configs/decoder_pretrain.yaml

### BASELINE: CONVERGES AFTER LONG

parameters:

  ### MODEL ARCHITECTURE                                                       
  MODEL:
    value:
      MODEL_CLASS: "UnReflect_Model"  # Main model class name (must match class in models.py)  # <<<<<<<<< DECODER PRETRAINING: NOT USING TOKEN INPAINTER (DIRECT FROM DINO)
      MODEL_MODULE: "models"  # Module name to import model classes from (default: "models")
      RGB_ENCODER:
        ENCODER: "facebook/dinov3-vitl16-pretrain-lvd1689m"  # DINOv3 encoder model name (HuggingFace format)
        IMAGE_SIZE: 896  # Input image size (height and width in pixels)
        RETURN_SELECTED_LAYERS: [3, 6, 9, 12]  # Transformer layer indices to extract features from (0-indexed)
        RGB_ENCODER_LR: 0.0  # Learning rate for RGB encoder (0.0 = frozen, must be explicitly set)
      DECODERS:
        diffuse:
          FEATURE_DIM: 1024  # Feature dimension for decoder (should match encoder output)
          REASSEMBLE_OUT_CHANNELS: [768,1024,1536,2048]  # Output channels for each decoder stage (DPT-style reassembly)
          REASSEMBLE_FACTORS: [4.0, 2.0, 1.0, 0.5]  # Spatial upsampling factors for each stage
          READOUT_TYPE: "ignore"  # Readout type for DPT decoder ("ignore", "project", etc.)
          # FROM_PRETRAINED: "rgb_decoder.pth"  # Path to pretrained decoder weights (optional)    # <<<<<<<<< DECODER PRETRAINING: NO WEIGHTS HERE
          USE_BN: False  # Use batch normalization in decoder
          DROPOUT: 0.1  # Dropout rate in decoder layers
          OUTPUT_IMAGE_SIZE: [896,896]  # Output image resolution [height, width]
          OUTPUT_CHANNELS: 3  # Number of output channels (3 for RGB diffuse image)
          DECODER_LR: 5.0e-5 # Custom learning rate for decoder (0.0 = frozen, 1.0 = same as base LR)
          NUM_FUSION_BLOCKS_TRAINABLE: null  # Number of fusion blocks to train (0-4, null = train all if DECODER_LR != 0)
          TRAIN_RGB_HEAD: True  # Whether to train RGB head (true/false, null = train if DECODER_LR != 0)
      TOKEN_INPAINTER: # <<<<<<<<<<<< DOESNT MATTER, MODEL CLASS IS NOT TOKEN INPAINTER
        TOKEN_INPAINTER_CLASS: "TokenInpainter_Prior"  # Token inpainter class name
        TOKEN_INPAINTER_MODULE: "token_inpainters"  # Module name to import token inpainter from
        FROM_PRETRAINED: "token_inpainter.pth"  # Path to pretrained token inpainter weights (optional)
        TOKEN_INPAINTER_LR: 1.0e-5  # Learning rate for token inpainter (can differ from base LR)
        DEPTH: 6  # Number of transformer blocks
        HEADS: 16  # Number of attention heads
        DROP: 0 # Dropout rate 
        USE_POSITIONAL_ENCODING: True  # Enable 2D sinusoidal positional encodings
        USE_FINAL_NORM: True  # Enable final LayerNorm before output projection
        USE_LOCAL_PRIOR: True  # Blend local mean prior for masked seeds
        LOCAL_PRIOR_WEIGHT: 0.5  # Weight for local prior blending (1.0 = only mask_token, 0.0 = only local mean)
        LOCAL_PRIOR_KERNEL: 5  # Kernel size for local prior blending (> 1)
        SEED_NOISE_STD: 0.02  # Standard deviation of noise added to masked seeds during training  
  INPAINT_MASK_DILATION:
    value: 3  # Dilation kernel size (pixels) for inpaint mask - Must be odd
  USE_TORCH_COMPILE:  # Enable PyTorch 2.0 torch.compile for faster training (experimental)
    value: False
  DISTRIBUTE:
    value: "ddp"

  ### DATA                                                                      
  DATASETS:
    value:
      SCRREAM:
        VAL_SCENES: ["scene10_full_00","scene11_full_00","scene044_full_00","scene04_reduced_00","scene04_reduced_01","scene04_reduced_02"]  # List of validation scene names
        TARGET_SIZE: [896,896]  # Target image size [height, width] in pixels
        RESIZE_MODE: "resize+crop"  # Image resizing mode: "resize", "crop", "resize+crop", or "pad"
        FEW_IMAGES: False  # If True, load only first 10 images per scene (for quick debugging)
        SAMPLE_EVERY_N: 2  # Load every Nth frame from each scene (1 = all frames, 4 = every 4th frame)
        LOAD_RGB_ONLY: True  # If True, ignore polarization data and load only RGB images
        
      HOUSECAT6D:
        VAL_SCENES: ["val_scene1","val_scene2"]  # Validation scene names
        TARGET_SIZE: [896,896]  # Target image size [height, width]
        RESIZE_MODE: "resize+crop"  # Image resizing mode
        FEW_IMAGES: False  # Load only first 10 images if True
        SAMPLE_EVERY_N: 2  # Load every Nth frame
        LOAD_RGB_ONLY: True  # Ignore polarization data if True

      CROMO:
        TRAIN_SCENES: ["kitchen"]  # Training scene names (list or string)
        # VAL_SCENES: "station"  # Validation scene names (optional)
        TARGET_SIZE: [896,896]  # Target image size [height, width]
        RESIZE_MODE: "resize"  # Image resizing mode
        FEW_IMAGES: False  # Load only first 10 images if True
        SAMPLE_EVERY_N: 2  # Load every Nth frame
        LOAD_RGB_ONLY: True  # Ignore polarization data if True
    
      PSD:
        TRAIN_SCENES: "PSD_Train"  # Training scene name (string or list)
        VAL_SCENES: "PSD_Val"  # Validation scene name (string or list)
        TARGET_SIZE: [896,896]  # Target image size [height, width]
        RESIZE_MODE: "resize+crop"  # Image resizing mode
        FEW_IMAGES: False  # Load only first 10 images if True
        SAMPLE_EVERY_N: 1  # Load every Nth frame (1 = all frames)
        LOAD_RGB_ONLY: True  # Ignore polarization data if True

      SCARED:
        VAL_SCENES: ["v22","v23","v24","v25","v26","v27","v28","v29","v30","v31","v32","v33","v34"]  # Validation scene names
        TARGET_SIZE: [896,896]  # Target image size [height, width]
        RESIZE_MODE: "resize+crop"  # Image resizing mode
        SAMPLE_EVERY_N: 8  # Load every Nth frame
        LOAD_RGB_ONLY: True  # Ignore polarization data if True
        FEW_IMAGES: False  # Load only first 10 images if True
        HIGHLIGHT_ENABLE: False  # Enable highlight detection/processing in dataset
        HIGHLIGHT_BRIGHTNESS_THRESHOLD: 0.9  # Brightness threshold for highlight detection (0-1)
        HIGHLIGHT_RETURN_MASK: True  # Return highlight mask in dataset output
        HIGHLIGHT_RECT_SIZE: [1000, 1000]  # Size of highlight rectangle region [height, width]
        HIGHLIGHT_RETURN_RECT_AS_RGB: False  # Return highlight rectangle as RGB if True
        HIGHLIGHT_RETURN_RECT: True  # Return highlight rectangle region if True
      
      STEREOMIS_TRACKING:
        VAL_SCENES: ["P2_2"]  # Validation scene names
        TARGET_SIZE: [896,896]  # Target image size [height, width]
        RESIZE_MODE: "resize+crop"  # Image resizing mode
        SAMPLE_EVERY_N: 4  # Load every Nth frame
        LOAD_RGB_ONLY: True  # Ignore polarization data if True
        FEW_IMAGES: False  # Load only first 10 imagas if True
        HIGHLIGHT_ENABLE: False  # Enable highlight detection/processing
        HIGHLIGHT_BRIGHTNESS_THRESHOLD: 0.9  # Brightness threshold for highlight detection
        HIGHLIGHT_RETURN_MASK: True  # Return highlight mask in dataset output
        HIGHLIGHT_RECT_SIZE: [800, 800]  # Size of highlight rectangle region
        HIGHLIGHT_RETURN_RECT_AS_RGB: False  # Return highlight rectangle as RGB if True
        HIGHLIGHT_RETURN_RECT: True  # Return highlight rectangle region if True

      CHOLEC80:
        VAL_SCENES: ["val"]  # Validation scene names
        TARGET_SIZE: [896,896]  # Target image size [height, width]
        RESIZE_MODE: "resize+crop"  # Image resizing mode
        SAMPLE_EVERY_N: 10  # Load every Nth frame
        LOAD_RGB_ONLY: True  # Ignore polarization data if True
        FEW_IMAGES: False  # Load only first 10 images if True
        HIGHLIGHT_ENABLE: False  # Enable highlight detection/processing
        HIGHLIGHT_BRIGHTNESS_THRESHOLD: 0.9  # Brightness threshold for highlight detection
        HIGHLIGHT_RETURN_MASK: True  # Return highlight mask in dataset output
        HIGHLIGHT_RECT_SIZE: [800, 800]  # Size of highlight rectangle region
        HIGHLIGHT_RETURN_RECT_AS_RGB: False  # Return highlight rectangle as RGB if True
        HIGHLIGHT_RETURN_RECT: True  # Return highlight rectangle region if True

      SUNRGBD:
        VAL_SCENES: ["realsense"]  # Validation scene names
        TARGET_SIZE: [896,896]  # Target image size [height, width]
        RESIZE_MODE: "resize+crop"  # Image resizing mode
        SAMPLE_EVERY_N: 1  # Load every Nth frame
        LOAD_RGB_ONLY: True  # Ignore polarization data if True
        FEW_IMAGES: False  # Load only first 10 images if True
        HIGHLIGHT_ENABLE: False  # Enable highlight detection/processing
        HIGHLIGHT_BRIGHTNESS_THRESHOLD: 0.9  # Brightness threshold for highlight detection
        HIGHLIGHT_RETURN_MASK: True  # Return highlight mask in dataset output
        HIGHLIGHT_RECT_SIZE: [800, 800]  # Size of highlight rectangle region
        HIGHLIGHT_RETURN_RECT_AS_RGB: False  # Return highlight rectangle as RGB if True
        HIGHLIGHT_RETURN_RECT: True  # Return highlight rectangle region if True

  FEW_IMAGES_ALL_DATASETS:
    value: False  # If True, override all datasets' FEW_IMAGES to True (for quick debugging across all datasets)

  BATCH_SIZE: # Max batch size with img size 896 is 32
    value: 4 # Number of samples per batch (adjust based on GPU memory)
  NUM_WORKERS:
    value: 12  # Number of data loading worker processes (0 = main process only, "auto" = 90% of CPU affinity)
  SHUFFLE:
    value: True  # Shuffle training data each epoch (False for validation/test)
  PIN_MEMORY:
    value: True  # Pin memory in DataLoader for faster GPU transfer (recommended: True)
  PREFETCH_FACTOR:
    value: 2  # Number of batches to prefetch per worker (higher = more memory usage)
  
  ### HIGHLIGHTS                                                        
  MOGE_MODEL:
    value: "Ruicheng/moge-2-vits-normal"  # MoGe model name for normal estimation (HuggingFace format)
  SURFACE_ROUGHNESS:
    value: 8.0  # Blinn-Phong surface roughness exponent (higher = sharper highlights)
  INTENSITY:
    value: 0.0  # Specular highlight intensity multiplier
  LIGHT_DISTANCE_RANGE:
    value: [0.0, 1]  # Range for light source distance sampling [min, max] (normalized)
  LIGHT_LEFT_RIGHT_ANGLE:
    value: [0, 360]  # Range for light source horizontal angle [min, max] in degrees
  LIGHT_ABOVE_BELOW_ANGLE:
    value: [0, 360]  # Range for light source vertical angle [min, max] in degrees
  DATASET_HIGHLIGHT_DILATION:
    value: 25  #sDilation kernel size (pixels) for dataset highlight masks
  DATASET_HIGHLIGHT_THRESHOLD:
    value: 0.9  # Brightness/luminance threshold (0-1) for detecting highlights in dataset images
  DATASET_HIGHLIGHT_USE_LUMINANCE:
    value: True  # If True, use perceptually-weighted luminance (0.299*R + 0.587*G + 0.114*B) for dataset highlights; if False, use simple mean brightness
  HIGHLIGHT_COLOR:
    value: [1.0, 1.0, 1.0]  # RGB color for synthetic highlights (normalized 0-1)
  CLAMP_RECONSTRUCTION:
    value: True  # Clamp reconstructed images to [0, 1] range if True

  ### OPTIMIZATION                                                                      
  EPOCHS:
    value: 20  # Maximum number of training epochs<
  LEARNING_RATE:
    value: 1.0e-4  # Base learning rate for optimizer
  WEIGHT_DECAY:   
    value: 0.0  # L2 regularization weight (0.0 = no weight decay)
  GRADIENT_ACCUMULATION_STEPS:
    value: 1  # Number of steps to accumulate gradients before optimizer step (1 = no accumulation)
  WARMUP:
    value: 100  # Number of warmup steps for learning rate schedule (linear warmup from 0 to LR)
  GRADIENT_CLIPPING_MAX_NORM:
    value: 8  # Maximum gradient norm for clipping (set to -1 to disable clipping)
  LR_SCHEDULER: 
    value: 
      ONPLATEAU:  # ReduceLROnPlateau scheduler (reduces LR when validation metric plateaus)
        PATIENCE: 5  # Number of epochs to wait before reducing LR
        FACTOR: 0.1  # Factor by which LR is reduced (new_lr = old_lr * factor)
      COSINE:  # CosineAnnealingLR scheduler (cosine annealing schedule)
        N_PERIODS: 1  # Number of cosine periods over training
      # STEPWISE:  # StepLR scheduler (reduces LR at fixed step intervals)
      #   N_STEPS: 5  # Number of times to reduce LR during training
      #   GAMMA: 0.25  # Factor by which LR is reduced at each step (new_lr = old_lr * gamma)
      # EXPONENTIAL:  # ExponentialLR scheduler (exponential decay)
      #   GAMMA: 0.5  # Multiplicative factor for exponential decay

  SWITCH_OPTIMIZER_EPOCH:
    value: null  # Epoch number to switch from bootstrap to refining optimizer (null = no switch)
  OPTIMIZER_BOOTSTRAP_NAME:
    value: "AdamW"  # Optimizer name for initial training phase ("Adam", "SGD", etc.)
  OPTIMIZER_REFINING_NAME:
    value: "AdamW"  # Optimizer name for refining phase (used after SWITCH_OPTIMIZER_EPOCH)
  EARLY_STOPPING_PATIENCE:
    value: 20  # Number of epochs without improvement before stopping training
  SAVE_INTERVAL:
    value: 1000  # Number of training steps between model checkpoints
  
  DATASET_HIGHLIGHT_SUPERVISION_THRESHOLD:
    value: 0.1  # Pixel highlights above this threshold (should be low) are excluded from supervision

  ### LOSS WEIGHTS (relative to the total loss, NOT NORMALIZED LATER)                                                                              
  SPECULAR_LOSS_WEIGHT:
    value: 0.0  # Weight for specular component reconstruction loss
  DIFFUSE_LOSS_WEIGHT:
    value: 1.0  # Weight for diffuse component reconstruction loss
  HIGHLIGHT_LOSS_WEIGHT:
    value: 0.0  # Weight for highlight mask regression loss
  TOKEN_INPAINT_LOSS_WEIGHT:
    value: 0.0  # Weight for token-space inpainting loss (L1 + cosine similarity in feature space)

  IMAGE_RECONSTRUCTION_LOSS_WEIGHT:
    value: 0.0  # Weight for full image reconstruction loss
  SATURATION_RING_LOSS_WEIGHT:
    value: 0.0  # Weight for saturation ring consistency loss (around highlight regions)
  RING_KERNEL_SIZE:
    value: 11  # Kernel size (odd number) for saturation ring dilation around highlights
  RING_VAR_WEIGHT:
    value: 0.5  # Weight for variance matching in saturation ring loss (vs mean matching)
  RING_TEXTURE_WEIGHT:
    value: 0.0  # Weight for texture consistency term in saturation ring loss
  HLREG_W_L1:
    value: 1.0  # Weight for L1 loss in highlight regression
  HLREG_USE_CHARB:
    value: True  # Use Charbonnier loss (smooth L1) instead of standard L1 if True
  HLREG_W_DICE:
    value: 0.2  # Weight for Dice loss in highlight regression (for mask overlap)
  HLREG_W_SSIM:
    value: 0.0  # Weight for SSIM loss in highlight regression
  HLREG_W_GRAD:
    value: 0.0  # Weight for gradient loss in highlight regression
  HLREG_W_TV:
    value: 0.0  # Weight for total variation loss in highlight regression
  HLREG_BALANCE_MODE:
    value: "auto"   # Class balancing mode for highlight regression: 'none' | 'auto' | 'pos_weight'
  HLREG_POS_WEIGHT:
    value: 1.0      # Positive class weight (used only if BALANCE_MODE == 'pos_weight')
  HLREG_FOCAL_GAMMA:
    value: 2.0      # Focal loss gamma parameter (0.0 = standard BCE, 1.0-2.0 helps with gradient vanishing)
  
  WEIGHT_CONTEXT_IDENTITY: 
    value: 0.0  # LEAVE TO 0.0: Weight for L1 loss on context (non-masked) regions (identity preservation) 
  WEIGHT_TV_IN_HOLE:
    value: 0.0  # LEAVE TO 0.0: Weight for total variation loss inside masked/hole regions
  RING_DILATE_KERNEL:
    value: 17  # Dilation kernel size (odd number) for creating ring mask around highlights
  WEIGHT_SEAM:
    value: 0.0  # Weight for gradient matching loss on saturation ring
  SEAM_USE_CHARB:
    value: True  # Use Charbonnier loss instead of L1 in seam loss (smooth L1 for boundary consistency)
  SEAM_WEIGHT_GRAD:
    value: 0.0  # Weight for gradient matching term inside seam loss (0.0 = disable gradient term)
  TOKEN_FEAT_ALPHA:
    value: 0.5  # Mixing factor for token feature loss: alpha * L1 + (1-alpha) * (1-cosine_sim)

  ### DIFFUSE HIGHLIGHT PENALTY
  WEIGHT_DIFFUSE_HIGHLIGHT_PENALTY:
    value: 0.0  # Weight for penalty loss on highlights in diffuse decoder output (0.0 = disabled)
  DIFFUSE_HL_THRESHOLD:
    value: 0.8  # Brightness/luminance threshold for detecting highlights in diffuse (0.0-1.0)
  DIFFUSE_HL_USE_CHARB:
    value: True  # Use Charbonnier loss instead of L1 for diffuse highlight penalty
  DIFFUSE_HL_PENALTY_MODE:
    value: "brightness"  # Penalty mode: "brightness" (penalize brightness/luminance above threshold) or "pixel" (penalize RGB values directly)
  DIFFUSE_HL_TARGET_BRIGHTNESS:
    value: null  # Target brightness/luminance for penalized pixels (null = use threshold value)
  DIFFUSE_HL_USE_LUMINANCE:
    value: True  # If True, use perceptually-weighted luminance (0.299*R + 0.587*G + 0.114*B); if False, use simple mean brightness

  ### LOGGING, RESULTS AND WANDB                                                                 
  LOG_INTERVAL:
    value: 1  # Number of training steps between console log outputs
  WANDB_LOG_INTERVAL:
    value: 1  # Number of training steps between WandB metric logs
  IMAGE_LOG_INTERVAL:
    value: 5  # Number of training steps between image logging to WandB
  NO_WANDB:
    value: False  # Disable WandB logging if True (useful for local debugging)
  MODEL_WATCHER_FREQ_WANDB:
    value: 50  # Frequency (in steps) for logging model parameter histograms to WandB
  WANDB_ENTITY:
    value: "unreflect-anything"  # WandB organization/entity name
  WANDB_PROJECT:
    value: "UnReflectAnything"  # WandB project name
  NOTES:
    value: ""  # Notes/description for this training run