Merge branch 'dev' into fix-issue-8780

Author: ytl0623

.github/workflows/pythonapp.yml

@@ -82,7 +82,7 @@ jobs:
         find  /opt/hostedtoolcache/* -maxdepth 0 ! -name 'Python' -exec rm -rf {} \;
     - name: Install the dependencies
       run: |
-        python -m pip install --user --upgrade pip wheel
+        python -m pip install --user --upgrade pip wheel pybind11
         python -m pip install torch==2.5.1 torchvision==0.20.1
         cat "requirements-dev.txt"
         python -m pip install --no-build-isolation -r requirements-dev.txt

.github/workflows/setupapp.yml

@@ -81,7 +81,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.9', '3.10', '3.11']
+        python-version: ['3.10', '3.11', '3.12']
     steps:
     - uses: actions/checkout@v6
       with:
@@ -90,23 +90,12 @@ jobs:
       uses: actions/setup-python@v6
       with:
         python-version: ${{ matrix.python-version }}
-    - name: cache weekly timestamp
-      id: pip-cache
-      run: |
-        echo "datew=$(date '+%Y-%V')" >> $GITHUB_OUTPUT
-    - name: cache for pip
-      uses: actions/cache@v5
-      id: cache
-      with:
-        path: |
-          ~/.cache/pip
-          ~/.cache/torch
-        key: ${{ runner.os }}-${{ matrix.python-version }}-pip-${{ steps.pip-cache.outputs.datew }}
+        cache: pip
     - name: Install the dependencies
       run: |
         find  /opt/hostedtoolcache/* -maxdepth 0 ! -name 'Python' -exec rm -rf {} \;
         python -m pip install --upgrade pip wheel
-        python -m pip install -r requirements-dev.txt
+        python -m pip install --no-build-isolation -r requirements-dev.txt
     - name: Run quick tests CPU ubuntu
       env:
         NGC_API_KEY: ${{ secrets.NGC_API_KEY }}
@@ -115,8 +104,8 @@ jobs:
       run: |
         python -m pip list
         python -c 'import torch; print(torch.__version__); print(torch.rand(5,3))'
-        BUILD_MONAI=0 ./runtests.sh --build --quick --unittests
-        BUILD_MONAI=1 ./runtests.sh --build --quick --min
+        BUILD_MONAI=0 ./runtests.sh --build --coverage --quick --unittests
+        BUILD_MONAI=1 ./runtests.sh --build --coverage --quick --min
         coverage xml --ignore-errors
     - name: Upload coverage
       uses: codecov/codecov-action@v5

docs/source/losses.rst

@@ -98,6 +98,11 @@ Segmentation Losses
 .. autoclass:: NACLLoss
     :members:
 
+`MCCLoss`
+~~~~~~~~~
+.. autoclass:: MCCLoss
+    :members:
+
 Registration Losses
 -------------------
 

monai/apps/auto3dseg/auto_runner.py

@@ -229,7 +229,7 @@ def __init__(
             input = os.path.join(os.path.abspath(work_dir), "input.yaml")
             logger.info(f"Input config is not provided, using the default {input}")
 
-        self.data_src_cfg = dict()
+        self.data_src_cfg = {}
         if isinstance(input, dict):
             self.data_src_cfg = input
         elif isinstance(input, str) and os.path.isfile(input):

monai/apps/detection/transforms/box_ops.py

@@ -267,7 +267,7 @@ def convert_box_to_mask(
             boxes_only_mask = np.ones(box_size, dtype=np.int16) * np.int16(labels_np[b])
         # apply to global mask
         slicing = [b]
-        slicing.extend(slice(boxes_np[b, d], boxes_np[b, d + spatial_dims]) for d in range(spatial_dims))  # type:ignore
+        slicing.extend(slice(boxes_np[b, d], boxes_np[b, d + spatial_dims]) for d in range(spatial_dims))  # type: ignore
         boxes_mask_np[tuple(slicing)] = boxes_only_mask
     return convert_to_dst_type(src=boxes_mask_np, dst=boxes, dtype=torch.int16)[0]
 

monai/apps/reconstruction/transforms/dictionary.py

@@ -20,6 +20,7 @@
 from monai.apps.reconstruction.transforms.array import EquispacedKspaceMask, RandomKspaceMask
 from monai.config import DtypeLike, KeysCollection
 from monai.config.type_definitions import NdarrayOrTensor
+from monai.data.meta_tensor import MetaTensor
 from monai.transforms import InvertibleTransform
 from monai.transforms.croppad.array import SpatialCrop
 from monai.transforms.intensity.array import NormalizeIntensity
@@ -33,15 +34,36 @@ class ExtractDataKeyFromMetaKeyd(MapTransform):
     Moves keys from meta to data. It is useful when a dataset of paired samples
     is loaded and certain keys should be moved from meta to data.
 
+    This transform supports two modes:
+
+    1. When ``meta_key`` references a metadata dictionary in the data (e.g., when
+       ``image_only=False`` was used with ``LoadImaged``), the requested keys are
+       extracted directly from that dictionary.
+
+    2. When ``meta_key`` references a ``MetaTensor`` in the data (e.g., when
+       ``image_only=True`` was used with ``LoadImaged``), the requested keys are
+       extracted from its ``.meta`` attribute.
+
     Args:
         keys: keys to be transferred from meta to data
-        meta_key: the meta key where all the meta-data is stored
+        meta_key: the key in the data dictionary where the metadata source is
+            stored. This can be either a metadata dictionary or a ``MetaTensor``.
         allow_missing_keys: don't raise exception if key is missing
 
     Example:
         When the fastMRI dataset is loaded, "kspace" is stored in the data dictionary,
         but the ground-truth image with the key "reconstruction_rss" is stored in the meta data.
         In this case, ExtractDataKeyFromMetaKeyd moves "reconstruction_rss" to data.
+
+        When ``LoadImaged`` is used with ``image_only=True`` (the default), the loaded
+        data is a ``MetaTensor`` with metadata accessible via ``.meta``. In this case,
+        set ``meta_key`` to the key of the ``MetaTensor`` itself::
+
+            li = LoadImaged(keys="image")  # image_only=True by default
+            dat = li({"image": "image.nii"})
+            e = ExtractDataKeyFromMetaKeyd("filename_or_obj", meta_key="image")
+            dat = e(dat)
+            assert dat["image"].meta["filename_or_obj"] == dat["filename_or_obj"]
     """
 
     def __init__(self, keys: KeysCollection, meta_key: str, allow_missing_keys: bool = False) -> None:
@@ -58,9 +80,18 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, T
             the new data dictionary
         """
         d = dict(data)
+        meta_obj = d[self.meta_key]
+
+        # If meta_key references a MetaTensor, extract from its .meta attribute;
+        # otherwise treat it as a metadata dictionary directly.
+        if isinstance(meta_obj, MetaTensor):
+            meta_dict: dict = meta_obj.meta
+        else:
+            meta_dict = dict(meta_obj)
+
         for key in self.keys:
-            if key in d[self.meta_key]:
-                d[key] = d[self.meta_key][key]  # type: ignore
+            if key in meta_dict:
+                d[key] = meta_dict[key]  # type: ignore
             elif not self.allow_missing_keys:
                 raise KeyError(
                     f"Key `{key}` of transform `{self.__class__.__name__}` was missing in the meta data"

monai/auto3dseg/analyzer.py

@@ -15,7 +15,7 @@
 from abc import ABC, abstractmethod
 from collections.abc import Hashable, Mapping
 from copy import deepcopy
-from typing import Any
+from typing import Any, cast
 
 import numpy as np
 import torch
@@ -105,7 +105,7 @@ def update_ops_nested_label(self, nested_key: str, op: Operations) -> None:
             raise ValueError("Nested_key input format is wrong. Please ensure it is like key1#0#key2")
         root: str
         child_key: str
-        (root, _, child_key) = keys
+        root, _, child_key = keys
         if root not in self.ops:
             self.ops[root] = [{}]
         self.ops[root][0].update({child_key: None})
@@ -468,21 +468,35 @@ def __call__(self, data: Mapping[Hashable, MetaTensor]) -> dict[Hashable, MetaTe
         """
         d: dict[Hashable, MetaTensor] = dict(data)
         start = time.time()
-        if isinstance(d[self.image_key], (torch.Tensor, MetaTensor)) and d[self.image_key].device.type == "cuda":
-            using_cuda = True
-        else:
-            using_cuda = False
+        image_tensor = d[self.image_key]
+        label_tensor = d[self.label_key]
+        # Check if either tensor is on CUDA to determine if we should move both to CUDA for processing
+        using_cuda = any(
+            isinstance(t, (torch.Tensor, MetaTensor)) and t.device.type == "cuda" for t in (image_tensor, label_tensor)
+        )
         restore_grad_state = torch.is_grad_enabled()
         torch.set_grad_enabled(False)
 
-        ndas: list[MetaTensor] = [d[self.image_key][i] for i in range(d[self.image_key].shape[0])]  # type: ignore
-        ndas_label: MetaTensor = d[self.label_key].astype(torch.int16)  # (H,W,D)
+        if isinstance(image_tensor, (MetaTensor, torch.Tensor)) and isinstance(
+            label_tensor, (MetaTensor, torch.Tensor)
+        ):
+            if label_tensor.device != image_tensor.device:
+                if using_cuda:
+                    # Move both tensors to CUDA when mixing devices
+                    cuda_device = image_tensor.device if image_tensor.device.type == "cuda" else label_tensor.device
+                    image_tensor = cast(MetaTensor, image_tensor.to(cuda_device))
+                    label_tensor = cast(MetaTensor, label_tensor.to(cuda_device))
+                else:
+                    label_tensor = cast(MetaTensor, label_tensor.to(image_tensor.device))
+
+        ndas: list[MetaTensor] = [image_tensor[i] for i in range(image_tensor.shape[0])]  # type: ignore
+        ndas_label: MetaTensor = label_tensor.astype(torch.int16)  # (H,W,D)
 
         if ndas_label.shape != ndas[0].shape:
             raise ValueError(f"Label shape {ndas_label.shape} is different from image shape {ndas[0].shape}")
 
         nda_foregrounds: list[torch.Tensor] = [get_foreground_label(nda, ndas_label) for nda in ndas]
-        nda_foregrounds = [nda if nda.numel() > 0 else torch.Tensor([0]) for nda in nda_foregrounds]
+        nda_foregrounds = [nda if nda.numel() > 0 else MetaTensor([0.0]) for nda in nda_foregrounds]
 
         unique_label = unique(ndas_label)
         if isinstance(ndas_label, (MetaTensor, torch.Tensor)):

monai/bundle/scripts.py

@@ -1948,7 +1948,7 @@ def create_workflow(
 
     """
     _args = update_kwargs(args=args_file, workflow_name=workflow_name, config_file=config_file, **kwargs)
-    (workflow_name, config_file) = _pop_args(
+    workflow_name, config_file = _pop_args(
         _args, workflow_name=ConfigWorkflow, config_file=None
     )  # the default workflow name is "ConfigWorkflow"
     if isinstance(workflow_name, str):

monai/data/dataset.py

@@ -139,7 +139,7 @@ class DatasetFunc(Dataset):
     """
 
     def __init__(self, data: Any, func: Callable, **kwargs) -> None:
-        super().__init__(data=None, transform=None)  # type:ignore
+        super().__init__(data=None, transform=None)  # type: ignore
         self.src = data
         self.func = func
         self.kwargs = kwargs
@@ -1635,7 +1635,7 @@ def _cachecheck(self, item_transformed):
                         return (_data, _meta)
                     return _data
                 else:
-                    item: list[dict[Any, Any]] = [{} for _ in range(len(item_transformed))]  # type:ignore
+                    item: list[dict[Any, Any]] = [{} for _ in range(len(item_transformed))]  # type: ignore
                     for i, _item in enumerate(item_transformed):
                         for k in _item:
                             meta_i_k = self._load_meta_cache(meta_hash_file_name=f"{hashfile.name}-{k}-meta-{i}")

monai/data/image_writer.py

@@ -324,7 +324,7 @@ def convert_to_channel_last(
                 data = data[..., 0, :]
         # if desired, remove trailing singleton dimensions
         while squeeze_end_dims and data.shape[-1] == 1:
-            data = np.squeeze(data, -1)
+            data = data.squeeze(-1)
         if contiguous:
             data = ascontiguousarray(data)
         return data