""" Copyright (c) 2018 Intel Corporation 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. """ import numpy as np from extensions.front.tf.conv_ext import Conv2DFrontExtractor, DepthwiseConv2dNativeFrontExtractor from mo.utils.unittest.extractors import PB, BaseExtractorsTestingClass class ConvExtractorTest(BaseExtractorsTestingClass): @classmethod def setUpClass(cls): cls.strides = [1, 2, 3, 4] cls.dilations = [1, 1, 1, 1] def test_conv_2d_defaults(self): node = PB({'pb': PB({'attr': { 'data_format': PB({ 's': b"NHWC" }), 'strides': PB({ 'list': PB({"i": self.strides}) }), 'padding': PB({ 's': b'VALID' }), 'dilations': PB({ 'list': PB({"i": [1, 1, 1, 1]}) }) }})}) self.expected = { 'bias_addable': True, 'dilation': np.array([1, 1, 1, 1], dtype=np.int8), 'type': 'Convolution', 'layout': 'NHWC', } Conv2DFrontExtractor.extract(node) self.res = node self.expected_call_args = (None, False) self.compare() def test_conv2d_nhwc(self): node = PB({'pb': PB({'attr': { 'data_format': PB({ 's': b"NHWC" }), 'strides': PB({ 'list': PB({"i": self.strides}) }), 'padding': PB({ 's': b'VALID' }), 'dilations': PB({ 'list': PB({"i": [1, 1, 1, 1]}) }) }})}) self.expected = { # spatial_dims = [1, 2] will be detected in infer function "channel_dims": [3], "batch_dims": [0], "input_feature_channel": 2, "output_feature_channel": 3, 'dilation': np.array([1, 1, 1, 1], dtype=np.int8), 'stride': np.array(self.strides, dtype=np.int8), } Conv2DFrontExtractor.extract(node) self.res = node self.expected_call_args = (None, False) self.compare() def test_conv2d_nchw(self): node = PB({'pb': PB({'attr': { 'data_format': PB({ 's': b"NCHW" }), 'strides': PB({ 'list': PB({"i": self.strides}) }), 'padding': PB({ 's': b'VALID' }), 'dilations': PB({ 'list': PB({"i": [1, 1, 1, 1]}) }) }})}) self.expected = { # spatial_dims = [2, 3] will be detected in infer function "channel_dims": [1], "batch_dims": [0], "input_feature_channel": 2, "output_feature_channel": 3, 'dilation': np.array([1, 1, 1, 1], dtype=np.int8), 'stride': np.array(self.strides, dtype=np.int8), } Conv2DFrontExtractor.extract(node) self.res = node self.expected_call_args = (None, False) self.compare() def test_conv2d_depthwise(self): node = PB({'pb': PB({'attr': { 'data_format': PB({ 's': b"NHWC" }), 'strides': PB({ 'list': PB({"i": self.strides}), }), 'dilations': PB({ 'list': PB({"i": self.dilations}), }), 'padding': PB({ 's': b'VALID' }) }})}) self.expected = { # spatial_dims = [1, 2] will be detected in infer function "channel_dims": [3], "batch_dims": [0], "input_feature_channel": 2, "output_feature_channel": 2, 'dilation': np.array([1, 1, 1, 1], dtype=np.int8), 'stride': np.array(self.strides, dtype=np.int8), } DepthwiseConv2dNativeFrontExtractor.extract(node) self.res = node self.expected_call_args = (None, True) self.compare()