@tensorflow-models/coco-ssd
Version:
Object detection model (coco-ssd) in TensorFlow.js
554 lines • 21.7 kB
JavaScript
"use strict";
/**
* @license
* Copyright 2017 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.
* =============================================================================
*/
Object.defineProperty(exports, "__esModule", { value: true });
var device_util = require("./device_util");
var engine_1 = require("./engine");
var environment_util_1 = require("./environment_util");
var tensor_1 = require("./tensor");
var tensor_util_1 = require("./tensor_util");
exports.EPSILON_FLOAT16 = 1e-4;
var TEST_EPSILON_FLOAT16 = 1e-1;
exports.EPSILON_FLOAT32 = 1e-7;
var TEST_EPSILON_FLOAT32 = 1e-3;
var Environment = /** @class */ (function () {
function Environment(features) {
this.features = {};
this.registry = {};
if (features != null) {
this.features = features;
}
if (this.get('DEBUG')) {
console.warn('Debugging mode is ON. The output of every math call will ' +
'be downloaded to CPU and checked for NaNs. ' +
'This significantly impacts performance.');
}
}
/**
* Sets the backend (cpu, webgl, etc) responsible for creating tensors and
* executing operations on those tensors.
*
* Note this disposes the current backend, if any, as well as any tensors
* associated with it. A new backend is initialized, even if it is of the
* same type as the previous one.
*
* @param backendName The name of the backend. Currently supports
* `'webgl'|'cpu'` in the browser, and `'tensorflow'` under node.js
* (requires tfjs-node).
* @param safeMode Defaults to false. In safe mode, you are forced to
* construct tensors and call math operations inside a `tidy()` which
* will automatically clean up intermediate tensors.
*/
/** @doc {heading: 'Environment'} */
Environment.setBackend = function (backendName, safeMode) {
if (safeMode === void 0) { safeMode = false; }
if (!(backendName in exports.ENV.registry)) {
throw new Error("Backend name '" + backendName + "' not found in registry");
}
exports.ENV.engine.backend = exports.ENV.findBackend(backendName);
exports.ENV.backendName = backendName;
};
/**
* Returns the current backend name (cpu, webgl, etc). The backend is
* responsible for creating tensors and executing operations on those tensors.
*/
/** @doc {heading: 'Environment'} */
Environment.getBackend = function () {
exports.ENV.initEngine();
return exports.ENV.backendName;
};
/**
* Dispose all variables kept in backend engine.
*/
/** @doc {heading: 'Environment'} */
Environment.disposeVariables = function () {
exports.ENV.engine.disposeVariables();
};
/**
* Returns memory info at the current time in the program. The result is an
* object with the following properties:
*
* - `numBytes`: Number of bytes allocated (undisposed) at this time.
* - `numTensors`: Number of unique tensors allocated.
* - `numDataBuffers`: Number of unique data buffers allocated
* (undisposed) at this time, which is ≤ the number of tensors
* (e.g. `a.reshape(newShape)` makes a new Tensor that shares the same
* data buffer with `a`).
* - `unreliable`: True if the memory usage is unreliable. See `reasons` when
* `unrealible` is true.
* - `reasons`: `string[]`, reasons why the memory is unreliable, present if
* `unreliable` is true.
*/
/** @doc {heading: 'Performance', subheading: 'Memory'} */
Environment.memory = function () {
return exports.ENV.engine.memory();
};
/**
* Executes the provided function `f()` and returns a promise that resolves
* with information about the function's memory use:
* - `newBytes`: tne number of new bytes allocated
* - `newTensors`: the number of new tensors created
* - `peakBytes`: the peak number of bytes allocated
* - `kernels`: an array of objects for each kernel involved that reports
* their input and output shapes, number of bytes used, and number of new
* tensors created.
*
* ```js
* const profile = await tf.profile(() => {
* const x = tf.tensor1d([1, 2, 3]);
* let x2 = x.square();
* x2.dispose();
* x2 = x.square();
* x2.dispose();
* return x;
* });
*
* console.log(`newBytes: ${profile.newBytes}`);
* console.log(`newTensors: ${profile.newTensors}`);
* console.log(`byte usage over all kernels: ${profile.kernels.map(k =>
* k.totalBytesSnapshot)}`);
* ```
*
*/
/** @doc {heading: 'Performance', subheading: 'Profile'} */
Environment.profile = function (f) {
return exports.ENV.engine.profile(f);
};
/**
* 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.
*
* When in safe mode, you must enclose all `tf.Tensor` creation and ops
* inside a `tf.tidy` to prevent memory leaks.
*
* ```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'} */
Environment.tidy = function (nameOrFn, fn) {
return exports.ENV.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'} */
Environment.dispose = function (container) {
var tensors = tensor_util_1.getTensorsInContainer(container);
tensors.forEach(function (tensor) { return tensor.dispose(); });
};
/**
* Keeps a `tf.Tensor` generated inside a `tf.tidy` from being disposed
* automatically.
*
* ```js
* let b;
* const y = tf.tidy(() => {
* const one = tf.scalar(1);
* const a = tf.scalar(2);
*
* // b will not be cleaned up by the tidy. a and one will be cleaned up
* // when the tidy ends.
* b = tf.keep(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);
* console.log('y:');
* y.print();
* console.log('b:');
* b.print();
* ```
*
* @param result The tensor to keep from being disposed.
*/
/** @doc {heading: 'Performance', subheading: 'Memory'} */
Environment.keep = function (result) {
return exports.ENV.engine.keep(result);
};
/**
* Executes `f()` and returns a promise that resolves with timing
* information.
*
* The result is an object with the following properties:
*
* - `wallMs`: Wall execution time.
* - `kernelMs`: Kernel execution time, ignoring data transfer.
* - On `WebGL` The following additional properties exist:
* - `uploadWaitMs`: CPU blocking time on texture uploads.
* - `downloadWaitMs`: CPU blocking time on texture downloads (readPixels).
*
* ```js
* const x = tf.randomNormal([20, 20]);
* const time = await tf.time(() => x.matMul(x));
*
* console.log(`kernelMs: ${time.kernelMs}, wallTimeMs: ${time.wallMs}`);
* ```
*
* @param f The function to execute and time.
*/
/** @doc {heading: 'Performance', subheading: 'Timing'} */
Environment.time = function (f) {
return exports.ENV.engine.time(f);
};
Environment.prototype.get = function (feature) {
if (feature in this.features) {
return this.features[feature];
}
this.features[feature] = this.evaluateFeature(feature);
return this.features[feature];
};
Environment.prototype.getFeatures = function () {
return this.features;
};
Environment.prototype.set = function (feature, value) {
this.features[feature] = value;
};
Environment.prototype.getBestBackendName = function () {
var _this = this;
if (Object.keys(this.registry).length === 0) {
throw new Error('No backend found in registry.');
}
var sortedBackends = Object.keys(this.registry)
.map(function (name) {
return { name: name, entry: _this.registry[name] };
})
.sort(function (a, b) {
// Highest priority comes first.
return b.entry.priority - a.entry.priority;
});
return sortedBackends[0].name;
};
Environment.prototype.evaluateFeature = function (feature) {
if (feature === 'DEBUG') {
return false;
}
else if (feature === 'IS_BROWSER') {
return typeof window !== 'undefined';
}
else if (feature === 'IS_NODE') {
return (typeof process !== 'undefined') &&
(typeof process.versions !== 'undefined') &&
(typeof process.versions.node !== 'undefined');
}
else if (feature === 'IS_CHROME') {
return environment_util_1.isChrome();
}
else if (feature === 'WEBGL_CPU_FORWARD') {
return true;
}
else if (feature === 'WEBGL_PACK') {
return this.get('WEBGL_VERSION') === 0 ? false : true;
}
else if (feature === 'WEBGL_PACK_BATCHNORMALIZATION') {
return this.get('WEBGL_PACK');
}
else if (feature === 'WEBGL_PACK_CLIP') {
return this.get('WEBGL_PACK');
}
else if (feature === 'WEBGL_PACK_DEPTHWISECONV') {
return this.get('WEBGL_PACK');
}
else if (feature === 'WEBGL_PACK_BINARY_OPERATIONS') {
return this.get('WEBGL_PACK');
}
else if (feature === 'WEBGL_PACK_ARRAY_OPERATIONS') {
return this.get('WEBGL_PACK');
}
else if (feature === 'WEBGL_PACK_IMAGE_OPERATIONS') {
return this.get('WEBGL_PACK');
}
else if (feature === 'WEBGL_LAZILY_UNPACK') {
return this.get('WEBGL_PACK');
}
else if (feature === 'WEBGL_CONV_IM2COL') {
return this.get('WEBGL_PACK');
}
else if (feature === 'WEBGL_NUM_MB_BEFORE_PAGING') {
if (this.get('PROD') || !this.get('IS_BROWSER')) {
return Number.POSITIVE_INFINITY;
}
return environment_util_1.getNumMBBeforePaging();
}
else if (feature === 'WEBGL_MAX_TEXTURE_SIZE') {
return environment_util_1.getWebGLMaxTextureSize(this.get('WEBGL_VERSION'));
}
else if (feature === 'WEBGL_MAX_TEXTURES_IN_SHADER') {
return environment_util_1.getMaxTexturesInShader(this.get('WEBGL_VERSION'));
}
else if (feature === 'IS_TEST') {
return false;
}
else if (feature === 'BACKEND') {
return this.getBestBackendName();
}
else if (feature === 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') {
var webGLVersion = this.get('WEBGL_VERSION');
if (webGLVersion === 0) {
return 0;
}
return environment_util_1.getWebGLDisjointQueryTimerVersion(webGLVersion);
}
else if (feature === 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') {
return this.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0 &&
!device_util.isMobile();
}
else if (feature === 'HAS_WEBGL') {
return this.get('WEBGL_VERSION') > 0;
}
else if (feature === 'WEBGL_VERSION') {
if (environment_util_1.isWebGLVersionEnabled(2)) {
return 2;
}
else if (environment_util_1.isWebGLVersionEnabled(1)) {
return 1;
}
return 0;
}
else if (feature === 'WEBGL_RENDER_FLOAT32_ENABLED') {
return environment_util_1.isRenderToFloatTextureEnabled(this.get('WEBGL_VERSION'));
}
else if (feature === 'WEBGL_DOWNLOAD_FLOAT_ENABLED') {
return environment_util_1.isDownloadFloatTextureEnabled(this.get('WEBGL_VERSION'));
}
else if (feature === 'WEBGL_FENCE_API_ENABLED') {
return environment_util_1.isWebGLFenceEnabled(this.get('WEBGL_VERSION'));
}
else if (feature === 'WEBGL_SIZE_UPLOAD_UNIFORM') {
// Use uniform uploads only when 32bit floats are supported. In 16bit
// environments there are problems with comparing a 16bit texture value
// with a 32bit uniform value.
var useUniforms = this.get('WEBGL_RENDER_FLOAT32_ENABLED');
return useUniforms ? 4 : 0;
}
else if (feature === 'TEST_EPSILON') {
return this.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 :
TEST_EPSILON_FLOAT16;
}
else if (feature === 'EPSILON') {
return this.backend.floatPrecision() === 32 ? exports.EPSILON_FLOAT32 :
exports.EPSILON_FLOAT16;
}
else if (feature === 'PROD') {
return false;
}
else if (feature === 'TENSORLIKE_CHECK_SHAPE_CONSISTENCY') {
return !this.get('PROD');
}
else if (feature === 'DEPRECATION_WARNINGS_ENABLED') {
return true;
}
throw new Error("Unknown feature " + feature + ".");
};
Environment.prototype.setFeatures = function (features) {
this.features = Object.assign({}, features);
};
Environment.prototype.reset = function () {
this.features = environment_util_1.getFeaturesFromURL();
if (this.globalEngine != null) {
this.globalEngine = null;
}
};
Object.defineProperty(Environment.prototype, "backend", {
get: function () {
return this.engine.backend;
},
enumerable: true,
configurable: true
});
Environment.prototype.findBackend = function (name) {
if (!(name in this.registry)) {
return null;
}
return this.registry[name].backend;
};
/**
* Registers a global backend. The registration should happen when importing
* a module file (e.g. when importing `backend_webgl.ts`), and is used for
* modular builds (e.g. custom tfjs bundle with only webgl support).
*
* @param factory The backend factory function. When called, it should
* return an instance of the backend.
* @param priority The priority of the backend (higher = more important).
* In case multiple backends are registered, the priority is used to find
* the best backend. Defaults to 1.
* @return False if the creation/registration failed. True otherwise.
*/
Environment.prototype.registerBackend = function (name, factory, priority) {
var _this = this;
if (priority === void 0) { priority = 1; }
if (name in this.registry) {
console.warn(name + " backend was already registered. Reusing existing backend");
return false;
}
try {
var backend = factory();
backend.setDataMover({ moveData: function (dataId) { return _this.engine.moveData(dataId); } });
this.registry[name] = { backend: backend, priority: priority };
return true;
}
catch (err) {
console.warn("Registration of backend " + name + " failed");
console.warn(err.stack || err.message);
return false;
}
};
Environment.prototype.removeBackend = function (name) {
if (!(name in this.registry)) {
throw new Error(name + " backend not found in registry");
}
this.registry[name].backend.dispose();
delete this.registry[name];
};
Object.defineProperty(Environment.prototype, "engine", {
get: function () {
this.initEngine();
return this.globalEngine;
},
enumerable: true,
configurable: true
});
Environment.prototype.initEngine = function () {
var _this = this;
if (this.globalEngine == null) {
this.backendName = this.get('BACKEND');
var backend = this.findBackend(this.backendName);
this.globalEngine =
new engine_1.Engine(backend, false /* safeMode */, function () { return _this.get('DEBUG'); });
}
};
Object.defineProperty(Environment.prototype, "global", {
get: function () {
return getGlobalNamespace();
},
enumerable: true,
configurable: true
});
return Environment;
}());
exports.Environment = Environment;
var _global;
function getGlobalNamespace() {
if (_global == null) {
// tslint:disable-next-line:no-any
var ns = void 0;
if (typeof (window) !== 'undefined') {
ns = window;
}
else if (typeof (global) !== 'undefined') {
ns = global;
}
else if (typeof (process) !== 'undefined') {
ns = process;
}
else {
throw new Error('Could not find a global object');
}
_global = ns;
}
return _global;
}
function getOrMakeEnvironment() {
var ns = getGlobalNamespace();
if (ns.ENV == null) {
ns.ENV = new Environment(environment_util_1.getFeaturesFromURL());
}
// Tell the current tensor interface that the global engine is responsible for
// tracking.
tensor_1.setTensorTracker(function () { return ns.ENV.engine; });
return ns.ENV;
}
/**
* Enables production mode which disables correctness checks in favor of
* performance.
*/
/** @doc {heading: 'Environment'} */
function enableProdMode() {
exports.ENV.set('PROD', true);
}
exports.enableProdMode = enableProdMode;
/**
* Enables debug mode which will log information about all executed kernels:
* the ellapsed time of the kernel execution, as well as the rank, shape, and
* size of the output tensor.
*
* Debug mode will significantly slow down your application as it will
* download the result of every operation to the CPU. This should not be used in
* production. Debug mode does not affect the timing information of the kernel
* execution as we do not measure download time in the kernel execution time.
*
* See also: `tf.profile`, `tf.memory`.
*/
/** @doc {heading: 'Environment'} */
function enableDebugMode() {
exports.ENV.set('DEBUG', true);
}
exports.enableDebugMode = enableDebugMode;
/** Globally disables deprecation warnings */
function disableDeprecationWarnings() {
exports.ENV.set('DEPRECATION_WARNINGS_ENABLED', false);
console.warn("TensorFlow.js deprecation warnings have been disabled.");
}
exports.disableDeprecationWarnings = disableDeprecationWarnings;
/** Warn users about deprecated functionality. */
function deprecationWarn(msg) {
if (exports.ENV.get('DEPRECATION_WARNINGS_ENABLED')) {
console.warn(msg + ' You can disable deprecation warnings with ' +
'tf.disableDeprecationWarnings().');
}
}
exports.deprecationWarn = deprecationWarn;
tensor_1.setDeprecationWarningFn(deprecationWarn);
exports.ENV = getOrMakeEnvironment();
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