Merge branch 'dev' into fix/GHSA-873f-pvrv-4x83

Signed-off-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com>

Author: ericspod

.github/workflows/cicd_tests.yml

@@ -56,7 +56,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        opt: ["codeformat", "mypy"]  #  "pytype" omitted for being essentially deprecated, see #8865
+        opt: ["codeformat", "pyrefly"]
     steps:
     - name: Clean unused tools
       run: |
@@ -80,8 +80,7 @@ jobs:
       run: |
         # clean up temporary files
         $(pwd)/runtests.sh --build --clean
-        # Github actions have multiple cores, so parallelize pytype
-        $(pwd)/runtests.sh --build --${{ matrix.opt }} -j $(nproc --all)
+        $(pwd)/runtests.sh --build --${{ matrix.opt }}
 
   min-dep:  # Test with minumum dependencies installed for different OS, Python, and PyTorch combinations
     runs-on: ${{ matrix.os }}

.github/workflows/cron.yml

@@ -214,7 +214,7 @@ jobs:
         python -c "import torch; print(torch.__version__); print('{} of GPUs available'.format(torch.cuda.device_count()))"
         python -c 'import torch; print(torch.rand(5,3, device=torch.device("cuda:0")))'
         ngc --version
-        BUILD_MONAI=1 ./runtests.sh --build --coverage --unittests --disttests  # unit tests with pytype checks, coverage report
+        BUILD_MONAI=1 ./runtests.sh --build --coverage --pyrefly --unittests --disttests  # unit tests with pyrefly checks, coverage report
         BUILD_MONAI=1 ./runtests.sh --build --coverage --net  # integration tests with coverage report
         coverage xml --ignore-errors
         if pgrep python; then pkill python; fi

.github/workflows/weekly-preview.yml

@@ -12,7 +12,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        opt: ["codeformat", "mypy"]
+        opt: ["codeformat", "pyrefly"]
     steps:
     - name: Clean unused tools
       run: |

.gitignore

@@ -110,12 +110,17 @@ venv.bak/
 # pytype cache
 .pytype/
 
+# pyrefly cache
+.pyrefly_cache/
+
 # mypy
 .mypy_cache/
+.dmypy.json
+
 examples/scd_lvsegs.npz
 temp/
 .idea/
-.dmypy.json
+.plans/
 
 *~
 

CONTRIBUTING.md

@@ -38,7 +38,7 @@ Please note that, as per PyTorch, MONAI uses American English spelling. This mea
 ### Preparing pull requests
 
 To ensure the code quality, MONAI relies on several linting tools ([black](https://github.com/psf/black), [isort](https://github.com/timothycrosley/isort), [ruff](https://github.com/astral-sh/ruff)),
-static type analysis tools ([mypy](https://github.com/python/mypy), [pytype](https://github.com/google/pytype)), as well as a set of unit/integration tests.
+static type analysis tools ([pyrefly](https://github.com/facebook/pyrefly)), as well as a set of unit/integration tests.
 
 This section highlights all the necessary preparation steps required before sending a pull request.
 To collaborate efficiently, please read through this section and follow them.

monai/apps/auto3dseg/auto_runner.py

@@ -790,6 +790,7 @@ def _train_algo_in_nni(self, history: list[dict[str, Any]]) -> None:
             nni_config_filename = os.path.abspath(os.path.join(self.work_dir, f"{name}_nni_config.yaml"))
             ConfigParser.export_config_file(nni_config, nni_config_filename, fmt="yaml", default_flow_style=None)
 
+            # pyrefly: ignore [redundant-cast]
             max_trial = min(self.hpo_tasks, cast(int, default_nni_config["maxTrialNumber"]))
             cmd = "nnictl create --config " + nni_config_filename + " --port 8088"
 
@@ -805,6 +806,7 @@ def _train_algo_in_nni(self, history: list[dict[str, Any]]) -> None:
                 n_trainings = len(import_bundle_algo_history(self.work_dir, only_trained=True))
 
             cmd = "nnictl stop --all"
+            # pyrefly: ignore [bad-argument-type]
             run_cmd(cmd.split(), check=True)
             logger.info(f"NNI completes HPO on {name}")
             last_total_tasks = n_trainings

monai/apps/deepedit/transforms.py

@@ -434,6 +434,7 @@ def _randomize(self, d, key_label):
         else:
             logger.info(f"Not slice IDs for label: {key_label}")
             sid = None
+        # pyrefly: ignore [unsupported-operation]
         self.sid[key_label] = sid
 
     def __call__(self, data: Mapping[Hashable, np.ndarray]) -> dict[Hashable, np.ndarray]:
@@ -561,6 +562,7 @@ def __init__(
         self.guidance: dict[str, list[list[int]]] = {}
 
     def randomize(self, data=None):
+        # pyrefly: ignore [unsupported-operation]
         probability = data[self.probability]
         self._will_interact = self.R.choice([True, False], p=[probability, 1.0 - probability])
 
@@ -885,6 +887,7 @@ def _randomize(self, d, key_label):
         else:
             logger.info(f"Not slice IDs for label: {key_label}")
             sid = None
+        # pyrefly: ignore [unsupported-operation]
         self.sid[key_label] = sid
 
     def __call__(self, data: Mapping[Hashable, np.ndarray]) -> dict[Hashable, np.ndarray]:

monai/apps/deepgrow/dataset.py

@@ -175,6 +175,7 @@ def _save_data_2d(vol_idx, vol_image, vol_label, dataset_dir, relative_path):
             continue
 
         # For all Labels
+        # pyrefly: ignore [missing-attribute]
         unique_labels = np.unique(label.flatten())
         unique_labels = unique_labels[unique_labels != 0]
         unique_labels_count = max(unique_labels_count, len(unique_labels))

monai/apps/deepgrow/transforms.py

@@ -288,6 +288,7 @@ def __init__(self, guidance: str = "guidance", discrepancy: str = "discrepancy",
         self._will_interact = None
 
     def randomize(self, data=None):
+        # pyrefly: ignore [unsupported-operation]
         probability = data[self.probability]
         self._will_interact = self.R.choice([True, False], p=[probability, 1.0 - probability])
 

monai/apps/detection/networks/retinanet_detector.py

@@ -525,6 +525,7 @@ def forward(
             )
 
         # 4. Generate anchors and store it in self.anchors: List[Tensor]
+        # pyrefly: ignore [bad-argument-type]
         self.generate_anchors(images, head_outputs)
         # num_anchor_locs_per_level: List[int], list of HW or HWD for each level
         num_anchor_locs_per_level = [x.shape[2:].numel() for x in head_outputs[self.cls_key]]
@@ -535,6 +536,7 @@ def forward(
             # reshape to Tensor sized(B, sum(HWA), self.num_classes) for self.cls_key
             # or (B, sum(HWA), 2* self.spatial_dims) for self.box_reg_key
             # A = self.num_anchors_per_loc
+            # pyrefly: ignore [bad-argument-type]
             head_outputs[key] = self._reshape_maps(head_outputs[key])
 
         # 6(1). If during training, return losses