eval_diffusion_methods.py

import os
import argparse
import torch
from torch.utils.data import DataLoader
import pandas as pd
import numpy as np
from sklearn.metrics import accuracy_score, roc_auc_score, average_precision_score, precision_recall_curve
import json
from datetime import datetime

from dataset import FeatureDataset
from model import FusionModel

def filter_metadata_for_diffusion_testing(metadata_path, diffusion_methods=['AniPortrait', 'Hallo', 'Sonic', 'Joyvasa', 'Ditto']):
    """Filter metadata to include only diffusion methods and real data for testing"""
    metadata = pd.read_csv(metadata_path)
    
    # Include real data and diffusion methods
    filtered_metadata = metadata[
        metadata['path'].str.contains('real', case=False) | 
        metadata['path'].str.contains('|'.join(diffusion_methods))
    ]
    
    return filtered_metadata

def get_eval_args():
    parser = argparse.ArgumentParser(description='Evaluate Diffusion Methods Model')
    
    # Model checkpoint
    parser.add_argument('--checkpoint_path', type=str, required=True,
                        help='Path to the trained model checkpoint (.pt file)')
    
    # Data paths
    parser.add_argument('--features_path', type=str, required=True,
                        help='Path to feature data directory')
    parser.add_argument('--metadata', type=str, required=True,
                        help='Path to test metadata file')
    
    # Model configuration
    parser.add_argument('--batch_size', type=int, default=1024,
                        help='Batch size for evaluation')
    parser.add_argument('--tau', type=int, default=15,
                        help='Temporal window size')
    
    return parser.parse_args()

def calculate_acc_at_eer(labels, scores):
    """Calculate accuracy at Equal Error Rate (EER)"""
    from sklearn.metrics import roc_curve
    
    fpr, tpr, thresholds = roc_curve(labels, scores)
    fnr = 1 - tpr
    
    # Find threshold where FPR = FNR (EER)
    eer_threshold_idx = np.nanargmin(np.abs(fpr - fnr))
    eer_threshold = thresholds[eer_threshold_idx]
    
    # Calculate accuracy at EER threshold
    binary_predictions = (scores >= eer_threshold).astype(int)
    acc_at_eer = accuracy_score(labels, binary_predictions)
    
    return acc_at_eer, eer_threshold

def save_predictions_to_csv(video_names, predictions, labels, output_path):
    """Save individual video predictions to CSV file"""
    results_df = pd.DataFrame({
        'video_name': video_names,
        'prediction_score': predictions,
        'predicted_label': (predictions > 0).astype(int),
        'true_label': labels,
        'correct': ((predictions > 0).astype(int) == labels).astype(int)
    })
    
    results_df.to_csv(output_path, index=False)
    print(f"Predictions saved to: {output_path}")

def main():
    args = get_eval_args()
    print("Evaluating Diffusion Methods Model")
    print(f"Checkpoint: {args.checkpoint_path}")
    print(f"Features: {args.features_path}")
    print(f"Metadata: {args.metadata}")
    print(f"Batch size: {args.batch_size}")
    
    # Set device
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    
    # Define diffusion methods
    diffusion_methods = ['AniPortrait', 'Hallo', 'Sonic', 'Joyvasa', 'Ditto']
    print(f"Testing on diffusion methods: {diffusion_methods}")
    
    # Load and filter test metadata
    test_metadata = filter_metadata_for_diffusion_testing(args.metadata, diffusion_methods)
    
    print(f"Test dataset size (filtered): {len(test_metadata)}")
    
    # Create test dataset
    # Save filtered metadata to temporary file
    temp_metadata_path = "/tmp/test_metadata_diffusion.csv"
    test_metadata.to_csv(temp_metadata_path, index=False)
    
    test_dataset = FeatureDataset(
        temp_metadata_path, args.features_path, tau=args.tau
    )
    
    # Create data loader
    test_loader = DataLoader(
        test_dataset, batch_size=args.batch_size, shuffle=False, num_workers=4
    )
    
    # Load model
    model = FusionModel().to(device)
    
    # Load checkpoint
    if os.path.exists(args.checkpoint_path):
        checkpoint = torch.load(args.checkpoint_path, map_location=device)
        model.load_state_dict(checkpoint['state_dict'])
        print(f"Loaded model from {args.checkpoint_path}")
        if 'best_val_loss' in checkpoint:
            print(f"Best validation loss: {checkpoint['best_val_loss']:.6f}")
    else:
        print(f"Checkpoint not found at {args.checkpoint_path}")
        return
    
    print(f"Using single GPU for evaluation")
    
    model.eval()
    
    # Evaluation
    total_loss = 0
    total_samples = 0
    logsoftmax = torch.nn.LogSoftmax(dim=1)
    
    # For ACC and AUC calculation and per-video results
    all_predictions = []
    all_labels = []
    all_video_names = []
    
    with torch.no_grad():
        for batch in test_loader:
            visual_frame, audio_window, video_name, video_frames, labels = batch
            current_batch_size = visual_frame.size()[0]

            visual_frame = visual_frame.to(device)
            audio_window = audio_window.to(device)

            # Repeat video frame to match audio frames (2*tau+1 times)
            visual_central_frame = visual_frame.unsqueeze(1).repeat(1, 2 * args.tau + 1, 1)

            outputs = model(visual_central_frame, audio_window)
            outputs = outputs.squeeze()
        
            synchronization_scores = logsoftmax(outputs)[:, args.tau]
            loss = -torch.sum(synchronization_scores)

            total_loss += loss.item()
            total_samples += current_batch_size
            
            # Collect predictions, labels, and video names for detailed analysis
            predictions = synchronization_scores.detach().cpu().numpy()
            all_predictions.extend(predictions)
            
            batch_labels = labels.detach().cpu().numpy()
            all_labels.extend(batch_labels)
            
            # Collect video names
            all_video_names.extend(video_name)
    
    avg_loss = total_loss / total_samples
    print(f"Test Loss: {avg_loss:.6f}")
    
    # Calculate comprehensive evaluation metrics
    if len(all_predictions) > 0 and len(all_labels) > 0:
        all_predictions = np.array(all_predictions)
        all_labels = np.array(all_labels)
        
        # Convert synchronization scores to binary predictions (threshold at 0)
        binary_predictions = (all_predictions > 0).astype(int)
        
        # Calculate Accuracy
        accuracy = accuracy_score(all_labels, binary_predictions)
        print(f"Accuracy (ACC): {accuracy:.4f}")
        
        # Calculate AUC
        try:
            auc = roc_auc_score(all_labels, all_predictions)
            print(f"AUC Score: {auc:.4f}")
        except ValueError as e:
            print(f"AUC calculation failed: {e}")
            auc = 0.0
        
        # Calculate Average Precision (AP)
        try:
            ap = average_precision_score(all_labels, all_predictions)
            print(f"Average Precision (AP): {ap:.4f}")
        except ValueError as e:
            print(f"AP calculation failed: {e}")
            ap = 0.0
        
        # Calculate Accuracy at EER
        try:
            acc_at_eer, eer_threshold = calculate_acc_at_eer(all_labels, all_predictions)
            print(f"Accuracy at EER: {acc_at_eer:.4f} (Threshold: {eer_threshold:.4f})")
        except Exception as e:
            print(f"ACC@EER calculation failed: {e}")
            acc_at_eer = 0.0
            eer_threshold = 0.0
        
        # Print class distribution
        unique, counts = np.unique(all_labels, return_counts=True)
        print(f"Class distribution: {dict(zip(unique, counts))}")
        print(f"Real samples: {counts[0] if 0 in unique else 0}, Fake samples: {counts[1] if 1 in unique else 0}")
        
        # Save detailed predictions to CSV
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        checkpoint_name = os.path.basename(args.checkpoint_path).replace('.pt', '')
        predictions_csv_path = f"predictions_{checkpoint_name}_diffusion_{timestamp}.csv"
        save_predictions_to_csv(all_video_names, all_predictions, all_labels, predictions_csv_path)
        
        # Save evaluation summary to JSON
        eval_summary = {
            'checkpoint_path': args.checkpoint_path,
            'test_metadata': args.metadata,
            'test_samples': int(len(all_predictions)),
            'real_samples': int(counts[0] if 0 in unique else 0),
            'fake_samples': int(counts[1] if 1 in unique else 0),
            'test_loss': float(avg_loss),
            'accuracy': float(accuracy),
            'auc': float(auc),
            'average_precision': float(ap),
            'acc_at_eer': float(acc_at_eer),
            'eer_threshold': float(eer_threshold),
            'predictions_file': predictions_csv_path,
            'evaluation_time': datetime.now().isoformat(),
            'diffusion_methods': diffusion_methods
        }
        
        summary_json_path = f"eval_summary_{checkpoint_name}_diffusion_{timestamp}.json"
        with open(summary_json_path, 'w') as f:
            json.dump(eval_summary, f, indent=2)
        
        print(f"Evaluation summary saved to: {summary_json_path}")
        print("\n=== Evaluation Summary ===")
        print(f"Test Loss: {avg_loss:.6f}")
        print(f"ACC: {accuracy:.4f}")
        print(f"AUC: {auc:.4f}")
        print(f"AP: {ap:.4f}")
        print(f"ACC@EER: {acc_at_eer:.4f}")
        print(f"Total samples: {len(all_predictions)}")

if __name__ == "__main__":
    main()