/**

* @license

* Copyright 2017 Google Inc. 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('conv3dTranspose', ALL_ENVS, () => {

// Reference Python TensorFlow code

// ```python

// import numpy as np

// import tensorflow as tf

// tf.enable_eager_execution()

// x = np.array([2], dtype = np.float32).reshape(1, 1, 1, 1, 1)

// w = np.array([5, 4, 8, 7, 1, 2, 6, 3], dtype = np.float32).reshape(2, 2, 2,

// 1, 1)

// tf.nn.conv3d_transpose(x, w, output_shape=[1, 2, 2, 2, 1], padding='VALID')

// ```

it('input=2x2x2x1,d2=1,f=2,s=1,p=valid', async () => {

const origInputDepth = 1;

const origOutputDepth = 1;

const inputShape: [number, number, number, number] =

[1, 1, 1, origOutputDepth];

const fSize = 2;

const origPad = 'valid';

const origStride = 1;

const x = tf.tensor4d([2], inputShape);

const w = tf.tensor5d(

[5, 4, 8, 7, 1, 2, 6, 3],

[fSize, fSize, fSize, origInputDepth, origOutputDepth]);

const result = tf.conv3dTranspose(x, w, [2, 2, 2, 1], origStride, origPad);

const expected = [10, 8, 16, 14, 2, 4, 12, 6];

expect(result.shape).toEqual([2, 2, 2, 1]);

expectArraysClose(await result.data(), expected);

});

// Reference Python TensorFlow code

// ```python

// import numpy as np

// import tensorflow as tf

// tf.enable_eager_execution()

// x = np.array([2, 3], dtype = np.float32).reshape(2, 1, 1, 1, 1, 1)

// w = np.array([5, 4, 8, 7, 1, 2, 6, 3], dtype = np.float32).reshape(2,

// 2, 2, 1, 1)

// tf.nn.conv3d_transpose(x, w, output_shape=[2, 2, 2, 2, 1], padding='VALID')

// ```

it('input=2x2x2x1,d2=1,f=2,s=1,p=valid, batch=2', async () => {

const origInputDepth = 1;

const origOutputDepth = 1;

const inputShape: [number, number, number, number, number] =

[2, 1, 1, 1, origOutputDepth];

const fSize = 2;

const origPad = 'valid';

const origStride = 1;

const x = tf.tensor5d([2, 3], inputShape);

const w = tf.tensor5d(

[5, 4, 8, 7, 1, 2, 6, 3],

[fSize, fSize, fSize, origInputDepth, origOutputDepth]);

const result =

tf.conv3dTranspose(x, w, [2, 2, 2, 2, 1], origStride, origPad);

const expected = [10, 8, 16, 14, 2, 4, 12, 6, 15, 12, 24, 21, 3, 6, 18, 9];

expect(result.shape).toEqual([2, 2, 2, 2, 1]);

expectArraysClose(await result.data(), expected);

});

});