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