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# Copyright 2022 Big Vision 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 vision-text-transformer."""
from absl.testing import absltest
from big_vision.models.proj.uvim import vtt
import jax
import jax.numpy as jnp
import ml_collections
class VTTTest(absltest.TestCase):
def test_vtt_with_1_step(self):
model_config = ml_collections.ConfigDict(dict(
input_size=(224, 224),
patches={"size": (16, 16)},
num_heads=2,
num_layers=2,
mlp_dim=128,
emb_dim=64,
vocab_size=500))
batch_size, max_len = 8, 50
image = jnp.ones((batch_size, 224, 224, 3))
text = jnp.ones((batch_size, max_len), dtype=jnp.int32)
m = vtt.Model(**model_config)
variables = m.init(jax.random.PRNGKey(42), image, text)
self.assertCountEqual(variables.keys(), ["params"])
params = variables["params"]
out = m.apply({"params": params}, image, text)
expected_shape = (batch_size, max_len, model_config.vocab_size)
self.assertEqual(out.shape, expected_shape)
if __name__ == "__main__":
absltest.main()
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