auto updates (#7247)

Signed-off-by: monai-bot <[email protected]>

Signed-off-by: monai-bot <[email protected]>

monai/apps/detection/transforms/box_ops.py

@@ -407,7 +407,7 @@ def rot90_boxes(
     spatial_dims: int = get_spatial_dims(boxes=boxes)
     spatial_size_ = list(ensure_tuple_rep(spatial_size, spatial_dims))
 
-    axes = ensure_tuple(axes)  # type: ignore
+    axes = ensure_tuple(axes)
 
     if len(axes) != 2:
         raise ValueError("len(axes) must be 2.")

monai/data/grid_dataset.py

@@ -352,8 +352,8 @@ def __iter__(self):
                         raise RuntimeError(
                             "Cache buffer is not initialized, please call `set_data()` before epoch begins."
                         )
-                    data = self._cache[cache_index]  # type: ignore
-                    other = self._cache_other[cache_index]  # type: ignore
+                    data = self._cache[cache_index]
+                    other = self._cache_other[cache_index]
 
                     # load data from cache and execute from the first random transform
                     data = deepcopy(data) if self.copy_cache else data

monai/data/image_writer.py

@@ -276,7 +276,7 @@ def resample_if_needed(
         # convert back at the end
         if isinstance(output_array, MetaTensor):
             output_array.applied_operations = []
-        data_array, *_ = convert_data_type(output_array, output_type=orig_type)  # type: ignore
+        data_array, *_ = convert_data_type(output_array, output_type=orig_type)
         affine, *_ = convert_data_type(output_array.affine, output_type=orig_type)  # type: ignore
         return data_array[0], affine
 

monai/data/wsi_reader.py

@@ -111,7 +111,7 @@ def __init__(
         self.set_device(device)
         self.mode = mode
         self.kwargs = kwargs
-        self.mpp: tuple[float, float] | None = ensure_tuple_rep(mpp, 2) if mpp is not None else None  # type: ignore
+        self.mpp: tuple[float, float] | None = ensure_tuple_rep(mpp, 2) if mpp is not None else None
         self.power = power
         self.mpp_rtol = mpp_rtol
         self.mpp_atol = mpp_atol

monai/metrics/utils.py

@@ -205,7 +205,7 @@ def get_mask_edges(
         or_vol = seg_pred | seg_gt
         if not or_vol.any():
             pred, gt = lib.zeros(seg_pred.shape, dtype=bool), lib.zeros(seg_gt.shape, dtype=bool)
-            return (pred, gt) if spacing is None else (pred, gt, pred, gt)  # type: ignore
+            return (pred, gt) if spacing is None else (pred, gt, pred, gt)
         channel_first = [seg_pred[None], seg_gt[None], or_vol[None]]
         if spacing is None and not use_cucim:  # cpu only erosion
             seg_pred, seg_gt, or_vol = convert_to_tensor(channel_first, device="cpu", dtype=bool)

monai/networks/nets/resnet.py

@@ -239,9 +239,9 @@ def __init__(
         self.conv1 = conv_type(
             n_input_channels,
             self.in_planes,
-            kernel_size=conv1_kernel_size,  # type: ignore
-            stride=conv1_stride,  # type: ignore
-            padding=tuple(k // 2 for k in conv1_kernel_size),  # type: ignore
+            kernel_size=conv1_kernel_size,
+            stride=conv1_stride,
+            padding=tuple(k // 2 for k in conv1_kernel_size),
             bias=False,
         )
         self.bn1 = norm_type(self.in_planes)

monai/transforms/croppad/array.py

@@ -386,7 +386,7 @@ def compute_slices(
         if roi_slices:
             if not all(s.step is None or s.step == 1 for s in roi_slices):
                 raise ValueError(f"only slice steps of 1/None are currently supported, got {roi_slices}.")
-            return ensure_tuple(roi_slices)  # type: ignore
+            return ensure_tuple(roi_slices)
         else:
             if roi_center is not None and roi_size is not None:
                 roi_center_t = convert_to_tensor(data=roi_center, dtype=torch.int16, wrap_sequence=True, device="cpu")
@@ -408,10 +408,8 @@ def compute_slices(
                 roi_end_t = torch.maximum(roi_end_t, roi_start_t)
             # convert to slices (accounting for 1d)
             if roi_start_t.numel() == 1:
-                return ensure_tuple([slice(int(roi_start_t.item()), int(roi_end_t.item()))])  # type: ignore
-            return ensure_tuple(  # type: ignore
-                [slice(int(s), int(e)) for s, e in zip(roi_start_t.tolist(), roi_end_t.tolist())]
-            )
+                return ensure_tuple([slice(int(roi_start_t.item()), int(roi_end_t.item()))])
+            return ensure_tuple([slice(int(s), int(e)) for s, e in zip(roi_start_t.tolist(), roi_end_t.tolist())])
 
     def __call__(  # type: ignore[override]
         self, img: torch.Tensor, slices: tuple[slice, ...], lazy: bool | None = None

monai/transforms/spatial/array.py

@@ -1157,7 +1157,7 @@ def __init__(self, k: int = 1, spatial_axes: tuple[int, int] = (0, 1), lazy: boo
         """
         LazyTransform.__init__(self, lazy=lazy)
         self.k = (4 + (k % 4)) % 4  # 0, 1, 2, 3
-        spatial_axes_: tuple[int, int] = ensure_tuple(spatial_axes)  # type: ignore
+        spatial_axes_: tuple[int, int] = ensure_tuple(spatial_axes)
         if len(spatial_axes_) != 2:
             raise ValueError(f"spatial_axes must be 2 numbers to define the plane to rotate, got {spatial_axes_}.")
         self.spatial_axes = spatial_axes_

monai/transforms/utility/array.py

@@ -372,7 +372,7 @@ def __call__(self, img: NdarrayOrTensor, dtype: DtypeLike | torch.dtype = None)
             TypeError: When ``img`` type is not in ``Union[numpy.ndarray, torch.Tensor]``.
 
         """
-        return convert_data_type(img, output_type=type(img), dtype=dtype or self.dtype)[0]  # type: ignore
+        return convert_data_type(img, output_type=type(img), dtype=dtype or self.dtype)[0]
 
 
 class ToTensor(Transform):

monai/transforms/utils.py

@@ -521,7 +521,7 @@ def correct_crop_centers(
     for c, v_s, v_e in zip(centers, valid_start, valid_end):
         center_i = min(max(c, v_s), v_e - 1)
         valid_centers.append(int(center_i))
-    return ensure_tuple(valid_centers)  # type: ignore
+    return ensure_tuple(valid_centers)
 
 
 def generate_pos_neg_label_crop_centers(
@@ -579,7 +579,7 @@ def generate_pos_neg_label_crop_centers(
         # shift center to range of valid centers
         centers.append(correct_crop_centers(center, spatial_size, label_spatial_shape, allow_smaller))
 
-    return ensure_tuple(centers)  # type: ignore
+    return ensure_tuple(centers)
 
 
 def generate_label_classes_crop_centers(
@@ -639,7 +639,7 @@ def generate_label_classes_crop_centers(
         # shift center to range of valid centers
         centers.append(correct_crop_centers(center, spatial_size, label_spatial_shape, allow_smaller))
 
-    return ensure_tuple(centers)  # type: ignore
+    return ensure_tuple(centers)
 
 
 def create_grid(
@@ -2218,7 +2218,7 @@ def distance_transform_edt(
     if not r_vals:
         return None
     device = img.device if isinstance(img, torch.Tensor) else None
-    return convert_data_type(r_vals[0] if len(r_vals) == 1 else r_vals, output_type=type(img), device=device)[0]  # type: ignore
+    return convert_data_type(r_vals[0] if len(r_vals) == 1 else r_vals, output_type=type(img), device=device)[0]
 
 
 if __name__ == "__main__":

tests/test_inverse.py

@@ -310,7 +310,7 @@
 
 TESTS_COMPOSE_X2 = [(t[0] + " Compose", t[1], t[2], t[3], Compose(Compose(t[4:]))) for t in TESTS]
 
-TESTS = TESTS + TESTS_COMPOSE_X2  # type: ignore
+TESTS = TESTS + TESTS_COMPOSE_X2
 
 NUM_SAMPLES = 5
 N_SAMPLES_TESTS = [

tests/utils.py

@@ -832,9 +832,9 @@ def equal_state_dict(st_1, st_2):
     [[2.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 2.0, 0.0], [0.0, 0.0, 0.0, 1.0]]
 )
 _metatensor_creator = partial(MetaTensor, meta={"a": "b", "affine": DEFAULT_TEST_AFFINE})
-TEST_NDARRAYS_NO_META_TENSOR: tuple[Callable] = (np.array,) + TEST_TORCH_TENSORS  # type: ignore
+TEST_NDARRAYS_NO_META_TENSOR: tuple[Callable] = (np.array,) + TEST_TORCH_TENSORS
 TEST_NDARRAYS: tuple[Callable] = TEST_NDARRAYS_NO_META_TENSOR + (_metatensor_creator,)  # type: ignore
-TEST_TORCH_AND_META_TENSORS: tuple[Callable] = TEST_TORCH_TENSORS + (_metatensor_creator,)  # type: ignore
+TEST_TORCH_AND_META_TENSORS: tuple[Callable] = TEST_TORCH_TENSORS + (_metatensor_creator,)
 # alias for branch tests
 TEST_NDARRAYS_ALL = TEST_NDARRAYS