Merge with remote branch and resolve test conflicts

- Keep the corrected test implementation that matches the actual behavior
- Maintain raise_on_missing_reader=False warning behavior instead of exception

Author: its-serah

.github/workflows/pythonapp-min.yml

@@ -124,7 +124,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        pytorch-version: ['2.4.1', '2.5.1', '2.6.0', '2.7.1']
+        pytorch-version: ['2.5.1', '2.6.0', '2.7.1', '2.8.0']
     timeout-minutes: 40
     steps:
     - uses: actions/checkout@v4

.github/workflows/pythonapp.yml

@@ -94,7 +94,7 @@ jobs:
     - if: runner.os == 'windows'
       name: Install torch cpu from pytorch.org (Windows only)
       run: |
-        python -m pip install torch==2.4.1 torchvision==0.19.1+cpu --index-url https://download.pytorch.org/whl/cpu
+        python -m pip install torch==2.5.1 torchvision==0.20.1+cpu --index-url https://download.pytorch.org/whl/cpu
     - if: runner.os == 'Linux'
       name: Install itk pre-release (Linux only)
       run: |
@@ -103,7 +103,7 @@ jobs:
     - name: Install the dependencies
       run: |
         python -m pip install --user --upgrade pip wheel
-        python -m pip install torch==2.4.1 torchvision==0.19.1
+        python -m pip install torch==2.5.1 torchvision==0.20.1
         cat "requirements-dev.txt"
         python -m pip install -r requirements-dev.txt
         python -m pip list
@@ -155,7 +155,7 @@ jobs:
         # install the latest pytorch for testing
         # however, "pip install monai*.tar.gz" will build cpp/cuda with an isolated
         # fresh torch installation according to pyproject.toml
-        python -m pip install torch>=2.4.1 torchvision
+        python -m pip install torch>=2.5.1 torchvision
     - name: Check packages
       run: |
         pip uninstall monai

monai/apps/auto3dseg/auto_runner.py

@@ -194,6 +194,15 @@ class AutoRunner:
             ├── segresnet2d_0       # network scripts/configs/checkpoints and pickle object of the algo
             └── swinunetr_0         # network scripts/configs/checkpoints and pickle object of the algo
 
+
+        The input config requires at least the following keys:
+            - ``modality``: the modality of the data, e.g. "ct", "mri", etc.
+            - ``datalist``: the path to the datalist file in JSON format.
+            - ``dataroot``: the root directory of the data files.
+
+        For the datalist file format, see the description under :py:func:`monai.data.load_decathlon_datalist`.
+        Note that the AutoRunner will use the "validation" key in the datalist file if it exists, otherwise
+        it will do cross-validation, by default with five folds (this is hardcoded).
     """
 
     analyze_params: dict | None

monai/apps/detection/metrics/coco.py

@@ -457,7 +457,7 @@ def _compute_statistics(self, results_list: list[dict[int, dict[str, np.ndarray]
                 dt_ignores = np.concatenate([r["dtIgnore"][:, 0:max_det] for r in results], axis=1)[:, inds]
                 self.check_number_of_iou(dt_matches, dt_ignores)
                 gt_ignore = np.concatenate([r["gtIgnore"] for r in results])
-                num_gt = np.count_nonzero(gt_ignore == 0)  # number of ground truth boxes (non ignored)
+                num_gt = int(np.count_nonzero(gt_ignore == 0))  # number of ground truth boxes (non ignored)
                 if num_gt == 0:
                     logger.warning(f"WARNING, no gt found for coco metric for class {cls_i}")
                     continue
@@ -523,13 +523,12 @@ def _compute_stats_single_threshold(
         recall = 0
 
     # array where precision values nearest to given recall th are saved
-    precision = np.zeros((num_recall_th,))
+    precision = [0.0] * num_recall_th
     # save scores for corresponding recall value in here
     th_scores = np.zeros((num_recall_th,))
     # numpy is slow without cython optimization for accessing elements
     # use python array gets significant speed improvement
     pr = pr.tolist()
-    precision = precision.tolist()
 
     # smooth precision curve (create box shape)
     for i in range(len(tp) - 1, 0, -1):

monai/apps/detection/networks/retinanet_detector.py

@@ -357,7 +357,7 @@ def set_atss_matcher(self, num_candidates: int = 4, center_in_gt: bool = False)
             num_candidates: number of positions to select candidates from.
                 Smaller value will result in a higher matcher threshold and less matched candidates.
             center_in_gt: If False (default), matched anchor center points do not need
-                to lie withing the ground truth box. Recommend False for small objects.
+                to lie within the ground truth box. Recommend False for small objects.
                 If True, will result in a strict matcher and less matched candidates.
         """
         self.proposal_matcher = ATSSMatcher(num_candidates, self.box_overlap_metric, center_in_gt, debug=self.debug)
@@ -611,7 +611,7 @@ def _reshape_maps(self, result_maps: list[Tensor]) -> Tensor:
             elif self.spatial_dims == 3:
                 reshaped_result_map = reshaped_result_map.permute(0, 3, 4, 5, 1, 2)
             else:
-                ValueError("Images can only be 2D or 3D.")
+                raise ValueError("Images can only be 2D or 3D.")
 
             # reshaped_result_map will become (B, HWA, num_channel) or (B, HWDA, num_channel)
             reshaped_result_map = reshaped_result_map.reshape(batch_size, -1, num_channel)

monai/apps/detection/utils/box_coder.py

@@ -210,7 +210,10 @@ def decode_single(self, rel_codes: Tensor, reference_boxes: Tensor) -> Tensor:
         offset = reference_boxes.shape[-1]
 
         pred_boxes = []
-        boxes_cccwhd = convert_box_mode(reference_boxes, src_mode=StandardMode, dst_mode=CenterSizeMode)
+        boxes_cccwhd: torch.Tensor = convert_box_mode(
+            reference_boxes, src_mode=StandardMode, dst_mode=CenterSizeMode
+        )  # type: ignore[assignment]
+
         for axis in range(self.spatial_dims):
             whd_axis = boxes_cccwhd[:, axis + self.spatial_dims]
             ctr_xyz_axis = boxes_cccwhd[:, axis]

monai/apps/generation/maisi/networks/diffusion_model_unet_maisi.py

@@ -358,8 +358,9 @@ def _apply_down_blocks(self, h, emb, context, down_block_additional_residuals):
 
     def _apply_up_blocks(self, h, emb, context, down_block_res_samples):
         for upsample_block in self.up_blocks:
-            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
-            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
+            idx: int = -len(upsample_block.resnets)  # type: ignore
+            res_samples = down_block_res_samples[idx:]
+            down_block_res_samples = down_block_res_samples[:idx]
             h = upsample_block(hidden_states=h, res_hidden_states_list=res_samples, temb=emb, context=context)
 
         return h

monai/auto3dseg/analyzer.py

@@ -217,6 +217,22 @@ def __init__(self, image_key: str, stats_name: str = DataStatsKeys.IMAGE_STATS)
         self.update_ops(ImageStatsKeys.INTENSITY, SampleOperations())
 
     def __call__(self, data):
+        # Input Validation Addition
+        if not isinstance(data, dict):
+            raise TypeError(f"Input data must be a dict, but got {type(data).__name__}.")
+        if self.image_key not in data:
+            raise KeyError(f"Key '{self.image_key}' not found in input data.")
+        image = data[self.image_key]
+        if not isinstance(image, (np.ndarray, torch.Tensor, MetaTensor)):
+            raise TypeError(
+                f"Value for '{self.image_key}' must be a numpy array, torch.Tensor, or MetaTensor, "
+                f"but got {type(image).__name__}."
+            )
+        if image.ndim < 3:
+            raise ValueError(
+                f"Image data under '{self.image_key}' must have at least 3 dimensions, but got shape {image.shape}."
+            )
+            # --- End of validation ---
         """
         Callable to execute the pre-defined functions
 

monai/data/box_utils.py

@@ -811,9 +811,9 @@ def _box_inter_union(
 
     # compute size for the intersection region for the NxM combinations
     wh = (rb - lt + TO_REMOVE).clamp(min=0)  # (N,M,spatial_dims)
-    inter = torch.prod(wh, dim=-1, keepdim=False)  # (N,M)
+    inter: torch.Tensor = torch.prod(wh, dim=-1, keepdim=False)  # (N,M)
 
-    union = area1[:, None] + area2 - inter
+    union: torch.Tensor = area1[:, None] + area2 - inter  # type: ignore
     return inter, union
 
 
@@ -981,7 +981,7 @@ def box_pair_giou(boxes1: NdarrayOrTensor, boxes2: NdarrayOrTensor) -> NdarrayOr
     wh = (rb - lt + TO_REMOVE).clamp(min=0)  # (N,spatial_dims)
     enclosure = torch.prod(wh, dim=-1, keepdim=False)  # (N,)
 
-    giou_t = iou - (enclosure - union) / (enclosure + torch.finfo(COMPUTE_DTYPE).eps)
+    giou_t: torch.Tensor = iou - (enclosure - union) / (enclosure + torch.finfo(COMPUTE_DTYPE).eps)  # type: ignore
     giou_t = giou_t.to(dtype=box_dtype)  # (N,spatial_dims)
     if torch.isnan(giou_t).any() or torch.isinf(giou_t).any():
         raise ValueError("Box GIoU is NaN or Inf.")

monai/data/dataset.py

@@ -1353,7 +1353,7 @@ def __len__(self) -> int:
         return len(self.dataset)
 
     def randomize(self, data: Any | None = None) -> None:
-        self._seed = self.R.randint(MAX_SEED, dtype="uint32")
+        self._seed = int(self.R.randint(MAX_SEED, dtype="uint32"))
 
     def __getitem__(self, index: int):
         self.randomize()