1 | /**
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2 | * @license
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3 | * Copyright 2017 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('conv2dTranspose', ALL_ENVS, () => {
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21 | it('input=2x2x1,d2=1,f=2,s=1,p=0', async () => {
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22 | const origInputDepth = 1;
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23 | const origOutputDepth = 1;
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24 | const inputShape = [1, 1, origOutputDepth];
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25 | const fSize = 2;
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26 | const origPad = 0;
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27 | const origStride = 1;
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28 | const x = tf.tensor3d([2], inputShape);
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29 | const w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);
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30 | const result = tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad);
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31 | const expected = [6, 2, 10, 0];
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32 | expect(result.shape).toEqual([2, 2, 1]);
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33 | expectArraysClose(await result.data(), expected);
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34 | });
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35 | it('input=3x3x1,d2=1,f=2,s=2,p=same', async () => {
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36 | const origInputDepth = 1;
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37 | const origOutputDepth = 4;
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38 | const inputShape = [1, 2, 2, origOutputDepth];
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39 | const fSize = 2;
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40 | const origPad = 'same';
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41 | const origStride = 2;
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42 | const x = tf.tensor4d([
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43 | 1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34, 0.28,
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44 | 0., 0.06, 0.14, 0.24
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45 | ], inputShape);
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46 | const w = tf.tensor4d([0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.], [fSize, fSize, origInputDepth, origOutputDepth]);
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47 | const result = tf.conv2dTranspose(x, w, [1, 3, 3, 1], origStride, origPad);
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48 | const expected = [7.63, 28.39, 2.94, 49.15, 69.91, 14.62, 1.69, 5.01, 1.06];
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49 | expect(result.shape).toEqual([1, 3, 3, 1]);
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50 | expectArraysClose(await result.data(), expected);
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51 | });
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52 | it('input=3x3x1,d2=1,f=2,s=2,p=explicit', async () => {
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53 | const origInputDepth = 1;
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54 | const origOutputDepth = 4;
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55 | const inputShape = [1, 2, 2, origOutputDepth];
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56 | const fSize = 2;
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57 | const origPad = [[0, 0], [0, 1], [0, 1], [0, 0]];
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58 | const origStride = 2;
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59 | const x = tf.tensor4d([
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60 | 1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34, 0.28,
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61 | 0., 0.06, 0.14, 0.24
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62 | ], inputShape);
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63 | const w = tf.tensor4d([0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.], [fSize, fSize, origInputDepth, origOutputDepth]);
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64 | const result = tf.conv2dTranspose(x, w, [1, 3, 3, 1], origStride, origPad);
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65 | const expected = [7.63, 28.39, 2.94, 49.15, 69.91, 14.62, 1.69, 5.01, 1.06];
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66 | expect(result.shape).toEqual([1, 3, 3, 1]);
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67 | expectArraysClose(await result.data(), expected);
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68 | });
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69 | it('input=2x2x1,d2=1,f=2,s=1,p=0, batch=2', async () => {
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70 | const origInputDepth = 1;
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71 | const origOutputDepth = 1;
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72 | const inputShape = [2, 1, 1, origOutputDepth];
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73 | const fSize = 2;
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74 | const origPad = 0;
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75 | const origStride = 1;
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76 | const x = tf.tensor4d([2, 3], inputShape);
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77 | const w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);
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78 | const result = tf.conv2dTranspose(x, w, [2, 2, 2, 1], origStride, origPad);
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79 | const expected = [6, 2, 10, 0, 9, 3, 15, 0];
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80 | expect(result.shape).toEqual([2, 2, 2, 1]);
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81 | expectArraysClose(await result.data(), expected);
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82 | });
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83 | it('input=2x2x2,output=3x3x2,f=2,s=2,inDepth=2,p=same', async () => {
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84 | const origInputDepth = 2;
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85 | const origOutputDepth = 2;
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86 | const inputShape = [1, 2, 2, origOutputDepth];
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87 | const fSize = 2;
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88 | const origPad = 'same';
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89 | const origStride = 2;
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90 | const x = tf.tensor4d([0., 1., 2., 3., 4., 5., 6., 7.], inputShape);
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91 | const w = tf.tensor4d([0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.], [fSize, fSize, origInputDepth, origOutputDepth]);
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92 | const result = tf.conv2dTranspose(x, w, [1, 3, 3, origInputDepth], origStride, origPad);
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93 | const expected = [1, 3, 5, 7, 3, 13, 9, 11, 13, 15, 43, 53, 5, 23, 41, 59, 7, 33.];
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94 | expect(result.shape).toEqual([1, 3, 3, origInputDepth]);
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95 | expectArraysClose(await result.data(), expected);
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96 | });
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97 | it('throws when dimRoundingMode is set and pad is same', async () => {
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98 | const origInputDepth = 1;
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99 | const origOutputDepth = 4;
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100 | const inputShape = [1, 2, 2, origOutputDepth];
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101 | const fSize = 2;
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102 | const origPad = 'same';
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103 | const origStride = 2;
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104 | const dimRoundingMode = 'round';
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105 | const x = tf.tensor4d([
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106 | 1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34, 0.28,
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107 | 0., 0.06, 0.14, 0.24
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108 | ], inputShape);
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109 | const w = tf.tensor4d([0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.], [fSize, fSize, origInputDepth, origOutputDepth]);
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110 | expect(() => tf.conv2dTranspose(x, w, [1, 3, 3, 1], origStride, origPad, dimRoundingMode))
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111 | .toThrowError();
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112 | });
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113 | it('throws when dimRoundingMode is set and pad is valid', async () => {
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114 | const origInputDepth = 1;
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115 | const origOutputDepth = 4;
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116 | const inputShape = [1, 2, 2, origOutputDepth];
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117 | const fSize = 2;
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118 | const origPad = 'valid';
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119 | const origStride = 2;
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120 | const dimRoundingMode = 'round';
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121 | const x = tf.tensor4d([
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122 | 1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34, 0.28,
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123 | 0., 0.06, 0.14, 0.24
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124 | ], inputShape);
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125 | const w = tf.tensor4d([0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.], [fSize, fSize, origInputDepth, origOutputDepth]);
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126 | expect(() => tf.conv2dTranspose(x, w, [1, 3, 3, 1], origStride, origPad, dimRoundingMode))
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127 | .toThrowError();
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128 | });
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129 | it('throws when dimRoundingMode is set and pad is a non-integer number', async () => {
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130 | const origInputDepth = 1;
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131 | const origOutputDepth = 4;
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132 | const inputShape = [1, 2, 2, origOutputDepth];
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133 | const fSize = 2;
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134 | const origPad = 1.2;
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135 | const origStride = 2;
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136 | const dimRoundingMode = 'round';
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137 | const x = tf.tensor4d([
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138 | 1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34,
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139 | 0.28, 0., 0.06, 0.14, 0.24
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140 | ], inputShape);
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141 | const w = tf.tensor4d([
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142 | 0., 1., 2., 3., 4., 5., 6., 7., 8.,
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143 | 9., 10., 11., 12., 13., 14., 15.
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144 | ], [fSize, fSize, origInputDepth, origOutputDepth]);
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145 | expect(() => tf.conv2dTranspose(x, w, [1, 3, 3, 1], origStride, origPad, dimRoundingMode))
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146 | .toThrowError();
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147 | });
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148 | it('throws when dimRoundingMode is set and pad is explicit by non-integer ' +
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149 | 'number', async () => {
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150 | const origInputDepth = 1;
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151 | const origOutputDepth = 4;
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152 | const inputShape = [1, 2, 2, origOutputDepth];
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153 | const fSize = 2;
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154 | const origPad = [[0, 0], [0, 1.1], [0, 1], [0, 0]];
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155 | const origStride = 2;
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156 | const dimRoundingMode = 'round';
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157 | const x = tf.tensor4d([
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158 | 1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34,
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159 | 0.28, 0., 0.06, 0.14, 0.24
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160 | ], inputShape);
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161 | const w = tf.tensor4d([
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162 | 0., 1., 2., 3., 4., 5., 6., 7., 8.,
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163 | 9., 10., 11., 12., 13., 14., 15.
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164 | ], [fSize, fSize, origInputDepth, origOutputDepth]);
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165 | expect(() => tf.conv2dTranspose(x, w, [1, 3, 3, 1], origStride, origPad, dimRoundingMode))
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166 | .toThrowError();
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167 | });
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168 | // Reference (Python) TensorFlow code:
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169 | //
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170 | // ```py
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171 | // import numpy as np
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172 | // import tensorflow as tf
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173 | //
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174 | // tf.enable_eager_execution()
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175 | //
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176 | // x = tf.constant(np.array([[
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177 | // [[-0.14656299], [0.32942239], [-1.90302866]],
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178 | // [[-0.06487813], [-2.02637842], [-1.83669377]],
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179 | // [[0.82650784], [-0.89249092], [0.01207666]]
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180 | // ]]).astype(np.float32))
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181 | // filt = tf.constant(np.array([
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182 | // [[[-0.48280062], [1.26770487]], [[-0.83083738], [0.54341856]]],
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183 | // [[[-0.274904], [0.73111374]], [[2.01885189], [-2.68975237]]]
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184 | // ]).astype(np.float32))
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185 | //
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186 | // with tf.GradientTape() as g:
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187 | // g.watch(x)
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188 | // g.watch(filt)
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189 | // y = tf.keras.backend.conv2d_transpose(x, filt, [1, 4, 4, 2])
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190 | // print(y)
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191 | // (x_grad, filt_grad) = g.gradient(y, [x, filt])
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192 | //
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193 | // print("x_grad = %s" % x_grad)
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194 | // print("filt_grad = %s" % filt_grad)
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195 | // ```
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196 | it('gradient with clones input=[1,3,3,1] f=[2,2,2,1] s=1 padding=valid', async () => {
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197 | const inputDepth = 1;
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198 | const outputDepth = 2;
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199 | const inputShape = [1, 3, 3, inputDepth];
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200 | const filterSize = 2;
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201 | const stride = 1;
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202 | const pad = 'valid';
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203 | const filterShape = [filterSize, filterSize, outputDepth, inputDepth];
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204 | const x = tf.tensor4d([[
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205 | [[-0.14656299], [0.32942239], [-1.90302866]],
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206 | [[-0.06487813], [-2.02637842], [-1.83669377]],
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207 | [[0.82650784], [-0.89249092], [0.01207666]]
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208 | ]], inputShape);
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209 | const filt = tf.tensor4d([
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210 | [[[-0.48280062], [1.26770487]], [[-0.83083738], [0.54341856]]],
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211 | [[[-0.274904], [0.73111374]], [[2.01885189], [-2.68975237]]]
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212 | ], filterShape);
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213 | const grads = tf.grads((x, filter) => tf.conv2dTranspose(x.clone(), filter.clone(), [1, 4, 4, outputDepth], stride, pad)
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214 | .clone());
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215 | const dy = tf.ones([1, 4, 4, outputDepth]);
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216 | const [xGrad, filtGrad] = grads([x, filt], dy);
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217 | const expectedXGrad = tf.ones([1, 3, 3, 1]).mul(tf.scalar(0.2827947));
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218 | expectArraysClose(await xGrad.data(), await expectedXGrad.data());
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219 | const expectedFiltGrad = tf.ones([2, 2, 2, 1]).mul(tf.scalar(-5.70202599));
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220 | expectArraysClose(await filtGrad.data(), await expectedFiltGrad.data());
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221 | });
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222 | // Reference (Python) TensorFlow code:
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223 | //
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224 | // ```py
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225 | // import numpy as np
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226 | // import tensorflow as tf
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227 | //
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228 | // tf.enable_eager_execution()
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229 | //
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230 | // x = tf.constant(np.array([
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231 | // [[[-0.36541713], [-0.53973116]], [[0.01731674], [0.90227772]]]
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232 | // ]).astype(np.float32))
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233 | // filt = tf.constant(np.array([
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234 | // [[[-0.01423461], [-1.00267384]], [[1.61163029], [0.66302646]]],
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235 | // [[[-0.46900087], [-0.78649444]], [[0.87780536], [-0.84551637]]]
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236 | // ]).astype(np.float32))
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237 | //
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238 | // with tf.GradientTape() as g:
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239 | // g.watch(x)
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240 | // g.watch(filt)
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241 | // y = tf.keras.backend.conv2d_transpose(x, filt, [1, 4, 4, 2], strides=(2,
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242 | // 2)) print(y)
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243 | // (x_grad, filt_grad) = g.gradient(y, [x, filt])
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244 | //
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245 | // print("x_grad = %s" % -x_grad)
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246 | // print("filt_grad = %s" % -filt_grad)
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247 | // ```
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248 | it('gradient input=[1,2,2,1] f=[2,2,2,1] s=[2,2] padding=valid', async () => {
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249 | const inputDepth = 1;
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250 | const outputDepth = 2;
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251 | const inputShape = [1, 2, 2, inputDepth];
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252 | const filterSize = 2;
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253 | const stride = [2, 2];
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254 | const pad = 'valid';
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255 | const filterShape = [filterSize, filterSize, outputDepth, inputDepth];
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256 | const x = tf.tensor4d([[[[-0.36541713], [-0.53973116]], [[0.01731674], [0.90227772]]]], inputShape);
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257 | const filt = tf.tensor4d([
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258 | [[[-0.01423461], [-1.00267384]], [[1.61163029], [0.66302646]]],
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259 | [[[-0.46900087], [-0.78649444]], [[0.87780536], [-0.84551637]]]
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260 | ], filterShape);
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261 | const grads = tf.grads((x, filter) => tf.conv2dTranspose(x, filter, [1, 4, 4, outputDepth], stride, pad));
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262 | const dy = tf.ones([1, 4, 4, outputDepth]).mul(tf.scalar(-1));
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263 | const [xGrad, filtGrad] = grads([x, filt], dy);
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264 | const expectedXGrad = tf.ones([1, 2, 2, 1]).mul(tf.scalar(-0.03454196));
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265 | expectArraysClose(await xGrad.data(), await expectedXGrad.data());
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266 | expect(xGrad.shape).toEqual([1, 2, 2, 1]);
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267 | const expectedFiltGrad = tf.ones([2, 2, 2, 1]).mul(tf.scalar(-0.01444618));
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268 | expectArraysClose(await filtGrad.data(), await expectedFiltGrad.data());
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269 | expect(filtGrad.shape).toEqual([2, 2, 2, 1]);
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270 | });
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271 | // Reference (Python) TensorFlow code:
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272 | //
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273 | // ```py
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274 | // import numpy as np
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275 | // import tensorflow as tf
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276 | //
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277 | // tf.enable_eager_execution()
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278 | //
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279 | // x = tf.constant(np.array([[
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280 | // [[1.52433065], [-0.77053435], [-0.64562341]],
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281 | // [[0.77962889], [1.58413887], [-0.25581856]],
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282 | // [[-0.58966221], [0.05411662], [0.70749138]]
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283 | // ]]).astype(np.float32))
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284 | // filt = tf.constant(np.array([
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285 | // [[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],
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286 | // [[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]
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287 | // ]).astype(np.float32))
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288 | //
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289 | // with tf.GradientTape() as g:
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290 | // g.watch(x)
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291 | // g.watch(filt)
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292 | // y = tf.keras.backend.conv2d_transpose(
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293 | // x, filt, [1, 3, 3, 2], strides=(1, 1), padding='same')
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294 | // (x_grad, filt_grad) = g.gradient(y, [x, filt])
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295 | //
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296 | // print("x_grad = %s" % x_grad)
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297 | // print("filt_grad = %s" % filt_grad)
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298 | // ```
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299 | it('gradient input=[1,3,3,1] f=[2,2,2,1] s=[1,1] padding=same', async () => {
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300 | const inputDepth = 1;
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301 | const outputDepth = 2;
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302 | const inputShape = [1, 3, 3, inputDepth];
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303 | const filterSize = 2;
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304 | const stride = [1, 1];
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305 | const pad = 'same';
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306 | const filterShape = [filterSize, filterSize, outputDepth, inputDepth];
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307 | const x = tf.tensor4d([[
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308 | [[1.52433065], [-0.77053435], [-0.64562341]],
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309 | [[0.77962889], [1.58413887], [-0.25581856]],
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310 | [[-0.58966221], [0.05411662], [0.70749138]]
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311 | ]], inputShape);
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312 | const filt = tf.tensor4d([
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313 | [[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],
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314 | [[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]
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315 | ], filterShape);
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316 | const grads = tf.grads((x, filter) => tf.conv2dTranspose(x, filter, [1, 3, 3, outputDepth], stride, pad));
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317 | const dy = tf.ones([1, 3, 3, outputDepth]);
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318 | const [xGrad, filtGrad] = grads([x, filt], dy);
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319 | expectArraysClose(await xGrad.array(), [[
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320 | [[1.30709858], [1.30709858], [-0.92814366]],
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321 | [[1.30709858], [1.30709858], [-0.92814366]],
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322 | [[1.19666437], [1.19666437], [-0.85476589]]
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323 | ]]);
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324 | expectArraysClose(await filtGrad.array(), [
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325 | [[[2.38806788], [2.38806788]], [[2.58201847], [2.58201847]]],
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326 | [[[2.2161221], [2.2161221]], [[3.11756406], [3.11756406]]]
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327 | ]);
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328 | });
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329 | it('gradient input=[1,3,3,1] f=[2,2,2,1] s=[1,1] p=explicit', async () => {
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330 | const inputDepth = 1;
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331 | const outputDepth = 2;
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332 | const inputShape = [1, 3, 3, inputDepth];
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333 | const filterSize = 2;
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334 | const stride = [1, 1];
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335 | const pad = [[0, 0], [0, 1], [0, 1], [0, 0]];
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336 | const filterShape = [filterSize, filterSize, outputDepth, inputDepth];
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337 | const x = tf.tensor4d([[
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338 | [[1.52433065], [-0.77053435], [-0.64562341]],
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339 | [[0.77962889], [1.58413887], [-0.25581856]],
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340 | [[-0.58966221], [0.05411662], [0.70749138]]
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341 | ]], inputShape);
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342 | const filt = tf.tensor4d([
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343 | [[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],
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344 | [[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]
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345 | ], filterShape);
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346 | const grads = tf.grads((x, filter) => tf.conv2dTranspose(x, filter, [1, 3, 3, outputDepth], stride, pad));
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347 | const dy = tf.ones([1, 3, 3, outputDepth]);
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348 | const [xGrad, filtGrad] = grads([x, filt], dy);
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349 | expectArraysClose(await xGrad.array(), [[
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350 | [[1.30709858], [1.30709858], [-0.92814366]],
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351 | [[1.30709858], [1.30709858], [-0.92814366]],
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352 | [[1.19666437], [1.19666437], [-0.85476589]]
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353 | ]]);
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354 | expectArraysClose(await filtGrad.array(), [
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355 | [[[2.38806788], [2.38806788]], [[2.58201847], [2.58201847]]],
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356 | [[[2.2161221], [2.2161221]], [[3.11756406], [3.11756406]]]
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357 | ]);
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358 | });
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359 | // Reference (Python) TensorFlow code:
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360 | //
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361 | // ```py
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362 | // import numpy as np
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363 | // import tensorflow as tf
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364 | //
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365 | // tf.enable_eager_execution()
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366 | //
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367 | // x = tf.constant(np.array([[
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368 | // [[1.52433065], [-0.77053435]], [[0.77962889], [1.58413887]],
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369 | // ]]).astype(np.float32))
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370 | // filt = tf.constant(np.array([
|
371 | // [[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],
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372 | // [[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]
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373 | // ]).astype(np.float32))
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374 | //
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375 | // with tf.GradientTape() as g:
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376 | // g.watch(x)
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377 | // g.watch(filt)
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378 | // y = tf.keras.backend.conv2d_transpose(
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379 | // x, filt, [1, 3, 3, 2], strides=(2, 2), padding='same')
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380 | // print(y.shape)
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381 | // (x_grad, filt_grad) = g.gradient(y, [x, filt])
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382 | //
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383 | // print("x_grad = %s" % x_grad)
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384 | // print("filt_grad = %s" % filt_grad)
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385 | // ```
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386 | it('gradient input=[1,2,2,2] f=[2,2,2,1] s=[2,2] padding=same', async () => {
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387 | const inputDepth = 2;
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388 | const outputDepth = 2;
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389 | const inputShape = [1, 2, 2, inputDepth];
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390 | const filterSize = 2;
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391 | const stride = [2, 2];
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392 | const pad = 'same';
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393 | const filterShape = [filterSize, filterSize, outputDepth, inputDepth];
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394 | const x = tf.tensor4d([[
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395 | [[-1.81506593, 1.00900095], [-0.05199118, 0.26311377]],
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396 | [[-1.18469792, -0.34780521], [2.04971242, -0.65154692]]
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397 | ]], inputShape);
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398 | const filt = tf.tensor4d([
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399 | [
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400 | [[0.19529686, -0.79594708], [0.70314057, -0.06081263]],
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401 | [[0.28724744, 0.88522715], [-0.51824096, -0.97120989]]
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402 | ],
|
403 | [
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404 | [[0.51872197, -1.17569193], [1.28316791, -0.81225092]],
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405 | [[-0.44221532, 0.70058174], [-0.4849217, 0.03806348]]
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406 | ]
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407 | ], filterShape);
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408 | const grads = tf.grads((x, filter) => tf.conv2dTranspose(x, filter, [1, 3, 3, outputDepth], stride, pad));
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409 | const dy = tf.ones([1, 3, 3, outputDepth]);
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410 | const [xGrad, filtGrad] = grads([x, filt], dy);
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411 | expectArraysClose(await xGrad.data(), [
|
412 | 1.54219678, -2.19204008, 2.70032732, -2.84470257, 0.66744391, -0.94274245,
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413 | 0.89843743, -0.85675972
|
414 | ]);
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415 | expect(xGrad.shape).toEqual([1, 2, 2, 2]);
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416 | expectArraysClose(await filtGrad.data(), [
|
417 | -1.00204261, 0.27276259, -1.00204261, 0.27276259, -2.99976385, 0.66119574,
|
418 | -2.99976385, 0.66119574, -1.86705711, 1.27211472, -1.86705711, 1.27211472,
|
419 | -1.81506593, 1.00900095, -1.81506593, 1.00900095
|
420 | ]);
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421 | expect(filtGrad.shape).toEqual([2, 2, 2, 2]);
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422 | });
|
423 | it('throws when x is not rank 3', () => {
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424 | const origInputDepth = 1;
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425 | const origOutputDepth = 1;
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426 | const fSize = 2;
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427 | const origPad = 0;
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428 | const origStride = 1;
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429 | // tslint:disable-next-line:no-any
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430 | const x = tf.tensor2d([2, 2], [2, 1]);
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431 | const w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);
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432 | expect(() => tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad))
|
433 | .toThrowError();
|
434 | });
|
435 | it('throws when weights is not rank 4', () => {
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436 | const origInputDepth = 1;
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437 | const origOutputDepth = 1;
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438 | const inputShape = [1, 1, origOutputDepth];
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439 | const fSize = 2;
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440 | const origPad = 0;
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441 | const origStride = 1;
|
442 | const x = tf.tensor3d([2], inputShape);
|
443 | // tslint:disable-next-line:no-any
|
444 | const w = tf.tensor3d([3, 1, 5, 0], [fSize, fSize, origInputDepth]);
|
445 | expect(() => tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad))
|
446 | .toThrowError();
|
447 | });
|
448 | it('throws when x depth does not match weights original output depth', () => {
|
449 | const origInputDepth = 1;
|
450 | const origOutputDepth = 2;
|
451 | const wrongOrigOutputDepth = 3;
|
452 | const inputShape = [1, 1, origOutputDepth];
|
453 | const fSize = 2;
|
454 | const origPad = 0;
|
455 | const origStride = 1;
|
456 | const x = tf.tensor3d([2, 2], inputShape);
|
457 | const w = tf.randomNormal([fSize, fSize, origInputDepth, wrongOrigOutputDepth]);
|
458 | expect(() => tf.conv2dTranspose(x, w, [2, 2, 2], origStride, origPad))
|
459 | .toThrowError();
|
460 | });
|
461 | it('throws when passed x as a non-tensor', () => {
|
462 | const origInputDepth = 1;
|
463 | const origOutputDepth = 1;
|
464 | const fSize = 2;
|
465 | const origPad = 0;
|
466 | const origStride = 1;
|
467 | const w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);
|
468 | expect(() => tf.conv2dTranspose({}, w, [2, 2, 1], origStride, origPad))
|
469 | .toThrowError(/Argument 'x' passed to 'conv2dTranspose' must be a Tensor/);
|
470 | });
|
471 | it('throws when passed filter as a non-tensor', () => {
|
472 | const origOutputDepth = 1;
|
473 | const inputShape = [1, 1, origOutputDepth];
|
474 | const origPad = 0;
|
475 | const origStride = 1;
|
476 | const x = tf.tensor3d([2], inputShape);
|
477 | expect(() => tf.conv2dTranspose(x, {}, [2, 2, 1], origStride, origPad))
|
478 | .toThrowError(/Argument 'filter' passed to 'conv2dTranspose' must be a Tensor/);
|
479 | });
|
480 | it('accepts a tensor-like object', async () => {
|
481 | const origPad = 0;
|
482 | const origStride = 1;
|
483 | const x = [[[2]]]; // 1x1x1
|
484 | const w = [[[[3]], [[1]]], [[[5]], [[0]]]]; // 2x2x1x1
|
485 | const result = tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad);
|
486 | const expected = [6, 2, 10, 0];
|
487 | expect(result.shape).toEqual([2, 2, 1]);
|
488 | expectArraysClose(await result.data(), expected);
|
489 | });
|
490 | });
|
491 | //# 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CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACxC,iBAAiB,CAAC,MAAM,MAAM,CAAC,IAAI,EAAE,EAAE,QAAQ,CAAC,CAAC;IACnD,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2017 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';\nimport {Rank} from '../types';\n\ndescribeWithFlags('conv2dTranspose', ALL_ENVS, () => {\n  it('input=2x2x1,d2=1,f=2,s=1,p=0', async () => {\n    const origInputDepth = 1;\n    const origOutputDepth = 1;\n    const inputShape: [number, number, number] = [1, 1, origOutputDepth];\n    const fSize = 2;\n    const origPad = 0;\n    const origStride = 1;\n\n    const x = tf.tensor3d([2], inputShape);\n    const w = tf.tensor4d(\n        [3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);\n\n    const result = tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad);\n    const expected = [6, 2, 10, 0];\n\n    expect(result.shape).toEqual([2, 2, 1]);\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('input=3x3x1,d2=1,f=2,s=2,p=same', async () => {\n    const origInputDepth = 1;\n    const origOutputDepth = 4;\n    const inputShape: [number, number, number, number] =\n        [1, 2, 2, origOutputDepth];\n    const fSize = 2;\n    const origPad = 'same';\n    const origStride = 2;\n\n    const x = tf.tensor4d(\n        [\n          1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34, 0.28,\n          0., 0.06, 0.14, 0.24\n        ],\n        inputShape);\n    const w = tf.tensor4d(\n        [0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.],\n        [fSize, fSize, origInputDepth, origOutputDepth]);\n\n    const result = tf.conv2dTranspose(x, w, [1, 3, 3, 1], origStride, origPad);\n    const expected = [7.63, 28.39, 2.94, 49.15, 69.91, 14.62, 1.69, 5.01, 1.06];\n\n    expect(result.shape).toEqual([1, 3, 3, 1]);\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('input=3x3x1,d2=1,f=2,s=2,p=explicit', async () => {\n    const origInputDepth = 1;\n    const origOutputDepth = 4;\n    const inputShape: [number, number, number, number] =\n        [1, 2, 2, origOutputDepth];\n    const fSize = 2;\n    const origPad =\n        [[0, 0], [0, 1], [0, 1], [0, 0]] as tf.backend_util.ExplicitPadding;\n    const origStride = 2;\n\n    const x = tf.tensor4d(\n        [\n          1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34, 0.28,\n          0., 0.06, 0.14, 0.24\n        ],\n        inputShape);\n    const w = tf.tensor4d(\n        [0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.],\n        [fSize, fSize, origInputDepth, origOutputDepth]);\n\n    const result = tf.conv2dTranspose(x, w, [1, 3, 3, 1], origStride, origPad);\n    const expected = [7.63, 28.39, 2.94, 49.15, 69.91, 14.62, 1.69, 5.01, 1.06];\n\n    expect(result.shape).toEqual([1, 3, 3, 1]);\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('input=2x2x1,d2=1,f=2,s=1,p=0, batch=2', async () => {\n    const origInputDepth = 1;\n    const origOutputDepth = 1;\n    const inputShape: [number, number, number, number] =\n        [2, 1, 1, origOutputDepth];\n    const fSize = 2;\n    const origPad = 0;\n    const origStride = 1;\n\n    const x = tf.tensor4d([2, 3], inputShape);\n    const w = tf.tensor4d(\n        [3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);\n\n    const result = tf.conv2dTranspose(x, w, [2, 2, 2, 1], origStride, origPad);\n    const expected = [6, 2, 10, 0, 9, 3, 15, 0];\n\n    expect(result.shape).toEqual([2, 2, 2, 1]);\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('input=2x2x2,output=3x3x2,f=2,s=2,inDepth=2,p=same', async () => {\n    const origInputDepth = 2;\n    const origOutputDepth = 2;\n    const inputShape: [number, number, number, number] =\n        [1, 2, 2, origOutputDepth];\n    const fSize = 2;\n    const origPad = 'same';\n    const origStride = 2;\n\n    const x = tf.tensor4d([0., 1., 2., 3., 4., 5., 6., 7.], inputShape);\n    const w = tf.tensor4d(\n        [0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.],\n        [fSize, fSize, origInputDepth, origOutputDepth]);\n\n    const result = tf.conv2dTranspose(\n        x, w, [1, 3, 3, origInputDepth], origStride, origPad);\n    const expected =\n        [1, 3, 5, 7, 3, 13, 9, 11, 13, 15, 43, 53, 5, 23, 41, 59, 7, 33.];\n\n    expect(result.shape).toEqual([1, 3, 3, origInputDepth]);\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('throws when dimRoundingMode is set and pad is same', async () => {\n    const origInputDepth = 1;\n    const origOutputDepth = 4;\n    const inputShape: [number, number, number, number] =\n        [1, 2, 2, origOutputDepth];\n    const fSize = 2;\n    const origPad = 'same';\n    const origStride = 2;\n    const dimRoundingMode = 'round';\n\n    const x = tf.tensor4d(\n        [\n          1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34, 0.28,\n          0., 0.06, 0.14, 0.24\n        ],\n        inputShape);\n    const w = tf.tensor4d(\n        [0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.],\n        [fSize, fSize, origInputDepth, origOutputDepth]);\n\n    expect(\n        () => tf.conv2dTranspose(\n            x, w, [1, 3, 3, 1], origStride, origPad, dimRoundingMode))\n        .toThrowError();\n  });\n\n  it('throws when dimRoundingMode is set and pad is valid', async () => {\n    const origInputDepth = 1;\n    const origOutputDepth = 4;\n    const inputShape: [number, number, number, number] =\n        [1, 2, 2, origOutputDepth];\n    const fSize = 2;\n    const origPad = 'valid';\n    const origStride = 2;\n    const dimRoundingMode = 'round';\n\n    const x = tf.tensor4d(\n        [\n          1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34, 0.28,\n          0., 0.06, 0.14, 0.24\n        ],\n        inputShape);\n    const w = tf.tensor4d(\n        [0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.],\n        [fSize, fSize, origInputDepth, origOutputDepth]);\n\n    expect(\n        () => tf.conv2dTranspose(\n            x, w, [1, 3, 3, 1], origStride, origPad, dimRoundingMode))\n        .toThrowError();\n  });\n\n  it('throws when dimRoundingMode is set and pad is a non-integer number',\n     async () => {\n       const origInputDepth = 1;\n       const origOutputDepth = 4;\n       const inputShape: [number, number, number, number] =\n           [1, 2, 2, origOutputDepth];\n       const fSize = 2;\n       const origPad = 1.2;\n       const origStride = 2;\n       const dimRoundingMode = 'round';\n\n       const x = tf.tensor4d(\n           [\n             1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34,\n             0.28, 0., 0.06, 0.14, 0.24\n           ],\n           inputShape);\n       const w = tf.tensor4d(\n           [\n             0., 1., 2., 3., 4., 5., 6., 7., 8.,\n             9., 10., 11., 12., 13., 14., 15.\n           ],\n           [fSize, fSize, origInputDepth, origOutputDepth]);\n\n       expect(\n           () => tf.conv2dTranspose(\n              x, w, [1, 3, 3, 1], origStride, origPad, dimRoundingMode))\n           .toThrowError();\n     });\n\n  it('throws when dimRoundingMode is set and pad is explicit by non-integer ' +\n         'number',\n     async () => {\n       const origInputDepth = 1;\n       const origOutputDepth = 4;\n       const inputShape: [number, number, number, number] =\n           [1, 2, 2, origOutputDepth];\n       const fSize = 2;\n       const origPad = [[0, 0], [0, 1.1], [0, 1], [0, 0]] as\n           tf.backend_util.ExplicitPadding;\n       const origStride = 2;\n       const dimRoundingMode = 'round';\n\n       const x = tf.tensor4d(\n           [\n             1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34,\n             0.28, 0., 0.06, 0.14, 0.24\n           ],\n           inputShape);\n       const w = tf.tensor4d(\n           [\n             0., 1., 2., 3., 4., 5., 6., 7., 8.,\n             9., 10., 11., 12., 13., 14., 15.\n           ],\n           [fSize, fSize, origInputDepth, origOutputDepth]);\n\n       expect(\n           () => tf.conv2dTranspose(\n              x, w, [1, 3, 3, 1], origStride, origPad, dimRoundingMode))\n           .toThrowError();\n     });\n\n  // Reference (Python) TensorFlow code:\n  //\n  // ```py\n  // import numpy as np\n  // import tensorflow as tf\n  //\n  // tf.enable_eager_execution()\n  //\n  // x = tf.constant(np.array([[\n  //     [[-0.14656299], [0.32942239], [-1.90302866]],\n  //     [[-0.06487813], [-2.02637842], [-1.83669377]],\n  //     [[0.82650784], [-0.89249092], [0.01207666]]\n  // ]]).astype(np.float32))\n  // filt = tf.constant(np.array([\n  //     [[[-0.48280062], [1.26770487]], [[-0.83083738], [0.54341856]]],\n  //     [[[-0.274904], [0.73111374]], [[2.01885189], [-2.68975237]]]\n  // ]).astype(np.float32))\n  //\n  // with tf.GradientTape() as g:\n  //   g.watch(x)\n  //   g.watch(filt)\n  //   y = tf.keras.backend.conv2d_transpose(x, filt, [1, 4, 4, 2])\n  //   print(y)\n  // (x_grad, filt_grad) = g.gradient(y, [x, filt])\n  //\n  // print(\"x_grad = %s\" % x_grad)\n  // print(\"filt_grad = %s\" % filt_grad)\n  // ```\n  it('gradient with clones input=[1,3,3,1] f=[2,2,2,1] s=1 padding=valid',\n     async () => {\n       const inputDepth = 1;\n       const outputDepth = 2;\n       const inputShape: [number, number, number, number] =\n           [1, 3, 3, inputDepth];\n       const filterSize = 2;\n       const stride = 1;\n       const pad = 'valid';\n\n       const filterShape: [number, number, number, number] =\n           [filterSize, filterSize, outputDepth, inputDepth];\n\n       const x = tf.tensor4d(\n           [[\n             [[-0.14656299], [0.32942239], [-1.90302866]],\n             [[-0.06487813], [-2.02637842], [-1.83669377]],\n             [[0.82650784], [-0.89249092], [0.01207666]]\n           ]],\n           inputShape);\n       const filt = tf.tensor4d(\n           [\n             [[[-0.48280062], [1.26770487]], [[-0.83083738], [0.54341856]]],\n             [[[-0.274904], [0.73111374]], [[2.01885189], [-2.68975237]]]\n           ],\n           filterShape);\n\n       const grads = tf.grads(\n           (x: tf.Tensor4D, filter: tf.Tensor4D) =>\n               tf.conv2dTranspose(\n                     x.clone(), filter.clone(), [1, 4, 4, outputDepth], stride,\n                     pad)\n                   .clone());\n       const dy = tf.ones([1, 4, 4, outputDepth]);\n       const [xGrad, filtGrad] = grads([x, filt], dy);\n\n       const expectedXGrad = tf.ones([1, 3, 3, 1]).mul(tf.scalar(0.2827947));\n       expectArraysClose(await xGrad.data(), await expectedXGrad.data());\n       const expectedFiltGrad =\n           tf.ones([2, 2, 2, 1]).mul(tf.scalar(-5.70202599));\n       expectArraysClose(await filtGrad.data(), await expectedFiltGrad.data());\n     });\n\n  // Reference (Python) TensorFlow code:\n  //\n  // ```py\n  // import numpy as np\n  // import tensorflow as tf\n  //\n  // tf.enable_eager_execution()\n  //\n  // x = tf.constant(np.array([\n  //     [[[-0.36541713], [-0.53973116]], [[0.01731674], [0.90227772]]]\n  // ]).astype(np.float32))\n  // filt = tf.constant(np.array([\n  //     [[[-0.01423461], [-1.00267384]], [[1.61163029], [0.66302646]]],\n  //     [[[-0.46900087], [-0.78649444]], [[0.87780536], [-0.84551637]]]\n  // ]).astype(np.float32))\n  //\n  // with tf.GradientTape() as g:\n  //   g.watch(x)\n  //   g.watch(filt)\n  //   y = tf.keras.backend.conv2d_transpose(x, filt, [1, 4, 4, 2], strides=(2,\n  //   2)) print(y)\n  // (x_grad, filt_grad) = g.gradient(y, [x, filt])\n  //\n  // print(\"x_grad = %s\" % -x_grad)\n  // print(\"filt_grad = %s\" % -filt_grad)\n  // ```\n  it('gradient input=[1,2,2,1] f=[2,2,2,1] s=[2,2] padding=valid', async () => {\n    const inputDepth = 1;\n    const outputDepth = 2;\n    const inputShape: [number, number, number, number] = [1, 2, 2, inputDepth];\n    const filterSize = 2;\n    const stride: [number, number] = [2, 2];\n    const pad = 'valid';\n\n    const filterShape: [number, number, number, number] =\n        [filterSize, filterSize, outputDepth, inputDepth];\n\n    const x = tf.tensor4d(\n        [[[[-0.36541713], [-0.53973116]], [[0.01731674], [0.90227772]]]],\n        inputShape);\n    const filt = tf.tensor4d(\n        [\n          [[[-0.01423461], [-1.00267384]], [[1.61163029], [0.66302646]]],\n          [[[-0.46900087], [-0.78649444]], [[0.87780536], [-0.84551637]]]\n        ],\n        filterShape);\n\n    const grads = tf.grads(\n        (x: tf.Tensor4D, filter: tf.Tensor4D) =>\n            tf.conv2dTranspose(x, filter, [1, 4, 4, outputDepth], stride, pad));\n    const dy = tf.ones([1, 4, 4, outputDepth]).mul(tf.scalar(-1));\n    const [xGrad, filtGrad] = grads([x, filt], dy);\n\n    const expectedXGrad = tf.ones([1, 2, 2, 1]).mul(tf.scalar(-0.03454196));\n    expectArraysClose(await xGrad.data(), await expectedXGrad.data());\n    expect(xGrad.shape).toEqual([1, 2, 2, 1]);\n\n    const expectedFiltGrad = tf.ones([2, 2, 2, 1]).mul(tf.scalar(-0.01444618));\n    expectArraysClose(await filtGrad.data(), await expectedFiltGrad.data());\n    expect(filtGrad.shape).toEqual([2, 2, 2, 1]);\n  });\n\n  // Reference (Python) TensorFlow code:\n  //\n  // ```py\n  // import numpy as np\n  // import tensorflow as tf\n  //\n  // tf.enable_eager_execution()\n  //\n  // x = tf.constant(np.array([[\n  //     [[1.52433065], [-0.77053435], [-0.64562341]],\n  //     [[0.77962889], [1.58413887], [-0.25581856]],\n  //     [[-0.58966221], [0.05411662], [0.70749138]]\n  // ]]).astype(np.float32))\n  // filt = tf.constant(np.array([\n  //     [[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],\n  //     [[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]\n  // ]).astype(np.float32))\n  //\n  // with tf.GradientTape() as g:\n  //   g.watch(x)\n  //   g.watch(filt)\n  //   y = tf.keras.backend.conv2d_transpose(\n  //       x, filt, [1, 3, 3, 2], strides=(1, 1), padding='same')\n  // (x_grad, filt_grad) = g.gradient(y, [x, filt])\n  //\n  // print(\"x_grad = %s\" % x_grad)\n  // print(\"filt_grad = %s\" % filt_grad)\n  // ```\n  it('gradient input=[1,3,3,1] f=[2,2,2,1] s=[1,1] padding=same', async () => {\n    const inputDepth = 1;\n    const outputDepth = 2;\n    const inputShape: [number, number, number, number] = [1, 3, 3, inputDepth];\n    const filterSize = 2;\n    const stride: [number, number] = [1, 1];\n    const pad = 'same';\n\n    const filterShape: [number, number, number, number] =\n        [filterSize, filterSize, outputDepth, inputDepth];\n\n    const x = tf.tensor4d(\n        [[\n          [[1.52433065], [-0.77053435], [-0.64562341]],\n          [[0.77962889], [1.58413887], [-0.25581856]],\n          [[-0.58966221], [0.05411662], [0.70749138]]\n        ]],\n        inputShape);\n    const filt = tf.tensor4d(\n        [\n          [[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],\n          [[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]\n        ],\n        filterShape);\n\n    const grads = tf.grads(\n        (x: tf.Tensor4D, filter: tf.Tensor4D) =>\n            tf.conv2dTranspose(x, filter, [1, 3, 3, outputDepth], stride, pad));\n    const dy = tf.ones([1, 3, 3, outputDepth]);\n    const [xGrad, filtGrad] = grads([x, filt], dy);\n\n    expectArraysClose(await xGrad.array(), [[\n                        [[1.30709858], [1.30709858], [-0.92814366]],\n                        [[1.30709858], [1.30709858], [-0.92814366]],\n                        [[1.19666437], [1.19666437], [-0.85476589]]\n                      ]]);\n    expectArraysClose(await filtGrad.array(), [\n      [[[2.38806788], [2.38806788]], [[2.58201847], [2.58201847]]],\n      [[[2.2161221], [2.2161221]], [[3.11756406], [3.11756406]]]\n    ]);\n  });\n\n  it('gradient input=[1,3,3,1] f=[2,2,2,1] s=[1,1] p=explicit', async () => {\n    const inputDepth = 1;\n    const outputDepth = 2;\n    const inputShape: [number, number, number, number] = [1, 3, 3, inputDepth];\n    const filterSize = 2;\n    const stride: [number, number] = [1, 1];\n    const pad =\n        [[0, 0], [0, 1], [0, 1], [0, 0]] as tf.backend_util.ExplicitPadding;\n\n    const filterShape: [number, number, number, number] =\n        [filterSize, filterSize, outputDepth, inputDepth];\n\n    const x = tf.tensor4d(\n        [[\n          [[1.52433065], [-0.77053435], [-0.64562341]],\n          [[0.77962889], [1.58413887], [-0.25581856]],\n          [[-0.58966221], [0.05411662], [0.70749138]]\n        ]],\n        inputShape);\n    const filt = tf.tensor4d(\n        [\n          [[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],\n          [[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]\n        ],\n        filterShape);\n\n    const grads = tf.grads(\n        (x: tf.Tensor4D, filter: tf.Tensor4D) =>\n            tf.conv2dTranspose(x, filter, [1, 3, 3, outputDepth], stride, pad));\n    const dy = tf.ones([1, 3, 3, outputDepth]);\n    const [xGrad, filtGrad] = grads([x, filt], dy);\n\n    expectArraysClose(await xGrad.array(), [[\n                        [[1.30709858], [1.30709858], [-0.92814366]],\n                        [[1.30709858], [1.30709858], [-0.92814366]],\n                        [[1.19666437], [1.19666437], [-0.85476589]]\n                      ]]);\n    expectArraysClose(await filtGrad.array(), [\n      [[[2.38806788], [2.38806788]], [[2.58201847], [2.58201847]]],\n      [[[2.2161221], [2.2161221]], [[3.11756406], [3.11756406]]]\n    ]);\n  });\n\n  // Reference (Python) TensorFlow code:\n  //\n  // ```py\n  // import numpy as np\n  // import tensorflow as tf\n  //\n  // tf.enable_eager_execution()\n  //\n  // x = tf.constant(np.array([[\n  //     [[1.52433065], [-0.77053435]], [[0.77962889], [1.58413887]],\n  // ]]).astype(np.float32))\n  // filt = tf.constant(np.array([\n  //     [[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],\n  //     [[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]\n  // ]).astype(np.float32))\n  //\n  // with tf.GradientTape() as g:\n  //   g.watch(x)\n  //   g.watch(filt)\n  //   y = tf.keras.backend.conv2d_transpose(\n  //       x, filt, [1, 3, 3, 2], strides=(2, 2), padding='same')\n  //   print(y.shape)\n  // (x_grad, filt_grad) = g.gradient(y, [x, filt])\n  //\n  // print(\"x_grad = %s\" % x_grad)\n  // print(\"filt_grad = %s\" % filt_grad)\n  // ```\n  it('gradient input=[1,2,2,2] f=[2,2,2,1] s=[2,2] padding=same', async () => {\n    const inputDepth = 2;\n    const outputDepth = 2;\n    const inputShape: [number, number, number, number] = [1, 2, 2, inputDepth];\n    const filterSize = 2;\n    const stride: [number, number] = [2, 2];\n    const pad = 'same';\n\n    const filterShape: [number, number, number, number] =\n        [filterSize, filterSize, outputDepth, inputDepth];\n\n    const x = tf.tensor4d(\n        [[\n          [[-1.81506593, 1.00900095], [-0.05199118, 0.26311377]],\n          [[-1.18469792, -0.34780521], [2.04971242, -0.65154692]]\n        ]],\n        inputShape);\n    const filt = tf.tensor4d(\n        [\n          [\n            [[0.19529686, -0.79594708], [0.70314057, -0.06081263]],\n            [[0.28724744, 0.88522715], [-0.51824096, -0.97120989]]\n          ],\n          [\n            [[0.51872197, -1.17569193], [1.28316791, -0.81225092]],\n            [[-0.44221532, 0.70058174], [-0.4849217, 0.03806348]]\n          ]\n        ],\n        filterShape);\n\n    const grads = tf.grads(\n        (x: tf.Tensor4D, filter: tf.Tensor4D) =>\n            tf.conv2dTranspose(x, filter, [1, 3, 3, outputDepth], stride, pad));\n    const dy = tf.ones([1, 3, 3, outputDepth]);\n    const [xGrad, filtGrad] = grads([x, filt], dy);\n\n    expectArraysClose(await xGrad.data(), [\n      1.54219678, -2.19204008, 2.70032732, -2.84470257, 0.66744391, -0.94274245,\n      0.89843743, -0.85675972\n    ]);\n    expect(xGrad.shape).toEqual([1, 2, 2, 2]);\n    expectArraysClose(await filtGrad.data(), [\n      -1.00204261, 0.27276259, -1.00204261, 0.27276259, -2.99976385, 0.66119574,\n      -2.99976385, 0.66119574, -1.86705711, 1.27211472, -1.86705711, 1.27211472,\n      -1.81506593, 1.00900095, -1.81506593, 1.00900095\n    ]);\n    expect(filtGrad.shape).toEqual([2, 2, 2, 2]);\n  });\n\n  it('throws when x is not rank 3', () => {\n    const origInputDepth = 1;\n    const origOutputDepth = 1;\n    const fSize = 2;\n    const origPad = 0;\n    const origStride = 1;\n\n    // tslint:disable-next-line:no-any\n    const x: any = tf.tensor2d([2, 2], [2, 1]);\n    const w = tf.tensor4d(\n        [3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);\n\n    expect(() => tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad))\n        .toThrowError();\n  });\n\n  it('throws when weights is not rank 4', () => {\n    const origInputDepth = 1;\n    const origOutputDepth = 1;\n    const inputShape: [number, number, number] = [1, 1, origOutputDepth];\n    const fSize = 2;\n    const origPad = 0;\n    const origStride = 1;\n\n    const x = tf.tensor3d([2], inputShape);\n    // tslint:disable-next-line:no-any\n    const w: any = tf.tensor3d([3, 1, 5, 0], [fSize, fSize, origInputDepth]);\n\n    expect(() => tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad))\n        .toThrowError();\n  });\n\n  it('throws when x depth does not match weights original output depth', () => {\n    const origInputDepth = 1;\n    const origOutputDepth = 2;\n    const wrongOrigOutputDepth = 3;\n    const inputShape: [number, number, number] = [1, 1, origOutputDepth];\n    const fSize = 2;\n    const origPad = 0;\n    const origStride = 1;\n\n    const x = tf.tensor3d([2, 2], inputShape);\n    const w = tf.randomNormal<Rank.R4>(\n        [fSize, fSize, origInputDepth, wrongOrigOutputDepth]);\n\n    expect(() => tf.conv2dTranspose(x, w, [2, 2, 2], origStride, origPad))\n        .toThrowError();\n  });\n\n  it('throws when passed x as a non-tensor', () => {\n    const origInputDepth = 1;\n    const origOutputDepth = 1;\n    const fSize = 2;\n    const origPad = 0;\n    const origStride = 1;\n\n    const w = tf.tensor4d(\n        [3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);\n\n    expect(\n        () => tf.conv2dTranspose(\n            {} as tf.Tensor3D, w, [2, 2, 1], origStride, origPad))\n        .toThrowError(\n            /Argument 'x' passed to 'conv2dTranspose' must be a Tensor/);\n  });\n\n  it('throws when passed filter as a non-tensor', () => {\n    const origOutputDepth = 1;\n    const inputShape: [number, number, number] = [1, 1, origOutputDepth];\n    const origPad = 0;\n    const origStride = 1;\n\n    const x = tf.tensor3d([2], inputShape);\n\n    expect(\n        () => tf.conv2dTranspose(\n            x, {} as tf.Tensor4D, [2, 2, 1], origStride, origPad))\n        .toThrowError(\n            /Argument 'filter' passed to 'conv2dTranspose' must be a Tensor/);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const origPad = 0;\n    const origStride = 1;\n\n    const x = [[[2]]];                           // 1x1x1\n    const w = [[[[3]], [[1]]], [[[5]], [[0]]]];  // 2x2x1x1\n\n    const result = tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad);\n    const expected = [6, 2, 10, 0];\n\n    expect(result.shape).toEqual([2, 2, 1]);\n    expectArraysClose(await result.data(), expected);\n  });\n});\n"]} |
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