Pytorch 2.8 Support (#8530)

Fixes #8529.

### Description

This adds support for PyTorch 2.8. This required a few minor changes to
how determinism worked, and skipping some tests which mysteriously
started failing under Windows which seems related to GLOO under 2.8.
Mypy seems to have updated with new things it found and a number of
spurious typing issues so many more files had to be fixed for that.

### 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`.
- [x] 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: Eric Kerfoot <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: ericspod

.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/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/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/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()

monai/data/image_dataset.py

@@ -97,7 +97,7 @@ def __len__(self) -> int:
         return len(self.image_files)
 
     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()

monai/data/image_reader.py

@@ -580,7 +580,7 @@ def _combine_dicom_series(self, data: Iterable, filenames: Sequence[PathLike]):
         shape = first_array.shape
         spacing = getattr(first_slice, "PixelSpacing", [1.0] * len(shape))
         prev_pos = getattr(first_slice, "ImagePositionPatient", (0.0, 0.0, 0.0))[2]
-        stack_array = [first_array]
+        stack_array_list: list = [first_array]
         for idx in range(1, len(slices)):
             slc_array = self._get_array_data(slices[idx][0], slices[idx][1])
             slc_shape = slc_array.shape
@@ -592,22 +592,24 @@ def _combine_dicom_series(self, data: Iterable, filenames: Sequence[PathLike]):
                 warnings.warn(f"the list contains slices that have different shapes {shape} and {slc_shape}.")
             average_distance += abs(prev_pos - slc_pos)
             prev_pos = slc_pos
-            stack_array.append(slc_array)
+            stack_array_list.append(slc_array)
 
         if len(slices) > 1:
             average_distance /= len(slices) - 1
             spacing.append(average_distance)
             if self.to_gpu:
-                stack_array = cp.stack(stack_array, axis=-1)
+                stack_array = cp.stack(stack_array_list, axis=-1)
             else:
-                stack_array = np.stack(stack_array, axis=-1)
+                stack_array = np.stack(stack_array_list, axis=-1)
+
+            del stack_array_list[:]
             stack_metadata = self._get_meta_dict(first_slice)
             stack_metadata["spacing"] = np.asarray(spacing)
             if hasattr(slices[-1][0], "ImagePositionPatient"):
                 stack_metadata["lastImagePositionPatient"] = np.asarray(slices[-1][0].ImagePositionPatient)
             stack_metadata[MetaKeys.SPATIAL_SHAPE] = shape + (len(slices),)
         else:
-            stack_array = stack_array[0]
+            stack_array = stack_array_list[0]
             stack_metadata = self._get_meta_dict(first_slice)
             stack_metadata["spacing"] = np.asarray(spacing)
             stack_metadata[MetaKeys.SPATIAL_SHAPE] = shape

monai/networks/nets/diffusion_model_unet.py

@@ -1795,8 +1795,9 @@ def forward(
 
         # 6. up
         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)
 
         # 7. output block