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|
| | import time |
| | import unittest |
| |
|
| | import torch |
| |
|
| | from protenix.model.modules.transformer import ConditionedTransitionBlock |
| |
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|
| | class TestConditionedTransitionBlock(unittest.TestCase): |
| | def setUp(self) -> None: |
| | self._start_time = time.time() |
| | self.device = "cuda" if torch.cuda.is_available() else "cpu" |
| | super().setUp() |
| |
|
| | def get_model(self, c_a: int = 768, c_s: int = 384, n: int = 2): |
| |
|
| | model = ConditionedTransitionBlock(c_a=c_a, c_s=c_s, n=n).to(self.device) |
| |
|
| | return model |
| |
|
| | def test_shape(self) -> None: |
| |
|
| | c_a = 5 * 55 |
| | c_s = 123 |
| |
|
| | N_token = 135 |
| | bs_dims = (2, 3, 5) |
| |
|
| | inputs = { |
| | "a": torch.rand(size=(*bs_dims, N_token, c_a)).to(self.device), |
| | "s": torch.rand(size=(*bs_dims, N_token, c_s)).to(self.device), |
| | } |
| |
|
| | model = self.get_model(c_a=c_a, c_s=c_s) |
| |
|
| | out = model(**inputs) |
| | target_shape = (*bs_dims, N_token, c_a) |
| | self.assertEqual(out.shape, out.reshape(target_shape).shape) |
| |
|
| | def tearDown(self): |
| | elapsed_time = time.time() - self._start_time |
| | print(f"Test {self.id()} took {elapsed_time:.6f}s") |
| |
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|
| | if __name__ == "__main__": |
| | unittest.main() |
| |
|