data2/LocalFaceRecognition.git

/**
 * @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.
 * =============================================================================
 */
/**
 * Executor: Evaluates SymbolicTensor based on feeds.
 */
import { cast, dispose, memory, util } from '@tensorflow/tfjs-core';
import { ValueError } from '../errors';
import { LruCache } from '../utils/executor_utils';
import { toList } from '../utils/generic_utils';
import { InputLayer } from './input_layer';
import { SymbolicTensor } from './topology';
/**
 * Helper function to check the dtype and shape compatibility of a feed value.
 */
function assertFeedCompatibility(key, val) {
    // Check dtype compatibility.
    if (key.dtype == null || key.dtype === val.dtype) {
        //  a.  If types match, return val tensor as is.
        return val;
    }
    try {
        //  b. Attempt to convert to expected type.
        return cast(val, key.dtype);
    }
    catch (err) {
        //  c. If conversion fails, return helpful error.
        throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype ` +
            `of the key '${key.name}' (${key.dtype}).`);
    }
}
/**
 * FeedDict: A mapping from unique SymbolicTensors to feed values for them.
 * A feed value is a concrete value represented as an `Tensor`.
 */
export class FeedDict {
    /**
     * Constructor, optionally does copy-construction.
     * @param feeds An Array of `Feed`s, or another `FeedDict`, in which case
     *   copy-construction will be performed.
     */
    constructor(feeds) {
        this.id2Value = {};
        this.id2Mask = {};
        this.name2Id = {};
        if (feeds instanceof FeedDict) {
            for (const id in feeds.id2Value) {
                this.id2Value[id] = feeds.id2Value[id];
                if (id in feeds.id2Mask) {
                    this.id2Mask[id] = feeds.id2Mask[id];
                }
            }
        }
        else {
            if (feeds == null) {
                return;
            }
            for (const feed of feeds) {
                this.add(feed.key, feed.value);
            }
        }
    }
    /**
     * Add a key-value pair to the FeedDict.
     *
     * @param key The key of the feed.
     * @param value The value of the tensor feed.
     * @param mask The value of the mask feed (optional).
     * @returns This `FeedDict`.
     * @throws ValueError: If the key `SymbolicTensor` already exists in the
     *   `FeedDict`.
     */
    add(key, value, mask) {
        if (this.id2Value[key.id] == null) {
            this.id2Value[key.id] = assertFeedCompatibility(key, value);
            this.name2Id[key.name] = key.id;
            if (mask != null) {
                this.id2Mask[key.id] = mask;
            }
        }
        else {
            throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);
        }
        return this;
    }
    /**
     * Add a Feed to the FeedDict.
     * @param feed The new `Feed` to add.
     * @returns This `FeedDict`.
     */
    addFeed(feed) {
        this.add(feed.key, feed.value);
    }
    /**
     * Probe whether a key already exists in the FeedDict.
     * @param key
     */
    hasKey(key) {
        return this.id2Value[key.id] != null;
    }
    /**
     * Get all the SymbolicTensor available in this FeedDict.
     */
    names() {
        return Object.keys(this.name2Id);
    }
    /**
     * Get the feed value for given key.
     * @param key The SymbolicTensor, or its name (as a string), of which the
     *     value is sought.
     * @returns If `key` exists, the corresponding feed value.
     * @throws ValueError: If `key` does not exist in this `FeedDict`.
     */
    getValue(key) {
        if (key instanceof SymbolicTensor) {
            if (this.id2Value[key.id] == null) {
                throw new ValueError(`Nonexistent key: ${key.name}`);
            }
            else {
                return this.id2Value[key.id];
            }
        }
        else {
            const id = this.name2Id[key];
            if (id == null) {
                throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);
            }
            return this.id2Value[id];
        }
    }
    /**
     * Get the feed mask for given key.
     * @param key The SymbolicTensor, or its name (as a string), of which the
     *     value is sought.
     * @returns If `key` exists, the corresponding feed mask.
     * @throws ValueError: If `key` does not exist in this `FeedDict`.
     */
    getMask(key) {
        if (key instanceof SymbolicTensor) {
            if (this.id2Value[key.id] == null) {
                throw new ValueError(`Nonexistent key: ${key.name}`);
            }
            else {
                return this.id2Mask[key.id];
            }
        }
        else {
            const id = this.name2Id[key];
            if (id == null) {
                throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);
            }
            return this.id2Mask[id];
        }
    }
    /** Dispose all mask Tensors held by this object. */
    disposeMasks() {
        if (this.id2Mask != null) {
            dispose(this.id2Mask);
        }
    }
}
// Cache for topologically sorted SymbolicTensors for given execution
// targets (i.e., fetches).
export const cachedSorted = new LruCache();
// Cache for recipient count maps for given execution targets (i.e., fetches).
export const cachedRecipientCounts = new LruCache();
export function updateCacheMaxEntries(maxEntries) {
    if (cachedSorted != null) {
        cachedSorted.setMaxEntries(maxEntries);
    }
    if (cachedRecipientCounts != null) {
        cachedRecipientCounts.setMaxEntries(maxEntries);
    }
}
/**
 * Execute a SymbolicTensor by using concrete feed values.
 *
 * A `SymbolicTensor` object is a node in a computation graph of TF.js
 * Layers. The object is backed by a source layer and input
 * `SymbolicTensor`s to the source layer. This method evaluates
 * the `call()` method of the source layer, using concrete values of the
 * inputs obtained from either
 * * `feedDict`, if the input key exists in `feedDict`, or else,
 * * a recursive call to `execute()` itself.
 *
 * @param x: The `SymbolicTensor` to execute.
 * @param feedDict: The feed values, as base condition of the recursion.
 *   execution.
 * @param kwargs: Optional keyword arguments.
 * @param probe: A probe object (of interface `ExecutionProbe`) used for
 *   testing memory footprint of `execute` calls.
 * @returns Result of the execution.
 * @throws ValueError: If any `SymbolicTensor`s from `InputLayer`s
 *   encountered during the execution lacks a feed value in `feedDict`.
 */
export function execute(fetches, feedDict, kwargs, probe) {
    const training = kwargs == null ? false : kwargs['training'];
    const arrayFetches = Array.isArray(fetches);
    const fetchArray = arrayFetches ? fetches : [fetches];
    const outputNames = fetchArray.map(t => t.name);
    const finalOutputs = [];
    const feedNames = feedDict.names();
    for (const outputName of outputNames) {
        if (feedNames.indexOf(outputName) !== -1) {
            finalOutputs.push(feedDict.getValue(outputName));
        }
        else {
            finalOutputs.push(null);
        }
    }
    if (probe != null) {
        // For optional probing of memory footprint during execution.
        probe.maxNumTensors = -Infinity;
        probe.minNumTensors = Infinity;
    }
    // Check cache.
    const fetchAndFeedKey = outputNames.join(',') + '|' + feedDict.names().sort().join(',');
    let sorted = cachedSorted.get(fetchAndFeedKey);
    let recipientCounts;
    if (sorted == null) {
        // Cache doesn't contain the desired combination of fetches. Compute
        // topological sort for the combination for the first time.
        const out = getTopologicalSortAndRecipientCounts(fetchArray, feedDict);
        sorted = out.sorted;
        recipientCounts = out.recipientCounts;
        // Store results in cache for future use.
        cachedSorted.put(fetchAndFeedKey, sorted);
        cachedRecipientCounts.put(fetchAndFeedKey, recipientCounts);
    }
    recipientCounts = {};
    if (!training) {
        Object.assign(recipientCounts, cachedRecipientCounts.get(fetchAndFeedKey));
    }
    const internalFeedDict = new FeedDict(feedDict);
    // Start iterative execution on the topologically-sorted SymbolicTensors.
    for (let i = 0; i < sorted.length; ++i) {
        if (probe != null) {
            // For optional probing of memory usage during execution.
            const numTensors = memory().numTensors;
            if (numTensors > probe.maxNumTensors) {
                probe.maxNumTensors = numTensors;
            }
            if (numTensors < probe.minNumTensors) {
                probe.minNumTensors = numTensors;
            }
        }
        const symbolic = sorted[i];
        const srcLayer = symbolic.sourceLayer;
        if (srcLayer instanceof InputLayer) {
            continue;
        }
        const inputValues = [];
        const inputMasks = [];
        const tensorsToDispose = [];
        let maskExists = false;
        for (const input of symbolic.inputs) {
            const value = internalFeedDict.getValue(input);
            const mask = internalFeedDict.getMask(input);
            inputValues.push(value);
            inputMasks.push(mask);
            if (mask != null) {
                maskExists = true;
            }
            if (!training) {
                recipientCounts[input.name]--;
                if (recipientCounts[input.name] === 0 && !feedDict.hasKey(input) &&
                    outputNames.indexOf(input.name) === -1 && !value.isDisposed &&
                    input.sourceLayer.stateful !== true) {
                    tensorsToDispose.push(value);
                }
            }
        }
        if (maskExists) {
            kwargs = kwargs || {};
            kwargs['mask'] = inputMasks[0];
        }
        const outputTensors = toList(srcLayer.apply(inputValues, kwargs));
        let outputMask = null;
        if (srcLayer.supportsMasking) {
            outputMask = srcLayer.computeMask(inputValues, inputMasks);
        }
        const layerOutputs = getNodeOutputs(symbolic);
        const outputSymbolicTensors = Array.isArray(layerOutputs) ? layerOutputs : [layerOutputs];
        for (let i = 0; i < outputSymbolicTensors.length; ++i) {
            if (!internalFeedDict.hasKey(outputSymbolicTensors[i])) {
                internalFeedDict.add(outputSymbolicTensors[i], outputTensors[i], Array.isArray(outputMask) ? outputMask[0] : outputMask);
            }
            const index = outputNames.indexOf(outputSymbolicTensors[i].name);
            if (index !== -1) {
                finalOutputs[index] = outputTensors[i];
            }
        }
        if (!training) {
            // Clean up Tensors that are no longer needed.
            dispose(tensorsToDispose);
        }
    }
    // NOTE(cais): Unlike intermediate tensors, we don't discard mask
    // tensors as we go, because these tensors are sometimes passed over a
    // series of mutliple layers, i.e., not obeying the immediate input
    // relations in the graph. If this becomes a memory-usage concern,
    // we can improve this in the future.
    internalFeedDict.disposeMasks();
    return arrayFetches ? finalOutputs : finalOutputs[0];
}
/**
 * Sort the `SymbolicTensor`s topologically, for an array of fetches.
 *
 * This function calls getTopologicalSortAndRecipientCountsForOneFetch and
 * merges their results.
 *
 * @param fetch The array of fetches requested. Must be a non-empty array.
 * @param feedDict The dictionary of fed values.
 * @returns sorted: Topologically-sorted array of SymbolicTensors.
 *   recipientCounts: Recipient counts for all SymbolicTensors in `sorted`.
 */
function getTopologicalSortAndRecipientCounts(fetches, feedDict) {
    util.assert(fetches != null && fetches.length > 0, () => `Expected at least one fetch, got none`);
    let finalSorted = [];
    let finalRecipientMap = {};
    if (fetches.length === 1) {
        // Special-casing 1 fetch for efficiency.
        const out = getTopologicalSortAndRecipientCountsForOneFetch(fetches[0], feedDict);
        finalSorted = out.sorted;
        finalRecipientMap = out.recipientMap;
    }
    else {
        const visited = new Set();
        for (const fetch of fetches) {
            const { sorted, recipientMap } = getTopologicalSortAndRecipientCountsForOneFetch(fetch, feedDict);
            // Merge sorted SymbolicTensor Arrays.
            for (const symbolicTensor of sorted) {
                if (!visited.has(symbolicTensor.name)) {
                    finalSorted.push(symbolicTensor);
                    visited.add(symbolicTensor.name);
                }
            }
            // Merge recipient maps.
            for (const name in recipientMap) {
                if (finalRecipientMap[name] == null) {
                    finalRecipientMap[name] = new Set();
                }
                recipientMap[name].forEach(recipient => finalRecipientMap[name].add(recipient));
            }
        }
    }
    return {
        sorted: finalSorted,
        recipientCounts: recipientMap2Counts(finalRecipientMap)
    };
}
function recipientMap2Counts(recipientMap) {
    const recipientCounts = {};
    for (const name in recipientMap) {
        recipientCounts[name] = recipientMap[name].size;
    }
    return recipientCounts;
}
/**
 * Sort the `SymbolicTensor`s topologically, for a single fetch.
 *
 * This helper function processes the upstream SymbolicTensors of a single
 * fetch.
 *
 * @param fetch The single fetch requested.
 * @param feedDict The dictionary of fed values.
 * @returns sorted: Topologically-sorted array of SymbolicTensors.
 *   recipientMap: Recipient names for all SymbolicTensors in `sorted`.
 */
export function getTopologicalSortAndRecipientCountsForOneFetch(fetch, feedDict) {
    const visited = new Set();
    const sorted = [];
    const recipientMap = {};
    // Put keys of the feedDict into visited first, so they don't have to be
    // walked. This is needed in case where there are feeds for intermediate
    // SymbolicTensors of the graph.
    for (const key of feedDict.names()) {
        visited.add(key);
    }
    const stack = [];
    const marks = [];
    // Initial population of stack and marks.
    stack.push(fetch);
    while (stack.length > 0) {
        const top = stack[stack.length - 1];
        if (visited.has(top.name)) {
            stack.pop();
            continue;
        }
        const topIsMarked = marks[marks.length - 1] === stack.length - 1;
        if (top.inputs.length === 0 || topIsMarked) {
            // Input SymbolicTensor or all children have been visited.
            stack.pop();
            sorted.push(top);
            visited.add(top.name);
            if (topIsMarked) {
                marks.pop();
            }
        }
        else {
            // A non-input SymbolicTensor whose upstream SymbolicTensors haven't
            // been visited yet. Push them onto the stack.
            marks.push(stack.length - 1);
            for (const input of top.inputs) {
                // Increment the recipient count. Note that this needs to happen
                // regardless of whether the SymbolicTensor has been visited before.
                if (recipientMap[input.name] == null) {
                    recipientMap[input.name] = new Set();
                }
                recipientMap[input.name].add(top.name);
                if (visited.has(input.name)) {
                    continue; // Avoid repeated visits to the same SymbolicTensor.
                }
                stack.push(input);
            }
        }
    }
    return { sorted, recipientMap };
}
/**
 * Get the symbolic output tensors of the node to which a given fetch belongs.
 * @param fetch The fetched symbolic tensor.
 * @returns The Array of symbolic tensors output by the node to which `fetch`
 *   belongs.
 */
function getNodeOutputs(fetch) {
    let layerOutputs;
    if (fetch.sourceLayer.inboundNodes.length === 1) {
        layerOutputs = fetch.sourceLayer.output;
    }
    else {
        let nodeIndex = null;
        for (let i = 0; i < fetch.sourceLayer.inboundNodes.length; ++i) {
            for (const outputTensor of fetch.sourceLayer.inboundNodes[i]
                .outputTensors) {
                if (outputTensor.id === fetch.id) {
                    nodeIndex = i;
                    break;
                }
            }
        }
        layerOutputs = fetch.sourceLayer.getOutputAt(nodeIndex);
    }
    return layerOutputs;
}
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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\n/**\n * Executor: Evaluates SymbolicTensor based on feeds.\n */\n\nimport {cast, dispose, memory, Tensor, util} from '@tensorflow/tfjs-core';\n\nimport {ValueError} from '../errors';\nimport {Kwargs} from '../types';\nimport {LruCache} from '../utils/executor_utils';\nimport {toList} from '../utils/generic_utils';\n\nimport {InputLayer} from './input_layer';\nimport {SymbolicTensor} from './topology';\n\n/**\n * Helper function to check the dtype and shape compatibility of a feed value.\n */\nfunction assertFeedCompatibility(key: SymbolicTensor, val: Tensor): Tensor {\n  // Check dtype compatibility.\n  if (key.dtype == null || key.dtype === val.dtype) {\n    //  a.  If types match, return val tensor as is.\n    return val;\n  }\n  try {\n    //  b. Attempt to convert to expected type.\n    return cast(val, key.dtype);\n  } catch (err) {\n    //  c. If conversion fails, return helpful error.\n    throw new ValueError(\n        `The dtype of the feed (${val.dtype}) can not be cast to the dtype ` +\n        `of the key '${key.name}' (${key.dtype}).`);\n  }\n}\n\n/**\n * A concrete Tensor value for a symbolic tensor as the key.\n */\nexport interface Feed {\n  key: SymbolicTensor;\n  value: Tensor;\n}\n\n/**\n * FeedDict: A mapping from unique SymbolicTensors to feed values for them.\n * A feed value is a concrete value represented as an `Tensor`.\n */\nexport class FeedDict {\n  private id2Value: {[id: number]: Tensor} = {};\n  private id2Mask: {[id: number]: Tensor} = {};\n  private name2Id: {[name: string]: number} = {};\n\n  /**\n   * Constructor, optionally does copy-construction.\n   * @param feeds An Array of `Feed`s, or another `FeedDict`, in which case\n   *   copy-construction will be performed.\n   */\n  constructor(feeds?: Feed[]|FeedDict) {\n    if (feeds instanceof FeedDict) {\n      for (const id in feeds.id2Value) {\n        this.id2Value[id] = feeds.id2Value[id];\n        if (id in feeds.id2Mask) {\n          this.id2Mask[id] = feeds.id2Mask[id];\n        }\n      }\n    } else {\n      if (feeds == null) {\n        return;\n      }\n      for (const feed of feeds) {\n        this.add(feed.key, feed.value);\n      }\n    }\n  }\n\n  /**\n   * Add a key-value pair to the FeedDict.\n   *\n   * @param key The key of the feed.\n   * @param value The value of the tensor feed.\n   * @param mask The value of the mask feed (optional).\n   * @returns This `FeedDict`.\n   * @throws ValueError: If the key `SymbolicTensor` already exists in the\n   *   `FeedDict`.\n   */\n  add(key: SymbolicTensor, value: Tensor, mask?: Tensor): FeedDict {\n    if (this.id2Value[key.id] == null) {\n      this.id2Value[key.id] = assertFeedCompatibility(key, value);\n      this.name2Id[key.name] = key.id;\n      if (mask != null) {\n        this.id2Mask[key.id] = mask;\n      }\n    } else {\n      throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);\n    }\n    return this;\n  }\n\n  /**\n   * Add a Feed to the FeedDict.\n   * @param feed The new `Feed` to add.\n   * @returns This `FeedDict`.\n   */\n  addFeed(feed: Feed) {\n    this.add(feed.key, feed.value);\n  }\n\n  /**\n   * Probe whether a key already exists in the FeedDict.\n   * @param key\n   */\n  hasKey(key: SymbolicTensor): boolean {\n    return this.id2Value[key.id] != null;\n  }\n\n  /**\n   * Get all the SymbolicTensor available in this FeedDict.\n   */\n  names(): string[] {\n    return Object.keys(this.name2Id);\n  }\n\n  /**\n   * Get the feed value for given key.\n   * @param key The SymbolicTensor, or its name (as a string), of which the\n   *     value is sought.\n   * @returns If `key` exists, the corresponding feed value.\n   * @throws ValueError: If `key` does not exist in this `FeedDict`.\n   */\n  getValue(key: SymbolicTensor|string): Tensor {\n    if (key instanceof SymbolicTensor) {\n      if (this.id2Value[key.id] == null) {\n        throw new ValueError(`Nonexistent key: ${key.name}`);\n      } else {\n        return this.id2Value[key.id];\n      }\n    } else {\n      const id = this.name2Id[key];\n      if (id == null) {\n        throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n      }\n      return this.id2Value[id];\n    }\n  }\n\n  /**\n   * Get the feed mask for given key.\n   * @param key The SymbolicTensor, or its name (as a string), of which the\n   *     value is sought.\n   * @returns If `key` exists, the corresponding feed mask.\n   * @throws ValueError: If `key` does not exist in this `FeedDict`.\n   */\n  getMask(key: SymbolicTensor|string): Tensor {\n    if (key instanceof SymbolicTensor) {\n      if (this.id2Value[key.id] == null) {\n        throw new ValueError(`Nonexistent key: ${key.name}`);\n      } else {\n        return this.id2Mask[key.id];\n      }\n    } else {\n      const id = this.name2Id[key];\n      if (id == null) {\n        throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n      }\n      return this.id2Mask[id];\n    }\n  }\n\n  /** Dispose all mask Tensors held by this object. */\n  disposeMasks() {\n    if (this.id2Mask != null) {\n      dispose(this.id2Mask);\n    }\n  }\n}\n\n// Cache for topologically sorted SymbolicTensors for given execution\n// targets (i.e., fetches).\nexport const cachedSorted: LruCache<SymbolicTensor[]> =\n    new LruCache<SymbolicTensor[]>();\n\n// Cache for recipient count maps for given execution targets (i.e., fetches).\nexport const cachedRecipientCounts: LruCache<RecipientCounts> =\n    new LruCache<RecipientCounts>();\n\nexport function updateCacheMaxEntries(maxEntries: number) {\n  if (cachedSorted != null) {\n    cachedSorted.setMaxEntries(maxEntries);\n  }\n  if (cachedRecipientCounts != null) {\n    cachedRecipientCounts.setMaxEntries(maxEntries);\n  }\n}\n\n/**\n * Interface for the optional object used for probing the memory\n * usage and 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Tensor counts are measured at the beginning of every\n   * step.\n   */\n  maxNumTensors?: number;\n\n  /**\n   * Minimum number of tensors that exist during all steps of the\n   * execution. Tensor counts are measured at the beginning of every\n   * step.\n   */\n  minNumTensors?: number;\n}\n\n/**\n * Execute a SymbolicTensor by using concrete feed values.\n *\n * A `SymbolicTensor` object is a node in a computation graph of TF.js\n * Layers. The object is backed by a source layer and input\n * `SymbolicTensor`s to the source layer. This method evaluates\n * the `call()` method of the source layer, using concrete values of the\n * inputs obtained from either\n * * `feedDict`, if the input key exists in `feedDict`, or else,\n * * a recursive call to `execute()` itself.\n *\n * @param x: The `SymbolicTensor` to execute.\n * @param feedDict: The feed values, as base condition of the recursion.\n *   execution.\n * @param kwargs: Optional keyword arguments.\n * @param probe: A probe object (of interface `ExecutionProbe`) used for\n *   testing memory footprint of `execute` calls.\n * @returns Result of the execution.\n * @throws ValueError: If any `SymbolicTensor`s from `InputLayer`s\n *   encountered during the execution lacks a feed value in `feedDict`.\n */\nexport function execute(\n    fetches: SymbolicTensor|SymbolicTensor[], feedDict: FeedDict,\n    kwargs?: Kwargs, probe?: ExecutionProbe): Tensor|\n    Tensor[]|[Tensor | Tensor[]] {\n  const training: boolean = kwargs == null ? false : kwargs['training'];\n\n  const arrayFetches = Array.isArray(fetches);\n  const fetchArray: SymbolicTensor[] =\n      arrayFetches ? fetches : [fetches];\n\n  const outputNames = fetchArray.map(t => t.name);\n  const finalOutputs: Tensor[] = [];\n  const feedNames = feedDict.names();\n  for (const outputName of outputNames) {\n    if (feedNames.indexOf(outputName) !== -1) {\n      finalOutputs.push(feedDict.getValue(outputName));\n    } else {\n      finalOutputs.push(null);\n    }\n  }\n\n  if (probe != null) {\n    // For optional probing of memory footprint during execution.\n    probe.maxNumTensors = -Infinity;\n    probe.minNumTensors = Infinity;\n  }\n\n  // Check cache.\n  const fetchAndFeedKey =\n      outputNames.join(',') + '|' + feedDict.names().sort().join(',');\n  let sorted: SymbolicTensor[] = cachedSorted.get(fetchAndFeedKey);\n  let recipientCounts: {[fetchName: string]: number};\n  if (sorted == null) {\n    // Cache doesn't contain the desired combination of fetches. Compute\n    // topological sort for the combination for the first time.\n    const out = getTopologicalSortAndRecipientCounts(fetchArray, feedDict);\n    sorted = out.sorted;\n    recipientCounts = out.recipientCounts;\n\n    // Store results in cache for future use.\n    cachedSorted.put(fetchAndFeedKey, sorted);\n    cachedRecipientCounts.put(fetchAndFeedKey, recipientCounts);\n  }\n  recipientCounts = {};\n  if (!training) {\n    Object.assign(recipientCounts, cachedRecipientCounts.get(fetchAndFeedKey));\n  }\n\n  const internalFeedDict = new FeedDict(feedDict);\n\n  // Start iterative execution on the topologically-sorted SymbolicTensors.\n  for (let i = 0; i < sorted.length; ++i) {\n    if (probe != null) {\n      // For optional probing of memory usage during execution.\n      const numTensors = memory().numTensors;\n      if (numTensors > probe.maxNumTensors) {\n        probe.maxNumTensors = numTensors;\n      }\n      if (numTensors < probe.minNumTensors) {\n        probe.minNumTensors = numTensors;\n      }\n    }\n\n    const symbolic = sorted[i];\n    const srcLayer = symbolic.sourceLayer;\n    if (srcLayer instanceof InputLayer) {\n      continue;\n    }\n    const inputValues: Tensor[] = [];\n    const inputMasks: Tensor[] = [];\n    const tensorsToDispose: Tensor[] = [];\n\n    let maskExists = false;\n    for (const input of symbolic.inputs) {\n      const value = internalFeedDict.getValue(input);\n      const mask = internalFeedDict.getMask(input);\n      inputValues.push(value);\n      inputMasks.push(mask);\n      if (mask != null) {\n        maskExists = true;\n      }\n      if (!training) {\n        recipientCounts[input.name]--;\n        if (recipientCounts[input.name] === 0 && !feedDict.hasKey(input) &&\n            outputNames.indexOf(input.name) === -1 && !value.isDisposed &&\n            input.sourceLayer.stateful !== true) {\n          tensorsToDispose.push(value);\n        }\n      }\n    }\n\n    if (maskExists) {\n      kwargs = kwargs || {};\n      kwargs['mask'] = inputMasks[0];\n    }\n    const outputTensors =\n        toList(srcLayer.apply(inputValues, kwargs)) as Tensor[];\n    let outputMask: Tensor|Tensor[] = null;\n    if (srcLayer.supportsMasking) {\n      outputMask = srcLayer.computeMask(inputValues, inputMasks);\n    }\n    const layerOutputs = getNodeOutputs(symbolic);\n    const outputSymbolicTensors =\n        Array.isArray(layerOutputs) ? layerOutputs : [layerOutputs];\n    for (let i = 0; i < outputSymbolicTensors.length; ++i) {\n      if (!internalFeedDict.hasKey(outputSymbolicTensors[i])) {\n        internalFeedDict.add(\n            outputSymbolicTensors[i], outputTensors[i],\n            Array.isArray(outputMask) ? outputMask[0] : outputMask);\n      }\n      const index = outputNames.indexOf(outputSymbolicTensors[i].name);\n      if (index !== -1) {\n        finalOutputs[index] = outputTensors[i];\n      }\n    }\n\n    if (!training) {\n      // Clean up Tensors that are no longer needed.\n      dispose(tensorsToDispose);\n    }\n  }\n  // NOTE(cais): Unlike intermediate tensors, we don't discard mask\n  // tensors as we go, because these tensors are sometimes passed over a\n  // series of mutliple layers, i.e., not obeying the immediate input\n  // relations in the graph. If this becomes a memory-usage concern,\n  // we can improve this in the future.\n  internalFeedDict.disposeMasks();\n\n  return arrayFetches ? finalOutputs : finalOutputs[0];\n}\n\ntype RecipientCounts = {\n  [fetchName: string]: number\n};\n\nexport type RecipientMap = {\n  [fetchName: string]: Set<string>;\n};\n\n/**\n * Sort the `SymbolicTensor`s topologically, for an array of fetches.\n *\n * This function calls getTopologicalSortAndRecipientCountsForOneFetch and\n * merges their results.\n *\n * @param fetch The array of fetches requested. Must be a non-empty array.\n * @param feedDict The dictionary of fed values.\n * @returns sorted: Topologically-sorted array of SymbolicTensors.\n *   recipientCounts: Recipient counts for all SymbolicTensors in `sorted`.\n */\nfunction getTopologicalSortAndRecipientCounts(\n    fetches: SymbolicTensor[], feedDict: FeedDict):\n    {sorted: SymbolicTensor[], recipientCounts: RecipientCounts} {\n  util.assert(\n      fetches != null && fetches.length > 0,\n      () => `Expected at least one fetch, got none`);\n\n  let finalSorted: SymbolicTensor[] = [];\n  let finalRecipientMap: RecipientMap = {};\n  if (fetches.length === 1) {\n    // Special-casing 1 fetch for efficiency.\n    const out =\n        getTopologicalSortAndRecipientCountsForOneFetch(fetches[0], feedDict);\n    finalSorted = out.sorted;\n    finalRecipientMap = out.recipientMap;\n  } else {\n    const visited = new Set<string>();\n    for (const fetch of fetches) {\n      const {sorted, recipientMap} =\n          getTopologicalSortAndRecipientCountsForOneFetch(fetch, feedDict);\n\n      // Merge sorted SymbolicTensor Arrays.\n      for (const symbolicTensor of sorted) {\n        if (!visited.has(symbolicTensor.name)) {\n          finalSorted.push(symbolicTensor);\n          visited.add(symbolicTensor.name);\n        }\n      }\n\n      // Merge recipient maps.\n      for (const name in recipientMap) {\n        if (finalRecipientMap[name] == null) {\n          finalRecipientMap[name] = new Set<string>();\n        }\n        recipientMap[name].forEach(\n            recipient => finalRecipientMap[name].add(recipient));\n      }\n    }\n  }\n  return {\n    sorted: finalSorted,\n    recipientCounts: recipientMap2Counts(finalRecipientMap)\n  };\n}\n\nfunction recipientMap2Counts(recipientMap: RecipientMap): RecipientCounts {\n  const recipientCounts: RecipientCounts = {};\n  for (const name in recipientMap) {\n    recipientCounts[name] = recipientMap[name].size;\n  }\n  return recipientCounts;\n}\n\n/**\n * Sort the `SymbolicTensor`s topologically, for a single fetch.\n *\n * This helper function processes the upstream SymbolicTensors of a single\n * fetch.\n *\n * @param fetch The single fetch requested.\n * @param feedDict The dictionary of fed values.\n * @returns sorted: Topologically-sorted array of SymbolicTensors.\n *   recipientMap: Recipient names for all SymbolicTensors in `sorted`.\n */\nexport function getTopologicalSortAndRecipientCountsForOneFetch(\n    fetch: SymbolicTensor, feedDict: FeedDict):\n    {sorted: SymbolicTensor[], recipientMap: RecipientMap} {\n  const visited = new Set<string>();\n  const sorted: SymbolicTensor[] = [];\n  const recipientMap: RecipientMap = {};\n\n  // Put keys of the feedDict into visited first, so they don't have to be\n  // walked. This is needed in case where there are feeds for intermediate\n  // SymbolicTensors of the graph.\n  for (const key of feedDict.names()) {\n    visited.add(key);\n  }\n\n  const stack: SymbolicTensor[] = [];\n  const marks: number[] = [];\n\n  // Initial population of stack and marks.\n  stack.push(fetch);\n\n  while (stack.length > 0) {\n    const top = stack[stack.length - 1];\n    if (visited.has(top.name)) {\n      stack.pop();\n      continue;\n    }\n    const topIsMarked = marks[marks.length - 1] === stack.length - 1;\n    if (top.inputs.length === 0 || topIsMarked) {\n      // Input SymbolicTensor or all children have been visited.\n      stack.pop();\n      sorted.push(top);\n      visited.add(top.name);\n      if (topIsMarked) {\n        marks.pop();\n      }\n    } else {\n      // A non-input SymbolicTensor whose upstream SymbolicTensors haven't\n      // been visited yet. Push them onto the stack.\n      marks.push(stack.length - 1);\n      for (const input of top.inputs) {\n        // Increment the recipient count. Note that this needs to happen\n        // regardless of whether the SymbolicTensor has been visited before.\n        if (recipientMap[input.name] == null) {\n          recipientMap[input.name] = new Set<string>();\n        }\n        recipientMap[input.name].add(top.name);\n\n        if (visited.has(input.name)) {\n          continue;  // Avoid repeated visits to the same SymbolicTensor.\n        }\n        stack.push(input);\n      }\n    }\n  }\n  return {sorted, recipientMap};\n}\n\n/**\n * Get the symbolic output tensors of the node to which a given fetch belongs.\n * @param fetch The fetched symbolic tensor.\n * @returns The Array of symbolic tensors output by the node to which `fetch`\n *   belongs.\n */\nfunction getNodeOutputs(fetch: SymbolicTensor): SymbolicTensor|\n    SymbolicTensor[] {\n  let layerOutputs: SymbolicTensor|SymbolicTensor[];\n  if (fetch.sourceLayer.inboundNodes.length === 1) {\n    layerOutputs = fetch.sourceLayer.output;\n  } else {\n    let nodeIndex: number = null;\n    for (let i = 0; i < fetch.sourceLayer.inboundNodes.length; ++i) {\n      for (const outputTensor of fetch.sourceLayer.inboundNodes[i]\n               .outputTensors) {\n        if (outputTensor.id === fetch.id) {\n          nodeIndex = i;\n          break;\n        }\n      }\n    }\n    layerOutputs = fetch.sourceLayer.getOutputAt(nodeIndex);\n  }\n  return layerOutputs;\n}\n"]}