Merge branch 'dev' into dev

.github/workflows/cron.yml

@@ -13,17 +13,13 @@ jobs:
     strategy:
       matrix:
         environment:
-          - "PT113+CUDA118"
-          - "PT210+CUDA121"
+          - "PT230+CUDA121"
           - "PT240+CUDA126"
           - "PTLATEST+CUDA126"
         include:
           # https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes
-          - environment: PT113+CUDA118
-            pytorch: "torch==1.13.1 torchvision==0.14.1 --extra-index-url https://download.pytorch.org/whl/cu121"
-            base: "nvcr.io/nvidia/pytorch:22.10-py3"  # CUDA 11.8
-          - environment: PT210+CUDA121
-            pytorch: "pytorch==2.1.0 torchvision==0.16.0 --extra-index-url https://download.pytorch.org/whl/cu121"
+          - environment: PT230+CUDA121
+            pytorch: "pytorch==2.3.0 torchvision==0.18.0 --extra-index-url https://download.pytorch.org/whl/cu121"
             base: "nvcr.io/nvidia/pytorch:23.08-py3"  # CUDA 12.1
           - environment: PT240+CUDA126
             pytorch: "pytorch==2.4.0 torchvision==0.19.0 --extra-index-url https://download.pytorch.org/whl/cu121"

.github/workflows/pythonapp-gpu.yml

@@ -22,19 +22,21 @@ jobs:
     strategy:
       matrix:
         environment:
-          - "PT113+CUDA116"
-          - "PT210+CUDA121DOCKER"
+          - "PT230+CUDA124DOCKER"
+          - "PT240+CUDA125DOCKER"
+          - "PT250+CUDA126DOCKER"
         include:
           # https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes
-          - environment: PT113+CUDA116
-            pytorch: "torch==1.13.1 torchvision==0.14.1"
-            base: "nvcr.io/nvidia/cuda:11.6.1-devel-ubuntu18.04"
-          - environment: PT210+CUDA121DOCKER
-            # 23.08: 2.1.0a0+29c30b1
+          - environment: PT230+CUDA124DOCKER
+            # 24.04: 2.3.0a0+6ddf5cf85e
             pytorch: "-h"  # we explicitly set pytorch to -h to avoid pip install error
-            base: "nvcr.io/nvidia/pytorch:23.08-py3"
-          - environment: PT210+CUDA121DOCKER
-            # 24.08: 2.3.0a0+40ec155e58.nv24.3
+            base: "nvcr.io/nvidia/pytorch:24.04-py3"
+          - environment: PT240+CUDA125DOCKER
+            # 24.06: 2.4.0a0+f70bd71a48
+            pytorch: "-h"  # we explicitly set pytorch to -h to avoid pip install error
+            base: "nvcr.io/nvidia/pytorch:24.06-py3"
+          - environment: PT250+CUDA126DOCKER
+            # 24.08: 2.5.0a0+872d972e41
             pytorch: "-h"  # we explicitly set pytorch to -h to avoid pip install error
             base: "nvcr.io/nvidia/pytorch:24.08-py3"
     container:
@@ -49,7 +51,7 @@ jobs:
         apt-get update
         apt-get install -y wget
 
-        if [ ${{ matrix.environment }} = "PT113+CUDA116" ]
+        if [ ${{ matrix.environment }} = "PT230+CUDA124" ]
         then
         PYVER=3.9 PYSFX=3 DISTUTILS=python3-distutils && \
         apt-get update && apt-get install -y --no-install-recommends \
@@ -114,7 +116,7 @@ jobs:
         # build for the current self-hosted CI Tesla V100
         BUILD_MONAI=1 TORCH_CUDA_ARCH_LIST="7.0" ./runtests.sh --build --disttests
         ./runtests.sh --quick --unittests
-        if [ ${{ matrix.environment }} = "PT113+CUDA116" ]; then
+        if [ ${{ matrix.environment }} = "PT230+CUDA124" ]; then
           # test the clang-format tool downloading once
           coverage run -m tests.clang_format_utils
         fi

.github/workflows/pythonapp-min.yml

@@ -124,7 +124,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        pytorch-version: ['1.13.1', '2.0.1', '2.2.2', '2.3.1', '2.4.1', 'latest']
+        pytorch-version: ['2.3.1', '2.4.1', '2.5.1', 'latest']
     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==1.13.1+cpu torchvision==0.14.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
+        python -m pip install torch==2.4.1 torchvision==0.19.1+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==1.13.1 torchvision==0.14.1
+        python -m pip install torch==2.4.1 torchvision==0.19.1
         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>=1.13.1 torchvision
+        python -m pip install torch>=2.3.0 torchvision
     - name: Check packages
       run: |
         pip uninstall monai

docs/requirements.txt

@@ -1,5 +1,5 @@
--f https://download.pytorch.org/whl/cpu/torch-1.13.1%2Bcpu-cp39-cp39-linux_x86_64.whl
-torch>=1.13.1
+-f https://download.pytorch.org/whl/cpu/torch-2.3.0%2Bcpu-cp39-cp39-linux_x86_64.whl
+torch>=2.3.0
 pytorch-ignite==0.4.11
 numpy>=1.20
 itk>=5.2

environment-dev.yml

@@ -5,8 +5,8 @@ channels:
   - nvidia
   - conda-forge
 dependencies:
-  - numpy>=1.24,<2.0
-  - pytorch>=1.13.1
+  - numpy>=1.24,<3.0
+  - pytorch>=2.3.0
   - torchio
   - torchvision
   - pytorch-cuda>=11.6

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/detection/networks/retinanet_detector.py

@@ -180,7 +180,7 @@ def forward(self, images: torch.Tensor):
                 nesterov=True,
             )
             torch.save(detector.network.state_dict(), 'model.pt')  # save model
-            detector.network.load_state_dict(torch.load('model.pt'))  # load model
+            detector.network.load_state_dict(torch.load('model.pt', weights_only=True))  # load model
     """
 
     def __init__(

monai/apps/detection/networks/retinanet_network.py

@@ -88,8 +88,8 @@ def __init__(
 
         for layer in self.conv.children():
             if isinstance(layer, conv_type):  # type: ignore
-                torch.nn.init.normal_(layer.weight, std=0.01)
-                torch.nn.init.constant_(layer.bias, 0)
+                torch.nn.init.normal_(layer.weight, std=0.01)  # type: ignore[arg-type]
+                torch.nn.init.constant_(layer.bias, 0)  # type: ignore[arg-type]
 
         self.cls_logits = conv_type(in_channels, num_anchors * num_classes, kernel_size=3, stride=1, padding=1)
         torch.nn.init.normal_(self.cls_logits.weight, std=0.01)
@@ -167,8 +167,8 @@ def __init__(self, in_channels: int, num_anchors: int, spatial_dims: int):
 
         for layer in self.conv.children():
             if isinstance(layer, conv_type):  # type: ignore
-                torch.nn.init.normal_(layer.weight, std=0.01)
-                torch.nn.init.zeros_(layer.bias)
+                torch.nn.init.normal_(layer.weight, std=0.01)  # type: ignore[arg-type]
+                torch.nn.init.zeros_(layer.bias)  # type: ignore[arg-type]
 
     def forward(self, x: list[Tensor]) -> list[Tensor]:
         """
@@ -297,7 +297,7 @@ def __init__(
             )
         self.feature_extractor = feature_extractor
 
-        self.feature_map_channels: int = self.feature_extractor.out_channels
+        self.feature_map_channels: int = self.feature_extractor.out_channels  # type: ignore[assignment]
         self.num_anchors = num_anchors
         self.classification_head = RetinaNetClassificationHead(
             self.feature_map_channels, self.num_anchors, self.num_classes, spatial_dims=self.spatial_dims

monai/apps/detection/utils/box_coder.py

@@ -221,15 +221,15 @@ def decode_single(self, rel_codes: Tensor, reference_boxes: Tensor) -> Tensor:
 
             pred_ctr_xyx_axis = dxyz_axis * whd_axis[:, None] + ctr_xyz_axis[:, None]
             pred_whd_axis = torch.exp(dwhd_axis) * whd_axis[:, None]
-            pred_whd_axis = pred_whd_axis.to(dxyz_axis.dtype)
+            pred_whd_axis = pred_whd_axis.to(dxyz_axis.dtype)  # type: ignore[union-attr]
 
             # When convert float32 to float16, Inf or Nan may occur
             if torch.isnan(pred_whd_axis).any() or torch.isinf(pred_whd_axis).any():
                 raise ValueError("pred_whd_axis is NaN or Inf.")
 
             # Distance from center to box's corner.
             c_to_c_whd_axis = (
-                torch.tensor(0.5, dtype=pred_ctr_xyx_axis.dtype, device=pred_whd_axis.device) * pred_whd_axis
+                torch.tensor(0.5, dtype=pred_ctr_xyx_axis.dtype, device=pred_whd_axis.device) * pred_whd_axis  # type: ignore[arg-type]
             )
 
             pred_boxes.append(pred_ctr_xyx_axis - c_to_c_whd_axis)

monai/apps/mmars/mmars.py

@@ -241,7 +241,7 @@ def load_from_mmar(
         return torch.jit.load(_model_file, map_location=map_location)
 
     # loading with `torch.load`
-    model_dict = torch.load(_model_file, map_location=map_location)
+    model_dict = torch.load(_model_file, map_location=map_location, weights_only=True)
     if weights_only:
         return model_dict.get(model_key, model_dict)  # model_dict[model_key] or model_dict directly
 

monai/apps/reconstruction/networks/blocks/varnetblock.py

@@ -55,7 +55,7 @@ def soft_dc(self, x: Tensor, ref_kspace: Tensor, mask: Tensor) -> Tensor:
         Returns:
             Output of DC block with the same shape as x
         """
-        return torch.where(mask, x - ref_kspace, self.zeros) * self.dc_weight
+        return torch.where(mask, x - ref_kspace, self.zeros) * self.dc_weight  # type: ignore
 
     def forward(self, current_kspace: Tensor, ref_kspace: Tensor, mask: Tensor, sens_maps: Tensor) -> Tensor:
         """

monai/bundle/scripts.py

@@ -760,7 +760,7 @@ def load(
     if load_ts_module is True:
         return load_net_with_metadata(full_path, map_location=torch.device(device), more_extra_files=config_files)
     # loading with `torch.load`
-    model_dict = torch.load(full_path, map_location=torch.device(device))
+    model_dict = torch.load(full_path, map_location=torch.device(device), weights_only=True)
 
     if not isinstance(model_dict, Mapping):
         warnings.warn(f"the state dictionary from {full_path} should be a dictionary but got {type(model_dict)}.")
@@ -1279,9 +1279,8 @@ def verify_net_in_out(
         if input_dtype == torch.float16:
             # fp16 can only be executed in gpu mode
             net.to("cuda")
-            from torch.cuda.amp import autocast
 
-            with autocast():
+            with torch.autocast("cuda"):
                 output = net(test_data.cuda(), **extra_forward_args_)
             net.to(device_)
         else:
@@ -1330,7 +1329,7 @@ def _export(
         # here we use ignite Checkpoint to support nested weights and be compatible with MONAI CheckpointSaver
         Checkpoint.load_objects(to_load={key_in_ckpt: net}, checkpoint=ckpt_file)
     else:
-        ckpt = torch.load(ckpt_file)
+        ckpt = torch.load(ckpt_file, weights_only=True)
         copy_model_state(dst=net, src=ckpt if key_in_ckpt == "" else ckpt[key_in_ckpt])
 
     # Use the given converter to convert a model and save with metadata, config content

monai/data/dataset.py

@@ -22,7 +22,6 @@
 import warnings
 from collections.abc import Callable, Sequence
 from copy import copy, deepcopy
-from inspect import signature
 from multiprocessing.managers import ListProxy
 from multiprocessing.pool import ThreadPool
 from pathlib import Path
@@ -372,10 +371,7 @@ def _cachecheck(self, item_transformed):
 
         if hashfile is not None and hashfile.is_file():  # cache hit
             try:
-                if "weights_only" in signature(torch.load).parameters:
-                    return torch.load(hashfile, weights_only=False)
-                else:
-                    return torch.load(hashfile)
+                return torch.load(hashfile, weights_only=False)
             except PermissionError as e:
                 if sys.platform != "win32":
                     raise e
@@ -1674,7 +1670,4 @@ def _load_meta_cache(self, meta_hash_file_name):
         if meta_hash_file_name in self._meta_cache:
             return self._meta_cache[meta_hash_file_name]
         else:
-            if "weights_only" in signature(torch.load).parameters:
-                return torch.load(self.cache_dir / meta_hash_file_name, weights_only=False)
-            else:
-                return torch.load(self.cache_dir / meta_hash_file_name)
+            return torch.load(self.cache_dir / meta_hash_file_name, weights_only=False)

monai/data/utils.py

@@ -753,7 +753,7 @@ def affine_to_spacing(affine: NdarrayTensor, r: int = 3, dtype=float, suppress_z
     if isinstance(_affine, torch.Tensor):
         spacing = torch.sqrt(torch.sum(_affine * _affine, dim=0))
     else:
-        spacing = np.sqrt(np.sum(_affine * _affine, axis=0))
+        spacing = np.sqrt(np.sum(_affine * _affine, axis=0))  # type: ignore[operator]
     if suppress_zeros:
         spacing[spacing == 0] = 1.0
     spacing_, *_ = convert_to_dst_type(spacing, dst=affine, dtype=dtype)

monai/data/video_dataset.py

@@ -177,7 +177,7 @@ def get_available_codecs() -> dict[str, str]:
                 for codec, ext in all_codecs.items():
                     writer = cv2.VideoWriter()
                     fname = os.path.join(tmp_dir, f"test{ext}")
-                    fourcc = cv2.VideoWriter_fourcc(*codec)
+                    fourcc = cv2.VideoWriter_fourcc(*codec)  # type: ignore[attr-defined]
                     noviderr = writer.open(fname, fourcc, 1, (10, 10))
                     if noviderr:
                         codecs[codec] = ext

monai/engines/evaluator.py

@@ -28,7 +28,7 @@
 from monai.utils import ForwardMode, IgniteInfo, ensure_tuple, min_version, optional_import
 from monai.utils.enums import CommonKeys as Keys
 from monai.utils.enums import EngineStatsKeys as ESKeys
-from monai.utils.module import look_up_option, pytorch_after
+from monai.utils.module import look_up_option
 
 if TYPE_CHECKING:
     from ignite.engine import Engine, EventEnum
@@ -82,8 +82,8 @@ class Evaluator(Workflow):
             default to `True`.
         to_kwargs: dict of other args for `prepare_batch` API when converting the input data, except for
             `device`, `non_blocking`.
-        amp_kwargs: dict of the args for `torch.cuda.amp.autocast()` API, for more details:
-            https://pytorch.org/docs/stable/amp.html#torch.cuda.amp.autocast.
+        amp_kwargs: dict of the args for `torch.autocast("cuda")` API, for more details:
+            https://pytorch.org/docs/stable/amp.html#torch.autocast.
 
     """
 
@@ -214,8 +214,8 @@ class SupervisedEvaluator(Evaluator):
             default to `True`.
         to_kwargs: dict of other args for `prepare_batch` API when converting the input data, except for
             `device`, `non_blocking`.
-        amp_kwargs: dict of the args for `torch.cuda.amp.autocast()` API, for more details:
-            https://pytorch.org/docs/stable/amp.html#torch.cuda.amp.autocast.
+        amp_kwargs: dict of the args for `torch.autocast("cuda")` API, for more details:
+            https://pytorch.org/docs/stable/amp.html#torch.autocast.
         compile: whether to use `torch.compile`, default is False. If True, MetaTensor inputs will be converted to
             `torch.Tensor` before forward pass,  then converted back afterward with copied meta information.
         compile_kwargs: dict of the args for `torch.compile()` API, for more details:
@@ -269,13 +269,8 @@ def __init__(
             amp_kwargs=amp_kwargs,
         )
         if compile:
-            if pytorch_after(2, 1):
-                compile_kwargs = {} if compile_kwargs is None else compile_kwargs
-                network = torch.compile(network, **compile_kwargs)  # type: ignore[assignment]
-            else:
-                warnings.warn(
-                    "Network compilation (compile=True) not supported for Pytorch versions before 2.1, no compilation done"
-                )
+            compile_kwargs = {} if compile_kwargs is None else compile_kwargs
+            network = torch.compile(network, **compile_kwargs)  # type: ignore[assignment]
         self.network = network
         self.compile = compile
         self.inferer = SimpleInferer() if inferer is None else inferer
@@ -329,7 +324,7 @@ def _iteration(self, engine: SupervisedEvaluator, batchdata: dict[str, torch.Ten
         # execute forward computation
         with engine.mode(engine.network):
             if engine.amp:
-                with torch.cuda.amp.autocast(**engine.amp_kwargs):
+                with torch.autocast("cuda", **engine.amp_kwargs):
                     engine.state.output[Keys.PRED] = engine.inferer(inputs, engine.network, *args, **kwargs)
             else:
                 engine.state.output[Keys.PRED] = engine.inferer(inputs, engine.network, *args, **kwargs)
@@ -399,8 +394,8 @@ class EnsembleEvaluator(Evaluator):
             default to `True`.
         to_kwargs: dict of other args for `prepare_batch` API when converting the input data, except for
             `device`, `non_blocking`.
-        amp_kwargs: dict of the args for `torch.cuda.amp.autocast()` API, for more details:
-            https://pytorch.org/docs/stable/amp.html#torch.cuda.amp.autocast.
+        amp_kwargs: dict of the args for `torch.autocast("cuda")` API, for more details:
+            https://pytorch.org/docs/stable/amp.html#torch.autocast.
 
     """
 
@@ -492,7 +487,7 @@ def _iteration(self, engine: EnsembleEvaluator, batchdata: dict[str, torch.Tenso
         for idx, network in enumerate(engine.networks):
             with engine.mode(network):
                 if engine.amp:
-                    with torch.cuda.amp.autocast(**engine.amp_kwargs):
+                    with torch.autocast("cuda", **engine.amp_kwargs):
                         if isinstance(engine.state.output, dict):
                             engine.state.output.update(
                                 {engine.pred_keys[idx]: engine.inferer(inputs, network, *args, **kwargs)}