Tediously update all enum usages to use Enum.KEY.value instead of Enu…

…m.KEY, which works on Python<3.11 but broke in 3.11 (see: python/cpython#100458)

kolena/_api/v1/batched_load.py

@@ -25,11 +25,11 @@ class Path(str, Enum):
 
         @classmethod
         def upload_signed_url(cls, load_uuid: str) -> str:
-            return f"{cls.UPLOAD_SIGNED_URL_STUB}/{load_uuid}"
+            return f"{cls.UPLOAD_SIGNED_URL_STUB.value}/{load_uuid}"
 
         @classmethod
         def download_by_path(cls, path: str) -> str:
-            return f"{cls.DOWNLOAD_BY_PATH_STUB}/{path}"
+            return f"{cls.DOWNLOAD_BY_PATH_STUB.value}/{path}"
 
     @dataclass(frozen=True)
     class WithLoadUUID:

kolena/_utils/_consts.py

@@ -15,7 +15,6 @@
 
 
 class _BatchSize(int, Enum):
-    UPLOAD_CHIPS = 5_000
     UPLOAD_RECORDS = 10_000_000
     UPLOAD_RESULTS = 1_000_000
 

kolena/_utils/batched_load.py

@@ -45,7 +45,7 @@
 
 
 def init_upload() -> API.InitiateUploadResponse:
-    init_res = krequests.put(endpoint_path=API.Path.INIT_UPLOAD)
+    init_res = krequests.put(endpoint_path=API.Path.INIT_UPLOAD.value)
     krequests.raise_for_status(init_res)
     init_response = from_dict(data_class=API.InitiateUploadResponse, data=init_res.json())
     return init_response
@@ -81,7 +81,7 @@ def upload_data_frame_chunk(df_chunk: pd.DataFrame, load_uuid: str) -> None:
 def upload_image_chips(
     df: _ImageChipsDataFrame,
     path_mapper: AssetPathMapper,
-    batch_size: int = _BatchSize.UPLOAD_CHIPS,
+    batch_size: int = _BatchSize.UPLOAD_CHIPS.value,
 ) -> None:
     def upload_batch(df_batch: _ImageChipsDataFrame) -> None:
         df_batch = df_batch.reset_index(drop=True)  # reset indices so we match the signed_url indices
@@ -106,7 +106,7 @@ def as_buffer(image_raw: np.ndarray) -> io.BytesIO:
             ],
         )
         upload_response = krequests.put(
-            endpoint_path=AssetAPI.Path.BULK_UPLOAD,
+            endpoint_path=AssetAPI.Path.BULK_UPLOAD.value,
             data=data,
             headers={"Content-Type": data.content_type},
         )
@@ -157,7 +157,7 @@ def complete_load(uuid: Optional[str]) -> None:
             return
         complete_request = API.CompleteDownloadRequest(uuid=uuid)
         complete_res = krequests.put(
-            endpoint_path=API.Path.COMPLETE_DOWNLOAD,
+            endpoint_path=API.Path.COMPLETE_DOWNLOAD.value,
             data=json.dumps(dataclasses.asdict(complete_request)),
         )
         krequests.raise_for_status(complete_res)

kolena/_utils/instrumentation.py

@@ -54,15 +54,15 @@ def upload_log(message: str, status: str) -> None:
         message=message,
         status=status,
     )
-    krequests.post(endpoint_path=API.Path.UPLOAD, json=dataclasses.asdict(request))
+    krequests.post(endpoint_path=API.Path.UPLOAD.value, json=dataclasses.asdict(request))
 
 
 def log_telemetry(e: BaseException) -> None:
     try:
         stack = tb.format_stack()
         exc_format = tb.format_exception(None, e, e.__traceback__)
         combined = stack + exc_format
-        upload_log("".join(combined), DatadogLogLevels.ERROR)
+        upload_log("".join(combined), DatadogLogLevels.ERROR.value)
     except BaseException:
         """
         Attempting to upload the telemetry is best-effort. We don't want to have exceptions in that

kolena/_utils/repository.py

@@ -21,7 +21,7 @@
 
 def create(repository: str) -> None:
     response = krequests.post(
-        endpoint_path=Path.CREATE,
+        endpoint_path=Path.CREATE.value,
         data=json.dumps(dataclasses.asdict(CreateRepositoryRequest(repository=repository))),
     )
     krequests.raise_for_status(response)

kolena/detection/_internal/model.py

@@ -93,7 +93,7 @@ def __init__(self, name: str, workflow: WorkflowType, metadata: Optional[Dict[st
     def _create(cls, workflow: WorkflowType, name: str, metadata: Dict[str, Any]) -> CoreAPI.EntityData:
         log.info(f"creating new model '{name}'")
         request = CoreAPI.CreateRequest(name=name, metadata=metadata, workflow=workflow.value)
-        res = krequests.post(endpoint_path=API.Path.CREATE, data=json.dumps(dataclasses.asdict(request)))
+        res = krequests.post(endpoint_path=API.Path.CREATE.value, data=json.dumps(dataclasses.asdict(request)))
         krequests.raise_for_status(res)
         log.success(f"created new model '{name}'")
         return from_dict(data_class=CoreAPI.EntityData, data=res.json())
@@ -102,7 +102,7 @@ def _create(cls, workflow: WorkflowType, name: str, metadata: Dict[str, Any]) ->
     @validate_arguments(config=ValidatorConfig)
     def _load_by_name(cls, name: str) -> CoreAPI.EntityData:
         request = CoreAPI.LoadByNameRequest(name=name)
-        res = krequests.put(endpoint_path=API.Path.LOAD_BY_NAME, data=json.dumps(dataclasses.asdict(request)))
+        res = krequests.put(endpoint_path=API.Path.LOAD_BY_NAME.value, data=json.dumps(dataclasses.asdict(request)))
         krequests.raise_for_status(res)
         return from_dict(data_class=CoreAPI.EntityData, data=res.json())
 
@@ -131,7 +131,7 @@ def iter_inferences(
     def _iter_inference_batch_for_reference(
         self,
         test_object: Union[_TestCaseClass, _TestSuiteClass],
-        batch_size: int = _BatchSize.LOAD_SAMPLES,
+        batch_size: int = _BatchSize.LOAD_SAMPLES.value,
     ) -> Iterator[_LoadInferencesDataFrameClass]:
         if batch_size <= 0:
             raise InputValidationError(f"invalid batch_size '{batch_size}': expected positive integer")
@@ -143,7 +143,7 @@ def _iter_inference_batch_for_reference(
         init_request = API.InitLoadInferencesRequest(**params)
         yield from _BatchedLoader.iter_data(
             init_request=init_request,
-            endpoint_path=API.Path.INIT_LOAD_INFERENCES,
+            endpoint_path=API.Path.INIT_LOAD_INFERENCES.value,
             df_class=self._LoadInferencesDataFrameClass,
         )
         log.success(f"loaded inferences from model '{self.name}' on {test_object_display_name}")
@@ -166,7 +166,7 @@ def load_inferences_by_test_case(
     def _iter_inference_batch_for_test_suite(
         self,
         test_suite: _TestSuiteClass,
-        batch_size: int = _BatchSize.LOAD_SAMPLES,
+        batch_size: int = _BatchSize.LOAD_SAMPLES.value,
     ) -> Iterator[_LoadInferencesDataFrameClass]:
         if batch_size <= 0:
             raise InputValidationError(f"invalid batch_size '{batch_size}': expected positive integer")
@@ -175,7 +175,7 @@ def _iter_inference_batch_for_test_suite(
         init_request = API.InitLoadInferencesByTestCaseRequest(**params)
         yield from _BatchedLoader.iter_data(
             init_request=init_request,
-            endpoint_path=API.Path.INIT_LOAD_INFERENCES_BY_TEST_CASE,
+            endpoint_path=API.Path.INIT_LOAD_INFERENCES_BY_TEST_CASE.value,
             df_class=self._LoadInferencesDataFrameClass,
         )
         log.success(f"loaded inferences from model '{self.name}' on test suite '{test_suite.name}'")

kolena/detection/_internal/test_case.py

@@ -128,7 +128,7 @@ def _create(
         """Create a new test case with the provided name."""
         log.info(f"creating new test case '{name}'")
         request = CoreAPI.CreateRequest(name=name, description=description or "", workflow=workflow.value)
-        res = krequests.post(endpoint_path=API.Path.CREATE, data=json.dumps(dataclasses.asdict(request)))
+        res = krequests.post(endpoint_path=API.Path.CREATE.value, data=json.dumps(dataclasses.asdict(request)))
         krequests.raise_for_status(res)
         data = from_dict(data_class=CoreAPI.EntityData, data=res.json())
         obj = cls._create_from_data(data)
@@ -142,7 +142,7 @@ def _create(
     def _load_by_name(cls, name: str, version: Optional[int] = None) -> CoreAPI.EntityData:
         """Load an existing test case with the provided name."""
         request = CoreAPI.LoadByNameRequest(name=name, version=version)
-        res = krequests.put(endpoint_path=API.Path.LOAD_BY_NAME, data=json.dumps(dataclasses.asdict(request)))
+        res = krequests.put(endpoint_path=API.Path.LOAD_BY_NAME.value, data=json.dumps(dataclasses.asdict(request)))
         krequests.raise_for_status(res)
         return from_dict(data_class=CoreAPI.EntityData, data=res.json())
 
@@ -173,10 +173,10 @@ def load_images(self) -> List[_TestImageClass]:
     def iter_images(self) -> Iterator[_TestImageClass]:
         """Iterate through all images with their associated ground truths in this test case."""
         log.info(f"loading test images for test case '{self.name}'")
-        init_request = CoreAPI.InitLoadContentsRequest(batch_size=_BatchSize.LOAD_SAMPLES, test_case_id=self._id)
+        init_request = CoreAPI.InitLoadContentsRequest(batch_size=_BatchSize.LOAD_SAMPLES.value, test_case_id=self._id)
         for df in _BatchedLoader.iter_data(
             init_request=init_request,
-            endpoint_path=API.Path.INIT_LOAD_IMAGES,
+            endpoint_path=API.Path.INIT_LOAD_IMAGES.value,
             df_class=self._TestImageDataFrameClass,
         ):
             for record in df.itertuples():
@@ -312,7 +312,7 @@ def edit(self, reset: bool = False) -> Iterator[Editor]:
         init_response = init_upload()
         df = self._to_data_frame(list(editor._images.values()))
         df_serialized = df.as_serializable()
-        upload_data_frame(df=df_serialized, batch_size=_BatchSize.UPLOAD_RECORDS, load_uuid=init_response.uuid)
+        upload_data_frame(df=df_serialized, batch_size=_BatchSize.UPLOAD_RECORDS.value, load_uuid=init_response.uuid)
 
         request = CoreAPI.CompleteEditRequest(
             test_case_id=self._id,
@@ -322,7 +322,7 @@ def edit(self, reset: bool = False) -> Iterator[Editor]:
             uuid=init_response.uuid,
         )
         complete_res = krequests.put(
-            endpoint_path=API.Path.COMPLETE_EDIT,
+            endpoint_path=API.Path.COMPLETE_EDIT.value,
             data=json.dumps(dataclasses.asdict(request)),
         )
         krequests.raise_for_status(complete_res)

kolena/detection/_internal/test_run.py

@@ -97,7 +97,10 @@ def __init__(
             test_suite_ids=[test_suite._id],
             config=config,
         )
-        res = krequests.post(endpoint_path=API.Path.CREATE_OR_RETRIEVE, data=json.dumps(dataclasses.asdict(request)))
+        res = krequests.post(
+            endpoint_path=API.Path.CREATE_OR_RETRIEVE.value,
+            data=json.dumps(dataclasses.asdict(request)),
+        )
         krequests.raise_for_status(res)
         response = from_dict(data_class=API.CreateOrRetrieveResponse, data=res.json())
         self._id = response.test_run_id
@@ -128,7 +131,7 @@ def __exit__(
         self._submit_custom_metrics()
         self._active = False
         if exc_type is not None:
-            report_crash(self._id, API.Path.MARK_CRASHED)
+            report_crash(self._id, API.Path.MARK_CRASHED.value)
 
     @validate_arguments(config=ValidatorConfig)
     def add_inferences(self, image: _TestImageClass, inferences: Optional[List[_InferenceClass]]) -> None:
@@ -160,7 +163,7 @@ def add_inferences(self, image: _TestImageClass, inferences: Optional[List[_Infe
 
             self._inferences[image_id] = context_image_inferences
 
-        if self._n_inferences >= _BatchSize.UPLOAD_RESULTS:
+        if self._n_inferences >= _BatchSize.UPLOAD_RESULTS.value:
             log.info(f"uploading batch of '{self._n_inferences}' inference results")
             self._upload_chunk()
             log.success(f"uploaded batch of '{self._n_inferences}' inference results")
@@ -176,7 +179,7 @@ def iter_images(self) -> Iterator[_TestImageClass]:
                 yield self._image_from_load_image_record(record)
 
     @validate_arguments(config=ValidatorConfig)
-    def load_images(self, batch_size: int = _BatchSize.LOAD_SAMPLES) -> List[_TestImageClass]:
+    def load_images(self, batch_size: int = _BatchSize.LOAD_SAMPLES.value) -> List[_TestImageClass]:
         """
         Returns a list of images that still need inferences evaluated, bounded in count
         by batch_size. Note that image ground truths will be excluded from the returned
@@ -195,7 +198,10 @@ def load_images(self, batch_size: int = _BatchSize.LOAD_SAMPLES) -> List[_TestIm
         return [self._image_from_load_image_record(record) for record in df_image_batch.itertuples()]
 
     @validate_arguments(config=ValidatorConfig)
-    def _iter_image_batch(self, batch_size: int = _BatchSize.LOAD_SAMPLES) -> Iterator[_LoadTestImagesDataFrameClass]:
+    def _iter_image_batch(
+        self,
+        batch_size: int = _BatchSize.LOAD_SAMPLES.value,
+    ) -> Iterator[_LoadTestImagesDataFrameClass]:
         if batch_size <= 0:
             raise InputValidationError(f"invalid batch_size '{batch_size}': expected positive integer")
         init_request = API.InitLoadRemainingImagesRequest(
@@ -205,7 +211,7 @@ def _iter_image_batch(self, batch_size: int = _BatchSize.LOAD_SAMPLES) -> Iterat
         )
         yield from _BatchedLoader.iter_data(
             init_request=init_request,
-            endpoint_path=API.Path.INIT_LOAD_REMAINING_IMAGES,
+            endpoint_path=API.Path.INIT_LOAD_REMAINING_IMAGES.value,
             df_class=self._LoadTestImagesDataFrameClass,
         )
 
@@ -239,7 +245,7 @@ def _finalize_upload(self) -> None:
         log.info("finalizing inference upload for test run")
         request = API.UploadImageResultsRequest(uuid=self._upload_uuid, test_run_id=self._id, reset=self._reset)
         finalize_res = krequests.put(
-            endpoint_path=API.Path.UPLOAD_IMAGE_RESULTS,
+            endpoint_path=API.Path.UPLOAD_IMAGE_RESULTS.value,
             data=json.dumps(dataclasses.asdict(request)),
         )
         krequests.raise_for_status(finalize_res)
@@ -289,6 +295,9 @@ def _submit_custom_metrics(self) -> None:
         log.info("submitting custom metrics for test run")
         custom_metrics = self._compute_custom_metrics()
         request = API.UpdateCustomMetricsRequest(model_id=self._model._id, metrics=custom_metrics)
-        res = krequests.put(endpoint_path=API.Path.UPLOAD_CUSTOM_METRICS, data=json.dumps(dataclasses.asdict(request)))
+        res = krequests.put(
+            endpoint_path=API.Path.UPLOAD_CUSTOM_METRICS.value,
+            data=json.dumps(dataclasses.asdict(request)),
+        )
         krequests.raise_for_status(res)
         log.success("submitted custom metrics for test run")

kolena/detection/_internal/test_suite.py

@@ -112,7 +112,7 @@ def _create(
         """Create a new test suite with the provided name."""
         log.info(f"creating new test suite '{name}'")
         request = CoreAPI.TestSuite.CreateRequest(name=name, description=description or "", workflow=workflow.value)
-        res = krequests.post(endpoint_path=API.Path.CREATE, data=json.dumps(dataclasses.asdict(request)))
+        res = krequests.post(endpoint_path=API.Path.CREATE.value, data=json.dumps(dataclasses.asdict(request)))
         krequests.raise_for_status(res)
         data = from_dict(data_class=CoreAPI.TestSuite.EntityData, data=res.json())
         obj = cls._create_from_data(data)
@@ -127,7 +127,7 @@ def _load_by_name(cls, name: str, version: Optional[int] = None) -> CoreAPI.Test
         """Retrieve the existing test suite with the provided name."""
         request = CoreAPI.TestSuite.LoadByNameRequest(name=name, version=version)
         data = json.dumps(dataclasses.asdict(request))
-        res = krequests.put(endpoint_path=API.Path.LOAD_BY_NAME, data=data)
+        res = krequests.put(endpoint_path=API.Path.LOAD_BY_NAME.value, data=data)
         krequests.raise_for_status(res)
         return from_dict(data_class=CoreAPI.TestSuite.EntityData, data=res.json())
 
@@ -298,7 +298,7 @@ def edit(self, reset: bool = False) -> Iterator[Editor]:
             test_case_ids=list(editor._test_cases.values()),
         )
         data = json.dumps(dataclasses.asdict(request))
-        res = krequests.post(endpoint_path=API.Path.EDIT, data=data)
+        res = krequests.post(endpoint_path=API.Path.EDIT.value, data=data)
         krequests.raise_for_status(res)
         log.success(f"updated test suite '{self.name}'")
         test_suite_data = from_dict(data_class=CoreAPI.TestSuite.EntityData, data=res.json())

kolena/detection/test_image.py

@@ -103,10 +103,10 @@ def iter_images(dataset: Optional[str] = None) -> Iterator[TestImage]:
     :param dataset: optionally specify the single dataset to be retrieved. By default, images from all
         datasets are returned
     """
-    init_request = API.InitLoadImagesRequest(dataset=dataset, batch_size=_BatchSize.LOAD_RECORDS)
+    init_request = API.InitLoadImagesRequest(dataset=dataset, batch_size=_BatchSize.LOAD_RECORDS.value)
     for df in _BatchedLoader.iter_data(
         init_request=init_request,
-        endpoint_path=API.Path.INIT_LOAD_IMAGES,
+        endpoint_path=API.Path.INIT_LOAD_IMAGES.value,
         df_class=TestImageDataFrame,
     ):
         for record in df.itertuples():

kolena/fr/model.py

@@ -58,7 +58,7 @@ def create(cls, name: str, metadata: Dict[str, Any]) -> "Model":
         """
         log.info(f"creating model '{name}'")
         request = API.CreateRequest(name=name, metadata=metadata)
-        res = krequests.post(endpoint_path=API.Path.CREATE, data=json.dumps(dataclasses.asdict(request)))
+        res = krequests.post(endpoint_path=API.Path.CREATE.value, data=json.dumps(dataclasses.asdict(request)))
         krequests.raise_for_status(res)
         log.success(f"created model '{name}'")
         return Model.__factory__(from_dict(data_class=Model.Data, data=res.json()))
@@ -74,7 +74,7 @@ def load_by_name(cls, name: str) -> "Model":
         """
         log.info(f"loading model '{name}'")
         request = API.LoadByNameRequest(name=name)
-        res = krequests.put(endpoint_path=API.Path.LOAD_BY_NAME, data=json.dumps(dataclasses.asdict(request)))
+        res = krequests.put(endpoint_path=API.Path.LOAD_BY_NAME.value, data=json.dumps(dataclasses.asdict(request)))
         krequests.raise_for_status(res)
         log.success(f"loaded model '{name}'")
         return Model.__factory__(from_dict(data_class=Model.Data, data=res.json()))
@@ -146,7 +146,7 @@ def iter_pair_results(
         init_request = API.InitLoadPairResultsRequest(batch_size=batch_size, **base_load_request)
         yield from _BatchedLoader.iter_data(
             init_request=init_request,
-            endpoint_path=API.Path.INIT_LOAD_PAIR_RESULTS,
+            endpoint_path=API.Path.INIT_LOAD_PAIR_RESULTS.value,
             df_class=LoadedPairResultDataFrame,
         )
         log.success(f"loaded pair results from model '{self.data.name}' on {test_object_display_name}")