@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @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('conv2dTranspose', ALL_ENVS, () => {
it('input=2x2x1,d2=1,f=2,s=1,p=0', async () => {
const origInputDepth = 1;
const origOutputDepth = 1;
const inputShape = [1, 1, origOutputDepth];
const fSize = 2;
const origPad = 0;
const origStride = 1;
const x = tf.tensor3d([2], inputShape);
const w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);
const result = tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad);
const expected = [6, 2, 10, 0];
expect(result.shape).toEqual([2, 2, 1]);
expectArraysClose(await result.data(), expected);
});
it('input=3x3x1,d2=1,f=2,s=2,p=same', async () => {
const origInputDepth = 1;
const origOutputDepth = 4;
const inputShape = [1, 2, 2, origOutputDepth];
const fSize = 2;
const origPad = 'same';
const origStride = 2;
const x = tf.tensor4d([
1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34, 0.28,
0., 0.06, 0.14, 0.24
], inputShape);
const w = tf.tensor4d([0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.], [fSize, fSize, origInputDepth, origOutputDepth]);
const result = tf.conv2dTranspose(x, w, [1, 3, 3, 1], origStride, origPad);
const expected = [7.63, 28.39, 2.94, 49.15, 69.91, 14.62, 1.69, 5.01, 1.06];
expect(result.shape).toEqual([1, 3, 3, 1]);
expectArraysClose(await result.data(), expected);
});
it('input=3x3x1,d2=1,f=2,s=2,p=explicit', async () => {
const origInputDepth = 1;
const origOutputDepth = 4;
const inputShape = [1, 2, 2, origOutputDepth];
const fSize = 2;
const origPad = [[0, 0], [0, 1], [0, 1], [0, 0]];
const origStride = 2;
const x = tf.tensor4d([
1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34, 0.28,
0., 0.06, 0.14, 0.24
], inputShape);
const w = tf.tensor4d([0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.], [fSize, fSize, origInputDepth, origOutputDepth]);
const result = tf.conv2dTranspose(x, w, [1, 3, 3, 1], origStride, origPad);
const expected = [7.63, 28.39, 2.94, 49.15, 69.91, 14.62, 1.69, 5.01, 1.06];
expect(result.shape).toEqual([1, 3, 3, 1]);
expectArraysClose(await result.data(), expected);
});
it('input=2x2x1,d2=1,f=2,s=1,p=0, batch=2', async () => {
const origInputDepth = 1;
const origOutputDepth = 1;
const inputShape = [2, 1, 1, origOutputDepth];
const fSize = 2;
const origPad = 0;
const origStride = 1;
const x = tf.tensor4d([2, 3], inputShape);
const w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);
const result = tf.conv2dTranspose(x, w, [2, 2, 2, 1], origStride, origPad);
const expected = [6, 2, 10, 0, 9, 3, 15, 0];
expect(result.shape).toEqual([2, 2, 2, 1]);
expectArraysClose(await result.data(), expected);
});
it('input=2x2x2,output=3x3x2,f=2,s=2,inDepth=2,p=same', async () => {
const origInputDepth = 2;
const origOutputDepth = 2;
const inputShape = [1, 2, 2, origOutputDepth];
const fSize = 2;
const origPad = 'same';
const origStride = 2;
const x = tf.tensor4d([0., 1., 2., 3., 4., 5., 6., 7.], inputShape);
const w = tf.tensor4d([0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.], [fSize, fSize, origInputDepth, origOutputDepth]);
const result = tf.conv2dTranspose(x, w, [1, 3, 3, origInputDepth], origStride, origPad);
const expected = [1, 3, 5, 7, 3, 13, 9, 11, 13, 15, 43, 53, 5, 23, 41, 59, 7, 33.];
expect(result.shape).toEqual([1, 3, 3, origInputDepth]);
expectArraysClose(await result.data(), expected);
});
it('throws when dimRoundingMode is set and pad is same', async () => {
const origInputDepth = 1;
const origOutputDepth = 4;
const inputShape = [1, 2, 2, origOutputDepth];
const fSize = 2;
const origPad = 'same';
const origStride = 2;
const dimRoundingMode = 'round';
const x = tf.tensor4d([
1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34, 0.28,
0., 0.06, 0.14, 0.24
], inputShape);
const w = tf.tensor4d([0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.], [fSize, fSize, origInputDepth, origOutputDepth]);
expect(() => tf.conv2dTranspose(x, w, [1, 3, 3, 1], origStride, origPad, dimRoundingMode))
.toThrowError();
});
it('throws when dimRoundingMode is set and pad is valid', async () => {
const origInputDepth = 1;
const origOutputDepth = 4;
const inputShape = [1, 2, 2, origOutputDepth];
const fSize = 2;
const origPad = 'valid';
const origStride = 2;
const dimRoundingMode = 'round';
const x = tf.tensor4d([
1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34, 0.28,
0., 0.06, 0.14, 0.24
], inputShape);
const w = tf.tensor4d([0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.], [fSize, fSize, origInputDepth, origOutputDepth]);
expect(() => tf.conv2dTranspose(x, w, [1, 3, 3, 1], origStride, origPad, dimRoundingMode))
.toThrowError();
});
it('throws when dimRoundingMode is set and pad is a non-integer number', async () => {
const origInputDepth = 1;
const origOutputDepth = 4;
const inputShape = [1, 2, 2, origOutputDepth];
const fSize = 2;
const origPad = 1.2;
const origStride = 2;
const dimRoundingMode = 'round';
const x = tf.tensor4d([
1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34,
0.28, 0., 0.06, 0.14, 0.24
], inputShape);
const w = tf.tensor4d([
0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14.,
15.
], [fSize, fSize, origInputDepth, origOutputDepth]);
expect(() => tf.conv2dTranspose(x, w, [1, 3, 3, 1], origStride, origPad, dimRoundingMode))
.toThrowError();
});
it('throws when dimRoundingMode is set and pad is explicit by non-integer ' +
'number', async () => {
const origInputDepth = 1;
const origOutputDepth = 4;
const inputShape = [1, 2, 2, origOutputDepth];
const fSize = 2;
const origPad = [[0, 0], [0, 1.1], [0, 1], [0, 0]];
const origStride = 2;
const dimRoundingMode = 'round';
const x = tf.tensor4d([
1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34,
0.28, 0., 0.06, 0.14, 0.24
], inputShape);
const w = tf.tensor4d([
0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14.,
15.
], [fSize, fSize, origInputDepth, origOutputDepth]);
expect(() => tf.conv2dTranspose(x, w, [1, 3, 3, 1], origStride, origPad, dimRoundingMode))
.toThrowError();
});
// Reference (Python) TensorFlow code:
//
// ```py
// import numpy as np
// import tensorflow as tf
//
// tf.enable_eager_execution()
//
// x = tf.constant(np.array([[
// [[-0.14656299], [0.32942239], [-1.90302866]],
// [[-0.06487813], [-2.02637842], [-1.83669377]],
// [[0.82650784], [-0.89249092], [0.01207666]]
// ]]).astype(np.float32))
// filt = tf.constant(np.array([
// [[[-0.48280062], [1.26770487]], [[-0.83083738], [0.54341856]]],
// [[[-0.274904], [0.73111374]], [[2.01885189], [-2.68975237]]]
// ]).astype(np.float32))
//
// with tf.GradientTape() as g:
// g.watch(x)
// g.watch(filt)
// y = tf.keras.backend.conv2d_transpose(x, filt, [1, 4, 4, 2])
// print(y)
// (x_grad, filt_grad) = g.gradient(y, [x, filt])
//
// print("x_grad = %s" % x_grad)
// print("filt_grad = %s" % filt_grad)
// ```
it('gradient with clones input=[1,3,3,1] f=[2,2,2,1] s=1 padding=valid', async () => {
const inputDepth = 1;
const outputDepth = 2;
const inputShape = [1, 3, 3, inputDepth];
const filterSize = 2;
const stride = 1;
const pad = 'valid';
const filterShape = [filterSize, filterSize, outputDepth, inputDepth];
const x = tf.tensor4d([[
[[-0.14656299], [0.32942239], [-1.90302866]],
[[-0.06487813], [-2.02637842], [-1.83669377]],
[[0.82650784], [-0.89249092], [0.01207666]]
]], inputShape);
const filt = tf.tensor4d([
[[[-0.48280062], [1.26770487]], [[-0.83083738], [0.54341856]]],
[[[-0.274904], [0.73111374]], [[2.01885189], [-2.68975237]]]
], filterShape);
const grads = tf.grads((x, filter) => tf.conv2dTranspose(x.clone(), filter.clone(), [1, 4, 4, outputDepth], stride, pad)
.clone());
const dy = tf.ones([1, 4, 4, outputDepth]);
const [xGrad, filtGrad] = grads([x, filt], dy);
const expectedXGrad = tf.ones([1, 3, 3, 1]).mul(tf.scalar(0.2827947));
expectArraysClose(await xGrad.data(), await expectedXGrad.data());
const expectedFiltGrad = tf.ones([2, 2, 2, 1]).mul(tf.scalar(-5.70202599));
expectArraysClose(await filtGrad.data(), await expectedFiltGrad.data());
});
// Reference (Python) TensorFlow code:
//
// ```py
// import numpy as np
// import tensorflow as tf
//
// tf.enable_eager_execution()
//
// x = tf.constant(np.array([
// [[[-0.36541713], [-0.53973116]], [[0.01731674], [0.90227772]]]
// ]).astype(np.float32))
// filt = tf.constant(np.array([
// [[[-0.01423461], [-1.00267384]], [[1.61163029], [0.66302646]]],
// [[[-0.46900087], [-0.78649444]], [[0.87780536], [-0.84551637]]]
// ]).astype(np.float32))
//
// with tf.GradientTape() as g:
// g.watch(x)
// g.watch(filt)
// y = tf.keras.backend.conv2d_transpose(x, filt, [1, 4, 4, 2], strides=(2,
// 2)) print(y)
// (x_grad, filt_grad) = g.gradient(y, [x, filt])
//
// print("x_grad = %s" % -x_grad)
// print("filt_grad = %s" % -filt_grad)
// ```
it('gradient input=[1,2,2,1] f=[2,2,2,1] s=[2,2] padding=valid', async () => {
const inputDepth = 1;
const outputDepth = 2;
const inputShape = [1, 2, 2, inputDepth];
const filterSize = 2;
const stride = [2, 2];
const pad = 'valid';
const filterShape = [filterSize, filterSize, outputDepth, inputDepth];
const x = tf.tensor4d([[[[-0.36541713], [-0.53973116]], [[0.01731674], [0.90227772]]]], inputShape);
const filt = tf.tensor4d([
[[[-0.01423461], [-1.00267384]], [[1.61163029], [0.66302646]]],
[[[-0.46900087], [-0.78649444]], [[0.87780536], [-0.84551637]]]
], filterShape);
const grads = tf.grads((x, filter) => tf.conv2dTranspose(x, filter, [1, 4, 4, outputDepth], stride, pad));
const dy = tf.ones([1, 4, 4, outputDepth]).mul(tf.scalar(-1));
const [xGrad, filtGrad] = grads([x, filt], dy);
const expectedXGrad = tf.ones([1, 2, 2, 1]).mul(tf.scalar(-0.03454196));
expectArraysClose(await xGrad.data(), await expectedXGrad.data());
expect(xGrad.shape).toEqual([1, 2, 2, 1]);
const expectedFiltGrad = tf.ones([2, 2, 2, 1]).mul(tf.scalar(-0.01444618));
expectArraysClose(await filtGrad.data(), await expectedFiltGrad.data());
expect(filtGrad.shape).toEqual([2, 2, 2, 1]);
});
// Reference (Python) TensorFlow code:
//
// ```py
// import numpy as np
// import tensorflow as tf
//
// tf.enable_eager_execution()
//
// x = tf.constant(np.array([[
// [[1.52433065], [-0.77053435], [-0.64562341]],
// [[0.77962889], [1.58413887], [-0.25581856]],
// [[-0.58966221], [0.05411662], [0.70749138]]
// ]]).astype(np.float32))
// filt = tf.constant(np.array([
// [[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],
// [[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]
// ]).astype(np.float32))
//
// with tf.GradientTape() as g:
// g.watch(x)
// g.watch(filt)
// y = tf.keras.backend.conv2d_transpose(
// x, filt, [1, 3, 3, 2], strides=(1, 1), padding='same')
// (x_grad, filt_grad) = g.gradient(y, [x, filt])
//
// print("x_grad = %s" % x_grad)
// print("filt_grad = %s" % filt_grad)
// ```
it('gradient input=[1,3,3,1] f=[2,2,2,1] s=[1,1] padding=same', async () => {
const inputDepth = 1;
const outputDepth = 2;
const inputShape = [1, 3, 3, inputDepth];
const filterSize = 2;
const stride = [1, 1];
const pad = 'same';
const filterShape = [filterSize, filterSize, outputDepth, inputDepth];
const x = tf.tensor4d([[
[[1.52433065], [-0.77053435], [-0.64562341]],
[[0.77962889], [1.58413887], [-0.25581856]],
[[-0.58966221], [0.05411662], [0.70749138]]
]], inputShape);
const filt = tf.tensor4d([
[[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],
[[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]
], filterShape);
const grads = tf.grads((x, filter) => tf.conv2dTranspose(x, filter, [1, 3, 3, outputDepth], stride, pad));
const dy = tf.ones([1, 3, 3, outputDepth]);
const [xGrad, filtGrad] = grads([x, filt], dy);
expectArraysClose(await xGrad.array(), [[
[[1.30709858], [1.30709858], [-0.92814366]],
[[1.30709858], [1.30709858], [-0.92814366]],
[[1.19666437], [1.19666437], [-0.85476589]]
]]);
expectArraysClose(await filtGrad.array(), [
[[[2.38806788], [2.38806788]], [[2.58201847], [2.58201847]]],
[[[2.2161221], [2.2161221]], [[3.11756406], [3.11756406]]]
]);
});
it('gradient input=[1,3,3,1] f=[2,2,2,1] s=[1,1] p=explicit', async () => {
const inputDepth = 1;
const outputDepth = 2;
const inputShape = [1, 3, 3, inputDepth];
const filterSize = 2;
const stride = [1, 1];
const pad = [[0, 0], [0, 1], [0, 1], [0, 0]];
const filterShape = [filterSize, filterSize, outputDepth, inputDepth];
const x = tf.tensor4d([[
[[1.52433065], [-0.77053435], [-0.64562341]],
[[0.77962889], [1.58413887], [-0.25581856]],
[[-0.58966221], [0.05411662], [0.70749138]]
]], inputShape);
const filt = tf.tensor4d([
[[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],
[[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]
], filterShape);
const grads = tf.grads((x, filter) => tf.conv2dTranspose(x, filter, [1, 3, 3, outputDepth], stride, pad));
const dy = tf.ones([1, 3, 3, outputDepth]);
const [xGrad, filtGrad] = grads([x, filt], dy);
expectArraysClose(await xGrad.array(), [[
[[1.30709858], [1.30709858], [-0.92814366]],
[[1.30709858], [1.30709858], [-0.92814366]],
[[1.19666437], [1.19666437], [-0.85476589]]
]]);
expectArraysClose(await filtGrad.array(), [
[[[2.38806788], [2.38806788]], [[2.58201847], [2.58201847]]],
[[[2.2161221], [2.2161221]], [[3.11756406], [3.11756406]]]
]);
});
// Reference (Python) TensorFlow code:
//
// ```py
// import numpy as np
// import tensorflow as tf
//
// tf.enable_eager_execution()
//
// x = tf.constant(np.array([[
// [[1.52433065], [-0.77053435]], [[0.77962889], [1.58413887]],
// ]]).astype(np.float32))
// filt = tf.constant(np.array([
// [[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],
// [[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]
// ]).astype(np.float32))
//
// with tf.GradientTape() as g:
// g.watch(x)
// g.watch(filt)
// y = tf.keras.backend.conv2d_transpose(
// x, filt, [1, 3, 3, 2], strides=(2, 2), padding='same')
// print(y.shape)
// (x_grad, filt_grad) = g.gradient(y, [x, filt])
//
// print("x_grad = %s" % x_grad)
// print("filt_grad = %s" % filt_grad)
// ```
it('gradient input=[1,2,2,2] f=[2,2,2,1] s=[2,2] padding=same', async () => {
const inputDepth = 2;
const outputDepth = 2;
const inputShape = [1, 2, 2, inputDepth];
const filterSize = 2;
const stride = [2, 2];
const pad = 'same';
const filterShape = [filterSize, filterSize, outputDepth, inputDepth];
const x = tf.tensor4d([[
[[-1.81506593, 1.00900095], [-0.05199118, 0.26311377]],
[[-1.18469792, -0.34780521], [2.04971242, -0.65154692]]
]], inputShape);
const filt = tf.tensor4d([
[
[[0.19529686, -0.79594708], [0.70314057, -0.06081263]],
[[0.28724744, 0.88522715], [-0.51824096, -0.97120989]]
],
[
[[0.51872197, -1.17569193], [1.28316791, -0.81225092]],
[[-0.44221532, 0.70058174], [-0.4849217, 0.03806348]]
]
], filterShape);
const grads = tf.grads((x, filter) => tf.conv2dTranspose(x, filter, [1, 3, 3, outputDepth], stride, pad));
const dy = tf.ones([1, 3, 3, outputDepth]);
const [xGrad, filtGrad] = grads([x, filt], dy);
expectArraysClose(await xGrad.data(), [
1.54219678, -2.19204008, 2.70032732, -2.84470257, 0.66744391, -0.94274245,
0.89843743, -0.85675972
]);
expect(xGrad.shape).toEqual([1, 2, 2, 2]);
expectArraysClose(await filtGrad.data(), [
-1.00204261, 0.27276259, -1.00204261, 0.27276259, -2.99976385, 0.66119574,
-2.99976385, 0.66119574, -1.86705711, 1.27211472, -1.86705711, 1.27211472,
-1.81506593, 1.00900095, -1.81506593, 1.00900095
]);
expect(filtGrad.shape).toEqual([2, 2, 2, 2]);
});
it('throws when x is not rank 3', () => {
const origInputDepth = 1;
const origOutputDepth = 1;
const fSize = 2;
const origPad = 0;
const origStride = 1;
// tslint:disable-next-line:no-any
const x = tf.tensor2d([2, 2], [2, 1]);
const w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);
expect(() => tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad))
.toThrowError();
});
it('throws when weights is not rank 4', () => {
const origInputDepth = 1;
const origOutputDepth = 1;
const inputShape = [1, 1, origOutputDepth];
const fSize = 2;
const origPad = 0;
const origStride = 1;
const x = tf.tensor3d([2], inputShape);
// tslint:disable-next-line:no-any
const w = tf.tensor3d([3, 1, 5, 0], [fSize, fSize, origInputDepth]);
expect(() => tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad))
.toThrowError();
});
it('throws when x depth does not match weights original output depth', () => {
const origInputDepth = 1;
const origOutputDepth = 2;
const wrongOrigOutputDepth = 3;
const inputShape = [1, 1, origOutputDepth];
const fSize = 2;
const origPad = 0;
const origStride = 1;
const x = tf.tensor3d([2, 2], inputShape);
const w = tf.randomNormal([fSize, fSize, origInputDepth, wrongOrigOutputDepth]);
expect(() => tf.conv2dTranspose(x, w, [2, 2, 2], origStride, origPad))
.toThrowError();
});
it('throws when passed x as a non-tensor', () => {
const origInputDepth = 1;
const origOutputDepth = 1;
const fSize = 2;
const origPad = 0;
const origStride = 1;
const w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);
expect(() => tf.conv2dTranspose({}, w, [2, 2, 1], origStride, origPad))
.toThrowError(/Argument 'x' passed to 'conv2dTranspose' must be a Tensor/);
});
it('throws when passed filter as a non-tensor', () => {
const origOutputDepth = 1;
const inputShape = [1, 1, origOutputDepth];
const origPad = 0;
const origStride = 1;
const x = tf.tensor3d([2], inputShape);
expect(() => tf.conv2dTranspose(x, {}, [2, 2, 1], origStride, origPad))
.toThrowError(/Argument 'filter' passed to 'conv2dTranspose' must be a Tensor/);
});
it('accepts a tensor-like object', async () => {
const origPad = 0;
const origStride = 1;
const x = [[[2]]]; // 1x1x1
const w = [[[[3]], [[1]]], [[[5]], [[0]]]]; // 2x2x1x1
const result = tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad);
const expected = [6, 2, 10, 0];
expect(result.shape).toEqual([2, 2, 1]);
expectArraysClose(await result.data(), expected);
});
it('input=8x8x8,output=4x4x8,f=8,s=1,inDepth=8,p=same vec4', async () => {
const origInputDepth = 8;
const origOutputDepth = 8;
const inputShape = [1, 8, 8, origOutputDepth];
const fSize = 8;
const origPad = 'same';
const origStride = [1, 1];
const wShape = [fSize, fSize, origInputDepth, origOutputDepth];
const inputData = [];
for (let i = 0; i < fSize * fSize * origInputDepth; i++) {
inputData.push(i % 5);
}
const wData = [];
for (let i = 0; i < fSize * fSize * origInputDepth * origOutputDepth; i++) {
wData.push(i % 5);
}
const x = tf.tensor4d(inputData, inputShape);
const w = tf.tensor4d(wData, wShape);
const result = tf.conv2dTranspose(x, w, [1, 4, 4, origInputDepth], origStride, origPad);
expect(result.shape).toEqual([1, 4, 4, 8]);
const expected = [
512, 533, 469, 550, 506, 512, 533, 469, 550, 506, 512, 533, 469, 550, 506,
512, 533, 469, 550, 506, 512, 533, 469, 550, 506, 512, 533, 469, 550, 506,
512, 533, 506, 512, 533, 469, 550, 506, 512, 533, 469, 550, 506, 512, 533,
469, 550, 506, 512, 533, 469, 550, 506, 512, 533, 469, 550, 506, 512, 533,
469, 550, 506, 512, 550, 506, 512, 533, 469, 550, 506, 512, 533, 469, 550,
506, 512, 533, 469, 550, 506, 512, 533, 469, 550, 506, 512, 533, 469, 550,
506, 512, 533, 469, 550, 506, 469, 550, 506, 512, 533, 469, 550, 506, 512,
533, 469, 550, 506, 512, 533, 469, 550, 506, 512, 533, 469, 550, 506, 512,
533, 469, 550, 506, 512, 533, 469, 550
];
expectArraysClose(await result.data(), expected);
});
});
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