| |
| import numpy as np |
| import pytest |
| import torch |
| from torch.autograd import gradcheck |
|
|
| from mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE |
|
|
|
|
| class TestCarafe: |
|
|
| def test_carafe_naive_gradcheck(self): |
| if not torch.cuda.is_available(): |
| return |
| from mmcv.ops import CARAFENaive |
| feat = torch.randn( |
| 2, 64, 3, 3, requires_grad=True, device='cuda').double() |
| mask = torch.randn( |
| 2, 100, 6, 6, requires_grad=True, |
| device='cuda').sigmoid().double() |
| gradcheck(CARAFENaive(5, 4, 2), (feat, mask), atol=1e-4, eps=1e-4) |
|
|
| def test_carafe_gradcheck(self): |
| if not torch.cuda.is_available(): |
| return |
| from mmcv.ops import CARAFE |
| feat = torch.randn( |
| 2, 64, 3, 3, requires_grad=True, device='cuda').double() |
| mask = torch.randn( |
| 2, 100, 6, 6, requires_grad=True, |
| device='cuda').sigmoid().double() |
| gradcheck(CARAFE(5, 4, 2), (feat, mask), atol=1e-4, eps=1e-4) |
|
|
| @pytest.mark.parametrize('device', [ |
| pytest.param( |
| 'cuda', |
| marks=pytest.mark.skipif( |
| not IS_CUDA_AVAILABLE, reason='requires CUDA support')), |
| pytest.param( |
| 'mlu', |
| marks=pytest.mark.skipif( |
| not IS_MLU_AVAILABLE, reason='requires MLU support')) |
| ]) |
| def test_carafe_allclose(self, device): |
| try: |
| from mmcv.ops import CARAFE |
| except ModuleNotFoundError: |
| pytest.skip('test requires compilation') |
|
|
| np_feat = np.fromfile( |
| 'tests/data/for_carafe/carafe_feat.bin', dtype=np.float32) |
| np_mask = np.fromfile( |
| 'tests/data/for_carafe/carafe_mask.bin', dtype=np.float32) |
| np_output = np.fromfile( |
| 'tests/data/for_carafe/carafe_output.bin', dtype=np.float32) |
| np_feat_grad = np.fromfile( |
| 'tests/data/for_carafe/carafe_feat_grad.bin', dtype=np.float32) |
| np_mask_grad = np.fromfile( |
| 'tests/data/for_carafe/carafe_mask_grad.bin', dtype=np.float32) |
|
|
| np_feat = np_feat.reshape((2, 64, 3, 3)) |
| np_mask = np_mask.reshape((2, 100, 6, 6)) |
| np_output = np_output.reshape((2, 64, 6, 6)) |
| np_feat_grad = np_feat_grad.reshape((2, 64, 3, 3)) |
| np_mask_grad = np_mask_grad.reshape((2, 100, 6, 6)) |
|
|
| feat = torch.tensor( |
| np_feat, dtype=torch.float, device=device, requires_grad=True) |
| mask = torch.tensor( |
| np_mask, dtype=torch.float, device=device, requires_grad=True) |
|
|
| carafe = CARAFE(5, 4, 2) |
|
|
| output = carafe(feat, mask) |
| output.backward(torch.ones_like(output)) |
| assert np.allclose( |
| output.data.type(torch.float).cpu().numpy(), np_output, atol=1e-3) |
| assert np.allclose( |
| feat.grad.data.type(torch.float).cpu().numpy(), |
| np_feat_grad, |
| atol=1e-3) |
| assert np.allclose( |
| mask.grad.data.type(torch.float).cpu().numpy(), |
| np_mask_grad, |
| atol=1e-3) |
|
|
