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Fix AttributeError when using torch.min and max #8041

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merged 4 commits into from
Aug 23, 2024
+15 −3
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Fix AttributeError when using torch.min and max #8041

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f5a7da8
fix #8040
KumoLiu Aug 22, 2024
6d57d5d
add max
KumoLiu Aug 22, 2024
5e27890
fix ci
KumoLiu Aug 22, 2024
7b444f1
fix ci
KumoLiu Aug 22, 2024
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4 changes: 4 additions & 0 deletions monai/transforms/utils_pytorch_numpy_unification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -480,6 +480,8 @@ def max(x: NdarrayTensor, dim: int | tuple | None = None, **kwargs) -> NdarrayTe
else:
ret = torch.max(x, int(dim), **kwargs) # type: ignore

if isinstance(ret, tuple):
return ret.values
return ret


Expand Down Expand Up @@ -546,6 +548,8 @@ def min(x: NdarrayTensor, dim: int | tuple | None = None, **kwargs) -> NdarrayTe
else:
ret = torch.min(x, int(dim), **kwargs) # type: ignore

if isinstance(ret, tuple):
return ret.values
return ret


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14 changes: 13 additions & 1 deletion tests/test_utils_pytorch_numpy_unification.py
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Expand Up @@ -17,7 +17,7 @@
import torch
from parameterized import parameterized

from monai.transforms.utils_pytorch_numpy_unification import mode, percentile
from monai.transforms.utils_pytorch_numpy_unification import min, max, mode, percentile
from monai.utils import set_determinism
from tests.utils import TEST_NDARRAYS, assert_allclose, skip_if_quick

Expand All @@ -27,6 +27,13 @@
TEST_MODE.append([p(np.array([3.1, 4.1, 4.1, 5.1])), p(4.1), False])
TEST_MODE.append([p(np.array([3.1, 4.1, 4.1, 5.1])), p(4), True])

TEST_MIN_MAX = []
for p in TEST_NDARRAYS:
TEST_MIN_MAX.append([p(np.array([1, 2, 3, 4, 4, 5])), {}, min, p(1)])
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TEST_MIN_MAX.append([p(np.array([[3.1, 4.1, 4.1, 5.1], [3, 5, 4.1, 5]])), {"dim": 1}, min, p([3.1, 3])])
TEST_MIN_MAX.append([p(np.array([1, 2, 3, 4, 4, 5])), {}, max, p(5)])
TEST_MIN_MAX.append([p(np.array([[3.1, 4.1, 4.1, 5.1], [3, 5, 4.1, 5]])), {"dim": 1}, max, p([5.1, 5])])


class TestPytorchNumpyUnification(unittest.TestCase):

Expand Down Expand Up @@ -74,6 +81,11 @@ def test_mode(self, array, expected, to_long):
res = mode(array, to_long=to_long)
assert_allclose(res, expected)

@parameterized.expand(TEST_MIN_MAX)
def test_min_max(self, array, input_params, func, expected):
res = func(array, **input_params)
assert_allclose(res, expected, type_test=False)


if __name__ == "__main__":
unittest.main()
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