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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('depthwiseConv2D', ALL_ENVS, () => {
| it('input=1x3x3x1,f=2,s=1,d=1,p=valid,chMul=1', async () => {
| const fSize = 2;
| const pad = 'valid';
| const stride = 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.depthwiseConv2d(x, w, stride, pad);
| expect(result.shape).toEqual([1, 2, 2, 1]);
| const expected = [1.07022, 1.03167, 0.67041, 0.778863];
| expectArraysClose(await result.data(), expected);
| });
| it('input=1x3x3x1,f=2,s=1,d=1,p=explicit,chMul=1', async () => {
| const fSize = 2;
| const pad = [[0, 0], [1, 2], [0, 1], [0, 0]];
| const stride = 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.depthwiseConv2d(x, w, stride, pad);
| expect(result.shape).toEqual([1, 5, 3, 1]);
| const expected = [
| 0.826533, 0.197560, 0.0098898, 1.070216, 1.031675, 0.126422, 0.6704096,
| 0.778863, 0.273041, 0.116357, 0.204908, 0.106774, 0, 0, 0
| ];
| expectArraysClose(await result.data(), expected);
| });
| it('input=1x5x5x1,f=3,s=1,d=1,p=valid,chMul=1', async () => {
| const fSize = 3;
| const pad = 'valid';
| const stride = 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.depthwiseConv2d(x, w, stride, pad);
| expect(result.shape).toEqual([1, 3, 3, 1]);
| const expected = [
| 2.540022, 2.505885, 2.454062, 2.351701, 2.459601, 3.076421, 3.29848,
| 3.437421, 2.93419
| ];
| expectArraysClose(await result.data(), expected);
| });
| it('input=1x3x3x1,f=2,s=1,d=2,p=valid,chMul=1', async () => {
| const fSize = 2;
| const pad = 'valid';
| const stride = 1;
| const dilation = 2;
| 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]);
| // adding a dilation rate is equivalent to using a filter
| // with 0s for the dilation rate
| const fSizeDilated = fSize + (fSize - 1) * (dilation - 1);
| const wDilated = tf.tensor4d([0.303873, 0, 0.229223, 0, 0, 0, 0.144333, 0, 0.803373], [fSizeDilated, fSizeDilated, inDepth, chMul]);
| const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);
| const expectedResult = tf.depthwiseConv2d(x, wDilated, stride, pad);
| expect(result.shape).toEqual(expectedResult.shape);
| expectArraysClose(await result.data(), await expectedResult.data());
| });
| it('input=1x5x5x1,f=3,s=1,d=2,p=valid,chMul=1', async () => {
| const fSize = 3;
| const pad = 'valid';
| const stride = 1;
| const dilation = 2;
| 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]);
| // adding a dilation rate is equivalent to using a filter
| // with 0s for the dilation rate
| const fSizeDilated = fSize + (fSize - 1) * (dilation - 1);
| const wDilated = tf.tensor4d([
| 0.125386, 0, 0.975199, 0, 0.640437, 0, 0, 0, 0, 0,
| 0.281895, 0, 0.990968, 0, 0.347208, 0, 0, 0, 0, 0,
| 0.889702, 0, 0.180695, 0, 0.691992
| ], [fSizeDilated, fSizeDilated, inDepth, chMul]);
| const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);
| const expectedResult = tf.depthwiseConv2d(x, wDilated, stride, pad);
| expect(result.shape).toEqual(expectedResult.shape);
| expectArraysClose(await result.data(), await expectedResult.data());
| });
| it('input=1x5x5x1,f=2,s=1,d=4,p=valid,chMul=1', async () => {
| const fSize = 2;
| const pad = 'valid';
| const stride = 1;
| const dilation = 4;
| 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], [fSize, fSize, inDepth, chMul]);
| // adding a dilation rate is equivalent to using a filter
| // with 0s for the dilation rate
| const fSizeDilated = fSize + (fSize - 1) * (dilation - 1);
| const wDilated = tf.tensor4d([
| 0.125386, 0, 0, 0, 0.975199, 0, 0, 0, 0, 0, 0, 0, 0,
| 0, 0, 0, 0, 0, 0, 0, 0.640437, 0, 0, 0, 0.281895
| ], [fSizeDilated, fSizeDilated, inDepth, chMul]);
| const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);
| const expectedResult = tf.depthwiseConv2d(x, wDilated, stride, pad);
| expect(result.shape).toEqual(expectedResult.shape);
| expectArraysClose(await result.data(), await expectedResult.data());
| });
| it('input=1x3x3x2,f=2,s=1,d=1,p=same,chMul=1', async () => {
| const fSize = 2;
| const pad = 'same';
| const stride = 1;
| const chMul = 1;
| const inDepth = 2;
| const x = tf.tensor4d([
| 0.111057, 0.661818, 0.701979, 0.424362, 0.992854, 0.417599, 0.423036,
| 0.500499, 0.368484, 0.714135, 0.456693, 0.531058, 0.636636, 0.345024,
| 0.0506303, 0.789682, 0.177473, 0.793569
| ], [1, 3, 3, inDepth]);
| const w = tf.tensor4d([
| 0.614293, 0.0648011, 0.101113, 0.452887, 0.0582746, 0.426481,
| 0.872743, 0.765767
| ], [fSize, fSize, inDepth, chMul]);
| const result = tf.depthwiseConv2d(x, w, stride, pad);
| expect(result.shape).toEqual([1, 3, 3, 2]);
| const expected = [
| 0.485445, 0.995389, 0.95166, 0.927856, 0.636516, 0.253547, 0.378414,
| 1.10771, 0.430373, 1.23126, 0.290885, 0.372855, 0.3962, 0.379995,
| 0.0490466, 0.410569, 0.10902, 0.0514242
| ];
| expectArraysClose(await result.data(), expected);
| });
| it('input=1x5x5x1,f=3,s=1,d=1,p=same,chMul=1', async () => {
| const fSize = 3;
| const pad = 'same';
| const stride = 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.depthwiseConv2d(x, w, stride, pad);
| expect(result.shape).toEqual([1, 5, 5, 1]);
| const expected = [
| 0.684796, 1.179251, 1.680593, 0.885615, 1.152995, 1.52291, 2.540022,
| 2.505885, 2.454062, 1.871258, 2.371015, 2.351701, 2.459601, 3.076421,
| 1.323994, 1.985572, 3.29848, 3.437421, 2.93419, 1.823238, 1.410545,
| 2.352186, 2.19622, 1.348218, 0.774635
| ];
| expectArraysClose(await result.data(), expected);
| });
| it('input=1x3x3x2,f=2,s=1,d=2,p=same,chMul=1', async () => {
| const fSize = 2;
| const pad = 'same';
| const stride = 1;
| const dilation = 2;
| const inDepth = 2;
| const x = tf.tensor4d([
| 0.111057, 0.661818, 0.701979, 0.424362, 0.992854, 0.417599, 0.423036,
| 0.500499, 0.368484, 0.714135, 0.456693, 0.531058, 0.636636, 0.345024,
| 0.0506303, 0.789682, 0.177473, 0.793569
| ], [1, 3, 3, inDepth]);
| const w = tf.stack([
| tf.tensor2d([0.614293, 0.0648011, 0.101113, 0.452887], [fSize, fSize]),
| tf.tensor2d([0.0582746, 0.426481, 0.872743, 0.765767], [fSize, fSize])
| ], 2)
| .expandDims(3);
| // adding a dilation rate is equivalent to using a filter
| // with 0s for the dilation rate
| const fSizeDilated = fSize + (fSize - 1) * (dilation - 1);
| const wDilated = tf.stack([
| tf.tensor2d([0.614293, 0, 0.0648011, 0, 0, 0, 0.101113, 0, 0.452887], [fSizeDilated, fSizeDilated]),
| tf.tensor2d([0.0582746, 0, 0.426481, 0, 0, 0, 0.872743, 0, 0.765767], [fSizeDilated, fSizeDilated])
| ], 2)
| .expandDims(3);
| expect(wDilated.shape).toEqual([fSizeDilated, fSizeDilated, inDepth, 1]);
| const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);
| const expectedResult = tf.depthwiseConv2d(x, wDilated, stride, pad);
| expect(result.shape).toEqual(expectedResult.shape);
| expectArraysClose(await result.data(), await expectedResult.data());
| });
| it('input=1x5x5x1,f=3,s=1,d=2,p=valid,chMul=1', async () => {
| const fSize = 3;
| const pad = 'valid';
| const stride = 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.depthwiseConv2d(x, w, stride, pad);
| expect(result.shape).toEqual([1, 3, 3, 1]);
| const expected = [
| 2.540022, 2.505885, 2.454062, 2.351701, 2.459601, 3.076421, 3.29848,
| 3.437421, 2.93419
| ];
| expectArraysClose(await result.data(), expected);
| });
| it('input=1x5x5x4,f=3,s=1,d=1,p=same,chMul=1', async () => {
| const fSize = 3;
| const pad = 'same';
| const stride = 1;
| const chMul = 1;
| const inDepth = 4;
| 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, 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, 0.653365,
| 0.442196, 0.261945, 0.0528113, 0.656698, 0.127345, 0.610039,
| 0.169131, 0.458647, 0.0988288, 0.966109, 0.0421747, 0.82035,
| 0.274711, 0.359377, 0.512113, 0.689682, 0.941571, 0.31961,
| 0.743826, 0.858147, 0.984766, 0.926973, 0.579597, 0.444104,
| 0.505969, 0.241437, 0.937999, 0.0957074, 0.773611, 0.46023,
| 0.469379, 0.363789, 0.269745, 0.486136, 0.894215, 0.794299,
| 0.724615, 0.261945, 0.0528113, 0.656698, 0.127345, 0.610039,
| 0.169131, 0.458647, 0.0988288, 0.966109, 0.0421747, 0.82035,
| 0.274711, 0.359377, 0.512113, 0.689682, 0.941571, 0.31961,
| 0.743826, 0.858147, 0.984766, 0.926973
| ], [1, 5, 5, inDepth]);
| const w = tf.tensor4d([
| 0.6511372, 0.8699447, 0.6511372, 0.8699447, 0.267792, 0.9981787,
| 0.267792, 0.9981787, 0.4913572, 0.3321196, 0.4913572, 0.3321196,
| 0.5286497, 0.4241803, 0.5286497, 0.4241803, 0.0175446, 0.8365464,
| 0.0175446, 0.8365464, 0.1768399, 0.2874831, 0.1768399, 0.2874831,
| 0.0933998, 0.5764548, 0.0933998, 0.5764548, 0.0661623, 0.8850273,
| 0.0661623, 0.8850273, 0.8700929, 0.205422, 0.8700929, 0.205422
| ], [fSize, fSize, inDepth, chMul]);
| const result = tf.depthwiseConv2d(x, w, stride, pad);
| expect(result.shape).toEqual([1, 5, 5, 4]);
| const expected = [
| 0.29389750957489014, 1.055132269859314, 0.8355544209480286,
| 0.7652503848075867, 1.116986632347107, 1.7007107734680176,
| 0.7228718996047974, 1.2455471754074097, 0.7690584063529968,
| 1.4749835729599, 1.1460752487182617, 1.5098011493682861,
| 0.7502411007881165, 2.056602716445923, 1.0519171953201294,
| 1.012758731842041, 0.37667199969291687, 1.6647151708602905,
| 0.4798099994659424, 0.532977283000946, 0.4293096363544464,
| 1.8309053182601929, 0.7433272004127502, 1.1491419076919556,
| 1.3050479888916016, 2.7769954204559326, 1.6411027908325195,
| 2.1799824237823486, 1.0364032983779907, 2.7503039836883545,
| 1.7060394287109375, 2.880652904510498, 1.8967751264572144,
| 3.3914175033569336, 1.734355092048645, 2.076633930206299,
| 0.7774094939231873, 3.1432321071624756, 0.9456352591514587,
| 1.0863502025604248, 0.8477171659469604, 2.5510711669921875,
| 1.169355869293213, 2.0218098163604736, 2.23183274269104,
| 3.257829189300537, 1.939490556716919, 2.96195650100708,
| 1.0946838855743408, 2.4252827167510986, 1.329919695854187,
| 3.0390005111694336, 1.8967963457107544, 2.775693416595459,
| 1.5250799655914307, 2.4470155239105225, 0.40530526638031006,
| 2.775503158569336, 0.8836789727210999, 1.1361782550811768,
| 0.4407186806201935, 2.3912413120269775, 0.38215696811676025,
| 2.047299861907959, 1.080580234527588, 3.09224534034729,
| 1.2943278551101685, 3.1656715869903564, 0.9704407453536987,
| 2.8066811561584473, 1.419780969619751, 3.1822099685668945,
| 1.720312237739563, 3.279745578765869, 2.0871992111206055,
| 2.6629819869995117, 0.5254714488983154, 3.3779194355010986,
| 0.73943030834198, 2.0616414546966553, 0.5148154497146606,
| 1.6852912902832031, 0.5320349931716919, 1.7935365438461304,
| 1.1387810707092285, 2.119696617126465, 1.2744661569595337,
| 2.3705403804779053, 1.0399315357208252, 1.6817822456359863,
| 0.8927359580993652, 1.6332063674926758, 1.3386595249176025,
| 1.8818190097808838, 1.267898440361023, 1.6589205265045166,
| 0.8288722038269043, 2.119757890701294, 0.8847255706787109,
| 1.5954076051712036
| ];
| expectArraysClose(await result.data(), expected);
| });
| it('input=1x5x5x4,f=5,s=2,d=1,p=same,chMul=1', async () => {
| const fSize = 5;
| const pad = 'same';
| const stride = 2;
| const chMul = 1;
| const inDepth = 4;
| 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, 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, 0.653365,
| 0.442196, 0.261945, 0.0528113, 0.656698, 0.127345, 0.610039,
| 0.169131, 0.458647, 0.0988288, 0.966109, 0.0421747, 0.82035,
| 0.274711, 0.359377, 0.512113, 0.689682, 0.941571, 0.31961,
| 0.743826, 0.858147, 0.984766, 0.926973, 0.579597, 0.444104,
| 0.505969, 0.241437, 0.937999, 0.0957074, 0.773611, 0.46023,
| 0.469379, 0.363789, 0.269745, 0.486136, 0.894215, 0.794299,
| 0.724615, 0.261945, 0.0528113, 0.656698, 0.127345, 0.610039,
| 0.169131, 0.458647, 0.0988288, 0.966109, 0.0421747, 0.82035,
| 0.274711, 0.359377, 0.512113, 0.689682, 0.941571, 0.31961,
| 0.743826, 0.858147, 0.984766, 0.926973
| ], [1, 5, 5, inDepth]);
| const w = tf.tensor4d([
| 0.6511372, 0.8699447, 0.6511372, 0.8699447, 0.267792, 0.9981787,
| 0.267792, 0.9981787, 0.4913572, 0.3321196, 0.4913572, 0.3321196,
| 0.5286497, 0.4241803, 0.5286497, 0.4241803, 0.0175446, 0.8365464,
| 0.0175446, 0.8365464, 0.1768399, 0.2874831, 0.1768399, 0.2874831,
| 0.0933998, 0.5764548, 0.0933998, 0.5764548, 0.0661623, 0.8850273,
| 0.0661623, 0.8850273, 0.8700929, 0.205422, 0.8700929, 0.205422,
| 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, 0.125386, 0.975199, 0.640437, 0.281895, 0.990968,
| 0.347208, 0.889702, 0.180695, 0.691992, 0.347154, 0.386692,
| 0.327191, 0.483784, 0.591807, 0.24263, 0.95182, 0.174353,
| 0.592136, 0.623469, 0.988244, 0.660731, 0.946534, 0.0801365,
| 0.864889, 0.874602, 0.240347, 0.906352, 0.478657, 0.825918,
| 0.380769, 0.184705, 0.238241, 0.201907, 0.294087, 0.181165,
| 0.191303, 0.7225, 0.430064, 0.900622
| ], [fSize, fSize, inDepth, chMul]);
| const result = tf.depthwiseConv2d(x, w, stride, pad);
| expect(result.shape).toEqual([1, 3, 3, 4]);
| const expected = [
| 2.2883458137512207, 2.5740344524383545, 2.3246560096740723,
| 2.27826189994812, 3.0600292682647705, 5.021538734436035,
| 4.432307720184326, 2.6976213455200195, 1.8467353582382202,
| 3.617821216583252, 2.0940940380096436, 1.3091316223144531,
| 2.4892354011535645, 4.767732620239258, 3.126866579055786,
| 3.4326541423797607, 4.181705474853516, 8.082467079162598,
| 6.922453880310059, 5.922790050506592, 2.819075345993042,
| 5.9510369300842285, 3.7211103439331055, 2.7263708114624023,
| 1.164026141166687, 3.3068809509277344, 1.6575196981430054,
| 2.738445997238159, 2.288442850112915, 5.463253021240234,
| 2.840029239654541, 3.8579823970794678, 1.440760612487793,
| 3.862100839614868, 2.3826799392700195, 2.2323575019836426
| ];
| expectArraysClose(await result.data(), expected);
| });
| it('input=1x5x5x1,f=3,s=1,d=2,p=explicit,chMul=1', async () => {
| const fSize = 3;
| const pad = [[0, 0], [0, 0], [0, 1], [0, 1]];
| const stride = 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.depthwiseConv2d(x, w, stride, pad);
| expect(result.shape).toEqual([1, 3, 4, 1]);
| const expected = [
| 2.540022, 2.505885, 2.454062, 1.871258, 2.35170, 2.459601, 3.076421,
| 1.32399, 3.298480, 3.437421, 2.93419, 1.823238
| ];
| expectArraysClose(await result.data(), expected);
| });
| it('input=1x3x3x4,f=3,s=1,d=2,p=same,chMul=1', async () => {
| const fSize = 3;
| const pad = 'same';
| const stride = 1;
| const chMul = 1;
| const inDepth = 4;
| const dilation = 2;
| const x = tf.tensor4d([
| 0.5227615, 0.3477598, 0.5227615, 0.3477598, 0.4690094, 0.408161,
| 0.4690094, 0.408161, 0.3239015, 0.2372907, 0.3239015, 0.2372907,
| 0.6136674, 0.7918105, 0.6136674, 0.7918105, 0.9145211, 0.218611,
| 0.9145211, 0.218611, 0.3778793, 0.2392365, 0.3778793, 0.2392365,
| 0.2340134, 0.1251984, 0.2340134, 0.1251984, 0.6222534, 0.1327361,
| 0.6222534, 0.1327361, 0.7697753, 0.1216059, 0.7697753, 0.1216059
| ], [1, 3, 3, inDepth]);
| const w = tf.tensor4d([
| 0.6511372, 0.8699447, 0.6511372, 0.8699447, 0.267792, 0.9981787,
| 0.267792, 0.9981787, 0.4913572, 0.3321196, 0.4913572, 0.3321196,
| 0.5286497, 0.4241803, 0.5286497, 0.4241803, 0.0175446, 0.8365464,
| 0.0175446, 0.8365464, 0.1768399, 0.2874831, 0.1768399, 0.2874831,
| 0.0933998, 0.5764548, 0.0933998, 0.5764548, 0.0661623, 0.8850273,
| 0.0661623, 0.8850273, 0.8700929, 0.205422, 0.8700929, 0.205422
| ], [fSize, fSize, inDepth, chMul]);
| const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);
| expect(result.shape).toEqual([1, 3, 3, 4]);
| const expected = [
| 0.7517092227935791, 0.4949187934398651, 0.7517092227935791,
| 0.4949187934398651, 0.04939830303192139, 0.4589206874370575,
| 0.04939830303192139, 0.4589206874370575, 0.3548273742198944,
| 0.5258132815361023, 0.3548273742198944, 0.5258132815361023,
| 0.0775906890630722, 0.7311626672744751, 0.0775906890630722,
| 0.7311626672744751, 0.01604490540921688, 0.1828782558441162,
| 0.01604490540921688, 0.1828782558441162, 0.3310448229312897,
| 0.5360028743743896, 0.3310448229312897, 0.5360028743743896,
| 0.4393753409385681, 0.565629243850708, 0.4393753409385681,
| 0.565629243850708, 0.13651414215564728, 0.5184575319290161,
| 0.13651414215564728, 0.5184575319290161, 0.5643441677093506,
| 0.6942259669303894, 0.5643441677093506, 0.6942259669303894
| ];
| expectArraysClose(await result.data(), expected);
| });
| it('input=1x3x3x2,f=2,s=1,p=same,chMul=2', async () => {
| const fSize = 2;
| const pad = 'same';
| const stride = 1;
| const chMul = 2;
| const inDepth = 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, 0.653365, 0.442196
| ], [1, 3, 3, inDepth]);
| const w = tf.tensor4d([
| 0.347154, 0.386692, 0.327191, 0.483784, 0.591807, 0.24263, 0.95182,
| 0.174353, 0.592136, 0.623469, 0.988244, 0.660731, 0.946534, 0.0801365,
| 0.864889, 0.874602
| ], [fSize, fSize, inDepth, chMul]);
| const result = tf.depthwiseConv2d(x, w, stride, pad);
| expect(result.shape).toEqual([1, 3, 3, 4]);
| const expected = [
| 1.83059, 0.937125, 2.1218, 1.39024, 0.990167, 0.803472,
| 1.31405, 1.14959, 0.182147, 0.196385, 0.241141, 0.188081,
| 0.950656, 0.622581, 1.92451, 1.20179, 1.07422, 0.483268,
| 1.36948, 1.14256, 0.449444, 0.477042, 0.505857, 0.393989,
| 0.0746509, 0.0633184, 0.74101, 0.41159, 0.403195, 0.176938,
| 0.602415, 0.345499, 0.226819, 0.252651, 0.144682, 0.213927
| ];
| expectArraysClose(await result.data(), expected);
| });
| it('input=2x3x3x2,f=2,s=1,p=same,chMul=2', async () => {
| const fSize = 2;
| const pad = 'same';
| const stride = 1;
| const chMul = 2;
| const inDepth = 2;
| const x = tf.tensor4d([
| 0.261945, 0.0528113, 0.656698, 0.127345, 0.610039, 0.169131,
| 0.458647, 0.0988288, 0.966109, 0.0421747, 0.82035, 0.274711,
| 0.359377, 0.512113, 0.689682, 0.941571, 0.31961, 0.743826,
| 0.858147, 0.984766, 0.926973, 0.579597, 0.444104, 0.505969,
| 0.241437, 0.937999, 0.0957074, 0.773611, 0.46023, 0.469379,
| 0.363789, 0.269745, 0.486136, 0.894215, 0.794299, 0.724615
| ], [2, 3, 3, inDepth]);
| const w = tf.tensor4d([
| 0.240347, 0.906352, 0.478657, 0.825918, 0.380769, 0.184705, 0.238241,
| 0.201907, 0.294087, 0.181165, 0.191303, 0.7225, 0.430064, 0.900622,
| 0.670338, 0.33478
| ], [fSize, fSize, inDepth, chMul]);
| const result = tf.depthwiseConv2d(x, w, stride, pad);
| expect(result.shape).toEqual([2, 3, 3, 4]);
| const expected = [
| 0.863379, 1.3119, 0.102795, 0.154853, 1.02704, 1.62173, 0.293466,
| 0.261764, 0.387876, 0.701529, 0.133508, 0.338167, 0.880395, 1.28039,
| 0.786492, 0.775361, 0.884845, 1.43995, 0.764374, 1.0196, 0.291162,
| 0.801428, 0.273788, 0.764303, 0.348985, 0.45311, 0.469447, 0.613073,
| 0.287461, 0.684128, 0.627899, 0.927844, 0.0768174, 0.28968, 0.356037,
| 0.614339, 0.67138, 1.07894, 1.30747, 1.86705, 0.617971, 1.35402,
| 0.860607, 1.29693, 0.242087, 0.485892, 0.331979, 0.757015, 0.410527,
| 0.740235, 1.28431, 1.42516, 0.68281, 0.975185, 1.13892, 1.62237,
| 0.344208, 0.561029, 0.363292, 0.911203, 0.272541, 0.419513, 0.342154,
| 0.403335, 0.419286, 0.587321, 0.600655, 0.884853, 0.190907, 0.719914,
| 0.346842, 0.598472
| ];
| expectArraysClose(await result.data(), expected);
| });
| it('input=2x3x3x2,f=2,s=1,d=2,p=same,chMul=2', async () => {
| const fSize = 2;
| const pad = 'same';
| const stride = 1;
| const inDepth = 2;
| const dilation = 2;
| const noDilation = 1;
| const x = tf.tensor4d([
| 0.261945, 0.0528113, 0.656698, 0.127345, 0.610039, 0.169131,
| 0.458647, 0.0988288, 0.966109, 0.0421747, 0.82035, 0.274711,
| 0.359377, 0.512113, 0.689682, 0.941571, 0.31961, 0.743826,
| 0.858147, 0.984766, 0.926973, 0.579597, 0.444104, 0.505969,
| 0.241437, 0.937999, 0.0957074, 0.773611, 0.46023, 0.469379,
| 0.363789, 0.269745, 0.486136, 0.894215, 0.794299, 0.724615
| ], [2, 3, 3, inDepth]);
| const w = tf.stack([
| tf.stack([
| tf.tensor2d([0.240347, 0.906352, 0.478657, 0.825918], [fSize, fSize]),
| tf.tensor2d([0.380769, 0.184705, 0.238241, 0.201907], [fSize, fSize])
| ], 2),
| tf.stack([
| tf.tensor2d([0.294087, 0.181165, 0.191303, 0.7225], [fSize, fSize]),
| tf.tensor2d([0.430064, 0.900622, 0.670338, 0.33478], [fSize, fSize])
| ], 2)
| ], 3);
| const fSizeDilated = fSize + (fSize - 1) * (dilation - 1);
| const wDilated = tf.stack([
| tf.stack([
| tf.tensor2d([0.240347, 0, 0.906352, 0, 0, 0, 0.478657, 0, 0.825918], [fSizeDilated, fSizeDilated]),
| tf.tensor2d([0.380769, 0, 0.184705, 0, 0, 0, 0.238241, 0, 0.201907], [fSizeDilated, fSizeDilated])
| ], 2),
| tf.stack([
| tf.tensor2d([0.294087, 0, 0.181165, 0, 0, 0, 0.191303, 0, 0.7225], [fSizeDilated, fSizeDilated]),
| tf.tensor2d([0.430064, 0, 0.900622, 0, 0, 0, 0.670338, 0, 0.33478], [fSizeDilated, fSizeDilated])
| ], 2)
| ], 3);
| const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);
| const expectedResult = tf.depthwiseConv2d(x, wDilated, stride, pad, 'NHWC', noDilation);
| expect(result.shape).toEqual(expectedResult.shape);
| expectArraysClose(await result.data(), await expectedResult.data());
| });
| it('input=2x3x3x2,f=3,s=1,d=2,p=same,chMul=2', async () => {
| const fSize = 3;
| const pad = 'same';
| const stride = 1;
| const inDepth = 2;
| const dilation = 2;
| const x = tf.tensor4d([[
| [
| [0.52276146, 0.34775984], [0.4690094, 0.40816104],
| [0.32390153, 0.23729074], [0.61366737, 0.7918105],
| [0.9145211, 0.218611], [0.37787926, 0.23923647],
| [0.23401344, 0.12519836]
| ],
| [
| [0.6222534, 0.13273609], [0.7697753, 0.12160587],
| [0.0448128, 0.94806635], [0.4199953, 0.7140714],
| [0.01420832, 0.47453713], [0.02061439, 0.37226152],
| [0.62741446, 0.23167181]
| ],
| [
| [0.7257557, 0.14352751], [0.3011638, 0.3869065],
| [0.09286129, 0.25151742], [0.7566397, 0.13099921],
| [0.65324724, 0.38959372], [0.65826, 0.7505318],
| [0.35919082, 0.85470796]
| ],
| [
| [0.24827361, 0.2826661], [0.24717247, 0.27446854],
| [0.27112448, 0.68068564], [0.11082292, 0.7948675],
| [0.41535318, 0.659986], [0.22165525, 0.18149579],
| [0.42273378, 0.9558281]
| ],
| [
| [0.943074, 0.6799041], [0.78851473, 0.07249606],
| [0.771909, 0.7925967], [0.9551083, 0.03087568],
| [0.82589805, 0.94797385], [0.5895462, 0.5045923],
| [0.9667754, 0.24292922]
| ],
| [
| [0.67123663, 0.109761], [0.04002762, 0.51942277],
| [0.37868536, 0.8467603], [0.77171385, 0.51604605],
| [0.8192849, 0.38843668], [0.19607484, 0.5591624],
| [0.45990825, 0.35768318]
| ],
| [
| [0.67443585, 0.6256168], [0.9373623, 0.6498393],
| [0.7623085, 0.13218105], [0.9349631, 0.7660191],
| [0.50054944, 0.7738123], [0.30201948, 0.525643],
| [0.30896342, 0.21111596]
| ]
| ]], [1, 7, 7, inDepth]);
| const w = tf.tensor4d([
| [
| [[0.65113723], [0.8699447]],
| [[0.267792], [0.9981787]],
| [[0.4913572], [0.33211958]]
| ],
| [
| [[0.5286497], [0.42418027]],
| [[0.01754463], [0.8365464]],
| [[0.17683995], [0.2874831]]
| ],
| [
| [[0.09339976], [0.57645476]],
| [[0.06616235], [0.8850273]],
| [[0.87009287], [0.20542204]]
| ]
| ], [fSize, fSize, inDepth, 1]);
| const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);
| expect(result.shape).toEqual([1, 7, 7, 2]);
| expectArraysClose(await result.data(), [
| 0.19526604, 0.5378273, 0.795022, 0.9384107, 1.0860794, 0.7942326,
| 0.9764694, 1.3974442, 0.5930813, 0.9848901, 0.44526684, 1.275759,
| 0.572345, 1.1784878, 0.27117175, 0.773588, 0.20055711, 0.71320784,
| 0.73477566, 1.8867722, 0.64123434, 1.6549369, 0.55551285, 2.0385633,
| 0.24740812, 1.233143, 0.08528192, 1.6214795, 1.062326, 1.3828603,
| 1.4494176, 1.1022222, 2.2350664, 2.283423, 1.5940895, 1.8871424,
| 1.6627852, 2.4903212, 1.0405337, 2.0754304, 1.1508893, 1.9568737,
| 0.6148571, 1.1505995, 1.1105528, 1.3823687, 1.4342139, 2.9909487,
| 1.0210396, 2.6467443, 1.0563798, 3.3963797, 0.42652097, 2.274134,
| 0.51121074, 2.264094, 1.1009313, 1.6042703, 1.510688, 1.2317145,
| 2.025515, 2.3658662, 1.6722159, 2.0787857, 1.3785586, 2.895031,
| 1.2915218, 2.2051222, 1.0423074, 2.4303207, 0.27844793, 0.84346974,
| 0.25781655, 1.1208354, 0.9447272, 2.0111258, 0.3689065, 1.9052455,
| 0.79137695, 2.355344, 0.5429248, 1.5593178, 0.8248403, 1.9922242,
| 0.77847, 1.5032601, 0.8622418, 0.84645665, 1.6850245, 2.2958806,
| 1.6242284, 1.329045, 1.6652328, 2.480535, 1.2793491, 1.2951884,
| 1.0667037, 1.5720158
| ]);
| });
| it('input=1x8x8x2,f=3,s=1,d=3,p=valid,chMul=1', async () => {
| const fSize = 3;
| const pad = 'valid';
| const stride = 1;
| const inDepth = 2;
| const dilation = 3;
| const x = tf.tensor4d([
| 0.09941668063402176, 0.05248984694480896, 0.4567521810531616,
| 0.8002573847770691, 0.810535192489624, 0.7010623216629028,
| 0.5898630023002625, 0.05883334204554558, 0.2314797043800354,
| 0.45427876710891724, 0.10960108041763306, 0.9710874557495117,
| 0.18139968812465668, 0.8959258794784546, 0.35156702995300293,
| 0.6495933532714844, 0.5185067653656006, 0.3260101079940796,
| 0.7837356925010681, 0.9170011281967163, 0.465780109167099,
| 0.0857422724366188, 0.38354963064193726, 0.8134718537330627,
| 0.8768209218978882, 0.38151195645332336, 0.5045309066772461,
| 0.8152258396148682, 0.2782581150531769, 0.545160174369812,
| 0.1587309092283249, 0.5507456064224243, 0.2704062759876251,
| 0.7736618518829346, 0.9871141314506531, 0.29300180077552795,
| 0.3038032352924347, 0.36257433891296387, 0.967268168926239,
| 0.7251133918762207, 0.6244085431098938, 0.8398842215538025,
| 0.42696574330329895, 0.25569799542427063, 0.5784937143325806,
| 0.22755105793476105, 0.8869972229003906, 0.05128923058509827,
| 0.6748542785644531, 0.97468101978302, 0.5549167394638062,
| 0.5639380812644958, 0.821204662322998, 0.5207878947257996,
| 0.8831672668457031, 0.6721863746643066, 0.23375047743320465,
| 0.040671784430742264, 0.24522553384304047, 0.6293181777000427,
| 0.6886807680130005, 0.29527169466018677, 0.48199158906936646,
| 0.5751473307609558, 0.817806601524353, 0.38846832513809204,
| 0.5553714036941528, 0.1839468777179718, 0.5287416577339172,
| 0.4813096523284912, 0.477756530046463, 0.641162633895874,
| 0.03040425479412079, 0.20608118176460266, 0.7930338978767395,
| 0.727353572845459, 0.42868077754974365, 0.6136374473571777,
| 0.06312728673219681, 0.4346885681152344, 0.004786544945091009,
| 0.4951920807361603, 0.588252604007721, 0.724294126033783,
| 0.07830118387937546, 0.07353833317756653, 0.7818689346313477,
| 0.8137099742889404, 0.6505773067474365, 0.5716961026191711,
| 0.5416423678398132, 0.855529248714447, 0.8958709239959717,
| 0.3598312437534332, 0.31329575181007385, 0.5971285104751587,
| 0.034069616347551346, 0.6229354739189148, 0.24074052274227142,
| 0.3356363773345947, 0.1049640029668808, 0.2543765604496002,
| 0.1635538637638092, 0.8082090616226196, 0.9097364544868469,
| 0.6435819268226624, 0.6100808382034302, 0.29750677943229675,
| 0.0738643929362297, 0.8887753486633301, 0.7692861557006836,
| 0.6412256360054016, 0.16205888986587524, 0.9414404034614563,
| 0.5698712468147278, 0.6834514737129211, 0.41202589869499207,
| 0.9096908569335938, 0.8094117045402527, 0.42103442549705505,
| 0.8905773162841797, 0.069722980260849, 0.014392468146979809,
| 0.22018849849700928, 0.30076053738594055, 0.8472294211387634,
| 0.852762758731842, 0.5004454851150513
| ], [1, 8, 8, inDepth]);
| const w = tf.tensor4d([
| 0.5785998106002808, 0.7439202666282654, 0.2178175300359726,
| 0.8782838582992554, 0.6579487919807434, 0.6556791067123413,
| 0.7341834306716919, 0.3332836329936981, 0.037182893604040146,
| 0.7394348382949829, 0.04031887650489807, 0.19104436039924622,
| 0.7014378309249878, 0.5309979319572449, 0.8485966920852661,
| 0.6609954237937927, 0.021728534251451492, 0.9289031624794006
| ], [fSize, fSize, inDepth, 1]);
| const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);
| expect(result.shape).toEqual([1, 2, 2, 2]);
| expectArraysClose(await result.data(), [
| 1.0257229804992676, 3.247040033340454, 1.9391249418258667,
| 2.9474055767059326, 2.0091731548309326, 3.600433826446533,
| 2.334312677383423, 2.548961877822876
| ]);
| });
| it('Tensor3D is allowed', async () => {
| const fSize = 2;
| const pad = 'same';
| const stride = 1;
| const chMul = 3;
| const inDepth = 2;
| const x = tf.zeros([3, 3, inDepth]);
| const w = tf.zeros([fSize, fSize, inDepth, chMul]);
| const result = tf.depthwiseConv2d(x, w, stride, pad);
| expect(result.shape).toEqual([3, 3, inDepth * chMul]);
| });
| it('Pass null for dilations, which defaults to [1, 1]', () => {
| const fSize = 2;
| const pad = 'same';
| const stride = 1;
| const chMul = 3;
| const inDepth = 2;
| const dilations = null;
| const x = tf.zeros([3, 3, inDepth]);
| const w = tf.zeros([fSize, fSize, inDepth, chMul]);
| const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilations);
| expect(result.shape).toEqual([3, 3, inDepth * chMul]);
| });
| it('TensorLike', async () => {
| const pad = 'valid';
| const stride = 1;
| const x = [[
| [[0.230664], [0.987388], [0.0685208]],
| [[0.419224], [0.887861], [0.731641]],
| [[0.0741907], [0.409265], [0.351377]]
| ]];
| const w = [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];
| const result = tf.depthwiseConv2d(x, w, stride, pad);
| const expected = [1.07022, 1.03167, 0.67041, 0.778863];
| expectArraysClose(await result.data(), expected);
| });
| it('TensorLike Chained', async () => {
| const pad = 'valid';
| const stride = 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 = [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];
| const result = x.depthwiseConv2d(w, stride, pad);
| expect(result.shape).toEqual([1, 2, 2, 1]);
| const expected = [1.07022, 1.03167, 0.67041, 0.778863];
| expectArraysClose(await result.data(), expected);
| });
| it('throws when passed x as a non-tensor', () => {
| const inputDepth = 1;
| const outputDepth = 1;
| const fSize = 1;
| const pad = 'same';
| const stride = 2;
| const dataFormat = 'NHWC';
| const dilation = 2;
| const w = tf.tensor4d([3], [fSize, fSize, inputDepth, outputDepth]);
| const e = /Argument 'x' passed to 'depthwiseConv2d' must be a Tensor/;
| expect(() => tf.depthwiseConv2d({}, w, stride, pad, dataFormat, dilation))
| .toThrowError(e);
| });
| it('throws when passed filter as a non-tensor', () => {
| const inputDepth = 1;
| const inputShape = [2, 2, inputDepth];
| const pad = 'same';
| const stride = 2;
| const dataFormat = 'NHWC';
| const dilation = 2;
| const x = tf.tensor3d([1, 2, 3, 4], inputShape);
| const e = /Argument 'filter' passed to 'depthwiseConv2d' must be a Tensor/;
| expect(() => tf.depthwiseConv2d(x, {}, stride, pad, dataFormat, dilation))
| .toThrowError(e);
| });
| it('throws when input is int32', async () => {
| const fSize = 2;
| const pad = 'valid';
| const stride = 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]);
| const errRegex = /Argument 'x' passed to 'depthwiseConv2d' must be float32/;
| expect(() => tf.depthwiseConv2d(x, w, stride, pad)).toThrowError(errRegex);
| });
| it('throws when filter is int32', async () => {
| const fSize = 2;
| const pad = 'valid';
| const stride = 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');
| const errRegex = /Argument 'filter' passed to 'depthwiseConv2d' must be float32/;
| expect(() => tf.depthwiseConv2d(x, w, stride, pad)).toThrowError(errRegex);
| });
| it('throws when dimRoundingMode is set and pad is same', () => {
| const fSize = 2;
| const pad = 'same';
| const stride = 1;
| const chMul = 1;
| const inDepth = 1;
| const dimRoundingMode = 'round';
| 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]);
| expect(() => tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', 1, dimRoundingMode))
| .toThrowError();
| });
| it('throws when dimRoundingMode is set and pad is valid', () => {
| const fSize = 2;
| const pad = 'valid';
| const stride = 1;
| const chMul = 1;
| const inDepth = 1;
| const dimRoundingMode = 'round';
| 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]);
| expect(() => tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', 1, dimRoundingMode))
| .toThrowError();
| });
| it('throws when dimRoundingMode is set and pad is a non-integer number', () => {
| const fSize = 2;
| const pad = 1.2;
| const stride = 1;
| const chMul = 1;
| const inDepth = 1;
| const dimRoundingMode = 'round';
| 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]);
| expect(() => tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', 1, dimRoundingMode))
| .toThrowError();
| });
| it('throws when dimRoundingMode is set and pad is explicit by non-integer ' +
| 'number', () => {
| const fSize = 2;
| const pad = [[0, 0], [0, 2.1], [1, 1], [0, 0]];
| const stride = 1;
| const chMul = 1;
| const inDepth = 1;
| const dimRoundingMode = 'round';
| 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]);
| expect(() => tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', 1, dimRoundingMode))
| .toThrowError();
| });
| it('accepts a tensor-like object', async () => {
| const pad = 'valid';
| const stride = 1;
| // 1x3x3x1
| const x = [[
| [[0.230664], [0.987388], [0.0685208]],
| [[0.419224], [0.887861], [0.731641]],
| [[0.0741907], [0.409265], [0.351377]]
| ]];
| // 2x2x1x1
| const w = [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];
| const result = tf.depthwiseConv2d(x, w, stride, pad);
| expect(result.shape).toEqual([1, 2, 2, 1]);
| const expected = [1.07022, 1.03167, 0.67041, 0.778863];
| expectArraysClose(await result.data(), expected);
| });
| });
| describeWithFlags('depthwiseConv2d gradients', ALL_ENVS, () => {
| let images;
| let filter;
| let result;
| const stride = 1;
| const pad = 'same';
| beforeEach(() => {
| // two 2x2 RGB images => 2x2x2x3
| images = tf.tensor4d([
| [[[2, 3, 1], [3, 0, 2]], [[0, 4, 1], [3, 1, 3]]],
| [[[2, 1, 0], [0, 3, 3]], [[4, 0, 1], [1, 4, 1]]]
| ]);
| // 2x2 filters, chMul = 2 => 2x2x3x2
| filter = tf.tensor4d([
| [[[1, 1], [1, 1], [0, 0]], [[0, 1], [1, 1], [1, 1]]],
| [[[1, 0], [1, 1], [0, 0]], [[0, 1], [1, 0], [0, 0]]]
| ]);
| // result of convolution operatoin
| result = tf.tensor4d([
| [
| [[2, 8, 8, 7, 2, 2], [6, 3, 1, 1, 0, 0]],
| [[0, 3, 5, 5, 3, 3], [3, 3, 1, 1, 0, 0]]
| ],
| [
| [[6, 3, 8, 4, 3, 3], [1, 0, 7, 7, 0, 0]],
| [[4, 5, 4, 4, 1, 1], [1, 1, 4, 4, 0, 0]]
| ]
| ]);
| });
| it('wrt input', async () => {
| const { value, grad } = tf.valueAndGrad((x) => tf.depthwiseConv2d(x, filter, stride, pad))(images);
| expectArraysClose(await value.data(), await result.data());
| const expectedGrad = tf.tensor4d([
| [[[2., 2., 0.], [3., 4., 2.]], [[3., 4., 0.], [5., 7., 2.]]],
| [[[2., 2., 0.], [3., 4., 2.]], [[3., 4., 0.], [5., 7., 2.]]]
| ]);
| expectArraysClose(await grad.data(), await expectedGrad.data());
| });
| // The gradients of normal and depthwise 2D convolutions are actually the same
| // in the special case that dy = 1, so we also test the gradient of a function
| // of the output to disambiguate the two methods.
| it('wrt input, squared output', async () => {
| const grad = tf.grad((x) => tf.square(tf.depthwiseConv2d(x, filter, stride, pad)))(images);
| const expectedGrad = tf.tensor4d([
| [[[20., 30., 0.], [34., 34., 8.]], [[10., 50., 0.], [46., 44., 12.]]],
| [[[18., 24., 0.], [8., 52., 12.]], [[30., 40., 0.], [22., 76., 4.]]]
| ]);
| expectArraysClose(await grad.data(), await expectedGrad.data());
| });
| it('wrt filter', async () => {
| const { value, grad } = tf.valueAndGrad((f) => tf.depthwiseConv2d(images, f, stride, pad))(filter);
| expectArraysClose(await value.data(), await result.data());
| const expectedGrad = tf.tensor4d([
| [[[15., 15.], [16., 16.], [12., 12.]], [[7., 7.], [8., 8.], [9., 9.]]],
| [[[8., 8.], [9., 9.], [6., 6.]], [[4., 4.], [5., 5.], [4., 4.]]]
| ]);
| expectArraysClose(await grad.data(), await expectedGrad.data());
| });
| it('gradient with clones', async () => {
| const [dx, dFilter] = tf.grads((x, filter) => tf.depthwiseConv2d(x.clone(), filter.clone(), stride, pad).clone())([images, filter]);
| expect(dx.shape).toEqual(images.shape);
| expect(dFilter.shape).toEqual(filter.shape);
| });
| // Also disambiguate regular vs. depthwise filter gradients
| it('wrt filter, squared output', async () => {
| const grad = tf.grad((f) => tf.square(tf.depthwiseConv2d(images, f, stride, pad)))(filter);
| const expectedGrad = tf.tensor4d([
| [
| [[120., 122.], [180., 166.], [12., 12.]],
| [[20., 76.], [90., 66.], [46., 46.]]
| ],
| [
| [[86., 42.], [122., 114.], [10., 10.]],
| [[24., 54.], [80., 46.], [18., 18.]]
| ]
| ]);
| expectArraysClose(await grad.data(), await expectedGrad.data());
| });
| it('throws error on dilations > 1', () => {
| const grad = tf.grad((x) => tf.depthwiseConv2d(x, filter, stride, pad, 'NHWC', 2));
| expect(() => grad(images))
| .toThrowError(/dilation rates greater than 1 are not yet supported/);
| });
| it('wrt input, stride=2, pad=valid', async () => {
| const dx = tf.grad((x) => tf.depthwiseConv2d(x, filter, 2, 'valid'))(images);
| expectArraysClose(await dx.data(), [
| 2., 2., 0., 1., 2., 2., 1., 2., 0., 1., 1., 0.,
| 2., 2., 0., 1., 2., 2., 1., 2., 0., 1., 1., 0.
| ]);
| expect(dx.shape).toEqual([2, 2, 2, 3]);
| });
| it('wrt filter, stride=2, pad=valid', async () => {
| const df = tf.grad((f) => tf.depthwiseConv2d(images, f, 2, 'valid'))(filter);
| expectArraysClose(await df.data(), [
| 4., 4., 4., 4., 1., 1., 3., 3., 3., 3., 5., 5.,
| 4., 4., 4., 4., 2., 2., 4., 4., 5., 5., 4., 4.
| ]);
| expect(df.shape).toEqual([2, 2, 3, 2]);
| });
| it('gradient with clones', async () => {
| const fSize = 2;
| const pad = 'valid';
| const stride = 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 f = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
| const [dx, df] = tf.grads((x, f) => tf.depthwiseConv2d(x.clone(), f.clone(), stride, pad).clone())([x, f]);
| expectArraysClose(await dx.data(), [
| 0.303873, 0.533096, 0.229223, 0.448206, 1.480802, 1.032596, 0.144333,
| 0.947706, 0.803373
| ]);
| expect(dx.shape).toEqual([1, 3, 3, 1]);
| expectArraysClose(await df.data(), [2.525137, 2.6754108, 1.7905407, 2.380144]);
| expect(df.shape).toEqual([2, 2, 1, 1]);
| });
| });
| //# 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* @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as tf from '../index';\nimport {ALL_ENVS, describeWithFlags} from '../jasmine_util';\nimport {expectArraysClose} from '../test_util';\nimport {Rank} from '../types';\n\ndescribeWithFlags('depthwiseConv2D', ALL_ENVS, () => {\n  it('input=1x3x3x1,f=2,s=1,d=1,p=valid,chMul=1', async () => {\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(\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.depthwiseConv2d(x, w, stride, pad);\n    expect(result.shape).toEqual([1, 2, 2, 1]);\n    const expected = [1.07022, 1.03167, 0.67041, 0.778863];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('input=1x3x3x1,f=2,s=1,d=1,p=explicit,chMul=1', async () => {\n    const fSize = 2;\n    const pad =\n        [[0, 0], [1, 2], [0, 1], [0, 0]] as tf.backend_util.ExplicitPadding;\n    const stride = 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.depthwiseConv2d(x, w, stride, pad);\n    expect(result.shape).toEqual([1, 5, 3, 1]);\n    const expected = [\n      0.826533, 0.197560, 0.0098898, 1.070216, 1.031675, 0.126422, 0.6704096,\n      0.778863, 0.273041, 0.116357, 0.204908, 0.106774, 0, 0, 0\n    ];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('input=1x5x5x1,f=3,s=1,d=1,p=valid,chMul=1', async () => {\n    const fSize = 3;\n    const pad = 'valid';\n    const stride = 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, 0.889702,\n          0.180695, 0.691992\n        ],\n        [fSize, fSize, inDepth, chMul],\n    );\n\n    const result = tf.depthwiseConv2d(x, w, stride, pad);\n    expect(result.shape).toEqual([1, 3, 3, 1]);\n    const expected = [\n      2.540022, 2.505885, 2.454062, 2.351701, 2.459601, 3.076421, 3.29848,\n      3.437421, 2.93419\n    ];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('input=1x3x3x1,f=2,s=1,d=2,p=valid,chMul=1', async () => {\n    const fSize = 2;\n    const pad = 'valid';\n    const stride = 1;\n    const dilation = 2;\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    // adding a dilation rate is equivalent to using a filter\n    // with 0s for the dilation rate\n    const fSizeDilated = fSize + (fSize - 1) * (dilation - 1);\n    const wDilated = tf.tensor4d(\n        [0.303873, 0, 0.229223, 0, 0, 0, 0.144333, 0, 0.803373],\n        [fSizeDilated, fSizeDilated, inDepth, chMul],\n    );\n\n    const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);\n\n    const expectedResult = tf.depthwiseConv2d(x, wDilated, stride, pad);\n\n    expect(result.shape).toEqual(expectedResult.shape);\n    expectArraysClose(await result.data(), await expectedResult.data());\n  });\n\n  it('input=1x5x5x1,f=3,s=1,d=2,p=valid,chMul=1', async () => {\n    const fSize = 3;\n    const pad = 'valid';\n    const stride = 1;\n    const dilation = 2;\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, 0.889702,\n          0.180695, 0.691992\n        ],\n        [fSize, fSize, inDepth, chMul],\n    );\n    // adding a dilation rate is equivalent to using a filter\n    // with 0s for the dilation rate\n    const fSizeDilated = fSize + (fSize - 1) * (dilation - 1);\n    const wDilated = tf.tensor4d(\n        [\n          0.125386, 0, 0.975199, 0, 0.640437, 0, 0, 0, 0, 0,\n          0.281895, 0, 0.990968, 0, 0.347208, 0, 0, 0, 0, 0,\n          0.889702, 0, 0.180695, 0, 0.691992\n        ],\n        [fSizeDilated, fSizeDilated, inDepth, chMul],\n    );\n\n    const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);\n\n    const expectedResult = tf.depthwiseConv2d(x, wDilated, stride, pad);\n\n    expect(result.shape).toEqual(expectedResult.shape);\n    expectArraysClose(await result.data(), await expectedResult.data());\n  });\n\n  it('input=1x5x5x1,f=2,s=1,d=4,p=valid,chMul=1', async () => {\n    const fSize = 2;\n    const pad = 'valid';\n    const stride = 1;\n    const dilation = 4;\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        [0.125386, 0.975199, 0.640437, 0.281895],\n        [fSize, fSize, inDepth, chMul],\n    );\n    // adding a dilation rate is equivalent to using a filter\n    // with 0s for the dilation rate\n    const fSizeDilated = fSize + (fSize - 1) * (dilation - 1);\n    const wDilated = tf.tensor4d(\n        [\n          0.125386, 0, 0, 0, 0.975199, 0, 0, 0,        0, 0, 0, 0,       0,\n          0,        0, 0, 0, 0,        0, 0, 0.640437, 0, 0, 0, 0.281895\n        ],\n        [fSizeDilated, fSizeDilated, inDepth, chMul],\n    );\n\n    const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);\n\n    const expectedResult = tf.depthwiseConv2d(x, wDilated, stride, pad);\n\n    expect(result.shape).toEqual(expectedResult.shape);\n    expectArraysClose(await result.data(), await expectedResult.data());\n  });\n\n  it('input=1x3x3x2,f=2,s=1,d=1,p=same,chMul=1', async () => {\n    const fSize = 2;\n    const pad = 'same';\n    const stride = 1;\n    const chMul = 1;\n    const inDepth = 2;\n\n    const x = tf.tensor4d(\n        [\n          0.111057, 0.661818, 0.701979, 0.424362, 0.992854, 0.417599, 0.423036,\n          0.500499, 0.368484, 0.714135, 0.456693, 0.531058, 0.636636, 0.345024,\n          0.0506303, 0.789682, 0.177473, 0.793569\n        ],\n        [1, 3, 3, inDepth]);\n    const w = tf.tensor4d(\n        [\n          0.614293, 0.0648011, 0.101113, 0.452887, 0.0582746, 0.426481,\n          0.872743, 0.765767\n        ],\n        [fSize, fSize, inDepth, chMul]);\n    const result = tf.depthwiseConv2d(x, w, stride, pad);\n    expect(result.shape).toEqual([1, 3, 3, 2]);\n\n    const expected = [\n      0.485445, 0.995389, 0.95166, 0.927856, 0.636516, 0.253547, 0.378414,\n      1.10771, 0.430373, 1.23126, 0.290885, 0.372855, 0.3962, 0.379995,\n      0.0490466, 0.410569, 0.10902, 0.0514242\n    ];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('input=1x5x5x1,f=3,s=1,d=1,p=same,chMul=1', async () => {\n    const fSize = 3;\n    const pad = 'same';\n    const stride = 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, 0.889702,\n          0.180695, 0.691992\n        ],\n        [fSize, fSize, inDepth, chMul],\n    );\n\n    const result = tf.depthwiseConv2d(x, w, stride, pad);\n    expect(result.shape).toEqual([1, 5, 5, 1]);\n    const expected = [\n      0.684796, 1.179251, 1.680593, 0.885615, 1.152995, 1.52291,  2.540022,\n      2.505885, 2.454062, 1.871258, 2.371015, 2.351701, 2.459601, 3.076421,\n      1.323994, 1.985572, 3.29848,  3.437421, 2.93419,  1.823238, 1.410545,\n      2.352186, 2.19622,  1.348218, 0.774635\n    ];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('input=1x3x3x2,f=2,s=1,d=2,p=same,chMul=1', async () => {\n    const fSize = 2;\n    const pad = 'same';\n    const stride = 1;\n    const dilation = 2;\n    const inDepth = 2;\n\n    const x = tf.tensor4d(\n        [\n          0.111057, 0.661818, 0.701979, 0.424362, 0.992854, 0.417599, 0.423036,\n          0.500499, 0.368484, 0.714135, 0.456693, 0.531058, 0.636636, 0.345024,\n          0.0506303, 0.789682, 0.177473, 0.793569\n        ],\n        [1, 3, 3, inDepth]);\n\n    const w: tf.Tensor4D =\n        tf.stack(\n              [\n                tf.tensor2d(\n                    [0.614293, 0.0648011, 0.101113, 0.452887], [fSize, fSize]),\n                tf.tensor2d(\n                    [0.0582746, 0.426481, 0.872743, 0.765767], [fSize, fSize])\n              ],\n              2)\n            .expandDims(3);\n\n    // adding a dilation rate is equivalent to using a filter\n    // with 0s for the dilation rate\n    const fSizeDilated = fSize + (fSize - 1) * (dilation - 1);\n    const wDilated: tf.Tensor4D =\n        tf.stack(\n              [\n                tf.tensor2d(\n                    [0.614293, 0, 0.0648011, 0, 0, 0, 0.101113, 0, 0.452887],\n                    [fSizeDilated, fSizeDilated]),\n                tf.tensor2d(\n                    [0.0582746, 0, 0.426481, 0, 0, 0, 0.872743, 0, 0.765767],\n                    [fSizeDilated, fSizeDilated])\n              ],\n              2)\n            .expandDims(3);\n\n    expect(wDilated.shape).toEqual([fSizeDilated, fSizeDilated, inDepth, 1]);\n\n    const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);\n\n    const expectedResult = tf.depthwiseConv2d(x, wDilated, stride, pad);\n\n    expect(result.shape).toEqual(expectedResult.shape);\n    expectArraysClose(await result.data(), await expectedResult.data());\n  });\n\n  it('input=1x5x5x1,f=3,s=1,d=2,p=valid,chMul=1', async () => {\n    const fSize = 3;\n    const pad = 'valid';\n    const stride = 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, 0.889702,\n          0.180695, 0.691992\n        ],\n        [fSize, fSize, inDepth, chMul],\n    );\n\n    const result = tf.depthwiseConv2d(x, w, stride, pad);\n    expect(result.shape).toEqual([1, 3, 3, 1]);\n    const expected = [\n      2.540022, 2.505885, 2.454062, 2.351701, 2.459601, 3.076421, 3.29848,\n      3.437421, 2.93419\n    ];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('input=1x5x5x4,f=3,s=1,d=1,p=same,chMul=1', async () => {\n    const fSize = 3;\n    const pad = 'same';\n    const stride = 1;\n    const chMul = 1;\n    const inDepth = 4;\n\n    const x = tf.tensor4d(\n        [\n          0.149194, 0.089009, 0.654891,  0.083324,  0.537043,  0.644331,\n          0.563037, 0.211859, 0.633501,  0.186427,  0.777034,  0.50001,\n          0.607341, 0.95303,  0.696479,  0.050387,  0.62045,   0.728049,\n          0.028043, 0.437009, 0.712881,  0.741935,  0.974474,  0.621102,\n          0.171411, 0.675707, 0.758567,  0.413529,  0.963967,  0.217291,\n          0.101335, 0.804231, 0.329673,  0.924503,  0.728742,  0.180217,\n          0.210459, 0.133869, 0.650827,  0.047613,  0.554795,  0.653365,\n          0.442196, 0.261945, 0.0528113, 0.656698,  0.127345,  0.610039,\n          0.169131, 0.458647, 0.0988288, 0.966109,  0.0421747, 0.82035,\n          0.274711, 0.359377, 0.512113,  0.689682,  0.941571,  0.31961,\n          0.743826, 0.858147, 0.984766,  0.926973,  0.579597,  0.444104,\n          0.505969, 0.241437, 0.937999,  0.0957074, 0.773611,  0.46023,\n          0.469379, 0.363789, 0.269745,  0.486136,  0.894215,  0.794299,\n          0.724615, 0.261945, 0.0528113, 0.656698,  0.127345,  0.610039,\n          0.169131, 0.458647, 0.0988288, 0.966109,  0.0421747, 0.82035,\n          0.274711, 0.359377, 0.512113,  0.689682,  0.941571,  0.31961,\n          0.743826, 0.858147, 0.984766,  0.926973\n        ],\n        [1, 5, 5, inDepth]);\n    const w = tf.tensor4d(\n        [\n          0.6511372, 0.8699447, 0.6511372, 0.8699447, 0.267792,  0.9981787,\n          0.267792,  0.9981787, 0.4913572, 0.3321196, 0.4913572, 0.3321196,\n          0.5286497, 0.4241803, 0.5286497, 0.4241803, 0.0175446, 0.8365464,\n          0.0175446, 0.8365464, 0.1768399, 0.2874831, 0.1768399, 0.2874831,\n          0.0933998, 0.5764548, 0.0933998, 0.5764548, 0.0661623, 0.8850273,\n          0.0661623, 0.8850273, 0.8700929, 0.205422,  0.8700929, 0.205422\n        ],\n        [fSize, fSize, inDepth, chMul],\n    );\n\n    const result = tf.depthwiseConv2d(x, w, stride, pad);\n    expect(result.shape).toEqual([1, 5, 5, 4]);\n    const expected = [\n      0.29389750957489014, 1.055132269859314,   0.8355544209480286,\n      0.7652503848075867,  1.116986632347107,   1.7007107734680176,\n      0.7228718996047974,  1.2455471754074097,  0.7690584063529968,\n      1.4749835729599,     1.1460752487182617,  1.5098011493682861,\n      0.7502411007881165,  2.056602716445923,   1.0519171953201294,\n      1.012758731842041,   0.37667199969291687, 1.6647151708602905,\n      0.4798099994659424,  0.532977283000946,   0.4293096363544464,\n      1.8309053182601929,  0.7433272004127502,  1.1491419076919556,\n      1.3050479888916016,  2.7769954204559326,  1.6411027908325195,\n      2.1799824237823486,  1.0364032983779907,  2.7503039836883545,\n      1.7060394287109375,  2.880652904510498,   1.8967751264572144,\n      3.3914175033569336,  1.734355092048645,   2.076633930206299,\n      0.7774094939231873,  3.1432321071624756,  0.9456352591514587,\n      1.0863502025604248,  0.8477171659469604,  2.5510711669921875,\n      1.169355869293213,   2.0218098163604736,  2.23183274269104,\n      3.257829189300537,   1.939490556716919,   2.96195650100708,\n      1.0946838855743408,  2.4252827167510986,  1.329919695854187,\n      3.0390005111694336,  1.8967963457107544,  2.775693416595459,\n      1.5250799655914307,  2.4470155239105225,  0.40530526638031006,\n      2.775503158569336,   0.8836789727210999,  1.1361782550811768,\n      0.4407186806201935,  2.3912413120269775,  0.38215696811676025,\n      2.047299861907959,   1.080580234527588,   3.09224534034729,\n      1.2943278551101685,  3.1656715869903564,  0.9704407453536987,\n      2.8066811561584473,  1.419780969619751,   3.1822099685668945,\n      1.720312237739563,   3.279745578765869,   2.0871992111206055,\n      2.6629819869995117,  0.5254714488983154,  3.3779194355010986,\n      0.73943030834198,    2.0616414546966553,  0.5148154497146606,\n      1.6852912902832031,  0.5320349931716919,  1.7935365438461304,\n      1.1387810707092285,  2.119696617126465,   1.2744661569595337,\n      2.3705403804779053,  1.0399315357208252,  1.6817822456359863,\n      0.8927359580993652,  1.6332063674926758,  1.3386595249176025,\n      1.8818190097808838,  1.267898440361023,   1.6589205265045166,\n      0.8288722038269043,  2.119757890701294,   0.8847255706787109,\n      1.5954076051712036\n    ];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('input=1x5x5x4,f=5,s=2,d=1,p=same,chMul=1', async () => {\n    const fSize = 5;\n    const pad = 'same';\n    const stride = 2;\n    const chMul = 1;\n    const inDepth = 4;\n\n    const x = tf.tensor4d(\n        [\n          0.149194, 0.089009, 0.654891,  0.083324,  0.537043,  0.644331,\n          0.563037, 0.211859, 0.633501,  0.186427,  0.777034,  0.50001,\n          0.607341, 0.95303,  0.696479,  0.050387,  0.62045,   0.728049,\n          0.028043, 0.437009, 0.712881,  0.741935,  0.974474,  0.621102,\n          0.171411, 0.675707, 0.758567,  0.413529,  0.963967,  0.217291,\n          0.101335, 0.804231, 0.329673,  0.924503,  0.728742,  0.180217,\n          0.210459, 0.133869, 0.650827,  0.047613,  0.554795,  0.653365,\n          0.442196, 0.261945, 0.0528113, 0.656698,  0.127345,  0.610039,\n          0.169131, 0.458647, 0.0988288, 0.966109,  0.0421747, 0.82035,\n          0.274711, 0.359377, 0.512113,  0.689682,  0.941571,  0.31961,\n          0.743826, 0.858147, 0.984766,  0.926973,  0.579597,  0.444104,\n          0.505969, 0.241437, 0.937999,  0.0957074, 0.773611,  0.46023,\n          0.469379, 0.363789, 0.269745,  0.486136,  0.894215,  0.794299,\n          0.724615, 0.261945, 0.0528113, 0.656698,  0.127345,  0.610039,\n          0.169131, 0.458647, 0.0988288, 0.966109,  0.0421747, 0.82035,\n          0.274711, 0.359377, 0.512113,  0.689682,  0.941571,  0.31961,\n          0.743826, 0.858147, 0.984766,  0.926973\n        ],\n        [1, 5, 5, inDepth]);\n    const w = tf.tensor4d(\n        [\n          0.6511372, 0.8699447, 0.6511372, 0.8699447, 0.267792,  0.9981787,\n          0.267792,  0.9981787, 0.4913572, 0.3321196, 0.4913572, 0.3321196,\n          0.5286497, 0.4241803, 0.5286497, 0.4241803, 0.0175446, 0.8365464,\n          0.0175446, 0.8365464, 0.1768399, 0.2874831, 0.1768399, 0.2874831,\n          0.0933998, 0.5764548, 0.0933998, 0.5764548, 0.0661623, 0.8850273,\n          0.0661623, 0.8850273, 0.8700929, 0.205422,  0.8700929, 0.205422,\n          0.149194,  0.089009,  0.654891,  0.083324,  0.537043,  0.644331,\n          0.563037,  0.211859,  0.633501,  0.186427,  0.777034,  0.50001,\n          0.607341,  0.95303,   0.696479,  0.050387,  0.62045,   0.728049,\n          0.028043,  0.437009,  0.712881,  0.741935,  0.974474,  0.621102,\n          0.171411,  0.125386,  0.975199,  0.640437,  0.281895,  0.990968,\n          0.347208,  0.889702,  0.180695,  0.691992,  0.347154,  0.386692,\n          0.327191,  0.483784,  0.591807,  0.24263,   0.95182,   0.174353,\n          0.592136,  0.623469,  0.988244,  0.660731,  0.946534,  0.0801365,\n          0.864889,  0.874602,  0.240347,  0.906352,  0.478657,  0.825918,\n          0.380769,  0.184705,  0.238241,  0.201907,  0.294087,  0.181165,\n          0.191303,  0.7225,    0.430064,  0.900622\n        ],\n        [fSize, fSize, inDepth, chMul],\n    );\n\n    const result = tf.depthwiseConv2d(x, w, stride, pad);\n    expect(result.shape).toEqual([1, 3, 3, 4]);\n    const expected = [\n      2.2883458137512207, 2.5740344524383545, 2.3246560096740723,\n      2.27826189994812,   3.0600292682647705, 5.021538734436035,\n      4.432307720184326,  2.6976213455200195, 1.8467353582382202,\n      3.617821216583252,  2.0940940380096436, 1.3091316223144531,\n      2.4892354011535645, 4.767732620239258,  3.126866579055786,\n      3.4326541423797607, 4.181705474853516,  8.082467079162598,\n      6.922453880310059,  5.922790050506592,  2.819075345993042,\n      5.9510369300842285, 3.7211103439331055, 2.7263708114624023,\n      1.164026141166687,  3.3068809509277344, 1.6575196981430054,\n      2.738445997238159,  2.288442850112915,  5.463253021240234,\n      2.840029239654541,  3.8579823970794678, 1.440760612487793,\n      3.862100839614868,  2.3826799392700195, 2.2323575019836426\n    ];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('input=1x5x5x1,f=3,s=1,d=2,p=explicit,chMul=1', async () => {\n    const fSize = 3;\n    const pad =\n        [[0, 0], [0, 0], [0, 1], [0, 1]] as tf.backend_util.ExplicitPadding;\n    const stride = 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, 0.889702,\n          0.180695, 0.691992\n        ],\n        [fSize, fSize, inDepth, chMul],\n    );\n\n    const result = tf.depthwiseConv2d(x, w, stride, pad);\n    expect(result.shape).toEqual([1, 3, 4, 1]);\n    const expected = [\n      2.540022, 2.505885, 2.454062, 1.871258, 2.35170, 2.459601, 3.076421,\n      1.32399, 3.298480, 3.437421, 2.93419, 1.823238\n    ];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('input=1x3x3x4,f=3,s=1,d=2,p=same,chMul=1', async () => {\n    const fSize = 3;\n    const pad = 'same';\n    const stride = 1;\n    const chMul = 1;\n    const inDepth = 4;\n    const dilation = 2;\n\n    const x = tf.tensor4d(\n        [\n          0.5227615, 0.3477598, 0.5227615, 0.3477598, 0.4690094, 0.408161,\n          0.4690094, 0.408161,  0.3239015, 0.2372907, 0.3239015, 0.2372907,\n          0.6136674, 0.7918105, 0.6136674, 0.7918105, 0.9145211, 0.218611,\n          0.9145211, 0.218611,  0.3778793, 0.2392365, 0.3778793, 0.2392365,\n          0.2340134, 0.1251984, 0.2340134, 0.1251984, 0.6222534, 0.1327361,\n          0.6222534, 0.1327361, 0.7697753, 0.1216059, 0.7697753, 0.1216059\n        ],\n        [1, 3, 3, inDepth]);\n    const w = tf.tensor4d(\n        [\n          0.6511372, 0.8699447, 0.6511372, 0.8699447, 0.267792,  0.9981787,\n          0.267792,  0.9981787, 0.4913572, 0.3321196, 0.4913572, 0.3321196,\n          0.5286497, 0.4241803, 0.5286497, 0.4241803, 0.0175446, 0.8365464,\n          0.0175446, 0.8365464, 0.1768399, 0.2874831, 0.1768399, 0.2874831,\n          0.0933998, 0.5764548, 0.0933998, 0.5764548, 0.0661623, 0.8850273,\n          0.0661623, 0.8850273, 0.8700929, 0.205422,  0.8700929, 0.205422\n        ],\n        [fSize, fSize, inDepth, chMul],\n    );\n\n    const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);\n    expect(result.shape).toEqual([1, 3, 3, 4]);\n    const expected = [\n      0.7517092227935791,  0.4949187934398651,  0.7517092227935791,\n      0.4949187934398651,  0.04939830303192139, 0.4589206874370575,\n      0.04939830303192139, 0.4589206874370575,  0.3548273742198944,\n      0.5258132815361023,  0.3548273742198944,  0.5258132815361023,\n      0.0775906890630722,  0.7311626672744751,  0.0775906890630722,\n      0.7311626672744751,  0.01604490540921688, 0.1828782558441162,\n      0.01604490540921688, 0.1828782558441162,  0.3310448229312897,\n      0.5360028743743896,  0.3310448229312897,  0.5360028743743896,\n      0.4393753409385681,  0.565629243850708,   0.4393753409385681,\n      0.565629243850708,   0.13651414215564728, 0.5184575319290161,\n      0.13651414215564728, 0.5184575319290161,  0.5643441677093506,\n      0.6942259669303894,  0.5643441677093506,  0.6942259669303894\n    ];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('input=1x3x3x2,f=2,s=1,p=same,chMul=2', async () => {\n    const fSize = 2;\n    const pad = 'same';\n    const stride = 1;\n    const chMul = 2;\n    const inDepth = 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, 0.653365, 0.442196\n        ],\n        [1, 3, 3, inDepth]);\n    const w = tf.tensor4d(\n        [\n          0.347154, 0.386692, 0.327191, 0.483784, 0.591807, 0.24263, 0.95182,\n          0.174353, 0.592136, 0.623469, 0.988244, 0.660731, 0.946534, 0.0801365,\n          0.864889, 0.874602\n        ],\n        [fSize, fSize, inDepth, chMul]);\n    const result = tf.depthwiseConv2d(x, w, stride, pad);\n    expect(result.shape).toEqual([1, 3, 3, 4]);\n\n    const expected = [\n      1.83059,   0.937125,  2.1218,   1.39024,  0.990167, 0.803472,\n      1.31405,   1.14959,   0.182147, 0.196385, 0.241141, 0.188081,\n      0.950656,  0.622581,  1.92451,  1.20179,  1.07422,  0.483268,\n      1.36948,   1.14256,   0.449444, 0.477042, 0.505857, 0.393989,\n      0.0746509, 0.0633184, 0.74101,  0.41159,  0.403195, 0.176938,\n      0.602415,  0.345499,  0.226819, 0.252651, 0.144682, 0.213927\n    ];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('input=2x3x3x2,f=2,s=1,p=same,chMul=2', async () => {\n    const fSize = 2;\n    const pad = 'same';\n    const stride = 1;\n    const chMul = 2;\n    const inDepth = 2;\n\n    const x = tf.tensor4d(\n        [\n          0.261945, 0.0528113, 0.656698,  0.127345,  0.610039, 0.169131,\n          0.458647, 0.0988288, 0.966109,  0.0421747, 0.82035,  0.274711,\n          0.359377, 0.512113,  0.689682,  0.941571,  0.31961,  0.743826,\n          0.858147, 0.984766,  0.926973,  0.579597,  0.444104, 0.505969,\n          0.241437, 0.937999,  0.0957074, 0.773611,  0.46023,  0.469379,\n          0.363789, 0.269745,  0.486136,  0.894215,  0.794299, 0.724615\n        ],\n        [2, 3, 3, inDepth]);\n    const w = tf.tensor4d(\n        [\n          0.240347, 0.906352, 0.478657, 0.825918, 0.380769, 0.184705, 0.238241,\n          0.201907, 0.294087, 0.181165, 0.191303, 0.7225, 0.430064, 0.900622,\n          0.670338, 0.33478\n        ],\n        [fSize, fSize, inDepth, chMul]);\n    const result = tf.depthwiseConv2d(x, w, stride, pad);\n    expect(result.shape).toEqual([2, 3, 3, 4]);\n\n    const expected = [\n      0.863379, 1.3119,   0.102795, 0.154853, 1.02704,   1.62173,  0.293466,\n      0.261764, 0.387876, 0.701529, 0.133508, 0.338167,  0.880395, 1.28039,\n      0.786492, 0.775361, 0.884845, 1.43995,  0.764374,  1.0196,   0.291162,\n      0.801428, 0.273788, 0.764303, 0.348985, 0.45311,   0.469447, 0.613073,\n      0.287461, 0.684128, 0.627899, 0.927844, 0.0768174, 0.28968,  0.356037,\n      0.614339, 0.67138,  1.07894,  1.30747,  1.86705,   0.617971, 1.35402,\n      0.860607, 1.29693,  0.242087, 0.485892, 0.331979,  0.757015, 0.410527,\n      0.740235, 1.28431,  1.42516,  0.68281,  0.975185,  1.13892,  1.62237,\n      0.344208, 0.561029, 0.363292, 0.911203, 0.272541,  0.419513, 0.342154,\n      0.403335, 0.419286, 0.587321, 0.600655, 0.884853,  0.190907, 0.719914,\n      0.346842, 0.598472\n    ];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('input=2x3x3x2,f=2,s=1,d=2,p=same,chMul=2',\n     async () => {\n       const fSize = 2;\n       const pad = 'same';\n       const stride = 1;\n       const inDepth = 2;\n       const dilation = 2;\n       const noDilation = 1;\n\n       const x = tf.tensor4d(\n           [\n             0.261945, 0.0528113, 0.656698,  0.127345,  0.610039, 0.169131,\n             0.458647, 0.0988288, 0.966109,  0.0421747, 0.82035,  0.274711,\n             0.359377, 0.512113,  0.689682,  0.941571,  0.31961,  0.743826,\n             0.858147, 0.984766,  0.926973,  0.579597,  0.444104, 0.505969,\n             0.241437, 0.937999,  0.0957074, 0.773611,  0.46023,  0.469379,\n             0.363789, 0.269745,  0.486136,  0.894215,  0.794299, 0.724615\n           ],\n           [2, 3, 3, inDepth]);\n\n       const w = tf.stack(\n                     [\n                       tf.stack(\n                           [\n                             tf.tensor2d(\n                                 [0.240347, 0.906352, 0.478657, 0.825918],\n                                 [fSize, fSize]),\n                             tf.tensor2d(\n                                 [0.380769, 0.184705, 0.238241, 0.201907],\n                                 [fSize, fSize])\n                           ],\n                           2),\n                       tf.stack(\n                           [\n                             tf.tensor2d(\n                                 [0.294087, 0.181165, 0.191303, 0.7225],\n                                 [fSize, fSize]),\n                             tf.tensor2d(\n                                 [0.430064, 0.900622, 0.670338, 0.33478],\n                                 [fSize, fSize])\n                           ],\n                           2)\n                     ],\n                     3) as tf.Tensor4D;\n\n       const fSizeDilated = fSize + (fSize - 1) * (dilation - 1);\n       const wDilated = tf.stack([\n      tf.stack(\n          [\n            tf.tensor2d(\n              [0.240347, 0, 0.906352, 0, 0, 0, 0.478657, 0, 0.825918],\n              [fSizeDilated, fSizeDilated]),\n            tf.tensor2d(\n              [0.380769, 0, 0.184705, 0, 0, 0, 0.238241, 0, 0.201907],\n              [fSizeDilated, fSizeDilated])\n          ],\n          2),\n      tf.stack(\n          [\n            tf.tensor2d([0.294087, 0, 0.181165, 0, 0, 0, 0.191303, 0, 0.7225],\n              [fSizeDilated, fSizeDilated]),\n            tf.tensor2d(\n              [0.430064, 0, 0.900622, 0, 0, 0, 0.670338, 0, 0.33478],\n              [fSizeDilated, fSizeDilated])\n          ],\n          2)\n    ], 3) as tf.Tensor4D;\n\n       const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);\n\n       const expectedResult =\n           tf.depthwiseConv2d(x, wDilated, stride, pad, 'NHWC', noDilation);\n\n       expect(result.shape).toEqual(expectedResult.shape);\n       expectArraysClose(await result.data(), await expectedResult.data());\n     });\n\n  it('input=2x3x3x2,f=3,s=1,d=2,p=same,chMul=2', async () => {\n    const fSize = 3;\n    const pad = 'same';\n    const stride = 1;\n    const inDepth = 2;\n    const dilation = 2;\n\n    const x = tf.tensor4d(\n        [[\n          [\n            [0.52276146, 0.34775984], [0.4690094, 0.40816104],\n            [0.32390153, 0.23729074], [0.61366737, 0.7918105],\n            [0.9145211, 0.218611], [0.37787926, 0.23923647],\n            [0.23401344, 0.12519836]\n          ],\n\n          [\n            [0.6222534, 0.13273609], [0.7697753, 0.12160587],\n            [0.0448128, 0.94806635], [0.4199953, 0.7140714],\n            [0.01420832, 0.47453713], [0.02061439, 0.37226152],\n            [0.62741446, 0.23167181]\n          ],\n\n          [\n            [0.7257557, 0.14352751], [0.3011638, 0.3869065],\n            [0.09286129, 0.25151742], [0.7566397, 0.13099921],\n            [0.65324724, 0.38959372], [0.65826, 0.7505318],\n            [0.35919082, 0.85470796]\n          ],\n\n          [\n            [0.24827361, 0.2826661], [0.24717247, 0.27446854],\n            [0.27112448, 0.68068564], [0.11082292, 0.7948675],\n            [0.41535318, 0.659986], [0.22165525, 0.18149579],\n            [0.42273378, 0.9558281]\n          ],\n\n          [\n            [0.943074, 0.6799041], [0.78851473, 0.07249606],\n            [0.771909, 0.7925967], [0.9551083, 0.03087568],\n            [0.82589805, 0.94797385], [0.5895462, 0.5045923],\n            [0.9667754, 0.24292922]\n          ],\n\n          [\n            [0.67123663, 0.109761], [0.04002762, 0.51942277],\n            [0.37868536, 0.8467603], [0.77171385, 0.51604605],\n            [0.8192849, 0.38843668], [0.19607484, 0.5591624],\n            [0.45990825, 0.35768318]\n          ],\n\n          [\n            [0.67443585, 0.6256168], [0.9373623, 0.6498393],\n            [0.7623085, 0.13218105], [0.9349631, 0.7660191],\n            [0.50054944, 0.7738123], [0.30201948, 0.525643],\n            [0.30896342, 0.21111596]\n          ]\n        ]],\n        [1, 7, 7, inDepth]);\n\n    const w = tf.tensor4d(\n        [\n          [\n            [[0.65113723], [0.8699447]],\n\n            [[0.267792], [0.9981787]],\n\n            [[0.4913572], [0.33211958]]\n          ],\n          [\n            [[0.5286497], [0.42418027]],\n\n            [[0.01754463], [0.8365464]],\n\n            [[0.17683995], [0.2874831]]\n          ],\n          [\n            [[0.09339976], [0.57645476]],\n\n            [[0.06616235], [0.8850273]],\n\n            [[0.87009287], [0.20542204]]\n          ]\n        ],\n        [fSize, fSize, inDepth, 1],\n    );\n    const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);\n\n    expect(result.shape).toEqual([1, 7, 7, 2]);\n    expectArraysClose(await result.data(), [\n      0.19526604, 0.5378273, 0.795022,   0.9384107,  1.0860794,  0.7942326,\n      0.9764694,  1.3974442, 0.5930813,  0.9848901,  0.44526684, 1.275759,\n      0.572345,   1.1784878, 0.27117175, 0.773588,   0.20055711, 0.71320784,\n      0.73477566, 1.8867722, 0.64123434, 1.6549369,  0.55551285, 2.0385633,\n      0.24740812, 1.233143,  0.08528192, 1.6214795,  1.062326,   1.3828603,\n      1.4494176,  1.1022222, 2.2350664,  2.283423,   1.5940895,  1.8871424,\n      1.6627852,  2.4903212, 1.0405337,  2.0754304,  1.1508893,  1.9568737,\n      0.6148571,  1.1505995, 1.1105528,  1.3823687,  1.4342139,  2.9909487,\n      1.0210396,  2.6467443, 1.0563798,  3.3963797,  0.42652097, 2.274134,\n      0.51121074, 2.264094,  1.1009313,  1.6042703,  1.510688,   1.2317145,\n      2.025515,   2.3658662, 1.6722159,  2.0787857,  1.3785586,  2.895031,\n      1.2915218,  2.2051222, 1.0423074,  2.4303207,  0.27844793, 0.84346974,\n      0.25781655, 1.1208354, 0.9447272,  2.0111258,  0.3689065,  1.9052455,\n      0.79137695, 2.355344,  0.5429248,  1.5593178,  0.8248403,  1.9922242,\n      0.77847,    1.5032601, 0.8622418,  0.84645665, 1.6850245,  2.2958806,\n      1.6242284,  1.329045,  1.6652328,  2.480535,   1.2793491,  1.2951884,\n      1.0667037,  1.5720158\n    ]);\n  });\n\n  it('input=1x8x8x2,f=3,s=1,d=3,p=valid,chMul=1', async () => {\n    const fSize = 3;\n    const pad = 'valid';\n    const stride = 1;\n    const inDepth = 2;\n    const dilation = 3;\n\n    const x = tf.tensor4d(\n        [\n          0.09941668063402176,  0.05248984694480896, 0.4567521810531616,\n          0.8002573847770691,   0.810535192489624,   0.7010623216629028,\n          0.5898630023002625,   0.05883334204554558, 0.2314797043800354,\n          0.45427876710891724,  0.10960108041763306, 0.9710874557495117,\n          0.18139968812465668,  0.8959258794784546,  0.35156702995300293,\n          0.6495933532714844,   0.5185067653656006,  0.3260101079940796,\n          0.7837356925010681,   0.9170011281967163,  0.465780109167099,\n          0.0857422724366188,   0.38354963064193726, 0.8134718537330627,\n          0.8768209218978882,   0.38151195645332336, 0.5045309066772461,\n          0.8152258396148682,   0.2782581150531769,  0.545160174369812,\n          0.1587309092283249,   0.5507456064224243,  0.2704062759876251,\n          0.7736618518829346,   0.9871141314506531,  0.29300180077552795,\n          0.3038032352924347,   0.36257433891296387, 0.967268168926239,\n          0.7251133918762207,   0.6244085431098938,  0.8398842215538025,\n          0.42696574330329895,  0.25569799542427063, 0.5784937143325806,\n          0.22755105793476105,  0.8869972229003906,  0.05128923058509827,\n          0.6748542785644531,   0.97468101978302,    0.5549167394638062,\n          0.5639380812644958,   0.821204662322998,   0.5207878947257996,\n          0.8831672668457031,   0.6721863746643066,  0.23375047743320465,\n          0.040671784430742264, 0.24522553384304047, 0.6293181777000427,\n          0.6886807680130005,   0.29527169466018677, 0.48199158906936646,\n          0.5751473307609558,   0.817806601524353,   0.38846832513809204,\n          0.5553714036941528,   0.1839468777179718,  0.5287416577339172,\n          0.4813096523284912,   0.477756530046463,   0.641162633895874,\n          0.03040425479412079,  0.20608118176460266, 0.7930338978767395,\n          0.727353572845459,    0.42868077754974365, 0.6136374473571777,\n          0.06312728673219681,  0.4346885681152344,  0.004786544945091009,\n          0.4951920807361603,   0.588252604007721,   0.724294126033783,\n          0.07830118387937546,  0.07353833317756653, 0.7818689346313477,\n          0.8137099742889404,   0.6505773067474365,  0.5716961026191711,\n          0.5416423678398132,   0.855529248714447,   0.8958709239959717,\n          0.3598312437534332,   0.31329575181007385, 0.5971285104751587,\n          0.034069616347551346, 0.6229354739189148,  0.24074052274227142,\n          0.3356363773345947,   0.1049640029668808,  0.2543765604496002,\n          0.1635538637638092,   0.8082090616226196,  0.9097364544868469,\n          0.6435819268226624,   0.6100808382034302,  0.29750677943229675,\n          0.0738643929362297,   0.8887753486633301,  0.7692861557006836,\n          0.6412256360054016,   0.16205888986587524, 0.9414404034614563,\n          0.5698712468147278,   0.6834514737129211,  0.41202589869499207,\n          0.9096908569335938,   0.8094117045402527,  0.42103442549705505,\n          0.8905773162841797,   0.069722980260849,   0.014392468146979809,\n          0.22018849849700928,  0.30076053738594055, 0.8472294211387634,\n          0.852762758731842,    0.5004454851150513\n        ],\n        [1, 8, 8, inDepth]);\n\n    const w = tf.tensor4d(\n        [\n          0.5785998106002808, 0.7439202666282654, 0.2178175300359726,\n          0.8782838582992554, 0.6579487919807434, 0.6556791067123413,\n          0.7341834306716919, 0.3332836329936981, 0.037182893604040146,\n          0.7394348382949829, 0.04031887650489807, 0.19104436039924622,\n          0.7014378309249878, 0.5309979319572449, 0.8485966920852661,\n          0.6609954237937927, 0.021728534251451492, 0.9289031624794006\n        ],\n        [fSize, fSize, inDepth, 1],\n    );\n    const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);\n\n    expect(result.shape).toEqual([1, 2, 2, 2]);\n    expectArraysClose(await result.data(), [\n      1.0257229804992676, 3.247040033340454, 1.9391249418258667,\n      2.9474055767059326, 2.0091731548309326, 3.600433826446533,\n      2.334312677383423, 2.548961877822876\n    ]);\n  });\n\n  it('Tensor3D is allowed', async () => {\n    const fSize = 2;\n    const pad = 'same';\n    const stride = 1;\n    const chMul = 3;\n    const inDepth = 2;\n\n    const x = tf.zeros<Rank.R3>([3, 3, inDepth]);\n    const w = tf.zeros<Rank.R4>([fSize, fSize, inDepth, chMul]);\n    const result = tf.depthwiseConv2d(x, w, stride, pad);\n    expect(result.shape).toEqual([3, 3, inDepth * chMul]);\n  });\n\n  it('Pass null for dilations, which defaults to [1, 1]', () => {\n    const fSize = 2;\n    const pad = 'same';\n    const stride = 1;\n    const chMul = 3;\n    const inDepth = 2;\n    const dilations: [number, number] = null;\n\n    const x = tf.zeros<Rank.R3>([3, 3, inDepth]);\n    const w = tf.zeros<Rank.R4>([fSize, fSize, inDepth, chMul]);\n    const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilations);\n    expect(result.shape).toEqual([3, 3, inDepth * chMul]);\n  });\n\n  it('TensorLike', async () => {\n    const pad = 'valid';\n    const stride = 1;\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 w = [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];\n\n    const result = tf.depthwiseConv2d(x, w, stride, pad);\n\n    const expected = [1.07022, 1.03167, 0.67041, 0.778863];\n    expectArraysClose(await result.data(), expected);\n  });\n  it('TensorLike Chained', async () => {\n    const pad = 'valid';\n    const stride = 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 = [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];\n\n    const result = x.depthwiseConv2d(w, stride, pad);\n    expect(result.shape).toEqual([1, 2, 2, 1]);\n\n    const expected = [1.07022, 1.03167, 0.67041, 0.778863];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('throws when passed x as a non-tensor', () => {\n    const inputDepth = 1;\n    const outputDepth = 1;\n    const fSize = 1;\n    const pad = 'same';\n    const stride = 2;\n    const dataFormat = 'NHWC';\n    const dilation = 2;\n\n    const w = tf.tensor4d([3], [fSize, fSize, inputDepth, outputDepth]);\n\n    const e = /Argument 'x' passed to 'depthwiseConv2d' must be a Tensor/;\n    expect(\n        () => tf.depthwiseConv2d(\n            {} as tf.Tensor3D, w, stride, pad, dataFormat, dilation))\n        .toThrowError(e);\n  });\n\n  it('throws when passed filter as a non-tensor', () => {\n    const inputDepth = 1;\n    const inputShape: [number, number, number] = [2, 2, inputDepth];\n    const pad = 'same';\n    const stride = 2;\n    const dataFormat = 'NHWC';\n    const dilation = 2;\n\n    const x = tf.tensor3d([1, 2, 3, 4], inputShape);\n\n    const e = /Argument 'filter' passed to 'depthwiseConv2d' must be a Tensor/;\n    expect(\n        () => tf.depthwiseConv2d(\n            x, {} as tf.Tensor4D, stride, pad, dataFormat, dilation))\n        .toThrowError(e);\n  });\n\n  it('throws when input is int32', async () => {\n    const fSize = 2;\n    const pad = 'valid';\n    const stride = 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    const errRegex = /Argument 'x' passed to 'depthwiseConv2d' must be float32/;\n    expect(() => tf.depthwiseConv2d(x, w, stride, pad)).toThrowError(errRegex);\n  });\n\n  it('throws when filter is int32', async () => {\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([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    const errRegex =\n        /Argument 'filter' passed to 'depthwiseConv2d' must be float32/;\n    expect(() => tf.depthwiseConv2d(x, w, stride, pad)).toThrowError(errRegex);\n  });\n\n  it('throws when dimRoundingMode is set and pad is same', () => {\n    const fSize = 2;\n    const pad = 'same';\n    const stride = 1;\n    const chMul = 1;\n    const inDepth = 1;\n    const dimRoundingMode = 'round';\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    expect(\n        () => tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', 1, dimRoundingMode))\n        .toThrowError();\n  });\n\n  it('throws when dimRoundingMode is set and pad is valid', () => {\n    const fSize = 2;\n    const pad = 'valid';\n    const stride = 1;\n    const chMul = 1;\n    const inDepth = 1;\n    const dimRoundingMode = 'round';\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    expect(\n        () => tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', 1, dimRoundingMode))\n        .toThrowError();\n  });\n\n  it('throws when dimRoundingMode is set and pad is a non-integer number',\n     () => {\n       const fSize = 2;\n       const pad = 1.2;\n       const stride = 1;\n       const chMul = 1;\n       const inDepth = 1;\n       const dimRoundingMode = 'round';\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       expect(\n           () => tf.depthwiseConv2d(\n               x, w, stride, pad, 'NHWC', 1, dimRoundingMode))\n           .toThrowError();\n     });\n\n  it('throws when dimRoundingMode is set and pad is explicit by non-integer ' +\n         'number',\n     () => {\n       const fSize = 2;\n       const pad = [[0, 0], [0, 2.1], [1, 1], [0, 0]] as\n           tf.backend_util.ExplicitPadding;\n       const stride = 1;\n       const chMul = 1;\n       const inDepth = 1;\n       const dimRoundingMode = 'round';\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       expect(\n           () => tf.depthwiseConv2d(\n               x, w, stride, pad, 'NHWC', 1, dimRoundingMode))\n           .toThrowError();\n     });\n\n  it('accepts a tensor-like object', async () => {\n    const pad = 'valid';\n    const stride = 1;\n    // 1x3x3x1\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 w = [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];\n    const result = tf.depthwiseConv2d(x, w, stride, pad);\n    expect(result.shape).toEqual([1, 2, 2, 1]);\n\n    const expected = [1.07022, 1.03167, 0.67041, 0.778863];\n    expectArraysClose(await result.data(), expected);\n  });\n});\n\ndescribeWithFlags('depthwiseConv2d gradients', ALL_ENVS, () => {\n  let images: tf.Tensor4D;\n  let filter: tf.Tensor4D;\n  let result: tf.Tensor4D;\n  const stride = 1;\n  const pad = 'same';\n\n  beforeEach(() => {\n    // two 2x2 RGB images => 2x2x2x3\n    images = tf.tensor4d([\n      [[[2, 3, 1], [3, 0, 2]], [[0, 4, 1], [3, 1, 3]]],\n      [[[2, 1, 0], [0, 3, 3]], [[4, 0, 1], [1, 4, 1]]]\n    ]);\n    // 2x2 filters, chMul = 2 => 2x2x3x2\n    filter = tf.tensor4d([\n      [[[1, 1], [1, 1], [0, 0]], [[0, 1], [1, 1], [1, 1]]],\n      [[[1, 0], [1, 1], [0, 0]], [[0, 1], [1, 0], [0, 0]]]\n    ]);\n    // result of convolution operatoin\n    result = tf.tensor4d([\n      [\n        [[2, 8, 8, 7, 2, 2], [6, 3, 1, 1, 0, 0]],\n        [[0, 3, 5, 5, 3, 3], [3, 3, 1, 1, 0, 0]]\n      ],\n      [\n        [[6, 3, 8, 4, 3, 3], [1, 0, 7, 7, 0, 0]],\n        [[4, 5, 4, 4, 1, 1], [1, 1, 4, 4, 0, 0]]\n      ]\n    ]);\n  });\n\n  it('wrt input', async () => {\n    const {value, grad} = tf.valueAndGrad(\n        (x: tf.Tensor4D) => tf.depthwiseConv2d(x, filter, stride, pad))(images);\n\n    expectArraysClose(await value.data(), await result.data());\n\n    const expectedGrad = tf.tensor4d([\n      [[[2., 2., 0.], [3., 4., 2.]], [[3., 4., 0.], [5., 7., 2.]]],\n      [[[2., 2., 0.], [3., 4., 2.]], [[3., 4., 0.], [5., 7., 2.]]]\n    ]);\n\n    expectArraysClose(await grad.data(), await expectedGrad.data());\n  });\n\n  // The gradients of normal and depthwise 2D convolutions are actually the same\n  // in the special case that dy = 1, so we also test the gradient of a function\n  // of the output to disambiguate the two methods.\n  it('wrt input, squared output', async () => {\n    const grad = tf.grad(\n        (x: tf.Tensor4D) =>\n            tf.square(tf.depthwiseConv2d(x, filter, stride, pad)))(images);\n\n    const expectedGrad = tf.tensor4d([\n      [[[20., 30., 0.], [34., 34., 8.]], [[10., 50., 0.], [46., 44., 12.]]],\n      [[[18., 24., 0.], [8., 52., 12.]], [[30., 40., 0.], [22., 76., 4.]]]\n    ]);\n\n    expectArraysClose(await grad.data(), await expectedGrad.data());\n  });\n\n  it('wrt filter', async () => {\n    const {value, grad} = tf.valueAndGrad(\n        (f: tf.Tensor4D) => tf.depthwiseConv2d(images, f, stride, pad))(filter);\n\n    expectArraysClose(await value.data(), await result.data());\n\n    const expectedGrad = tf.tensor4d([\n      [[[15., 15.], [16., 16.], [12., 12.]], [[7., 7.], [8., 8.], [9., 9.]]],\n      [[[8., 8.], [9., 9.], [6., 6.]], [[4., 4.], [5., 5.], [4., 4.]]]\n    ]);\n\n    expectArraysClose(await grad.data(), await expectedGrad.data());\n  });\n\n  it('gradient with clones', async () => {\n    const [dx, dFilter] = tf.grads(\n        (x: tf.Tensor4D, filter: tf.Tensor4D) =>\n            tf.depthwiseConv2d(x.clone(), filter.clone(), stride, pad).clone())(\n        [images, filter]);\n    expect(dx.shape).toEqual(images.shape);\n    expect(dFilter.shape).toEqual(filter.shape);\n  });\n\n  // Also disambiguate regular vs. depthwise filter gradients\n  it('wrt filter, squared output', async () => {\n    const grad = tf.grad(\n        (f: tf.Tensor4D) =>\n            tf.square(tf.depthwiseConv2d(images, f, stride, pad)))(filter);\n\n    const expectedGrad = tf.tensor4d([\n      [\n        [[120., 122.], [180., 166.], [12., 12.]],\n        [[20., 76.], [90., 66.], [46., 46.]]\n      ],\n      [\n        [[86., 42.], [122., 114.], [10., 10.]],\n        [[24., 54.], [80., 46.], [18., 18.]]\n      ]\n    ]);\n\n    expectArraysClose(await grad.data(), await expectedGrad.data());\n  });\n\n  it('throws error on dilations > 1', () => {\n    const grad = tf.grad(\n        (x: tf.Tensor4D) =>\n            tf.depthwiseConv2d(x, filter, stride, pad, 'NHWC', 2));\n\n    expect(() => grad(images))\n        .toThrowError(/dilation rates greater than 1 are not yet supported/);\n  });\n\n  it('wrt input, stride=2, pad=valid', async () => {\n    const dx = tf.grad(\n        (x: tf.Tensor4D) => tf.depthwiseConv2d(x, filter, 2, 'valid'))(images);\n\n    expectArraysClose(await dx.data(), [\n      2., 2., 0., 1., 2., 2., 1., 2., 0., 1., 1., 0.,\n      2., 2., 0., 1., 2., 2., 1., 2., 0., 1., 1., 0.\n    ]);\n    expect(dx.shape).toEqual([2, 2, 2, 3]);\n  });\n\n  it('wrt filter, stride=2, pad=valid', async () => {\n    const df = tf.grad(\n        (f: tf.Tensor4D) => tf.depthwiseConv2d(images, f, 2, 'valid'))(filter);\n\n    expectArraysClose(await df.data(), [\n      4., 4., 4., 4., 1., 1., 3., 3., 3., 3., 5., 5.,\n      4., 4., 4., 4., 2., 2., 4., 4., 5., 5., 4., 4.\n    ]);\n    expect(df.shape).toEqual([2, 2, 3, 2]);\n  });\n\n  it('gradient with clones', async () => {\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(\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\n    const f = tf.tensor4d(\n        [0.303873, 0.229223, 0.144333, 0.803373],\n        [fSize, fSize, inDepth, chMul],\n    );\n\n    const [dx, df] = tf.grads(\n        (x: tf.Tensor4D, f: tf.Tensor4D) =>\n            tf.depthwiseConv2d(x.clone(), f.clone(), stride, pad).clone())(\n        [x, f]);\n\n    expectArraysClose(await dx.data(), [\n      0.303873, 0.533096, 0.229223, 0.448206, 1.480802, 1.032596, 0.144333,\n      0.947706, 0.803373\n    ]);\n    expect(dx.shape).toEqual([1, 3, 3, 1]);\n\n    expectArraysClose(\n        await df.data(), [2.525137, 2.6754108, 1.7905407, 2.380144]);\n    expect(df.shape).toEqual([2, 2, 1, 1]);\n  });\n});\n"]}
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