@handtracking.io/yoha
Version:
Yoha is currently available for the web via JavaScript. More languages will be added in the future. If you want to port Yoha to another language and need help feel free reach out.
315 lines • 39.5 kB
JavaScript
/**
* @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.
* =============================================================================
*/
import { tensor, Tensor } from '@tensorflow/tfjs-core';
import * as tfliteWebAPIClient from './tflite_web_api_client';
const TFHUB_SEARCH_PARAM = '?lite-format=tflite';
/**
* A `tflite.TFLiteModel` is built from a TFLite model flatbuffer and executable
* on TFLite interpreter. To load it, use the `loadTFLiteModel` function below.
*
* Sample usage:
*
* ```js
* // Load the MobilenetV2 tflite model from tfhub.
* const tfliteModel = tflite.loadTFLiteModel(
* 'https://tfhub.dev/tensorflow/lite-model/mobilenet_v2_1.0_224/1/metadata/1');
*
* const outputTensor = tf.tidy(() => {
* // Get pixels data from an image.
* const img = tf.browser.fromPixels(document.querySelector('img'));
* // Normalize (might also do resize here if necessary).
* const input = tf.sub(tf.div(tf.expandDims(img), 127.5), 1);
* // Run the inference.
* let outputTensor = tfliteModel.predict(input) as tf.Tensor;
* // De-normalize the result.
* return tf.mul(tf.add(outputTensor, 1), 127.5)
* });
* console.log(outputTensor);
*
* ```
*
* @doc {heading: 'Models', subheading: 'Classes'}
*/
export class TFLiteModel {
constructor(modelRunner) {
this.modelRunner = modelRunner;
}
get inputs() {
const modelInputs = this.modelRunner.getInputs();
return this.convertTFLiteTensorInfos(modelInputs);
}
get outputs() {
const modelOutputs = this.modelRunner.getOutputs();
return this.convertTFLiteTensorInfos(modelOutputs);
}
/**
* Execute the inference for the input tensors.
*
* @param inputs The input tensors, when there is single input for the model,
* inputs param should be a Tensor. For models with multiple inputs,
* inputs params should be in either Tensor[] if the input order is fixed,
* or otherwise NamedTensorMap format.
*
* @param config Prediction configuration for specifying the batch size.
* Currently this field is not used, and batch inference is not supported.
*
* @returns Inference result tensors. The output would be single Tensor if
* model has single output node, otherwise NamedTensorMap will be returned
* for model with multiple outputs. Tensor[] is not used.
*
* @doc {heading: 'Models', subheading: 'Classes'}
*/
predict(inputs, config) {
const modelInputs = this.modelRunner.getInputs();
const modelOutputs = this.modelRunner.getOutputs();
// Set model inputs from the given tensors.
// A single tensor or a tensor array.
if (inputs instanceof Tensor || Array.isArray(inputs)) {
let inputTensors;
if (inputs instanceof Tensor) {
inputTensors = [inputs];
}
else {
inputTensors = inputs;
}
if (modelInputs.length !== inputTensors.length) {
throw new Error(`The size of TFLite model inputs (${modelInputs
.length}) does not match the size of the input tensors (${inputTensors.length})`);
}
for (let i = 0; i < modelInputs.length; i++) {
this.setModelInputFromTensor(modelInputs[i], inputTensors[i]);
}
}
// Named tensors.
else {
const inputTensorNames = Object.keys(inputs);
const modelInputMap = {};
modelInputs.forEach(modelInput => {
modelInputMap[modelInput.name] = modelInput;
});
const modelInputNames = Object.keys(modelInputMap);
this.checkMapInputs(inputTensorNames, modelInputNames);
for (const name of inputTensorNames) {
this.setModelInputFromTensor(modelInputMap[name], inputs[name]);
}
}
// Run inference.
const success = this.modelRunner.infer();
if (!success) {
throw new Error('Failed running inference');
}
// Convert model outputs to tensors.
const outputTensors = {};
for (let i = 0; i < modelOutputs.length; i++) {
const modelOutput = modelOutputs[i];
let data = modelOutput.data();
// Convert TFLite tensor types that are not supported by TFJS to
// compatible types.
switch (modelOutput.dataType) {
case 'int8':
case 'int16':
case 'uint32':
data = Int32Array.from(data);
break;
case 'float64':
console.warn(`WARNING: converting output tensor from 'float64' to 'float32'`);
data = Float32Array.from(data);
break;
default:
break;
}
const outputTensor = tensor(data, this.getShapeFromTFLiteTensorInfo(modelOutput));
outputTensors[modelOutput.name] = outputTensor;
}
const names = Object.keys(outputTensors);
return names.length === 1 ? outputTensors[names[0]] : outputTensors;
}
/**
* Execute the inference for the input tensors and return activation
* values for specified output node names without batching.
*
* @param inputs The input tensors, when there is single input for the model,
* inputs param should be a Tensor. For models with multiple inputs,
* inputs params should be in either Tensor[] if the input order is fixed,
* or otherwise NamedTensorMap format.
*
* @param outputs string|string[]. List of output node names to retrieve
* activation from.
*
* @returns Activation values for the output nodes result tensors. The return
* type matches specified parameter outputs type. The output would be
* single Tensor if single output is specified, otherwise Tensor[] for
* multiple outputs.
*/
execute(inputs, outputs) {
throw new Error('execute() of TFLiteModel is not supported yet.');
}
getProfilingResults() {
return this.modelRunner.getProfilingResults();
}
getProfilingSummary() {
return this.modelRunner.getProfilingSummary();
}
setModelInputFromTensor(modelInput, tensor) {
// String and complex tensors are not supported.
if (tensor.dtype === 'string' || tensor.dtype === 'complex64') {
throw new Error(`Data type '${tensor.dtype}' not supported.`);
}
// Check shape.
//
// At this point, we've already checked that input tensors and model inputs
// have the same size.
const modelInputShape = modelInput.shape.split(',').map(dim => Number(dim));
if (!tensor.shape.every((dim, index) => modelInputShape[index] === -1 ||
modelInputShape[index] === dim)) {
throw new Error(`Input tensor shape mismatch: expect '${modelInput.shape}', got '${tensor.shape.join(',')}'.`);
}
// Check types.
switch (modelInput.dataType) {
// All 'bool' and 'int' tflite types accpet 'bool' or 'int32' tfjs types.
// Will throw error for 'float32' tfjs type.
case 'bool':
case 'int8':
case 'uint8':
case 'int16':
case 'uint32':
case 'int32':
if (tensor.dtype === 'float32') {
throw this.getDataTypeMismatchError(modelInput.dataType, tensor.dtype);
}
else if (modelInput.dataType !== tensor.dtype) {
console.warn(`WARNING: converting '${tensor.dtype}' to '${modelInput.dataType}'`);
}
break;
// All 'float' tflite types accept all tfjs types.
case 'float32':
case 'float64':
if (modelInput.dataType !== tensor.dtype) {
console.warn(`WARNING: converting '${tensor.dtype}' to '${modelInput.dataType}'`);
}
break;
default:
break;
}
const modelInputBuffer = modelInput.data();
switch (modelInput.dataType) {
case 'int8':
modelInputBuffer.set(Int8Array.from(tensor.dataSync()));
break;
case 'uint8':
case 'bool':
modelInputBuffer.set(Uint8Array.from(tensor.dataSync()));
break;
case 'int16':
modelInputBuffer.set(Int16Array.from(tensor.dataSync()));
break;
case 'int32':
modelInputBuffer.set(Int32Array.from(tensor.dataSync()));
break;
case 'uint32':
modelInputBuffer.set(Uint32Array.from(tensor.dataSync()));
break;
case 'float32':
modelInputBuffer.set(Float32Array.from(tensor.dataSync()));
break;
case 'float64':
modelInputBuffer.set(Float64Array.from(tensor.dataSync()));
break;
default:
break;
}
}
convertTFLiteTensorInfos(infos) {
return infos.map(info => {
const dtype = getDTypeFromTFLiteType(info.dataType);
return {
name: info.name,
shape: this.getShapeFromTFLiteTensorInfo(info),
dtype,
};
});
}
checkMapInputs(inputTensorNames, modelInputNames) {
const notInModel = inputTensorNames.filter(name => !modelInputNames.includes(name));
const notInInput = modelInputNames.filter(name => !inputTensorNames.includes(name));
if (notInModel.length === 0 && notInInput.length === 0) {
return;
}
const msgParts = ['The model input names don\'t match the model input names.'];
if (notInModel.length > 0) {
msgParts.push(`Names in input but missing in model: [${notInModel}].`);
}
if (notInInput.length > 0) {
msgParts.push(`Names in model but missing in inputs: [${notInInput}].`);
}
throw new Error(msgParts.join(' '));
}
getShapeFromTFLiteTensorInfo(info) {
return info.shape.split(',').map(s => Number(s));
}
getDataTypeMismatchError(expected, got) {
return new Error(`Data type mismatch: input tensor expects '${expected}', got '${got}'`);
}
}
/**
* Loads a TFLiteModel from the given model url.
*
* @param model The path to the model (string), or the model content in memory
* (ArrayBuffer).
* @param options Options related to model inference.
*
* @doc {heading: 'Models', subheading: 'Loading'}
*/
export async function loadTFLiteModel(model, options) {
// Handle tfhub links.
if (typeof model === 'string' && model.includes('tfhub.dev') &&
model.includes('lite-model') && !model.endsWith(TFHUB_SEARCH_PARAM)) {
model = `${model}${TFHUB_SEARCH_PARAM}`;
}
const tfliteModelRunner = await tfliteWebAPIClient.tfweb.TFLiteWebModelRunner.create(model, options);
return new TFLiteModel(tfliteModelRunner);
}
/**
* Returns the compatible tfjs DataType from the given TFLite data type.
*
* @param tfliteType The type in TFLite.
*
* @doc {heading: 'Models', subheading: 'Utilities'}
*/
export function getDTypeFromTFLiteType(tfliteType) {
let dtype;
switch (tfliteType) {
case 'float32':
case 'float64':
dtype = 'float32';
break;
case 'int8':
case 'uint8':
case 'int16':
case 'int32':
case 'uint32':
dtype = 'int32';
break;
case 'bool':
dtype = 'bool';
break;
default:
break;
}
return dtype;
}
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* @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, InferenceModel, ModelPredictConfig, ModelTensorInfo, NamedTensorMap, tensor, Tensor} from '@tensorflow/tfjs-core';\n\nimport * as tfliteWebAPIClient from './tflite_web_api_client';\nimport {ProfileItem, TFLiteDataType, TFLiteWebModelRunner, TFLiteWebModelRunnerOptions, TFLiteWebModelRunnerTensorInfo} from './types/tflite_web_model_runner';\n\nconst TFHUB_SEARCH_PARAM = '?lite-format=tflite';\n\n/**\n * A `tflite.TFLiteModel` is built from a TFLite model flatbuffer and executable\n * on TFLite interpreter. To load it, use the `loadTFLiteModel` function below.\n *\n * Sample usage:\n *\n * ```js\n * // Load the MobilenetV2 tflite model from tfhub.\n * const tfliteModel = tflite.loadTFLiteModel(\n *     'https://tfhub.dev/tensorflow/lite-model/mobilenet_v2_1.0_224/1/metadata/1');\n *\n * const outputTensor = tf.tidy(() => {\n *    // Get pixels data from an image.\n *    const img = tf.browser.fromPixels(document.querySelector('img'));\n *    // Normalize (might also do resize here if necessary).\n *    const input = tf.sub(tf.div(tf.expandDims(img), 127.5), 1);\n *    // Run the inference.\n *    let outputTensor = tfliteModel.predict(input) as tf.Tensor;\n *    // De-normalize the result.\n *    return tf.mul(tf.add(outputTensor, 1), 127.5)\n *  });\n * console.log(outputTensor);\n *\n * ```\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class TFLiteModel implements InferenceModel {\n  constructor(private readonly modelRunner: TFLiteWebModelRunner) {}\n\n  get inputs(): ModelTensorInfo[] {\n    const modelInputs = this.modelRunner.getInputs();\n    return this.convertTFLiteTensorInfos(modelInputs);\n  }\n\n  get outputs(): ModelTensorInfo[] {\n    const modelOutputs = this.modelRunner.getOutputs();\n    return this.convertTFLiteTensorInfos(modelOutputs);\n  }\n\n  /**\n   * Execute the inference for the input tensors.\n   *\n   * @param inputs The input tensors, when there is single input for the model,\n   *     inputs param should be a Tensor. For models with multiple inputs,\n   *     inputs params should be in either Tensor[] if the input order is fixed,\n   *     or otherwise NamedTensorMap format.\n   *\n   * @param config Prediction configuration for specifying the batch size.\n   *     Currently this field is not used, and batch inference is not supported.\n   *\n   * @returns Inference result tensors. The output would be single Tensor if\n   *     model has single output node, otherwise NamedTensorMap will be returned\n   *     for model with multiple outputs. Tensor[] is not used.\n   *\n   * @doc {heading: 'Models', subheading: 'Classes'}\n   */\n  predict(inputs: Tensor|Tensor[]|NamedTensorMap, config?: ModelPredictConfig):\n      Tensor|Tensor[]|NamedTensorMap {\n    const modelInputs = this.modelRunner.getInputs();\n    const modelOutputs = this.modelRunner.getOutputs();\n\n    // Set model inputs from the given tensors.\n\n    // A single tensor or a tensor array.\n    if (inputs instanceof Tensor || Array.isArray(inputs)) {\n      let inputTensors: Tensor[];\n      if (inputs instanceof Tensor) {\n        inputTensors = [inputs];\n      } else {\n        inputTensors = inputs;\n      }\n      if (modelInputs.length !== inputTensors.length) {\n        throw new Error(`The size of TFLite model inputs (${\n            modelInputs\n                .length}) does not match the size of the input tensors (${\n            inputTensors.length})`);\n      }\n      for (let i = 0; i < modelInputs.length; i++) {\n        this.setModelInputFromTensor(modelInputs[i], inputTensors[i]);\n      }\n    }\n    // Named tensors.\n    else {\n      const inputTensorNames = Object.keys(inputs);\n      const modelInputMap:\n          {[name: string]: TFLiteWebModelRunnerTensorInfo} = {};\n      modelInputs.forEach(modelInput => {\n        modelInputMap[modelInput.name] = modelInput;\n      });\n      const modelInputNames = Object.keys(modelInputMap);\n      this.checkMapInputs(inputTensorNames, modelInputNames);\n      for (const name of inputTensorNames) {\n        this.setModelInputFromTensor(modelInputMap[name], inputs[name]);\n      }\n    }\n\n    // Run inference.\n    const success = this.modelRunner.infer();\n    if (!success) {\n      throw new Error('Failed running inference');\n    }\n\n    // Convert model outputs to tensors.\n    const outputTensors: NamedTensorMap = {};\n    for (let i = 0; i < modelOutputs.length; i++) {\n      const modelOutput = modelOutputs[i];\n      let data = modelOutput.data();\n\n      // Convert TFLite tensor types that are not supported by TFJS to\n      // compatible types.\n      switch (modelOutput.dataType) {\n        case 'int8':\n        case 'int16':\n        case 'uint32':\n          data = Int32Array.from(data);\n          break;\n        case 'float64':\n          console.warn(\n              `WARNING: converting output tensor from 'float64' to 'float32'`);\n          data = Float32Array.from(data);\n          break;\n        default:\n          break;\n      }\n      const outputTensor =\n          tensor(data, this.getShapeFromTFLiteTensorInfo(modelOutput));\n      outputTensors[modelOutput.name] = outputTensor;\n    }\n    const names = Object.keys(outputTensors);\n    return names.length === 1 ? outputTensors[names[0]] : outputTensors;\n  }\n\n  /**\n   * Execute the inference for the input tensors and return activation\n   * values for specified output node names without batching.\n   *\n   * @param inputs The input tensors, when there is single input for the model,\n   *     inputs param should be a Tensor. For models with multiple inputs,\n   *     inputs params should be in either Tensor[] if the input order is fixed,\n   *     or otherwise NamedTensorMap format.\n   *\n   * @param outputs string|string[]. List of output node names to retrieve\n   *     activation from.\n   *\n   * @returns Activation values for the output nodes result tensors. The return\n   *     type matches specified parameter outputs type. The output would be\n   *     single Tensor if single output is specified, otherwise Tensor[] for\n   *     multiple outputs.\n   */\n  execute(inputs: Tensor|Tensor[]|NamedTensorMap, outputs: string|string[]):\n      Tensor|Tensor[] {\n    throw new Error('execute() of TFLiteModel is not supported yet.');\n  }\n\n  getProfilingResults(): ProfileItem[] {\n    return this.modelRunner.getProfilingResults();\n  }\n\n  getProfilingSummary(): string {\n    return this.modelRunner.getProfilingSummary();\n  }\n\n  private setModelInputFromTensor(\n      modelInput: TFLiteWebModelRunnerTensorInfo, tensor: Tensor) {\n    // String and complex tensors are not supported.\n    if (tensor.dtype === 'string' || tensor.dtype === 'complex64') {\n      throw new Error(`Data type '${tensor.dtype}' not supported.`);\n    }\n\n    // Check shape.\n    //\n    // At this point, we've already checked that input tensors and model inputs\n    // have the same size.\n    const modelInputShape = modelInput.shape.split(',').map(dim => Number(dim));\n    if (!tensor.shape.every(\n            (dim, index) => modelInputShape[index] === -1 ||\n                modelInputShape[index] === dim)) {\n      throw new Error(`Input tensor shape mismatch: expect '${\n          modelInput.shape}', got '${tensor.shape.join(',')}'.`);\n    }\n\n    // Check types.\n    switch (modelInput.dataType) {\n      // All 'bool' and 'int' tflite types accpet 'bool' or 'int32' tfjs types.\n      // Will throw error for 'float32' tfjs type.\n      case 'bool':\n      case 'int8':\n      case 'uint8':\n      case 'int16':\n      case 'uint32':\n      case 'int32':\n        if (tensor.dtype === 'float32') {\n          throw this.getDataTypeMismatchError(\n              modelInput.dataType, tensor.dtype);\n        } else if (modelInput.dataType !== tensor.dtype) {\n          console.warn(`WARNING: converting '${tensor.dtype}' to '${\n              modelInput.dataType}'`);\n        }\n        break;\n      // All 'float' tflite types accept all tfjs types.\n      case 'float32':\n      case 'float64':\n        if (modelInput.dataType !== tensor.dtype) {\n          console.warn(`WARNING: converting '${tensor.dtype}' to '${\n              modelInput.dataType}'`);\n        }\n        break;\n      default:\n        break;\n    }\n\n    const modelInputBuffer = modelInput.data();\n    switch (modelInput.dataType) {\n      case 'int8':\n        modelInputBuffer.set(Int8Array.from(tensor.dataSync()));\n        break;\n      case 'uint8':\n      case 'bool':\n        modelInputBuffer.set(Uint8Array.from(tensor.dataSync()));\n        break;\n      case 'int16':\n        modelInputBuffer.set(Int16Array.from(tensor.dataSync()));\n        break;\n      case 'int32':\n        modelInputBuffer.set(Int32Array.from(tensor.dataSync()));\n        break;\n      case 'uint32':\n        modelInputBuffer.set(Uint32Array.from(tensor.dataSync()));\n        break;\n      case 'float32':\n        modelInputBuffer.set(Float32Array.from(tensor.dataSync()));\n        break;\n      case 'float64':\n        modelInputBuffer.set(Float64Array.from(tensor.dataSync()));\n        break;\n      default:\n        break;\n    }\n  }\n\n  private convertTFLiteTensorInfos(infos: TFLiteWebModelRunnerTensorInfo[]):\n      ModelTensorInfo[] {\n    return infos.map(info => {\n      const dtype = getDTypeFromTFLiteType(info.dataType);\n      return {\n        name: info.name,\n        shape: this.getShapeFromTFLiteTensorInfo(info),\n        dtype,\n      };\n    });\n  }\n\n  private checkMapInputs(\n      inputTensorNames: string[], modelInputNames: string[]) {\n    const notInModel =\n        inputTensorNames.filter(name => !modelInputNames.includes(name));\n    const notInInput =\n        modelInputNames.filter(name => !inputTensorNames.includes(name));\n    if (notInModel.length === 0 && notInInput.length === 0) {\n      return;\n    }\n\n    const msgParts =\n        ['The model input names don\\'t match the model input names.'];\n    if (notInModel.length > 0) {\n      msgParts.push(`Names in input but missing in model: [${notInModel}].`);\n    }\n    if (notInInput.length > 0) {\n      msgParts.push(`Names in model but missing in inputs: [${notInInput}].`);\n    }\n    throw new Error(msgParts.join(' '));\n  }\n\n  private getShapeFromTFLiteTensorInfo(info: TFLiteWebModelRunnerTensorInfo) {\n    return info.shape.split(',').map(s => Number(s));\n  }\n\n  private getDataTypeMismatchError(expected: string, got: string) {\n    return new Error(\n        `Data type mismatch: input tensor expects '${expected}', got '${got}'`);\n  }\n}\n\n/**\n * Loads a TFLiteModel from the given model url.\n *\n * @param model The path to the model (string), or the model content in memory\n *     (ArrayBuffer).\n * @param options Options related to model inference.\n *\n * @doc {heading: 'Models', subheading: 'Loading'}\n */\nexport async function loadTFLiteModel(\n    model: string|ArrayBuffer,\n    options?: TFLiteWebModelRunnerOptions): Promise<TFLiteModel> {\n  // Handle tfhub links.\n  if (typeof model === 'string' && model.includes('tfhub.dev') &&\n      model.includes('lite-model') && !model.endsWith(TFHUB_SEARCH_PARAM)) {\n    model = `${model}${TFHUB_SEARCH_PARAM}`;\n  }\n\n  const tfliteModelRunner =\n      await tfliteWebAPIClient.tfweb.TFLiteWebModelRunner.create(\n          model, options);\n  return new TFLiteModel(tfliteModelRunner);\n}\n\n/**\n * Returns the compatible tfjs DataType from the given TFLite data type.\n *\n * @param tfliteType The type in TFLite.\n *\n * @doc {heading: 'Models', subheading: 'Utilities'}\n */\nexport function getDTypeFromTFLiteType(tfliteType: TFLiteDataType): DataType {\n  let dtype: DataType;\n  switch (tfliteType) {\n    case 'float32':\n    case 'float64':\n      dtype = 'float32';\n      break;\n    case 'int8':\n    case 'uint8':\n    case 'int16':\n    case 'int32':\n    case 'uint32':\n      dtype = 'int32';\n      break;\n    case 'bool':\n      dtype = 'bool';\n      break;\n    default:\n      break;\n  }\n  return dtype;\n}\n"]}