@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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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.
* =============================================================================
*/
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('pad 1d', jasmine_util_1.ALL_ENVS, function () {
it('Should pad 1D arrays', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([1, 2, 3, 4, 5, 6], 'int32');
b = tf.pad1d(a, [2, 3]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, b.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0, 0, 1, 2, 3, 4, 5, 6, 0, 0, 0]]);
return [2 /*return*/];
}
});
}); });
it('Should not pad 1D arrays with 0s', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([1, 2, 3, 4], 'int32');
b = tf.pad1d(a, [0, 0]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, b.data()];
case 1:
_a.apply(void 0, [_b.sent(), [1, 2, 3, 4]]);
return [2 /*return*/];
}
});
}); });
it('Should handle padding with custom value', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, _a, _b, _c;
return __generator(this, function (_d) {
switch (_d.label) {
case 0:
a = tf.tensor1d([1, 2, 3, 4], 'int32');
b = tf.pad1d(a, [2, 3], 9);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, b.data()];
case 1:
_a.apply(void 0, [_d.sent(), [9, 9, 1, 2, 3, 4, 9, 9, 9]]);
a = tf.tensor1d([1, 2, 3, 4]);
b = tf.pad1d(a, [2, 1], 1.1);
_b = test_util_1.expectArraysClose;
return [4 /*yield*/, b.data()];
case 2:
_b.apply(void 0, [_d.sent(), [1.1, 1.1, 1, 2, 3, 4, 1.1]]);
a = tf.tensor1d([1, 2, 3, 4]);
b = tf.pad1d(a, [2, 1], 1);
_c = test_util_1.expectArraysClose;
return [4 /*yield*/, b.data()];
case 3:
_c.apply(void 0, [_d.sent(), [1, 1, 1, 2, 3, 4, 1]]);
return [2 /*return*/];
}
});
}); });
it('Should handle NaNs with 1D arrays', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([1, NaN, 2, NaN]);
b = tf.pad1d(a, [1, 1]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, b.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0, 1, NaN, 2, NaN, 0]]);
return [2 /*return*/];
}
});
}); });
it('Should handle invalid paddings', function () {
var a = tf.tensor1d([1, 2, 3, 4], 'int32');
var f = function () {
// tslint:disable-next-line:no-any
tf.pad1d(a, [2, 2, 2]);
};
expect(f).toThrowError();
});
it('grad', function () { return __awaiter(_this, void 0, void 0, function () {
var a, dy, da, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([1, 2, 3]);
dy = tf.tensor1d([10, 20, 30, 40, 50, 60]);
da = tf.grad(function (a) { return tf.pad1d(a, [2, 1]); })(a, dy);
expect(da.shape).toEqual([3]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, da.data()];
case 1:
_a.apply(void 0, [_b.sent(), [30, 40, 50]]);
return [2 /*return*/];
}
});
}); });
it('gradient with clones', function () { return __awaiter(_this, void 0, void 0, function () {
var a, dy, da, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([1, 2, 3]);
dy = tf.tensor1d([10, 20, 30, 40, 50, 60]);
da = tf.grad(function (a) { return tf.pad1d(a.clone(), [2, 1]).clone(); })(a, dy);
expect(da.shape).toEqual([3]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, da.data()];
case 1:
_a.apply(void 0, [_b.sent(), [30, 40, 50]]);
return [2 /*return*/];
}
});
}); });
it('accepts a tensor-like object', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = [1, 2, 3, 4, 5, 6];
b = tf.pad1d(a, [2, 3]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, b.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0, 0, 1, 2, 3, 4, 5, 6, 0, 0, 0]]);
return [2 /*return*/];
}
});
}); });
});
jasmine_util_1.describeWithFlags('pad 2d', jasmine_util_1.ALL_ENVS, function () {
it('Should pad 2D arrays', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, _a, _b;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
a = tf.tensor2d([[1], [2]], [2, 1], 'int32');
b = tf.pad2d(a, [[1, 1], [1, 1]]);
// 0, 0, 0
// 0, 1, 0
// 0, 2, 0
// 0, 0, 0
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, b.data()];
case 1:
// 0, 0, 0
// 0, 1, 0
// 0, 2, 0
// 0, 0, 0
_a.apply(void 0, [_c.sent(), [0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0]]);
a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3], 'int32');
b = tf.pad2d(a, [[2, 2], [1, 1]]);
// 0, 0, 0, 0, 0
// 0, 0, 0, 0, 0
// 0, 1, 2, 3, 0
// 0, 4, 5, 6, 0
// 0, 0, 0, 0, 0
// 0, 0, 0, 0, 0
_b = test_util_1.expectArraysClose;
return [4 /*yield*/, b.data()];
case 2:
// 0, 0, 0, 0, 0
// 0, 0, 0, 0, 0
// 0, 1, 2, 3, 0
// 0, 4, 5, 6, 0
// 0, 0, 0, 0, 0
// 0, 0, 0, 0, 0
_b.apply(void 0, [_c.sent(), [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 0,
0, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
]]);
return [2 /*return*/];
}
});
}); });
it('Should not pad 2D arrays with 0s', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3], 'int32');
b = tf.pad2d(a, [[0, 0], [0, 0]]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, b.data()];
case 1:
_a.apply(void 0, [_b.sent(), [1, 2, 3, 4, 5, 6]]);
return [2 /*return*/];
}
});
}); });
it('Should handle padding with custom value', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, _a, _b, _c;
return __generator(this, function (_d) {
switch (_d.label) {
case 0:
a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3], 'int32');
b = tf.pad2d(a, [[1, 1], [1, 1]], 10);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, b.data()];
case 1:
_a.apply(void 0, [_d.sent(), [
10, 10, 10, 10, 10, 10, 1, 2, 3, 10,
10, 4, 5, 6, 10, 10, 10, 10, 10, 10
]]);
a = tf.tensor2d([[1], [1]], [2, 1]);
b = tf.pad2d(a, [[1, 1], [1, 1]], -2.1);
_b = test_util_1.expectArraysClose;
return [4 /*yield*/, b.data()];
case 2:
_b.apply(void 0, [_d.sent(),
[-2.1, -2.1, -2.1, -2.1, 1, -2.1, -2.1, 1, -2.1, -2.1, -2.1, -2.1]]);
a = tf.tensor2d([[1], [1]], [2, 1]);
b = tf.pad2d(a, [[1, 1], [1, 1]], -2);
_c = test_util_1.expectArraysClose;
return [4 /*yield*/, b.data()];
case 3:
_c.apply(void 0, [_d.sent(), [-2, -2, -2, -2, 1, -2, -2, 1, -2, -2, -2, -2]]);
return [2 /*return*/];
}
});
}); });
it('Should handle NaNs with 2D arrays', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor2d([[1, NaN], [1, NaN]], [2, 2]);
b = tf.pad2d(a, [[1, 1], [1, 1]]);
// 0, 0, 0, 0
// 0, 1, NaN, 0
// 0, 1, NaN, 0
// 0, 0, 0, 0
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, b.data()];
case 1:
// 0, 0, 0, 0
// 0, 1, NaN, 0
// 0, 1, NaN, 0
// 0, 0, 0, 0
_a.apply(void 0, [_b.sent(), [0, 0, 0, 0, 0, 1, NaN, 0, 0, 1, NaN, 0, 0, 0, 0, 0]]);
return [2 /*return*/];
}
});
}); });
it('Should handle invalid paddings', function () {
var a = tf.tensor2d([[1], [2]], [2, 1], 'int32');
var f = function () {
// tslint:disable-next-line:no-any
tf.pad2d(a, [[2, 2, 2], [1, 1, 1]]);
};
expect(f).toThrowError();
});
it('grad', function () { return __awaiter(_this, void 0, void 0, function () {
var a, dy, da, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor2d([[1, 2], [3, 4]]);
dy = tf.tensor2d([[0, 0, 0], [10, 20, 0], [30, 40, 0]], [3, 3]);
da = tf.grad(function (a) { return tf.pad2d(a, [[1, 0], [0, 1]]); })(a, dy);
expect(da.shape).toEqual([2, 2]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, da.data()];
case 1:
_a.apply(void 0, [_b.sent(), [10, 20, 30, 40]]);
return [2 /*return*/];
}
});
}); });
it('accepts a tensor-like object', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = [[1, 2, 3], [4, 5, 6]];
b = tf.pad2d(a, [[0, 0], [0, 0]]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, b.data()];
case 1:
_a.apply(void 0, [_b.sent(), [1, 2, 3, 4, 5, 6]]);
return [2 /*return*/];
}
});
}); });
});
jasmine_util_1.describeWithFlags('pad 3d', jasmine_util_1.ALL_ENVS, function () {
it('works with 3d tensor, float32', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor3d([[[1]], [[2]]], [2, 1, 1], 'float32');
b = tf.pad3d(a, [[1, 1], [1, 1], [1, 1]]);
// 0, 0, 0
// 0, 0, 0
// 0, 0, 0
// 0, 0, 0
// 0, 1, 0
// 0, 0, 0
// 0, 0, 0
// 0, 2, 0
// 0, 0, 0
// 0, 0, 0
// 0, 0, 0
// 0, 0, 0
expect(b.shape).toEqual([4, 3, 3]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, b.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
]]);
return [2 /*return*/];
}
});
}); });
});
jasmine_util_1.describeWithFlags('pad 4d', jasmine_util_1.ALL_ENVS, function () {
it('Should pad 4D arrays', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, expected, _a, _b;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
a = tf.tensor4d([[[[9]]]], [1, 1, 1, 1], 'int32');
b = tf.pad4d(a, [[0, 0], [1, 1], [1, 1], [0, 0]]);
expected = tf.tensor4d([[[[0], [0], [0]], [[0], [9], [0]], [[0], [0], [0]]]], [1, 3, 3, 1], 'int32');
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, b.data()];
case 1:
_b = [_c.sent()];
return [4 /*yield*/, expected.data()];
case 2:
_a.apply(void 0, _b.concat([_c.sent()]));
expect(b.dtype).toBe('int32');
expect(b.shape).toEqual([1, 3, 3, 1]);
return [2 /*return*/];
}
});
}); });
it('does not leak memory', function () {
var a = tf.tensor4d([[[[9]]]], [1, 1, 1, 1], 'int32');
// The first call to pad may create and keeps internal singleton tensors.
// Subsequent calls should always create exactly one new tensor.
tf.pad4d(a, [[0, 0], [1, 1], [1, 1], [0, 0]]);
// Count before real call.
var numTensors = tf.memory().numTensors;
tf.pad4d(a, [[0, 0], [1, 1], [1, 1], [0, 0]]);
expect(tf.memory().numTensors).toEqual(numTensors + 1);
});
it('accepts a tensor-like object', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, expected, _a, _b;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
a = [[[[9]]]];
b = tf.pad4d(a, [[0, 0], [1, 1], [1, 1], [0, 0]]);
expected = tf.tensor4d([[[[0], [0], [0]], [[0], [9], [0]], [[0], [0], [0]]]], [1, 3, 3, 1], 'float32');
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, b.data()];
case 1:
_b = [_c.sent()];
return [4 /*yield*/, expected.data()];
case 2:
_a.apply(void 0, _b.concat([_c.sent()]));
expect(b.dtype).toBe('float32');
expect(b.shape).toEqual([1, 3, 3, 1]);
return [2 /*return*/];
}
});
}); });
});
jasmine_util_1.describeWithFlags('pad', jasmine_util_1.ALL_ENVS, function () {
it('Pad tensor2d', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, _a, _b;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
a = tf.tensor2d([[1], [2]], [2, 1], 'int32');
b = tf.pad(a, [[1, 1], [1, 1]]);
// 0, 0, 0
// 0, 1, 0
// 0, 2, 0
// 0, 0, 0
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, b.data()];
case 1:
// 0, 0, 0
// 0, 1, 0
// 0, 2, 0
// 0, 0, 0
_a.apply(void 0, [_c.sent(), [0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0]]);
a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3], 'int32');
b = tf.pad(a, [[2, 2], [1, 1]]);
// 0, 0, 0, 0, 0
// 0, 0, 0, 0, 0
// 0, 1, 2, 3, 0
// 0, 4, 5, 6, 0
// 0, 0, 0, 0, 0
// 0, 0, 0, 0, 0
_b = test_util_1.expectArraysClose;
return [4 /*yield*/, b.data()];
case 2:
// 0, 0, 0, 0, 0
// 0, 0, 0, 0, 0
// 0, 1, 2, 3, 0
// 0, 4, 5, 6, 0
// 0, 0, 0, 0, 0
// 0, 0, 0, 0, 0
_b.apply(void 0, [_c.sent(), [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 0,
0, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
]]);
return [2 /*return*/];
}
});
}); });
it('throws when passed a non-tensor', function () {
expect(function () { return tf.pad({}, [[0, 0]]); })
.toThrowError(/Argument 'x' passed to 'pad' must be a Tensor/);
});
it('accepts a tensor-like object', function () { return __awaiter(_this, void 0, void 0, function () {
var x, res, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
x = [[1], [2]];
res = tf.pad(x, [[1, 1], [1, 1]]);
// 0, 0, 0
// 0, 1, 0
// 0, 2, 0
// 0, 0, 0
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, res.data()];
case 1:
// 0, 0, 0
// 0, 1, 0
// 0, 2, 0
// 0, 0, 0
_a.apply(void 0, [_b.sent(), [0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0]]);
return [2 /*return*/];
}
});
}); });
});
//# sourceMappingURL=pad_test.js.map