Merge branch 'Project-MONAI:dev' into add-tf32-doc

.github/workflows/cron.yml

@@ -15,8 +15,8 @@ jobs:
         environment:
           - "PT191+CUDA113"
           - "PT110+CUDA113"
-          - "PT112+CUDA113"
-          - "PTLATEST+CUDA118"
+          - "PT113+CUDA113"
+          - "PTLATEST+CUDA121"
         include:
           # https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes
           - environment: PT191+CUDA113
@@ -25,12 +25,12 @@ jobs:
           - environment: PT110+CUDA113
             pytorch: "torch==1.10.2 torchvision==0.11.3 --extra-index-url https://download.pytorch.org/whl/cu113"
             base: "nvcr.io/nvidia/pytorch:21.06-py3"  # CUDA 11.3
-          - environment: PT112+CUDA113
-            pytorch: "torch==1.12.1 torchvision==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu113"
+          - environment: PT113+CUDA113
+            pytorch: "torch==1.13.1 torchvision==0.14.1 --extra-index-url https://download.pytorch.org/whl/cu113"
             base: "nvcr.io/nvidia/pytorch:21.06-py3"  # CUDA 11.3
-          - environment: PTLATEST+CUDA118
+          - environment: PTLATEST+CUDA121
             pytorch: "-U torch torchvision --extra-index-url https://download.pytorch.org/whl/cu118"
-            base: "nvcr.io/nvidia/pytorch:23.03-py3"  # CUDA 11.8
+            base: "nvcr.io/nvidia/pytorch:23.06-py3"  # CUDA 12.1
     container:
       image: ${{ matrix.base }}
       options: "--gpus all"
@@ -76,7 +76,7 @@ jobs:
     if: github.repository == 'Project-MONAI/MONAI'
     strategy:
       matrix:
-        container: ["pytorch:22.09", "pytorch:22.11", "pytorch:23.03"]
+        container: ["pytorch:22.10", "pytorch:23.06"]
     container:
       image: nvcr.io/nvidia/${{ matrix.container }}-py3  # testing with the latest pytorch base image
       options: "--gpus all"
@@ -121,7 +121,7 @@ jobs:
     if: github.repository == 'Project-MONAI/MONAI'
     strategy:
       matrix:
-        container: ["pytorch:22.09", "pytorch:22.11", "pytorch:23.03"]
+        container: ["pytorch:23.06"]
     container:
       image: nvcr.io/nvidia/${{ matrix.container }}-py3  # testing with the latest pytorch base image
       options: "--gpus all"
@@ -221,7 +221,7 @@ jobs:
     if: github.repository == 'Project-MONAI/MONAI'
     needs: cron-gpu  # so that monai itself is verified first
     container:
-      image: nvcr.io/nvidia/pytorch:23.03-py3  # testing with the latest pytorch base image
+      image: nvcr.io/nvidia/pytorch:23.06-py3  # testing with the latest pytorch base image
       options: "--gpus all --ipc=host"
     runs-on: [self-hosted, linux, x64, integration]
     steps:

.github/workflows/docker.yml

@@ -85,12 +85,11 @@ jobs:
     container:
       image: docker://projectmonai/monai:latest
       options: "--shm-size=4g --ipc=host"
-    runs-on: ubuntu-latest
+    runs-on: [self-hosted, linux, X64, docker]
     steps:
     - name: Import
       run: |
-        export CUDA_VISIBLE_DEVICES=$(python -m tests.utils | tail -n 1)
-        echo $CUDA_VISIBLE_DEVICES
+        export CUDA_VISIBLE_DEVICES=  # cpu-only
         python -c 'import monai; monai.config.print_debug_info()'
         cd /opt/monai
         ls -al

.github/workflows/pythonapp-gpu.yml

@@ -25,7 +25,7 @@ jobs:
           - "PT110+CUDA111"
           - "PT112+CUDA118DOCKER"
           - "PT113+CUDA116"
-          - "PT114+CUDA120DOCKER"
+          - "PT210+CUDA121DOCKER"
         include:
           # https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes
           - environment: PT19+CUDA114DOCKER
@@ -42,10 +42,10 @@ jobs:
           - environment: PT113+CUDA116
             pytorch: "torch==1.13.1 torchvision==0.14.1"
             base: "nvcr.io/nvidia/cuda:11.6.1-devel-ubuntu18.04"
-          - environment: PT114+CUDA120DOCKER
-            # 23.03: 2.0.0a0+1767026
+          - environment: PT210+CUDA121DOCKER
+            # 23.06: 2.1.0a0+4136153
             pytorch: "-h"  # we explicitly set pytorch to -h to avoid pip install error
-            base: "nvcr.io/nvidia/pytorch:23.03-py3"
+            base: "nvcr.io/nvidia/pytorch:23.06-py3"
     container:
       image: ${{ matrix.base }}
       options: --gpus all --env NVIDIA_DISABLE_REQUIRE=true  # workaround for unsatisfied condition: cuda>=11.6

Dockerfile

@@ -11,7 +11,7 @@
 
 # To build with a different base image
 # please run `docker build` using the `--build-arg PYTORCH_IMAGE=...` flag.
-ARG PYTORCH_IMAGE=nvcr.io/nvidia/pytorch:23.03-py3
+ARG PYTORCH_IMAGE=nvcr.io/nvidia/pytorch:23.06-py3
 FROM ${PYTORCH_IMAGE}
 
 LABEL maintainer="[email protected]"

docs/requirements.txt

@@ -6,6 +6,7 @@ itk>=5.2
 nibabel
 parameterized
 scikit-image>=0.19.0
+scipy>=1.7.1
 tensorboard
 commonmark==0.9.1
 recommonmark==0.6.0

docs/source/installation.md

@@ -10,8 +10,8 @@
 		- [Uninstall the packages](#uninstall-the-packages)
 	- [From conda-forge](#from-conda-forge)
 	- [From GitHub](#from-github)
-		- [Option 1 (as a part of your system-wide module)](#option-1-as-a-part-of-your-system-wide-module)
-		- [Option 2 (editable installation)](#option-2-editable-installation)
+		- [Option 1 (as a part of your system-wide module):](#option-1-as-a-part-of-your-system-wide-module)
+		- [Option 2 (editable installation):](#option-2-editable-installation)
 	- [Validating the install](#validating-the-install)
 	- [MONAI version string](#monai-version-string)
 	- [From DockerHub](#from-dockerhub)
@@ -254,10 +254,10 @@ Since MONAI v0.2.0, the extras syntax such as `pip install 'monai[nibabel]'` is
 - The options are
 
 ```
-[nibabel, skimage, pillow, tensorboard, gdown, ignite, torchvision, itk, tqdm, lmdb, psutil, cucim, openslide, pandas, einops, transformers, mlflow, clearml, matplotlib, tensorboardX, tifffile, imagecodecs, pyyaml, fire, jsonschema, ninja, pynrrd, pydicom, h5py, nni, optuna, onnx, onnxruntime, zarr]
+[nibabel, skimage, scipy, pillow, tensorboard, gdown, ignite, torchvision, itk, tqdm, lmdb, psutil, cucim, openslide, pandas, einops, transformers, mlflow, clearml, matplotlib, tensorboardX, tifffile, imagecodecs, pyyaml, fire, jsonschema, ninja, pynrrd, pydicom, h5py, nni, optuna, onnx, onnxruntime, zarr]
 ```
 
-which correspond to `nibabel`, `scikit-image`, `pillow`, `tensorboard`,
+which correspond to `nibabel`, `scikit-image`, `scipy`, `pillow`, `tensorboard`,
 `gdown`, `pytorch-ignite`, `torchvision`, `itk`, `tqdm`, `lmdb`, `psutil`, `cucim`, `openslide-python`, `pandas`, `einops`, `transformers`, `mlflow`, `clearml`, `matplotlib`, `tensorboardX`, `tifffile`, `imagecodecs`, `pyyaml`, `fire`, `jsonschema`, `ninja`, `pynrrd`, `pydicom`, `h5py`, `nni`, `optuna`, `onnx`, `onnxruntime`, and `zarr` respectively.
 
 - `pip install 'monai[all]'` installs all the optional dependencies.

monai/apps/auto3dseg/data_analyzer.py

@@ -70,7 +70,7 @@ class DataAnalyzer:
             the DataAnalyzer will skip looking for labels and all label-related operations.
         hist_bins: bins to compute histogram for each image channel.
         hist_range: ranges to compute histogram for each image channel.
-        fmt: format used to save the analysis results. Defaults to "yaml".
+        fmt: format used to save the analysis results. Currently support ``"json"`` and ``"yaml"``, defaults to "yaml".
         histogram_only: whether to only compute histograms. Defaults to False.
         extra_params: other optional arguments. Currently supported arguments are :
             'allowed_shape_difference' (default 5) can be used to change the default tolerance of
@@ -164,6 +164,7 @@ def _check_data_uniformity(keys: list[str], result: dict) -> bool:
         constant_props = [result[DataStatsKeys.SUMMARY][DataStatsKeys.IMAGE_STATS][key] for key in keys]
         for prop in constant_props:
             if "stdev" in prop and np.any(prop["stdev"]):
+                logger.debug(f"summary image_stats {prop} has non-zero stdev {prop['stdev']}.")
                 return False
 
         return True
@@ -242,15 +243,16 @@ def get_all_case_stats(self, key="training", transform_list=None):
         if not self._check_data_uniformity([ImageStatsKeys.SPACING], result):
             logger.info("Data spacing is not completely uniform. MONAI transforms may provide unexpected result")
         if self.output_path:
+            logger.info(f"Writing data stats to {self.output_path}.")
             ConfigParser.export_config_file(
                 result, self.output_path, fmt=self.fmt, default_flow_style=None, sort_keys=False
             )
+            by_case_path = self.output_path.replace(f".{self.fmt}", f"_by_case.{self.fmt}")
+            if by_case_path == self.output_path:  # self.output_path not ended with self.fmt?
+                by_case_path += f".by_case.{self.fmt}"
+            logger.info(f"Writing by-case data stats to {by_case_path}, this may take a while.")
             ConfigParser.export_config_file(
-                result_bycase,
-                self.output_path.replace(".yaml", "_by_case.yaml"),
-                fmt=self.fmt,
-                default_flow_style=None,
-                sort_keys=False,
+                result_bycase, by_case_path, fmt=self.fmt, default_flow_style=None, sort_keys=False
             )
         # release memory
         if self.device.type == "cuda":

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])  # type: ignore
-            box_end = np.array([s.stop for s in cropper.slices])  # type: ignore
+            box_start = np.array([s.start for s in cropper.slices])
+            box_end = np.array([s.stop for s in cropper.slices])
         else:
             cropper = SpatialCrop(roi_start=box_start, roi_end=box_end)
 

monai/bundle/config_parser.py

@@ -438,12 +438,12 @@ def export_config_file(cls, config: dict, filepath: PathLike, fmt: str = "json",
 
         """
         _filepath: str = str(Path(filepath))
-        writer = look_up_option(fmt.lower(), {"json", "yaml"})
+        writer = look_up_option(fmt.lower(), {"json", "yaml", "yml"})
         with open(_filepath, "w") as f:
             if writer == "json":
                 json.dump(config, f, **kwargs)
                 return
-            if writer == "yaml":
+            if writer == "yaml" or writer == "yml":
                 return yaml.safe_dump(config, f, **kwargs)
             raise ValueError(f"only support JSON or YAML config file so far, got {writer}.")
 

monai/config/deviceconfig.py

@@ -71,6 +71,7 @@ def get_optional_config_values():
     output["ITK"] = get_package_version("itk")
     output["Nibabel"] = get_package_version("nibabel")
     output["scikit-image"] = get_package_version("skimage")
+    output["scipy"] = get_package_version("scipy")
     output["Pillow"] = get_package_version("PIL")
     output["Tensorboard"] = get_package_version("tensorboard")
     output["gdown"] = get_package_version("gdown")

monai/data/__init__.py

@@ -150,3 +150,5 @@ def reduce_meta_tensor(meta_tensor):
         return _rebuild_meta, (type(meta_tensor), storage, dtype, metadata)
 
     ForkingPickler.register(MetaTensor, reduce_meta_tensor)
+
+from .ultrasound_confidence_map import UltrasoundConfidenceMap