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# Copyright 2017 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 conv_utils."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import itertools
from absl.testing import parameterized
import numpy as np
from tensorflow.python.keras.utils import conv_utils
from tensorflow.python.platform import test
def _get_const_output_shape(input_shape, dim):
return tuple([min(d, dim) for d in input_shape])
input_shapes = [
(0,),
(0, 0),
(1,),
(2,),
(3,),
(1, 0),
(0, 3),
(1, 1),
(1, 2),
(3, 1),
(2, 2),
(3, 3),
(1, 0, 1),
(5, 2, 3),
(3, 5, 6, 7, 0),
(3, 2, 2, 4, 4),
(1, 2, 3, 4, 7, 2),
]
@parameterized.parameters(input_shapes)
class TestConvUtils(test.TestCase, parameterized.TestCase):
def test_conv_kernel_mask_fc(self, *input_shape):
padding = 'valid'
kernel_shape = input_shape
ndims = len(input_shape)
strides = (1,) * ndims
output_shape = _get_const_output_shape(input_shape, dim=1)
mask = np.ones(input_shape + output_shape, np.bool)
self.assertAllEqual(
mask,
conv_utils.conv_kernel_mask(
input_shape,
kernel_shape,
strides,
padding
)
)
def test_conv_kernel_mask_diag(self, *input_shape):
ndims = len(input_shape)
kernel_shape = (1,) * ndims
strides = (1,) * ndims
for padding in ['valid', 'same']:
mask = np.identity(int(np.prod(input_shape)), np.bool)
mask = np.reshape(mask, input_shape * 2)
self.assertAllEqual(
mask,
conv_utils.conv_kernel_mask(
input_shape,
kernel_shape,
strides,
padding
)
)
def test_conv_kernel_mask_full_stride(self, *input_shape):
padding = 'valid'
ndims = len(input_shape)
kernel_shape = (1,) * ndims
strides = tuple([max(d, 1) for d in input_shape])
output_shape = _get_const_output_shape(input_shape, dim=1)
mask = np.zeros(input_shape + output_shape, np.bool)
if all(d > 0 for d in mask.shape):
mask[(0,) * len(output_shape)] = True
self.assertAllEqual(
mask,
conv_utils.conv_kernel_mask(
input_shape,
kernel_shape,
strides,
padding
)
)
def test_conv_kernel_mask_almost_full_stride(self, *input_shape):
padding = 'valid'
ndims = len(input_shape)
kernel_shape = (1,) * ndims
strides = tuple([max(d - 1, 1) for d in input_shape])
output_shape = _get_const_output_shape(input_shape, dim=2)
mask = np.zeros(input_shape + output_shape, np.bool)
if all(d > 0 for d in mask.shape):
for in_position in itertools.product(*[[0, d - 1] for d in input_shape]):
out_position = tuple([min(p, 1) for p in in_position])
mask[in_position + out_position] = True
self.assertAllEqual(
mask,
conv_utils.conv_kernel_mask(
input_shape,
kernel_shape,
strides,
padding
)
)
def test_conv_kernel_mask_rect_kernel(self, *input_shape):
padding = 'valid'
ndims = len(input_shape)
strides = (1,) * ndims
for d in range(ndims):
kernel_shape = [1] * ndims
kernel_shape[d] = input_shape[d]
output_shape = list(input_shape)
output_shape[d] = min(1, input_shape[d])
mask = np.identity(int(np.prod(input_shape)), np.bool)
mask = np.reshape(mask, input_shape * 2)
for p in itertools.product(*[range(input_shape[dim])
for dim in range(ndims)]):
p = list(p)
p[d] = slice(None)
mask[p * 2] = True
mask = np.take(mask, range(0, min(1, input_shape[d])), ndims + d)
self.assertAllEqual(
mask,
conv_utils.conv_kernel_mask(
input_shape,
kernel_shape,
strides,
padding
)
)
def test_conv_kernel_mask_wrong_padding(self, *input_shape):
ndims = len(input_shape)
kernel_shape = (1,) * ndims
strides = (1,) * ndims
conv_utils.conv_kernel_mask(
input_shape,
kernel_shape,
strides,
'valid'
)
conv_utils.conv_kernel_mask(
input_shape,
kernel_shape,
strides,
'same'
)
self.assertRaises(NotImplementedError,
conv_utils.conv_kernel_mask,
input_shape, kernel_shape, strides, 'full')
def test_conv_kernel_mask_wrong_dims(self, *input_shape):
kernel_shape = 1
strides = 1
conv_utils.conv_kernel_mask(
input_shape,
kernel_shape,
strides,
'valid'
)
ndims = len(input_shape)
kernel_shape = (2,) * (ndims + 1)
self.assertRaises(ValueError,
conv_utils.conv_kernel_mask,
input_shape, kernel_shape, strides, 'same')
strides = (1,) * ndims
self.assertRaises(ValueError,
conv_utils.conv_kernel_mask,
input_shape, kernel_shape, strides, 'valid')
kernel_shape = (1,) * ndims
strides = (2,) * (ndims - 1)
self.assertRaises(ValueError,
conv_utils.conv_kernel_mask,
input_shape, kernel_shape, strides, 'valid')
strides = (2,) * ndims
conv_utils.conv_kernel_mask(
input_shape,
kernel_shape,
strides,
'valid'
)
if __name__ == '__main__':
test.main()
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