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import time |
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import unittest |
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import torch |
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import torch.nn as nn |
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from protenix.utils.lr_scheduler import AlphaFold3LRScheduler |
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class SimpleModel(nn.Module): |
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def __init__(self): |
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super(SimpleModel, self).__init__() |
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self.linear = nn.Linear(in_features=1, out_features=1) |
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def forward(self, x): |
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return self.linear(x) |
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class TestSchedule(unittest.TestCase): |
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def setUp(self): |
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self._start_time = time.time() |
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return super().setUp() |
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def test_af3_lr_schedule(self): |
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model = SimpleModel() |
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base_lr = 1.8e-3 |
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optimizer = torch.optim.Adam( |
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model.parameters(), lr=base_lr, betas=(0.9, 0.95), eps=1e-8 |
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) |
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scheduler = AlphaFold3LRScheduler(optimizer=optimizer) |
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learning_rates = [] |
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test_steps = 60000 |
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for step in range(test_steps): |
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learning_rates.append(scheduler._get_step_lr(step)) |
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optimizer.step() |
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scheduler.step() |
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self.assertEqual(learning_rates[0], 0) |
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self.assertEqual(learning_rates[1], 1.8e-6) |
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self.assertEqual(learning_rates[1000], 1.8e-3) |
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self.assertEqual(learning_rates[50000], 0.95 * 1.8e-3) |
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def tearDown(self): |
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elapsed_time = time.time() - self._start_time |
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print(f"Test {self.id()} took {elapsed_time:.6f}s") |
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if __name__ == "__main__": |
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unittest.main() |
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