syncnet.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Dict, Optional, Tuple
import numpy as np
from scipy.spatial.transform import Rotation
from torchmetrics.functional import structural_similarity_index_measure as ssim
from torchmetrics.image.fid import FrechetInceptionDistance

from mysixdrepnet import *

'''
Key components:

SyncNet Implementation:

pythonCopy- Video encoder for lip movements
- Audio encoder for speech features
- Synchronization confidence computation

CAPP Score Components:

pythonCopy- Pose sequence extraction
- Audio-pose alignment
- Contrastive learning based evaluation

Motion Analysis:

pythonCopyMetrics:
- Pose variation intensity
- Head movement statistics
- Temporal consistency

Visual Quality:

pythonCopyWhen ground truth available:
- FID Score
- SSIM
- L1/L2 distances
To use in training:
pythonCopy# Initialize evaluator
evaluator = Evaluator(device)

# During training
metrics = evaluator.evaluate_batch(batch, generated_video)
logger.log_metrics(metrics, step)

# During validation
val_metrics = {}
for batch in val_loader:
    generated = model(batch)
    batch_metrics = evaluator.evaluate_batch(batch, generated)
    for k, v in batch_metrics.items():
        val_metrics[k] = val_metrics.get(k, 0) + v
'''
class SyncNet(nn.Module):
    """
    SyncNet implementation for lip-sync evaluation
    As referenced in paper for computing Cl and Dl
    """
    def __init__(self):
        super().__init__()
        # Initialize SyncNet architecture
        # This should be pretrained SyncNet as used in paper
        self.video_encoder = nn.Sequential(
            # Conv3D layers for video processing
            nn.Conv3d(3, 96, kernel_size=(5, 7, 7), stride=(1, 2, 2), padding=(2, 3, 3)),
            nn.BatchNorm3d(96),
            nn.ReLU(inplace=True),
            nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2)),
            nn.Conv3d(96, 256, kernel_size=(5, 5, 5), stride=(1, 2, 2), padding=(2, 2, 2)),
            nn.BatchNorm3d(256),
            nn.ReLU(inplace=True),
            nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2)),
            nn.Conv3d(256, 256, kernel_size=(3, 3, 3), padding=(1, 1, 1)),
            nn.BatchNorm3d(256),
            nn.ReLU(inplace=True),
            nn.Conv3d(256, 256, kernel_size=(3, 3, 3), padding=(1, 1, 1)),
            nn.BatchNorm3d(256),
            nn.ReLU(inplace=True),
            nn.Conv3d(256, 256, kernel_size=(3, 3, 3), padding=(1, 1, 1)),
            nn.BatchNorm3d(256),
            nn.ReLU(inplace=True)
        )
        
        self.audio_encoder = nn.Sequential(
            # 1D Conv layers for audio processing
            nn.Conv1d(1, 96, kernel_size=7, stride=2, padding=3),
            nn.BatchNorm1d(96),
            nn.ReLU(inplace=True),
            nn.MaxPool1d(kernel_size=3, stride=2),
            nn.Conv1d(96, 256, kernel_size=5, stride=2, padding=2),
            nn.BatchNorm1d(256),
            nn.ReLU(inplace=True),
            nn.MaxPool1d(kernel_size=3, stride=2),
            nn.Conv1d(256, 256, kernel_size=3, padding=1),
            nn.BatchNorm1d(256),
            nn.ReLU(inplace=True),
            nn.Conv1d(256, 256, kernel_size=3, padding=1),
            nn.BatchNorm1d(256),
            nn.ReLU(inplace=True),
            nn.Conv1d(256, 256, kernel_size=3, padding=1),
            nn.BatchNorm1d(256),
            nn.ReLU(inplace=True)
        )

    def forward(
        self,
        video: torch.Tensor,
        audio: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Compute confidence score (Cl) and feature distance (Dl)
        """
        # Extract features
        video_features = self.video_encoder(video)
        audio_features = self.audio_encoder(audio)
        
        # Compute confidence score (cosine similarity)
        confidence = F.cosine_similarity(
            video_features.view(video_features.size(0), -1),
            audio_features.view(audio_features.size(0), -1)
        )
        
        # Compute feature distance
        distance = F.pairwise_distance(
            video_features.view(video_features.size(0), -1),
            audio_features.view(audio_features.size(0), -1)
        )
        
        return confidence, distance

class PoseExtractor(nn.Module):
    """Extract pose sequences from videos"""
    def __init__(self):
        super().__init__()
        # Initialize pose estimation network (e.g., SixDRepNet)
        self.pose_net = SixDRepNet_Detector()
        self.eval()

    @torch.no_grad()
    def forward(self, video: torch.Tensor) -> torch.Tensor:
        """
        Extract pose sequences from video frames
        Args:
            video: Video frames [B, T, C, H, W]
        Returns:
            poses: Pose parameters [B, T, 6] (3 rotation + 3 translation)
        """
        B, T = video.shape[:2]
        poses = []
        
        for t in range(T):
            frame_poses = self.pose_net(video[:, t])
            poses.append(frame_poses)
        
        return torch.stack(poses, dim=1)

def compute_pose_intensity(pose_sequences: torch.Tensor) -> float:
    """
    Compute pose variation intensity
    Args:
        pose_sequences: Pose parameters [B, T, 6]
    Returns:
        intensity: Average pose variation between consecutive frames
    """
    # Convert rotation vectors to matrices
    rotations = Rotation.from_rotvec(
        pose_sequences[..., :3].cpu().numpy()
    ).as_matrix()
    
    # Compute angular differences between consecutive frames
    angular_diffs = []
    for t in range(rotations.shape[1] - 1):
        R1 = rotations[:, t]
        R2 = rotations[:, t + 1]
        R_diff = np.matmul(R1.transpose(0, 2, 1), R2)
        angle = np.arccos((np.trace(R_diff, axis1=1, axis2=2) - 1) / 2)
        angular_diffs.append(angle)
    
    # Compute translation differences
    trans_diffs = torch.norm(
        pose_sequences[..., 3:, 1:] - pose_sequences[..., 3:, :-1],
        dim=-1
    ).mean().item()
    
    # Combine rotation and translation variations
    intensity = (np.mean(angular_diffs) + trans_diffs) / 2
    return intensity

class Evaluator:
    """Comprehensive evaluation metrics for VASA"""
    def __init__(self, device: torch.device):
        self.device = device
        
        # Initialize evaluation networks
        self.syncnet = SyncNet().to(device)
        self.capp_scorer = CAPPScore(pose_dim=6, audio_dim=768).to(device)
        self.pose_extractor = PoseExtractor().to(device)
        self.fid = FrechetInceptionDistance(normalize=True).to(device)
        
        # Put networks in eval mode
        for module in [self.syncnet, self.capp_scorer, self.pose_extractor]:
            module.eval()
    
    @torch.no_grad()
    def compute_metrics(self,
                       generated_video: torch.Tensor,
                       audio_features: torch.Tensor,
                       real_video: Optional[torch.Tensor] = None) -> Dict[str, float]:
        """
        Compute comprehensive evaluation metrics
        Args:
            generated_video: Generated video frames [B, T, C, H, W]
            audio_features: Audio features [B, T, C]
            real_video: Optional ground truth video for comparison
        Returns:
            Dictionary of computed metrics
        """
        metrics = {}
        
        # 1. Audio-Visual Synchronization
        sync_conf, sync_dist = self.syncnet(generated_video, audio_features)
        metrics['sync_confidence'] = sync_conf.mean().item()
        metrics['sync_distance'] = sync_dist.mean().item()
        
        # 2. Audio-Pose Alignment
        pose_sequences = self.pose_extractor(generated_video)
        capp_similarity = self.capp_scorer(pose_sequences, audio_features)
        metrics['capp_score'] = capp_similarity.diagonal().mean().item()
        
        # 3. Motion Analysis
        # Pose variation intensity
        metrics['pose_intensity'] = compute_pose_intensity(pose_sequences)
        
        # Head movement statistics
        head_motion = pose_sequences[..., :3]  # rotation components
        metrics.update({
            'head_motion_mean': head_motion.abs().mean().item(),
            'head_motion_std': head_motion.std().item(),
            'head_motion_max': head_motion.abs().max().item()
        })
        
        # 4. Visual Quality
        if real_video is not None:
            # FID Score
            self.fid.update(real_video, real=True)
            self.fid.update(generated_video, real=False)
            metrics['fid'] = self.fid.compute().item()
            
            # SSIM
            metrics['ssim'] = ssim(generated_video, real_video).item()
            
            # L1 and L2 distances
            metrics.update({
                'l1_distance': F.l1_loss(generated_video, real_video).item(),
                'l2_distance': F.mse_loss(generated_video, real_video).item()
            })
        
        # 5. Temporal Consistency
        temp_diff = torch.diff(generated_video, dim=1)
        metrics.update({
            'temporal_consistency': -temp_diff.abs().mean().item(),
            'temporal_smoothness': -temp_diff.std().item()
        })
        
        return metrics
    
    def evaluate_batch(self,
                      batch: Dict[str, torch.Tensor],
                      generated_video: torch.Tensor) -> Dict[str, float]:
        """
        Evaluate a batch of generated videos
        Args:
            batch: Dictionary containing ground truth data
            generated_video: Generated video frames
        """
        metrics = self.compute_metrics(
            generated_video,
            batch['audio_features'],
            batch['frames']
        )
        
        # Add any batch-specific metrics
        if 'emotion' in batch:
            emotion_consistency = F.cosine_similarity(
                batch['emotion'],
                self.emotion_extractor(generated_video)
            ).mean()
            metrics['emotion_consistency'] = emotion_consistency.item()
        
        return metrics
    
    def log_metrics(self,
                   metrics: Dict[str, float],
                   logger: Any,
                   step: int,
                   prefix: str = ''):
        """Log computed metrics"""
        if prefix and not prefix.endswith('/'):
            prefix = prefix + '/'
            
        for name, value in metrics.items():
            logger.log_metrics({f"{prefix}{name}": value}, step)