47 lines
1.7 KiB
Python
47 lines
1.7 KiB
Python
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# Copyright 2022 Google LLC
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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"""Tests for image_tokenizer."""
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from absl.testing import parameterized
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from robotics_transformer.tokenizers import image_tokenizer
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import tensorflow as tf
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class ImageTokenizerTest(tf.test.TestCase, parameterized.TestCase):
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@parameterized.named_parameters(
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('sample_image', 512, 224, False, 8),
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('sample_image_token_learner', 512, 224, True, 8))
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def testTokenize(self, output_dim, image_resolution, use_token_learner,
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num_tokens):
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batch = 1
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seq = 2
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tokenizer = image_tokenizer.RT1ImageTokenizer(
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embedding_output_dim=output_dim,
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use_token_learner=use_token_learner,
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num_tokens=num_tokens)
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image = tf.random.normal(
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shape=(batch, seq, image_resolution, image_resolution, 3))
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image = tf.clip_by_value(image, 0.0, 1.0)
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context_vector = tf.random.uniform((batch, seq, 512))
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image_tokens = tokenizer(image, context_vector)
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if use_token_learner:
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self.assertEqual(image_tokens.shape, [batch, seq, num_tokens, 512])
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else:
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self.assertEqual(image_tokens.shape, [batch, seq, 81, 512])
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if __name__ == '__main__':
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tf.test.main()
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