data2/LocalFaceRecognition.git

/**
 * @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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* @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('conv2dTranspose', ALL_ENVS, () => {\n  it('input=2x2x1,d2=1,f=2,s=1,p=0', async () => {\n    const origInputDepth = 1;\n    const origOutputDepth = 1;\n    const inputShape: [number, number, number] = [1, 1, origOutputDepth];\n    const fSize = 2;\n    const origPad = 0;\n    const origStride = 1;\n\n    const x = tf.tensor3d([2], inputShape);\n    const w = tf.tensor4d(\n        [3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);\n\n    const result = tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad);\n    const expected = [6, 2, 10, 0];\n\n    expect(result.shape).toEqual([2, 2, 1]);\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('input=3x3x1,d2=1,f=2,s=2,p=same', async () => {\n    const origInputDepth = 1;\n    const origOutputDepth = 4;\n    const inputShape: [number, number, number, number] =\n        [1, 2, 2, origOutputDepth];\n    const fSize = 2;\n    const origPad = 'same';\n    const origStride = 2;\n\n    const x = tf.tensor4d(\n        [\n          1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34, 0.28,\n          0., 0.06, 0.14, 0.24\n        ],\n        inputShape);\n    const w = tf.tensor4d(\n        [0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.],\n        [fSize, fSize, origInputDepth, origOutputDepth]);\n\n    const result = tf.conv2dTranspose(x, w, [1, 3, 3, 1], origStride, origPad);\n    const expected = [7.63, 28.39, 2.94, 49.15, 69.91, 14.62, 1.69, 5.01, 1.06];\n\n    expect(result.shape).toEqual([1, 3, 3, 1]);\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('input=3x3x1,d2=1,f=2,s=2,p=explicit', async () => {\n    const origInputDepth = 1;\n    const origOutputDepth = 4;\n    const inputShape: [number, number, number, number] =\n        [1, 2, 2, origOutputDepth];\n    const fSize = 2;\n    const origPad =\n        [[0, 0], [0, 1], [0, 1], [0, 0]] as tf.backend_util.ExplicitPadding;\n    const origStride = 2;\n\n    const x = tf.tensor4d(\n        [\n          1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34, 0.28,\n          0., 0.06, 0.14, 0.24\n        ],\n        inputShape);\n    const w = tf.tensor4d(\n        [0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.],\n        [fSize, fSize, origInputDepth, origOutputDepth]);\n\n    const result = tf.conv2dTranspose(x, w, [1, 3, 3, 1], origStride, origPad);\n    const expected = [7.63, 28.39, 2.94, 49.15, 69.91, 14.62, 1.69, 5.01, 1.06];\n\n    expect(result.shape).toEqual([1, 3, 3, 1]);\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('input=2x2x1,d2=1,f=2,s=1,p=0, batch=2', async () => {\n    const origInputDepth = 1;\n    const origOutputDepth = 1;\n    const inputShape: [number, number, number, number] =\n        [2, 1, 1, origOutputDepth];\n    const fSize = 2;\n    const origPad = 0;\n    const origStride = 1;\n\n    const x = tf.tensor4d([2, 3], inputShape);\n    const w = tf.tensor4d(\n        [3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);\n\n    const result = tf.conv2dTranspose(x, w, [2, 2, 2, 1], origStride, origPad);\n    const expected = [6, 2, 10, 0, 9, 3, 15, 0];\n\n    expect(result.shape).toEqual([2, 2, 2, 1]);\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('input=2x2x2,output=3x3x2,f=2,s=2,inDepth=2,p=same', async () => {\n    const origInputDepth = 2;\n    const origOutputDepth = 2;\n    const inputShape: [number, number, number, number] =\n        [1, 2, 2, origOutputDepth];\n    const fSize = 2;\n    const origPad = 'same';\n    const origStride = 2;\n\n    const x = tf.tensor4d([0., 1., 2., 3., 4., 5., 6., 7.], inputShape);\n    const w = tf.tensor4d(\n        [0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.],\n        [fSize, fSize, origInputDepth, origOutputDepth]);\n\n    const result = tf.conv2dTranspose(\n        x, w, [1, 3, 3, origInputDepth], origStride, origPad);\n    const expected =\n        [1, 3, 5, 7, 3, 13, 9, 11, 13, 15, 43, 53, 5, 23, 41, 59, 7, 33.];\n\n    expect(result.shape).toEqual([1, 3, 3, origInputDepth]);\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('throws when dimRoundingMode is set and pad is same', async () => {\n    const origInputDepth = 1;\n    const origOutputDepth = 4;\n    const inputShape: [number, number, number, number] =\n        [1, 2, 2, origOutputDepth];\n    const fSize = 2;\n    const origPad = 'same';\n    const origStride = 2;\n    const dimRoundingMode = 'round';\n\n    const x = tf.tensor4d(\n        [\n          1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34, 0.28,\n          0., 0.06, 0.14, 0.24\n        ],\n        inputShape);\n    const w = tf.tensor4d(\n        [0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.],\n        [fSize, fSize, origInputDepth, origOutputDepth]);\n\n    expect(\n        () => tf.conv2dTranspose(\n            x, w, [1, 3, 3, 1], origStride, origPad, dimRoundingMode))\n        .toThrowError();\n  });\n\n  it('throws when dimRoundingMode is set and pad is valid', async () => {\n    const origInputDepth = 1;\n    const origOutputDepth = 4;\n    const inputShape: [number, number, number, number] =\n        [1, 2, 2, origOutputDepth];\n    const fSize = 2;\n    const origPad = 'valid';\n    const origStride = 2;\n    const dimRoundingMode = 'round';\n\n    const x = tf.tensor4d(\n        [\n          1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34, 0.28,\n          0., 0.06, 0.14, 0.24\n        ],\n        inputShape);\n    const w = tf.tensor4d(\n        [0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.],\n        [fSize, fSize, origInputDepth, origOutputDepth]);\n\n    expect(\n        () => tf.conv2dTranspose(\n            x, w, [1, 3, 3, 1], origStride, origPad, dimRoundingMode))\n        .toThrowError();\n  });\n\n  it('throws when dimRoundingMode is set and pad is a non-integer number',\n     async () => {\n       const origInputDepth = 1;\n       const origOutputDepth = 4;\n       const inputShape: [number, number, number, number] =\n           [1, 2, 2, origOutputDepth];\n       const fSize = 2;\n       const origPad = 1.2;\n       const origStride = 2;\n       const dimRoundingMode = 'round';\n\n       const x = tf.tensor4d(\n           [\n             1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34,\n             0.28, 0., 0.06, 0.14, 0.24\n           ],\n           inputShape);\n       const w = tf.tensor4d(\n           [\n             0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14.,\n             15.\n           ],\n           [fSize, fSize, origInputDepth, origOutputDepth]);\n\n       expect(\n           () => tf.conv2dTranspose(\n               x, w, [1, 3, 3, 1], origStride, origPad, dimRoundingMode))\n           .toThrowError();\n     });\n\n  it('throws when dimRoundingMode is set and pad is explicit by non-integer ' +\n         'number',\n     async () => {\n       const origInputDepth = 1;\n       const origOutputDepth = 4;\n       const inputShape: [number, number, number, number] =\n           [1, 2, 2, origOutputDepth];\n       const fSize = 2;\n       const origPad = [[0, 0], [0, 1.1], [0, 1], [0, 0]] as\n           tf.backend_util.ExplicitPadding;\n       const origStride = 2;\n       const dimRoundingMode = 'round';\n\n       const x = tf.tensor4d(\n           [\n             1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34,\n             0.28, 0., 0.06, 0.14, 0.24\n           ],\n           inputShape);\n       const w = tf.tensor4d(\n           [\n             0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14.,\n             15.\n           ],\n           [fSize, fSize, origInputDepth, origOutputDepth]);\n\n       expect(\n           () => tf.conv2dTranspose(\n               x, w, [1, 3, 3, 1], origStride, origPad, dimRoundingMode))\n           .toThrowError();\n     });\n\n  // Reference (Python) TensorFlow code:\n  //\n  // ```py\n  // import numpy as np\n  // import tensorflow as tf\n  //\n  // tf.enable_eager_execution()\n  //\n  // x = tf.constant(np.array([[\n  //     [[-0.14656299], [0.32942239], [-1.90302866]],\n  //     [[-0.06487813], [-2.02637842], [-1.83669377]],\n  //     [[0.82650784], [-0.89249092], [0.01207666]]\n  // ]]).astype(np.float32))\n  // filt = tf.constant(np.array([\n  //     [[[-0.48280062], [1.26770487]], [[-0.83083738], [0.54341856]]],\n  //     [[[-0.274904], [0.73111374]], [[2.01885189], [-2.68975237]]]\n  // ]).astype(np.float32))\n  //\n  // with tf.GradientTape() as g:\n  //   g.watch(x)\n  //   g.watch(filt)\n  //   y = tf.keras.backend.conv2d_transpose(x, filt, [1, 4, 4, 2])\n  //   print(y)\n  // (x_grad, filt_grad) = g.gradient(y, [x, filt])\n  //\n  // print(\"x_grad = %s\" % x_grad)\n  // print(\"filt_grad = %s\" % filt_grad)\n  // ```\n  it('gradient with clones input=[1,3,3,1] f=[2,2,2,1] s=1 padding=valid',\n     async () => {\n       const inputDepth = 1;\n       const outputDepth = 2;\n       const inputShape: [number, number, number, number] =\n           [1, 3, 3, inputDepth];\n       const filterSize = 2;\n       const stride = 1;\n       const pad = 'valid';\n\n       const filterShape: [number, number, number, number] =\n           [filterSize, filterSize, outputDepth, inputDepth];\n\n       const x = tf.tensor4d(\n           [[\n             [[-0.14656299], [0.32942239], [-1.90302866]],\n             [[-0.06487813], [-2.02637842], [-1.83669377]],\n             [[0.82650784], [-0.89249092], [0.01207666]]\n           ]],\n           inputShape);\n       const filt = tf.tensor4d(\n           [\n             [[[-0.48280062], [1.26770487]], [[-0.83083738], [0.54341856]]],\n             [[[-0.274904], [0.73111374]], [[2.01885189], [-2.68975237]]]\n           ],\n           filterShape);\n\n       const grads = tf.grads(\n           (x: tf.Tensor4D, filter: tf.Tensor4D) =>\n               tf.conv2dTranspose(\n                     x.clone(), filter.clone(), [1, 4, 4, outputDepth], stride,\n                     pad)\n                   .clone());\n       const dy = tf.ones([1, 4, 4, outputDepth]);\n       const [xGrad, filtGrad] = grads([x, filt], dy);\n\n       const expectedXGrad = tf.ones([1, 3, 3, 1]).mul(tf.scalar(0.2827947));\n       expectArraysClose(await xGrad.data(), await expectedXGrad.data());\n       const expectedFiltGrad =\n           tf.ones([2, 2, 2, 1]).mul(tf.scalar(-5.70202599));\n       expectArraysClose(await filtGrad.data(), await expectedFiltGrad.data());\n     });\n\n  // Reference (Python) TensorFlow code:\n  //\n  // ```py\n  // import numpy as np\n  // import tensorflow as tf\n  //\n  // tf.enable_eager_execution()\n  //\n  // x = tf.constant(np.array([\n  //     [[[-0.36541713], [-0.53973116]], [[0.01731674], [0.90227772]]]\n  // ]).astype(np.float32))\n  // filt = tf.constant(np.array([\n  //     [[[-0.01423461], [-1.00267384]], [[1.61163029], [0.66302646]]],\n  //     [[[-0.46900087], [-0.78649444]], [[0.87780536], [-0.84551637]]]\n  // ]).astype(np.float32))\n  //\n  // with tf.GradientTape() as g:\n  //   g.watch(x)\n  //   g.watch(filt)\n  //   y = tf.keras.backend.conv2d_transpose(x, filt, [1, 4, 4, 2], strides=(2,\n  //   2)) print(y)\n  // (x_grad, filt_grad) = g.gradient(y, [x, filt])\n  //\n  // print(\"x_grad = %s\" % -x_grad)\n  // print(\"filt_grad = %s\" % -filt_grad)\n  // ```\n  it('gradient input=[1,2,2,1] f=[2,2,2,1] s=[2,2] padding=valid', async () => {\n    const inputDepth = 1;\n    const outputDepth = 2;\n    const inputShape: [number, number, number, number] = [1, 2, 2, inputDepth];\n    const filterSize = 2;\n    const stride: [number, number] = [2, 2];\n    const pad = 'valid';\n\n    const filterShape: [number, number, number, number] =\n        [filterSize, filterSize, outputDepth, inputDepth];\n\n    const x = tf.tensor4d(\n        [[[[-0.36541713], [-0.53973116]], [[0.01731674], [0.90227772]]]],\n        inputShape);\n    const filt = tf.tensor4d(\n        [\n          [[[-0.01423461], [-1.00267384]], [[1.61163029], [0.66302646]]],\n          [[[-0.46900087], [-0.78649444]], [[0.87780536], [-0.84551637]]]\n        ],\n        filterShape);\n\n    const grads = tf.grads(\n        (x: tf.Tensor4D, filter: tf.Tensor4D) =>\n            tf.conv2dTranspose(x, filter, [1, 4, 4, outputDepth], stride, pad));\n    const dy = tf.ones([1, 4, 4, outputDepth]).mul(tf.scalar(-1));\n    const [xGrad, filtGrad] = grads([x, filt], dy);\n\n    const expectedXGrad = tf.ones([1, 2, 2, 1]).mul(tf.scalar(-0.03454196));\n    expectArraysClose(await xGrad.data(), await expectedXGrad.data());\n    expect(xGrad.shape).toEqual([1, 2, 2, 1]);\n\n    const expectedFiltGrad = tf.ones([2, 2, 2, 1]).mul(tf.scalar(-0.01444618));\n    expectArraysClose(await filtGrad.data(), await expectedFiltGrad.data());\n    expect(filtGrad.shape).toEqual([2, 2, 2, 1]);\n  });\n\n  // Reference (Python) TensorFlow code:\n  //\n  // ```py\n  // import numpy as np\n  // import tensorflow as tf\n  //\n  // tf.enable_eager_execution()\n  //\n  // x = tf.constant(np.array([[\n  //     [[1.52433065], [-0.77053435], [-0.64562341]],\n  //     [[0.77962889], [1.58413887], [-0.25581856]],\n  //     [[-0.58966221], [0.05411662], [0.70749138]]\n  // ]]).astype(np.float32))\n  // filt = tf.constant(np.array([\n  //     [[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],\n  //     [[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]\n  // ]).astype(np.float32))\n  //\n  // with tf.GradientTape() as g:\n  //   g.watch(x)\n  //   g.watch(filt)\n  //   y = tf.keras.backend.conv2d_transpose(\n  //       x, filt, [1, 3, 3, 2], strides=(1, 1), padding='same')\n  // (x_grad, filt_grad) = g.gradient(y, [x, filt])\n  //\n  // print(\"x_grad = %s\" % x_grad)\n  // print(\"filt_grad = %s\" % filt_grad)\n  // ```\n  it('gradient input=[1,3,3,1] f=[2,2,2,1] s=[1,1] padding=same', async () => {\n    const inputDepth = 1;\n    const outputDepth = 2;\n    const inputShape: [number, number, number, number] = [1, 3, 3, inputDepth];\n    const filterSize = 2;\n    const stride: [number, number] = [1, 1];\n    const pad = 'same';\n\n    const filterShape: [number, number, number, number] =\n        [filterSize, filterSize, outputDepth, inputDepth];\n\n    const x = tf.tensor4d(\n        [[\n          [[1.52433065], [-0.77053435], [-0.64562341]],\n          [[0.77962889], [1.58413887], [-0.25581856]],\n          [[-0.58966221], [0.05411662], [0.70749138]]\n        ]],\n        inputShape);\n    const filt = tf.tensor4d(\n        [\n          [[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],\n          [[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]\n        ],\n        filterShape);\n\n    const grads = tf.grads(\n        (x: tf.Tensor4D, filter: tf.Tensor4D) =>\n            tf.conv2dTranspose(x, filter, [1, 3, 3, outputDepth], stride, pad));\n    const dy = tf.ones([1, 3, 3, outputDepth]);\n    const [xGrad, filtGrad] = grads([x, filt], dy);\n\n    expectArraysClose(await xGrad.array(), [[\n                        [[1.30709858], [1.30709858], [-0.92814366]],\n                        [[1.30709858], [1.30709858], [-0.92814366]],\n                        [[1.19666437], [1.19666437], [-0.85476589]]\n                      ]]);\n    expectArraysClose(await filtGrad.array(), [\n      [[[2.38806788], [2.38806788]], [[2.58201847], [2.58201847]]],\n      [[[2.2161221], [2.2161221]], [[3.11756406], [3.11756406]]]\n    ]);\n  });\n\n  it('gradient input=[1,3,3,1] f=[2,2,2,1] s=[1,1] p=explicit', async () => {\n    const inputDepth = 1;\n    const outputDepth = 2;\n    const inputShape: [number, number, number, number] = [1, 3, 3, inputDepth];\n    const filterSize = 2;\n    const stride: [number, number] = [1, 1];\n    const pad =\n        [[0, 0], [0, 1], [0, 1], [0, 0]] as tf.backend_util.ExplicitPadding;\n\n    const filterShape: [number, number, number, number] =\n        [filterSize, filterSize, outputDepth, inputDepth];\n\n    const x = tf.tensor4d(\n        [[\n          [[1.52433065], [-0.77053435], [-0.64562341]],\n          [[0.77962889], [1.58413887], [-0.25581856]],\n          [[-0.58966221], [0.05411662], [0.70749138]]\n        ]],\n        inputShape);\n    const filt = tf.tensor4d(\n        [\n          [[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],\n          [[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]\n        ],\n        filterShape);\n\n    const grads = tf.grads(\n        (x: tf.Tensor4D, filter: tf.Tensor4D) =>\n            tf.conv2dTranspose(x, filter, [1, 3, 3, outputDepth], stride, pad));\n    const dy = tf.ones([1, 3, 3, outputDepth]);\n    const [xGrad, filtGrad] = grads([x, filt], dy);\n\n    expectArraysClose(await xGrad.array(), [[\n                        [[1.30709858], [1.30709858], [-0.92814366]],\n                        [[1.30709858], [1.30709858], [-0.92814366]],\n                        [[1.19666437], [1.19666437], [-0.85476589]]\n                      ]]);\n    expectArraysClose(await filtGrad.array(), [\n      [[[2.38806788], [2.38806788]], [[2.58201847], [2.58201847]]],\n      [[[2.2161221], [2.2161221]], [[3.11756406], [3.11756406]]]\n    ]);\n  });\n\n  // Reference (Python) TensorFlow code:\n  //\n  // ```py\n  // import numpy as np\n  // import tensorflow as tf\n  //\n  // tf.enable_eager_execution()\n  //\n  // x = tf.constant(np.array([[\n  //     [[1.52433065], [-0.77053435]], [[0.77962889], [1.58413887]],\n  // ]]).astype(np.float32))\n  // filt = tf.constant(np.array([\n  //     [[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],\n  //     [[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]\n  // ]).astype(np.float32))\n  //\n  // with tf.GradientTape() as g:\n  //   g.watch(x)\n  //   g.watch(filt)\n  //   y = tf.keras.backend.conv2d_transpose(\n  //       x, filt, [1, 3, 3, 2], strides=(2, 2), padding='same')\n  //   print(y.shape)\n  // (x_grad, filt_grad) = g.gradient(y, [x, filt])\n  //\n  // print(\"x_grad = %s\" % x_grad)\n  // print(\"filt_grad = %s\" % filt_grad)\n  // ```\n  it('gradient input=[1,2,2,2] f=[2,2,2,1] s=[2,2] padding=same', async () => {\n    const inputDepth = 2;\n    const outputDepth = 2;\n    const inputShape: [number, number, number, number] = [1, 2, 2, inputDepth];\n    const filterSize = 2;\n    const stride: [number, number] = [2, 2];\n    const pad = 'same';\n\n    const filterShape: [number, number, number, number] =\n        [filterSize, filterSize, outputDepth, inputDepth];\n\n    const x = tf.tensor4d(\n        [[\n          [[-1.81506593, 1.00900095], [-0.05199118, 0.26311377]],\n          [[-1.18469792, -0.34780521], [2.04971242, -0.65154692]]\n        ]],\n        inputShape);\n    const filt = tf.tensor4d(\n        [\n          [\n            [[0.19529686, -0.79594708], [0.70314057, -0.06081263]],\n            [[0.28724744, 0.88522715], [-0.51824096, -0.97120989]]\n          ],\n          [\n            [[0.51872197, -1.17569193], [1.28316791, -0.81225092]],\n            [[-0.44221532, 0.70058174], [-0.4849217, 0.03806348]]\n          ]\n        ],\n        filterShape);\n\n    const grads = tf.grads(\n        (x: tf.Tensor4D, filter: tf.Tensor4D) =>\n            tf.conv2dTranspose(x, filter, [1, 3, 3, outputDepth], stride, pad));\n    const dy = tf.ones([1, 3, 3, outputDepth]);\n    const [xGrad, filtGrad] = grads([x, filt], dy);\n\n    expectArraysClose(await xGrad.data(), [\n      1.54219678, -2.19204008, 2.70032732, -2.84470257, 0.66744391, -0.94274245,\n      0.89843743, -0.85675972\n    ]);\n    expect(xGrad.shape).toEqual([1, 2, 2, 2]);\n    expectArraysClose(await filtGrad.data(), [\n      -1.00204261, 0.27276259, -1.00204261, 0.27276259, -2.99976385, 0.66119574,\n      -2.99976385, 0.66119574, -1.86705711, 1.27211472, -1.86705711, 1.27211472,\n      -1.81506593, 1.00900095, -1.81506593, 1.00900095\n    ]);\n    expect(filtGrad.shape).toEqual([2, 2, 2, 2]);\n  });\n\n  it('throws when x is not rank 3', () => {\n    const origInputDepth = 1;\n    const origOutputDepth = 1;\n    const fSize = 2;\n    const origPad = 0;\n    const origStride = 1;\n\n    // tslint:disable-next-line:no-any\n    const x: any = tf.tensor2d([2, 2], [2, 1]);\n    const w = tf.tensor4d(\n        [3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);\n\n    expect(() => tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad))\n        .toThrowError();\n  });\n\n  it('throws when weights is not rank 4', () => {\n    const origInputDepth = 1;\n    const origOutputDepth = 1;\n    const inputShape: [number, number, number] = [1, 1, origOutputDepth];\n    const fSize = 2;\n    const origPad = 0;\n    const origStride = 1;\n\n    const x = tf.tensor3d([2], inputShape);\n    // tslint:disable-next-line:no-any\n    const w: any = tf.tensor3d([3, 1, 5, 0], [fSize, fSize, origInputDepth]);\n\n    expect(() => tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad))\n        .toThrowError();\n  });\n\n  it('throws when x depth does not match weights original output depth', () => {\n    const origInputDepth = 1;\n    const origOutputDepth = 2;\n    const wrongOrigOutputDepth = 3;\n    const inputShape: [number, number, number] = [1, 1, origOutputDepth];\n    const fSize = 2;\n    const origPad = 0;\n    const origStride = 1;\n\n    const x = tf.tensor3d([2, 2], inputShape);\n    const w = tf.randomNormal<Rank.R4>(\n        [fSize, fSize, origInputDepth, wrongOrigOutputDepth]);\n\n    expect(() => tf.conv2dTranspose(x, w, [2, 2, 2], origStride, origPad))\n        .toThrowError();\n  });\n\n  it('throws when passed x as a non-tensor', () => {\n    const origInputDepth = 1;\n    const origOutputDepth = 1;\n    const fSize = 2;\n    const origPad = 0;\n    const origStride = 1;\n\n    const w = tf.tensor4d(\n        [3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);\n\n    expect(\n        () => tf.conv2dTranspose(\n            {} as tf.Tensor3D, w, [2, 2, 1], origStride, origPad))\n        .toThrowError(\n            /Argument 'x' passed to 'conv2dTranspose' must be a Tensor/);\n  });\n\n  it('throws when passed filter as a non-tensor', () => {\n    const origOutputDepth = 1;\n    const inputShape: [number, number, number] = [1, 1, origOutputDepth];\n    const origPad = 0;\n    const origStride = 1;\n\n    const x = tf.tensor3d([2], inputShape);\n\n    expect(\n        () => tf.conv2dTranspose(\n            x, {} as tf.Tensor4D, [2, 2, 1], origStride, origPad))\n        .toThrowError(\n            /Argument 'filter' passed to 'conv2dTranspose' must be a Tensor/);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const origPad = 0;\n    const origStride = 1;\n\n    const x = [[[2]]];                           // 1x1x1\n    const w = [[[[3]], [[1]]], [[[5]], [[0]]]];  // 2x2x1x1\n\n    const result = tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad);\n    const expected = [6, 2, 10, 0];\n\n    expect(result.shape).toEqual([2, 2, 1]);\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('input=8x8x8,output=4x4x8,f=8,s=1,inDepth=8,p=same vec4', async () => {\n    const origInputDepth = 8;\n    const origOutputDepth = 8;\n    const inputShape: [number, number, number, number] =\n        [1, 8, 8, origOutputDepth];\n    const fSize = 8;\n    const origPad = 'same';\n    const origStride: [number, number] = [1, 1];\n    const wShape: [number, number, number, number] =\n        [fSize, fSize, origInputDepth, origOutputDepth];\n\n    const inputData = [];\n    for (let i = 0; i < fSize * fSize * origInputDepth; i++) {\n      inputData.push(i % 5);\n    }\n    const wData = [];\n    for (let i = 0; i < fSize * fSize * origInputDepth * origOutputDepth; i++) {\n      wData.push(i % 5);\n    }\n\n    const x = tf.tensor4d(inputData, inputShape);\n    const w = tf.tensor4d(wData, wShape);\n    const result = tf.conv2dTranspose(\n        x, w, [1, 4, 4, origInputDepth], origStride, origPad);\n    expect(result.shape).toEqual([1, 4, 4, 8]);\n\n    const expected = [\n      512, 533, 469, 550, 506, 512, 533, 469, 550, 506, 512, 533, 469, 550, 506,\n      512, 533, 469, 550, 506, 512, 533, 469, 550, 506, 512, 533, 469, 550, 506,\n      512, 533, 506, 512, 533, 469, 550, 506, 512, 533, 469, 550, 506, 512, 533,\n      469, 550, 506, 512, 533, 469, 550, 506, 512, 533, 469, 550, 506, 512, 533,\n      469, 550, 506, 512, 550, 506, 512, 533, 469, 550, 506, 512, 533, 469, 550,\n      506, 512, 533, 469, 550, 506, 512, 533, 469, 550, 506, 512, 533, 469, 550,\n      506, 512, 533, 469, 550, 506, 469, 550, 506, 512, 533, 469, 550, 506, 512,\n      533, 469, 550, 506, 512, 533, 469, 550, 506, 512, 533, 469, 550, 506, 512,\n      533, 469, 550, 506, 512, 533, 469, 550\n    ];\n    expectArraysClose(await result.data(), expected);\n  });\n});\n"]}