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# Copyright 2015 The TensorFlow Authors. 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.
# ==============================================================================
"""Tests for convolution related functionality in tensorflow.ops.nn."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.platform import test
class Conv1DTest(test.TestCase):
def testBasic(self):
"""Test that argument passing to conv2d is handled properly."""
x = constant_op.constant([1, 2, 3, 4], dtype=dtypes.float32)
x = array_ops.expand_dims(x, 0) # Add batch dimension
x = array_ops.expand_dims(x, 2) # And depth dimension
filters = constant_op.constant([2, 1], dtype=dtypes.float32)
filters = array_ops.expand_dims(filters, 1) # in_channels
filters = array_ops.expand_dims(filters, 2) # out_channels
# Filters is 2x1x1
for stride in [1, 2]:
with self.test_session(use_gpu=test.is_gpu_available()):
c = nn_ops.conv1d(x, filters, stride, padding="VALID")
reduced = array_ops.squeeze(c)
output = reduced.eval()
if stride == 1:
self.assertEqual(len(output), 3)
self.assertAllClose(output,
[2 * 1 + 1 * 2, 2 * 2 + 1 * 3, 2 * 3 + 1 * 4])
else:
self.assertEqual(len(output), 2)
self.assertAllClose(output, [2 * 1 + 1 * 2, 2 * 3 + 1 * 4])
def testConv1DTranspose(self):
with self.test_session():
stride = 2
# Input, output: [batch, width, depth]
x_shape = [2, 4, 3]
y_shape = [2, 9, 2]
# Filter: [kernel_width, output_depth, input_depth]
f_shape = [3, 2, 3]
x = constant_op.constant(
1.0, shape=x_shape, name="x", dtype=dtypes.float32)
f = constant_op.constant(
1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
output = nn_ops.conv1d_transpose(
x, f, y_shape, stride=stride, padding="VALID")
value = output.eval()
cache_values = np.zeros(y_shape, dtype=np.float32)
# The amount of padding added
pad = 1
for n in xrange(x_shape[0]):
for k in xrange(f_shape[1]):
for w in xrange(pad, y_shape[1] - pad):
target = 3.0
# We add a case for locations divisible by the stride.
w_in = w % stride == 0 and w > pad and w < y_shape[1] - 1 - pad
if w_in:
target += 3.0
cache_values[n, w, k] = target
# copy values in the border
cache_values[n, 0, k] = cache_values[n, 1, k]
cache_values[n, -1, k] = cache_values[n, -2, k]
self.assertAllClose(cache_values, value)
if __name__ == "__main__":
test.main()
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