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Training utilities and metrics
Implements Critical Fix #9: Dataset-Aware Metric Computation
"""
import torch
import numpy as np
from typing import Dict, List, Optional
from sklearn.metrics import (
accuracy_score, f1_score, precision_score, recall_score,
confusion_matrix
)
class SegmentationMetrics:
"""
Segmentation metrics (IoU, Dice)
Only computed for datasets with pixel masks (Critical Fix #9)
"""
def __init__(self):
"""Initialize metrics"""
self.reset()
def reset(self):
"""Reset all metrics"""
self.intersection = 0
self.union = 0
self.pred_sum = 0
self.target_sum = 0
self.total_samples = 0
def update(self,
pred: torch.Tensor,
target: torch.Tensor,
has_pixel_mask: bool = True):
"""
Update metrics with batch
Args:
pred: Predicted probabilities (B, 1, H, W)
target: Ground truth masks (B, 1, H, W)
has_pixel_mask: Whether to compute metrics (Critical Fix #9)
"""
if not has_pixel_mask:
return
# Binarize predictions
pred_binary = (pred > 0.5).float()
# Compute intersection and union
intersection = (pred_binary * target).sum().item()
union = pred_binary.sum().item() + target.sum().item() - intersection
self.intersection += intersection
self.union += union
self.pred_sum += pred_binary.sum().item()
self.target_sum += target.sum().item()
self.total_samples += pred.shape[0]
def compute(self) -> Dict[str, float]:
"""
Compute final metrics
Returns:
Dictionary with IoU, Dice, Precision, Recall
"""
# IoU (Jaccard)
iou = self.intersection / (self.union + 1e-8)
# Dice (F1)
dice = (2 * self.intersection) / (self.pred_sum + self.target_sum + 1e-8)
# Precision
precision = self.intersection / (self.pred_sum + 1e-8)
# Recall
recall = self.intersection / (self.target_sum + 1e-8)
return {
'iou': iou,
'dice': dice,
'precision': precision,
'recall': recall
}
class ClassificationMetrics:
"""Classification metrics for forgery type classification"""
def __init__(self, num_classes: int = 3):
"""
Initialize metrics
Args:
num_classes: Number of forgery types
"""
self.num_classes = num_classes
self.reset()
def reset(self):
"""Reset all metrics"""
self.predictions = []
self.targets = []
self.confidences = []
def update(self,
pred: np.ndarray,
target: np.ndarray,
confidence: Optional[np.ndarray] = None):
"""
Update metrics with predictions
Args:
pred: Predicted class indices
target: Ground truth class indices
confidence: Optional prediction confidences
"""
self.predictions.extend(pred.tolist())
self.targets.extend(target.tolist())
if confidence is not None:
self.confidences.extend(confidence.tolist())
def compute(self) -> Dict[str, float]:
"""
Compute final metrics
Returns:
Dictionary with Accuracy, F1, Precision, Recall
"""
if len(self.predictions) == 0:
return {
'accuracy': 0.0,
'f1_macro': 0.0,
'f1_weighted': 0.0,
'precision': 0.0,
'recall': 0.0
}
preds = np.array(self.predictions)
targets = np.array(self.targets)
# Accuracy
accuracy = accuracy_score(targets, preds)
# F1 score (macro and weighted)
f1_macro = f1_score(targets, preds, average='macro', zero_division=0)
f1_weighted = f1_score(targets, preds, average='weighted', zero_division=0)
# Precision and Recall
precision = precision_score(targets, preds, average='macro', zero_division=0)
recall = recall_score(targets, preds, average='macro', zero_division=0)
# Confusion matrix
cm = confusion_matrix(targets, preds, labels=range(self.num_classes))
return {
'accuracy': accuracy,
'f1_macro': f1_macro,
'f1_weighted': f1_weighted,
'precision': precision,
'recall': recall,
'confusion_matrix': cm.tolist()
}
class MetricsTracker:
"""Track all metrics during training"""
def __init__(self, config):
"""
Initialize metrics tracker
Args:
config: Configuration object
"""
self.config = config
self.num_classes = config.get('data.num_classes', 3)
self.seg_metrics = SegmentationMetrics()
self.cls_metrics = ClassificationMetrics(self.num_classes)
self.history = {
'train_loss': [],
'val_loss': [],
'train_iou': [],
'val_iou': [],
'train_dice': [],
'val_dice': [],
'train_precision': [],
'val_precision': [],
'train_recall': [],
'val_recall': []
}
def reset(self):
"""Reset metrics for new epoch"""
self.seg_metrics.reset()
self.cls_metrics.reset()
def update_segmentation(self,
pred: torch.Tensor,
target: torch.Tensor,
dataset_name: str):
"""Update segmentation metrics (dataset-aware)"""
has_pixel_mask = self.config.should_compute_localization_metrics(dataset_name)
self.seg_metrics.update(pred, target, has_pixel_mask)
def update_classification(self,
pred: np.ndarray,
target: np.ndarray,
confidence: Optional[np.ndarray] = None):
"""Update classification metrics"""
self.cls_metrics.update(pred, target, confidence)
def compute_all(self) -> Dict[str, float]:
"""Compute all metrics"""
seg = self.seg_metrics.compute()
# Only include classification metrics if they have data
if len(self.cls_metrics.predictions) > 0:
cls = self.cls_metrics.compute()
# Prefix classification metrics to avoid collision
cls_prefixed = {f'cls_{k}': v for k, v in cls.items()}
return {**seg, **cls_prefixed}
return seg
def log_epoch(self, epoch: int, phase: str, loss: float, metrics: Dict):
"""Log metrics for epoch"""
prefix = f'{phase}_'
self.history[f'{phase}_loss'].append(loss)
if 'iou' in metrics:
self.history[f'{phase}_iou'].append(metrics['iou'])
if 'dice' in metrics:
self.history[f'{phase}_dice'].append(metrics['dice'])
if 'precision' in metrics:
self.history[f'{phase}_precision'].append(metrics['precision'])
if 'recall' in metrics:
self.history[f'{phase}_recall'].append(metrics['recall'])
def get_history(self) -> Dict:
"""Get full training history"""
return self.history
class EarlyStopping:
"""Early stopping to prevent overfitting"""
def __init__(self,
patience: int = 10,
min_delta: float = 0.001,
mode: str = 'max'):
"""
Initialize early stopping
Args:
patience: Number of epochs to wait
min_delta: Minimum improvement required
mode: 'min' for loss, 'max' for metrics
"""
self.patience = patience
self.min_delta = min_delta
self.mode = mode
self.counter = 0
self.best_value = None
self.should_stop = False
def __call__(self, value: float) -> bool:
"""
Check if training should stop
Args:
value: Current metric value
Returns:
True if should stop
"""
if self.best_value is None:
self.best_value = value
return False
if self.mode == 'max':
improved = value > self.best_value + self.min_delta
else:
improved = value < self.best_value - self.min_delta
if improved:
self.best_value = value
self.counter = 0
else:
self.counter += 1
if self.counter >= self.patience:
self.should_stop = True
return self.should_stop
def get_metrics_tracker(config) -> MetricsTracker:
"""Factory function for metrics tracker"""
return MetricsTracker(config)
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