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| # 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 VIT.""" | |
| import math | |
| from absl.testing import parameterized | |
| import tensorflow as tf, tf_keras | |
| from official.vision.modeling.backbones import vit | |
| class VisionTransformerTest(parameterized.TestCase, tf.test.TestCase): | |
| def test_network_creation(self, input_size, params_count): | |
| """Test creation of VisionTransformer family models.""" | |
| tf_keras.backend.set_image_data_format('channels_last') | |
| input_specs = tf_keras.layers.InputSpec( | |
| shape=[2, input_size, input_size, 3]) | |
| network = vit.VisionTransformer(input_specs=input_specs) | |
| inputs = tf_keras.Input(shape=(input_size, input_size, 3), batch_size=1) | |
| _ = network(inputs) | |
| self.assertEqual(network.count_params(), params_count) | |
| def test_network_with_diferent_configs( | |
| self, patch_size, output_2d_feature_maps, pooler): | |
| tf_keras.backend.set_image_data_format('channels_last') | |
| input_size = 24 | |
| expected_feat_level = str(round(math.log2(patch_size))) | |
| num_patch_rows = input_size // patch_size | |
| input_specs = tf_keras.layers.InputSpec( | |
| shape=[2, input_size, input_size, 3]) | |
| network = vit.VisionTransformer( | |
| input_specs=input_specs, | |
| patch_size=patch_size, | |
| pooler=pooler, | |
| hidden_size=8, | |
| mlp_dim=8, | |
| num_layers=1, | |
| num_heads=2, | |
| representation_size=16, | |
| output_2d_feature_maps=output_2d_feature_maps) | |
| inputs = tf_keras.Input(shape=(input_size, input_size, 3), batch_size=1) | |
| output = network(inputs) | |
| if pooler == 'none': | |
| self.assertEqual( | |
| output['encoded_tokens'].shape, [1, num_patch_rows**2, 16]) | |
| else: | |
| self.assertEqual(output['pre_logits'].shape, [1, 1, 1, 16]) | |
| if output_2d_feature_maps: | |
| self.assertIn(expected_feat_level, output) | |
| self.assertIn(expected_feat_level, network.output_specs) | |
| self.assertEqual( | |
| network.output_specs[expected_feat_level][1:], | |
| [num_patch_rows, num_patch_rows, 8]) | |
| else: | |
| self.assertNotIn(expected_feat_level, output) | |
| def test_posembedding_interpolation(self): | |
| tf_keras.backend.set_image_data_format('channels_last') | |
| input_size = 256 | |
| input_specs = tf_keras.layers.InputSpec( | |
| shape=[2, input_size, input_size, 3]) | |
| network = vit.VisionTransformer( | |
| input_specs=input_specs, | |
| patch_size=16, | |
| pooler='gap', | |
| pos_embed_shape=(14, 14)) # (224 // 16) | |
| inputs = tf_keras.Input(shape=(input_size, input_size, 3), batch_size=1) | |
| output = network(inputs)['pre_logits'] | |
| self.assertEqual(output.shape, [1, 1, 1, 768]) | |
| if __name__ == '__main__': | |
| tf.test.main() | |