redoing model loading design

image_prediction/exceptions.py

@@ -10,5 +10,9 @@ class UnknownModelLoader(ValueError):
     pass
 
 
+class UnknownDatabaseType(ValueError):
+    pass
+
+
 class IncorrectInstantiation(RuntimeError):
     pass

image_prediction/model_loader/database/connectors/mock.py

@@ -0,0 +1,10 @@
+from image_prediction.model_loader.database.connector import DatabaseConnector
+
+
+class DatabaseConnectorMock(DatabaseConnector):
+
+    def __init__(self, store: dict):
+        self.store = store
+
+    def get_object(self, identifier):
+        return self.store[identifier]

image_prediction/model_loader/loader.py

@@ -1,12 +1,19 @@
-import abc
+from functools import lru_cache
+
+from image_prediction.model_loader.database.connector import DatabaseConnector
 
 
-class ModelLoader(abc.ABC):
+class ModelLoader:
 
-    @abc.abstractmethod
-    def load_model(self, *args, **kwargs):
-        pass
+    def __init__(self, database_connector: DatabaseConnector):
+        self.database_connector = database_connector
 
-    @abc.abstractmethod
-    def load_classes(self, *args, **kwargs):
-        pass
+    @lru_cache(maxsize=None)
+    def __get_object(self, identifier):
+        return self.database_connector.get_object(identifier)
+
+    def load_model(self, identifier):
+        return self.__get_object(identifier)["model"]
+
+    def load_classes(self, identifier):
+        return self.__get_object(identifier)["classes"]

image_prediction/model_loader/loaders/mlflow.py

@@ -1,101 +1,123 @@
-"""This module translates between the new ModelLoader API and the inconsistent and historically grown redai model and
-MLflow API as well as the circumstance, that the model artifacts are currently not stored at a single place, due to the
-need of loading the base weights of the pre-trained model, that became apparent at a later point than the design of the
-MLflow storage and MlflowModelReader class; that is why the code in this module is so unclean. In the future, a
-non-adhoc solution should be used that offers a clean API and storage solution. Either implement a well-designed MLflow
-based solution or look into an alternative such as WandB or use a platform solution such as AWS.
-"""
-import importlib
-import json
-import os
-import warnings
-
-import numpy as np
-
-from image_prediction.exceptions import IncorrectInstantiation
-from image_prediction.model_loader.loader import ModelLoader
-
-warnings.filterwarnings("ignore", category=DeprecationWarning, module="pkg_resources")
-
-import mlflow
-
-
-def load_object(object_path):
-    path_fragments = object_path.split(".")
-
-    module_path = ".".join(path_fragments[:-1])
-    object_name = path_fragments[-1]
-
-    module = importlib.import_module(module_path)
-    return getattr(module, object_name)
-
-
-def to_local_path(uri):
-    return uri[7:]
-
-
-class MlflowModelReader:
-
-    def __init__(self, run_id, mlruns_dir=None):
-        mlflow.set_tracking_uri(mlruns_dir)
-
-        self.run_id = run_id
-        self.run = mlflow.get_run(run_id)
-        self.artifact_uri = self.__correct_artifact_uri(self.run.info.to_proto().artifact_uri, mlruns_dir)
-
-    @staticmethod
-    def __correct_artifact_uri(run_artifact_uri, base_path):
-        _, suffix = run_artifact_uri.split("mlruns/")
-        return os.path.join(base_path, suffix)
-
-    def get_weights_path(self, prefix="tt"):
-        path = os.path.join(self.artifact_uri, prefix, "train_dev", "estimator", "weights.h5")
-        return path
-
-    def get_classes(self, prefix="tt"):
-        classes = json.loads(
-            self.run.data.params[os.path.join(prefix, "train_dev/estimator/classes")].replace("'", '"')
-        )
-        return classes
-
-    def get_model_handle(self, base_weights=None):
-        weights_path = self.get_weights_path()
-        model_handle_builder = load_object(self.run.data.params["model_handle_builder"].strip())
-        model_handle = model_handle_builder(self.get_classes(), base_weights=base_weights)
-        model_handle.load_top_weights(weights_path)
-        return model_handle
-
-
-class MlflowLoader(ModelLoader):
-
-    def __init__(self, mlruns_dir):
-        self.__mlruns_dir = mlruns_dir
-        self._model_handle = None
-        self.__last_run_id = None
-        self._base_weights = None
-
-    def load_model(self, run_id, base_weights=None):
-
-        if not base_weights:
-
-            if not self._base_weights:
-                raise IncorrectInstantiation("MlflowReader needs to be initialized via get_model_loader.")
-
-            base_weights = self._base_weights
-
-        if not self._model_handle and run_id == self.__last_run_id:
-            mlflow_reader = MlflowModelReader(run_id, mlruns_dir=self.__mlruns_dir)
-            model_handel = mlflow_reader.get_model_handle(base_weights)
-            self._model_handle = model_handel
-            self.__last_run_id = run_id
-
-        return self._model_handle
-
-    def load_classes(self, run_id):
-        model_handle = self.load_model(run_id)
-
-        classes = model_handle.model.classes_
-        classes_readable = np.array(model_handle.classes)
-        classes_readable_aligned = classes_readable[classes[list(range(len(classes)))]]
-
-        return classes_readable_aligned
+# """This module translates between the new ModelLoader API and the inconsistent and historically grown redai model and
+# MLflow API as well as the circumstance, that the model artifacts are currently not stored at a single place, due to the
+# need of loading the base weights of the pre-trained model, that became apparent at a later point than the design of the
+# MLflow storage and MlflowModelReader class; that is why the code in this module is so unclean. In the future, a
+# non-adhoc solution should be used that offers a clean API and storage solution. Either implement a well-designed MLflow
+# based solution or look into an alternative such as WandB or use a platform solution such as AWS.
+# """
+# import importlib
+# import json
+# import os
+# import warnings
+# from typing import Mapping
+#
+# import numpy as np
+# from funcy import rcompose
+#
+# from image_prediction.exceptions import IncorrectInstantiation
+# from image_prediction.model_loader.loader import ModelLoader
+#
+# warnings.filterwarnings("ignore", category=DeprecationWarning, module="pkg_resources")
+#
+# import mlflow
+#
+#
+# def load_object(object_path):
+#     path_fragments = object_path.split(".")
+#
+#     module_path = ".".join(path_fragments[:-1])
+#     object_name = path_fragments[-1]
+#
+#     module = importlib.import_module(module_path)
+#     return getattr(module, object_name)
+#
+#
+# def to_local_path(uri):
+#     return uri[7:]
+#
+#
+# class MlflowModelReader:
+#
+#     def __init__(self, run_id, mlruns_dir=None):
+#         mlflow.set_tracking_uri(mlruns_dir)
+#
+#         self.run_id = run_id
+#         self.run = mlflow.get_run(run_id)
+#         self.artifact_uri = self.__correct_artifact_uri(self.run.info.to_proto().artifact_uri, mlruns_dir)
+#
+#     @staticmethod
+#     def __correct_artifact_uri(run_artifact_uri, base_path):
+#         _, suffix = run_artifact_uri.split("mlruns/")
+#         return os.path.join(base_path, suffix)
+#
+#     def get_weights_path(self, prefix="tt"):
+#         path = os.path.join(self.artifact_uri, prefix, "train_dev", "estimator", "weights.h5")
+#         return path
+#
+#     def get_classes(self, prefix="tt"):
+#         classes = json.loads(
+#             self.run.data.params[os.path.join(prefix, "train_dev/estimator/classes")].replace("'", '"')
+#         )
+#         return classes
+#
+#     def get_model_handle(self, base_weights=None):
+#         weights_path = self.get_weights_path()
+#         model_handle_builder = load_object(self.run.data.params["model_handle_builder"].strip())
+#         model_handle = model_handle_builder(self.get_classes(), base_weights=base_weights)
+#         model_handle.load_top_weights(weights_path)
+#         return model_handle
+#
+#
+# class PredictionModelHandle:
+#     """Simplifies usage of ModelHandle instances for prediction purposes."""
+#
+#     def __init__(self, model_handle, classes_readable: Mapping[int, str]):
+#         self.__model_handle = model_handle
+#         self.__classes_readable = classes_readable
+#
+#     @property
+#     def classes(self):
+#         return self.__classes_readable
+#
+#     def predict(self, *args, **kwargs):
+#         predict = rcompose(self.__model_handle.prep_images, self.__model_handle.model.predict)
+#         return predict(*args, **kwargs)
+#
+#     def predict_proba(self, *args, **kwargs):
+#         predict = rcompose(self.__model_handle.prep_images, self.__model_handle.model.predict_proba)
+#         return predict(*args, **kwargs)
+#
+#
+# class MlflowLoader(ModelLoader):
+#
+#     def __init__(self, mlruns_dir):
+#         self.__mlruns_dir = mlruns_dir
+#         self._base_weights = None
+#
+#     def load_model(self, run_id, base_weights=None) -> PredictionModelHandle:
+#
+#         # TODO: refac https://stackoverflow.com/questions/42735421/how-to-restrict-object-instantiation-only-via-a-factory-in-python
+#         if not base_weights:
+#
+#             if not self._base_weights:
+#                 raise IncorrectInstantiation("MlflowReader needs to be initialized via get_model_loader.")
+#
+#             base_weights = self._base_weights
+#
+#         mlflow_reader = MlflowModelReader(run_id, mlruns_dir=self.__mlruns_dir)
+#         model_handel = mlflow_reader.get_model_handle(base_weights)
+#         model_handle = model_handel
+#         classes_readable = self.__load_classes(model_handle)
+#
+#         model = PredictionModelHandle(model_handle, classes_readable)
+#
+#         return model
+#
+#     @staticmethod
+#     def __load_classes(model_handle):
+#
+#         classes = model_handle.model.classes_
+#         classes_readable = np.array(model_handle.classes)
+#         classes_readable_aligned = classes_readable[classes[list(range(len(classes)))]]
+#
+#         return classes_readable_aligned

image_prediction/model_loader/loaders/mock.py

@@ -9,7 +9,3 @@ class ModelLoaderMock(ModelLoader):
     def load_model(self, identifier):
         assert self.model is not None, "Set the model to be returned first via monkeypatching"
         return self.model
-
-    def load_classes(self, identifier):
-        assert self.classes is not None, "Set the classes to be returned first via monkeypatching"
-        return self.classes

image_prediction/model_loading.py

@@ -1,17 +0,0 @@
-from collections import namedtuple
-
-from image_prediction.locations import MLRUNS_DIR
-from image_prediction.model_loader.loader import ModelLoader
-from image_prediction.model_loader.loaders.mlflow import MlflowLoader
-
-ModelClassesPair = namedtuple("ModelClassesPair", ["model", "classes"])
-
-
-def load_model_and_classes(identifier, model_loader: ModelLoader = None) -> ModelClassesPair:
-    if not model_loader:
-        model_loader = MlflowLoader(MLRUNS_DIR)
-
-    model = model_loader.load_model(identifier)
-    classes = model_loader.load_classes(identifier)
-
-    return ModelClassesPair(model, classes)

test/unit_tests/conftest.py

@@ -1,4 +1,5 @@
 import random
+import string
 import tempfile
 from itertools import starmap
 from operator import itemgetter
@@ -13,13 +14,12 @@ from image_prediction.classifier.image_classifier import ImageClassifier
 from image_prediction.estimator.adapter.adapters.keras import KerasEstimatorAdapter
 from image_prediction.estimator.adapter.adapters.mock import EstimatorMock, EstimatorAdapterMock
 from image_prediction.estimator.preprocessor.preprocessors.basic import BasicPreprocessor
-from image_prediction.exceptions import UnknownEstimatorAdapter, UnknownImageExtractor, UnknownModelLoader
+from image_prediction.exceptions import UnknownEstimatorAdapter, UnknownImageExtractor, UnknownDatabaseType
 from image_prediction.image_extractor.extractor import ImageMetadataPair
 from image_prediction.image_extractor.extractors.mock import ImageExtractorMock
 from image_prediction.image_extractor.extractors.parsable import ParsablePDFImageExtractor
-from image_prediction.model_loader.loaders.loaders import get_mlflow_loader
-from image_prediction.model_loader.loaders.mlflow import MlflowLoader
-from image_prediction.model_loader.loaders.mock import ModelLoaderMock
+from image_prediction.model_loader.database.connectors.mock import DatabaseConnectorMock
+from image_prediction.model_loader.loader import ModelLoader
 
 
 @pytest.fixture
@@ -207,29 +207,79 @@ def add_image_to_last_page(pdf: fpdf.fpdf.FPDF, image_metadata_pair):
         pdf.image(temp_image.name, x=x, y=y, w=w, h=h, type="png")
 
 
-@pytest.fixture
-def model_handle_mock(classes, classifier):
-
-    class ModelHandleMock:
-
-        def __init__(self, classes):
-            classifier.classes_ = np.array(list(range(len(classes))))
-            self.classes = classes
-            self.model = classifier
-
-    return ModelHandleMock(classes)
+# @pytest.fixture
+# def model_handle_mock(classes, classifier):
+#
+#     class ModelHandleMock:
+#
+#         def __init__(self, classes):
+#             classifier.classes_ = np.array(list(range(len(classes))))
+#             self.classes = classes
+#             self.model = classifier
+#
+#     return ModelHandleMock(classes)
+#
+#
+# @pytest.fixture
+# def prediction_model_handle_mock(model_handle_mock, classes):
+#     return PredictionModelHandle(model_handle_mock, classes)
 
 
 @pytest.fixture
-def model_loader(loader_type, monkeypatch, model_handle_mock, classes):
-    if loader_type == "mock":
-        loader = ModelLoaderMock()
-        monkeypatch.setattr(loader, "model", model_handle_mock)
-        monkeypatch.setattr(loader, "classes", classes)
-    elif loader_type == "mlflow":
-        loader = get_mlflow_loader()
-        monkeypatch.setattr(loader, "_model_handle", model_handle_mock)
+def model():
+    
+    class Model:
+        
+        @staticmethod
+        def predict(*args):
+            return True
+
+        @staticmethod
+        def predict_proba(*args):
+            return True
+
+    return Model()
+        
+
+@pytest.fixture
+def model_database_record_identifier():
+    return "".join(random.sample(string.ascii_letters, k=10))
+
+
+@pytest.fixture
+def model_database_record(model, classes):
+    return {"model": model, "classes": classes}
+
+
+@pytest.fixture
+def model_database(model_database_record, model_database_record_identifier):
+    return {model_database_record_identifier: model_database_record}
+
+
+@pytest.fixture
+def database_connector(database_type, model_database):
+    if database_type == "mock":
+        return DatabaseConnectorMock(model_database)
     else:
-        raise UnknownModelLoader(f"No model loader for type {loader_type} was specified.")
+        raise UnknownDatabaseType(f"No connector for database type {database_type} was specified.")
 
-    return loader
+
+@pytest.fixture
+def model_loader(database_connector):
+    return ModelLoader(database_connector)
+
+
+# @pytest.fixture
+# def model_loader(loader_type, monkeypatch, model_handle_mock, classes):
+#     if loader_type == "mock":
+#         loader = ModelLoaderMock()
+#         monkeypatch.setattr(loader, "model", model_handle_mock)
+#
+#     # elif loader_type == "mlflow":
+#     #     loader = get_mlflow_loader()
+#     #     monkeypatch.setattr(loader, "_model_handle", model_handle_mock)
+#
+#     else:
+#         raise UnknownModelLoader(f"No model loader for type {loader_type} was specified.")
+#
+#     return loader

test/unit_tests/model_loader_test.py

@@ -1,15 +1,19 @@
 import numpy as np
 import pytest
 
-from image_prediction.model_loading import load_model_and_classes
 
+# @pytest.mark.parametrize("loader_type", ["mock"])
+# @pytest.mark.parametrize("estimator_type", ["mock"])
+# @pytest.mark.parametrize("batch_size", [3])
+# def test_load_model_and_classes(model_loader, model_handle_mock, classes):
+#     model_loaded, classes_loaded = model_loader.load_model_and_classes("an identifier")
+#     assert model_loaded == model_handle_mock
+#     assert np.all(classes_loaded == classes)
 
-@pytest.mark.parametrize("loader_type", ["mock", "mlflow"])
-@pytest.mark.parametrize("estimator_type", ["mock"])
-@pytest.mark.parametrize("batch_size", [3])
-def test_load_model_and_classes(model_loader, model_handle_mock, classes):
-    # Load twice to test caching logic
-    for _ in range(2):
-        model_loaded, classes_loaded = load_model_and_classes("some random identifier", model_loader=model_loader)
-        assert model_loaded == model_handle_mock
-        assert np.all(classes_loaded == classes)
+@pytest.mark.parametrize("database_type", ["mock"])
+def test_load_model_and_classes(model_loader, model_database_record_identifier, model, classes):
+    model_loaded = model_loader.load_model(model_database_record_identifier)
+    classes_loaded = model_loader.load_classes(model_database_record_identifier)
+
+    assert model_loaded == model
+    assert classes_loaded == classes