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| /**
| * @license
| * Copyright 2020 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';
| import { identityPoolTest } from './identity_pool_test';
| describeWithFlags('avgPool', ALL_ENVS, () => {
| it('x=[1,1,1] f=[1,1] s=1 [0] => [0]', async () => {
| const a = tf.tensor3d([0], [1, 1, 1]);
| const result = tf.avgPool(a, 1, 1, 0);
| expectArraysClose(await result.data(), [0]);
| });
| it('x=[3,3,1] f=[2,2] s=1', async () => {
| // Feed forward.
| const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 9, 8], [3, 3, 1]);
| const result = tf.avgPool(a, 2, 1, 0);
| expect(result.shape).toEqual([2, 2, 1]);
| expect(result.dtype).toBe('float32');
| expectArraysClose(await result.data(), [3, 4, 6.25, 7]);
| });
| it('input int32 throws error', () => {
| // Feed forward.
| const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 9, 8], [3, 3, 1], 'int32');
| expect(() => tf.avgPool(a, 2, 1, 0)).toThrowError();
| });
| it('x=[2,3,3,1] f=[2,2], s=1', async () => {
| // Feed forward.
| const a = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 9, 8, 1, 2, 3, 4, 5, 6, 7, 8, 9], [2, 3, 3, 1]);
| const result = tf.avgPool(a, 2, 1, 0);
| expect(result.shape).toEqual([2, 2, 2, 1]);
| expectArraysClose(await result.data(), [3, 4, 6.25, 7, 3, 4, 6, 7]);
| });
| it('x=[3,3,1] f=[2,2] s=1 propagates NaNs', async () => {
| // Feed forward.
| const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, NaN, 8], [3, 3, 1]);
| const result = tf.avgPool(a, 2, 1, 0);
| expect(result.shape).toEqual([2, 2, 1]);
| expectArraysClose(await result.data(), [3, 4, NaN, NaN]);
| });
| it('x=[3,3,2] f=[2,2] s=1', async () => {
| // Feed forward.
| const a = tf.tensor3d([1, 99, 2, 88, 3, 77, 4, 66, 5, 55, 6, 44, 7, 33, 9, 22, 8, 11], [3, 3, 2]);
| const result = tf.avgPool(a, 2, 1, 0);
| expect(result.shape).toEqual([2, 2, 2]);
| expectArraysClose(await result.data(), [3, 77, 4, 66, 6.25, 44, 7, 33]);
| });
| it('x=[4,4,1] f=[2,2] s=2', async () => {
| // Feed forward.
| const a = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [4, 4, 1]);
| const result = tf.avgPool(a, 2, 2, 0);
| expect(result.shape).toEqual([2, 2, 1]);
| expectArraysClose(await result.data(), [2.5, 4.5, 10.5, 12.5]);
| });
| it('x=[2,2,1] f=[2,2] s=1 p=same', async () => {
| // Feed forward.
| const a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
| const fSize = 2;
| const strides = 1;
| const result = tf.avgPool(a, fSize, strides, 'same');
| expect(result.shape).toEqual([2, 2, 1]);
| expectArraysClose(await result.data(), [2.5, 3, 3.5, 4]);
| });
| it('x=[2,2,3] f=[2,2] s=1 p=valid', async () => {
| // Feed forward.
| const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);
| const fSize = 2;
| const strides = 1;
| const result = tf.avgPool(a, fSize, strides, 'valid');
| expect(result.shape).toEqual([1, 1, 3]);
| expectArraysClose(await result.data(), [5.5, 6.5, 7.5]);
| });
| it('x=[3,3,1] f=[3,3] s=1 p=explicit', async () => {
| // Feed forward.
| const x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8], [3, 3, 1]);
| const padding = [[0, 0], [1, 2], [0, 1], [0, 0]];
| const result = tf.avgPool(x, 3, 1, padding);
| expect(result.shape).toEqual([4, 2, 1]);
| expectArraysClose(await result.data(), [2.5, 3, 4, 4.5, 5.5, 6, 7, 7.5]);
| });
| it('x=[2,2,3] f=[2,2] s=3 p=1 default dimRoundingMode', () => {
| // Feed forward.
| const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);
| const result = tf.avgPool(x, 2, 3, 1);
| expect(result.shape).toEqual([1, 1, 3]);
| });
| it('x=[2,2,3] f=[1,1] s=2 p=1 dimRoundingMode=floor', () => {
| // Feed forward.
| const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);
| const result = tf.avgPool(x, 1, 2, 1, 'floor');
| expect(result.shape).toEqual([2, 2, 3]);
| });
| it('x=[2,2,3] f=[2,2] s=3 p=1 dimRoundingMode=floor', () => {
| // Feed forward.
| const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);
| const result = tf.avgPool(x, 2, 3, 1, 'floor');
| expect(result.shape).toEqual([1, 1, 3]);
| });
| it('x=[2,2,3] f=[2,2] s=3 p=1 dimRoundingMode=round', () => {
| // Feed forward.
| const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);
| const result = tf.avgPool(x, 2, 3, 1, 'round');
| expect(result.shape).toEqual([2, 2, 3]);
| });
| it('x=[2,2,3] f=[2,2] s=3 p=1 dimRoundingMode=ceil', () => {
| // Feed forward.
| const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);
| const result = tf.avgPool(x, 2, 3, 1, 'ceil');
| expect(result.shape).toEqual([2, 2, 3]);
| });
| it('gradient x=[1,1,1] f=[1,1] s=1 [0] => [0]', async () => {
| const x = tf.tensor3d([0], [1, 1, 1]);
| const dy = tf.tensor3d([0], [1, 1, 1]);
| const dx = tf.grad((x) => x.avgPool(1, 1, 0))(x, dy);
| expect(dx.shape).toEqual(x.shape);
| expectArraysClose(await dx.data(), [0]);
| });
| it('gradient with clones', async () => {
| const x = tf.tensor3d([0], [1, 1, 1]);
| const dy = tf.tensor3d([0], [1, 1, 1]);
| const dx = tf.grad((x) => tf.avgPool(x.clone(), 1, 1, 0).clone())(x, dy);
| expect(dx.shape).toEqual(x.shape);
| expectArraysClose(await dx.data(), [0]);
| });
| it('gradient x=[3,3,1] f=[2,2] s=1', async () => {
| // Feed forward.
| const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 9, 8], [3, 3, 1]);
| const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
| const avgMultiplier = 1 / (2 * 2);
| const dx = tf.grad((x) => x.avgPool(2, 1, 0))(x, dy);
| expect(dx.shape).toEqual(x.shape);
| expectArraysClose(await dx.data(), [
| 1 * avgMultiplier, 3 * avgMultiplier, 2 * avgMultiplier,
| 4 * avgMultiplier, 10 * avgMultiplier, 6 * avgMultiplier,
| 3 * avgMultiplier, 7 * avgMultiplier, 4 * avgMultiplier
| ]);
| });
| it('gradient x=[3,3,1] f=[3,3] s=1 p=explicit', async () => {
| // Feed forward.
| const x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8], [3, 3, 1]);
| const padding = [[0, 0], [1, 2], [0, 1], [0, 0]];
| const dy = tf.tensor3d([0, 3, 0, 6, 0, 9, 0, 12], [4, 2, 1]);
| const dx = tf.grad((x) => x.avgPool(3, 1, padding))(x, dy);
| expect(dx.shape).toEqual(x.shape);
| expectArraysClose(await dx.data(), [0, 1, 1, 0, 2, 2, 0, 3, 3]);
| });
| it('gradient x=[2,3,3,1] f=[2,2], s=1', async () => {
| // Feed forward.
| 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]);
| const dy = tf.tensor4d([1, 2, 3, 4, 1, 2, 3, 4], [2, 2, 2, 1]);
| const avgMultiplier = 1 / (2 * 2);
| const dx = tf.grad((x) => x.avgPool(2, 1, 0))(x, dy);
| expect(dx.shape).toEqual(x.shape);
| expectArraysClose(await dx.data(), [
| 1 * avgMultiplier, 3 * avgMultiplier, 2 * avgMultiplier,
| 4 * avgMultiplier, 10 * avgMultiplier, 6 * avgMultiplier,
| 3 * avgMultiplier, 7 * avgMultiplier, 4 * avgMultiplier,
| 1 * avgMultiplier, 3 * avgMultiplier, 2 * avgMultiplier,
| 4 * avgMultiplier, 10 * avgMultiplier, 6 * avgMultiplier,
| 3 * avgMultiplier, 7 * avgMultiplier, 4 * avgMultiplier
| ]);
| });
| it('throws when dimRoundingMode is set and pad is same', () => {
| const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
| const pad = 'same';
| const dimRoundingMode = 'round';
| expect(() => tf.avgPool(x, 2, 1, pad, dimRoundingMode)).toThrowError();
| });
| it('throws when dimRoundingMode is set and pad is valid', () => {
| const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
| const pad = 'valid';
| const dimRoundingMode = 'round';
| expect(() => tf.avgPool(x, 2, 1, pad, dimRoundingMode)).toThrowError();
| });
| it('throws when dimRoundingMode is set and pad is a non-integer number', () => {
| const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
| const pad = 1.2;
| const dimRoundingMode = 'round';
| expect(() => tf.avgPool(x, 2, 1, pad, dimRoundingMode)).toThrowError();
| });
| it('throws when dimRoundingMode is set and pad is explicit by non-integer ' +
| 'number', () => {
| const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
| const pad = [[0, 0], [0, 2.1], [1, 1], [0, 0]];
| const dimRoundingMode = 'round';
| expect(() => tf.avgPool(x, 2, 1, pad, dimRoundingMode)).toThrowError();
| });
| it('throws when passed a non-tensor', () => {
| expect(() => tf.avgPool({}, 2, 1, 'valid'))
| .toThrowError(/Argument 'x' passed to 'avgPool' must be a Tensor/);
| });
| it('accepts a tensor-like object', async () => {
| const a = [[[0]]]; // 1x1x1
| const result = tf.avgPool(a, 1, 1, 0);
| expectArraysClose(await result.data(), [0]);
| });
| identityPoolTest(tf.avgPool);
| });
| //# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as tf from '../index';\nimport {ALL_ENVS, describeWithFlags} from '../jasmine_util';\nimport {expectArraysClose} from '../test_util';\nimport {identityPoolTest} from './identity_pool_test';\n\ndescribeWithFlags('avgPool', ALL_ENVS, () => {\n  it('x=[1,1,1] f=[1,1] s=1 [0] => [0]', async () => {\n    const a = tf.tensor3d([0], [1, 1, 1]);\n    const result = tf.avgPool(a, 1, 1, 0);\n    expectArraysClose(await result.data(), [0]);\n  });\n\n  it('x=[3,3,1] f=[2,2] s=1', async () => {\n    // Feed forward.\n    const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 9, 8], [3, 3, 1]);\n    const result = tf.avgPool(a, 2, 1, 0);\n\n    expect(result.shape).toEqual([2, 2, 1]);\n    expect(result.dtype).toBe('float32');\n    expectArraysClose(await result.data(), [3, 4, 6.25, 7]);\n  });\n\n  it('input int32 throws error', () => {\n    // Feed forward.\n    const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 9, 8], [3, 3, 1], 'int32');\n    expect(() => tf.avgPool(a, 2, 1, 0)).toThrowError();\n  });\n\n  it('x=[2,3,3,1] f=[2,2], s=1', async () => {\n    // Feed forward.\n    const a = 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    const result = tf.avgPool(a, 2, 1, 0);\n\n    expect(result.shape).toEqual([2, 2, 2, 1]);\n    expectArraysClose(await result.data(), [3, 4, 6.25, 7, 3, 4, 6, 7]);\n  });\n\n  it('x=[3,3,1] f=[2,2] s=1 propagates NaNs', async () => {\n    // Feed forward.\n    const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, NaN, 8], [3, 3, 1]);\n    const result = tf.avgPool(a, 2, 1, 0);\n\n    expect(result.shape).toEqual([2, 2, 1]);\n    expectArraysClose(await result.data(), [3, 4, NaN, NaN]);\n  });\n\n  it('x=[3,3,2] f=[2,2] s=1', async () => {\n    // Feed forward.\n    const a = tf.tensor3d(\n        [1, 99, 2, 88, 3, 77, 4, 66, 5, 55, 6, 44, 7, 33, 9, 22, 8, 11],\n        [3, 3, 2]);\n    const result = tf.avgPool(a, 2, 1, 0);\n\n    expect(result.shape).toEqual([2, 2, 2]);\n    expectArraysClose(await result.data(), [3, 77, 4, 66, 6.25, 44, 7, 33]);\n  });\n\n  it('x=[4,4,1] f=[2,2] s=2', async () => {\n    // Feed forward.\n    const a = tf.tensor3d(\n        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [4, 4, 1]);\n    const result = tf.avgPool(a, 2, 2, 0);\n\n    expect(result.shape).toEqual([2, 2, 1]);\n    expectArraysClose(await result.data(), [2.5, 4.5, 10.5, 12.5]);\n  });\n\n  it('x=[2,2,1] f=[2,2] s=1 p=same', async () => {\n    // Feed forward.\n    const a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);\n    const fSize = 2;\n    const strides = 1;\n    const result = tf.avgPool(a, fSize, strides, 'same');\n\n    expect(result.shape).toEqual([2, 2, 1]);\n    expectArraysClose(await result.data(), [2.5, 3, 3.5, 4]);\n  });\n\n  it('x=[2,2,3] f=[2,2] s=1 p=valid', async () => {\n    // Feed forward.\n    const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);\n    const fSize = 2;\n    const strides = 1;\n    const result = tf.avgPool(a, fSize, strides, 'valid');\n\n    expect(result.shape).toEqual([1, 1, 3]);\n    expectArraysClose(await result.data(), [5.5, 6.5, 7.5]);\n  });\n\n  it('x=[3,3,1] f=[3,3] s=1 p=explicit', async () => {\n    // Feed forward.\n    const x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8], [3, 3, 1]);\n    const padding =\n        [[0, 0], [1, 2], [0, 1], [0, 0]] as tf.backend_util.ExplicitPadding;\n    const result = tf.avgPool(x, 3, 1, padding);\n\n    expect(result.shape).toEqual([4, 2, 1]);\n    expectArraysClose(await result.data(), [2.5, 3, 4, 4.5, 5.5, 6, 7, 7.5]);\n  });\n\n  it('x=[2,2,3] f=[2,2] s=3 p=1 default dimRoundingMode', () => {\n    // Feed forward.\n    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);\n    const result = tf.avgPool(x, 2, 3, 1);\n\n    expect(result.shape).toEqual([1, 1, 3]);\n  });\n\n  it('x=[2,2,3] f=[1,1] s=2 p=1 dimRoundingMode=floor', () => {\n    // Feed forward.\n    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);\n    const result = tf.avgPool(x, 1, 2, 1, 'floor');\n\n    expect(result.shape).toEqual([2, 2, 3]);\n  });\n\n  it('x=[2,2,3] f=[2,2] s=3 p=1 dimRoundingMode=floor', () => {\n    // Feed forward.\n    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);\n    const result = tf.avgPool(x, 2, 3, 1, 'floor');\n\n    expect(result.shape).toEqual([1, 1, 3]);\n  });\n\n  it('x=[2,2,3] f=[2,2] s=3 p=1 dimRoundingMode=round', () => {\n    // Feed forward.\n    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);\n    const result = tf.avgPool(x, 2, 3, 1, 'round');\n\n    expect(result.shape).toEqual([2, 2, 3]);\n  });\n\n  it('x=[2,2,3] f=[2,2] s=3 p=1 dimRoundingMode=ceil', () => {\n    // Feed forward.\n    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);\n    const result = tf.avgPool(x, 2, 3, 1, 'ceil');\n\n    expect(result.shape).toEqual([2, 2, 3]);\n  });\n\n  it('gradient x=[1,1,1] f=[1,1] s=1 [0] => [0]', async () => {\n    const x = tf.tensor3d([0], [1, 1, 1]);\n    const dy = tf.tensor3d([0], [1, 1, 1]);\n    const dx = tf.grad((x: tf.Tensor3D) => x.avgPool(1, 1, 0))(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), [0]);\n  });\n\n  it('gradient with clones', async () => {\n    const x = tf.tensor3d([0], [1, 1, 1]);\n    const dy = tf.tensor3d([0], [1, 1, 1]);\n    const dx = tf.grad(\n        (x: tf.Tensor3D) => tf.avgPool(x.clone(), 1, 1, 0).clone())(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), [0]);\n  });\n\n  it('gradient x=[3,3,1] f=[2,2] s=1', async () => {\n    // Feed forward.\n    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 9, 8], [3, 3, 1]);\n    const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);\n    const avgMultiplier = 1 / (2 * 2);\n\n    const dx = tf.grad((x: tf.Tensor3D) => x.avgPool(2, 1, 0))(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), [\n      1 * avgMultiplier, 3 * avgMultiplier, 2 * avgMultiplier,\n      4 * avgMultiplier, 10 * avgMultiplier, 6 * avgMultiplier,\n      3 * avgMultiplier, 7 * avgMultiplier, 4 * avgMultiplier\n    ]);\n  });\n\n  it('gradient x=[3,3,1] f=[3,3] s=1 p=explicit', async () => {\n    // Feed forward.\n    const x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8], [3, 3, 1]);\n    const padding =\n        [[0, 0], [1, 2], [0, 1], [0, 0]] as tf.backend_util.ExplicitPadding;\n    const dy = tf.tensor3d([0, 3, 0, 6, 0, 9, 0, 12], [4, 2, 1]);\n    const dx = tf.grad((x: tf.Tensor3D) => x.avgPool(3, 1, padding))(x, dy);\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), [0, 1, 1, 0, 2, 2, 0, 3, 3]);\n  });\n\n  it('gradient x=[2,3,3,1] f=[2,2], s=1', async () => {\n    // Feed forward.\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    const dy = tf.tensor4d([1, 2, 3, 4, 1, 2, 3, 4], [2, 2, 2, 1]);\n    const avgMultiplier = 1 / (2 * 2);\n\n    const dx = tf.grad((x: tf.Tensor4D) => x.avgPool(2, 1, 0))(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), [\n      1 * avgMultiplier, 3 * avgMultiplier, 2 * avgMultiplier,\n      4 * avgMultiplier, 10 * avgMultiplier, 6 * avgMultiplier,\n      3 * avgMultiplier, 7 * avgMultiplier, 4 * avgMultiplier,\n      1 * avgMultiplier, 3 * avgMultiplier, 2 * avgMultiplier,\n      4 * avgMultiplier, 10 * avgMultiplier, 6 * avgMultiplier,\n      3 * avgMultiplier, 7 * avgMultiplier, 4 * avgMultiplier\n    ]);\n  });\n\n  it('throws when dimRoundingMode is set and pad is same', () => {\n    const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);\n\n    const pad = 'same';\n    const dimRoundingMode = 'round';\n\n    expect(() => tf.avgPool(x, 2, 1, pad, dimRoundingMode)).toThrowError();\n  });\n\n  it('throws when dimRoundingMode is set and pad is valid', () => {\n    const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);\n\n    const pad = 'valid';\n    const dimRoundingMode = 'round';\n\n    expect(() => tf.avgPool(x, 2, 1, pad, dimRoundingMode)).toThrowError();\n  });\n\n  it('throws when dimRoundingMode is set and pad is a non-integer number',\n     () => {\n       const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);\n\n       const pad = 1.2;\n       const dimRoundingMode = 'round';\n\n       expect(() => tf.avgPool(x, 2, 1, pad, dimRoundingMode)).toThrowError();\n     });\n\n  it('throws when dimRoundingMode is set and pad is explicit by non-integer ' +\n         'number',\n     () => {\n       const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);\n\n       const pad = [[0, 0], [0, 2.1], [1, 1], [0, 0]] as\n           tf.backend_util.ExplicitPadding;\n       const dimRoundingMode = 'round';\n\n       expect(() => tf.avgPool(x, 2, 1, pad, dimRoundingMode)).toThrowError();\n     });\n\n  it('throws when passed a non-tensor', () => {\n    expect(() => tf.avgPool({} as tf.Tensor3D, 2, 1, 'valid'))\n        .toThrowError(/Argument 'x' passed to 'avgPool' must be a Tensor/);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const a = [[[0]]];  // 1x1x1\n    const result = tf.avgPool(a, 1, 1, 0);\n    expectArraysClose(await result.data(), [0]);\n  });\n\n  identityPoolTest(tf.avgPool);\n});\n"]}
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