/**
 * @license
 * Copyright 2018 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('separableConv2d', ALL_ENVS, () => {
    it('input=1x3x3x1,f=2,s=1,d=1,p=valid,chMul=1,outDepth=2', async () => {
        const fSize = 2;
        const pad = 'valid';
        const stride = 1;
        const chMul = 1;
        const inDepth = 1;
        const outDepth = 2;
        const x = tf.tensor4d([
            0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
            0.0741907, 0.409265, 0.351377
        ], [1, 3, 3, inDepth]);
        const depthwiseFilter = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
        const pointwiseFilter = tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
        const result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
        expectArraysClose(await result.data(), [
            0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096, -0.13408193,
            0.07788632, -0.15577264
        ]);
        expect(result.shape).toEqual([1, 2, 2, outDepth]);
    });
    it('input=1x3x3x1,f=2,s=1,d=1,p=valid,chMul=1,outDepth=2 in tensor', async () => {
        const fSize = 2;
        const pad = 'valid';
        const stride = 1;
        const chMul = 1;
        const inDepth = 1;
        const outDepth = 2;
        const x = tf.tensor4d([
            0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
            0.0741907, 0.409265, 0.351377
        ], [1, 3, 3, inDepth]);
        const depthwiseFilter = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
        const pointwiseFilter = tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
        const result = x.separableConv2d(depthwiseFilter, pointwiseFilter, stride, pad);
        expectArraysClose(await result.data(), [
            0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096,
            -0.13408193, 0.07788632, -0.15577264
        ]);
        expect(result.shape).toEqual([1, 2, 2, outDepth]);
    });
    it('input=1x3x3x1,f=2,s=1,d=1,p=valid,chMul=2,outDepth=2', async () => {
        const fSize = 2;
        const pad = 'valid';
        const stride = 1;
        const chMul = 2;
        const inDepth = 1;
        const outDepth = 3;
        const x = tf.tensor4d([
            0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
            0.0741907, 0.409265, 0.351377
        ], [1, 3, 3, inDepth]);
        const depthwiseFilter = tf.tensor4d([
            0.303873, 0.229223, 0.144333, 0.803373, -0.303873, -0.229223,
            -0.144333, -0.803373
        ], [fSize, fSize, inDepth, chMul]);
        const pointwiseFilter = tf.tensor4d([0.1, -0.2, -0.1, 0.2, 0.15, 0.15], [1, 1, inDepth * chMul, outDepth]);
        const result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
        expectArraysClose(await result.data(), [
            0.00305368, 0.0140969, 0.00980358, -0.10853045, -0.06339455, -0.0699412,
            0.11010849, 0.0347524, 0.05214475, 0.10307151, 0.02221644, 0.04224815
        ]);
        expect(result.shape).toEqual([1, 2, 2, outDepth]);
    });
    it('input=1x3x3x1,f=2,s=1,d=1,p=valid,chMul=1,outDepth=2,3D input', async () => {
        const fSize = 2;
        const pad = 'valid';
        const stride = 1;
        const chMul = 1;
        const inDepth = 1;
        const outDepth = 2;
        const x = tf.tensor3d([
            0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
            0.0741907, 0.409265, 0.351377
        ], [3, 3, inDepth]);
        const depthwiseFilter = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
        const pointwiseFilter = tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
        const result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
        expectArraysClose(await result.data(), [
            0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096,
            -0.13408193, 0.07788632, -0.15577264
        ]);
        expect(result.shape).toEqual([2, 2, outDepth]);
    });
    it('input=1x4x4x1,f=2,s=2,d=1,p=valid,chMul=1,outDepth=2', async () => {
        const fSize = 2;
        const pad = 'valid';
        const stride = [2, 2];
        const chMul = 1;
        const inDepth = 1;
        const outDepth = 2;
        const x = tf.tensor4d([
            0.675707, 0.758567, 0.413529, 0.963967, 0.217291, 0.101335, 0.804231,
            0.329673, 0.924503, 0.728742, 0.180217, 0.210459, 0.133869, 0.650827,
            0.047613, 0.554795
        ], [1, 4, 4, inDepth]);
        const depthwiseFilter = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
        const pointwiseFilter = tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
        const result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
        expectArraysClose(await result.data(), [
            0.04919822, -0.09839644, 0.07275512, -0.14551024, 0.09901544, -0.19803089,
            0.05555845, -0.11111691
        ]);
        expect(result.shape).toEqual([1, 2, 2, outDepth]);
    });
    it('input=2x4x4x1,f=2,s=2,d=1,p=valid,chMul=1,outDepth=2', async () => {
        const fSize = 2;
        const pad = 'valid';
        const stride = [2, 2];
        const chMul = 1;
        const inDepth = 1;
        const outDepth = 2;
        const x = tf.tensor4d([
            0.675707, 0.758567, 0.413529, 0.963967, 0.217291, 0.101335,
            0.804231, 0.329673, 0.924503, 0.728742, 0.180217, 0.210459,
            0.133869, 0.650827, 0.047613, 0.554795, -0.675707, -0.758567,
            -0.413529, -0.963967, -0.217291, -0.101335, -0.804231, -0.329673,
            -0.924503, -0.728742, -0.180217, -0.210459, -0.133869, -0.650827,
            -0.047613, -0.554795
        ], [2, 4, 4, inDepth]);
        const depthwiseFilter = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
        const pointwiseFilter = tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
        const result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
        expectArraysClose(await result.data(), [
            0.04919822, -0.09839644, 0.07275512, -0.14551024, 0.09901544, -0.19803089,
            0.05555845, -0.11111691, -0.04919822, 0.09839644, -0.07275512, 0.14551024,
            -0.09901544, 0.19803089, -0.05555845, 0.11111691
        ]);
        expect(result.shape).toEqual([2, 2, 2, outDepth]);
    });
    it('input=1x4x4x2,f=2,s=2,d=1,p=valid,chMul=1,outDepth=2', async () => {
        const fSize = 2;
        const pad = 'valid';
        const stride = [2, 2];
        const chMul = 1;
        const inDepth = 2;
        const outDepth = 2;
        const x = tf.tensor4d([
            0.675707, 0.758567, 0.413529, 0.963967, 0.217291, 0.101335,
            0.804231, 0.329673, 0.924503, 0.728742, 0.180217, 0.210459,
            0.133869, 0.650827, 0.047613, 0.554795, -0.675707, -0.758567,
            -0.413529, -0.963967, -0.217291, -0.101335, -0.804231, -0.329673,
            -0.924503, -0.728742, -0.180217, -0.210459, -0.133869, -0.650827,
            -0.047613, -0.554795
        ], [1, 4, 4, inDepth]);
        const depthwiseFilter = tf.tensor4d([
            0.303873, 0.229223, 0.144333, 0.803373, 0.98976838, 0.56597068,
            0.42654137, 0.66445535
        ], [fSize, fSize, inDepth, chMul]);
        const pointwiseFilter = tf.tensor4d([0.1, -0.2, 0.05, -0.05], [1, 1, inDepth * chMul, outDepth]);
        const result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
        expectArraysClose(await result.data(), [
            0.20072255, -0.32641545, 0.08474462, -0.11823604, -0.20072255, 0.32641545,
            -0.08474462, 0.11823604
        ]);
        expect(result.shape).toEqual([1, 2, 2, outDepth]);
    });
    it('input=1x4x4x1,f=2,s=1,d=2,p=valid,chMul=1,outDepth=2', async () => {
        const fSize = 2;
        const pad = 'valid';
        const stride = 1;
        const chMul = 1;
        const inDepth = 1;
        const outDepth = 2;
        const dilationRate = 2;
        const x = tf.tensor4d([
            0.675707, 0.758567, 0.413529, 0.963967, 0.217291, 0.101335, 0.804231,
            0.329673, 0.924503, 0.728742, 0.180217, 0.210459, 0.133869, 0.650827,
            0.047613, 0.554795
        ], [1, 4, 4, inDepth]);
        const depthwiseFilter = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
        const pointwiseFilter = tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
        const result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad, dilationRate);
        expectArraysClose(await result.data(), [
            0.05783373, -0.11566745, 0.07257301, -0.14514601, 0.03079498, -0.06158997,
            0.06460048, -0.12920095
        ]);
        expect(result.shape).toEqual([1, 2, 2, outDepth]);
    });
    it('input=1x4x4x1,f=2,s=1,d=1,p=same,chMul=1,outDepth=2', async () => {
        const fSize = 2;
        const pad = 'same';
        const stride = 1;
        const chMul = 1;
        const inDepth = 1;
        const outDepth = 2;
        const x = tf.tensor4d([
            0.675707, 0.758567, 0.413529, 0.963967, 0.217291, 0.101335, 0.804231,
            0.329673, 0.924503, 0.728742, 0.180217, 0.210459, 0.133869, 0.650827,
            0.047613, 0.554795
        ], [1, 4, 4, inDepth]);
        const depthwiseFilter = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
        const pointwiseFilter = tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
        const result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
        expectArraysClose(await result.data(), [
            0.04919822, -0.09839644, 0.09860218, -0.19720435, 0.07275512, -0.14551024,
            0.03405062, -0.06810125, 0.08081452, -0.16162904, 0.04651042, -0.09302084,
            0.05150411, -0.10300821, 0.01305549, -0.02611098, 0.09901544, -0.19803089,
            0.03949417, -0.07898834, 0.05555845, -0.11111691, 0.0144028, -0.02880561,
            0.01898637, -0.03797274, 0.02086828, -0.04173655, 0.01416401, -0.02832802,
            0.01685872, -0.03371745
        ]);
        expect(result.shape).toEqual([1, 4, 4, outDepth]);
    });
    it('TensorLike', async () => {
        const pad = 'valid';
        const stride = 1;
        const outDepth = 2;
        const x = [[
                [[0.230664], [0.987388], [0.0685208]],
                [[0.419224], [0.887861], [0.731641]],
                [[0.0741907], [0.409265], [0.351377]]
            ]];
        const depthwiseFilter = [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];
        const pointwiseFilter = [[[[0.1, -0.2]]]];
        const result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
        expectArraysClose(await result.data(), [
            0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096, -0.13408193,
            0.07788632, -0.15577264
        ]);
        expect(result.shape).toEqual([1, 2, 2, outDepth]);
    });
    it('TensorLike Chained', async () => {
        const pad = 'valid';
        const stride = 1;
        const outDepth = 2;
        const inDepth = 1;
        const x = tf.tensor4d([
            0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
            0.0741907, 0.409265, 0.351377
        ], [1, 3, 3, inDepth]);
        const depthwiseFilter = [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];
        const pointwiseFilter = [[[[0.1, -0.2]]]];
        const result = x.separableConv2d(depthwiseFilter, pointwiseFilter, stride, pad);
        expectArraysClose(await result.data(), [
            0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096, -0.13408193,
            0.07788632, -0.15577264
        ]);
        expect(result.shape).toEqual([1, 2, 2, outDepth]);
    });
    it('Incorrect input rank raises error', () => {
        // tslint:disable-next-line:no-any
        const x = tf.zeros([4, 4]);
        const depthwiseFilter = tf.zeros([2, 2, 1, 3]);
        const pointwiseFilter = tf.zeros([1, 1, 2, 4]);
        expect(() => tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid'))
            .toThrowError(/rank 4/);
    });
    it('Incorrect depthwise filter rank raises error', () => {
        const x = tf.zeros([1, 4, 4, 1]);
        // tslint:disable-next-line:no-any
        const depthwiseFilter = tf.zeros([2, 2, 1]);
        const pointwiseFilter = tf.zeros([1, 1, 2, 4]);
        expect(() => tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid'))
            .toThrowError(/rank 4/);
    });
    it('Incorrect depthwise filter rank raises error', () => {
        const x = tf.zeros([1, 4, 4, 1]);
        const depthwiseFilter = tf.zeros([2, 2, 1, 3]);
        // tslint:disable-next-line:no-any
        const pointwiseFilter = tf.zeros([1, 1, 2]);
        expect(() => tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid'))
            .toThrowError(/rank 4/);
    });
    it('Incorrect point filter 1st dimension raises error', () => {
        const x = tf.zeros([1, 4, 4, 1]);
        const depthwiseFilter = tf.zeros([2, 2, 1, 3]);
        const pointwiseFilter = tf.zeros([2, 1, 3, 6]);
        expect(() => tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid'))
            .toThrowError(/must be 1, but got 2/);
    });
    it('Incorrect point filter 2nd dimension raises error', () => {
        const x = tf.zeros([1, 4, 4, 1]);
        const depthwiseFilter = tf.zeros([2, 2, 1, 3]);
        const pointwiseFilter = tf.zeros([1, 5, 3, 6]);
        expect(() => tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid'))
            .toThrowError(/must be 1, but got 5/);
    });
    it('Incorrect pointwise filter 3rd dimension raises error', () => {
        const x = tf.zeros([1, 4, 4, 1]);
        const depthwiseFilter = tf.zeros([2, 2, 1, 3]);
        const pointwiseFilter = tf.zeros([1, 1, 4, 6]);
        expect(() => tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid'))
            .toThrowError(/must be 3, but got 4/);
    });
    it('throws when passed x as a non-tensor', () => {
        const fSize = 2;
        const pad = 'valid';
        const stride = 1;
        const chMul = 1;
        const inDepth = 1;
        const outDepth = 2;
        const depthwiseFilter = tf.zeros([fSize, fSize, inDepth, chMul]);
        const pointwiseFilter = tf.zeros([1, 1, inDepth * chMul, outDepth]);
        const e = /Argument 'x' passed to 'separableConv2d' must be a Tensor/;
        expect(() => tf.separableConv2d({}, depthwiseFilter, pointwiseFilter, stride, pad))
            .toThrowError(e);
    });
    it('throws when passed depthwiseFilter as a non-tensor', () => {
        const pad = 'valid';
        const stride = 1;
        const chMul = 1;
        const inDepth = 1;
        const outDepth = 2;
        const x = tf.zeros([1, 3, 3, inDepth]);
        const pointwiseFilter = tf.zeros([1, 1, inDepth * chMul, outDepth]);
        const e = new RegExp('Argument \'depthwiseFilter\' passed to \'separableConv2d\'' +
            ' must be a Tensor');
        expect(() => tf.separableConv2d(x, {}, pointwiseFilter, stride, pad))
            .toThrowError(e);
    });
    it('throws when passed pointwiseFilter as a non-tensor', () => {
        const fSize = 2;
        const pad = 'valid';
        const stride = 1;
        const chMul = 1;
        const inDepth = 1;
        const x = tf.zeros([1, 3, 3, inDepth]);
        const depthwiseFilter = tf.zeros([fSize, fSize, inDepth, chMul]);
        const e = new RegExp('Argument \'pointwiseFilter\' passed to \'separableConv2d\'' +
            ' must be a Tensor');
        expect(() => tf.separableConv2d(x, depthwiseFilter, {}, stride, pad))
            .toThrowError(e);
    });
    it('accepts a tensor-like object', async () => {
        const pad = 'valid';
        const stride = 1;
        const outDepth = 2;
        // 3x3x1
        const x = [
            [[0.230664], [0.987388], [0.0685208]],
            [[0.419224], [0.887861], [0.731641]],
            [[0.0741907], [0.409265], [0.351377]]
        ];
        // 2x2x1x1
        const depthwiseFilter = [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];
        // 1x1x1x2
        const pointwiseFilter = [[[[0.1, -0.2]]]];
        const result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
        expectArraysClose(await result.data(), [
            0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096, -0.13408193,
            0.07788632, -0.15577264
        ]);
        expect(result.shape).toEqual([2, 2, outDepth]);
    });
});
//# 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* @license\n * Copyright 2018 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('separableConv2d', ALL_ENVS, () => {\n  it('input=1x3x3x1,f=2,s=1,d=1,p=valid,chMul=1,outDepth=2', async () => {\n    const fSize = 2;\n    const pad = 'valid';\n    const stride = 1;\n    const chMul = 1;\n    const inDepth = 1;\n    const outDepth = 2;\n\n    const x = tf.tensor4d(\n        [\n          0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,\n          0.0741907, 0.409265, 0.351377\n        ],\n        [1, 3, 3, inDepth]);\n    const depthwiseFilter = tf.tensor4d(\n        [0.303873, 0.229223, 0.144333, 0.803373],\n        [fSize, fSize, inDepth, chMul],\n    );\n    const pointwiseFilter =\n        tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);\n\n    const result =\n        tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);\n\n    expectArraysClose(await result.data(), [\n      0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096, -0.13408193,\n      0.07788632, -0.15577264\n    ]);\n    expect(result.shape).toEqual([1, 2, 2, outDepth]);\n  });\n\n  it('input=1x3x3x1,f=2,s=1,d=1,p=valid,chMul=1,outDepth=2 in tensor',\n     async () => {\n       const fSize = 2;\n       const pad = 'valid';\n       const stride = 1;\n       const chMul = 1;\n       const inDepth = 1;\n       const outDepth = 2;\n\n       const x = tf.tensor4d(\n           [\n             0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,\n             0.0741907, 0.409265, 0.351377\n           ],\n           [1, 3, 3, inDepth]);\n       const depthwiseFilter = tf.tensor4d(\n           [0.303873, 0.229223, 0.144333, 0.803373],\n           [fSize, fSize, inDepth, chMul],\n       );\n       const pointwiseFilter =\n           tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);\n\n       const result =\n           x.separableConv2d(depthwiseFilter, pointwiseFilter, stride, pad);\n\n       expectArraysClose(await result.data(), [\n         0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096,\n         -0.13408193, 0.07788632, -0.15577264\n       ]);\n       expect(result.shape).toEqual([1, 2, 2, outDepth]);\n     });\n\n  it('input=1x3x3x1,f=2,s=1,d=1,p=valid,chMul=2,outDepth=2', async () => {\n    const fSize = 2;\n    const pad = 'valid';\n    const stride = 1;\n    const chMul = 2;\n    const inDepth = 1;\n    const outDepth = 3;\n\n    const x = tf.tensor4d(\n        [\n          0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,\n          0.0741907, 0.409265, 0.351377\n        ],\n        [1, 3, 3, inDepth]);\n    const depthwiseFilter = tf.tensor4d(\n        [\n          0.303873, 0.229223, 0.144333, 0.803373, -0.303873, -0.229223,\n          -0.144333, -0.803373\n        ],\n        [fSize, fSize, inDepth, chMul],\n    );\n    const pointwiseFilter = tf.tensor4d(\n        [0.1, -0.2, -0.1, 0.2, 0.15, 0.15], [1, 1, inDepth * chMul, outDepth]);\n\n    const result =\n        tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);\n\n    expectArraysClose(await result.data(), [\n      0.00305368, 0.0140969, 0.00980358, -0.10853045, -0.06339455, -0.0699412,\n      0.11010849, 0.0347524, 0.05214475, 0.10307151, 0.02221644, 0.04224815\n    ]);\n    expect(result.shape).toEqual([1, 2, 2, outDepth]);\n  });\n\n  it('input=1x3x3x1,f=2,s=1,d=1,p=valid,chMul=1,outDepth=2,3D input',\n     async () => {\n       const fSize = 2;\n       const pad = 'valid';\n       const stride = 1;\n       const chMul = 1;\n       const inDepth = 1;\n       const outDepth = 2;\n\n       const x = tf.tensor3d(\n           [\n             0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,\n             0.0741907, 0.409265, 0.351377\n           ],\n           [3, 3, inDepth]);\n       const depthwiseFilter = tf.tensor4d(\n           [0.303873, 0.229223, 0.144333, 0.803373],\n           [fSize, fSize, inDepth, chMul],\n       );\n       const pointwiseFilter =\n           tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);\n\n       const result =\n           tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);\n\n       expectArraysClose(await result.data(), [\n         0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096,\n         -0.13408193, 0.07788632, -0.15577264\n       ]);\n       expect(result.shape).toEqual([2, 2, outDepth]);\n     });\n\n  it('input=1x4x4x1,f=2,s=2,d=1,p=valid,chMul=1,outDepth=2', async () => {\n    const fSize = 2;\n    const pad = 'valid';\n    const stride: [number, number] = [2, 2];\n    const chMul = 1;\n    const inDepth = 1;\n    const outDepth = 2;\n\n    const x = tf.tensor4d(\n        [\n          0.675707, 0.758567, 0.413529, 0.963967, 0.217291, 0.101335, 0.804231,\n          0.329673, 0.924503, 0.728742, 0.180217, 0.210459, 0.133869, 0.650827,\n          0.047613, 0.554795\n        ],\n        [1, 4, 4, inDepth]);\n    const depthwiseFilter = tf.tensor4d(\n        [0.303873, 0.229223, 0.144333, 0.803373],\n        [fSize, fSize, inDepth, chMul],\n    );\n    const pointwiseFilter =\n        tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);\n\n    const result =\n        tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);\n\n    expectArraysClose(await result.data(), [\n      0.04919822, -0.09839644, 0.07275512, -0.14551024, 0.09901544, -0.19803089,\n      0.05555845, -0.11111691\n    ]);\n    expect(result.shape).toEqual([1, 2, 2, outDepth]);\n  });\n\n  it('input=2x4x4x1,f=2,s=2,d=1,p=valid,chMul=1,outDepth=2', async () => {\n    const fSize = 2;\n    const pad = 'valid';\n    const stride: [number, number] = [2, 2];\n    const chMul = 1;\n    const inDepth = 1;\n    const outDepth = 2;\n\n    const x = tf.tensor4d(\n        [\n          0.675707,  0.758567,  0.413529,  0.963967,  0.217291,  0.101335,\n          0.804231,  0.329673,  0.924503,  0.728742,  0.180217,  0.210459,\n          0.133869,  0.650827,  0.047613,  0.554795,  -0.675707, -0.758567,\n          -0.413529, -0.963967, -0.217291, -0.101335, -0.804231, -0.329673,\n          -0.924503, -0.728742, -0.180217, -0.210459, -0.133869, -0.650827,\n          -0.047613, -0.554795\n        ],\n        [2, 4, 4, inDepth]);\n    const depthwiseFilter = tf.tensor4d(\n        [0.303873, 0.229223, 0.144333, 0.803373],\n        [fSize, fSize, inDepth, chMul],\n    );\n    const pointwiseFilter =\n        tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);\n\n    const result =\n        tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);\n\n    expectArraysClose(await result.data(), [\n      0.04919822, -0.09839644, 0.07275512, -0.14551024, 0.09901544, -0.19803089,\n      0.05555845, -0.11111691, -0.04919822, 0.09839644, -0.07275512, 0.14551024,\n      -0.09901544, 0.19803089, -0.05555845, 0.11111691\n    ]);\n    expect(result.shape).toEqual([2, 2, 2, outDepth]);\n  });\n\n  it('input=1x4x4x2,f=2,s=2,d=1,p=valid,chMul=1,outDepth=2', async () => {\n    const fSize = 2;\n    const pad = 'valid';\n    const stride: [number, number] = [2, 2];\n    const chMul = 1;\n    const inDepth = 2;\n    const outDepth = 2;\n\n    const x = tf.tensor4d(\n        [\n          0.675707,  0.758567,  0.413529,  0.963967,  0.217291,  0.101335,\n          0.804231,  0.329673,  0.924503,  0.728742,  0.180217,  0.210459,\n          0.133869,  0.650827,  0.047613,  0.554795,  -0.675707, -0.758567,\n          -0.413529, -0.963967, -0.217291, -0.101335, -0.804231, -0.329673,\n          -0.924503, -0.728742, -0.180217, -0.210459, -0.133869, -0.650827,\n          -0.047613, -0.554795\n        ],\n        [1, 4, 4, inDepth]);\n    const depthwiseFilter = tf.tensor4d(\n        [\n          0.303873, 0.229223, 0.144333, 0.803373, 0.98976838, 0.56597068,\n          0.42654137, 0.66445535\n        ],\n        [fSize, fSize, inDepth, chMul],\n    );\n    const pointwiseFilter = tf.tensor4d(\n        [0.1, -0.2, 0.05, -0.05], [1, 1, inDepth * chMul, outDepth]);\n\n    const result =\n        tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);\n\n    expectArraysClose(await result.data(), [\n      0.20072255, -0.32641545, 0.08474462, -0.11823604, -0.20072255, 0.32641545,\n      -0.08474462, 0.11823604\n    ]);\n    expect(result.shape).toEqual([1, 2, 2, outDepth]);\n  });\n\n  it('input=1x4x4x1,f=2,s=1,d=2,p=valid,chMul=1,outDepth=2', async () => {\n    const fSize = 2;\n    const pad = 'valid';\n    const stride = 1;\n    const chMul = 1;\n    const inDepth = 1;\n    const outDepth = 2;\n    const dilationRate = 2;\n\n    const x = tf.tensor4d(\n        [\n          0.675707, 0.758567, 0.413529, 0.963967, 0.217291, 0.101335, 0.804231,\n          0.329673, 0.924503, 0.728742, 0.180217, 0.210459, 0.133869, 0.650827,\n          0.047613, 0.554795\n        ],\n        [1, 4, 4, inDepth]);\n    const depthwiseFilter = tf.tensor4d(\n        [0.303873, 0.229223, 0.144333, 0.803373],\n        [fSize, fSize, inDepth, chMul],\n    );\n    const pointwiseFilter =\n        tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);\n\n    const result = tf.separableConv2d(\n        x, depthwiseFilter, pointwiseFilter, stride, pad, dilationRate);\n\n    expectArraysClose(await result.data(), [\n      0.05783373, -0.11566745, 0.07257301, -0.14514601, 0.03079498, -0.06158997,\n      0.06460048, -0.12920095\n    ]);\n    expect(result.shape).toEqual([1, 2, 2, outDepth]);\n  });\n\n  it('input=1x4x4x1,f=2,s=1,d=1,p=same,chMul=1,outDepth=2', async () => {\n    const fSize = 2;\n    const pad = 'same';\n    const stride = 1;\n    const chMul = 1;\n    const inDepth = 1;\n    const outDepth = 2;\n\n    const x = tf.tensor4d(\n        [\n          0.675707, 0.758567, 0.413529, 0.963967, 0.217291, 0.101335, 0.804231,\n          0.329673, 0.924503, 0.728742, 0.180217, 0.210459, 0.133869, 0.650827,\n          0.047613, 0.554795\n        ],\n        [1, 4, 4, inDepth]);\n    const depthwiseFilter = tf.tensor4d(\n        [0.303873, 0.229223, 0.144333, 0.803373],\n        [fSize, fSize, inDepth, chMul],\n    );\n    const pointwiseFilter =\n        tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);\n\n    const result =\n        tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);\n\n    expectArraysClose(await result.data(), [\n      0.04919822, -0.09839644, 0.09860218, -0.19720435, 0.07275512, -0.14551024,\n      0.03405062, -0.06810125, 0.08081452, -0.16162904, 0.04651042, -0.09302084,\n      0.05150411, -0.10300821, 0.01305549, -0.02611098, 0.09901544, -0.19803089,\n      0.03949417, -0.07898834, 0.05555845, -0.11111691, 0.0144028,  -0.02880561,\n      0.01898637, -0.03797274, 0.02086828, -0.04173655, 0.01416401, -0.02832802,\n      0.01685872, -0.03371745\n    ]);\n    expect(result.shape).toEqual([1, 4, 4, outDepth]);\n  });\n\n  it('TensorLike', async () => {\n    const pad = 'valid';\n    const stride = 1;\n    const outDepth = 2;\n\n    const x = [[\n      [[0.230664], [0.987388], [0.0685208]],\n      [[0.419224], [0.887861], [0.731641]],\n      [[0.0741907], [0.409265], [0.351377]]\n    ]];\n    const depthwiseFilter =\n        [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];\n    const pointwiseFilter = [[[[0.1, -0.2]]]];\n\n    const result =\n        tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);\n\n    expectArraysClose(await result.data(), [\n      0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096, -0.13408193,\n      0.07788632, -0.15577264\n    ]);\n    expect(result.shape).toEqual([1, 2, 2, outDepth]);\n  });\n\n  it('TensorLike Chained', async () => {\n    const pad = 'valid';\n    const stride = 1;\n    const outDepth = 2;\n    const inDepth = 1;\n\n    const x = tf.tensor4d(\n        [\n          0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,\n          0.0741907, 0.409265, 0.351377\n        ],\n        [1, 3, 3, inDepth]);\n    const depthwiseFilter =\n        [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];\n    const pointwiseFilter = [[[[0.1, -0.2]]]];\n\n    const result =\n        x.separableConv2d(depthwiseFilter, pointwiseFilter, stride, pad);\n\n    expectArraysClose(await result.data(), [\n      0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096, -0.13408193,\n      0.07788632, -0.15577264\n    ]);\n    expect(result.shape).toEqual([1, 2, 2, outDepth]);\n  });\n\n  it('Incorrect input rank raises error', () => {\n    // tslint:disable-next-line:no-any\n    const x = tf.zeros([4, 4]) as any;\n    const depthwiseFilter: tf.Tensor4D = tf.zeros([2, 2, 1, 3]);\n    const pointwiseFilter: tf.Tensor4D = tf.zeros([1, 1, 2, 4]);\n    expect(\n        () =>\n            tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid'))\n        .toThrowError(/rank 4/);\n  });\n\n  it('Incorrect depthwise filter rank raises error', () => {\n    const x: tf.Tensor4D = tf.zeros([1, 4, 4, 1]);\n    // tslint:disable-next-line:no-any\n    const depthwiseFilter = tf.zeros([2, 2, 1]) as any;\n    const pointwiseFilter: tf.Tensor4D = tf.zeros([1, 1, 2, 4]);\n    expect(\n        () =>\n            tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid'))\n        .toThrowError(/rank 4/);\n  });\n\n  it('Incorrect depthwise filter rank raises error', () => {\n    const x: tf.Tensor4D = tf.zeros([1, 4, 4, 1]);\n    const depthwiseFilter: tf.Tensor4D = tf.zeros([2, 2, 1, 3]);\n    // tslint:disable-next-line:no-any\n    const pointwiseFilter = tf.zeros([1, 1, 2]) as any;\n    expect(\n        () =>\n            tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid'))\n        .toThrowError(/rank 4/);\n  });\n\n  it('Incorrect point filter 1st dimension raises error', () => {\n    const x: tf.Tensor4D = tf.zeros([1, 4, 4, 1]);\n    const depthwiseFilter: tf.Tensor4D = tf.zeros([2, 2, 1, 3]);\n    const pointwiseFilter: tf.Tensor4D = tf.zeros([2, 1, 3, 6]);\n    expect(\n        () =>\n            tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid'))\n        .toThrowError(/must be 1, but got 2/);\n  });\n\n  it('Incorrect point filter 2nd dimension raises error', () => {\n    const x: tf.Tensor4D = tf.zeros([1, 4, 4, 1]);\n    const depthwiseFilter: tf.Tensor4D = tf.zeros([2, 2, 1, 3]);\n    const pointwiseFilter: tf.Tensor4D = tf.zeros([1, 5, 3, 6]);\n    expect(\n        () =>\n            tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid'))\n        .toThrowError(/must be 1, but got 5/);\n  });\n\n  it('Incorrect pointwise filter 3rd dimension raises error', () => {\n    const x: tf.Tensor4D = tf.zeros([1, 4, 4, 1]);\n    const depthwiseFilter: tf.Tensor4D = tf.zeros([2, 2, 1, 3]);\n    const pointwiseFilter: tf.Tensor4D = tf.zeros([1, 1, 4, 6]);\n    expect(\n        () =>\n            tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid'))\n        .toThrowError(/must be 3, but got 4/);\n  });\n\n  it('throws when passed x as a non-tensor', () => {\n    const fSize = 2;\n    const pad = 'valid';\n    const stride = 1;\n    const chMul = 1;\n    const inDepth = 1;\n    const outDepth = 2;\n\n    const depthwiseFilter: tf.Tensor4D =\n        tf.zeros([fSize, fSize, inDepth, chMul]);\n    const pointwiseFilter: tf.Tensor4D =\n        tf.zeros([1, 1, inDepth * chMul, outDepth]);\n\n    const e = /Argument 'x' passed to 'separableConv2d' must be a Tensor/;\n    expect(\n        () => tf.separableConv2d(\n            {} as tf.Tensor3D, depthwiseFilter, pointwiseFilter, stride, pad))\n        .toThrowError(e);\n  });\n\n  it('throws when passed depthwiseFilter as a non-tensor', () => {\n    const pad = 'valid';\n    const stride = 1;\n    const chMul = 1;\n    const inDepth = 1;\n    const outDepth = 2;\n\n    const x: tf.Tensor4D = tf.zeros([1, 3, 3, inDepth]);\n    const pointwiseFilter: tf.Tensor4D =\n        tf.zeros([1, 1, inDepth * chMul, outDepth]);\n\n    const e = new RegExp(\n        'Argument \\'depthwiseFilter\\' passed to \\'separableConv2d\\'' +\n        ' must be a Tensor');\n    expect(\n        () => tf.separableConv2d(\n            x, {} as tf.Tensor4D, pointwiseFilter, stride, pad))\n        .toThrowError(e);\n  });\n\n  it('throws when passed pointwiseFilter as a non-tensor', () => {\n    const fSize = 2;\n    const pad = 'valid';\n    const stride = 1;\n    const chMul = 1;\n    const inDepth = 1;\n\n    const x: tf.Tensor4D = tf.zeros([1, 3, 3, inDepth]);\n    const depthwiseFilter: tf.Tensor4D =\n        tf.zeros([fSize, fSize, inDepth, chMul]);\n\n    const e = new RegExp(\n        'Argument \\'pointwiseFilter\\' passed to \\'separableConv2d\\'' +\n        ' must be a Tensor');\n    expect(\n        () => tf.separableConv2d(\n            x, depthwiseFilter, {} as tf.Tensor4D, stride, pad))\n        .toThrowError(e);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const pad = 'valid';\n    const stride = 1;\n    const outDepth = 2;\n\n    // 3x3x1\n    const x = [\n      [[0.230664], [0.987388], [0.0685208]],\n      [[0.419224], [0.887861], [0.731641]],\n      [[0.0741907], [0.409265], [0.351377]]\n    ];\n    // 2x2x1x1\n    const depthwiseFilter =\n        [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];\n    // 1x1x1x2\n    const pointwiseFilter = [[[[0.1, -0.2]]]];\n\n    const result =\n        tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);\n\n    expectArraysClose(await result.data(), [\n      0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096, -0.13408193,\n      0.07788632, -0.15577264\n    ]);\n    expect(result.shape).toEqual([2, 2, outDepth]);\n  });\n});\n"]}