feat: replace mypy and pytype with pyrefly for static type analysis

.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: |
@@ -80,7 +80,7 @@ jobs:
       run: |
         # clean up temporary files
         $(pwd)/runtests.sh --build --clean
-        # Github actions have multiple cores, so parallelize pytype
+        # Github actions have multiple cores, so parallelize pylint
         $(pwd)/runtests.sh --build --${{ matrix.opt }} -j $(nproc --all)
 
   min-dep:  # Test with minumum dependencies installed for different OS, Python, and PyTorch combinations
@@ -259,7 +259,7 @@ jobs:
         cache: 'pip'
     - name: Install dependencies
       run: |
-        python -m pip install --user --upgrade pip setuptools wheel twine packaging
+        python -m pip install --user --upgrade pip "setuptools<70" wheel twine packaging
         # 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
@@ -291,8 +291,8 @@ jobs:
     - name: Install wheel file
       working-directory: ${{ steps.mktemp.outputs.tmp_dir }}
       run: |
-        # install from wheel
-        python -m pip install monai*.whl --extra-index-url https://download.pytorch.org/whl/cpu
+        # install from wheel (use --no-build-isolation to keep system setuptools with pkg_resources)
+        python -m pip install monai*.whl --no-build-isolation --extra-index-url https://download.pytorch.org/whl/cpu
         python -c 'import monai; monai.config.print_config()' 2>&1 | grep -iv "unknown"
         python -c 'import monai; print(monai.__file__)'
         python -m pip uninstall -y monai
@@ -302,6 +302,6 @@ jobs:
       run: |
         for name in *.tar.gz; do break; done
         echo $name
-        python -m pip install ${name}[all] --extra-index-url https://download.pytorch.org/whl/cpu
+        python -m pip install ${name}[all] --no-build-isolation --extra-index-url https://download.pytorch.org/whl/cpu
         python -c 'import monai; monai.config.print_config()' 2>&1 | grep -iv "unknown"
         python -c 'import monai; print(monai.__file__)'

.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

@@ -107,15 +107,11 @@ venv.bak/
 # mkdocs documentation
 /site
 
-# pytype cache
-.pytype/
-
-# mypy
-.mypy_cache/
+# pyrefly cache
+.pyrefly_cache/
 examples/scd_lvsegs.npz
 temp/
 .idea/
-.dmypy.json
 
 *~
 

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

@@ -405,6 +405,7 @@ def inspect_datalist_folds(self, datalist_filename: str) -> int:
 
         datalist = ConfigParser.load_config_file(datalist_filename)
         if "training" not in datalist:
+            # pyrefly: ignore [unnecessary-type-conversion]
             raise ValueError("Datalist files has no training key:" + str(datalist_filename))
 
         fold_list = [int(d["fold"]) for d in datalist["training"] if "fold" in d]
@@ -790,6 +791,7 @@ 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 +807,7 @@ 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/auto3dseg/bundle_gen.py

@@ -344,6 +344,7 @@ def infer(self, image_file):
         config_dir = os.path.join(self.output_path, "configs")
         configs_path = [os.path.join(config_dir, f) for f in os.listdir(config_dir)]
 
+        # pyrefly: ignore [implicit-import]
         spec = importlib.util.spec_from_file_location("InferClass", infer_py)
         infer_class = importlib.util.module_from_spec(spec)  # type: ignore
         sys.modules["InferClass"] = infer_class

monai/apps/auto3dseg/ensemble_builder.py

@@ -337,6 +337,7 @@ def __init__(self, history: Sequence[dict[str, Any]], data_src_cfg_name: str | N
         self.ensemble: AlgoEnsemble
         self.data_src_cfg = ConfigParser(globals=False)
 
+        # pyrefly: ignore [unnecessary-type-conversion]
         if data_src_cfg_name is not None and os.path.exists(str(data_src_cfg_name)):
             self.data_src_cfg.read_config(data_src_cfg_name)
 

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

monai/apps/detection/transforms/array.py

@@ -257,10 +257,14 @@ def __call__(self, boxes: NdarrayTensor, src_spatial_size: Sequence[int] | int |
                 diff = od - zd
                 half = abs(diff) // 2
                 if diff > 0:  # need padding (half, diff - half)
+                    # pyrefly: ignore [bad-index, unsupported-operation]
                     zoomed_boxes[:, axis] = zoomed_boxes[:, axis] + half
+                    # pyrefly: ignore [bad-index, unsupported-operation]
                     zoomed_boxes[:, axis + spatial_dims] = zoomed_boxes[:, axis + spatial_dims] + half
                 elif diff < 0:  # need slicing (half, half + od)
+                    # pyrefly: ignore [bad-index, unsupported-operation]
                     zoomed_boxes[:, axis] = zoomed_boxes[:, axis] - half
+                    # pyrefly: ignore [bad-index, unsupported-operation]
                     zoomed_boxes[:, axis + spatial_dims] = zoomed_boxes[:, axis + spatial_dims] - half
         return zoomed_boxes
 

monai/apps/detection/transforms/box_ops.py

@@ -186,7 +186,9 @@ def flip_boxes(
     _flip_boxes: NdarrayTensor = boxes.clone() if isinstance(boxes, torch.Tensor) else deepcopy(boxes)  # type: ignore[assignment]
 
     for axis in flip_axes:
+        # pyrefly: ignore [bad-index, unsupported-operation]
         _flip_boxes[:, axis + spatial_dims] = spatial_size[axis] - boxes[:, axis] - TO_REMOVE
+        # pyrefly: ignore [bad-index, unsupported-operation]
         _flip_boxes[:, axis] = spatial_size[axis] - boxes[:, axis + spatial_dims] - TO_REMOVE
 
     return _flip_boxes

monai/apps/detection/transforms/dictionary.py

@@ -1200,6 +1200,7 @@ def __call__(self, data: Mapping[Hashable, torch.Tensor]) -> list[dict[Hashable,
             cropper = SpatialCrop(roi_center=tuple(center), roi_size=self.spatial_size)
             crop_start = [max(s.start, 0) for s in cropper.slices]
             crop_end = [min(s.stop, image_size_a) for s, image_size_a in zip(cropper.slices, image_size)]
+            # pyrefly: ignore [unnecessary-type-conversion]
             crop_slices = [slice(int(s), int(e)) for s, e in zip(crop_start, crop_end)]
 
             # crop images

monai/apps/detection/utils/hard_negative_sampler.py

@@ -273,6 +273,7 @@ def get_num_neg(self, negative: torch.Tensor, num_pos: int) -> int:
             number of negative samples
         """
         # always assume at least one pos sample was sampled
+        # pyrefly: ignore [unnecessary-type-conversion]
         num_neg = int(max(1, num_pos) * abs(1 - 1.0 / float(self.positive_fraction)))
         # protect against not enough negative examples and sample at least self.min_neg if possible
         num_neg = min(negative.numel(), max(num_neg, self.min_neg))

monai/apps/generation/maisi/networks/autoencoderkl_maisi.py

@@ -246,7 +246,9 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
         # update padding length if necessary
         padding = 3
+        # pyrefly: ignore [unsupported-operation]
         if padding % self.stride > 0:
+            # pyrefly: ignore [unsupported-operation]
             padding = (padding // self.stride + 1) * self.stride
         if self.print_info:
             logger.info(f"Padding size: {padding}")

monai/apps/nnunet/nnunetv2_runner.py

@@ -31,6 +31,7 @@
 tqdm, has_tqdm = optional_import("tqdm", name="tqdm")
 nib, _ = optional_import("nibabel")
 
+# pyrefly: ignore [implicit-import]
 logger = monai.apps.utils.get_logger(__name__)
 
 __all__ = ["nnUNetV2Runner"]
@@ -274,6 +275,7 @@ def convert_dataset(self):
                 modality = [modality]
 
             create_new_dataset_json(
+                # pyrefly: ignore [bad-argument-type]
                 modality=modality,
                 num_foreground_classes=num_foreground_classes,
                 num_input_channels=num_input_channels,

monai/apps/nnunet/utils.py

@@ -23,6 +23,7 @@
 tqdm, has_tqdm = optional_import("tqdm", name="tqdm")
 nib, _ = optional_import("nibabel")
 
+# pyrefly: ignore [implicit-import]
 logger = monai.apps.utils.get_logger(__name__)
 
 __all__ = ["analyze_data", "create_new_data_copy", "create_new_dataset_json", "NNUNETMode"]
@@ -43,6 +44,7 @@ def analyze_data(datalist_json: dict, data_dir: str) -> tuple[int, int]:
         datalist_json: original data list .json (required by most monai tutorials).
         data_dir: raw data directory.
     """
+    # pyrefly: ignore [implicit-import]
     img = monai.transforms.LoadImage(image_only=True, ensure_channel_first=True, simple_keys=True)(
         os.path.join(data_dir, datalist_json["training"][0]["image"])
     )
@@ -51,6 +53,7 @@ def analyze_data(datalist_json: dict, data_dir: str) -> tuple[int, int]:
 
     num_foreground_classes = 0
     for _i in range(len(datalist_json["training"])):
+        # pyrefly: ignore [implicit-import]
         seg = monai.transforms.LoadImage(image_only=True, ensure_channel_first=True, simple_keys=True)(
             os.path.join(data_dir, datalist_json["training"][_i]["label"])
         )
@@ -93,6 +96,7 @@ def create_new_data_copy(
             _index += 1
 
             # copy image
+            # pyrefly: ignore [implicit-import]
             nda = monai.transforms.LoadImage(image_only=True, ensure_channel_first=True, simple_keys=True)(
                 os.path.join(data_dir, orig_img_name)
             )
@@ -105,6 +109,7 @@ def create_new_data_copy(
 
             # copy label
             if isinstance(datalist_json[_key][_k], dict) and "label" in datalist_json[_key][_k]:
+                # pyrefly: ignore [implicit-import]
                 nda = monai.transforms.LoadImage(image_only=True, ensure_channel_first=True, simple_keys=True)(
                     os.path.join(data_dir, datalist_json[_key][_k]["label"])
                 )

monai/apps/nuclick/transforms.py

@@ -115,7 +115,9 @@ def bbox(self, patch_size, centroid, size):
         x, y = centroid
         m, n = size
 
+        # pyrefly: ignore [unnecessary-type-conversion]
         x_start = int(max(x - patch_size / 2, 0))
+        # pyrefly: ignore [unnecessary-type-conversion]
         y_start = int(max(y - patch_size / 2, 0))
         x_end = x_start + patch_size
         y_end = y_start + patch_size

monai/apps/pathology/transforms/post/dictionary.py

@@ -403,6 +403,7 @@ def __call__(self, data):
         d = dict(data)
         for key in self.key_iterator(d):
             offset = d[self.offset_key] if self.offset_key else None
+            # pyrefly: ignore [bad-argument-type]
             centroid = self.converter(d[key], offset)
             key_to_add = f"{key}_{self.centroid_key_postfix}"
             if key_to_add in d:

monai/apps/tcia/utils.py

@@ -100,6 +100,7 @@ def download_tcia_series_instance(
     query_name = "getImageWithMD5Hash" if check_md5 else "getImage"
     download_url = f"{BASE_URL}{query_name}?SeriesInstanceUID={series_uid}"
 
+    # pyrefly: ignore [implicit-import]
     monai.apps.utils.download_and_extract(
         url=download_url,
         filepath=os.path.join(download_dir, f"{series_uid}.zip"),
@@ -111,6 +112,7 @@ def download_tcia_series_instance(
             raise ValueError("pandas package is necessary, please install it.")
         hashes_df = pd.read_csv(os.path.join(output_dir, hashes_filename))
         for dcm, md5hash in hashes_df.values:
+            # pyrefly: ignore [implicit-import]
             monai.apps.utils.check_hash(filepath=os.path.join(output_dir, dcm), val=md5hash, hash_type="md5")
 
 

monai/apps/vista3d/inferer.py

@@ -89,8 +89,11 @@ def point_based_window_inferer(
                     unravel_slice = (
                         slice(None),
                         slice(None),
+                        # pyrefly: ignore [unnecessary-type-conversion]
                         slice(int(lx), int(rx)),
+                        # pyrefly: ignore [unnecessary-type-conversion]
                         slice(int(ly), int(ry)),
+                        # pyrefly: ignore [unnecessary-type-conversion]
                         slice(int(lz), int(rz)),
                     )
                     batch_image = image[unravel_slice]
@@ -151,6 +154,7 @@ def _get_window_idx_c(p: int, roi: int, s: int) -> tuple[int, int]:
     elif p + roi // 2 > s:
         left, right = s - roi, s
     else:
+        # pyrefly: ignore [unnecessary-type-conversion]
         left, right = int(p) - roi // 2, int(p) + roi // 2
     return left, right
 

monai/apps/vista3d/transforms.py

@@ -46,7 +46,9 @@ def _convert_name_to_index(name_to_index_mapping: dict, label_prompt: list | Non
         for l in label_prompt:
             if isinstance(l, (int, str)):
                 converted_label_prompt.append(
-                    name_to_index_mapping.get(l.lower(), int(l) if l.isdigit() else 0) if isinstance(l, str) else int(l)
+                    name_to_index_mapping.get(l.lower(), int(l) if l.isdigit() else 0)
+                    if isinstance(l, str)
+                    else int(l)  # pyrefly: ignore [unnecessary-type-conversion]
                 )
             else:
                 converted_label_prompt.append(l)
@@ -208,8 +210,8 @@ def __init__(
         self.dataset_key = dataset_key
         for name, mapping in label_mappings.items():
             self.mappers[name] = MapLabelValue(
-                orig_labels=[int(pair[0]) for pair in mapping],
-                target_labels=[int(pair[1]) for pair in mapping],
+                orig_labels=[int(pair[0]) for pair in mapping],  # pyrefly: ignore [unnecessary-type-conversion]
+                target_labels=[int(pair[1]) for pair in mapping],  # pyrefly: ignore [unnecessary-type-conversion]
                 dtype=dtype,
             )
 

monai/auto3dseg/operations.py

@@ -149,4 +149,5 @@ def evaluate(self, data: Any, **kwargs: Any) -> dict:
         Args:
             data: input data
         """
+        # pyrefly: ignore [missing-attribute]
         return {k: v(data[k], **kwargs).tolist() for k, v in self.data.items() if (callable(v) and k in data)}

monai/auto3dseg/seg_summarizer.py

@@ -208,6 +208,7 @@ def summarize(self, data: list[dict]) -> dict[str, dict]:
 
         for analyzer in self.summary_analyzers:
             if callable(analyzer):
+                # pyrefly: ignore [missing-attribute]
                 report.update({analyzer.stats_name: analyzer(data)})
 
         return report

monai/bundle/reference_resolver.py

@@ -254,6 +254,7 @@ def iter_subconfigs(cls, id: str, config: Any) -> Iterator[tuple[str, str, Any]]
         """
         for k, v in config.items() if isinstance(config, dict) else enumerate(config):
             sub_id = f"{id}{cls.sep}{k}" if id != "" else f"{k}"
+            # pyrefly: ignore [invalid-yield]
             yield k, sub_id, v
 
     @classmethod