Feature/callbacks Completed

callbacks/__init__.py

@@ -0,0 +1,4 @@
+from callbacks.csv_logging import CSVLogging
+from callbacks.epoch_results_logging import EpochResultsLogging
+from callbacks.early_stopping import EarlyStopping
+from callbacks.checkpoint import Checkpoint

callbacks/callback.py

@@ -0,0 +1,73 @@
+class Callback:
+    """
+    A base class for defining callbacks in a training process.
+
+    Callbacks are functions that can be executed at various stages during training.
+    They can be used to perform additional actions or modify the behavior of the training process.
+
+    Methods:
+        should_continue(logs=None) -> bool:
+            Determines whether the training process should continue or stop.
+
+        on_epoch_begin(epoch, logs=None) -> None:
+            Executed at the beginning of each epoch.
+
+        on_epoch_end(epoch, logs=None) -> None:
+            Executed at the end of each epoch.
+
+        on_train_begin(logs=None) -> None:
+            Executed at the beginning of the training process.
+
+        on_train_end(logs=None) -> None:
+            Executed at the end of the training process.
+    """
+
+    def should_continue(self, logs=None) -> bool:
+        """
+        Determines whether the training process should continue or stop.
+
+        Args:
+            logs (dict): Optional dictionary containing training logs.
+
+        Returns:
+            bool: True if the training process should continue, False otherwise.
+        """
+        return True
+
+    def on_epoch_begin(self, epoch, logs=None) -> None:
+        """
+        Executed at the beginning of each epoch.
+
+        Args:
+            epoch (int): The current epoch number.
+            logs (dict): Optional dictionary containing training logs.
+        """
+        pass
+
+    def on_epoch_end(self, epoch, logs=None) -> None:
+        """
+        Executed at the end of each epoch.
+
+        Args:
+            epoch (int): The current epoch number.
+            logs (dict): Optional dictionary containing training logs.
+        """
+        pass
+
+    def on_train_begin(self, logs=None) -> None:
+        """
+        Executed at the beginning of the training process.
+
+        Args:
+            logs (dict): Optional dictionary containing training logs.
+        """
+        pass
+
+    def on_train_end(self, logs=None) -> None:
+        """
+        Executed at the end of the training process.
+
+        Args:
+            logs (dict): Optional dictionary containing training logs.
+        """
+        pass

callbacks/checkpoint.py

@@ -0,0 +1,60 @@
+from callbacks.callback import Callback
+import os
+import torch
+
+class Checkpoint(Callback):
+    """
+    Callback class for saving model checkpoints during training.
+
+    Args:
+        checkpoint_dir (str): Directory to save the checkpoints.
+        model (torch.nn.Module): The model to be saved.
+        optimizer (torch.optim.Optimizer): The optimizer to be saved.
+        scheduler (torch.optim.lr_scheduler._LRScheduler, optional): The scheduler to be saved. Default is None.
+        save_freq (int, optional): Frequency of saving checkpoints. Default is 1.
+        verbose (bool, optional): Whether to print the checkpoint save path. Default is False.
+    """
+
+    def __init__(self, checkpoint_dir, model, optimizer, scheduler=None, save_freq=1, verbose=False):
+        self.checkpoint_dir = checkpoint_dir
+        self.model = model
+        self.optimizer = optimizer
+        self.scheduler = scheduler
+        self.save_freq = save_freq
+        self.verbose = verbose
+        if not os.path.exists(checkpoint_dir):
+            os.makedirs(checkpoint_dir, exist_ok=True)
+
+    def on_epoch_end(self, epoch, logs=None):
+        """
+        Callback function called at the end of each epoch.
+
+        Args:
+            epoch (int): The current epoch number.
+            logs (dict, optional): Dictionary containing training and validation losses. Default is None.
+        """
+        if (epoch + 1) % self.save_freq == 0:
+            self.save_checkpoint(epoch, logs)
+
+    def save_checkpoint(self, epoch, logs=None):
+        """
+        Save the model checkpoint.
+
+        Args:
+            epoch (int): The current epoch number.
+            logs (dict, optional): Dictionary containing training and validation losses. Default is None.
+        """
+        state = {
+            'epoch': epoch,
+            'model_state_dict': self.model.state_dict(),
+            'optimizer_state_dict': self.optimizer.state_dict(),
+            'train_losses': logs.get('training_losses', []),
+            'val_losses': logs.get('validation_losses', []),
+        }
+        if self.scheduler:
+            state['scheduler_state_dict'] = self.scheduler.state_dict()
+
+        save_path = os.path.join(self.checkpoint_dir, f'checkpoint_epoch_{epoch+1}.pth')
+        torch.save(state, save_path)
+        if self.verbose:
+            print(f"Checkpoint saved at {save_path}")

callbacks/csv_logging.py

@@ -0,0 +1,54 @@
+from callbacks.callback import Callback
+import csv
+
+class CSVLogging(Callback):
+    """
+    Callback for logging training and validation metrics to a CSV file.
+
+    Args:
+        csv_path (str): The path to the CSV file.
+
+    Attributes:
+        csv_path (str): The path to the CSV file.
+        headers_written (bool): Flag indicating whether the headers have been written to the CSV file.
+    """
+
+    def __init__(self, csv_path):
+        self.csv_path = csv_path
+        self.headers_written = False
+
+    def on_epoch_end(self, epoch, logs=None):
+        """
+        Method called at the end of each epoch during training.
+
+        Args:
+            epoch (int): The current epoch number.
+            logs (dict): Dictionary containing the training and validation metrics.
+
+        Returns:
+            None
+        """
+        if logs is None:
+            return
+
+        epoch_data = logs.get('epoch')
+        train_loss = logs.get('train_loss')
+        val_loss = logs.get('val_loss')
+        train_metrics = logs.get('train_metrics', {})
+        val_metrics = logs.get('val_metrics', {})
+
+        metrics = {'train_loss': train_loss, 'val_loss': val_loss}
+        metrics.update({f'train_{key}': value for key, value in train_metrics.items()})
+        metrics.update({f'val_{key}': value for key, value in val_metrics.items()})
+
+        if not self.headers_written:
+            headers = ['epoch'] + list(metrics.keys())
+            with open(self.csv_path, 'w', newline='') as file:
+                writer = csv.writer(file)
+                writer.writerow(headers)
+                self.headers_written = True
+
+        values = [epoch_data] + [metrics[key] for key in headers[1:]]  # Ensure the order matches headers
+        with open(self.csv_path, 'a', newline='') as file:
+            writer = csv.writer(file)
+            writer.writerow(values)

callbacks/early_stopping.py

@@ -0,0 +1,34 @@
+from callbacks.callback import Callback
+
+class EarlyStopping(Callback):
+    def __init__(self, monitor='val_loss', patience=5, verbose=False, delta=0):
+        self.monitor = monitor
+        self.patience = patience
+        self.verbose = verbose
+        self.delta = delta
+        self.best_score = None
+        self.epochs_no_improve = 0
+        self.stopped_epoch = 0
+        self.early_stop = False
+
+    def on_epoch_end(self, epoch, logs=None):
+        current = logs.get(self.monitor)
+        if current is None:
+            return
+
+        score = -current if 'loss' in self.monitor else current
+        if self.best_score is None:
+            self.best_score = score
+        elif score < self.best_score + self.delta:
+            self.epochs_no_improve += 1
+            if self.epochs_no_improve >= self.patience:
+                self.early_stop = True
+                self.stopped_epoch = epoch + 1
+                if self.verbose:
+                    print(f"Early stopping triggered at epoch {self.stopped_epoch}")
+        else:
+            self.best_score = score
+            self.epochs_no_improve = 0
+
+    def should_continue(self, logs=None):
+        return not self.early_stop

callbacks/epoch_results_logging.py

@@ -0,0 +1,39 @@
+from callbacks.callback import Callback
+
+class EpochResultsLogging(Callback):
+    """
+    Callback for logging epoch results during training.
+
+    This callback prints the training loss, training metrics, and validation metrics at the end of each epoch.
+
+    Args:
+        Callback: The base class for Keras callbacks.
+
+    Methods:
+        on_epoch_end: Called at the end of each epoch to print the epoch results.
+    """
+
+    def on_epoch_end(self, epoch, logs=None):
+        """
+        Prints the epoch results at the end of each epoch.
+
+        Args:
+            epoch (int): The current epoch number.
+            logs (dict): A dictionary containing the training and validation metrics.
+
+        Returns:
+            None
+        """
+        epoch_loss_train = logs.get('train_loss')
+        epoch_metrics_train = logs.get('train_metrics', {})
+        epoch_metrics_valid = logs.get('val_metrics', {})
+        num_epochs = logs.get('num_epochs', 0)
+
+        print(f"Epoch {epoch+1}/{num_epochs}, Training loss: {epoch_loss_train:.4f}")
+
+        for metric_name, value in epoch_metrics_train.items():
+            print(f"Training {metric_name}: {value:.4f}")
+
+        if epoch_metrics_valid:
+            for metric_name, value in epoch_metrics_valid.items():
+                print(f"Validation {metric_name}: {value if value is not None else 'N/A'}")

fine_tuning.py

@@ -4,7 +4,7 @@
 from torch.utils.data import DataLoader, random_split
 from datasets.dataset import get_dataset
 from datasets.transformations import get_transforms
-from utils.metrics import Accuracy, Precision, Recall, F1Score
+from utils.metrics import Accuracy, Precision
 from models import get_model
 from trainers import get_trainer
 from os import path
@@ -62,7 +62,6 @@ def main(config_path):
         train_loader=train_loader,
         valid_loader=valid_loader,
         num_epochs=config['training']['initial_epochs'],
-        log_path=log_filename,
         plot_path=plot_filename
     )
 
@@ -83,7 +82,6 @@ def main(config_path):
         train_loader=train_loader,
         valid_loader=valid_loader,
         num_epochs=config['training']['fine_tuning_epochs'],
-        log_path=log_filename,
         plot_path=plot_filename
     )
 

main.py

@@ -3,6 +3,7 @@
 from datetime import datetime
 from torch.utils.data import DataLoader, random_split
 from datasets.dataset import get_dataset
+from callbacks import EarlyStopping, CSVLogging, EpochResultsLogging
 from datasets.transformations import get_transforms
 from utils.metrics import Accuracy, Precision, Recall, F1Score
 from models import get_model
@@ -58,12 +59,22 @@ def main(config_path):
         metrics=metrics
     )
 
+    callbacks = [
+        CSVLogging(log_filename),
+        EpochResultsLogging(),
+        EarlyStopping(
+            monitor='val_loss', 
+            patience=2,
+            delta=0.1, 
+            verbose=True
+            )
+    ]
+
     trainer.train(
         train_loader=train_loader,
         valid_loader=valid_loader,
         num_epochs=config['training']['num_epochs'],
-        log_path=log_filename,
-        plot_path=plot_filename
+        callbacks=callbacks,
     )
 
     trainer.evaluate(data_loader=test_loader)