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