Fix issue 410 where the latest typeguard version 3.x is incompatible

examples/apps/ai_livertumor_seg_app/livertumor_seg_operator.py

@@ -52,14 +52,12 @@ class LiverTumorSegOperator(Operator):
     """
 
     def __init__(self):
-
         self.logger = logging.getLogger("{}.{}".format(__name__, type(self).__name__))
         super().__init__()
         self._input_dataset_key = "image"
         self._pred_dataset_key = "pred"
 
     def compute(self, op_input: InputContext, op_output: OutputContext, context: ExecutionContext):
-
         input_image = op_input.get("image")
         if not input_image:
             raise ValueError("Input image is not found.")

examples/apps/ai_unetr_seg_app/unetr_seg_operator.py

@@ -46,14 +46,12 @@ class UnetrSegOperator(Operator):
     """
 
     def __init__(self):
-
         self.logger = logging.getLogger("{}.{}".format(__name__, type(self).__name__))
         super().__init__()
         self._input_dataset_key = "image"
         self._pred_dataset_key = "pred"
 
     def compute(self, op_input: InputContext, op_output: OutputContext, context: ExecutionContext):
-
         input_image = op_input.get("image")
         if not input_image:
             raise ValueError("Input image is not found.")

monai/deploy/core/application.py

@@ -82,7 +82,7 @@ def __init__(
             self.description = get_docstring(self.__class__)
         if not self.version:
             try:
-                from _version import get_versions
+                from monai.deploy._version import get_versions
 
                 self.version = get_versions()["version"]
             except ImportError:

monai/deploy/core/executors/single_process_executor.py

@@ -141,7 +141,7 @@ def run(self):
                         )
 
                     next_op_exec_context = ExecutionContext(exec_context, next_op)
-                    for (out_label, in_labels) in io_map.items():
+                    for out_label, in_labels in io_map.items():
                         output = op_exec_context.output_context.get(out_label)
                         for in_label in in_labels:
                             next_op_exec_context.input_context.set(output, in_label)

monai/deploy/core/graphs/nx_digraph.py

@@ -52,5 +52,5 @@ def gen_worklist(self) -> Generator[Optional[Operator], None, None]:
         return worklist
 
     def gen_next_operators(self, op: Operator) -> Generator[Optional[Operator], None, None]:
-        for (_, v) in self._graph.out_edges(op):
+        for _, v in self._graph.out_edges(op):
             yield v

monai/deploy/operators/dicom_data_loader_operator.py

@@ -118,7 +118,6 @@ def _load_data(self, files: List[str]):
                 logging.warning(f"Ignored {file}, reason being: {ex}")
 
         for sop_instance in sop_instances:
-
             study_instance_uid = sop_instance[0x0020, 0x000D].value.name  # name is the UID as str
 
             if study_instance_uid not in study_dict:

monai/deploy/operators/dicom_encapsulated_pdf_writer_operator.py

@@ -42,7 +42,6 @@
 @md.output("dicom_instance", DataPath, IOType.DISK)
 @md.env(pip_packages=["pydicom >= 1.4.2", "PyPDF2 >= 2.11.1", "monai"])
 class DICOMEncapsulatedPDFWriterOperator(Operator):
-
     DCM_EXTENSION = ".dcm"
 
     def __init__(

monai/deploy/operators/dicom_seg_writer_operator.py

@@ -11,6 +11,7 @@
 
 import datetime
 import logging
+import os
 from pathlib import Path
 from random import randint
 from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Union

monai/deploy/operators/dicom_series_to_volume_operator.py

@@ -389,7 +389,6 @@ def _get_instance_properties(obj: object, not_none: bool = True) -> Dict:
 
 
 def test():
-
     from pathlib import Path
 
     from monai.deploy.operators.dicom_data_loader_operator import DICOMDataLoaderOperator

monai/deploy/operators/dicom_text_sr_writer_operator.py

@@ -39,7 +39,6 @@
 @md.output("dicom_instance", DataPath, IOType.DISK)
 @md.env(pip_packages=["pydicom >= 1.4.2", "monai"])
 class DICOMTextSRWriterOperator(Operator):
-
     # File extension for the generated DICOM Part 10 file.
     DCM_EXTENSION = ".dcm"
 

monai/deploy/operators/dicom_utils.py

@@ -60,7 +60,6 @@ class ModelInfo(object):
     """
 
     def __init__(self, creator: str = "", name: str = "", version: str = "", uid: str = ""):
-
         self.creator = creator if isinstance(creator, str) else ""
         self.name = name if isinstance(name, str) else ""
         self.version = version if isinstance(version, str) else ""
@@ -77,7 +76,6 @@ def __init__(
         series_number: str = "0000",
         software_version_number: str = "",
     ):
-
         self.manufacturer = manufacturer if isinstance(manufacturer, str) else ""
         self.manufacturer_model = manufacturer_model if isinstance(manufacturer_model, str) else ""
         self.series_number = series_number if isinstance(series_number, str) else ""

monai/deploy/operators/monai_bundle_inference_operator.py

@@ -78,8 +78,8 @@ def _read_from_archive(archive, root_name: str, config_name: str, do_search=True
 
         # Try directly read with constructed and expected path into the archive
         for suffix in bundle_suffixes:
+            path = Path(root_name, config_folder, config_name).with_suffix(suffix)
             try:
-                path = Path(root_name, config_folder, config_name).with_suffix(suffix)
                 logging.debug(f"Trying to read config '{config_name}' content from {path}.")
                 content_text = archive.read(str(path))
                 break
@@ -268,6 +268,7 @@ def _ensure_str_list(config_names):
 
 DEFAULT_BundleConfigNames = BundleConfigNames()
 
+
 # The operator env decorator defines the required pip packages commonly used in the Bundles.
 # The MONAI Deploy App SDK packager currently relies on the App to consolidate all required packages in order to
 # install them in the MAP Docker image.
@@ -768,7 +769,6 @@ def _convert_from_image(self, img: Image) -> Tuple[np.ndarray, Dict]:
             or ("spacing" in img_meta_dict and "original_affine" in img_meta_dict)
             or "row_pixel_spacing" not in img_meta_dict
         ):
-
             return img.asnumpy(), img_meta_dict
         else:
             return self._convert_from_image_dicom_source(img)

monai/deploy/utils/importutil.py

@@ -155,7 +155,7 @@ def exact_version(the_module, version_str: str = "") -> bool:
     Returns True if the module's __version__ matches version_str
     """
     if not hasattr(the_module, "__version__"):
-        warnings.warn(f"{the_module} has no attribute __version__ in exact_version check.")
+        warnings.warn(f"{the_module} has no attribute __version__ in exact_version check.", stacklevel=2)
         return False
     return bool(the_module.__version__ == version_str)
 

notebooks/tutorials/02_mednist_app-prebuilt.ipynb

@@ -84,7 +84,7 @@
       "Collecting networkx>=2.4\n",
       "  Downloading networkx-2.5.1-py3-none-any.whl (1.6 MB)\n",
       "\u001b[K     |████████████████████████████████| 1.6 MB 12.8 MB/s eta 0:00:01\n",
-      "\u001b[?25hCollecting typeguard>=2.12.1\n",
+      "\u001b[?25hCollecting typeguard~=2.12.1\n",
       "  Downloading typeguard-2.12.1-py3-none-any.whl (17 kB)\n",
       "Collecting numpy>=1.17\n",
       "  Downloading numpy-1.19.5-cp36-cp36m-manylinux2010_x86_64.whl (14.8 MB)\n",

notebooks/tutorials/03_segmentation_app.ipynb

@@ -104,7 +104,7 @@
     "!python -c \"import pydicom\" || pip install -q \"pydicom>=1.4.2\"\n",
     "!python -c \"import highdicom\" || pip install -q \"highdicom>=0.18.2\"\n",
     "!python -c \"import SimpleITK\" || pip install -q \"SimpleITK>=2.0.0\"\n",
-    "!python -c \"import typeguard\" || pip install -q \"typeguard>=2.12.1\"\n",
+    "!python -c \"import typeguard\" || pip install -q \"typeguard~=2.12.1\"\n",
     "\n",
     "# Install MONAI Deploy App SDK package\n",
     "!python -c \"import monai.deploy\" || pip install --upgrade -q \"monai-deploy-app-sdk\""

notebooks/tutorials/03_segmentation_viz_app.ipynb

@@ -113,7 +113,7 @@
     "!python -c \"import pydicom\" || pip install -q \"pydicom>=1.4.2\"\n",
     "!python -c \"import highdicom\" || pip install -q \"highdicom>=0.18.2\"\n",
     "!python -c \"import SimpleITK\" || pip install -q \"SimpleITK>=2.0.0\"\n",
-    "!python -c \"import typeguard\" || pip install -q \"typeguard>=2.12.1\"\n",
+    "!python -c \"import typeguard\" || pip install -q \"typeguard~=2.12.1\"\n",
     "\n",
     "# Install MONAI Deploy App SDK package\n",
     "!python -c \"import monai.deploy\" || pip install --upgrade -q \"monai-deploy-app-sdk\"\n",

notebooks/tutorials/06_monai_bundle_app.ipynb

@@ -106,7 +106,7 @@
     "!python -c \"import pydicom\" || pip install -q \"pydicom>=1.4.2\"\n",
     "!python -c \"import highdicom\" || pip install -q \"highdicom>=0.18.2\"\n",
     "!python -c \"import SimpleITK\" || pip install -q \"SimpleITK>=2.0.0\"\n",
-    "!python -c \"import typeguard\" || pip install -q \"typeguard>=2.12.1\"\n",
+    "!python -c \"import typeguard\" || pip install -q \"typeguard~=2.12.1\"\n",
     "\n",
     "# Install MONAI Deploy App SDK package\n",
     "!python -c \"import monai.deploy\" || pip install --upgrade -q \"monai-deploy-app-sdk\""

notebooks/tutorials/07_multi_model_app.ipynb

@@ -144,7 +144,7 @@
     "!python -c \"import pydicom\" || pip install -q \"pydicom>=1.4.2\"\n",
     "!python -c \"import highdicom\" || pip install -q \"highdicom>=0.18.2\"\n",
     "!python -c \"import SimpleITK\" || pip install -q \"SimpleITK>=2.0.0\"\n",
-    "!python -c \"import typeguard\" || pip install -q \"typeguard>=2.12.1\"\n",
+    "!python -c \"import typeguard\" || pip install -q \"typeguard~=2.12.1\"\n",
     "\n",
     "# Install MONAI Deploy App SDK package\n",
     "!python -c \"import monai.deploy\" || pip install --upgrade -q \"monai-deploy-app-sdk\""

platforms/nuance_pin/app/inference.py

@@ -82,7 +82,6 @@ def detection_list(self):
 @md.env(pip_packages=["monai>=0.8.1", "torch>=1.5", "numpy>=1.21", "nibabel"])
 class LungNoduleInferenceOperator(Operator):
     def __init__(self, model_path: str = "model/model.ts"):
-
         self.logger = logging.getLogger("{}.{}".format(__name__, type(self).__name__))
         super().__init__()
         self._input_dataset_key = "image"
@@ -125,7 +124,6 @@ def __init__(self, model_path: str = "model/model.ts"):
         self.detector.eval()
 
     def compute(self, op_input: InputContext, op_output: OutputContext, context: ExecutionContext):
-
         input_image = op_input.get("image")
         if not input_image:
             raise ValueError("Input image not found.")

platforms/nuance_pin/app/post_inference_ops.py

@@ -36,7 +36,6 @@ def __init__(self, pin_processor: Optional[AiJobProcessor], *args, **kwargs):
         self.pin_processor = pin_processor
 
     def compute(self, op_input: InputContext, op_output: OutputContext, context: ExecutionContext):
-
         selected_study = op_input.get("original_dicom")[0]  # assuming a single study
         selected_series = selected_study.selected_series[0]  # assuming a single series
         detection_result: DetectionResult = op_input.get("detection_predictions").detection_list[0]
@@ -60,7 +59,6 @@ def compute(self, op_input: InputContext, op_output: OutputContext, context: Exe
         accession = all_ref_images[0].AccessionNumber
 
         for inst_num, (box_data, box_score) in enumerate(zip(detection_result.box_data, detection_result.score_data)):
-
             tracking_id = f"{accession}_nodule_{inst_num}"  # site-specific ID
             tracking_uid = hd.UID()
 
@@ -160,7 +158,6 @@ def __init__(self, pin_processor: Optional[AiJobProcessor], *args, **kwargs):
         self.pin_processor = pin_processor
 
     def compute(self, op_input: InputContext, op_output: OutputContext, context: ExecutionContext):
-
         selected_study = op_input.get("original_dicom")[0]  # assuming a single study
         selected_series = selected_study.selected_series[0]  # assuming a single series
         detection_result: DetectionResult = op_input.get("detection_predictions").detection_list[0]
@@ -190,7 +187,6 @@ def compute(self, op_input: InputContext, op_output: OutputContext, context: Exe
         slice_coords = list(range(len(selected_series.series.get_sop_instances())))
 
         for box_data, box_score in zip(detection_result.box_data, detection_result.score_data):
-
             affected_slice_idx = [
                 idx
                 for idx, slice_coord in enumerate(slice_coords)

platforms/nuance_pin/app_wrapper.py

@@ -50,7 +50,6 @@
 
 
 class MONAIAppWrapper(AiJobProcessor):
-
     partner_name = os.environ["AI_PARTNER_NAME"]
     service_name = os.environ["AI_SVC_NAME"]
     service_version = os.environ["AI_SVC_VERSION"]

pyproject.toml

@@ -36,5 +36,78 @@ exclude = '''
 '''
 
 # https://github.com/microsoft/pyright/blob/main/docs/configuration.md
+# NOTE: All relative paths are relative to the location of this file.
 [tool.pyright]
-ignore = ["versioneer.py", "_version.py"]
+ignore = ["versioneer.py", "_version.py"]
+
+# https://google.github.io/pytype/
+[tool.pytype]
+# Space-separated list of files or directories to exclude.
+exclude = [
+    'versioneer.py',
+    '_version.py',
+    '**/_version.py',
+]
+# Space-separated list of files or directories to process.
+inputs = [
+    'monai',
+]
+# Keep going past errors to analyze as many files as possible.
+keep_going = true
+# Run N jobs in parallel. When 'auto' is used, this will be equivalent to the
+# number of CPUs on the host system.
+jobs = 8
+# All pytype output goes here.
+output = '.pytype'
+# Platform (e.g., "linux", "win32") that the target code runs on.
+platform = 'linux'
+# Paths to source code directories, separated by ':'.
+pythonpath = '.'
+
+# Always use function return type annotations. This flag is temporary and will
+# be removed once this behavior is enabled by default.
+always_use_return_annotations = false
+
+# Enable parameter count checks for overriding methods. This flag is temporary
+# and will be removed once this behavior is enabled by default.
+overriding_parameter_count_checks = false
+
+# Enable return type checks for overriding methods. This flag is temporary and
+# will be removed once this behavior is enabled by default.
+overriding_return_type_checks = true
+
+# Use the enum overlay for more precise enum checking. This flag is temporary
+# and will be removed once this behavior is enabled by default.
+use_enum_overlay = false
+
+# Opt-in: Do not allow Any as a return type.
+no_return_any = false
+
+# Experimental: Support pyglib's @cached.property.
+enable_cached_property = false
+
+# Experimental: Infer precise return types even for invalid function calls.
+precise_return = false
+
+# Experimental: Solve unknown types to label with structural types.
+protocols = false
+
+# Experimental: Only load submodules that are explicitly imported.
+strict_import = false
+
+# Experimental: Enable exhaustive checking of function parameter types.
+strict_parameter_checks = false
+
+# Experimental: Emit errors for comparisons between incompatible primitive
+# types.
+strict_primitive_comparisons = false
+
+# Space-separated list of error names to ignore.
+disable = [
+    'pyi-error',
+    'container-type-mismatch',
+    'attribute-error',
+]
+
+# Don't report errors.
+report_errors = true

requirements.txt

@@ -1,4 +1,4 @@
 numpy>=1.21.6
 networkx>=2.4
 colorama>=0.4.1
-typeguard>=2.12.1
+typeguard~=2.12.1

setup.cfg

@@ -26,7 +26,7 @@ install_requires =
     numpy>=1.21.6
     networkx>=2.4
     colorama>=0.4.1
-    typeguard>=2.12.1
+    typeguard~=2.12.1
 
 [options.extras_require]
 all =
@@ -43,8 +43,13 @@ ignore =
     E203,E305,E402,E501,E721,E741,F821,F841,F999,W503,W504,C408,E302,W291,E303,
     # N812 lowercase 'torch.nn.functional' imported as non lowercase 'F'
     N812,
-    B024, #abstract base class, but it has no abstract methods
-    B027  #method in base class with no implementation
+    #B024, abstract base class, but it has no abstract methods
+    B024,
+    # B027, #method in base class with no implementation
+    B027,
+    # B905 `zip()` without an explicit `strict=` parameter, but conflicting with pytype
+    B905
+
 per_file_ignores =
     __init__.py: F401
     # Allow using camel case for variable/argument names for the sake of readability.
@@ -105,38 +110,6 @@ ignore_errors = True
 # Ignores all non-fatal errors.
 ignore_errors = True
 
-[pytype]
-# Space-separated list of files or directories to exclude.
-exclude = versioneer.py _version.py
-# Space-separated list of files or directories to process.
-inputs = monai
-# Keep going past errors to analyze as many files as possible.
-keep_going = True
-# Run N jobs in parallel.
-jobs = 8
-# All pytype output goes here.
-output = .pytype
-# Paths to source code directories, separated by ':'.
-pythonpath = .
-# Check attribute values against their annotations.
-check_attribute_types = True
-# Check container mutations against their annotations.
-check_container_types = True
-# Check parameter defaults and assignments against their annotations.
-check_parameter_types = True
-# Check variable values against their annotations.
-check_variable_types = True
-# Comma or space separated list of error names to ignore.
-disable = pyi-error
-# Report errors.
-report_errors = True
-# Experimental: Infer precise return types even for invalid function calls.
-precise_return = True
-# Experimental: solve unknown types to label with structural types.
-protocols = True
-# Experimental: Only load submodules that are explicitly imported.
-strict_import = False
-
 [tool:pytest]
 # If a pytest section is found in one of the possible config files
 # (pytest.ini, tox.ini or setup.cfg), then pytest will not look for any others,