101 lines
3.0 KiB
Python
101 lines
3.0 KiB
Python
"""
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Copyright (C) 2018-2020 Intel Corporation
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import numpy as np
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from extensions.front.tf.deconv_ext import Conv2DBackpropInputFrontExtractor
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from mo.utils.unittest.extractors import PB, BaseExtractorsTestingClass
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class DeconvolutionExtractorTest(BaseExtractorsTestingClass):
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@classmethod
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def setUpClass(cls):
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cls.strides = [1, 2, 3, 4]
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def test_deconv2d_defaults(self):
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node = PB({'pb': PB({'attr': {
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'data_format': PB({
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's': b"NHWC"
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}),
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'strides': PB({
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'list': PB({"i": self.strides})
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}),
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'padding': PB({
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's': b'VALID'
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})
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}})})
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self.expected = {
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'bias_addable': True,
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'pad': None, # will be inferred when input shape is known
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'pad_spatial_shape': None,
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'output_spatial_shape': None,
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'output_shape': None,
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'group': None,
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}
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Conv2DBackpropInputFrontExtractor.extract(node)
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self.res = node
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self.expected_call_args = (None, False)
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self.compare()
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def test_deconv2d_nhwc(self):
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node = PB({'pb': PB({'attr': {
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'data_format': PB({
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's': b"NHWC"
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}),
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'strides': PB({
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'list': PB({"i": self.strides})
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}),
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'padding': PB({
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's': b'VALID'
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})
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}})})
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self.expected = {
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"spatial_dims": [1, 2],
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"channel_dims": [3],
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"batch_dims": [0],
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'stride': np.array(self.strides, dtype=np.int8),
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}
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Conv2DBackpropInputFrontExtractor.extract(node)
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self.res = node
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self.expected_call_args = (None, False)
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self.compare()
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def test_deconv2d_nchw(self):
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node = PB({'pb': PB({'attr': {
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'data_format': PB({
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's': b"NCHW"
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}),
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'strides': PB({
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'list': PB({"i": self.strides})
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}),
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'padding': PB({
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's': b'VALID'
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})
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}})})
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self.expected = {
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"spatial_dims": [2, 3],
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"channel_dims": [1],
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"batch_dims": [0],
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'stride': np.array(self.strides, dtype=np.int8),
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}
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Conv2DBackpropInputFrontExtractor.extract(node)
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self.res = node
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self.expected_call_args = (None, False)
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self.compare()
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