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Fix sliding_window_inference #6325

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Merged
wyli merged 9 commits into from
Apr 11, 2023
Merged

Fix sliding_window_inference #6325

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a958190
fix #6320
KumoLiu Apr 10, 2023
dd92ba1
Merge branch 'dev' of https://github.com/Project-MONAI/MONAI into fix…
KumoLiu Apr 10, 2023
c873507
fix flake8
KumoLiu Apr 10, 2023
d24652e
address comments
KumoLiu Apr 11, 2023
41b3a49
update type hints
KumoLiu Apr 11, 2023
bb4d3b2
fix flake8
KumoLiu Apr 11, 2023
16fcbbc
use `convert_to_dst_type` instead
KumoLiu Apr 11, 2023
04da439
minor fix
KumoLiu Apr 11, 2023
02ab606
Merge branch 'auto-update' into fix-sliding-window-inference
wyli Apr 11, 2023
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8 changes: 5 additions & 3 deletions monai/inferers/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,7 +41,7 @@


def sliding_window_inference(
inputs: torch.Tensor,
inputs: torch.Tensor | MetaTensor,
roi_size: Sequence[int] | int,
sw_batch_size: int,
predictor: Callable[..., torch.Tensor | Sequence[torch.Tensor] | dict[Any, torch.Tensor]],
Expand Down Expand Up @@ -307,9 +307,11 @@ def sliding_window_inference(
output_image_list[ss] = output_i[(slice(None), slice(None), *final_slicing)]

final_output = _pack_struct(output_image_list, dict_keys)
final_output = convert_to_dst_type(final_output, inputs, device=device)[0]
if temp_meta is not None:
final_output = MetaTensor(final_output).copy_meta_from(temp_meta)
final_output = convert_to_dst_type(final_output, temp_meta, device=device)[0] # type: ignore
else:
final_output = convert_to_dst_type(final_output, inputs, device=device)[0]

return final_output # type: ignore


Expand Down
8 changes: 6 additions & 2 deletions tests/test_sliding_window_hovernet_inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,7 @@
from parameterized import parameterized

from monai.apps.pathology.inferers import SlidingWindowHoVerNetInferer
from monai.data import MetaTensor
from monai.inferers import sliding_window_inference
from monai.utils import optional_import
from tests.test_sliding_window_inference import TEST_CASES
Expand All @@ -31,6 +32,8 @@
["hover", (1, 3, 16, 8), (4, 4), 7, 0.5, "constant", torch.device("cpu:0"), (1,) * 4],
]

TEST_CASES_MULTIOUTPUT = [[torch.ones((1, 6, 20, 20))], [MetaTensor(torch.ones((1, 6, 20, 20)))]]


class TestSlidingWindowHoVerNetInference(unittest.TestCase):
@parameterized.expand(TEST_CASES_PADDING)
Expand Down Expand Up @@ -245,9 +248,10 @@ def compute(data, test1, test2):
)(inputs, compute, t1, test2=t2)
np.testing.assert_allclose(result.cpu().numpy(), expected, rtol=1e-4)

def test_multioutput(self):
@parameterized.expand(TEST_CASES_MULTIOUTPUT)
def test_multioutput(self, inputs):
device = "cuda" if torch.cuda.is_available() else "cpu:0"
inputs = torch.ones((1, 6, 20, 20)).to(device=device)
inputs = inputs.to(device=device)
roi_shape = (8, 8)
sw_batch_size = 10

Expand Down