eliminated redai dependency; updated requirement versions

image_prediction/redai_adapter/mlflow.py

@@ -47,8 +47,9 @@ class MlflowModelReader:
 
         base_weights_path, weights_path = self.__get_weights_path(run_id)
 
-        model_handle = model_handle_builder(self.__get_classes(run_id), base_weights=base_weights_path)
-        model_handle.load_top_weights(weights_path)
+        model_handle = model_handle_builder(
+            self.__get_classes(run_id), base_weights_path=base_weights_path, weights_path=weights_path
+        )
 
         return model_handle
 

image_prediction/redai_adapter/model.py

@@ -5,7 +5,7 @@ class PredictionModelHandle:
     """Simplifies usage of ModelHandle instances for prediction purposes."""
 
     def __init__(self, model_handle):
-        self.__predict = rcompose(model_handle.prep_images, model_handle.model.predict_proba)
+        self.__predict = rcompose(model_handle.prep_images, model_handle.model.predict)
 
     def predict(self, *args, **kwargs):
         return self.__predict(*args, **kwargs)

image_prediction/redai_adapter/model_wrapper.py

@@ -0,0 +1,75 @@
+import os
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+import numpy as np
+
+import tensorflow as tf
+
+
+class EfficientNetWrapper:
+
+    def __init__(self, classes, base_weights_path=None, weights_path=None):
+        self.__classes = classes
+        self.__input_shape = (224, 224, 3)
+        self.model = self.__build(base_weights_path)
+        self.model.load_weights(weights_path)
+
+    @property
+    def input_shape(self):
+        return self.__input_shape
+
+    @property
+    def classes(self):
+        return self.__classes
+
+    @staticmethod
+    def __preprocess_tensor(tensor):
+        return tf.keras.applications.efficientnet.preprocess_input(tensor)
+
+    @staticmethod
+    def __images_to_tensor(images):
+        return np.array(list(map(tf.keras.preprocessing.image.img_to_array, images)))
+
+    def __resize_and_convert(self, image):
+        return image.resize(self.input_shape[:-1]).convert("RGB")
+
+    def prep_images(self, images):
+        images = map(self.__resize_and_convert, images)
+        tensor = self.__images_to_tensor(images)
+        tensor = self.__preprocess_tensor(tensor)
+
+        return tensor
+
+    def __build(self, base_weights=None) -> tf.keras.models.Model:
+        input_img = tf.keras.layers.Input(shape=self.input_shape)
+
+        pretrained = tf.keras.applications.efficientnet.EfficientNetB0(
+            include_top=False, input_tensor=tf.keras.layers.Input(shape=self.input_shape), weights=base_weights
+        )
+
+        pretrained.trainable = False
+
+        for layer in pretrained.layers:
+            layer.trainable = False
+
+        pretrained = pretrained(input_img)
+
+        finetuned = tf.keras.layers.Flatten()(pretrained)
+        finetuned = tf.keras.layers.Dense(512, activation="relu")(finetuned)
+        finetuned = tf.keras.layers.Dropout(0.2)(finetuned)
+        finetuned = tf.keras.layers.Dense(128, activation="relu")(finetuned)
+        finetuned = tf.keras.layers.Dropout(0.2)(finetuned)
+        finetuned = tf.keras.layers.Dense(32, activation="relu")(finetuned)
+        finetuned = tf.keras.layers.Dropout(0.2)(finetuned)
+        finetuned = tf.keras.layers.Dense(len(self.classes), activation="softmax")(finetuned)
+
+        model = tf.keras.models.Model(inputs=input_img, outputs=finetuned)
+
+        model.compile(
+            loss="categorical_crossentropy",
+            optimizer="adam",
+            metrics=[tf.keras.metrics.Recall(), tf.keras.metrics.Precision()],
+        )
+
+        return model

pytest.ini

@@ -1,5 +1,5 @@
 [pytest]
 norecursedirs = incl
 filterwarnings =
-    ignore:.*imp.*:DeprecationWarning
-    ignore:.*Use.*:DeprecationWarning
+    ignore:.*:DeprecationWarning
+    ignore:.*:DeprecationWarning

requirements.txt

@@ -1,26 +1,23 @@
-Flask==2.0.2
+Flask==2.1.1
 requests==2.27.1
 iteration-utilities==0.11.0
-dvc==2.9.3
+dvc==2.10.0
 dvc[ssh]
 frozendict==2.3.0
-waitress==2.0.0
-envyaml~=1.8.210417
+waitress==2.1.1
+envyaml==1.10.211231
 dependency-check==0.6.*
-envyaml~=1.8.210417
-mlflow~=1.20.2
-numpy~=1.19.3
-PDFNetPython3~=9.1.0
-tqdm~=4.62.2
-pandas~=1.3.1
-mlflow~=1.20.2
-tensorflow~=2.5.0
-PDFNetPython3~=9.1.0
-Pillow~=8.3.2
+mlflow==1.24.0
+numpy==1.22.3
+tqdm==4.63.1
+pandas==1.4.1
+mlflow==1.24.0
+tensorflow==2.8.0
 PyYAML~=5.4.1
-scikit_learn~=0.24.2
+scikit_learn==1.0.2
 pytest~=7.1.0
 funcy==1.17
 PyMuPDF==1.19.6
 fpdf==1.7.2
 coverage==6.3.2
+Pillow==9.1.0

test/integration_tests/server_test.py

@@ -63,7 +63,7 @@ def server_ready(url):
 @pytest.fixture(autouse=True, scope="function")
 def server_process(server, host_and_port, url):
     def get_server_process():
-        coverage.process_startup()
+        # coverage.process_startup()
         return Process(target=run_prediction_server, kwargs={"app": server, **host_and_port})
 
     server = get_server_process()