Spaces:
Runtime error
Runtime error
| # Copyright 2023 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 aspp.""" | |
| # Import libraries | |
| from absl.testing import parameterized | |
| import tensorflow as tf, tf_keras | |
| from official.vision.modeling.backbones import resnet | |
| from official.vision.modeling.decoders import aspp | |
| class ASPPTest(parameterized.TestCase, tf.test.TestCase): | |
| def test_network_creation(self, level, dilation_rates, num_filters, | |
| spp_layer_version): | |
| """Test creation of ASPP.""" | |
| input_size = 256 | |
| tf_keras.backend.set_image_data_format('channels_last') | |
| inputs = tf_keras.Input(shape=(input_size, input_size, 3), batch_size=1) | |
| backbone = resnet.ResNet(model_id=50) | |
| network = aspp.ASPP( | |
| level=level, | |
| dilation_rates=dilation_rates, | |
| num_filters=num_filters, | |
| spp_layer_version=spp_layer_version) | |
| endpoints = backbone(inputs) | |
| feats = network(endpoints) | |
| self.assertIn(str(level), feats) | |
| self.assertAllEqual( | |
| [1, input_size // 2**level, input_size // 2**level, num_filters], | |
| feats[str(level)].shape.as_list()) | |
| def test_serialize_deserialize(self): | |
| # Create a network object that sets all of its config options. | |
| kwargs = dict( | |
| level=3, | |
| dilation_rates=[6, 12], | |
| num_filters=256, | |
| pool_kernel_size=None, | |
| use_sync_bn=False, | |
| norm_momentum=0.99, | |
| norm_epsilon=0.001, | |
| activation='relu', | |
| kernel_initializer='VarianceScaling', | |
| kernel_regularizer=None, | |
| interpolation='bilinear', | |
| dropout_rate=0.2, | |
| use_depthwise_convolution='false', | |
| spp_layer_version='v1', | |
| output_tensor=False, | |
| dtype='float32', | |
| name='aspp', | |
| trainable=True) | |
| network = aspp.ASPP(**kwargs) | |
| expected_config = dict(kwargs) | |
| self.assertEqual(network.get_config(), expected_config) | |
| # Create another network object from the first object's config. | |
| new_network = aspp.ASPP.from_config(network.get_config()) | |
| # If the serialization was successful, the new config should match the old. | |
| self.assertAllEqual(network.get_config(), new_network.get_config()) | |
| if __name__ == '__main__': | |
| tf.test.main() | |