Current W&B logging implementation is missing several critical metrics and visualizations needed for proper training monitoring. Need to implement comprehensive logging of all training aspects.
Missing Metrics
VAE Training:
Reconstruction loss
Latent space statistics
VAE gradients
UNet Training:
Per-step sigma values
Noise prediction accuracy
Gradient norms
Validation:
ZTSNR effectiveness metrics
High-resolution coherence scores
Sample image quality metrics
Implementation Plan
# Add to training loop:
if args.use_wandb:
wandb.log({
# Training metrics
'train/unet_loss': loss.item(),
'train/weighted_loss': weighted_loss.item(),
'train/grad_norm': grad_norm,
'train/sigma': sigma.mean().item(),
# VAE metrics
'vae/reconstruction_loss': vae_loss,
'vae/latent_mean': latent_mean,
'vae/latent_std': latent_std,
# Learning rates
'lr/unet': lr_scheduler.get_last_lr()[0],
'lr/vae': vae_lr_scheduler.get_last_lr()[0],
# System metrics
'system/gpu_memory': torch.cuda.memory_allocated(),
'system/gpu_utilization': gpu_utilization,
})
Additional Features Needed
Custom W&B panels for:
Training progress visualization
Sample image comparison
Validation metrics tracking
System resource monitoring
Automatic logging of:
Model architecture
Training configuration
System information
Git commit information
Priority: High
Proper logging is crucial for debugging and monitoring training progress.
Description
Current W&B logging implementation is missing several critical metrics and visualizations needed for proper training monitoring. Need to implement comprehensive logging of all training aspects.
Missing Metrics
VAE Training:
UNet Training:
Validation:
Implementation Plan
Additional Features Needed
Custom W&B panels for:
Automatic logging of:
Priority: High
Proper logging is crucial for debugging and monitoring training progress.