8587 test erros on pytorch release 2508 on series 50

monai/apps/auto3dseg/bundle_gen.py

@@ -264,8 +264,7 @@ def _run_cmd(self, cmd: str, devices_info: str = "") -> subprocess.CompletedProc
                 look_up_option(self.device_setting["MN_START_METHOD"], ["bcprun"])
             except ValueError as err:
                 raise NotImplementedError(
-                    f"{self.device_setting['MN_START_METHOD']} is not supported yet."
-                    "Try modify BundleAlgo._run_cmd for your cluster."
+                    f"{self.device_setting['MN_START_METHOD']} is not supported yet. Try modify BundleAlgo._run_cmd for your cluster."
                 ) from err
 
             return _run_cmd_bcprun(cmd, n=self.device_setting["NUM_NODES"], p=self.device_setting["n_devices"])

monai/apps/detection/networks/retinanet_detector.py

@@ -342,8 +342,7 @@ def set_regular_matcher(
         """
         if fg_iou_thresh < bg_iou_thresh:
             raise ValueError(
-                "Require fg_iou_thresh >= bg_iou_thresh. "
-                f"Got fg_iou_thresh={fg_iou_thresh}, bg_iou_thresh={bg_iou_thresh}."
+                f"Require fg_iou_thresh >= bg_iou_thresh. Got fg_iou_thresh={fg_iou_thresh}, bg_iou_thresh={bg_iou_thresh}."
             )
         self.proposal_matcher = Matcher(
             fg_iou_thresh, bg_iou_thresh, allow_low_quality_matches=allow_low_quality_matches
@@ -519,7 +518,7 @@ def forward(
         else:
             if self.inferer is None:
                 raise ValueError(
-                    "`self.inferer` is not defined." "Please refer to function self.set_sliding_window_inferer(*)."
+                    "`self.inferer` is not defined. Please refer to function self.set_sliding_window_inferer(*)."
                 )
             head_outputs = predict_with_inferer(
                 images, self.network, keys=[self.cls_key, self.box_reg_key], inferer=self.inferer

monai/apps/detection/transforms/box_ops.py

@@ -267,7 +267,7 @@ def convert_box_to_mask(
             boxes_only_mask = np.ones(box_size, dtype=np.int16) * np.int16(labels_np[b])
         # apply to global mask
         slicing = [b]
-        slicing.extend(slice(boxes_np[b, d], boxes_np[b, d + spatial_dims]) for d in range(spatial_dims))  # type:ignore
+        slicing.extend(slice(boxes_np[b, d], boxes_np[b, d + spatial_dims]) for d in range(spatial_dims))  # type: ignore
         boxes_mask_np[tuple(slicing)] = boxes_only_mask
     return convert_to_dst_type(src=boxes_mask_np, dst=boxes, dtype=torch.int16)[0]
 

monai/apps/detection/transforms/dictionary.py

@@ -125,10 +125,8 @@ def __init__(self, box_keys: KeysCollection, box_ref_image_keys: str, allow_miss
         super().__init__(box_keys, allow_missing_keys)
         box_ref_image_keys_tuple = ensure_tuple(box_ref_image_keys)
         if len(box_ref_image_keys_tuple) > 1:
-            raise ValueError(
-                "Please provide a single key for box_ref_image_keys.\
-                All boxes of box_keys are attached to box_ref_image_keys."
-            )
+            raise ValueError("Please provide a single key for box_ref_image_keys.\
+                All boxes of box_keys are attached to box_ref_image_keys.")
         self.box_ref_image_keys = box_ref_image_keys
 
     def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, NdarrayOrTensor]:
@@ -289,10 +287,8 @@ def __init__(
         super().__init__(box_keys, allow_missing_keys)
         box_ref_image_keys_tuple = ensure_tuple(box_ref_image_keys)
         if len(box_ref_image_keys_tuple) > 1:
-            raise ValueError(
-                "Please provide a single key for box_ref_image_keys.\
-                All boxes of box_keys are attached to box_ref_image_keys."
-            )
+            raise ValueError("Please provide a single key for box_ref_image_keys.\
+                All boxes of box_keys are attached to box_ref_image_keys.")
         self.box_ref_image_keys = box_ref_image_keys
         self.image_meta_key = image_meta_key or f"{box_ref_image_keys}_{image_meta_key_postfix}"
         self.converter_to_image_coordinate = AffineBox()
@@ -310,10 +306,8 @@ def extract_affine(self, data: Mapping[Hashable, torch.Tensor]) -> tuple[Ndarray
         else:
             raise ValueError(f"{meta_key} is not found. Please check whether it is the correct the image meta key.")
         if "affine" not in meta_dict:
-            raise ValueError(
-                f"'affine' is not found in {meta_key}. \
-                Please check whether it is the correct the image meta key."
-            )
+            raise ValueError(f"'affine' is not found in {meta_key}. \
+                Please check whether it is the correct the image meta key.")
         affine: NdarrayOrTensor = meta_dict["affine"]
 
         if self.affine_lps_to_ras:  # RAS affine
@@ -815,16 +809,12 @@ def __init__(
     ) -> None:
         box_keys_tuple = ensure_tuple(box_keys)
         if len(box_keys_tuple) != 1:
-            raise ValueError(
-                "Please provide a single key for box_keys.\
-                All label_keys are attached to this box_keys."
-            )
+            raise ValueError("Please provide a single key for box_keys.\
+                All label_keys are attached to this box_keys.")
         box_ref_image_keys_tuple = ensure_tuple(box_ref_image_keys)
         if len(box_ref_image_keys_tuple) != 1:
-            raise ValueError(
-                "Please provide a single key for box_ref_image_keys.\
-                All box_keys and label_keys are attached to this box_ref_image_keys."
-            )
+            raise ValueError("Please provide a single key for box_ref_image_keys.\
+                All box_keys and label_keys are attached to this box_ref_image_keys.")
         self.label_keys = ensure_tuple(label_keys)
         super().__init__(box_keys_tuple, allow_missing_keys)
 
@@ -1091,10 +1081,8 @@ def __init__(
 
         box_keys_tuple = ensure_tuple(box_keys)
         if len(box_keys_tuple) != 1:
-            raise ValueError(
-                "Please provide a single key for box_keys.\
-                All label_keys are attached to this box_keys."
-            )
+            raise ValueError("Please provide a single key for box_keys.\
+                All label_keys are attached to this box_keys.")
         self.box_keys = box_keys_tuple[0]
         self.label_keys = ensure_tuple(label_keys)
 

monai/apps/detection/utils/anchor_utils.py

@@ -124,10 +124,8 @@ def __init__(
             aspect_ratios = (aspect_ratios,) * len(self.sizes)
 
         if len(self.sizes) != len(aspect_ratios):
-            raise ValueError(
-                "len(sizes) and len(aspect_ratios) should be equal. \
-                It represents the number of feature maps."
-            )
+            raise ValueError("len(sizes) and len(aspect_ratios) should be equal. \
+                It represents the number of feature maps.")
 
         spatial_dims = len(ensure_tuple(aspect_ratios[0][0])) + 1
         spatial_dims = look_up_option(spatial_dims, [2, 3])
@@ -172,16 +170,12 @@ def generate_anchors(
         scales_t = torch.as_tensor(scales, dtype=dtype, device=device)  # sized (N,)
         aspect_ratios_t = torch.as_tensor(aspect_ratios, dtype=dtype, device=device)  # sized (M,) or (M,2)
         if (self.spatial_dims >= 3) and (len(aspect_ratios_t.shape) != 2):
-            raise ValueError(
-                f"In {self.spatial_dims}-D image, aspect_ratios for each level should be \
-                {len(aspect_ratios_t.shape) - 1}-D. But got aspect_ratios with shape {aspect_ratios_t.shape}."
-            )
+            raise ValueError(f"In {self.spatial_dims}-D image, aspect_ratios for each level should be \
+                {len(aspect_ratios_t.shape) - 1}-D. But got aspect_ratios with shape {aspect_ratios_t.shape}.")
 
         if (self.spatial_dims >= 3) and (aspect_ratios_t.shape[1] != self.spatial_dims - 1):
-            raise ValueError(
-                f"In {self.spatial_dims}-D image, aspect_ratios for each level should has \
-                shape (_,{self.spatial_dims - 1}). But got aspect_ratios with shape {aspect_ratios_t.shape}."
-            )
+            raise ValueError(f"In {self.spatial_dims}-D image, aspect_ratios for each level should has \
+                shape (_,{self.spatial_dims - 1}). But got aspect_ratios with shape {aspect_ratios_t.shape}.")
 
         # if 2d, w:h = 1:aspect_ratios
         if self.spatial_dims == 2:

monai/apps/reconstruction/transforms/array.py

@@ -61,10 +61,8 @@ def __init__(
                 real/imaginary parts.
         """
         if len(center_fractions) != len(accelerations):
-            raise ValueError(
-                "Number of center fractions \
-                should match number of accelerations"
-            )
+            raise ValueError("Number of center fractions \
+                should match number of accelerations")
 
         self.center_fractions = center_fractions
         self.accelerations = accelerations

monai/auto3dseg/analyzer.py

@@ -105,7 +105,7 @@ def update_ops_nested_label(self, nested_key: str, op: Operations) -> None:
             raise ValueError("Nested_key input format is wrong. Please ensure it is like key1#0#key2")
         root: str
         child_key: str
-        (root, _, child_key) = keys
+        root, _, child_key = keys
         if root not in self.ops:
             self.ops[root] = [{}]
         self.ops[root][0].update({child_key: None})
@@ -952,8 +952,7 @@ def __call__(self, data: dict) -> dict:
             self.hist_range = nr_channels * self.hist_range
         if len(self.hist_range) != nr_channels:
             raise ValueError(
-                f"There is a mismatch between the number of channels ({nr_channels}) "
-                f"and histogram ranges ({len(self.hist_range)})."
+                f"There is a mismatch between the number of channels ({nr_channels}) and histogram ranges ({len(self.hist_range)})."
             )
 
         # perform calculation

monai/bundle/scripts.py

@@ -1948,7 +1948,7 @@ def create_workflow(
 
     """
     _args = update_kwargs(args=args_file, workflow_name=workflow_name, config_file=config_file, **kwargs)
-    (workflow_name, config_file) = _pop_args(
+    workflow_name, config_file = _pop_args(
         _args, workflow_name=ConfigWorkflow, config_file=None
     )  # the default workflow name is "ConfigWorkflow"
     if isinstance(workflow_name, str):

monai/bundle/utils.py

@@ -124,10 +124,8 @@
         "run_name": None,
         # may fill it at runtime
         "save_execute_config": True,
-        "is_not_rank0": (
-            "$torch.distributed.is_available() \
-                and torch.distributed.is_initialized() and torch.distributed.get_rank() > 0"
-        ),
+        "is_not_rank0": ("$torch.distributed.is_available() \
+                and torch.distributed.is_initialized() and torch.distributed.get_rank() > 0"),
         # MLFlowHandler config for the trainer
         "trainer": {
             "_target_": "MLFlowHandler",

monai/data/dataset.py

@@ -139,7 +139,7 @@ class DatasetFunc(Dataset):
     """
 
     def __init__(self, data: Any, func: Callable, **kwargs) -> None:
-        super().__init__(data=None, transform=None)  # type:ignore
+        super().__init__(data=None, transform=None)  # type: ignore
         self.src = data
         self.func = func
         self.kwargs = kwargs
@@ -1635,7 +1635,7 @@ def _cachecheck(self, item_transformed):
                         return (_data, _meta)
                     return _data
                 else:
-                    item: list[dict[Any, Any]] = [{} for _ in range(len(item_transformed))]  # type:ignore
+                    item: list[dict[Any, Any]] = [{} for _ in range(len(item_transformed))]  # type: ignore
                     for i, _item in enumerate(item_transformed):
                         for k in _item:
                             meta_i_k = self._load_meta_cache(meta_hash_file_name=f"{hashfile.name}-{k}-meta-{i}")

monai/data/wsi_reader.py

@@ -416,8 +416,7 @@ def get_data(
             # Check if there are three color channels for RGB
             elif mode in "RGB" and patch.shape[self.channel_dim] != 3:
                 raise ValueError(
-                    f"The image is expected to have three color channels in '{mode}' mode but has "
-                    f"{patch.shape[self.channel_dim]}. "
+                    f"The image is expected to have three color channels in '{mode}' mode but has {patch.shape[self.channel_dim]}. "
                 )
             # Get patch-related metadata
             metadata: dict = self._get_metadata(wsi=each_wsi, patch=patch, location=location, size=size, level=level)

monai/handlers/utils.py

@@ -48,7 +48,7 @@ def stopping_fn_from_loss() -> Callable[[Engine], Any]:
     """
 
     def stopping_fn(engine: Engine) -> Any:
-        return -engine.state.output  # type:ignore
+        return -engine.state.output  # type: ignore
 
     return stopping_fn
 

monai/losses/dice.py

@@ -203,11 +203,9 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
                 self.class_weight = torch.as_tensor([self.class_weight] * num_of_classes)
             else:
                 if self.class_weight.shape[0] != num_of_classes:
-                    raise ValueError(
-                        """the length of the `weight` sequence should be the same as the number of classes.
+                    raise ValueError("""the length of the `weight` sequence should be the same as the number of classes.
                         If `include_background=False`, the weight should not include
-                        the background category class 0."""
-                    )
+                        the background category class 0.""")
             if self.class_weight.min() < 0:
                 raise ValueError("the value/values of the `weight` should be no less than 0.")
             # apply class_weight to loss

monai/losses/focal_loss.py

@@ -183,11 +183,9 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
                 self.class_weight = torch.as_tensor([self.class_weight] * num_of_classes)
             else:
                 if self.class_weight.shape[0] != num_of_classes:
-                    raise ValueError(
-                        """the length of the `weight` sequence should be the same as the number of classes.
+                    raise ValueError("""the length of the `weight` sequence should be the same as the number of classes.
                         If `include_background=False`, the weight should not include
-                        the background category class 0."""
-                    )
+                        the background category class 0.""")
             if self.class_weight.min() < 0:
                 raise ValueError("the value/values of the `weight` should be no less than 0.")
             # apply class_weight to loss

monai/metrics/utils.py

@@ -320,7 +320,7 @@ def get_edge_surface_distance(
     edges_spacing = None
     if use_subvoxels:
         edges_spacing = spacing if spacing is not None else ([1] * len(y_pred.shape))
-    (edges_pred, edges_gt, *areas) = get_mask_edges(
+    edges_pred, edges_gt, *areas = get_mask_edges(
         y_pred, y, crop=True, spacing=edges_spacing, always_return_as_numpy=False
     )
     if not edges_gt.any():

monai/networks/blocks/warp.py

@@ -20,6 +20,7 @@
 from monai.config.deviceconfig import USE_COMPILED
 from monai.networks.layers.spatial_transforms import grid_pull
 from monai.networks.utils import meshgrid_ij
+from monai.transforms.spatial.functional import _compiled_unsupported
 from monai.utils import GridSampleMode, GridSamplePadMode, optional_import
 
 _C, _ = optional_import("monai._C")
@@ -138,7 +139,9 @@ def forward(self, image: torch.Tensor, ddf: torch.Tensor):
         grid = self.get_reference_grid(ddf, jitter=self.jitter) + ddf
         grid = grid.permute([0] + list(range(2, 2 + spatial_dims)) + [1])  # (batch, ..., spatial_dims)
 
-        if not USE_COMPILED:  # pytorch native grid_sample
+        _use_compiled = USE_COMPILED and not _compiled_unsupported(image.device)
+
+        if not _use_compiled:  # pytorch native grid_sample
             for i, dim in enumerate(grid.shape[1:-1]):
                 grid[..., i] = grid[..., i] * 2 / (dim - 1) - 1
             index_ordering: list[int] = list(range(spatial_dims - 1, -1, -1))

monai/transforms/io/array.py

@@ -11,6 +11,7 @@
 """
 A collection of "vanilla" transforms for IO functions.
 """
+
 from __future__ import annotations
 
 import inspect

monai/transforms/regularization/array.py

@@ -24,7 +24,6 @@
 
 
 class Mixer(RandomizableTransform):
-
     def __init__(self, batch_size: int, alpha: float = 1.0) -> None:
         """
         Mixer is a base class providing the basic logic for the mixup-class of

monai/transforms/spatial/array.py

@@ -34,6 +34,7 @@
 from monai.transforms.croppad.array import CenterSpatialCrop, ResizeWithPadOrCrop
 from monai.transforms.inverse import InvertibleTransform
 from monai.transforms.spatial.functional import (
+    _compiled_unsupported,
     affine_func,
     convert_box_to_points,
     convert_points_to_box,
@@ -2104,14 +2105,15 @@ def __call__(
         _align_corners = self.align_corners if align_corners is None else align_corners
         img_t, *_ = convert_data_type(img, torch.Tensor, dtype=_dtype, device=_device)
         sr = min(len(img_t.peek_pending_shape() if isinstance(img_t, MetaTensor) else img_t.shape[1:]), 3)
+        _use_compiled = USE_COMPILED and not _compiled_unsupported(img_t.device)
         backend, _interp_mode, _padding_mode, _ = resolves_modes(
             self.mode if mode is None else mode,
             self.padding_mode if padding_mode is None else padding_mode,
             backend=None,
-            use_compiled=USE_COMPILED,
+            use_compiled=_use_compiled,
         )
 
-        if USE_COMPILED or backend == TransformBackends.NUMPY:
+        if _use_compiled or backend == TransformBackends.NUMPY:
             grid_t, *_ = convert_to_dst_type(grid[:sr], img_t, dtype=grid.dtype, wrap_sequence=True)
             if isinstance(grid, torch.Tensor) and grid_t.data_ptr() == grid.data_ptr():
                 grid_t = grid_t.clone(memory_format=torch.contiguous_format)
@@ -2122,7 +2124,7 @@ def __call__(
                     grid_t[i] = ((_dim - 1) / _dim) * grid_t[i] + t if _align_corners else grid_t[i] + t
                 elif _align_corners:
                     grid_t[i] = ((_dim - 1) / _dim) * (grid_t[i] + 0.5)
-            if USE_COMPILED and backend == TransformBackends.TORCH:  # compiled is using torch backend param name
+            if _use_compiled and backend == TransformBackends.TORCH:  # compiled is using torch backend param name
                 grid_t = moveaxis(grid_t, 0, -1)  # type: ignore
                 out = grid_pull(
                     img_t.unsqueeze(0),
@@ -2140,6 +2142,20 @@ def __call__(
                     [_map_coord(c, grid_np, order=_interp_mode, mode=_padding_mode) for c in img_np]
                 )
                 out = convert_to_dst_type(out, img_t)[0]
+            else:
+                # Fallback to PyTorch grid_sample when compiled extension is unsupported.
+                # Convert grid coordinates from compiled convention [0, size-1] to PyTorch [-1, 1]
+                for i, dim in enumerate(img_t.shape[1 : 1 + sr]):
+                    _dim = max(2, dim)
+                    grid_t[i] = (grid_t[i] * 2.0 / _dim) - 1.0
+                grid_t = moveaxis(grid_t, 0, -1)  # type: ignore
+                out = torch.nn.functional.grid_sample(
+                    img_t.unsqueeze(0),
+                    grid_t.unsqueeze(0),
+                    mode=_interp_mode,
+                    padding_mode=_padding_mode,
+                    align_corners=None if _align_corners == TraceKeys.NONE else _align_corners,  # type: ignore
+                )[0]
         else:
             grid_t = moveaxis(grid[list(range(sr - 1, -1, -1))], 0, -1)  # type: ignore
             grid_t = convert_to_dst_type(grid_t, img_t, wrap_sequence=True)[0].unsqueeze(0)