@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
269 lines • 14.6 kB
JavaScript
;
/**
* @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.
* =============================================================================
*/
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
return new (P || (P = Promise))(function (resolve, reject) {
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
step((generator = generator.apply(thisArg, _arguments || [])).next());
});
};
var __generator = (this && this.__generator) || function (thisArg, body) {
var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
function verb(n) { return function (v) { return step([n, v]); }; }
function step(op) {
if (f) throw new TypeError("Generator is already executing.");
while (_) try {
if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
if (y = 0, t) op = [op[0] & 2, t.value];
switch (op[0]) {
case 0: case 1: t = op; break;
case 4: _.label++; return { value: op[1], done: false };
case 5: _.label++; y = op[1]; op = [0]; continue;
case 7: op = _.ops.pop(); _.trys.pop(); continue;
default:
if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
if (t[2]) _.ops.pop();
_.trys.pop(); continue;
}
op = body.call(thisArg, _);
} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
}
};
Object.defineProperty(exports, "__esModule", { value: true });
var environment_1 = require("../environment");
var util = require("../util");
var io_utils_1 = require("./io_utils");
var progress_1 = require("./progress");
var types_1 = require("./types");
/**
* Reads binary weights data from a number of URLs.
*
* @param fetchURLs URLs to send the HTTP requests at, using `fetch` calls.
* @param requestOptions RequestInit (options) for the HTTP requests.
* @param fetchFunc Optional overriding value for the `window.fetch` function.
* @param onProgress Optional, progress callback function, fired periodically
* before the load is completed.
* @returns A `Promise` of an Array of `ArrayBuffer`. The Array has the same
* length as `fetchURLs`.
*/
function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) {
return __awaiter(this, void 0, void 0, function () {
var fetchFunc, requests, fetchStartFraction, fetchEndFraction, responses, _a, bufferPromises, bufferStartFraction, bufferEndFraction, buffers, _b;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
if (loadOptions == null) {
loadOptions = {};
}
fetchFunc = loadOptions.fetchFunc == null ? environment_1.env().platform.fetch :
loadOptions.fetchFunc;
requests = fetchURLs.map(function (fetchURL) {
return fetchFunc(fetchURL, loadOptions.requestInit, { isBinary: true });
});
fetchStartFraction = 0;
fetchEndFraction = 0.5;
if (!(loadOptions.onProgress == null)) return [3 /*break*/, 2];
return [4 /*yield*/, Promise.all(requests)];
case 1:
_a = _c.sent();
return [3 /*break*/, 4];
case 2: return [4 /*yield*/, progress_1.monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction)];
case 3:
_a = _c.sent();
_c.label = 4;
case 4:
responses = _a;
bufferPromises = responses.map(function (response) { return response.arrayBuffer(); });
bufferStartFraction = 0.5;
bufferEndFraction = 1;
if (!(loadOptions.onProgress == null)) return [3 /*break*/, 6];
return [4 /*yield*/, Promise.all(bufferPromises)];
case 5:
_b = _c.sent();
return [3 /*break*/, 8];
case 6: return [4 /*yield*/, progress_1.monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction)];
case 7:
_b = _c.sent();
_c.label = 8;
case 8:
buffers = _b;
return [2 /*return*/, buffers];
}
});
});
}
exports.loadWeightsAsArrayBuffer = loadWeightsAsArrayBuffer;
/**
* Reads a weights manifest JSON configuration, fetches the weights and
* returns them as `Tensor`s.
*
* @param manifest The weights manifest JSON.
* @param filePathPrefix The path prefix for filenames given in the manifest.
* Defaults to the empty string.
* @param weightNames The names of the weights to be fetched.
*/
function loadWeights(manifest, filePathPrefix, weightNames, requestInit) {
if (filePathPrefix === void 0) { filePathPrefix = ''; }
return __awaiter(this, void 0, void 0, function () {
var fetchWeights, loadWeights;
return __generator(this, function (_a) {
fetchWeights = function (fetchUrls) {
return loadWeightsAsArrayBuffer(fetchUrls, { requestInit: requestInit });
};
loadWeights = weightsLoaderFactory(fetchWeights);
return [2 /*return*/, loadWeights(manifest, filePathPrefix, weightNames)];
});
});
}
exports.loadWeights = loadWeights;
/**
* Creates a function, which reads a weights manifest JSON configuration,
* fetches the weight files using the specified function and returns them as
* `Tensor`s.
*
* ```js
* // example for creating a nodejs weight loader, which reads the weight files
* // from disk using fs.readFileSync
*
* import * as fs from 'fs'
*
* const fetchWeightsFromDisk = (filePaths: string[]) =>
* filePaths.map(filePath => fs.readFileSync(filePath).buffer)
*
* const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)
*
* const manifest = JSON.parse(
* fs.readFileSync('./my_model-weights_manifest').toString()
* )
* const weightMap = await loadWeights(manifest, './')
* ```
* @param fetchWeightsFunction The function used for fetching the weight files.
* @returns Weight loading function.
*/
function weightsLoaderFactory(fetchWeightsFunction) {
var _this = this;
return function (manifest, filePathPrefix, weightNames) {
if (filePathPrefix === void 0) { filePathPrefix = ''; }
return __awaiter(_this, void 0, void 0, function () {
var groupIndicesToFetchMap, groupWeightsToFetch, weightsFound, allManifestWeightNames, weightsNotFound, groupIndicesToFetch, fetchUrls, buffers, weightsTensorMap, bufferIndexOffset;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
groupIndicesToFetchMap = manifest.map(function () { return false; });
groupWeightsToFetch = {};
weightsFound = weightNames != null ? weightNames.map(function () { return false; }) : [];
allManifestWeightNames = [];
manifest.forEach(function (manifestGroupConfig, groupIndex) {
var groupOffset = 0;
manifestGroupConfig.weights.forEach(function (weightsEntry) {
var rawDtype = ('quantization' in weightsEntry) ?
weightsEntry.quantization.dtype :
weightsEntry.dtype;
var weightsBytes = types_1.DTYPE_VALUE_SIZE_MAP[rawDtype] *
util.sizeFromShape(weightsEntry.shape);
var enqueueWeightsForFetchingFn = function () {
groupIndicesToFetchMap[groupIndex] = true;
if (groupWeightsToFetch[groupIndex] == null) {
groupWeightsToFetch[groupIndex] = [];
}
groupWeightsToFetch[groupIndex].push({
manifestEntry: weightsEntry,
groupOffset: groupOffset,
sizeBytes: weightsBytes
});
};
if (weightNames != null) {
weightNames.forEach(function (weightName, weightIndex) {
if (weightName === weightsEntry.name) {
enqueueWeightsForFetchingFn();
weightsFound[weightIndex] = true;
}
});
}
else {
enqueueWeightsForFetchingFn();
}
allManifestWeightNames.push(weightsEntry.name);
groupOffset += weightsBytes;
});
});
if (!weightsFound.every(function (found) { return found; })) {
weightsNotFound = weightNames.filter(function (_, i) { return !weightsFound[i]; });
throw new Error("Could not find weights in manifest with names: " +
(weightsNotFound.join(', ') + ". \n") +
"Manifest JSON has weights with names: " +
(allManifestWeightNames.join(', ') + "."));
}
groupIndicesToFetch = groupIndicesToFetchMap.reduce(function (accumulator, shouldFetch, i) {
if (shouldFetch) {
accumulator.push(i);
}
return accumulator;
}, []);
fetchUrls = [];
groupIndicesToFetch.forEach(function (i) {
manifest[i].paths.forEach(function (filepath) {
var fetchUrl = filePathPrefix +
(!filePathPrefix.endsWith('/') ? '/' : '') + filepath;
fetchUrls.push(fetchUrl);
});
});
return [4 /*yield*/, fetchWeightsFunction(fetchUrls)];
case 1:
buffers = _a.sent();
weightsTensorMap = {};
bufferIndexOffset = 0;
groupIndicesToFetch.forEach(function (i) {
var numBuffers = manifest[i].paths.length;
var groupBytes = 0;
for (var i_1 = 0; i_1 < numBuffers; i_1++) {
groupBytes += buffers[bufferIndexOffset + i_1].byteLength;
}
// Create a buffer for the whole group.
var groupBuffer = new ArrayBuffer(groupBytes);
var groupByteBuffer = new Uint8Array(groupBuffer);
var groupBufferOffset = 0;
for (var i_2 = 0; i_2 < numBuffers; i_2++) {
var buffer = new Uint8Array(buffers[bufferIndexOffset + i_2]);
groupByteBuffer.set(buffer, groupBufferOffset);
groupBufferOffset += buffer.byteLength;
}
var weightsEntries = groupWeightsToFetch[i];
weightsEntries.forEach(function (weightsEntry) {
var byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);
var nameToTensorMap = io_utils_1.decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);
for (var name_1 in nameToTensorMap) {
weightsTensorMap[name_1] = nameToTensorMap[name_1];
}
});
bufferIndexOffset += numBuffers;
});
return [2 /*return*/, weightsTensorMap];
}
});
});
};
}
exports.weightsLoaderFactory = weightsLoaderFactory;
//# sourceMappingURL=weights_loader.js.map