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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 vit vqvae model."""
from absl.testing import absltest
from big_vision.models.proj.uvim import vit
import jax
import jax.numpy as jnp
import ml_collections
class ViTVQVAEModelTest(absltest.TestCase):
def test_model(self):
model_config = ml_collections.ConfigDict({
"input_size": (32, 32),
"code_len": 4,
"width": 16,
"mlp_dim": 64,
"num_heads": 4,
"enc_depth": 1,
"dec_depth": 1,
"with_encoder_ctx": True,
"with_decoder_ctx": True,
"statistics_axis_name": None,
"inputs": {
"in1": (10, 3),
"in2": (25,),
},
"outputs": {
"out1": (5,),
"out2": (20,),
},
})
model = vit.Model(**model_config)
batch_size = 4
seq_len = (32 // 8) ** 2
x = {
"in1": jnp.zeros((batch_size, seq_len, 10, 3)),
"in2": jnp.zeros((batch_size, seq_len, 25)),
}
ctx_image = jnp.zeros((batch_size,) + model_config.input_size + (3,))
init_rngs = {
"params": jax.random.PRNGKey(0),
"state": jax.random.PRNGKey(1),
}
params = model.init(init_rngs, x, ctx=ctx_image)
self.assertEqual(params.keys(), set(["params", "state"]))
apply_rngs = {
"dropout": jax.random.PRNGKey(0),
"vqvae": jax.random.PRNGKey(0),
}
(logits, _), params = model.apply(
params, x, ctx=ctx_image, train=True, update_dict=True,
rngs=apply_rngs, mutable=["state"])
self.assertEqual(logits.keys(), set(["out1", "out2"]))
self.assertEqual(logits["out1"].shape, (batch_size, seq_len, 5))
self.assertEqual(logits["out2"].shape, (batch_size, seq_len, 20))
if __name__ == "__main__":
absltest.main()
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