Merge branch 'main' into feature/precision_improvement

# Conflicts:
#	training/train.py

training/old/vision.py

@@ -115,7 +115,9 @@
 
 # Train model
 epochs = 20
-with tf.device('/GPU:0'):
+device = tf.test.gpu_device_name() if tf.test.is_gpu_available() else '/CPU:0'
+print("Using Device:", device)
+with tf.device(device):
     history = model.fit(
         train_ds,
         validation_data=val_ds,

training/old/with_augmentation.py

@@ -69,7 +69,9 @@
 
 # Train model
 epochs = 20
-with tf.device('/GPU:1'):
+device = tf.test.gpu_device_name() if tf.test.is_gpu_available() else '/CPU:0'
+print("Using Device:", device)
+with tf.device(device):
     history = model.fit(
       train_ds,
       validation_data=val_ds,

training/old/without_augmentation.py

@@ -62,7 +62,9 @@
 
 # Train model
 epochs = 20
-with tf.device('/GPU:1'):
+device = tf.test.gpu_device_name() if tf.test.is_gpu_available() else '/CPU:0'
+print("Using Device:", device)
+with tf.device(device):
     history = model.fit(
         train_ds,
         validation_data=val_ds,

training/pre_filter.py

@@ -115,7 +115,9 @@
 
 # Train model
 epochs = 15
-with tf.device('/GPU:0'):
+device = tf.test.gpu_device_name() if tf.test.is_gpu_available() else '/CPU:0'
+print("Using Device:", device)
+with tf.device(device):
     history = model.fit(
         train_ds,
         validation_data=val_ds,

training/train.py

@@ -1,15 +1,18 @@
 # This file contains the code for training a model with data augmentation and a pretrained base.
 # Import libraries
+import keras
+from keras import layers
 from keras.models import Sequential
 from keras.applications import EfficientNetV2B1
-from utilities.tools import *
+from utilities.tools import get_data_path_addon, get_base_path, suppress_tf_warnings, load_dataset, show_augmented_batch, create_augmentation_layer, plot_model_score, show_sample_batch, show_batch_shape
 from utilities.discord_callback import DiscordCallback
 from keras.optimizers import AdamW
 from keras.regularizers import l1_l2
 from keras.callbacks import EarlyStopping, ModelCheckpoint
 import os
 import random
 
+import tensorflow as tf
 # Ignore warnings
 import warnings
 
@@ -32,9 +35,10 @@
 # Set seed for reproducibility
 random_seed = True
 # Config
+base_path = get_base_path()
 path_addon = get_data_path_addon(model_type)
 config = {
-    "path": f"C:/Users\phili/.keras/datasets/resized_DVM/{path_addon}",
+    "path": f"{base_path}/{path_addon}",
     "batch_size": 32,
     "img_height": img_height,
     "img_width": img_width,

utilities/tools.py

@@ -8,6 +8,7 @@
 from keras import layers
 import os
 import logging
+import platform
 
 
 def load_dataset(path: str, batch_size: int, img_height: int, img_width: int, seed: int) -> tuple[tf.data.Dataset, tf.data.Dataset, list]:
@@ -317,3 +318,17 @@ def get_data_path_addon(name: str) -> str:
         return "pre_filter"
     else:
         raise ValueError("Invalid model name")
+
+
+def get_base_path():
+    # Determine the base path depending on the operating system
+    if platform.system() == 'Windows':
+        base_path = r"C:/Users\phili/.keras/datasets/resized_DVM"
+    elif platform.system() == 'Linux':
+        base_path = "/home/luke/datasets/"
+    elif platform.system() == 'Darwin':  # Darwin is the system name for macOS
+        base_path = "/Users/flippchen/datasets/"
+    else:
+        raise ValueError("Operating system not supported.")
+
+    return base_path