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| # coding=utf-8 | |
| # Copyright 2021 The Deeplab2 Authors. | |
| # | |
| # 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 transformer_layers.""" | |
| import tensorflow as tf | |
| from deeplab2.model.layers import dual_path_transformer | |
| class TransformerLayersTest(tf.test.TestCase): | |
| def test_default_attention_operation_output_shape(self): | |
| layer = dual_path_transformer.AttentionOperation( | |
| 'attention', 'relu', 'softmax') | |
| output = layer((tf.zeros([2, 8, 4225, 127]), | |
| tf.zeros([2, 8, 422, 127]), | |
| tf.zeros([2, 422, 8, 128]))) | |
| self.assertListEqual(output.get_shape().as_list(), [2, 4225, 1024]) | |
| def test_default_transformer_layer_output_shape(self): | |
| layer = dual_path_transformer.DualPathTransformerLayer() | |
| float_training_tensor = tf.constant(0.0, dtype=tf.float32) | |
| output = layer((tf.zeros([2, 4225, 126]), | |
| tf.zeros([2, 127, 128]), | |
| float_training_tensor)) | |
| self.assertListEqual(output[0].get_shape().as_list(), [2, 4225, 126]) | |
| self.assertListEqual(output[1].get_shape().as_list(), [2, 4225, 126]) | |
| self.assertListEqual(output[2].get_shape().as_list(), [2, 127, 128]) | |
| def test_zero_feed_forward_network_output_shape(self): | |
| layer = dual_path_transformer.DualPathTransformerLayer( | |
| feed_forward_network_channels=0) | |
| float_training_tensor = tf.constant(0.0, dtype=tf.float32) | |
| output = layer((tf.zeros([2, 4225, 128]), | |
| tf.zeros([2, 128, 128]), | |
| float_training_tensor)) | |
| self.assertListEqual(output[0].get_shape().as_list(), [2, 4225, 128]) | |
| self.assertListEqual(output[1].get_shape().as_list(), [2, 4225, 128]) | |
| self.assertListEqual(output[2].get_shape().as_list(), [2, 128, 128]) | |
| def test_attention_types_output_shape(self): | |
| layer = dual_path_transformer.DualPathTransformerLayer( | |
| use_memory_self_attention=False, | |
| use_pixel2memory_feedback_attention=False) | |
| float_training_tensor = tf.constant(0.0, dtype=tf.float32) | |
| output = layer((tf.zeros([2, 4225, 128]), | |
| tf.zeros([2, 128, 128]), | |
| float_training_tensor)) | |
| self.assertListEqual(output[0].get_shape().as_list(), [2, 4225, 128]) | |
| self.assertListEqual(output[1].get_shape().as_list(), [2, 4225, 128]) | |
| self.assertListEqual(output[2].get_shape().as_list(), [2, 128, 128]) | |
| if __name__ == '__main__': | |
| tf.test.main() | |