic_rnn, where the intermediate * state needs to be fed manually. * * For batch inference execution, the tensors for each input need to be * concatenated together. For example with mobilenet, the required input shape * is [1, 244, 244, 3], which represents the [batch, height, width, channel]. * If we are provide a batched data of 100 images, the input tensor should be * in the shape of [100, 244, 244, 3]. * * @param config Prediction configuration for specifying the batch size. * Currently the batch size option is ignored for graph model. * * @returns Inference result tensors. If the model is converted and it * originally had structured_outputs in tensorflow, then a NamedTensorMap * will be returned matching the structured_outputs. If no structured_outputs * are present, the output will be single `tf.Tensor` if the model has single * output node, otherwise Tensor[]. * * @doc {heading: 'Models', subheading: 'Classes'} */ predict(inputs, config) { const outputTensors = this.execute(inputs, this.outputNodes); return this.addStructuredOutputNames(outputTensors); } /** * Execute the inference for the input tensors in async fashion, use this * method when your model contains control flow ops. * * @param input The input tensors, when there is single input for the model, * inputs param should be a `tf.Tensor`. For models with mutliple inputs, * inputs params should be in either `tf.Tensor`[] if the input order is * fixed, or otherwise NamedTensorMap format. * * For model with multiple inputs, we recommend you use NamedTensorMap as the * input type, if you use `tf.Tensor`[], the order of the array needs to * follow the * order of inputNodes array. @see {@link GraphModel.inputNodes} * * You can also feed any intermediate nodes using the NamedTensorMap as the * input type. For example, given the graph * InputNode => Intermediate => OutputNode, * you can execute the subgraph Intermediate => OutputNode by calling * model.execute('IntermediateNode' : tf.tensor(...)); * * This is useful for models that uses tf.dynamic_rnn, where the intermediate * state needs to be fed manually. * * For batch inference execution, the tensors for each input need to be * concatenated together. For example with mobilenet, the required input shape * is [1, 244, 244, 3], which represents the [batch, height, width, channel]. * If we are provide a batched data of 100 images, the input tensor should be * in the shape of [100, 244, 244, 3]. * * @param config Prediction configuration for specifying the batch size. * Currently the batch size option is ignored for graph model. * * @returns A Promise of inference result tensors. If the model is converted * and it originally had structured_outputs in tensorflow, then a * NamedTensorMap will be returned matching the structured_outputs. If no * structured_outputs are present, the output will be single `tf.Tensor` if * the model has single output node, otherwise Tensor[]. * * @doc {heading: 'Models', subheading: 'Classes'} */ async predictAsync(inputs, config) { const outputTensors = await this.executeAsync(inputs, this.outputNodes); return this.addStructuredOutputNames(outputTensors); } normalizeInputs(inputs) { var _a; if (!(inputs instanceof tfc.Tensor) && !Array.isArray(inputs)) { // The input is already a NamedTensorMap. const signatureInputs = (_a = this.signature) === null || _a === void 0 ? void 0 : _a.inputs; if (signatureInputs != null) { for (const input in signatureInputs) { const tensor = signatureInputs[input]; if (tensor.resourceId != null) { inputs[input] = this.resourceIdToCapturedInput[tensor.resourceId]; } } } return inputs; } inputs = Array.isArray(inputs) ? inputs : [inputs]; const numCapturedInputs = Object.keys(this.resourceIdToCapturedInput).length; if (inputs.length + numCapturedInputs !== this.inputNodes.length) { throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length - numCapturedInputs} non-resource placeholders, while there are ${inputs.length} input tensors provided.`); } let inputIndex = 0; return this.inputNodes.reduce((map, inputName) => { var _a, _b, _c; const resourceId = (_c = (_b = (_a = this.signature) === null || _a === void 0 ? void 0 : _a.inputs) === null || _b === void 0 ? void 0 : _b[inputName]) === null || _c === void 0 ? void 0 : _c.resourceId; if (resourceId != null) { map[inputName] = this.resourceIdToCapturedInput[resourceId]; } else { map[inputName] = inputs[inputIndex++]; } return map; }, {}); } normalizeOutputs(outputs) { outputs = outputs || this.outputNodes; return !Array.isArray(outputs) ? [outputs] : outputs; } executeInitializerGraph() { if (this.initializer == null) { return []; } if (this.initializerSignature == null) { return this.initializer.execute({}, []); } else { return this.initializer.execute({}, Object.keys(this.initializerSignature.outputs)); } } async executeInitializerGraphAsync() { if (this.initializer == null) { return []; } if (this.initializerSignature == null) { return this.initializer.executeAsync({}, []); } else { return this.initializer.executeAsync({}, Object.keys(this.initializerSignature.outputs)); } } setResourceIdToCapturedInput(outputs) { this.resourceIdToCapturedInput = {}; if (this.initializerSignature) { const signatureOutputs = this.initializerSignature.outputs; const outputNames = Object.keys(signatureOutputs); for (let i = 0; i < outputNames.length; i++) { const outputName = outputNames[i]; const tensorInfo = signatureOutputs[outputName]; this.resourceIdToCapturedInput[tensorInfo.resourceId] = outputs[i]; } } } /** * Executes inference for the model for given input tensors. * @param inputs tensor, tensor array or tensor map of the inputs for the * model, keyed by the input node names. * @param outputs output node name from the TensorFlow model, if no * outputs are specified, the default outputs of the model would be used. * You can inspect intermediate nodes of the model by adding them to the * outputs array. * * @returns A single tensor if provided with a single output or no outputs * are provided and there is only one default output, otherwise return a * tensor array. The order of the tensor array is the same as the outputs * if provided, otherwise the order of outputNodes attribute of the model. * * @doc {heading: 'Models', subheading: 'Classes'} */ execute(inputs, outputs) { if (this.resourceIdToCapturedInput == null) { this.setResourceIdToCapturedInput(this.executeInitializerGraph()); } inputs = this.normalizeInputs(inputs); outputs = this.normalizeOutputs(outputs); const result = this.executor.execute(inputs, outputs); return result.length > 1 ? result : result[0]; } /** * Executes inference for the model for given input tensors in async * fashion, use this method when your model contains control flow ops. * @param inputs tensor, tensor array or tensor map of the inputs for the * model, keyed by the input node names. * @param outputs output node name from the TensorFlow model, if no outputs * are specified, the default outputs of the model would be used. You can * inspect intermediate nodes of the model by adding them to the outputs * array. * * @returns A Promise of single tensor if provided with a single output or * no outputs are provided and there is only one default output, otherwise * return a tensor map. * * @doc {heading: 'Models', subheading: 'Classes'} */ async executeAsync(inputs, outputs) { if (this.resourceIdToCapturedInput == null) { this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()); } inputs = this.normalizeInputs(inputs); outputs = this.normalizeOutputs(outputs); const result = await this.executor.executeAsync(inputs, outputs); return result.length > 1 ? result : result[0]; } /** * Get intermediate tensors for model debugging mode (flag * KEEP_INTERMEDIATE_TENSORS is true). * * @doc {heading: 'Models', subheading: 'Classes'} */ getIntermediateTensors() { return this.executor.getIntermediateTensors(); } /** * Dispose intermediate tensors for model debugging mode (flag * KEEP_INTERMEDIATE_TENSORS is true). * * @doc {heading: 'Models', subheading: 'Classes'} */ disposeIntermediateTensors() { this.executor.disposeIntermediateTensors(); } convertTensorMapToTensorsMap(map) { return Object.keys(map).reduce((newMap, key) => { newMap[key] = [map[key]]; return newMap; }, {}); } /** * Releases the memory used by the weight tensors and resourceManager. * * @doc {heading: 'Models', subheading: 'Classes'} */ dispose() { this.executor.dispose(); if (this.initializer) { this.initializer.dispose(); if (this.resourceIdToCapturedInput) { tfc.dispose(this.resourceIdToCapturedInput); } } this.resourceManager.dispose(); } } /** * Load a graph model given a URL to the model definition. * * Example of loading MobileNetV2 from a URL and making a prediction with a * zeros input: * * ```js * const modelUrl = * 'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json'; * const model = await tf.loadGraphModel(modelUrl); * const zeros = tf.zeros([1, 224, 224, 3]); * model.predict(zeros).print(); * ``` * * Example of loading MobileNetV2 from a TF Hub URL and making a prediction * with a zeros input: * * ```js * const modelUrl = * 'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/2'; * const model = await tf.loadGraphModel(modelUrl, {fromTFHub: true}); * const zeros = tf.zeros([1, 224, 224, 3]); * model.predict(zeros).print(); * ``` * @param modelUrl The url or an `io.IOHandler` that loads the model. * @param options Options for the HTTP request, which allows to send * credentials * and custom headers. * * @doc {heading: 'Models', subheading: 'Loading'} */ async function loadGraphModel(modelUrl, options = {}, tfio = tfc.io) { if (modelUrl == null) { throw new Error('modelUrl in loadGraphModel() cannot be null. Please provide a url ' + 'or an IOHandler that loads the model'); } if (options == null) { options = {}; } if (options.fromTFHub && typeof modelUrl === 'string') { modelUrl = getTFHubUrl(modelUrl); } const model = new GraphModel(modelUrl, options, tfio); await model.load(); return model; } /** * Load a graph model given a synchronous IO handler with a 'load' method. * * @param modelSource The `io.IOHandlerSync` that loads the model, or the * `io.ModelArtifacts` that encode the model, or a tuple of * `[io.ModelJSON, ArrayBuffer]` of which the first element encodes the * model and the second contains the weights. * * @doc {heading: 'Models', subheading: 'Loading'} */ function loadGraphModelSync(modelSource) { if (modelSource == null) { throw new Error('modelUrl in loadGraphModelSync() cannot be null. Please provide ' + 'model artifacts or an IOHandler that loads the model'); } let ioHandler; if (modelSource instanceof Array) { const [modelJSON, weights] = modelSource; if (!modelJSON) { throw new Error('modelJSON must be the first element of the array'); } if (!weights || !(weights instanceof ArrayBuffer)) { throw new Error('An ArrayBuffer of weights must be the second element of' + ' the array'); } if (!('modelTopology' in modelJSON)) { throw new Error('Model JSON is missing \'modelTopology\''); } if (!('weightsManifest' in modelJSON)) { throw new Error('Model JSON is missing \'weightsManifest\''); } const weightSpecs = tfc.io.getWeightSpecs(modelJSON.weightsManifest); const modelArtifacts = tfc.io.getModelArtifactsForJSONSync(modelJSON, weightSpecs, weights); ioHandler = tfc.io.fromMemorySync(modelArtifacts); } else if ('load' in modelSource) { // Then modelSource is already an IOHandlerSync. ioHandler = modelSource; } else if ('modelTopology' in modelSource && 'weightSpecs' in modelSource && 'weightData' in modelSource) { // modelSource is of type ModelArtifacts. ioHandler = tfc.io.fromMemorySync(modelSource); } else { throw new Error('Unknown model format'); } const model = new GraphModel(ioHandler); model.load(); return model; } function getTFHubUrl(modelUrl) { if (!modelUrl.endsWith('/')) { modelUrl = (modelUrl) + '/'; } return `${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`; } /** @license See the LICENSE file. */ // This code is auto-generated, do not modify this file! const version = '4.15.0'; exports.GraphModel = GraphModel; exports.deregisterOp = deregisterOp; exports.loadGraphModel = loadGraphModel; exports.loadGraphModelSync = loadGraphModelSync; exports.registerOp = registerOp; exports.version_converter = version; })); //# sourceMappingURL=tf-converter.es2017.js.map