Merge branch 'Project-MONAI:dev' into dev

Author: SimoneBendazzoli93

.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

docs/requirements.txt

@@ -40,6 +40,6 @@ onnx>=1.13.0
 onnxruntime; python_version <= '3.10'
 zarr
 huggingface_hub
-pyamg>=5.0.0
+pyamg>=5.0.0, <5.3.0
 packaging
 polygraphy

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/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/decathlon_datalist.py

@@ -92,8 +92,42 @@ def load_decathlon_datalist(
 ) -> list[dict]:
     """Load image/label paths of decathlon challenge from JSON file
 
-    Json file is similar to what you get from http://medicaldecathlon.com/
-    Those dataset.json files
+    JSON file should follow the format of the Medical Segmentation Decathlon
+    datalist.json files, see http://medicaldecathlon.com.
+    The files are structured as follows:
+
+    .. code-block:: python
+
+        {
+            "metadata_key_0": "metadata_value_0",
+            "metadata_key_1": "metadata_value_1",
+            ...,
+            "training": [
+                {"image": "path/to/image_1.nii.gz", "label": "path/to/label_1.nii.gz"},
+                {"image": "path/to/image_2.nii.gz", "label": "path/to/label_2.nii.gz"},
+                ...
+            ],
+            "test": [
+                "path/to/image_3.nii.gz",
+                "path/to/image_4.nii.gz",
+                ...
+            ]
+        }
+
+
+    The metadata keys are optional for loading the datalist, but include:
+        - some string items: ``name``, ``description``, ``reference``, ``licence``, ``release``, ``tensorImageSize``
+        - two dict items: ``modality`` (keyed by channel index), and ``labels`` (keyed by label index)
+        - and two integer items: ``numTraining`` and ``numTest``, with the number of items.
+
+    The ``training`` key contains a list of dictionaries, each of which has at least
+    the ``image`` and ``label`` keys.
+    The image and label are loaded by :py:func:`monai.transforms.LoadImaged`, so both can be either
+    a single file path or a list of file paths, in which case they are loaded as multi-channel images.
+    Each item can also include a ``fold`` key for cross-validation purposes.
+    The "test" key contains a list of image paths, without labels, MONAI also supports a "validation" list
+    with the same format as the "training" list.
+
 
     Args:
         data_list_file_path: the path to the json file of datalist.
@@ -107,11 +141,11 @@ def load_decathlon_datalist(
 
     Returns a list of data items, each of which is a dict keyed by element names, for example:
 
-    .. code-block::
+    .. code-block:: python
 
         [
-            {'image': '/workspace/data/chest_19.nii.gz',  'label': 0},
-            {'image': '/workspace/data/chest_31.nii.gz',  'label': 1}
+            {'image': '/workspace/data/chest_19.nii.gz',  'label': '/workspace/labels/chest_19.nii.gz'},
+            {'image': '/workspace/data/chest_31.nii.gz',  'label': '/workspace/labels/chest_31.nii.gz'},
         ]
 
     """
@@ -134,7 +168,8 @@ def load_decathlon_datalist(
 
 
 def load_decathlon_properties(data_property_file_path: PathLike, property_keys: Sequence[str] | str) -> dict:
-    """Load the properties from the JSON file contains data property with specified `property_keys`.
+    """Extract the properties with the specified keys from the Decathlon JSON file.
+    See under `load_decathlon_datalist` for the expected keys in the Decathlon challenge.
 
     Args:
         data_property_file_path: the path to the JSON file of data properties.