EnsureSameShaped, adds warn option (#6455)

Adds an option to hide warning messages of EnsureSameShaped transform.

### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [ ] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [ ] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [ ] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

Signed-off-by: myron <[email protected]>

monai/apps/auto3dseg/transforms.py

@@ -39,6 +39,7 @@ def __init__(
         allow_missing_keys: bool = False,
         source_key: str = "image",
         allowed_shape_difference: int = 5,
+        warn: bool = True,
     ) -> None:
         """
         Args:
@@ -47,29 +48,38 @@ def __init__(
             source_key: key of the item with the reference shape.
             allowed_shape_difference: raises error if shapes are different more than this value in any dimension,
                 otherwise corrects for the shape mismatch using nearest interpolation.
+            warn: if `True` prints a warning if the label image is resized
+
 
         """
         super().__init__(keys=keys, allow_missing_keys=allow_missing_keys)
         self.source_key = source_key
         self.allowed_shape_difference = allowed_shape_difference
+        self.warn = warn
 
     def __call__(self, data: Mapping[Hashable, torch.Tensor]) -> dict[Hashable, torch.Tensor]:
         d = dict(data)
         image_shape = d[self.source_key].shape[1:]
         for key in self.key_iterator(d):
             label_shape = d[key].shape[1:]
             if label_shape != image_shape:
+                filename = ""
+                if hasattr(d[key], "meta") and isinstance(d[key].meta, Mapping):  # type: ignore[attr-defined]
+                    filename = d[key].meta.get(ImageMetaKey.FILENAME_OR_OBJ)  # type: ignore[attr-defined]
+
                 if np.allclose(list(label_shape), list(image_shape), atol=self.allowed_shape_difference):
-                    msg = f"The {key} with shape {label_shape} was resized to match the source shape {image_shape}"
-                    if hasattr(d[key], "meta") and isinstance(d[key].meta, Mapping):  # type: ignore[attr-defined]
-                        filename = d[key].meta.get(ImageMetaKey.FILENAME_OR_OBJ)  # type: ignore[attr-defined]
-                        msg += f", the metadata was not updated: filename={filename}"
-                    warnings.warn(msg)
+                    if self.warn:
+                        warnings.warn(
+                            f"The {key} with shape {label_shape} was resized to match the source shape {image_shape}"
+                            f", the metadata was not updated {filename}."
+                        )
                     d[key] = torch.nn.functional.interpolate(
                         input=d[key].unsqueeze(0),
                         size=image_shape,
                         mode="nearest-exact" if pytorch_after(1, 11) else "nearest",
                     ).squeeze(0)
                 else:
-                    raise ValueError(f"The {key} shape {label_shape} is different from the source shape {image_shape}.")
+                    raise ValueError(
+                        f"The {key} shape {label_shape} is different from the source shape {image_shape} {filename}."
+                    )
         return d

tests/test_densenet.py

@@ -21,7 +21,7 @@
 from monai.networks import eval_mode
 from monai.networks.nets import DenseNet121, Densenet169, DenseNet264, densenet201
 from monai.utils import optional_import
-from tests.utils import skip_if_quick, test_script_save
+from tests.utils import skip_if_downloading_fails, skip_if_quick, test_script_save
 
 if TYPE_CHECKING:
     import torchvision
@@ -82,7 +82,8 @@ class TestPretrainedDENSENET(unittest.TestCase):
     @parameterized.expand([TEST_PRETRAINED_2D_CASE_1, TEST_PRETRAINED_2D_CASE_2])
     @skip_if_quick
     def test_121_2d_shape_pretrain(self, model, input_param, input_shape, expected_shape):
-        net = model(**input_param).to(device)
+        with skip_if_downloading_fails():
+            net = model(**input_param).to(device)
         with eval_mode(net):
             result = net.forward(torch.randn(input_shape).to(device))
             self.assertEqual(result.shape, expected_shape)
@@ -91,7 +92,8 @@ def test_121_2d_shape_pretrain(self, model, input_param, input_shape, expected_s
     @skipUnless(has_torchvision, "Requires `torchvision` package.")
     def test_pretrain_consistency(self, model, input_param, input_shape):
         example = torch.randn(input_shape).to(device)
-        net = model(**input_param).to(device)
+        with skip_if_downloading_fails():
+            net = model(**input_param).to(device)
         with eval_mode(net):
             result = net.features.forward(example)
         torchvision_net = torchvision.models.densenet121(pretrained=True).to(device)

tests/test_torchvision_fc_model.py

@@ -21,6 +21,7 @@
 from monai.networks.nets import TorchVisionFCModel, UNet
 from monai.networks.utils import look_up_named_module, set_named_module
 from monai.utils import min_version, optional_import
+from tests.utils import skip_if_downloading_fails
 
 Inception_V3_Weights, has_enum = optional_import("torchvision.models.inception", name="Inception_V3_Weights")
 
@@ -176,7 +177,8 @@ def test_without_pretrained(self, input_param, input_shape, expected_shape):
     )
     @skipUnless(has_tv, "Requires TorchVision.")
     def test_with_pretrained(self, input_param, input_shape, expected_shape, expected_value):
-        net = TorchVisionFCModel(**input_param).to(device)
+        with skip_if_downloading_fails():
+            net = TorchVisionFCModel(**input_param).to(device)
         with eval_mode(net):
             result = net.forward(torch.randn(input_shape).to(device))
             value = next(net.features.parameters())[0, 0, 0, 0].item()