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avg_pool_3d_test.ts
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avg_pool_3d_test.ts
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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: number[] = [];
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]) as tf.Tensor5D;
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]) as tf.Tensor5D;
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: any = tf.tensor1d([1]);
expect(() => tf.avgPool3d(x as tf.Tensor5D, 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({} as tf.Tensor5D, 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.Tensor4D = tf.ones([2, 2, 2, 1]);
const expected = [1, 2, 3, 4, 5, 6, 7, 8];
const dx = tf.grad((x: tf.Tensor4D) => 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.Tensor5D = tf.ones([1, 2, 2, 2, 1]);
const expected = [1, 2, 3, 4, 5, 6, 7, 8];
const dx = tf.grad((x: tf.Tensor5D) => 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.Tensor5D = tf.ones([1, 2, 2, 2, 1]);
const expected = [1, 1, 1, 1, 1, 1, 1, 1];
const dx = tf.grad((x: tf.Tensor5D) => 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.Tensor5D = tf.ones([1, 2, 2, 2, 1]);
const expected = [1, 1, 1, 1, 1, 1, 1, 1];
const dx = tf.grad(
(x: tf.Tensor5D) => 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.Tensor5D = 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.Tensor5D) => 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.Tensor5D = 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.Tensor5D) => 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.Tensor5D = 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.Tensor5D) => 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.Tensor5D = 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.Tensor5D) => tf.avgPool3d(x, 2, 1, 0))(x, dy);
expect(dx.shape).toEqual(x.shape);
expectArraysClose(await dx.data(), expected);
});
});