@tensorflow/tfjs-layers/dist/engine/dataset_fakes.js

/**
 * @license
 * Copyright 2018 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
import * as tfc from '@tensorflow/tfjs-core';
import { Dataset, LazyIterator } from './dataset_stub';
function mergeBatchSizeAndShape(batchSize, shape) {
    if (Array.isArray(shape)) {
        return [batchSize].concat(shape);
    }
    else {
        const output = {};
        for (const name in shape) {
            output[name] = [batchSize].concat(shape[name]);
        }
        return output;
    }
}
function generateRandomTensorContainer(shape) {
    let output;
    if (Array.isArray(shape)) {
        output = tfc.randomNormal(shape);
    }
    else {
        output = {};
        for (const name in shape) {
            output[name] = tfc.randomNormal(shape[name]);
        }
    }
    return output;
}
class FakeNumericIterator extends LazyIterator {
    constructor(args) {
        super();
        this.tensorIndex = 0;
        this.xBatchShape = mergeBatchSizeAndShape(args.batchSize, args.xShape);
        this.yBatchShape = mergeBatchSizeAndShape(args.batchSize, args.yShape);
        this.numBatches = args.numBatches;
        this.batchCount = 0;
        this.xTensorsFunc = args.xTensorsFunc;
        this.yTensorsFunc = args.yTensorsFunc;
        // Sanity check on the preset tensors.
        tfc.util.assert(this.xTensorsFunc == null && this.yTensorsFunc == null ||
            this.xTensorsFunc != null && this.yTensorsFunc != null, () => 'presetXTensors and presetYTensors must be both null/undefined ' +
            'or both set.');
    }
    async next() {
        const done = ++this.batchCount > this.numBatches;
        if (done) {
            return { done, value: null };
        }
        if (this.xTensorsFunc == null) {
            // Generate data randomly.
            return {
                done,
                value: done ? null : {
                    xs: generateRandomTensorContainer(this.xBatchShape),
                    ys: generateRandomTensorContainer(this.yBatchShape)
                }
            };
        }
        else {
            // Use preset tensors.
            if ((this.batchCount - 1) % this.numBatches === 0) {
                this.xTensorValues = this.xTensorsFunc();
                this.yTensorValues = this.yTensorsFunc();
                this.tensorIndex = 0;
            }
            const index = this.tensorIndex++;
            let xs;
            if (Array.isArray(this.xTensorValues)) {
                xs = this.xTensorValues[index];
                tfc.util.assert(tfc.util.arraysEqual(xs.shape, this.xBatchShape), () => `Shape mismatch: expected: ${JSON.stringify(this.xBatchShape)}; ` +
                    `actual: ${JSON.stringify(xs.shape)}`);
            }
            else {
                xs = {};
                for (const key in this.xTensorValues) {
                    xs[key] = this.xTensorValues[key][index];
                    tfc.util.assert(tfc.util.arraysEqual(xs[key].shape, this.xBatchShape), () => `Shape mismatch: expected: ${JSON.stringify(this.xBatchShape)}; ` +
                        `actual: ${JSON.stringify(xs.shape)}`);
                }
            }
            let ys;
            if (Array.isArray(this.yTensorValues)) {
                // Get preset ys tensors for single-output models.
                ys = this.yTensorValues[index];
                tfc.util.assert(tfc.util.arraysEqual(ys.shape, this.yBatchShape), () => `Shape mismatch: expected: ${JSON.stringify(this.yBatchShape)}; ` +
                    `actual: ${JSON.stringify(ys.shape)}`);
            }
            else {
                // Get preset ys tensors for multi-output models.
                ys = {};
                this.yBatchShape = this.yBatchShape;
                for (const key in this.yTensorValues) {
                    ys[key] = this.yTensorValues[key][index];
                    tfc.util.assert(tfc.util.arraysEqual(ys[key].shape, this.yBatchShape[key]), () => `Shape mismatch: expected: ${JSON.stringify(this.yBatchShape)}; ` +
                        `actual: ${JSON.stringify(ys[key].shape)}`);
                }
            }
            return { done, value: { xs, ys } };
        }
    }
}
/**
 * A fake dataset with configurable feature and target shapes.
 *
 * The batch size and # of batches are also configurable.
 *
 * The iterator from the dataset always generate random-normal float32 values.
 */
export class FakeNumericDataset extends Dataset {
    constructor(args) {
        super();
        this.args = args;
        tfc.util.assert(args.batchSize > 0 && Number.isInteger(args.batchSize), () => `batchSize must be a positive integer, but got ${args.batchSize}`);
        tfc.util.assert(args.numBatches > 0 && Number.isInteger(args.numBatches), () => `numBatches must be positive integer, but got ${args.numBatches}`);
        this.size = args.numBatches;
    }
    async iterator() {
        return new FakeNumericIterator(this.args);
    }
}
// We can't use Dataset.map(...) because we don't depend on tfjs-data here,
// so we manually transform the above {xs, ys} dataset to the [xs, ys] form.
export class FakeNumericDatasetLegacyArrayForm extends Dataset {
    constructor(args) {
        super();
        this.args = args;
        this.ds = new FakeNumericDataset(args);
    }
    async iterator() {
        const it = await this.ds.iterator();
        return new FakeNumericIteratorLegacyArrayForm(it);
    }
}
class FakeNumericIteratorLegacyArrayForm extends LazyIterator {
    constructor(it) {
        super();
        this.it = it;
    }
    async next() {
        const result = await this.it.next();
        return {
            done: result.done,
            value: result.value == null ? null : [result.value.xs, result.value.ys]
        };
    }
}
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* @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n\nimport * as tfc from '@tensorflow/tfjs-core';\n\nimport {Shape} from '../keras_format/common';\nimport {TensorOrArrayOrMap} from '../types';\n\nimport {Dataset, LazyIterator} from './dataset_stub';\nimport {FitDatasetElement} from './training_dataset';\n\nexport interface FakeDatasetArgs {\n  /**\n   * The shape(s) of the features of a single example.\n   *\n   * Use an object mapping name to shape, if more than one feature tensors\n   * are required.\n   */\n  xShape: Shape|{[name: string]: Shape};\n\n  /**\n   * The shape of the target(s) of a single exapmle.\n   */\n  yShape: Shape|{[name: string]: Shape};\n\n  /**\n   * A function that generates preset sequence of X tensors.\n   *\n   * This function is invoked each time a new iterator is created.\n   */\n  xTensorsFunc?: () => tfc.Tensor[] | {[name: string]: tfc.Tensor[]};\n\n  /**\n   * A function that generates preset sequence of Y tensors.\n   *\n   * This function is invoked each time a new iterator is created.\n   */\n  yTensorsFunc?: () => tfc.Tensor[] | {[name: string]: tfc.Tensor[]};\n\n  /**\n   * The size of each batch generated by the iterator.\n   */\n  batchSize: number;\n\n  /**\n   * The number of batches an iterator generates before declaring done to be\n   * true.\n   */\n  numBatches: number;\n}\n\nfunction mergeBatchSizeAndShape(\n    batchSize: number, shape: Shape|{[name: string]: Shape}): Shape|\n    {[name: string]: Shape} {\n  if (Array.isArray(shape)) {\n    return [batchSize].concat(shape);\n  } else {\n    const output: {[name: string]: Shape} = {};\n    for (const name in shape) {\n      output[name] = [batchSize].concat(shape[name]);\n    }\n    return output;\n  }\n}\n\nfunction generateRandomTensorContainer(shape: Shape|{[name: string]: Shape}):\n    tfc.Tensor|{[name: string]: tfc.Tensor} {\n  let output: tfc.Tensor|{[name: string]: tfc.Tensor};\n  if (Array.isArray(shape)) {\n    output = tfc.randomNormal(shape);\n  } else {\n    output = {};\n    for (const name in shape) {\n      output[name] = tfc.randomNormal(shape[name]);\n    }\n  }\n  return output;\n}\n\nclass FakeNumericIterator extends LazyIterator<FitDatasetElement> {\n  private xBatchShape: Shape|{[name: string]: Shape};\n  private yBatchShape: Shape|{[name: string]: Shape};\n  private numBatches: number;\n  private batchCount: number;\n  private xTensorsFunc: () => tfc.Tensor[] | {[name: string]: tfc.Tensor[]};\n  private yTensorsFunc: () => tfc.Tensor[] | {[name: string]: tfc.Tensor[]};\n  private xTensorValues: tfc.Tensor[]|{[name: string]: tfc.Tensor[]};\n  private yTensorValues: tfc.Tensor[]|{[name: string]: tfc.Tensor[]};\n  private tensorIndex = 0;\n\n  constructor(args: FakeDatasetArgs) {\n    super();\n    this.xBatchShape = mergeBatchSizeAndShape(args.batchSize, args.xShape);\n    this.yBatchShape = mergeBatchSizeAndShape(args.batchSize, args.yShape);\n    this.numBatches = args.numBatches;\n    this.batchCount = 0;\n    this.xTensorsFunc = args.xTensorsFunc;\n    this.yTensorsFunc = args.yTensorsFunc;\n\n    // Sanity check on the preset tensors.\n    tfc.util.assert(\n        this.xTensorsFunc == null && this.yTensorsFunc == null ||\n            this.xTensorsFunc != null && this.yTensorsFunc != null,\n        () => 'presetXTensors and presetYTensors must be both null/undefined ' +\n            'or both set.');\n  }\n\n  async next(): Promise<IteratorResult<FitDatasetElement>> {\n    const done = ++this.batchCount > this.numBatches;\n    if (done) {\n      return {done, value: null};\n    }\n    if (this.xTensorsFunc == null) {\n      // Generate data randomly.\n      return {\n        done,\n        value: done ? null : {\n          xs: generateRandomTensorContainer(this.xBatchShape),\n          ys: generateRandomTensorContainer(this.yBatchShape)\n        }\n      };\n    } else {\n      // Use preset tensors.\n      if ((this.batchCount - 1) % this.numBatches === 0) {\n        this.xTensorValues = this.xTensorsFunc();\n        this.yTensorValues = this.yTensorsFunc();\n        this.tensorIndex = 0;\n      }\n      const index = this.tensorIndex++;\n\n      let xs: tfc.Tensor|{[name: string]: tfc.Tensor};\n      if (Array.isArray(this.xTensorValues)) {\n        xs = this.xTensorValues[index];\n        tfc.util.assert(\n            tfc.util.arraysEqual(xs.shape, this.xBatchShape as Shape),\n            () => `Shape mismatch: expected: ${\n                      JSON.stringify(this.xBatchShape)}; ` +\n                `actual: ${JSON.stringify((xs as tfc.Tensor).shape)}`);\n      } else {\n        xs = {};\n        for (const key in this.xTensorValues) {\n          xs[key] = this.xTensorValues[key][index];\n          tfc.util.assert(\n              tfc.util.arraysEqual(xs[key].shape, this.xBatchShape as Shape),\n              () => `Shape mismatch: expected: ${\n                        JSON.stringify(this.xBatchShape)}; ` +\n                  `actual: ${JSON.stringify((xs as tfc.Tensor).shape)}`);\n        }\n      }\n\n      let ys: tfc.Tensor|{[name: string]: tfc.Tensor};\n      if (Array.isArray(this.yTensorValues)) {\n        // Get preset ys tensors for single-output models.\n        ys = this.yTensorValues[index];\n        tfc.util.assert(\n            tfc.util.arraysEqual(ys.shape, this.yBatchShape as Shape),\n            () => `Shape mismatch: expected: ${\n                      JSON.stringify(this.yBatchShape)}; ` +\n                `actual: ${JSON.stringify((ys as tfc.Tensor).shape)}`);\n      } else {\n        // Get preset ys tensors for multi-output models.\n        ys = {};\n        this.yBatchShape = this.yBatchShape as {[name: string]: Shape};\n        for (const key in this.yTensorValues) {\n          ys[key] = this.yTensorValues[key][index];\n          tfc.util.assert(\n              tfc.util.arraysEqual(ys[key].shape, this.yBatchShape[key]),\n              () => `Shape mismatch: expected: ${\n                        JSON.stringify(this.yBatchShape)}; ` +\n                  `actual: ${\n                        JSON.stringify(\n                            (ys as {[name: string]: tfc.Tensor})[key].shape)}`);\n        }\n      }\n\n      return {done, value: {xs, ys}};\n    }\n  }\n}\n\n/**\n * A fake dataset with configurable feature and target shapes.\n *\n * The batch size and # of batches are also configurable.\n *\n * The iterator from the dataset always generate random-normal float32 values.\n */\nexport class FakeNumericDataset extends Dataset<FitDatasetElement> {\n  constructor(readonly args: FakeDatasetArgs) {\n    super();\n    tfc.util.assert(\n        args.batchSize > 0 && Number.isInteger(args.batchSize),\n        () =>\n            `batchSize must be a positive integer, but got ${args.batchSize}`);\n    tfc.util.assert(\n        args.numBatches > 0 && Number.isInteger(args.numBatches),\n        () =>\n            `numBatches must be positive integer, but got ${args.numBatches}`);\n    this.size = args.numBatches;\n  }\n\n  async iterator(): Promise<LazyIterator<FitDatasetElement>> {\n    return new FakeNumericIterator(this.args);\n  }\n}\n\n// We can't use Dataset.map(...) because we don't depend on tfjs-data here,\n// so we manually transform the above {xs, ys} dataset to the [xs, ys] form.\nexport class FakeNumericDatasetLegacyArrayForm extends\n    Dataset<[TensorOrArrayOrMap, TensorOrArrayOrMap]> {\n  ds: FakeNumericDataset;\n  constructor(readonly args: FakeDatasetArgs) {\n    super();\n    this.ds = new FakeNumericDataset(args);\n  }\n\n  async iterator():\n      Promise<LazyIterator<[TensorOrArrayOrMap, TensorOrArrayOrMap]>> {\n    const it = await this.ds.iterator();\n    return new FakeNumericIteratorLegacyArrayForm(it);\n  }\n}\n\nclass FakeNumericIteratorLegacyArrayForm extends\n    LazyIterator<[TensorOrArrayOrMap, TensorOrArrayOrMap]> {\n  constructor(private readonly it: LazyIterator<FitDatasetElement>) {\n    super();\n  }\n\n  async next():\n      Promise<IteratorResult<[TensorOrArrayOrMap, TensorOrArrayOrMap]>> {\n    const result = await this.it.next();\n    return {\n      done: result.done,\n      value: result.value == null ? null : [result.value.xs, result.value.ys]\n    };\n  }\n}\n"]}