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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('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 = [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 = [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);
| });
| });
| //# 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
|
|
