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| /**
| * @license
| * Copyright 2017 Google LLC. All Rights Reserved.
| * Licensed under the Apache License, Version 2.0 (the "License");
| * you may not use this file except in compliance with the License.
| * You may obtain a copy of the License at
| *
| * http://www.apache.org/licenses/LICENSE-2.0
| *
| * Unless required by applicable law or agreed to in writing, software
| * distributed under the License is distributed on an "AS IS" BASIS,
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
| * See the License for the specific language governing permissions and
| * limitations under the License.
| * =============================================================================
| */
| import * as tf from '../index';
| import { ALL_ENVS, describeWithFlags } from '../jasmine_util';
| import { expectArraysClose } from '../test_util';
| describeWithFlags('avgPool3d', ALL_ENVS, () => {
| it('x=[2,2,2,1] f=[2,2,2] s=1 p=valid', async () => {
| const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
| const result = tf.avgPool3d(x, 2, 1, 'valid');
| expect(result.shape).toEqual([1, 1, 1, 1]);
| expectArraysClose(await result.data(), [4.5]);
| });
| it('x=[2,2,2,1] f=[1,2,2] s=1 p=valid', async () => {
| const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
| const result = tf.avgPool3d(x, [1, 2, 2], 1, 'valid');
| expect(result.shape).toEqual([2, 1, 1, 1]);
| expectArraysClose(await result.data(), [2.5, 6.5]);
| });
| it('x=[1,1,1,1,1] f=[1,1,1] s=1 [0] => [0]', async () => {
| const x = tf.tensor5d([0], [1, 1, 1, 1, 1]);
| const result = tf.avgPool3d(x, 1, 1, 0);
| expect(result.shape).toEqual([1, 1, 1, 1, 1]);
| expectArraysClose(await result.data(), [0]);
| });
| it('x=[1,2,2,2,1] f=[2,2,2] s=1 p=valid', async () => {
| const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
| const result = tf.avgPool3d(x, 2, 1, 'valid');
| expect(result.shape).toEqual([1, 1, 1, 1, 1]);
| expectArraysClose(await result.data(), [4.5]);
| });
| it('x=[1,2,2,2,1] f=[2,2,2] s=1 p=same', async () => {
| const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
| const expected = [4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8];
| const result = tf.avgPool3d(x, 2, 1, 'same');
| expect(result.shape).toEqual([1, 2, 2, 2, 1]);
| expectArraysClose(await result.data(), expected);
| });
| it('x=[1,3,3,3,1] f=[2,2,2] s=1 p=valid', async () => {
| const x = tf.tensor5d([
| 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
| 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27
| ], [1, 3, 3, 3, 1]);
| const expected = [7.5, 8.5, 10.5, 11.5, 16.5, 17.5, 19.5, 20.5];
| const result = tf.avgPool3d(x, 2, 1, 'valid');
| expect(result.shape).toEqual([1, 2, 2, 2, 1]);
| expectArraysClose(await result.data(), expected);
| });
| it('x=[1,3,3,3,1] f=[2,2,2] s=1 p=same', async () => {
| const x = tf.tensor5d([
| 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
| 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27
| ], [1, 3, 3, 3, 1]);
| const expected = [
| 7.5, 8.5, 9, 10.5, 11.5, 12, 12, 13, 13.5,
| 16.5, 17.5, 18, 19.5, 20.5, 21, 21, 22, 22.5,
| 21, 22, 22.5, 24, 25, 25.5, 25.5, 26.5, 27
| ];
| const result = tf.avgPool3d(x, 2, 1, 'same');
| expect(result.shape).toEqual([1, 3, 3, 3, 1]);
| expectArraysClose(await result.data(), expected);
| });
| it('x=[1,3,3,3,1] f=[2,2,2] s=1 p=valid, propagates NaNs', async () => {
| const x = tf.tensor5d([
| 1, 2, 3, 4, 5, 6, 7, 8, NaN, 10, 11, 12, 13, 14,
| 15, 16, 17, 18, 19, 20, 21, NaN, 23, 24, 25, 26, 27
| ], [1, 3, 3, 3, 1]);
| const expected = [7.5, 8.5, 10.5, NaN, NaN, 17.5, NaN, 20.5];
| const result = tf.avgPool3d(x, 2, 1, 'valid');
| expect(result.shape).toEqual([1, 2, 2, 2, 1]);
| expectArraysClose(await result.data(), expected);
| });
| it('x=[2,3,3,3,1] f=[2,2,2] s=1 p=valid', async () => {
| const x = tf.tensor5d([
| 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
| 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
| 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,
| 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54
| ], [2, 3, 3, 3, 1]);
| const expected = [
| 7.5, 8.5, 10.5, 11.5, 16.5, 17.5, 19.5, 20.5, 34.5, 35.5, 37.5, 38.5,
| 43.5, 44.5, 46.5, 47.5
| ];
| const result = tf.avgPool3d(x, 2, 1, 'valid');
| expect(result.shape).toEqual([2, 2, 2, 2, 1]);
| expectArraysClose(await result.data(), expected);
| });
| it('x=[1,3,3,3,2] f=[2,2,2] s=1 p=valid', async () => {
| const x = tf.tensor5d([
| 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
| 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
| 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,
| 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54
| ], [1, 3, 3, 3, 2]);
| const expected = [14, 15, 16, 17, 20, 21, 22, 23, 32, 33, 34, 35, 38, 39, 40, 41];
| const result = tf.avgPool3d(x, 2, 1, 'valid');
| expect(result.shape).toEqual([1, 2, 2, 2, 2]);
| expectArraysClose(await result.data(), expected);
| });
| it('x=[1,2,2,2,1] f=[2,2,2] s=1 p=1 roundingMode=floor', async () => {
| const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
| const expected = [
| 1, 1.5, 2, 2, 2.5, 3, 3, 3.5, 4, 3, 3.5, 4, 4, 4.5,
| 5, 5, 5.5, 6, 5, 5.5, 6, 6, 6.5, 7, 7, 7.5, 8
| ];
| const result = tf.avgPool3d(x, 2, 1, 1, 'floor');
| expect(result.shape).toEqual([1, 3, 3, 3, 1]);
| expectArraysClose(await result.data(), expected);
| });
| it('x=[1,1,1,1,1] f=[1,1,3] s=1 p=valid', async () => {
| // Output tensor would have a dimension of zero, if a certain filter's
| // dimension is larger than the input's.
| const x = tf.tensor5d([1], [1, 1, 1, 1, 1]);
| const expected = [];
| const result = tf.avgPool3d(x, [1, 1, 3], 1, 'valid');
| expect(result.shape).toEqual([1, 1, 1, 0, 1]);
| expectArraysClose(await result.data(), expected);
| });
| it('x=[1,1,1,4,1] f=[1,1,1] s=[1,1,2] p=0', async () => {
| // Works if the padding is a number.
| const x = tf.ones([1, 1, 1, 4, 1]);
| const expected = [1, 1];
| const result = tf.avgPool3d(x, [1, 1, 1], [1, 1, 2], 0);
| expect(result.shape).toEqual([1, 1, 1, 2, 1]);
| expectArraysClose(await result.data(), expected);
| });
| it('x=[1,1,1,1,1] f=[2,2,2] s=1 p=2', async () => {
| // Works if the padding is larger than filter size.
| const x = tf.ones([1, 1, 1, 1, 1]);
| const expected = [
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
| 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0,
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
| ];
| const result = tf.avgPool3d(x, [2, 2, 2], 1, 2);
| expect(result.shape).toEqual([1, 4, 4, 4, 1]);
| expectArraysClose(await result.data(), expected);
| });
| it('throws when x is not rank 5', async () => {
| // tslint:disable-next-line:no-any
| const x = tf.tensor1d([1]);
| expect(() => tf.avgPool3d(x, 2, 1, 'valid')).toThrowError();
| });
| it('throws when dimRoundingMode is set and pad is same', async () => {
| const x = tf.tensor5d([1], [1, 1, 1, 1, 1]);
| const pad = 'same';
| const dimRoundingMode = 'round';
| expect(() => tf.avgPool3d(x, 2, 1, pad, dimRoundingMode)).toThrowError();
| });
| it('throws when dimRoundingMode is set and pad is valid', async () => {
| const x = tf.tensor5d([1], [1, 1, 1, 1, 1]);
| const pad = 'valid';
| const dimRoundingMode = 'round';
| expect(() => tf.avgPool3d(x, 2, 1, pad, dimRoundingMode)).toThrowError();
| });
| it('throws when dimRoundingMode is set and pad is a non-integer number', async () => {
| const x = tf.tensor5d([1], [1, 1, 1, 1, 1]);
| const pad = 1.2;
| const dimRoundingMode = 'round';
| expect(() => tf.avgPool3d(x, 2, 1, pad, dimRoundingMode)).toThrowError();
| });
| it('throws when passed a non-tensor', () => {
| expect(() => tf.avgPool3d({}, 2, 1, 'valid')).toThrowError();
| });
| it('throws when input dtype is not float32', () => {
| const a = tf.tensor5d([1], [1, 1, 1, 1, 1], 'int32');
| expect(() => tf.avgPool3d(a, 2, 1, 0)).toThrowError();
| });
| it('accepts a tensor-like object', async () => {
| const x = [[[[[0]]]]]; // 1x1x1x1x1
| const result = tf.avgPool3d(x, 1, 1, 0);
| expect(result.shape).toEqual([1, 1, 1, 1, 1]);
| expectArraysClose(await result.data(), [0]);
| });
| });
| describeWithFlags('avgPool3dBackprop', ALL_ENVS, () => {
| it('gradient x=[2,2,2,1] f=[1,1,1] s=1', async () => {
| const dy = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
| const x = tf.ones([2, 2, 2, 1]);
| const expected = [1, 2, 3, 4, 5, 6, 7, 8];
| const dx = tf.grad((x) => tf.avgPool3d(x, 1, 1, 0))(x, dy);
| expect(dx.shape).toEqual(x.shape);
| expectArraysClose(await dx.data(), expected);
| });
| it('gradient x=[1,2,2,2,1] f=[1,1,1] s=1', async () => {
| const dy = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
| const x = tf.ones([1, 2, 2, 2, 1]);
| const expected = [1, 2, 3, 4, 5, 6, 7, 8];
| const dx = tf.grad((x) => tf.avgPool3d(x, 1, 1, 0))(x, dy);
| expect(dx.shape).toEqual(x.shape);
| expectArraysClose(await dx.data(), expected);
| });
| it('gradient x=[1,2,2,2,1] f=[2,2,2] s=2', async () => {
| const dy = tf.tensor5d([8], [1, 1, 1, 1, 1]);
| const x = tf.ones([1, 2, 2, 2, 1]);
| const expected = [1, 1, 1, 1, 1, 1, 1, 1];
| const dx = tf.grad((x) => tf.avgPool3d(x, 2, 1, 0))(x, dy);
| expect(dx.shape).toEqual(x.shape);
| expectArraysClose(await dx.data(), expected);
| });
| it('gradient with clone x=[1,2,2,2,1] f=[2,2,2] s=1', async () => {
| const dy = tf.tensor5d([8], [1, 1, 1, 1, 1]);
| const x = tf.ones([1, 2, 2, 2, 1]);
| const expected = [1, 1, 1, 1, 1, 1, 1, 1];
| const dx = tf.grad((x) => tf.avgPool3d(x.clone(), 2, 1, 0).clone())(x, dy);
| expect(dx.shape).toEqual(x.shape);
| expectArraysClose(await dx.data(), expected);
| });
| it('gradient x=[1,3,3,3,1] f=[2,2,2] s=1', async () => {
| const dy = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
| const x = tf.ones([1, 3, 3, 3, 1]);
| const expected = [
| 0.125, 0.375, 0.25, 0.5, 1.25, 0.75, 0.375, 0.875, 0.5,
| 0.75, 1.75, 1, 2, 4.5, 2.5, 1.25, 2.75, 1.5,
| 0.625, 1.375, 0.75, 1.5, 3.25, 1.75, 0.875, 1.875, 1
| ];
| const dx = tf.grad((x) => tf.avgPool3d(x, 2, 1, 0))(x, dy);
| expect(dx.shape).toEqual(x.shape);
| expectArraysClose(await dx.data(), expected);
| });
| it('gradient x=[1,4,4,4,1] f=[2,2,2] s=2', async () => {
| const dy = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
| const x = tf.ones([1, 4, 4, 4, 1]);
| const expected = [
| 0.125, 0.125, 0.25, 0.25, 0.125, 0.125, 0.25, 0.25, 0.375, 0.375,
| 0.5, 0.5, 0.375, 0.375, 0.5, 0.5, 0.125, 0.125, 0.25, 0.25,
| 0.125, 0.125, 0.25, 0.25, 0.375, 0.375, 0.5, 0.5, 0.375, 0.375,
| 0.5, 0.5, 0.625, 0.625, 0.75, 0.75, 0.625, 0.625, 0.75, 0.75,
| 0.875, 0.875, 1, 1, 0.875, 0.875, 1, 1, 0.625, 0.625,
| 0.75, 0.75, 0.625, 0.625, 0.75, 0.75, 0.875, 0.875, 1, 1,
| 0.875, 0.875, 1, 1
| ];
| const dx = tf.grad((x) => tf.avgPool3d(x, 2, 2, 0))(x, dy);
| expect(dx.shape).toEqual(x.shape);
| expectArraysClose(await dx.data(), expected);
| });
| it('gradient x=[1,3,3,3,2] f=[2,2,2] s=1', async () => {
| const dy = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 2, 2, 2, 2]);
| const x = tf.ones([1, 3, 3, 3, 2]);
| const expected = [
| 0.125, 0.25, 0.5, 0.75, 0.375, 0.5, 0.75, 1, 2, 2.5, 1.25,
| 1.5, 0.625, 0.75, 1.5, 1.75, 0.875, 1, 1.25, 1.5, 3, 3.5,
| 1.75, 2, 3.5, 4, 8, 9, 4.5, 5, 2.25, 2.5, 5,
| 5.5, 2.75, 3, 1.125, 1.25, 2.5, 2.75, 1.375, 1.5, 2.75, 3,
| 6, 6.5, 3.25, 3.5, 1.625, 1.75, 3.5, 3.75, 1.875, 2
| ];
| const dx = tf.grad((x) => tf.avgPool3d(x, 2, 1, 0))(x, dy);
| expect(dx.shape).toEqual(x.shape);
| expectArraysClose(await dx.data(), expected);
| });
| it('gradient x=[2,3,3,3,1] f=[2,2,2] s=1', async () => {
| const dy = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [2, 2, 2, 2, 1]);
| const x = tf.ones([2, 3, 3, 3, 1]);
| const expected = [
| 0.125, 0.375, 0.25, 0.5, 1.25, 0.75, 0.375, 0.875, 0.5, 0.75, 1.75,
| 1, 2, 4.5, 2.5, 1.25, 2.75, 1.5, 0.625, 1.375, 0.75, 1.5,
| 3.25, 1.75, 0.875, 1.875, 1, 1.125, 2.375, 1.25, 2.5, 5.25, 2.75,
| 1.375, 2.875, 1.5, 2.75, 5.75, 3, 6, 12.5, 6.5, 3.25, 6.75,
| 3.5, 1.625, 3.375, 1.75, 3.5, 7.25, 3.75, 1.875, 3.875, 2
| ];
| const dx = tf.grad((x) => tf.avgPool3d(x, 2, 1, 0))(x, dy);
| expect(dx.shape).toEqual(x.shape);
| expectArraysClose(await dx.data(), expected);
| });
| });
| //# 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* @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('avgPool3d', ALL_ENVS, () => {\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 = tf.avgPool3d(x, 2, 1, 'valid');\n\n    expect(result.shape).toEqual([1, 1, 1, 1]);\n    expectArraysClose(await result.data(), [4.5]);\n  });\n\n  it('x=[2,2,2,1] f=[1,2,2] 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 = tf.avgPool3d(x, [1, 2, 2], 1, 'valid');\n\n    expect(result.shape).toEqual([2, 1, 1, 1]);\n    expectArraysClose(await result.data(), [2.5, 6.5]);\n  });\n\n  it('x=[1,1,1,1,1] f=[1,1,1] s=1 [0] => [0]', async () => {\n    const x = tf.tensor5d([0], [1, 1, 1, 1, 1]);\n\n    const result = tf.avgPool3d(x, 1, 1, 0);\n\n    expect(result.shape).toEqual([1, 1, 1, 1, 1]);\n    expectArraysClose(await result.data(), [0]);\n  });\n\n  it('x=[1,2,2,2,1] f=[2,2,2] s=1 p=valid', async () => {\n    const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n\n    const result = tf.avgPool3d(x, 2, 1, 'valid');\n\n    expect(result.shape).toEqual([1, 1, 1, 1, 1]);\n    expectArraysClose(await result.data(), [4.5]);\n  });\n\n  it('x=[1,2,2,2,1] f=[2,2,2] s=1 p=same', async () => {\n    const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n    const expected = [4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8];\n    const result = tf.avgPool3d(x, 2, 1, 'same');\n\n    expect(result.shape).toEqual([1, 2, 2, 2, 1]);\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('x=[1,3,3,3,1] f=[2,2,2] s=1 p=valid', async () => {\n    const x = tf.tensor5d(\n        [\n          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14,\n          15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27\n        ],\n        [1, 3, 3, 3, 1]);\n    const expected = [7.5, 8.5, 10.5, 11.5, 16.5, 17.5, 19.5, 20.5];\n    const result = tf.avgPool3d(x, 2, 1, 'valid');\n\n    expect(result.shape).toEqual([1, 2, 2, 2, 1]);\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('x=[1,3,3,3,1] f=[2,2,2] s=1 p=same', async () => {\n    const x = tf.tensor5d(\n        [\n          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14,\n          15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27\n        ],\n        [1, 3, 3, 3, 1]);\n    const expected = [\n      7.5,  8.5,  9,    10.5, 11.5, 12,   12,   13,   13.5,\n      16.5, 17.5, 18,   19.5, 20.5, 21,   21,   22,   22.5,\n      21,   22,   22.5, 24,   25,   25.5, 25.5, 26.5, 27\n    ];\n    const result = tf.avgPool3d(x, 2, 1, 'same');\n\n    expect(result.shape).toEqual([1, 3, 3, 3, 1]);\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('x=[1,3,3,3,1] f=[2,2,2] s=1 p=valid, propagates NaNs', async () => {\n    const x = tf.tensor5d(\n        [\n          1,  2,  3,  4,  5,  6,  7,  8,   NaN, 10, 11, 12, 13, 14,\n          15, 16, 17, 18, 19, 20, 21, NaN, 23,  24, 25, 26, 27\n        ],\n        [1, 3, 3, 3, 1]);\n    const expected = [7.5, 8.5, 10.5, NaN, NaN, 17.5, NaN, 20.5];\n    const result = tf.avgPool3d(x, 2, 1, 'valid');\n\n    expect(result.shape).toEqual([1, 2, 2, 2, 1]);\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('x=[2,3,3,3,1] f=[2,2,2] s=1 p=valid', async () => {\n    const x = tf.tensor5d(\n        [\n          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14,\n          15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,\n          29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,\n          43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54\n        ],\n        [2, 3, 3, 3, 1]);\n    const expected = [\n      7.5, 8.5, 10.5, 11.5, 16.5, 17.5, 19.5, 20.5, 34.5, 35.5, 37.5, 38.5,\n      43.5, 44.5, 46.5, 47.5\n    ];\n    const result = tf.avgPool3d(x, 2, 1, 'valid');\n\n    expect(result.shape).toEqual([2, 2, 2, 2, 1]);\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('x=[1,3,3,3,2] f=[2,2,2] s=1 p=valid', async () => {\n    const x = tf.tensor5d(\n        [\n          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14,\n          15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,\n          29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,\n          43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54\n        ],\n        [1, 3, 3, 3, 2]);\n    const expected =\n        [14, 15, 16, 17, 20, 21, 22, 23, 32, 33, 34, 35, 38, 39, 40, 41];\n    const result = tf.avgPool3d(x, 2, 1, 'valid');\n\n    expect(result.shape).toEqual([1, 2, 2, 2, 2]);\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('x=[1,2,2,2,1] f=[2,2,2] s=1 p=1 roundingMode=floor', async () => {\n    const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n    const expected = [\n      1, 1.5, 2,   2, 2.5, 3,   3, 3.5, 4,   3, 3.5, 4,   4, 4.5,\n      5, 5,   5.5, 6, 5,   5.5, 6, 6,   6.5, 7, 7,   7.5, 8\n    ];\n    const result = tf.avgPool3d(x, 2, 1, 1, 'floor');\n\n    expect(result.shape).toEqual([1, 3, 3, 3, 1]);\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('x=[1,1,1,1,1] f=[1,1,3] s=1 p=valid', async () => {\n    // Output tensor would have a dimension of zero, if a certain filter's\n    // dimension is larger than the input's.\n    const x = tf.tensor5d([1], [1, 1, 1, 1, 1]);\n    const expected: number[] = [];\n    const result = tf.avgPool3d(x, [1, 1, 3], 1, 'valid');\n\n    expect(result.shape).toEqual([1, 1, 1, 0, 1]);\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('x=[1,1,1,4,1] f=[1,1,1] s=[1,1,2] p=0', async () => {\n    // Works if the padding is a number.\n    const x = tf.ones([1, 1, 1, 4, 1]) as tf.Tensor5D;\n    const expected = [1, 1];\n    const result = tf.avgPool3d(x, [1, 1, 1], [1, 1, 2], 0);\n\n    expect(result.shape).toEqual([1, 1, 1, 2, 1]);\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('x=[1,1,1,1,1] f=[2,2,2] s=1 p=2', async () => {\n    // Works if the padding is larger than filter size.\n    const x = tf.ones([1, 1, 1, 1, 1]) as tf.Tensor5D;\n    const expected = [\n      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n      1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0,\n      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n    ];\n    const result = tf.avgPool3d(x, [2, 2, 2], 1, 2);\n\n    expect(result.shape).toEqual([1, 4, 4, 4, 1]);\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('throws when x is not rank 5', async () => {\n    // tslint:disable-next-line:no-any\n    const x: any = tf.tensor1d([1]);\n\n    expect(() => tf.avgPool3d(x as tf.Tensor5D, 2, 1, 'valid')).toThrowError();\n  });\n\n  it('throws when dimRoundingMode is set and pad is same', async () => {\n    const x = tf.tensor5d([1], [1, 1, 1, 1, 1]);\n    const pad = 'same';\n    const dimRoundingMode = 'round';\n\n    expect(() => tf.avgPool3d(x, 2, 1, pad, dimRoundingMode)).toThrowError();\n  });\n\n  it('throws when dimRoundingMode is set and pad is valid', async () => {\n    const x = tf.tensor5d([1], [1, 1, 1, 1, 1]);\n    const pad = 'valid';\n    const dimRoundingMode = 'round';\n\n    expect(() => tf.avgPool3d(x, 2, 1, pad, dimRoundingMode)).toThrowError();\n  });\n\n  it('throws when dimRoundingMode is set and pad is a non-integer number',\n     async () => {\n       const x = tf.tensor5d([1], [1, 1, 1, 1, 1]);\n       const pad = 1.2;\n       const dimRoundingMode = 'round';\n\n       expect(() => tf.avgPool3d(x, 2, 1, pad, dimRoundingMode)).toThrowError();\n     });\n\n  it('throws when passed a non-tensor', () => {\n    expect(() => tf.avgPool3d({} as tf.Tensor5D, 2, 1, 'valid')).toThrowError();\n  });\n\n  it('throws when input dtype is not float32', () => {\n    const a = tf.tensor5d([1], [1, 1, 1, 1, 1], 'int32');\n    expect(() => tf.avgPool3d(a, 2, 1, 0)).toThrowError();\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const x = [[[[[0]]]]];  // 1x1x1x1x1\n    const result = tf.avgPool3d(x, 1, 1, 0);\n    expect(result.shape).toEqual([1, 1, 1, 1, 1]);\n    expectArraysClose(await result.data(), [0]);\n  });\n});\n\ndescribeWithFlags('avgPool3dBackprop', ALL_ENVS, () => {\n  it('gradient x=[2,2,2,1] f=[1,1,1] s=1', async () => {\n    const dy = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);\n    const x: tf.Tensor4D = tf.ones([2, 2, 2, 1]);\n    const expected = [1, 2, 3, 4, 5, 6, 7, 8];\n\n    const dx = tf.grad((x: tf.Tensor4D) => tf.avgPool3d(x, 1, 1, 0))(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), expected);\n  });\n\n  it('gradient x=[1,2,2,2,1] f=[1,1,1] s=1', async () => {\n    const dy = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n    const x: tf.Tensor5D = tf.ones([1, 2, 2, 2, 1]);\n    const expected = [1, 2, 3, 4, 5, 6, 7, 8];\n\n    const dx = tf.grad((x: tf.Tensor5D) => tf.avgPool3d(x, 1, 1, 0))(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), expected);\n  });\n\n  it('gradient x=[1,2,2,2,1] f=[2,2,2] s=2', async () => {\n    const dy = tf.tensor5d([8], [1, 1, 1, 1, 1]);\n    const x: tf.Tensor5D = tf.ones([1, 2, 2, 2, 1]);\n    const expected = [1, 1, 1, 1, 1, 1, 1, 1];\n\n    const dx = tf.grad((x: tf.Tensor5D) => tf.avgPool3d(x, 2, 1, 0))(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), expected);\n  });\n\n  it('gradient with clone x=[1,2,2,2,1] f=[2,2,2] s=1', async () => {\n    const dy = tf.tensor5d([8], [1, 1, 1, 1, 1]);\n    const x: tf.Tensor5D = tf.ones([1, 2, 2, 2, 1]);\n    const expected = [1, 1, 1, 1, 1, 1, 1, 1];\n\n    const dx = tf.grad(\n        (x: tf.Tensor5D) => tf.avgPool3d(x.clone(), 2, 1, 0).clone())(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), expected);\n  });\n\n  it('gradient x=[1,3,3,3,1] f=[2,2,2] s=1', async () => {\n    const dy = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n    const x: tf.Tensor5D = tf.ones([1, 3, 3, 3, 1]);\n    const expected = [\n      0.125, 0.375, 0.25, 0.5, 1.25, 0.75, 0.375, 0.875, 0.5,\n      0.75,  1.75,  1,    2,   4.5,  2.5,  1.25,  2.75,  1.5,\n      0.625, 1.375, 0.75, 1.5, 3.25, 1.75, 0.875, 1.875, 1\n    ];\n\n    const dx = tf.grad((x: tf.Tensor5D) => tf.avgPool3d(x, 2, 1, 0))(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), expected);\n  });\n\n  it('gradient x=[1,4,4,4,1] f=[2,2,2] s=2', async () => {\n    const dy = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n    const x: tf.Tensor5D = tf.ones([1, 4, 4, 4, 1]);\n    const expected = [\n      0.125, 0.125, 0.25,  0.25,  0.125, 0.125, 0.25,  0.25,  0.375, 0.375,\n      0.5,   0.5,   0.375, 0.375, 0.5,   0.5,   0.125, 0.125, 0.25,  0.25,\n      0.125, 0.125, 0.25,  0.25,  0.375, 0.375, 0.5,   0.5,   0.375, 0.375,\n      0.5,   0.5,   0.625, 0.625, 0.75,  0.75,  0.625, 0.625, 0.75,  0.75,\n      0.875, 0.875, 1,     1,     0.875, 0.875, 1,     1,     0.625, 0.625,\n      0.75,  0.75,  0.625, 0.625, 0.75,  0.75,  0.875, 0.875, 1,     1,\n      0.875, 0.875, 1,     1\n    ];\n\n    const dx = tf.grad((x: tf.Tensor5D) => tf.avgPool3d(x, 2, 2, 0))(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), expected);\n  });\n\n  it('gradient x=[1,3,3,3,2] f=[2,2,2] s=1', async () => {\n    const dy = tf.tensor5d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],\n        [1, 2, 2, 2, 2]);\n    const x: tf.Tensor5D = tf.ones([1, 3, 3, 3, 2]);\n    const expected = [\n      0.125, 0.25,  0.5,  0.75,  0.375, 0.5,   0.75, 1,     2,     2.5,  1.25,\n      1.5,   0.625, 0.75, 1.5,   1.75,  0.875, 1,    1.25,  1.5,   3,    3.5,\n      1.75,  2,     3.5,  4,     8,     9,     4.5,  5,     2.25,  2.5,  5,\n      5.5,   2.75,  3,    1.125, 1.25,  2.5,   2.75, 1.375, 1.5,   2.75, 3,\n      6,     6.5,   3.25, 3.5,   1.625, 1.75,  3.5,  3.75,  1.875, 2\n    ];\n\n    const dx = tf.grad((x: tf.Tensor5D) => tf.avgPool3d(x, 2, 1, 0))(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), expected);\n  });\n\n  it('gradient x=[2,3,3,3,1] f=[2,2,2] s=1', async () => {\n    const dy = tf.tensor5d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],\n        [2, 2, 2, 2, 1]);\n    const x: tf.Tensor5D = tf.ones([2, 3, 3, 3, 1]);\n    const expected = [\n      0.125, 0.375, 0.25,  0.5,   1.25, 0.75,  0.375, 0.875, 0.5,   0.75, 1.75,\n      1,     2,     4.5,   2.5,   1.25, 2.75,  1.5,   0.625, 1.375, 0.75, 1.5,\n      3.25,  1.75,  0.875, 1.875, 1,    1.125, 2.375, 1.25,  2.5,   5.25, 2.75,\n      1.375, 2.875, 1.5,   2.75,  5.75, 3,     6,     12.5,  6.5,   3.25, 6.75,\n      3.5,   1.625, 3.375, 1.75,  3.5,  7.25,  3.75,  1.875, 3.875, 2\n    ];\n\n    const dx = tf.grad((x: tf.Tensor5D) => tf.avgPool3d(x, 2, 1, 0))(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), expected);\n  });\n});\n"]}
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