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fixes tutorial issue 352 #2968

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Merged
wyli merged 2 commits into from
Sep 17, 2021
Merged

fixes tutorial issue 352 #2968

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5fd339b
fixes tutorial issue 352
wyli Sep 17, 2021
6d7d661
simplified
wyli Sep 17, 2021
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6 changes: 2 additions & 4 deletions monai/transforms/spatial/array.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1065,7 +1065,7 @@ def __call__(
"""
if grid is None:
if spatial_size is not None:
grid = create_grid(spatial_size)
grid = create_grid(spatial_size, dtype=float)
else:
raise ValueError("Incompatible values: grid=None and spatial_size=None.")

Expand All @@ -1084,9 +1084,7 @@ def __call__(
else:
affine = self.affine

if self.device not in (None, torch.device("cpu"), "cpu"):
grid, *_ = convert_data_type(grid, torch.Tensor, device=self.device)
grid, *_ = convert_data_type(grid, dtype=float)
grid, *_ = convert_data_type(grid, torch.Tensor, device=self.device, dtype=float)
affine, *_ = convert_to_dst_type(affine, grid)

grid = (affine @ grid.reshape((grid.shape[0], -1))).reshape([-1] + list(grid.shape[1:]))
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6 changes: 2 additions & 4 deletions monai/transforms/spatial/dictionary.py
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Original file line number Diff line number Diff line change
Expand Up @@ -795,10 +795,8 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, N
sp_size = fall_back_tuple(self.rand_affine.spatial_size, data[self.keys[0]].shape[1:])
# change image size or do random transform
do_resampling = self._do_transform or (sp_size != ensure_tuple(data[self.keys[0]].shape[1:]))

affine: NdarrayOrTensor = np.eye(len(sp_size) + 1, dtype=np.float64)
if device not in (None, torch.device("cpu"), "cpu"):
affine, *_ = convert_data_type(affine, torch.Tensor, device=device)
affine: torch.Tensor = torch.eye(len(sp_size) + 1, dtype=torch.float64, device=device)
# converting affine to tensor because the resampler currently only support torch backend
grid = None
if do_resampling: # need to prepare grid
grid = self.rand_affine.get_identity_grid(sp_size)
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5 changes: 5 additions & 0 deletions tests/test_rand_affined.py
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Expand Up @@ -211,6 +211,11 @@ def test_rand_affined(self, input_param, input_data, expected_val):
expected = expected_val[key] if isinstance(expected_val, dict) else expected_val
assert_allclose(result, expected, rtol=1e-4, atol=1e-4)

g.set_random_state(4)
res = g(input_data)
# affine should be tensor because the resampler only supports pytorch backend
self.assertTrue(isinstance(res["img_transforms"][0]["extra_info"]["affine"], torch.Tensor))

def test_ill_cache(self):
with self.assertWarns(UserWarning):
# spatial size is None
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