| 1 | /**
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| 2 | * @license
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| 3 | * Copyright 2020 Google LLC. All Rights Reserved.
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| 4 | * Licensed under the Apache License, Version 2.0 (the "License");
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| 5 | * you may not use this file except in compliance with the License.
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| 6 | * You may obtain a copy of the License at
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| 7 | *
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| 8 | * http://www.apache.org/licenses/LICENSE-2.0
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| 9 | *
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| 10 | * Unless required by applicable law or agreed to in writing, software
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| 11 | * distributed under the License is distributed on an "AS IS" BASIS,
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| 12 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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| 13 | * See the License for the specific language governing permissions and
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| 14 | * limitations under the License.
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| 15 | * =============================================================================
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| 16 | */
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| 17 | import * as tf from '../index';
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| 18 | import { ALL_ENVS, describeWithFlags } from '../jasmine_util';
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| 19 | import { expectArraysClose } from '../test_util';
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| 20 | describeWithFlags('dilation2d', ALL_ENVS, () => {
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| 21 | it('valid padding.', async () => {
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| 22 | const inputShape = [1, 2, 2, 1];
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| 23 | const filterShape = [2, 2, 1];
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| 24 | const x = tf.tensor4d([.1, .2, .3, .4], inputShape);
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| 25 | const filter = tf.tensor3d([.4, .3, .1, .0], filterShape);
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| 26 | const result = tf.dilation2d(x, filter, 1 /* strides */, 'valid');
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| 27 | expect(result.shape).toEqual([1, 1, 1, 1]);
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| 28 | expectArraysClose(await result.data(), [.5]);
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| 29 | });
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| 30 | it('same padding.', async () => {
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| 31 | const inputShape = [1, 2, 2, 1];
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| 32 | const filterShape = [2, 2, 1];
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| 33 | const x = tf.tensor4d([.1, .2, .3, .4], inputShape);
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| 34 | const filter = tf.tensor3d([.4, .3, .1, .0], filterShape);
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| 35 | const result = tf.dilation2d(x, filter, 1 /* strides */, 'same');
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| 36 | expect(result.shape).toEqual([1, 2, 2, 1]);
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| 37 | expectArraysClose(await result.data(), [.5, .6, .7, .8]);
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| 38 | });
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| 39 | it('same padding depth 3.', async () => {
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| 40 | const inputShape = [1, 2, 2, 3];
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| 41 | const filterShape = [2, 2, 3];
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| 42 | const x = tf.tensor4d([.1, .2, .0, .2, .3, .1, .3, .4, .2, .4, .5, .3], inputShape);
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| 43 | const filter = tf.tensor3d([.4, .5, .3, .3, .4, .2, .1, .2, .0, .0, .1, -.1], filterShape);
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| 44 | const result = tf.dilation2d(x, filter, 1 /* strides */, 'same');
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| 45 | expect(result.shape).toEqual([1, 2, 2, 3]);
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| 46 | expectArraysClose(await result.data(), [.5, .7, .3, .6, .8, .4, .7, .9, .5, .8, 1., .6]);
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| 47 | });
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| 48 | it('same padding batch 2.', async () => {
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| 49 | const inputShape = [2, 2, 2, 1];
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| 50 | const filterShape = [2, 2, 1];
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| 51 | const x = tf.tensor4d([.1, .2, .3, .4, .2, .3, .4, .5], inputShape);
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| 52 | const filter = tf.tensor3d([.4, .3, .1, .0], filterShape);
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| 53 | const result = tf.dilation2d(x, filter, 1 /* strides */, 'same');
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| 54 | expect(result.shape).toEqual([2, 2, 2, 1]);
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| 55 | expectArraysClose(await result.data(), [.5, .6, .7, .8, .6, .7, .8, .9]);
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| 56 | });
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| 57 | it('same padding filter 2.', async () => {
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| 58 | const inputShape = [1, 3, 3, 1];
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| 59 | const filterShape = [2, 2, 1];
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| 60 | const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9], inputShape);
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| 61 | const filter = tf.tensor3d([.4, .3, .1, .2], filterShape);
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| 62 | const result = tf.dilation2d(x, filter, 1 /* strides */, 'same');
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| 63 | expect(result.shape).toEqual([1, 3, 3, 1]);
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| 64 | expectArraysClose(await result.data(), [.7, .8, .7, 1, 1.1, 1, 1.1, 1.2, 1.3]);
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| 65 | });
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| 66 | it('valid padding non-square-window.', async () => {
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| 67 | const inputShape = [1, 2, 2, 1];
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| 68 | const filterShape = [1, 2, 1];
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| 69 | const x = tf.tensor4d([.1, .2, .3, .4], inputShape);
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| 70 | const filter = tf.tensor3d([.4, .3], filterShape);
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| 71 | const result = tf.dilation2d(x, filter, 1 /* strides */, 'valid');
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| 72 | expect(result.shape).toEqual([1, 2, 1, 1]);
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| 73 | expectArraysClose(await result.data(), [.5, .7]);
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| 74 | });
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| 75 | it('same padding dilations 2.', async () => {
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| 76 | const inputShape = [1, 3, 3, 1];
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| 77 | const filterShape = [2, 2, 1];
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| 78 | const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9], inputShape);
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| 79 | const filter = tf.tensor3d([.4, .3, .1, .2], filterShape);
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| 80 | const result = tf.dilation2d(x, filter, 1 /* strides */, 'same', 2);
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| 81 | // Because dilations = 2, the effective filter is [3, 3, 1]:
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| 82 | // filter_eff = [[[.4], [.0], [.3]],
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| 83 | // [[.0], [.0], [.0]],
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| 84 | // [[.1], [.0], [.2]]]
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| 85 | expect(result.shape).toEqual([1, 3, 3, 1]);
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| 86 | expectArraysClose(await result.data(), [.7, .8, .6, 1., 1.1, .9, .8, .9, .9]);
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| 87 | });
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| 88 | it('valid padding uneven stride.', async () => {
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| 89 | const inputShape = [1, 3, 4, 1];
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| 90 | const filterShape = [2, 2, 1];
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| 91 | const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9, 1., 1.1, 1.2], inputShape);
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| 92 | const filter = tf.tensor3d([.4, .3, .1, .2], filterShape);
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| 93 | const result = tf.dilation2d(x, filter, [1, 2] /* strides */, 'valid');
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| 94 | expect(result.shape).toEqual([1, 2, 2, 1]);
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| 95 | expectArraysClose(await result.data(), [.8, 1., 1.2, 1.4]);
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| 96 | });
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| 97 | it('throws when input rank is not 3 or 4.', async () => {
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| 98 | const filterShape = [1, 1, 1];
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| 99 | // tslint:disable-next-line:no-any
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| 100 | const x = tf.tensor1d([.5]);
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| 101 | const filter = tf.tensor3d([.4], filterShape);
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| 102 | expect(() => tf.dilation2d(x, filter, 1, 'valid')).toThrowError();
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| 103 | });
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| 104 | it('thorws when filter is not rank 3.', async () => {
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| 105 | const inputShape = [1, 2, 2, 1];
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| 106 | const filterShape = [2, 2];
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| 107 | const x = tf.tensor4d([.1, .2, .3, .4], inputShape);
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| 108 | // tslint:disable-next-line:no-any
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| 109 | const filter = tf.tensor2d([.4, .3, .1, .0], filterShape);
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| 110 | expect(() => tf.dilation2d(x, filter, 1, 'valid')).toThrowError();
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| 111 | });
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| 112 | it('throws when data format is not NHWC.', async () => {
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| 113 | const inputShape = [1, 2, 2, 1];
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| 114 | const filterShape = [2, 2, 1];
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| 115 | const x = tf.tensor4d([.1, .2, .3, .4], inputShape);
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| 116 | const filter = tf.tensor3d([.4, .3, .1, .0], filterShape);
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| 117 | // tslint:disable-next-line:no-any
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| 118 | const dataFormat = 'NCHW';
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| 119 | expect(() => tf.dilation2d(x, filter, 1 /* strides */, 'valid', 1, dataFormat))
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| 120 | .toThrowError();
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| 121 | });
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| 122 | it('dilation gradient valid padding.', async () => {
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| 123 | const inputShape = [1, 3, 3, 1];
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| 124 | const filterShape = [1, 1, 1];
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| 125 | const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9], inputShape);
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| 126 | const filter = tf.tensor3d([.5], filterShape);
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| 127 | const dy = tf.tensor4d([.2, .3, .4, .2, .1, 1., .2, .3, .4], inputShape);
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| 128 | const grads = tf.grads((x, filter) => x.dilation2d(filter, 1, 'valid'));
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| 129 | const [dx, dfilter] = grads([x, filter], dy);
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| 130 | expect(dx.shape).toEqual(x.shape);
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| 131 | expectArraysClose(await dx.data(), [.2, .3, .4, .2, .1, 1., .2, .3, .4]);
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| 132 | expect(dfilter.shape).toEqual(filterShape);
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| 133 | expectArraysClose(await dfilter.data(), [3.1]);
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| 134 | });
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| 135 | it('dilation gradient same padding.', async () => {
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| 136 | const inputShape = [1, 3, 3, 1];
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| 137 | const filterShape = [1, 1, 1];
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| 138 | const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9], inputShape);
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| 139 | const filter = tf.tensor3d([.5], filterShape);
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| 140 | const dy = tf.tensor4d([.2, .3, .4, .2, .1, 1., .2, .3, .4], inputShape);
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| 141 | const grads = tf.grads((x, filter) => x.dilation2d(filter, 1, 'same'));
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| 142 | const [dx, dfilter] = grads([x, filter], dy);
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| 143 | expect(dx.shape).toEqual(x.shape);
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| 144 | expectArraysClose(await dx.data(), [.2, .3, .4, .2, .1, 1., .2, .3, .4]);
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| 145 | expect(dfilter.shape).toEqual(filterShape);
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| 146 | expectArraysClose(await dfilter.data(), [3.1]);
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| 147 | });
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| 148 | it('dilation gradient same padding depth 2.', async () => {
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| 149 | const inputShape = [1, 2, 2, 3];
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| 150 | const filterShape = [1, 1, 3];
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| 151 | const x = tf.tensor4d([.1, .2, .0, .2, .3, .1, .3, .4, .2, .4, .5, .3], inputShape);
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| 152 | const filter = tf.tensor3d([.4, .5, .6], filterShape);
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| 153 | const dy = tf.tensor4d([.2, .3, .4, .2, .1, 1., .2, .3, .4, .8, -.1, .1], inputShape);
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| 154 | const grads = tf.grads((x, filter) => x.dilation2d(filter, 1, 'same'));
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| 155 | const [dx, dfilter] = grads([x, filter], dy);
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| 156 | expect(dx.shape).toEqual(x.shape);
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| 157 | expectArraysClose(await dx.data(), [.2, .3, .4, .2, .1, 1., .2, .3, .4, .8, -.1, .1]);
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| 158 | expect(dfilter.shape).toEqual(filterShape);
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| 159 | expectArraysClose(await dfilter.data(), [1.4, .6, 1.9]);
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| 160 | });
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| 161 | it('dilation gradient valid padding filter 2.', async () => {
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| 162 | const inputShape = [1, 3, 3, 1];
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| 163 | const filterShape = [2, 2, 1];
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| 164 | const dyShape = [1, 2, 2, 1];
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| 165 | const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9], inputShape);
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| 166 | const filter = tf.tensor3d([.4, .3, .1, .2], filterShape);
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| 167 | const dy = tf.tensor4d([.2, .3, .4, .2], dyShape);
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| 168 | const grads = tf.grads((x, filter) => x.dilation2d(filter, 1, 'valid'));
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| 169 | const [dx, dfilter] = grads([x, filter], dy);
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| 170 | expect(dx.shape).toEqual(x.shape);
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| 171 | expectArraysClose(await dx.data(), [0, 0, 0, 0, .2, .3, 0, .4, .2]);
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| 172 | expect(dfilter.shape).toEqual(filterShape);
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| 173 | expectArraysClose(await dfilter.data(), [0, 0, 0, 1.1]);
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| 174 | });
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| 175 | it('dilation gradient same padding filter 2 depth 3.', async () => {
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| 176 | const inputShape = [1, 3, 3, 3];
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| 177 | const filterShape = [2, 2, 3];
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| 178 | const x = tf.tensor4d([
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| 179 | .1, .2, .3, .4, .5, .6, .7, .8, .9, .3, .2, .3, .4, .5,
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| 180 | .1, .9, .6, .3, .4, .5, .6, .2, .3, .5, .1, .2, .3
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| 181 | ], inputShape);
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| 182 | const filter = tf.tensor3d([.4, .3, .1, .2, .2, .1, .7, .3, .8, .4, .9, .1], filterShape);
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| 183 | const dy = tf.tensor4d([
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| 184 | .2, .3, .4, .2, .1, .5, 0, .8, .7, .1, .2, .1, .2, .3,
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| 185 | .4, .5, .6, .6, .6, .7, .8, .3, .2, .1, .2, .4, .2
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| 186 | ], inputShape);
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| 187 | const grads = tf.grads((x, filter) => x.dilation2d(filter, 1, 'same'));
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| 188 | const [dx, dfilter] = grads([x, filter], dy);
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| 189 | expect(dx.shape).toEqual(x.shape);
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| 190 | expectArraysClose(await dx.data(), [
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| 191 | 0, 0, 0, 0, 0, 0, 0, .8, .5, .2, 0, .4, 0, .3,
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| 192 | 0, .9, .7, .7, .7, .7, .9, .3, .4, .5, .2, .7, .8
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| 193 | ]);
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| 194 | expect(dfilter.shape).toEqual(filterShape);
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| 195 | expectArraysClose(await dfilter.data(), [1.6, 2.7, 1.1, .2, 0, .5, .3, 0, 2.2, .2, .9, 0]);
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| 196 | });
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| 197 | });
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| 198 | //# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as tf from '../index';\nimport {ALL_ENVS, describeWithFlags} from '../jasmine_util';\nimport {expectArraysClose} from '../test_util';\n\ndescribeWithFlags('dilation2d', ALL_ENVS, () => {\n  it('valid padding.', async () => {\n    const inputShape: [number, number, number, number] = [1, 2, 2, 1];\n    const filterShape: [number, number, number] = [2, 2, 1];\n    const x = tf.tensor4d([.1, .2, .3, .4], inputShape);\n    const filter = tf.tensor3d([.4, .3, .1, .0], filterShape);\n\n    const result = tf.dilation2d(x, filter, 1 /* strides */, 'valid');\n\n    expect(result.shape).toEqual([1, 1, 1, 1]);\n    expectArraysClose(await result.data(), [.5]);\n  });\n\n  it('same padding.', async () => {\n    const inputShape: [number, number, number, number] = [1, 2, 2, 1];\n    const filterShape: [number, number, number] = [2, 2, 1];\n    const x = tf.tensor4d([.1, .2, .3, .4], inputShape);\n    const filter = tf.tensor3d([.4, .3, .1, .0], filterShape);\n\n    const result = tf.dilation2d(x, filter, 1 /* strides */, 'same');\n\n    expect(result.shape).toEqual([1, 2, 2, 1]);\n    expectArraysClose(await result.data(), [.5, .6, .7, .8]);\n  });\n\n  it('same padding depth 3.', async () => {\n    const inputShape: [number, number, number, number] = [1, 2, 2, 3];\n    const filterShape: [number, number, number] = [2, 2, 3];\n    const x = tf.tensor4d(\n        [.1, .2, .0, .2, .3, .1, .3, .4, .2, .4, .5, .3], inputShape);\n    const filter = tf.tensor3d(\n        [.4, .5, .3, .3, .4, .2, .1, .2, .0, .0, .1, -.1], filterShape);\n\n    const result = tf.dilation2d(x, filter, 1 /* strides */, 'same');\n\n    expect(result.shape).toEqual([1, 2, 2, 3]);\n    expectArraysClose(\n        await result.data(), [.5, .7, .3, .6, .8, .4, .7, .9, .5, .8, 1., .6]);\n  });\n\n  it('same padding batch 2.', async () => {\n    const inputShape: [number, number, number, number] = [2, 2, 2, 1];\n    const filterShape: [number, number, number] = [2, 2, 1];\n    const x = tf.tensor4d([.1, .2, .3, .4, .2, .3, .4, .5], inputShape);\n    const filter = tf.tensor3d([.4, .3, .1, .0], filterShape);\n\n    const result = tf.dilation2d(x, filter, 1 /* strides */, 'same');\n\n    expect(result.shape).toEqual([2, 2, 2, 1]);\n    expectArraysClose(await result.data(), [.5, .6, .7, .8, .6, .7, .8, .9]);\n  });\n\n  it('same padding filter 2.', async () => {\n    const inputShape: [number, number, number, number] = [1, 3, 3, 1];\n    const filterShape: [number, number, number] = [2, 2, 1];\n    const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9], inputShape);\n    const filter = tf.tensor3d([.4, .3, .1, .2], filterShape);\n\n    const result = tf.dilation2d(x, filter, 1 /* strides */, 'same');\n\n    expect(result.shape).toEqual([1, 3, 3, 1]);\n    expectArraysClose(\n        await result.data(), [.7, .8, .7, 1, 1.1, 1, 1.1, 1.2, 1.3]);\n  });\n\n  it('valid padding non-square-window.', async () => {\n    const inputShape: [number, number, number, number] = [1, 2, 2, 1];\n    const filterShape: [number, number, number] = [1, 2, 1];\n    const x = tf.tensor4d([.1, .2, .3, .4], inputShape);\n    const filter = tf.tensor3d([.4, .3], filterShape);\n\n    const result = tf.dilation2d(x, filter, 1 /* strides */, 'valid');\n\n    expect(result.shape).toEqual([1, 2, 1, 1]);\n    expectArraysClose(await result.data(), [.5, .7]);\n  });\n\n  it('same padding dilations 2.', async () => {\n    const inputShape: [number, number, number, number] = [1, 3, 3, 1];\n    const filterShape: [number, number, number] = [2, 2, 1];\n    const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9], inputShape);\n    const filter = tf.tensor3d([.4, .3, .1, .2], filterShape);\n\n    const result = tf.dilation2d(x, filter, 1 /* strides */, 'same', 2);\n\n    // Because dilations = 2, the effective filter is [3, 3, 1]:\n    // filter_eff = [[[.4], [.0], [.3]],\n    //               [[.0], [.0], [.0]],\n    //               [[.1], [.0], [.2]]]\n    expect(result.shape).toEqual([1, 3, 3, 1]);\n    expectArraysClose(\n        await result.data(), [.7, .8, .6, 1., 1.1, .9, .8, .9, .9]);\n  });\n\n  it('valid padding uneven stride.', async () => {\n    const inputShape: [number, number, number, number] = [1, 3, 4, 1];\n    const filterShape: [number, number, number] = [2, 2, 1];\n    const x = tf.tensor4d(\n        [.1, .2, .3, .4, .5, .6, .7, .8, .9, 1., 1.1, 1.2], inputShape);\n    const filter = tf.tensor3d([.4, .3, .1, .2], filterShape);\n\n    const result = tf.dilation2d(x, filter, [1, 2] /* strides */, 'valid');\n\n    expect(result.shape).toEqual([1, 2, 2, 1]);\n    expectArraysClose(await result.data(), [.8, 1., 1.2, 1.4]);\n  });\n\n  it('throws when input rank is not 3 or 4.', async () => {\n    const filterShape: [number, number, number] = [1, 1, 1];\n    // tslint:disable-next-line:no-any\n    const x: any = tf.tensor1d([.5]);\n    const filter = tf.tensor3d([.4], filterShape);\n\n    expect(() => tf.dilation2d(x, filter, 1, 'valid')).toThrowError();\n  });\n\n  it('thorws when filter is not rank 3.', async () => {\n    const inputShape: [number, number, number, number] = [1, 2, 2, 1];\n    const filterShape: [number, number] = [2, 2];\n    const x = tf.tensor4d([.1, .2, .3, .4], inputShape);\n    // tslint:disable-next-line:no-any\n    const filter: any = tf.tensor2d([.4, .3, .1, .0], filterShape);\n\n    expect(() => tf.dilation2d(x, filter, 1, 'valid')).toThrowError();\n  });\n\n  it('throws when data format is not NHWC.', async () => {\n    const inputShape: [number, number, number, number] = [1, 2, 2, 1];\n    const filterShape: [number, number, number] = [2, 2, 1];\n    const x = tf.tensor4d([.1, .2, .3, .4], inputShape);\n    const filter = tf.tensor3d([.4, .3, .1, .0], filterShape);\n    // tslint:disable-next-line:no-any\n    const dataFormat: any = 'NCHW';\n\n    expect(\n        () => tf.dilation2d(x, filter, 1 /* strides */, 'valid', 1, dataFormat))\n        .toThrowError();\n  });\n\n  it('dilation gradient valid padding.', async () => {\n    const inputShape: [number, number, number, number] = [1, 3, 3, 1];\n    const filterShape: [number, number, number] = [1, 1, 1];\n    const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9], inputShape);\n    const filter = tf.tensor3d([.5], filterShape);\n    const dy = tf.tensor4d([.2, .3, .4, .2, .1, 1., .2, .3, .4], inputShape);\n\n    const grads = tf.grads(\n        (x: tf.Tensor4D, filter: tf.Tensor3D) =>\n            x.dilation2d(filter, 1, 'valid'));\n\n    const [dx, dfilter] = grads([x, filter], dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), [.2, .3, .4, .2, .1, 1., .2, .3, .4]);\n\n    expect(dfilter.shape).toEqual(filterShape);\n    expectArraysClose(await dfilter.data(), [3.1]);\n  });\n\n  it('dilation gradient same padding.', async () => {\n    const inputShape: [number, number, number, number] = [1, 3, 3, 1];\n    const filterShape: [number, number, number] = [1, 1, 1];\n    const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9], inputShape);\n    const filter = tf.tensor3d([.5], filterShape);\n    const dy = tf.tensor4d([.2, .3, .4, .2, .1, 1., .2, .3, .4], inputShape);\n\n    const grads = tf.grads(\n        (x: tf.Tensor4D, filter: tf.Tensor3D) =>\n            x.dilation2d(filter, 1, 'same'));\n\n    const [dx, dfilter] = grads([x, filter], dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), [.2, .3, .4, .2, .1, 1., .2, .3, .4]);\n\n    expect(dfilter.shape).toEqual(filterShape);\n    expectArraysClose(await dfilter.data(), [3.1]);\n  });\n\n  it('dilation gradient same padding depth 2.', async () => {\n    const inputShape: [number, number, number, number] = [1, 2, 2, 3];\n    const filterShape: [number, number, number] = [1, 1, 3];\n    const x = tf.tensor4d(\n        [.1, .2, .0, .2, .3, .1, .3, .4, .2, .4, .5, .3], inputShape);\n    const filter = tf.tensor3d([.4, .5, .6], filterShape);\n    const dy = tf.tensor4d(\n        [.2, .3, .4, .2, .1, 1., .2, .3, .4, .8, -.1, .1], inputShape);\n\n    const grads = tf.grads(\n        (x: tf.Tensor4D, filter: tf.Tensor3D) =>\n            x.dilation2d(filter, 1, 'same'));\n\n    const [dx, dfilter] = grads([x, filter], dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(\n        await dx.data(), [.2, .3, .4, .2, .1, 1., .2, .3, .4, .8, -.1, .1]);\n\n    expect(dfilter.shape).toEqual(filterShape);\n    expectArraysClose(await dfilter.data(), [1.4, .6, 1.9]);\n  });\n\n  it('dilation gradient valid padding filter 2.', async () => {\n    const inputShape: [number, number, number, number] = [1, 3, 3, 1];\n    const filterShape: [number, number, number] = [2, 2, 1];\n    const dyShape: [number, number, number, number] = [1, 2, 2, 1];\n    const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9], inputShape);\n    const filter = tf.tensor3d([.4, .3, .1, .2], filterShape);\n    const dy = tf.tensor4d([.2, .3, .4, .2], dyShape);\n\n    const grads = tf.grads(\n        (x: tf.Tensor4D, filter: tf.Tensor3D) =>\n            x.dilation2d(filter, 1, 'valid'));\n\n    const [dx, dfilter] = grads([x, filter], dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), [0, 0, 0, 0, .2, .3, 0, .4, .2]);\n\n    expect(dfilter.shape).toEqual(filterShape);\n    expectArraysClose(await dfilter.data(), [0, 0, 0, 1.1]);\n  });\n\n  it('dilation gradient same padding filter 2 depth 3.', async () => {\n    const inputShape: [number, number, number, number] = [1, 3, 3, 3];\n    const filterShape: [number, number, number] = [2, 2, 3];\n    const x = tf.tensor4d(\n        [\n          .1, .2, .3, .4, .5, .6, .7, .8, .9, .3, .2, .3, .4, .5,\n          .1, .9, .6, .3, .4, .5, .6, .2, .3, .5, .1, .2, .3\n        ],\n        inputShape);\n    const filter = tf.tensor3d(\n        [.4, .3, .1, .2, .2, .1, .7, .3, .8, .4, .9, .1], filterShape);\n    const dy = tf.tensor4d(\n        [\n          .2, .3, .4, .2, .1, .5, 0,  .8, .7, .1, .2, .1, .2, .3,\n          .4, .5, .6, .6, .6, .7, .8, .3, .2, .1, .2, .4, .2\n        ],\n        inputShape);\n\n    const grads = tf.grads(\n        (x: tf.Tensor4D, filter: tf.Tensor3D) =>\n            x.dilation2d(filter, 1, 'same'));\n\n    const [dx, dfilter] = grads([x, filter], dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), [\n      0, 0,  0,  0,  0,  0,  0,  .8, .5, .2, 0,  .4, 0, .3,\n      0, .9, .7, .7, .7, .7, .9, .3, .4, .5, .2, .7, .8\n    ]);\n\n    expect(dfilter.shape).toEqual(filterShape);\n    expectArraysClose(\n        await dfilter.data(),\n        [1.6, 2.7, 1.1, .2, 0, .5, .3, 0, 2.2, .2, .9, 0]);\n  });\n});\n"]} |
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