| 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('maxPoolWithArgmax', ALL_ENVS, () => {
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| 21 | it('x=[1,1,1] f=[1,1] s=1 d=1 [0] => [0]', async () => {
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| 22 | const x = tf.tensor4d([0], [1, 1, 1, 1]);
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| 23 | const padding = 0;
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| 24 | const { result, indexes } = tf.maxPoolWithArgmax(x, [1, 1], [1, 1], padding);
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| 25 | expectArraysClose(await result.data(), [0]);
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| 26 | expectArraysClose(await indexes.data(), [0]);
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| 27 | });
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| 28 | it('x=[2,2,2,1] f=[2,2,2] s=1 p=valid', async () => {
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| 29 | const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
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| 30 | const { result, indexes } = tf.maxPoolWithArgmax(x, 2, 1, 'valid');
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| 31 | expect(result.shape).toEqual([2, 1, 1, 1]);
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| 32 | expectArraysClose(await result.data(), [4, 8]);
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| 33 | expect(indexes.shape).toEqual([2, 1, 1, 1]);
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| 34 | expectArraysClose(await indexes.data(), [3, 3]);
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| 35 | });
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| 36 | it('x=[2,2,2,1] f=[2,2,2] s=1 p=valid includeBatchInIndex=true', async () => {
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| 37 | const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
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| 38 | const { result, indexes } = tf.maxPoolWithArgmax(x, 2, 1, 'valid', true);
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| 39 | expect(result.shape).toEqual([2, 1, 1, 1]);
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| 40 | expectArraysClose(await result.data(), [4, 8]);
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| 41 | expect(indexes.shape).toEqual([2, 1, 1, 1]);
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| 42 | expectArraysClose(await indexes.data(), [3, 7]);
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| 43 | });
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| 44 | it('x=[1,3,3,1] f=[2,2] s=1, p=0', async () => {
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| 45 | // Feed forward.
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| 46 | const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 9, 8], [1, 3, 3, 1]);
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| 47 | const { result, indexes } = tf.maxPoolWithArgmax(x, 2, 1, 0);
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| 48 | expect(result.shape).toEqual([1, 2, 2, 1]);
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| 49 | expectArraysClose(await result.data(), [5, 6, 9, 9]);
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| 50 | expect(indexes.shape).toEqual([1, 2, 2, 1]);
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| 51 | expectArraysClose(await indexes.data(), [4, 5, 7, 7]);
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| 52 | });
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| 53 | it('x=[1,3,3,1] f=[2,2] s=1 p=same', async () => {
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| 54 | const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 9, 8], [1, 3, 3, 1]);
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| 55 | const { result, indexes } = tf.maxPoolWithArgmax(x, 2, 1, 'same');
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| 56 | expect(result.shape).toEqual([1, 3, 3, 1]);
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| 57 | tf.test_util.expectArraysClose(await result.data(), new Float32Array([5, 6, 6, 9, 9, 8, 9, 9, 8]));
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| 58 | expect(indexes.shape).toEqual([1, 3, 3, 1]);
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| 59 | expectArraysClose(await indexes.data(), [4, 5, 5, 7, 7, 8, 7, 7, 8]);
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| 60 | });
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| 61 | it('x=[2,3,3,1] f=[2,2] s=1', async () => {
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| 62 | const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 9, 8, 1, 2, 3, 4, 5, 6, 7, 8, 9], [2, 3, 3, 1]);
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| 63 | const { result, indexes } = tf.maxPoolWithArgmax(x, 2, 1, 0);
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| 64 | expect(result.shape).toEqual([2, 2, 2, 1]);
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| 65 | expectArraysClose(await result.data(), [5, 6, 9, 9, 5, 6, 8, 9]);
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| 66 | expect(indexes.shape).toEqual([2, 2, 2, 1]);
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| 67 | expectArraysClose(await indexes.data(), [4, 5, 7, 7, 4, 5, 7, 8]);
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| 68 | });
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| 69 | it('x=[2,3,3,1] f=[2,2] s=1 includeBatchInIndex=true', async () => {
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| 70 | const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 9, 8, 1, 2, 3, 4, 5, 6, 7, 8, 9], [2, 3, 3, 1]);
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| 71 | const { result, indexes } = tf.maxPoolWithArgmax(x, 2, 1, 0, true);
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| 72 | expect(result.shape).toEqual([2, 2, 2, 1]);
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| 73 | expectArraysClose(await result.data(), [5, 6, 9, 9, 5, 6, 8, 9]);
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| 74 | expect(indexes.shape).toEqual([2, 2, 2, 1]);
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| 75 | expectArraysClose(await indexes.data(), [4, 5, 7, 7, 13, 14, 16, 17]);
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| 76 | });
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| 77 | it('[x=[1,3,3,1] f=[2,2] s=1 ignores NaNs', async () => {
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| 78 | const x = tf.tensor4d([NaN, 1, 2, 3, 4, 5, 6, 7, 9], [1, 3, 3, 1]);
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| 79 | const { result, indexes } = tf.maxPoolWithArgmax(x, 2, 1, 0);
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| 80 | expect(result.shape).toEqual([1, 2, 2, 1]);
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| 81 | expectArraysClose(await result.data(), [4, 5, 7, 9]);
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| 82 | expect(indexes.shape).toEqual([1, 2, 2, 1]);
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| 83 | expectArraysClose(await indexes.data(), [4, 5, 7, 8]);
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| 84 | });
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| 85 | it('x=[1, 3,3,2] f=[2,2] s=1', async () => {
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| 86 | // Feed forward.
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| 87 | const x = tf.tensor4d([1, 99, 2, 88, 3, 77, 4, 66, 5, 55, 6, 44, 7, 33, 9, 22, 8, 11], [1, 3, 3, 2]);
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| 88 | const { result, indexes } = tf.maxPoolWithArgmax(x, 2, 1, 0);
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| 89 | expect(result.shape).toEqual([1, 2, 2, 2]);
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| 90 | expectArraysClose(await result.data(), [5, 99, 6, 88, 9, 66, 9, 55]);
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| 91 | expect(indexes.shape).toEqual([1, 2, 2, 2]);
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| 92 | expectArraysClose(await indexes.data(), [8, 1, 10, 3, 14, 7, 14, 9]);
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| 93 | });
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| 94 | it('x=[1,4,4,1] f=[2,2] s=2', async () => {
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| 95 | // Feed forward.
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| 96 | const x = tf.tensor4d([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [1, 4, 4, 1]);
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| 97 | const { result, indexes } = tf.maxPoolWithArgmax(x, 2, 2, 0);
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| 98 | expect(result.shape).toEqual([1, 2, 2, 1]);
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| 99 | expectArraysClose(await result.data(), [5, 7, 13, 15]);
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| 100 | expect(indexes.shape).toEqual([1, 2, 2, 1]);
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| 101 | expectArraysClose(await indexes.data(), [5, 7, 13, 15]);
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| 102 | });
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| 103 | it('x=[1,2,2,1] f=[2,2] s=1 p=same', async () => {
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| 104 | // Feed forward.
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| 105 | const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
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| 106 | const { result, indexes } = tf.maxPoolWithArgmax(x, 2, 1, 'same');
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| 107 | expect(result.shape).toEqual([1, 2, 2, 1]);
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| 108 | expectArraysClose(await result.data(), [4, 4, 4, 4]);
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| 109 | expect(indexes.shape).toEqual([1, 2, 2, 1]);
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| 110 | expectArraysClose(await indexes.data(), [3, 3, 3, 3]);
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| 111 | });
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| 112 | it('throws when x is not rank 4', () => {
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| 113 | // tslint:disable-next-line:no-any
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| 114 | const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 3, 3]);
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| 115 | expect(() => tf.maxPoolWithArgmax(x, 2, 1, 0)).toThrowError();
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| 116 | });
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| 117 | it('throws when passed a non-tensor', () => {
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| 118 | expect(() => tf.maxPoolWithArgmax({}, 2, 1, 'valid'))
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| 119 | .toThrowError(/Argument 'x' passed to 'maxPoolWithArgmax' must be a Tensor/);
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| 120 | });
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| 121 | it('accepts a tensor-like object', async () => {
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| 122 | const x = [[[[0]]]]; // 1x1x1
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| 123 | const { result, indexes } = tf.maxPoolWithArgmax(x, 1, 1, 0);
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| 124 | expectArraysClose(await result.data(), [0]);
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| 125 | expect(indexes.shape).toEqual([1, 1, 1, 1]);
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| 126 | expectArraysClose(await indexes.data(), [0]);
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| 127 | });
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| 128 | });
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| 129 | //# 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CAAC,8BAA8B,EAAE,KAAK,IAAI,EAAE;QAC5C,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAE,QAAQ;QAC9B,MAAM,EAAC,MAAM,EAAE,OAAO,EAAC,GAAG,EAAE,CAAC,iBAAiB,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC;QAC3D,iBAAiB,CAAC,MAAM,MAAM,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QAC5C,MAAM,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC5C,iBAAiB,CAAC,MAAM,OAAO,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;IAC/C,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';\n\ndescribeWithFlags('maxPoolWithArgmax', ALL_ENVS, () => {\n  it('x=[1,1,1] f=[1,1] s=1 d=1 [0] => [0]', async () => {\n    const x = tf.tensor4d([0], [1, 1, 1, 1]);\n\n    const padding = 0;\n\n    const {result, indexes} = tf.maxPoolWithArgmax(x, [1, 1], [1, 1], padding);\n    expectArraysClose(await result.data(), [0]);\n    expectArraysClose(await indexes.data(), [0]);\n  });\n\n  it('x=[2,2,2,1] f=[2,2,2] s=1 p=valid', async () => {\n    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);\n\n    const {result, indexes} = tf.maxPoolWithArgmax(x, 2, 1, 'valid');\n\n    expect(result.shape).toEqual([2, 1, 1, 1]);\n    expectArraysClose(await result.data(), [4, 8]);\n    expect(indexes.shape).toEqual([2, 1, 1, 1]);\n    expectArraysClose(await indexes.data(), [3, 3]);\n  });\n\n  it('x=[2,2,2,1] f=[2,2,2] s=1 p=valid includeBatchInIndex=true', async () => {\n    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);\n\n    const {result, indexes} = tf.maxPoolWithArgmax(x, 2, 1, 'valid', true);\n\n    expect(result.shape).toEqual([2, 1, 1, 1]);\n    expectArraysClose(await result.data(), [4, 8]);\n    expect(indexes.shape).toEqual([2, 1, 1, 1]);\n    expectArraysClose(await indexes.data(), [3, 7]);\n  });\n\n  it('x=[1,3,3,1] f=[2,2] s=1, p=0', async () => {\n    // Feed forward.\n    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 9, 8], [1, 3, 3, 1]);\n\n    const {result, indexes} = tf.maxPoolWithArgmax(x, 2, 1, 0);\n\n    expect(result.shape).toEqual([1, 2, 2, 1]);\n    expectArraysClose(await result.data(), [5, 6, 9, 9]);\n    expect(indexes.shape).toEqual([1, 2, 2, 1]);\n    expectArraysClose(await indexes.data(), [4, 5, 7, 7]);\n  });\n\n  it('x=[1,3,3,1] f=[2,2] s=1 p=same', async () => {\n    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 9, 8], [1, 3, 3, 1]);\n\n    const {result, indexes} = tf.maxPoolWithArgmax(x, 2, 1, 'same');\n\n    expect(result.shape).toEqual([1, 3, 3, 1]);\n    tf.test_util.expectArraysClose(\n        await result.data(), new Float32Array([5, 6, 6, 9, 9, 8, 9, 9, 8]));\n    expect(indexes.shape).toEqual([1, 3, 3, 1]);\n    expectArraysClose(await indexes.data(), [4, 5, 5, 7, 7, 8, 7, 7, 8]);\n  });\n\n  it('x=[2,3,3,1] f=[2,2] s=1', async () => {\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 9, 8, 1, 2, 3, 4, 5, 6, 7, 8, 9], [2, 3, 3, 1]);\n\n    const {result, indexes} = tf.maxPoolWithArgmax(x, 2, 1, 0);\n    expect(result.shape).toEqual([2, 2, 2, 1]);\n    expectArraysClose(await result.data(), [5, 6, 9, 9, 5, 6, 8, 9]);\n    expect(indexes.shape).toEqual([2, 2, 2, 1]);\n    expectArraysClose(await indexes.data(), [4, 5, 7, 7, 4, 5, 7, 8]);\n  });\n\n  it('x=[2,3,3,1] f=[2,2] s=1 includeBatchInIndex=true', async () => {\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 9, 8, 1, 2, 3, 4, 5, 6, 7, 8, 9], [2, 3, 3, 1]);\n\n    const {result, indexes} = tf.maxPoolWithArgmax(x, 2, 1, 0, true);\n    expect(result.shape).toEqual([2, 2, 2, 1]);\n    expectArraysClose(await result.data(), [5, 6, 9, 9, 5, 6, 8, 9]);\n    expect(indexes.shape).toEqual([2, 2, 2, 1]);\n    expectArraysClose(await indexes.data(), [4, 5, 7, 7, 13, 14, 16, 17]);\n  });\n\n  it('[x=[1,3,3,1] f=[2,2] s=1 ignores NaNs', async () => {\n    const x = tf.tensor4d([NaN, 1, 2, 3, 4, 5, 6, 7, 9], [1, 3, 3, 1]);\n\n    const {result, indexes} = tf.maxPoolWithArgmax(x, 2, 1, 0);\n\n    expect(result.shape).toEqual([1, 2, 2, 1]);\n    expectArraysClose(await result.data(), [4, 5, 7, 9]);\n    expect(indexes.shape).toEqual([1, 2, 2, 1]);\n    expectArraysClose(await indexes.data(), [4, 5, 7, 8]);\n  });\n\n  it('x=[1, 3,3,2] f=[2,2] s=1', async () => {\n    // Feed forward.\n    const x = tf.tensor4d(\n        [1, 99, 2, 88, 3, 77, 4, 66, 5, 55, 6, 44, 7, 33, 9, 22, 8, 11],\n        [1, 3, 3, 2]);\n\n    const {result, indexes} = tf.maxPoolWithArgmax(x, 2, 1, 0);\n\n    expect(result.shape).toEqual([1, 2, 2, 2]);\n    expectArraysClose(await result.data(), [5, 99, 6, 88, 9, 66, 9, 55]);\n    expect(indexes.shape).toEqual([1, 2, 2, 2]);\n    expectArraysClose(await indexes.data(), [8, 1, 10, 3, 14, 7, 14, 9]);\n  });\n\n  it('x=[1,4,4,1] f=[2,2] s=2', async () => {\n    // Feed forward.\n    const x = tf.tensor4d(\n        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [1, 4, 4, 1]);\n\n    const {result, indexes} = tf.maxPoolWithArgmax(x, 2, 2, 0);\n\n    expect(result.shape).toEqual([1, 2, 2, 1]);\n    expectArraysClose(await result.data(), [5, 7, 13, 15]);\n    expect(indexes.shape).toEqual([1, 2, 2, 1]);\n    expectArraysClose(await indexes.data(), [5, 7, 13, 15]);\n  });\n\n  it('x=[1,2,2,1] f=[2,2] s=1 p=same', async () => {\n    // Feed forward.\n    const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);\n\n    const {result, indexes} = tf.maxPoolWithArgmax(x, 2, 1, 'same');\n    expect(result.shape).toEqual([1, 2, 2, 1]);\n    expectArraysClose(await result.data(), [4, 4, 4, 4]);\n    expect(indexes.shape).toEqual([1, 2, 2, 1]);\n    expectArraysClose(await indexes.data(), [3, 3, 3, 3]);\n  });\n\n  it('throws when x is not rank 4', () => {\n    // tslint:disable-next-line:no-any\n    const x: any = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 3, 3]);\n\n    expect(() => tf.maxPoolWithArgmax(x, 2, 1, 0)).toThrowError();\n  });\n\n  it('throws when passed a non-tensor', () => {\n    expect(() => tf.maxPoolWithArgmax({} as tf.Tensor4D, 2, 1, 'valid'))\n        .toThrowError(\n            /Argument 'x' passed to 'maxPoolWithArgmax' must be a Tensor/);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const x = [[[[0]]]];  // 1x1x1\n    const {result, indexes} = tf.maxPoolWithArgmax(x, 1, 1, 0);\n    expectArraysClose(await result.data(), [0]);\n    expect(indexes.shape).toEqual([1, 1, 1, 1]);\n    expectArraysClose(await indexes.data(), [0]);\n  });\n});\n"]} |
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