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Diffstat (limited to 'tensorflow/python/keras/utils/conv_utils_test.py')
| -rw-r--r-- | tensorflow/python/keras/utils/conv_utils_test.py | 232 |
1 files changed, 232 insertions, 0 deletions
diff --git a/tensorflow/python/keras/utils/conv_utils_test.py b/tensorflow/python/keras/utils/conv_utils_test.py new file mode 100644 index 0000000000..eb2a360bfd --- /dev/null +++ b/tensorflow/python/keras/utils/conv_utils_test.py @@ -0,0 +1,232 @@ +# 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() |