data2/LocalFaceRecognition.git

/**
 * @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';
describeWithFlags('fused depthwiseConv2D', ALL_ENVS, () => {
    it('basic', async () => {
        const fSize = 2;
        const pad = 'valid';
        const strides = 1;
        const chMul = 1;
        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 w = tf.tensor4d([-0.303873, -0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
        const result = tf.fused.depthwiseConv2d({ x, filter: w, strides, pad });
        expect(result.shape).toEqual([1, 2, 2, 1]);
        const expected = [0.47737, 0.40018, 0.00859, -0.09615];
        expectArraysClose(await result.data(), expected);
    });
    it('basic with relu', async () => {
        const fSize = 2;
        const pad = 'valid';
        const strides = 1;
        const chMul = 1;
        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 w = tf.tensor4d([-0.303873, -0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
        const result = tf.fused.depthwiseConv2d({ x, filter: w, strides, pad, activation: 'relu' });
        expect(result.shape).toEqual([1, 2, 2, 1]);
        const expected = [0.47737, 0.40018, 0.00859, 0];
        expectArraysClose(await result.data(), expected);
    });
    it('basic with channel-wise broadcasted bias and relu', async () => {
        const strides = 1;
        const pad = 'same';
        const x = tf.tensor4d([
            0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8,
            0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8
        ], [1, 3, 3, 4]);
        const w = tf.tensor4d([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [2, 2, 4, 1]);
        const bias = tf.tensor1d([0, 1, 2, 3]);
        const result = tf.fused.depthwiseConv2d({ x, filter: w, strides, pad, bias });
        expect(result.shape).toEqual([1, 3, 3, 4]);
        const expected = [
            124, 167, 92, 142, 112, 117, 76, 124, 16, 28, 44, 64,
            88, 134, 134, 88, 76, 120, 154, 205, 40, 58, 80, 106,
            4, 18, 36, 31, 20, 33, 50, 71, 0, 7, 16, 27
        ];
        expectArraysClose(await result.data(), expected);
    });
    it('basic with broadcasted bias and relu', async () => {
        const fSize = 2;
        const pad = 'valid';
        const strides = 1;
        const chMul = 1;
        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 w = tf.tensor4d([-0.303873, -0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
        const result = tf.fused.depthwiseConv2d({ x, filter: w, strides, pad, bias: tf.scalar(1), activation: 'relu' });
        expect(result.shape).toEqual([1, 2, 2, 1]);
        const expected = [1.47737, 1.40018, 1.00859, 0.90385];
        expectArraysClose(await result.data(), expected);
    });
    // For WebGPU DepthwiseConv2D3x3Program.
    it('basic with channel-wise broadcasted bias and relu filter 3x3', async () => {
        const fSize = 3;
        const pad = 'same';
        const strides = 1;
        const chMul = 1;
        const inDepth = 4;
        const x = tf.tensor4d([
            0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
            0.0741907, 0.409265, 0.351377, 0.230664, 0.987388, 0.0685208,
            0.419224, 0.887861, 0.731641, 0.0741907, 0.409265, 0.351377,
            0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
            0.0741907, 0.409265, 0.351377, 0.230664, 0.987388, 0.0685208,
            0.419224, 0.887861, 0.731641, 0.0741907, 0.409265, 0.351377
        ], [1, 3, 3, inDepth]);
        const w = tf.tensor4d([
            -0.303873, -0.229223, 0.144333, 0.803373, -0.303873, -0.229223,
            0.144333, 0.803373, -0.303873, -0.229223, 0.144333, 0.803373,
            -0.303873, -0.229223, 0.144333, 0.803373, -0.303873, -0.229223,
            0.144333, 0.803373, -0.303873, -0.229223, 0.144333, 0.803373,
            -0.303873, -0.229223, 0.144333, 0.803373, -0.303873, -0.229223,
            0.144333, 0.803373, -0.303873, -0.229223, 0.144333, 0.803373
        ], [fSize, fSize, inDepth, chMul]);
        const bias = tf.tensor1d([0, 1, 2, 3]);
        const result = tf.fused.depthwiseConv2d({ x, filter: w, strides, pad, bias });
        expect(result.shape).toEqual([1, 3, 3, 4]);
        const expected = [
            -0.5916450023651123, 0.32189714908599854, 2.1594903469085693,
            4.518429279327393, -0.7192406058311462, 0.1729278564453125,
            2.4301507472991943, 5.161257743835449, -0.521757185459137,
            0.6027780771255493, 2.3146610260009766, 4.764861583709717,
            -0.9142301082611084, 0.212377667427063, 2.2707135677337646,
            5.417022228240967, -1.264151692390442, 0.046402156352996826,
            2.6004443168640137, 6.342137336730957, -1.044123649597168,
            0.5700653791427612, 2.434239149093628, 5.760432243347168,
            -0.5743405818939209, 0.6064186692237854, 2.250115394592285,
            4.751436233520508, -0.8174881339073181, 0.4933167099952698,
            2.437333583831787, 5.621503829956055, -0.6675527095794678,
            0.7906477451324463, 2.2810182571411133, 5.376591682434082
        ];
        expectArraysClose(await result.data(), expected);
    });
    it('prelu', async () => {
        const fSize = 3;
        const pad = 'valid';
        const strides = 1;
        const chMul = 1;
        const inDepth = 1;
        const x = tf.tensor4d([
            0.149194, 0.089009, 0.654891, 0.083324, 0.537043, 0.644331, 0.563037,
            0.211859, 0.633501, 0.186427, 0.777034, 0.50001, 0.607341, 0.95303,
            0.696479, 0.050387, 0.62045, 0.728049, 0.028043, 0.437009, 0.712881,
            0.741935, 0.974474, 0.621102, 0.171411
        ], [1, 5, 5, inDepth]);
        const alpha = tf.tensor4d([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], [1, 3, 3, 1]);
        const w = tf.tensor4d([
            -0.125386, -0.975199, -0.640437, -0.281895, -0.990968, -0.347208,
            -0.889702, -0.180695, -0.691992
        ], [fSize, fSize, inDepth, chMul]);
        const result = tf.fused.depthwiseConv2d({
            x,
            filter: w,
            strides,
            pad,
            activation: 'prelu',
            preluActivationWeights: alpha
        });
        expect(result.shape).toEqual([1, 3, 3, 1]);
        const expected = [
            -0.25400, -0.50118, -0.73622, -0.94068, -1.2298, -1.84585, -2.3089,
            -2.7499, -2.64077
        ];
        expectArraysClose(await result.data(), expected);
    });
    it('leakyrelu', async () => {
        const fSize = 3;
        const pad = 'valid';
        const strides = 1;
        const chMul = 1;
        const inDepth = 1;
        const x = tf.tensor4d([
            0.149194, 0.089009, 0.654891, 0.083324, 0.537043, 0.644331, 0.563037,
            0.211859, 0.633501, 0.186427, 0.777034, 0.50001, 0.607341, 0.95303,
            0.696479, 0.050387, 0.62045, 0.728049, 0.028043, 0.437009, 0.712881,
            0.741935, 0.974474, 0.621102, 0.171411
        ], [1, 5, 5, inDepth]);
        const alpha = 0.3;
        const w = tf.tensor4d([
            -0.125386, -0.975199, -0.640437, -0.281895, -0.990968, -0.347208,
            -0.889702, -0.180695, -0.691992
        ], [fSize, fSize, inDepth, chMul]);
        const result = tf.fused.depthwiseConv2d({
            x,
            filter: w,
            strides,
            pad,
            activation: 'leakyrelu',
            leakyreluAlpha: alpha
        });
        expect(result.shape).toEqual([1, 3, 3, 1]);
        const expected = [
            -0.7620067596435547, -0.7517655491828918, -0.7362186312675476,
            -0.7055101990699768, -0.7378802299499512, -0.9229262471199036,
            -0.9895440340042114, -1.031226396560669, -0.8802568912506104
        ];
        expectArraysClose(await result.data(), expected);
    });
    it('sigmoid', async () => {
        const fSize = 3;
        const pad = 'valid';
        const strides = 1;
        const chMul = 1;
        const inDepth = 1;
        const x = tf.tensor4d([
            0.149194, 0.089009, 0.654891, 0.083324, 0.537043, 0.644331, 0.563037,
            0.211859, 0.633501, 0.186427, 0.777034, 0.50001, 0.607341, 0.95303,
            0.696479, 0.050387, 0.62045, 0.728049, 0.028043, 0.437009, 0.712881,
            0.741935, 0.974474, 0.621102, 0.171411
        ], [1, 5, 5, inDepth]);
        const w = tf.tensor4d([
            -0.125386, -0.975199, -0.640437, -0.281895, -0.990968, -0.347208,
            -0.889702, -0.180695, -0.691992
        ], [fSize, fSize, inDepth, chMul]);
        const result = tf.fused.depthwiseConv2d({ x, filter: w, strides, pad, activation: 'sigmoid' });
        expect(result.shape).toEqual([1, 3, 3, 1]);
        const expected = [
            0.07309964, 0.07544667, 0.07914197, 0.08693069, 0.07873929, 0.04409045,
            0.03562334, 0.0311462, 0.05048907
        ];
        expectArraysClose(await result.data(), expected);
    });
    it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0', async () => {
        const inputDepth = 1;
        const outputDepth = 1;
        const inputShape = [2, 3, 3, inputDepth];
        const filterSize = 2;
        const strides = 1;
        const pad = 0;
        const filterShape = [filterSize, filterSize, inputDepth, outputDepth];
        const filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
        const dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);
        const grads = tf.grads((x, filter) => tf.fused.depthwiseConv2d({ x, filter, strides, pad }));
        const [dx, dfilter] = grads([x, filter], dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), [-3, 2, 1, -8, 1.5, 0.5, -4, 1, 0, -3, 2, 1, -8, 1.5, 0.5, -4, 1, 0]);
        expect(dfilter.shape).toEqual(filterShape);
        expectArraysClose(await dfilter.data(), [26, 38, 62, 74]);
    });
    it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0 with bias', async () => {
        const inputDepth = 1;
        const outputDepth = 1;
        const inputShape = [2, 3, 3, inputDepth];
        const filterSize = 2;
        const strides = 1;
        const pad = 0;
        const filterShape = [filterSize, filterSize, inputDepth, outputDepth];
        const filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);
        const bias = tf.ones([2, 2, 2, 1]);
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
        const dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);
        const fusedGrads = tf.grads((x, w, b) => tf.fused.depthwiseConv2d({
            x,
            filter: w,
            strides,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            bias: b
        }));
        const [dxFused, dfilterFused, dbiasFused] = fusedGrads([x, filter, bias], dy);
        const grads = tf.grads((x, filter, bias) => {
            const conv = tf.depthwiseConv2d(x, filter, strides, pad);
            const sum = tf.add(conv, bias);
            return sum;
        });
        const [dx, dfilter, dbias] = grads([x, filter, bias], dy);
        expectArraysClose(await dxFused.array(), await dx.array());
        expectArraysClose(await dfilterFused.array(), await dfilter.array());
        expectArraysClose(await dbiasFused.array(), await dbias.array());
    });
    it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0 with bias and activation', async () => {
        const inputDepth = 1;
        const outputDepth = 1;
        const inputShape = [2, 3, 3, inputDepth];
        const filterSize = 2;
        const strides = 1;
        const pad = 0;
        const filterShape = [filterSize, filterSize, inputDepth, outputDepth];
        const filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);
        const bias = tf.ones([2, 2, 2, 1]);
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
        const dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);
        const fusedGrads = tf.grads((x, w, b) => tf.fused.depthwiseConv2d({
            x,
            filter: w,
            strides,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            bias: b,
            activation: 'relu'
        }));
        const [dxFused, dfilterFused, dbiasFused] = fusedGrads([x, filter, bias], dy);
        const grads = tf.grads((x, filter, bias) => {
            const conv = tf.depthwiseConv2d(x, filter, strides, pad);
            const sum = tf.add(conv, bias);
            return tf.relu(sum);
        });
        const [dx, dfilter, dbias] = grads([x, filter, bias], dy);
        expectArraysClose(await dxFused.array(), await dx.array());
        expectArraysClose(await dfilterFused.array(), await dfilter.array());
        expectArraysClose(await dbiasFused.array(), await dbias.array());
    });
    it('throws when input is int32', async () => {
        const fSize = 2;
        const pad = 'valid';
        const strides = 1;
        const chMul = 1;
        const inDepth = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 3, 3, inDepth], 'int32');
        const w = tf.tensor4d([-0.303873, -0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
        expect(() => tf.fused.depthwiseConv2d({ x, filter: w, strides, pad }))
            .toThrowError(/Argument 'x' passed to 'depthwiseConv2d' must be float32/);
    });
    it('throws when filter is int32', async () => {
        const fSize = 2;
        const pad = 'valid';
        const strides = 1;
        const chMul = 1;
        const inDepth = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 3, 3, inDepth]);
        const w = tf.tensor4d([1, 2, 3, 4], [fSize, fSize, inDepth, chMul], 'int32');
        expect(() => tf.fused.depthwiseConv2d({ x, filter: w, strides, pad }))
            .toThrowError(/Argument 'filter' passed to 'depthwiseConv2d' must be float32/);
    });
});
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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';\n\ndescribeWithFlags('fused depthwiseConv2D', ALL_ENVS, () => {\n  it('basic', async () => {\n    const fSize = 2;\n    const pad = 'valid';\n    const strides = 1;\n    const chMul = 1;\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 w = tf.tensor4d(\n        [-0.303873, -0.229223, 0.144333, 0.803373],\n        [fSize, fSize, inDepth, chMul],\n    );\n\n    const result = tf.fused.depthwiseConv2d({x, filter: w, strides, pad});\n    expect(result.shape).toEqual([1, 2, 2, 1]);\n    const expected = [0.47737, 0.40018, 0.00859, -0.09615];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('basic with relu', async () => {\n    const fSize = 2;\n    const pad = 'valid';\n    const strides = 1;\n    const chMul = 1;\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 w = tf.tensor4d(\n        [-0.303873, -0.229223, 0.144333, 0.803373],\n        [fSize, fSize, inDepth, chMul],\n    );\n\n    const result = tf.fused.depthwiseConv2d(\n        {x, filter: w, strides, pad, activation: 'relu'});\n    expect(result.shape).toEqual([1, 2, 2, 1]);\n    const expected = [0.47737, 0.40018, 0.00859, 0];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('basic with channel-wise broadcasted bias and relu', async () => {\n    const strides = 1;\n    const pad = 'same';\n    const x = tf.tensor4d(\n        [\n          0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8,\n          0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8\n        ],\n        [1, 3, 3, 4]);\n    const w = tf.tensor4d(\n        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [2, 2, 4, 1]);\n    const bias = tf.tensor1d([0, 1, 2, 3]);\n\n    const result = tf.fused.depthwiseConv2d({x, filter: w, strides, pad, bias});\n    expect(result.shape).toEqual([1, 3, 3, 4]);\n    const expected = [\n      124, 167, 92,  142, 112, 117, 76,  124, 16, 28, 44, 64,\n      88,  134, 134, 88,  76,  120, 154, 205, 40, 58, 80, 106,\n      4,   18,  36,  31,  20,  33,  50,  71,  0,  7,  16, 27\n    ];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('basic with broadcasted bias and relu', async () => {\n    const fSize = 2;\n    const pad = 'valid';\n    const strides = 1;\n    const chMul = 1;\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 w = tf.tensor4d(\n        [-0.303873, -0.229223, 0.144333, 0.803373],\n        [fSize, fSize, inDepth, chMul],\n    );\n\n    const result = tf.fused.depthwiseConv2d(\n        {x, filter: w, strides, pad, bias: tf.scalar(1), activation: 'relu'});\n    expect(result.shape).toEqual([1, 2, 2, 1]);\n    const expected = [1.47737, 1.40018, 1.00859, 0.90385];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  // For WebGPU DepthwiseConv2D3x3Program.\n  it('basic with channel-wise broadcasted bias and relu filter 3x3',\n     async () => {\n       const fSize = 3;\n       const pad = 'same';\n       const strides = 1;\n       const chMul = 1;\n       const inDepth = 4;\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,  0.230664,  0.987388, 0.0685208,\n             0.419224,  0.887861, 0.731641,  0.0741907, 0.409265, 0.351377,\n             0.230664,  0.987388, 0.0685208, 0.419224,  0.887861, 0.731641,\n             0.0741907, 0.409265, 0.351377,  0.230664,  0.987388, 0.0685208,\n             0.419224,  0.887861, 0.731641,  0.0741907, 0.409265, 0.351377\n           ],\n           [1, 3, 3, inDepth]);\n       const w = tf.tensor4d(\n           [\n             -0.303873, -0.229223, 0.144333,  0.803373,  -0.303873, -0.229223,\n             0.144333,  0.803373,  -0.303873, -0.229223, 0.144333,  0.803373,\n             -0.303873, -0.229223, 0.144333,  0.803373,  -0.303873, -0.229223,\n             0.144333,  0.803373,  -0.303873, -0.229223, 0.144333,  0.803373,\n             -0.303873, -0.229223, 0.144333,  0.803373,  -0.303873, -0.229223,\n             0.144333,  0.803373,  -0.303873, -0.229223, 0.144333,  0.803373\n           ],\n           [fSize, fSize, inDepth, chMul],\n       );\n       const bias = tf.tensor1d([0, 1, 2, 3]);\n       const result =\n           tf.fused.depthwiseConv2d({x, filter: w, strides, pad, bias});\n       expect(result.shape).toEqual([1, 3, 3, 4]);\n       const expected = [\n         -0.5916450023651123, 0.32189714908599854, 2.1594903469085693,\n         4.518429279327393,   -0.7192406058311462, 0.1729278564453125,\n         2.4301507472991943,  5.161257743835449,   -0.521757185459137,\n         0.6027780771255493,  2.3146610260009766,  4.764861583709717,\n         -0.9142301082611084, 0.212377667427063,   2.2707135677337646,\n         5.417022228240967,   -1.264151692390442,  0.046402156352996826,\n         2.6004443168640137,  6.342137336730957,   -1.044123649597168,\n         0.5700653791427612,  2.434239149093628,   5.760432243347168,\n         -0.5743405818939209, 0.6064186692237854,  2.250115394592285,\n         4.751436233520508,   -0.8174881339073181, 0.4933167099952698,\n         2.437333583831787,   5.621503829956055,   -0.6675527095794678,\n         0.7906477451324463,  2.2810182571411133,  5.376591682434082\n       ];\n       expectArraysClose(await result.data(), expected);\n     });\n\n  it('prelu', async () => {\n    const fSize = 3;\n    const pad = 'valid';\n    const strides = 1;\n    const chMul = 1;\n    const inDepth = 1;\n\n    const x = tf.tensor4d(\n        [\n          0.149194, 0.089009, 0.654891, 0.083324, 0.537043, 0.644331, 0.563037,\n          0.211859, 0.633501, 0.186427, 0.777034, 0.50001,  0.607341, 0.95303,\n          0.696479, 0.050387, 0.62045,  0.728049, 0.028043, 0.437009, 0.712881,\n          0.741935, 0.974474, 0.621102, 0.171411\n        ],\n        [1, 5, 5, inDepth]);\n    const alpha = tf.tensor4d(\n        [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], [1, 3, 3, 1]);\n    const w = tf.tensor4d(\n        [\n          -0.125386, -0.975199, -0.640437, -0.281895, -0.990968, -0.347208,\n          -0.889702, -0.180695, -0.691992\n        ],\n        [fSize, fSize, inDepth, chMul],\n    );\n\n    const result = tf.fused.depthwiseConv2d({\n      x,\n      filter: w,\n      strides,\n      pad,\n      activation: 'prelu',\n      preluActivationWeights: alpha\n    });\n    expect(result.shape).toEqual([1, 3, 3, 1]);\n    const expected = [\n      -0.25400, -0.50118, -0.73622, -0.94068, -1.2298, -1.84585, -2.3089,\n      -2.7499, -2.64077\n    ];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('leakyrelu', async () => {\n    const fSize = 3;\n    const pad = 'valid';\n    const strides = 1;\n    const chMul = 1;\n    const inDepth = 1;\n\n    const x = tf.tensor4d(\n        [\n          0.149194, 0.089009, 0.654891, 0.083324, 0.537043, 0.644331, 0.563037,\n          0.211859, 0.633501, 0.186427, 0.777034, 0.50001,  0.607341, 0.95303,\n          0.696479, 0.050387, 0.62045,  0.728049, 0.028043, 0.437009, 0.712881,\n          0.741935, 0.974474, 0.621102, 0.171411\n        ],\n        [1, 5, 5, inDepth]);\n    const alpha = 0.3;\n    const w = tf.tensor4d(\n        [\n          -0.125386, -0.975199, -0.640437, -0.281895, -0.990968, -0.347208,\n          -0.889702, -0.180695, -0.691992\n        ],\n        [fSize, fSize, inDepth, chMul],\n    );\n\n    const result = tf.fused.depthwiseConv2d({\n      x,\n      filter: w,\n      strides,\n      pad,\n      activation: 'leakyrelu',\n      leakyreluAlpha: alpha\n    });\n\n    expect(result.shape).toEqual([1, 3, 3, 1]);\n    const expected = [\n      -0.7620067596435547, -0.7517655491828918, -0.7362186312675476,\n      -0.7055101990699768, -0.7378802299499512, -0.9229262471199036,\n      -0.9895440340042114, -1.031226396560669, -0.8802568912506104\n    ];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('sigmoid', async () => {\n    const fSize = 3;\n    const pad = 'valid';\n    const strides = 1;\n    const chMul = 1;\n    const inDepth = 1;\n\n    const x = tf.tensor4d(\n        [\n          0.149194, 0.089009, 0.654891, 0.083324, 0.537043, 0.644331, 0.563037,\n          0.211859, 0.633501, 0.186427, 0.777034, 0.50001,  0.607341, 0.95303,\n          0.696479, 0.050387, 0.62045,  0.728049, 0.028043, 0.437009, 0.712881,\n          0.741935, 0.974474, 0.621102, 0.171411\n        ],\n        [1, 5, 5, inDepth]);\n    const w = tf.tensor4d(\n        [\n          -0.125386, -0.975199, -0.640437, -0.281895, -0.990968, -0.347208,\n          -0.889702, -0.180695, -0.691992\n        ],\n        [fSize, fSize, inDepth, chMul],\n    );\n\n    const result = tf.fused.depthwiseConv2d(\n        {x, filter: w, strides, pad, activation: 'sigmoid'});\n\n    expect(result.shape).toEqual([1, 3, 3, 1]);\n    const expected = [\n      0.07309964, 0.07544667, 0.07914197, 0.08693069, 0.07873929, 0.04409045,\n      0.03562334, 0.0311462, 0.05048907\n    ];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0', async () => {\n    const inputDepth = 1;\n    const outputDepth = 1;\n    const inputShape: [number, number, number, number] = [2, 3, 3, inputDepth];\n    const filterSize = 2;\n    const strides = 1;\n    const pad = 0;\n\n    const filterShape: [number, number, number, number] =\n        [filterSize, filterSize, inputDepth, outputDepth];\n    const filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);\n    const dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);\n\n    const grads = tf.grads(\n        (x: tf.Tensor4D, filter: tf.Tensor4D) =>\n            tf.fused.depthwiseConv2d({x, filter, strides, pad}));\n    const [dx, dfilter] = grads([x, filter], dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(\n        await dx.data(),\n        [-3, 2, 1, -8, 1.5, 0.5, -4, 1, 0, -3, 2, 1, -8, 1.5, 0.5, -4, 1, 0]);\n\n    expect(dfilter.shape).toEqual(filterShape);\n    expectArraysClose(await dfilter.data(), [26, 38, 62, 74]);\n  });\n\n  it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0 with bias', async () => {\n    const inputDepth = 1;\n    const outputDepth = 1;\n    const inputShape: [number, number, number, number] = [2, 3, 3, inputDepth];\n    const filterSize = 2;\n    const strides = 1;\n    const pad = 0;\n\n    const filterShape: [number, number, number, number] =\n        [filterSize, filterSize, inputDepth, outputDepth];\n    const filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);\n    const bias = tf.ones([2, 2, 2, 1]);\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);\n    const dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);\n\n    const fusedGrads = tf.grads(\n        (x: tf.Tensor4D, w: tf.Tensor4D, b) => tf.fused.depthwiseConv2d({\n          x,\n          filter: w,\n          strides,\n          pad,\n          dataFormat: 'NHWC',\n          dilations: [1, 1],\n          bias: b\n        }));\n    const [dxFused, dfilterFused, dbiasFused] =\n        fusedGrads([x, filter, bias], dy);\n\n    const grads = tf.grads((x: tf.Tensor4D, filter: tf.Tensor4D, bias) => {\n      const conv = tf.depthwiseConv2d(x, filter, strides, pad);\n      const sum = tf.add(conv, bias);\n      return sum;\n    });\n    const [dx, dfilter, dbias] = grads([x, filter, bias], dy);\n\n    expectArraysClose(await dxFused.array(), await dx.array());\n    expectArraysClose(await dfilterFused.array(), await dfilter.array());\n    expectArraysClose(await dbiasFused.array(), await dbias.array());\n  });\n\n  it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0 with bias and activation',\n     async () => {\n       const inputDepth = 1;\n       const outputDepth = 1;\n       const inputShape: [number, number, number, number] =\n           [2, 3, 3, inputDepth];\n       const filterSize = 2;\n       const strides = 1;\n       const pad = 0;\n\n       const filterShape: [number, number, number, number] =\n           [filterSize, filterSize, inputDepth, outputDepth];\n       const filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);\n       const bias = tf.ones([2, 2, 2, 1]);\n\n       const x = tf.tensor4d(\n           [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);\n       const dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);\n\n       const fusedGrads = tf.grads(\n           (x: tf.Tensor4D, w: tf.Tensor4D, b) => tf.fused.depthwiseConv2d({\n             x,\n             filter: w,\n             strides,\n             pad,\n             dataFormat: 'NHWC',\n             dilations: [1, 1],\n             bias: b,\n             activation: 'relu'\n           }));\n       const [dxFused, dfilterFused, dbiasFused] =\n           fusedGrads([x, filter, bias], dy);\n\n       const grads = tf.grads((x: tf.Tensor4D, filter: tf.Tensor4D, bias) => {\n         const conv = tf.depthwiseConv2d(x, filter, strides, pad);\n         const sum = tf.add(conv, bias);\n         return tf.relu(sum);\n       });\n       const [dx, dfilter, dbias] = grads([x, filter, bias], dy);\n\n       expectArraysClose(await dxFused.array(), await dx.array());\n       expectArraysClose(await dfilterFused.array(), await dfilter.array());\n       expectArraysClose(await dbiasFused.array(), await dbias.array());\n     });\n\n  it('throws when input is int32', async () => {\n    const fSize = 2;\n    const pad = 'valid';\n    const strides = 1;\n    const chMul = 1;\n    const inDepth = 1;\n\n    const x =\n        tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 3, 3, inDepth], 'int32');\n    const w = tf.tensor4d(\n        [-0.303873, -0.229223, 0.144333, 0.803373],\n        [fSize, fSize, inDepth, chMul],\n    );\n\n    expect(() => tf.fused.depthwiseConv2d({x, filter: w, strides, pad}))\n        .toThrowError(\n            /Argument 'x' passed to 'depthwiseConv2d' must be float32/);\n  });\n\n  it('throws when filter is int32', async () => {\n    const fSize = 2;\n    const pad = 'valid';\n    const strides = 1;\n    const chMul = 1;\n    const inDepth = 1;\n\n    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 3, 3, inDepth]);\n    const w = tf.tensor4d(\n        [1, 2, 3, 4],\n        [fSize, fSize, inDepth, chMul],\n        'int32',\n    );\n\n    expect(() => tf.fused.depthwiseConv2d({x, filter: w, strides, pad}))\n        .toThrowError(\n            /Argument 'filter' passed to 'depthwiseConv2d' must be float32/);\n  });\n});\n"]}