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1# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7#     http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ==============================================================================
15"""Tests for convolution related functionality in tensorflow.ops.nn."""
16from __future__ import absolute_import
17from __future__ import division
18from __future__ import print_function
19
20import numpy as np
21from six.moves import xrange  # pylint: disable=redefined-builtin
22
23from tensorflow.python.framework import constant_op
24from tensorflow.python.framework import dtypes
25from tensorflow.python.ops import array_ops
26from tensorflow.python.ops import nn_ops
27from tensorflow.python.platform import test
28
29
30class Conv1DTest(test.TestCase):
31
32  def testBasic(self):
33    """Test that argument passing to conv1d is handled properly."""
34    for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]:
35      x = constant_op.constant([1, 2, 3, 4], dtype=dtype)
36      x = array_ops.expand_dims(x, 0)  # Add batch dimension
37      x = array_ops.expand_dims(x, 2)  # And depth dimension
38      filters = constant_op.constant([2, 1], dtype=dtype)
39      filters = array_ops.expand_dims(filters, 1)  # in_channels
40      filters = array_ops.expand_dims(filters, 2)  # out_channels
41      # Filters is 2x1x1
42      for stride in [1, 2]:
43        with self.cached_session(use_gpu=test.is_gpu_available()):
44          c = nn_ops.conv1d(x, filters, stride, padding="VALID")
45          reduced = array_ops.squeeze(c)
46          output = self.evaluate(reduced)
47          if stride == 1:
48            self.assertEqual(len(output), 3)
49            self.assertAllClose(output,
50                                [2 * 1 + 1 * 2, 2 * 2 + 1 * 3, 2 * 3 + 1 * 4])
51          else:
52            self.assertEqual(len(output), 2)
53            self.assertAllClose(output, [2 * 1 + 1 * 2, 2 * 3 + 1 * 4])
54
55  def testConv1DTranspose(self):
56    with self.cached_session():
57      stride = 2
58
59      # Input, output: [batch, width, depth]
60      x_shape = [2, 4, 3]
61      y_shape = [2, 9, 2]
62
63      # Filter: [kernel_width, output_depth, input_depth]
64      f_shape = [3, 2, 3]
65
66      x = constant_op.constant(
67          1.0, shape=x_shape, name="x", dtype=dtypes.float32)
68      f = constant_op.constant(
69          1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
70      output = nn_ops.conv1d_transpose(
71          x, f, y_shape, strides=stride, padding="VALID")
72      value = self.evaluate(output)
73
74      cache_values = np.zeros(y_shape, dtype=np.float32)
75
76      # The amount of padding added
77      pad = 1
78
79      for n in xrange(x_shape[0]):
80        for k in xrange(f_shape[1]):
81          for w in xrange(pad, y_shape[1] - pad):
82            target = 3.0
83            # We add a case for locations divisible by the stride.
84            w_in = w % stride == 0 and w > pad and w < y_shape[1] - 1 - pad
85            if w_in:
86              target += 3.0
87            cache_values[n, w, k] = target
88
89          # copy values in the border
90          cache_values[n, 0, k] = cache_values[n, 1, k]
91          cache_values[n, -1, k] = cache_values[n, -2, k]
92
93    self.assertAllClose(cache_values, value)
94
95
96if __name__ == "__main__":
97  test.main()
98