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import unittest |
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import torch |
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from torch.autograd import gradcheck |
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from tensormask.layers.swap_align2nat import SwapAlign2Nat |
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class SwapAlign2NatTest(unittest.TestCase): |
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@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") |
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def test_swap_align2nat_gradcheck_cuda(self): |
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dtype = torch.float64 |
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device = torch.device("cuda") |
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m = SwapAlign2Nat(2).to(dtype=dtype, device=device) |
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x = torch.rand(2, 4, 10, 10, dtype=dtype, device=device, requires_grad=True) |
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self.assertTrue(gradcheck(m, x), "gradcheck failed for SwapAlign2Nat CUDA") |
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def _swap_align2nat(self, tensor, lambda_val): |
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""" |
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The basic setup for testing Swap_Align |
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""" |
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op = SwapAlign2Nat(lambda_val, pad_val=0.0) |
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input = torch.from_numpy(tensor[None, :, :, :].astype("float32")) |
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output = op.forward(input.cuda()).cpu().numpy() |
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return output[0] |
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if __name__ == "__main__": |
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unittest.main() |
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