Bugfix/file paths

docker-compose.yml

@@ -3,13 +3,13 @@ services:
   mosquitto:
     container_name: mosquitto_broker
     build:
-      context: ./src/mosquitto_broker
+      context: ./src/server/mosquitto_broker
       dockerfile: ./dockerfiles/Dockerfile
     hostname: mosquitto_broker
     volumes:
-      - ./src/mosquitto_broker/conf:/mosquitto/config
-      - ./src/mosquitto_broker/log:/mosquitto/log
-      - ./src/mosquitto_broker/data:/mosquitto/data
+      - ./src/server/mosquitto_broker/conf:/mosquitto/config
+      - ./src/server/mosquitto_broker/log:/mosquitto/log
+      - ./src/server/mosquitto_broker/data:/mosquitto/data
     restart: always
     ports:
       - "1883:1883"

src/server/commons.py

@@ -1,20 +1,31 @@
+from pathlib import Path
 import struct
 
 import numpy as np
 from tensorflow.keras.preprocessing.image import load_img, img_to_array
 
 
+BASE_DIR = Path(__file__).resolve().parent
+
+
 class OffloadingDataFiles:
-    data_file_path_device: str = "../device_inference_times.json"
-    data_file_path_edge: str = "../edge_inference_times.json"
-    data_file_path_sizes: str = "../layer_sizes.json"
-    evaluation_file_path: str = "../evaluations/evaluations.csv"
-    web_file_path: str = "../evaluations/web.csv"
+    data_file_path_device: str = str(BASE_DIR / "device_inference_times.json")
+    data_file_path_edge: str = str(BASE_DIR / "edge_inference_times.json")
+    data_file_path_sizes: str = str(BASE_DIR / "layer_sizes.json")
+
+
+class EvaluationFiles:
+    evaluation_file_path: str = str(BASE_DIR / "evaluations/evaluations.csv")
+    web_file_path: str = str(BASE_DIR / "evaluations/web.csv")
+
+
+class ModelFiles:
+    model_save_path: str = str(BASE_DIR / "models")
 
 
 class InputDataFiles:
-    test_data_file_path = "src/server/models/test/test_model/pred_data/input_data.png"  # Path to test image
-    input_data_file_path = "../input_data.png"  # Input image save path
+    test_data_file_path: str = str(BASE_DIR / "models/test/test_model/pred_data/input_data.png")  # Path to test image
+    input_data_file_path: str = str(BASE_DIR / "input_data.png")  # Input image save path
 
 
 class InputData:

src/server/models/model_manager.py

@@ -5,6 +5,7 @@
 import tensorflow as tf
 
 from server.commons import OffloadingDataFiles
+from server.commons import ModelFiles
 from server.logger.log import logger
 from server.models.model_manager_config import ModelManagerConfig
 
@@ -66,7 +67,7 @@ def load_model(self, model_path: str = ModelManagerConfig.MODEL_PATH):
         logger.debug(f"Loading model from path: {model_path}")
         try:
             self.model_path = model_path
-            self.model = tf.keras.models.load_model(f'src/server/models/test/{model_path}')
+            self.model = tf.keras.models.load_model(f'{ModelFiles.model_save_path}/test/{model_path}')
             self.num_layers = len(self.model.layers)
         except Exception as e:
             print(f"Error loading model: {e}")
@@ -127,7 +128,7 @@ def predict_single_layer(self, layer_id: int, layer_offset: int, layer_input_dat
 
         # initialize interepreter with layer tflite model
         interpreter = tf.lite.Interpreter(
-            model_path=f'src/server/models/test/test_model/layers/tflite/submodel_{layer_id - layer_offset}.tflite')
+            model_path=f'{ModelFiles.model_save_path}/test/{ModelManagerConfig.MODEL_DIR_PATH}/layers/tflite/submodel_{layer_id - layer_offset}.tflite')
         interpreter.allocate_tensors()
         input_details = interpreter.get_input_details()
         output_details = interpreter.get_output_details()
@@ -153,6 +154,6 @@ def save_inference_times(self, save_path: str | None = None):
             self.save_path = save_path
         self.save_path = self.save_path[:-1] if self.save_path[-1] == "/" else self.save_path
         inference_times = self.inference_times
-        with open(f"{self.save_path}/{OffloadingDataFiles.data_file_path_edge}", "w") as f:
+        with open(OffloadingDataFiles.data_file_path_edge, "w") as f:
             json.dump(inference_times, f, indent=4)
         logger.debug(f"Inference times saved")

src/server/mqtt_client/mqtt_client.py

@@ -13,6 +13,7 @@
 import queue
 
 from server.commons import OffloadingDataFiles
+from server.commons import EvaluationFiles
 from server.commons import InputData
 from server.commons import InputDataFiles
 from server.logger.log import logger
@@ -143,7 +144,7 @@ def handle_message_task(self, message, received_timestamp):
                 message_content="InputImage",
                 timestamp=None,
             )
-            MqttMessageData.save_to_file(OffloadingDataFiles.evaluation_file_path, message_data.to_dict())
+            MqttMessageData.save_to_file(EvaluationFiles.evaluation_file_path, message_data.to_dict())
             logger.debug("Input image saved")
             return
 
@@ -162,7 +163,7 @@ def handle_message_task(self, message, received_timestamp):
         # Extend message data
         message_data = self.extend_message_data(message_data, received_timestamp, message.payload)
         # Save message data to file
-        MqttMessageData.save_to_file(OffloadingDataFiles.evaluation_file_path, message_data.to_dict())
+        MqttMessageData.save_to_file(EvaluationFiles.evaluation_file_path, message_data.to_dict())
 
         # run offloading algorithm and ask for prediction after the device sends the registration message
         if message_data.topic == Topics.registration.value:
@@ -195,7 +196,7 @@ def handle_message_task(self, message, received_timestamp):
             # finish inference
             prediction = Edge.run_inference(message_data.offloading_layer_index, np.array(message_data.layer_output, dtype=np.float32))
             logger.debug(f"Prediction: {prediction.tolist()}")
-            MqttMessageData.save_to_file(OffloadingDataFiles.web_file_path, message_data.to_dict())
+            MqttMessageData.save_to_file(EvaluationFiles.web_file_path, message_data.to_dict())
             # run offloading algorithm
             offloading_algo = OffloadingAlgo(
                 avg_speed=message_data.avg_speed,