@tensorflow-models/coco-ssd
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Object detection model (coco-ssd) in TensorFlow.js
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JavaScript
;
/**
* @license
* Copyright 2018 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.
* =============================================================================
*/
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 };
}
};
var _this = this;
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("./index");
var jasmine_util_1 = require("./jasmine_util");
var test_util_1 = require("./test_util");
jasmine_util_1.describeWithFlags('time webgl', test_util_1.WEBGL_ENVS, function () {
it('upload + compute', function () { return __awaiter(_this, void 0, void 0, function () {
var a, time;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
a = tf.zeros([10, 10]);
return [4 /*yield*/, tf.time(function () { return a.square(); })];
case 1:
time = _a.sent();
expect(time.uploadWaitMs > 0);
expect(time.downloadWaitMs === 0);
expect(time.kernelMs > 0);
expect(time.wallMs >= time.kernelMs);
return [2 /*return*/];
}
});
}); });
it('upload + compute + dataSync', function () { return __awaiter(_this, void 0, void 0, function () {
var a, time;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
a = tf.zeros([10, 10]);
return [4 /*yield*/, tf.time(function () { return a.square().dataSync(); })];
case 1:
time = _a.sent();
expect(time.uploadWaitMs > 0);
expect(time.downloadWaitMs > 0);
expect(time.kernelMs > 0);
expect(time.wallMs >= time.kernelMs);
return [2 /*return*/];
}
});
}); });
it('upload + compute + data', function () { return __awaiter(_this, void 0, void 0, function () {
var a, time;
var _this = this;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
a = tf.zeros([10, 10]);
return [4 /*yield*/, tf.time(function () { return __awaiter(_this, void 0, void 0, function () { return __generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, a.square().data()];
case 1: return [2 /*return*/, _a.sent()];
}
}); }); })];
case 1:
time = _a.sent();
expect(time.uploadWaitMs > 0);
expect(time.downloadWaitMs > 0);
expect(time.kernelMs > 0);
expect(time.wallMs >= time.kernelMs);
return [2 /*return*/];
}
});
}); });
it('preupload (not included) + compute + data', function () { return __awaiter(_this, void 0, void 0, function () {
var a, time;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
a = tf.zeros([10, 10]);
// Pre-upload a on gpu.
a.square();
return [4 /*yield*/, tf.time(function () { return a.sqrt(); })];
case 1:
time = _a.sent();
// The tensor was already on gpu.
expect(time.uploadWaitMs === 0);
expect(time.downloadWaitMs === 0);
expect(time.kernelMs > 0);
expect(time.wallMs >= time.kernelMs);
return [2 /*return*/];
}
});
}); });
});
jasmine_util_1.describeWithFlags('time cpu', test_util_1.NODE_ENVS, function () {
it('simple upload', function () { return __awaiter(_this, void 0, void 0, function () {
var a, time;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
a = tf.zeros([10, 10]);
return [4 /*yield*/, tf.time(function () { return a.square(); })];
case 1:
time = _a.sent();
expect(time.kernelMs > 0);
expect(time.wallMs >= time.kernelMs);
return [2 /*return*/];
}
});
}); });
});
jasmine_util_1.describeWithFlags('tidy', test_util_1.ALL_ENVS, function () {
it('returns Tensor', function () {
tf.tidy(function () {
var a = tf.tensor1d([1, 2, 3]);
var b = tf.tensor1d([0, 0, 0]);
expect(tf.memory().numTensors).toBe(2);
tf.tidy(function () {
var result = tf.tidy(function () {
b = tf.addStrict(a, b);
b = tf.addStrict(a, b);
b = tf.addStrict(a, b);
return tf.add(a, b);
});
// result is new. All intermediates should be disposed.
expect(tf.memory().numTensors).toBe(2 + 1);
test_util_1.expectArraysClose(result, [4, 8, 12]);
});
// a, b are still here, result should be disposed.
expect(tf.memory().numTensors).toBe(2);
});
expect(tf.memory().numTensors).toBe(0);
});
it('multiple disposes does not affect num arrays', function () {
expect(tf.memory().numTensors).toBe(0);
var a = tf.tensor1d([1, 2, 3]);
var b = tf.tensor1d([1, 2, 3]);
expect(tf.memory().numTensors).toBe(2);
a.dispose();
a.dispose();
expect(tf.memory().numTensors).toBe(1);
b.dispose();
expect(tf.memory().numTensors).toBe(0);
});
it('allows primitive types', function () {
var a = tf.tidy(function () { return 5; });
expect(a).toBe(5);
var b = tf.tidy(function () { return 'hello'; });
expect(b).toBe('hello');
});
it('allows complex types', function () {
var res = tf.tidy(function () {
return { a: tf.scalar(1), b: 'hello', c: [tf.scalar(2), 'world'] };
});
test_util_1.expectArraysClose(res.a, [1]);
test_util_1.expectArraysClose(res.c[0], [2]);
});
it('returns Tensor[]', function () {
var a = tf.tensor1d([1, 2, 3]);
var b = tf.tensor1d([0, -1, 1]);
expect(tf.memory().numTensors).toBe(2);
tf.tidy(function () {
var result = tf.tidy(function () {
tf.add(a, b);
return [tf.add(a, b), tf.sub(a, b)];
});
// the 2 results are new. All intermediates should be disposed.
expect(tf.memory().numTensors).toBe(4);
test_util_1.expectArraysClose(result[0], [1, 1, 4]);
test_util_1.expectArraysClose(result[1], [1, 3, 2]);
expect(tf.memory().numTensors).toBe(4);
});
// the 2 results should be disposed.
expect(tf.memory().numTensors).toBe(2);
a.dispose();
b.dispose();
expect(tf.memory().numTensors).toBe(0);
});
it('basic usage without return', function () {
var a = tf.tensor1d([1, 2, 3]);
var b = tf.tensor1d([0, 0, 0]);
expect(tf.memory().numTensors).toBe(2);
tf.tidy(function () {
b = tf.addStrict(a, b);
b = tf.addStrict(a, b);
b = tf.addStrict(a, b);
tf.add(a, b);
});
// all intermediates should be disposed.
expect(tf.memory().numTensors).toBe(2);
});
it('nested usage', function () {
var a = tf.tensor1d([1, 2, 3]);
var b = tf.tensor1d([0, 0, 0]);
expect(tf.memory().numTensors).toBe(2);
tf.tidy(function () {
var result = tf.tidy(function () {
b = tf.addStrict(a, b);
b = tf.tidy(function () {
b = tf.tidy(function () {
return tf.addStrict(a, b);
});
// original a, b, and two intermediates.
expect(tf.memory().numTensors).toBe(4);
tf.tidy(function () {
tf.addStrict(a, b);
});
// All the intermediates should be cleaned up.
expect(tf.memory().numTensors).toBe(4);
return tf.addStrict(a, b);
});
expect(tf.memory().numTensors).toBe(4);
return tf.addStrict(a, b);
});
expect(tf.memory().numTensors).toBe(3);
test_util_1.expectArraysClose(result, [4, 8, 12]);
});
expect(tf.memory().numTensors).toBe(2);
});
it('nested usage returns tensor created from outside scope', function () {
var x = tf.scalar(1);
tf.tidy(function () {
tf.tidy(function () {
return x;
});
});
expect(x.isDisposed).toBe(false);
});
it('nested usage with keep works', function () {
var b;
tf.tidy(function () {
var a = tf.scalar(1);
tf.tidy(function () {
b = tf.keep(a);
});
});
expect(b.isDisposed).toBe(false);
b.dispose();
});
it('single argument', function () {
var hasRan = false;
tf.tidy(function () {
hasRan = true;
});
expect(hasRan).toBe(true);
});
it('single argument, but not a function throws error', function () {
expect(function () {
tf.tidy('asdf');
}).toThrowError();
});
it('2 arguments, first is string', function () {
var hasRan = false;
tf.tidy('name', function () {
hasRan = true;
});
expect(hasRan).toBe(true);
});
it('2 arguments, but first is not string throws error', function () {
expect(function () {
// tslint:disable-next-line:no-any
tf.tidy(4, function () { });
}).toThrowError();
});
it('2 arguments, but second is not a function throws error', function () {
expect(function () {
// tslint:disable-next-line:no-any
tf.tidy('name', 'another name');
}).toThrowError();
});
it('works with arbitrary depth of result', function () {
tf.tidy(function () {
var res = tf.tidy(function () {
return [tf.scalar(1), [[tf.scalar(2)]], { list: [tf.scalar(3)] }];
});
test_util_1.expectArraysEqual(res[0], [1]);
// tslint:disable-next-line:no-any
test_util_1.expectArraysEqual(res[1][0][0], [2]);
// tslint:disable-next-line:no-any
test_util_1.expectArraysEqual(res[2].list[0], [3]);
expect(tf.memory().numTensors).toBe(3);
return res[0];
});
// Everything but scalar(1) got disposed.
expect(tf.memory().numTensors).toBe(1);
});
});
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