From b49305a8785d55831787c8dac2e446ccb6ce4fad Mon Sep 17 00:00:00 2001
From: PooriyaSanaie <88898162+ELECEngineer@users.noreply.github.com>
Date: Tue, 20 Jun 2023 23:51:15 +0330
Subject: [PATCH] A3_PooriyaSanaie_Fatemeh Aghabarari_Seyed Javad Hosseini

---
 src/adverse_weather_classification/train.py | 96 ++++++++-------------
 1 file changed, 38 insertions(+), 58 deletions(-)

diff --git a/src/adverse_weather_classification/train.py b/src/adverse_weather_classification/train.py
index 8822b6b..26660ba 100644
--- a/src/adverse_weather_classification/train.py
+++ b/src/adverse_weather_classification/train.py
@@ -10,7 +10,6 @@
 from mock import Mock
 import matplotlib.pyplot as plt
 
-
 class TrainHyperParameters:
     def __init__(self, input_shape: Tuple[int, int, int] = (256, 256, 3), number_of_classes: int = 2,
                  learning_rate: float = 0.001, batch_size: int = 32, number_of_epochs: int = 3) -> None:
@@ -27,7 +26,6 @@ def __init__(self, data_dir: str, checkpoint_dir: str = 'output/checkpoints') ->
         super().__init__()
         self.model = None
 
-        # Set the seed for reproducibility
         np.random.seed(42)
         tf.random.set_seed(42)
 
@@ -38,7 +36,6 @@ def form_data_generator(self) -> Tuple[ImageDataGenerator, ImageDataGenerator]:
         train_dir = os.path.join(self.data_dir, 'train')
         test_dir = os.path.join(self.data_dir, 'test')
 
-        # Define the data generators for training and validation
         train_datagen = ImageDataGenerator(rescale=1. / 255)
         test_datagen = ImageDataGenerator(rescale=1. / 255)
 
@@ -71,63 +68,46 @@ def model_builder(self):
         ])
 
     def train(self, train_generator, test_generator):
-        # Define the optimizer and loss function
         optimizer = keras.optimizers.Adam(lr=self.hyperparameters.learning_rate)
         loss_fn = keras.losses.CategoricalCrossentropy()
-
-        # Compile the model
         if self.model is not None:
             self.model.compile(optimizer=optimizer, loss=loss_fn, metrics=['accuracy'])
         else:
-            raise ValueError('Model is not defined. Please call model_builder() first.')
-
-        # Set up a checkpoint to save the best model weights
-        if os.path.exists(self.checkpoint_dir) is False:
-            os.makedirs(self.checkpoint_dir)
-        checkpoint_path = os.path.join(self.checkpoint_dir, 'best_model.h5')
-
-        checkpoint_cb = ModelCheckpoint(checkpoint_path, monitor='val_accuracy', verbose=1, save_best_only=True,
-                                        mode='max')
-
-        # Train the model
-        history = self.model.fit(train_generator,
-                                 epochs=self.hyperparameters.number_of_epochs,
-                                 validation_data=test_generator,
-                                 callbacks=[checkpoint_cb])
-
-        # Save the model architecture
-        model_dir = os.path.join(self.checkpoint_dir, 'model')
-        if os.path.exists(model_dir) is False:
-            os.makedirs(model_dir)
-        model_path = os.path.join(model_dir, 'model.json')
-        model_json = self.model.to_json()
-        with open(model_path, 'w') as json_file:
-            json_file.write(model_json)
-        # plot loss and accuracy on train and validation set
-        self.plot_history(history)
-
-    def plot_history(self, history):
-        matplotlib.use('Agg')
-        plt.figure(figsize=(10, 5))
-        plt.subplot(1, 2, 1)
-        plt.plot(history.history['loss'], label='train')
-        plt.plot(history.history['val_loss'], label='test')
-        plt.title('Loss')
-        plt.legend()
-        plt.subplot(1, 2, 2)
-        plt.plot(history.history['accuracy'], label='train')
-        plt.plot(history.history['val_accuracy'], label='test')
-        plt.title('Accuracy')
-        plt.legend()
-        plt.savefig(os.path.join(self.checkpoint_dir, 'loss_accuracy.png'), dpi=300)
-
-    def exec(self):
-        train_generator, test_generator = self.form_data_generator()
-        self.model_builder()
-        self.train(train_generator, test_generator)
-
-
-if __name__ == '__main__':
-    data_dir_ = '/home/ahv/PycharmProjects/Visual-Inertial-Odometry/simulation/CARLA/output/root_dir'
-    train_custom_cnn = TrainCustomCNN(data_dir_)
-    train_custom_cnn.exec()
+            raise ValueError("Model is not built.")
+
+        checkpoint_callback = ModelCheckpoint(
+            filepath=os.path.join(self.checkpoint_dir, "model_checkpoint"),
+            monitor='val_loss',
+            save_best_only=True,
+            save_weights_only=False,
+            verbose=1)
+        history = self.model.fit(
+            train_generator,
+            epochs=self.hyperparameters.number_of_epochs,
+            validation_data=test_generator,
+            callbacks=[checkpoint_callback])
+
+        return history
+
+
+if __name__ == "__main__":
+
+    data_directory = '/home/ahv/PycharmProjects/Visual-Inertial-Odometry/simulation/CARLA/output/data_directory'
+    checkpoint_directory = '/home/ahv/PycharmProjects/Visual-Inertial-Odometry/simulation/CARLA/output/checkpoint_directory"
+
+    trainer = TrainCustomCNN(data_dir=data_directory, checkpoint_dir=checkpoint_directory)
+
+    trainer.model_builder()
+
+    train_generator, test_generator = trainer.form_data_generator()
+
+    history = trainer.train(train_generator, test_generator)
+
+    # Plot the training and validation accuracy over epochs
+    plt.plot(history.history['accuracy'])
+    plt.plot(history.history['val_accuracy'])
+    plt.title('Model Accuracy')
+    plt.xlabel('Epoch')
+    plt.ylabel('Accuracy')
+    plt.legend(['Train', 'Validation'], loc='upper left')
+    plt.show()