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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 tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import nn_ops
import tensorflow.python.ops.nn_grad # pylint: disable=unused-import
from tensorflow.python.platform import test
class Conv2DBackpropFilterGradTest(test.TestCase):
def testGradient(self):
with self.cached_session():
for padding in ["SAME", "VALID"]:
for stride in [1, 2]:
np.random.seed(1)
in_shape = [5, 8, 6, 4]
in_val = constant_op.constant(
2 * np.random.random_sample(in_shape) - 1, dtype=dtypes.float32)
filter_shape = [3, 3, 4, 6]
# Make a convolution op with the current settings, just to easily get
# the shape of the output.
conv_out = nn_ops.conv2d(
in_val,
array_ops.zeros(filter_shape),
strides=[1, stride, stride, 1],
padding=padding)
out_backprop_shape = conv_out.get_shape().as_list()
out_backprop_val = constant_op.constant(
2 * np.random.random_sample(out_backprop_shape) - 1,
dtype=dtypes.float32)
output = nn_ops.conv2d_backprop_filter(
in_val,
filter_shape,
out_backprop_val,
strides=[1, stride, stride, 1],
padding=padding)
err = gradient_checker.compute_gradient_error(
[in_val, out_backprop_val], [in_shape, out_backprop_shape],
output, filter_shape)
print("conv2d_backprop_filter gradient err = %g " % err)
err_tolerance = 2e-3
self.assertLess(err, err_tolerance)
def testGradientDilatedConv(self):
if test.is_gpu_available(cuda_only=True):
with self.test_session(use_gpu=True):
for padding in ["SAME", "VALID"]:
for stride in [1, 2]:
np.random.seed(1)
in_shape = [5, 8, 6, 4]
in_val = constant_op.constant(
2 * np.random.random_sample(in_shape) - 1, dtype=dtypes.float32)
filter_shape = [3, 3, 4, 6]
# Make a convolution op with the current settings,
# just to easily get the shape of the output.
conv_out = nn_ops.conv2d(
in_val,
array_ops.zeros(filter_shape),
dilations=[1, 2, 2, 1],
strides=[1, stride, stride, 1],
padding=padding)
out_backprop_shape = conv_out.get_shape().as_list()
out_backprop_val = constant_op.constant(
2 * np.random.random_sample(out_backprop_shape) - 1,
dtype=dtypes.float32)
output = nn_ops.conv2d_backprop_filter(
in_val,
filter_shape,
out_backprop_val,
dilations=[1, 2, 2, 1],
strides=[1, stride, stride, 1],
padding=padding)
err = gradient_checker.compute_gradient_error(
[in_val, out_backprop_val], [in_shape, out_backprop_shape],
output, filter_shape)
print("conv2d_backprop_filter gradient err = %g " % err)
err_tolerance = 2e-3
self.assertLess(err, err_tolerance)
if __name__ == "__main__":
test.main()
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