Fix Incompatible types in assignment issue (#7950)

Fixes #7947

### Description
Add type ignore in several redefinitions cases

### 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: YunLiu <55491388+KumoLiu@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: KumoLiu

monai/auto3dseg/analyzer.py

@@ -470,7 +470,7 @@ def __call__(self, data: Mapping[Hashable, MetaTensor]) -> dict[Hashable, MetaTe
 
         unique_label = unique(ndas_label)
         if isinstance(ndas_label, (MetaTensor, torch.Tensor)):
-            unique_label = unique_label.data.cpu().numpy()
+            unique_label = unique_label.data.cpu().numpy()  # type: ignore[assignment]
 
         unique_label = unique_label.astype(np.int16).tolist()
 

monai/metrics/cumulative_average.py

@@ -65,6 +65,7 @@ def get_current(self, to_numpy: bool = True) -> NdarrayOrTensor:
         if self.val is None:
             return 0
 
+        val: NdarrayOrTensor
         val = self.val.clone()
         val[~torch.isfinite(val)] = 0
 
@@ -96,6 +97,7 @@ def aggregate(self, to_numpy: bool = True) -> NdarrayOrTensor:
             dist.all_reduce(sum)
             dist.all_reduce(count)
 
+        val: NdarrayOrTensor
         val = torch.where(count > 0, sum / count, sum)
 
         if to_numpy:

monai/metrics/panoptic_quality.py

@@ -274,7 +274,7 @@ def _get_paired_iou(
 
         return paired_iou, paired_true, paired_pred
 
-    pairwise_iou = pairwise_iou.cpu().numpy()
+    pairwise_iou = pairwise_iou.cpu().numpy()  # type: ignore[assignment]
     paired_true, paired_pred = linear_sum_assignment(-pairwise_iou)
     paired_iou = pairwise_iou[paired_true, paired_pred]
     paired_true = torch.as_tensor(list(paired_true[paired_iou > match_iou_threshold] + 1), device=device)

monai/metrics/rocauc.py

@@ -88,8 +88,8 @@ def _calculate(y_pred: torch.Tensor, y: torch.Tensor) -> float:
 
     n = len(y)
     indices = y_pred.argsort()
-    y = y[indices].cpu().numpy()
-    y_pred = y_pred[indices].cpu().numpy()
+    y = y[indices].cpu().numpy()  # type: ignore[assignment]
+    y_pred = y_pred[indices].cpu().numpy()  # type: ignore[assignment]
     nneg = auc = tmp_pos = tmp_neg = 0.0
 
     for i in range(n):

monai/transforms/croppad/functional.py

@@ -48,7 +48,7 @@ def _np_pad(img: NdarrayTensor, pad_width: list[tuple[int, int]], mode: str, **k
             warnings.warn(f"Padding: moving img {img.shape} from cuda to cpu for dtype={img.dtype} mode={mode}.")
         img_np = img.detach().cpu().numpy()
     else:
-        img_np = img
+        img_np = np.asarray(img)
     mode = convert_pad_mode(dst=img_np, mode=mode).value
     if mode == "constant" and "value" in kwargs:
         kwargs["constant_values"] = kwargs.pop("value")

monai/visualize/img2tensorboard.py

@@ -176,7 +176,9 @@ def plot_2d_or_3d_image(
     # as the `d` data has no batch dim, reduce the spatial dim index if positive
     frame_dim = frame_dim - 1 if frame_dim > 0 else frame_dim
 
-    d: np.ndarray = data_index.detach().cpu().numpy() if isinstance(data_index, torch.Tensor) else data_index
+    d: np.ndarray = (
+        data_index.detach().cpu().numpy() if isinstance(data_index, torch.Tensor) else np.asarray(data_index)
+    )
 
     if d.ndim == 2:
         d = rescale_array(d, 0, 1)  # type: ignore