Merge branch 'dev' into fix-6840

Author: slicepaste

.github/workflows/pythonapp-min.yml

@@ -124,7 +124,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        pytorch-version: ['2.3.1', '2.4.1', '2.5.1', 'latest']
+        pytorch-version: ['2.4.1', '2.5.1', '2.6.0']  # FIXME: add 'latest' back once PyTorch 2.7 issues are resolved
     timeout-minutes: 40
     steps:
     - uses: actions/checkout@v4

.github/workflows/pythonapp.yml

@@ -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>=2.3.0 torchvision
+        python -m pip install torch>=2.4.1 torchvision
     - name: Check packages
       run: |
         pip uninstall monai

docs/requirements.txt

@@ -1,5 +1,5 @@
 -f https://download.pytorch.org/whl/cpu/torch-2.3.0%2Bcpu-cp39-cp39-linux_x86_64.whl
-torch>=2.3.0
+torch>=2.4.1, <2.7.0
 pytorch-ignite==0.4.11
 numpy>=1.20
 itk>=5.2

docs/source/apps.rst

@@ -277,3 +277,15 @@ FastMRIReader
 
 .. autoclass:: monai.apps.nnunet.nnUNetV2Runner
   :members:
+
+`nnUNet Bundle`
+---------------
+.. autoclass:: monai.apps.nnunet.ModelnnUNetWrapper
+    :members:
+    :special-members:
+
+.. autofunction:: monai.apps.nnunet.get_nnunet_trainer
+.. autofunction:: monai.apps.nnunet.get_nnunet_monai_predictor
+.. autofunction:: monai.apps.nnunet.convert_nnunet_to_monai_bundle
+.. autofunction:: monai.apps.nnunet.convert_monai_bundle_to_nnunet
+.. autofunction:: monai.apps.nnunet.get_network_from_nnunet_plans

docs/source/bundle.rst

@@ -34,6 +34,7 @@ Model Bundle
     :members:
     :special-members:
 
+
 `Scripts`
 ---------
 .. autofunction:: ckpt_export

docs/source/metrics.rst

@@ -98,7 +98,6 @@ Metrics
 `Hausdorff distance`
 --------------------
 .. autofunction:: compute_hausdorff_distance
-.. autofunction:: compute_percent_hausdorff_distance
 
 .. autoclass:: HausdorffDistanceMetric
     :members:

docs/source/networks.rst

@@ -109,6 +109,16 @@ Blocks
 .. autoclass:: SABlock
     :members:
 
+`CABlock Block`
+~~~~~~~~~~~~~~~
+.. autoclass:: CABlock
+    :members:
+
+`FeedForward Block`
+~~~~~~~~~~~~~~~~~~~
+.. autoclass:: FeedForward
+    :members:
+
 `Squeeze-and-Excitation`
 ~~~~~~~~~~~~~~~~~~~~~~~~
 .. autoclass:: ChannelSELayer
@@ -173,6 +183,16 @@ Blocks
 .. autoclass:: Subpixelupsample
 .. autoclass:: SubpixelUpSample
 
+`Downsampling`
+~~~~~~~~~~~~~~
+.. autoclass:: DownSample
+    :members:
+.. autoclass:: Downsample
+.. autoclass:: SubpixelDownsample
+    :members:
+.. autoclass:: Subpixeldownsample
+.. autoclass:: SubpixelDownSample
+
 `Registration Residual Conv Block`
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 .. autoclass:: RegistrationResidualConvBlock
@@ -625,6 +645,11 @@ Nets
 .. autoclass:: ViT
   :members:
 
+`Restormer`
+~~~~~~~~~~~
+.. autoclass:: restormer
+  :members:
+
 `ViTAutoEnc`
 ~~~~~~~~~~~~
 .. autoclass:: ViTAutoEnc
@@ -750,3 +775,38 @@ Utilities
 
 .. automodule:: monai.apps.reconstruction.networks.nets.utils
   :members:
+
+Noise Schedulers
+----------------
+.. automodule:: monai.networks.schedulers
+.. currentmodule:: monai.networks.schedulers
+
+`Scheduler`
+~~~~~~~~~~~
+.. autoclass:: Scheduler
+  :members:
+
+`NoiseSchedules`
+~~~~~~~~~~~~~~~~
+.. autoclass:: NoiseSchedules
+  :members:
+
+`DDPMScheduler`
+~~~~~~~~~~~~~~~
+.. autoclass:: DDPMScheduler
+  :members:
+
+`DDIMScheduler`
+~~~~~~~~~~~~~~~
+.. autoclass:: DDIMScheduler
+  :members:
+
+`PNDMScheduler`
+~~~~~~~~~~~~~~~
+.. autoclass:: PNDMScheduler
+  :members:
+
+`RFlowScheduler`
+~~~~~~~~~~~~~~~~
+.. autoclass:: RFlowScheduler
+  :members:

monai/apps/deepedit/interaction.py

@@ -72,7 +72,7 @@ def __call__(self, engine: SupervisedTrainer | SupervisedEvaluator, batchdata: d
 
                 with torch.no_grad():
                     if engine.amp:
-                        with torch.cuda.amp.autocast():
+                        with torch.autocast("cuda"):
                             predictions = engine.inferer(inputs, engine.network)
                     else:
                         predictions = engine.inferer(inputs, engine.network)

monai/apps/deepgrow/interaction.py

@@ -67,7 +67,7 @@ def __call__(self, engine: SupervisedTrainer | SupervisedEvaluator, batchdata: d
             engine.network.eval()
             with torch.no_grad():
                 if engine.amp:
-                    with torch.cuda.amp.autocast():
+                    with torch.autocast("cuda"):
                         predictions = engine.inferer(inputs, engine.network)
                 else:
                     predictions = engine.inferer(inputs, engine.network)

monai/apps/deepgrow/transforms.py

@@ -441,8 +441,8 @@ def __call__(self, data):
 
         if np.all(np.less(current_size, self.spatial_size)):
             cropper = SpatialCrop(roi_center=center, roi_size=self.spatial_size)
-            box_start = np.array([s.start for s in cropper.slices])
-            box_end = np.array([s.stop for s in cropper.slices])
+            box_start = [s.start for s in cropper.slices]
+            box_end = [s.stop for s in cropper.slices]
         else:
             cropper = SpatialCrop(roi_start=box_start, roi_end=box_end)