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configs/config_inference.yaml

@@ -1,29 +0,0 @@
-# weights_path: run "unreflectanything download-weights" then use ~/.cache/unreflectanything/weights/ (or set path below)
-run: "gallant-bush-806"
-runs_dir: "/anvme/workspace/v120bb18-unreflectanything/runs"
-weights_path: "/anvme/workspace/v120bb18-unreflectanything/results/gallant-bush-806/models/full_model_weights.pt"
-
-model_config_path: "/anvme/workspace/v120bb18-unreflectanything/config_train.yaml"
-model_module: "models"
-input_dir: "/anvme/workspace/v120bb18-unreflectanything/benchmark/data/input"
-output_dir: "/anvme/workspace/v120bb18-unreflectanything/benchmark/data/OURS_L1"
-batch_size: 32
-num_workers: 4
-chunk_size: 8
-device: "cuda"
-image_extensions: [".png", ".jpg", ".jpeg"]
-resize_output: True
-brightness_threshold: 0.8
-inpaint_mask_dilation: 11
-monitor_usage: True
-
-# serene-terrain-817   :  SoftTHR ablation - Rebuttal
-# smooth-vortex-816    :  Dice ablation - Rebuttal
-# super-microwave-815  :  TV ablation - Rebuttal
-# magic-brook-814      :  DWConv ablation - Rebuttal
-# faithful-music-813   :  Learned mask token ablation - Rebuttal
-# fresh-fire-811       :  Positional encoding ablation- Rebuttal
-# clean-haze-809       :  Spec ablation - Rebuttal
-# dainty-paper-808     :  Seam ablation - Rebuttal
-# leafy-glade-807      :  RGB ablation - Rebuttal
-# gallant-bush-806     :  L1 ablation - Rebuttal

configs/decoder_pretrain_448.yaml

@@ -1,307 +0,0 @@
-### BASELINE: CONVERGES AFTER LONG
-
-parameters:
-
-  ### MODEL ARCHITECTURE                                                       
-  MODEL:
-    value:
-      MODEL_CLASS: "UnReflect_Model"  # Main model class name (must match class in models.py)
-      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: 448  # 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:
-          USE_FILM: False  # Enable FiLM (Feature-wise Linear Modulation) conditioning in decoder
-          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: "weights/rgb_decoder.pth"  # Path to pretrained decoder weights (optional)
-          USE_BN: False  # Use batch normalization in decoder
-          DROPOUT: 0.1  # Dropout rate in decoder layers
-          OUTPUT_IMAGE_SIZE: [448,448]  # Output image resolution [height, width]
-          OUTPUT_CHANNELS: 3  # Number of output channels (3 for RGB diffuse image)
-          DECODER_LR: 1.0e-3 # 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)
-        highlight:
-          USE_FILM: False  # Enable FiLM conditioning in highlight decoder
-          FEATURE_DIM: 1024  # Feature dimension for highlight decoder
-          REASSEMBLE_OUT_CHANNELS: [96,192,384,768]  # Output channels for each decoder stage
-          REASSEMBLE_FACTORS: [4.0, 2.0, 1.0, 0.5]  # Spatial upsampling factors for each stage
-          READOUT_TYPE: "ignore"  # Readout type for DPT decoder
-          USE_BN: False  # Use batch normalization in decoder
-          DROPOUT: 0.1  # Dropout rate in decoder layers
-          OUTPUT_IMAGE_SIZE: [448,448]  # Output image resolution [height, width]
-          OUTPUT_CHANNELS: 1  # Number of output channels (1 for highlight mask)
-          DECODER_LR: 5.0e-4  # 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)
-      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: "weights/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: 11  # Dilation kernel size (pixels) for inpaint mask - Must be odd
-  USE_TORCH_COMPILE:  # Enable PyTorch 2.0 torch.compile for faster training (experimental)
-    value: True
-
-  ### 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: [448,448]  # 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: 4  # 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: [448,448]  # 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: 4  # 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: [448,448]  # Target image size [height, width]
-        RESIZE_MODE: "resize"  # Image resizing mode
-        FEW_IMAGES: False  # Load only first 10 images if True
-        SAMPLE_EVERY_N: 8  # 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: [448,448]  # 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: [448,448]  # 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 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: [448,448]  # 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
-
-      CHOLEC80:
-        VAL_SCENES: ["val"]  # Validation scene names
-        TARGET_SIZE: [448,448]  # Target image size [height, width]
-        RESIZE_MODE: "resize+crop"  # Image resizing mode
-        SAMPLE_EVERY_N: 50  # 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
-
-      # POLARGB:
-      #   TRAIN_SCENES: "train"
-      #   VAL_SCENES: "test"
-      #   TARGET_SIZE: [448,448]
-      #   RESIZE_MODE: "resize+crop"
-      #   SAMPLE_EVERY_N: 1
-      #   LOAD_RGB_ONLY: True
-
-  BATCH_SIZE: # Max batch size with img size 448 is 32
-    value: 16  # Number of samples per batch (adjust based on GPU memory)
-  NUM_WORKERS:
-    value: 12  # Number of data loading worker processes (0 = main process only)
-  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  # Dilation 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                                                                      
-  LEARNING_RATE:
-    value: 1.0e-3  # Base learning rate for optimizer
-  WEIGHT_DECAY:   
-    value: 0.0  # L2 regularization weight (0.0 = no weight decay)
-  EPOCHS:
-    value: 50  # Maximum number of training epochs
-  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: 5  # Number of cosine periods over training
-      STEPWISE:  # StepLR scheduler (reduces LR at fixed step intervals)
-        N_STEPS: 4  # Number of times to reduce LR during training
-        GAMMA: 0.5  # 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: 1.0  # Weight for specular component reconstruction loss
-  DIFFUSE_LOSS_WEIGHT:
-    value: 1.0  # Weight for diffuse component reconstruction loss
-  HIGHLIGHT_LOSS_WEIGHT:
-    value: 1.0  # Weight for highlight mask regression loss
-  IMAGE_RECONSTRUCTION_LOSS_WEIGHT:
-    value: 0.0  # Weight for full image reconstruction loss
-  SATURATION_RING_LOSS_WEIGHT:
-    value: 0.5  # 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_TOKEN_INPAINT:
-    value: 0.0  # Weight for token-space inpainting loss (L1 + cosine similarity in feature space)
-  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: 0.5  # 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: "896 Decoder Pretraining"  # Notes/description for this training run
-  

configs/decoder_pretrain_896.yaml

@@ -1,295 +0,0 @@
-### 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:
-          USE_FILM: False  # Enable FiLM (Feature-wise Linear Modulation) conditioning in decoder
-          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: "weights/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:
-        TOKEN_INPAINTER_CLASS: "TokenInpainter_Prior"  # Token inpainter class name
-        TOKEN_INPAINTER_MODULE: "token_inpainters"  # Module name to import token inpainter from
-        FROM_PRETRAINED: "weights/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: 11  # 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
-
-  ### 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: 4  # 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: 4  # Load every Nth frame
-        LOAD_RGB_ONLY: True  # Ignore polarization data if True
-
-      CROMO:
-        TRAIN_SCENES: ["kitchen","station","facades"]  # 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: 4  # 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: 4  # 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: 5  # 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
-
-      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: 50  # 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
-
-      # POLARGB:
-      #   TRAIN_SCENES: "train"
-      #   VAL_SCENES: "test"
-      #   TARGET_SIZE: [896,896]
-      #   RESIZE_MODE: "resize+crop"
-      #   SAMPLE_EVERY_N: 1
-      #   LOAD_RGB_ONLY: True
-
-  BATCH_SIZE: # Max batch size with img size 896x896 is 20
-    value: 20  # Number of samples per batch (adjust based on GPU memory)
-  NUM_WORKERS:
-    value: 12  # Number of data loading worker processes (0 = main process only)
-  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  # Dilation 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
-  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_TOKEN_INPAINT:
-    value: 0.0  # Weight for token-space inpainting loss (L1 + cosine similarity in feature space)
-  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: "896 Decoder Pretraining"  # Notes/description for this training run
-  

configs/figuremeout.yaml

@@ -1,306 +0,0 @@
-### BASELINE: CONVERGES AFTER LONG
-
-parameters:
-
-  ### MODEL ARCHITECTURE                                                       
-  MODEL:
-    value:
-      MODEL_CLASS: "UnReflect_Model "  # Main model class name (must match class in models.py)
-      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: 448  # 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:
-          USE_FILM: False  # Enable FiLM (Feature-wise Linear Modulation) conditioning in decoder
-          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: "weights/rgb_decoder.pth"  # Path to pretrained decoder weights (optional)
-          USE_BN: False  # Use batch normalization in decoder
-          DROPOUT: 0.1  # Dropout rate in decoder layers
-          OUTPUT_IMAGE_SIZE: [448,448]  # Output image resolution [height, width]
-          OUTPUT_CHANNELS: 3  # Number of output channels (3 for RGB diffuse image)
-          DECODER_LR: 1.0e-4 # Custom learning rate for decoder (0.0 = frozen, 1.0 = same as base LR)
-          NUM_FUSION_BLOCKS_TRAINABLE: 1  # 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)
-        highlight:
-          USE_FILM: False  # Enable FiLM conditioning in highlight decoder
-          FEATURE_DIM: 1024  # Feature dimension for highlight decoder
-          REASSEMBLE_OUT_CHANNELS: [96,192,384,768]  # Output channels for each decoder stage
-          REASSEMBLE_FACTORS: [4.0, 2.0, 1.0, 0.5]  # Spatial upsampling factors for each stage
-          READOUT_TYPE: "ignore"  # Readout type for DPT decoder
-          USE_BN: False  # Use batch normalization in decoder
-          DROPOUT: 0.1  # Dropout rate in decoder layers
-          OUTPUT_IMAGE_SIZE: [448,448]  # Output image resolution [height, width]
-          OUTPUT_CHANNELS: 1  # Number of output channels (1 for highlight mask)
-          DECODER_LR: 5.0e-4  # 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)
-      TOKEN_INPAINTER:
-        TOKEN_INPAINTER_CLASS: "TokenInpainter_Naive"  # Token inpainter class name
-        # TOKEN_INPAINTER_MODULE: "token_inpainters"  # Module name to import token inpainter from
-        # FROM_PRETRAINED: "weights/token_inpainter.pth"  # Path to pretrained token inpainter weights (optional)
-        TOKEN_INPAINTER_LR: 0.0  # 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: 1  # 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
-
-  ### 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: [448,448]  # 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: [448,448]  # 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: [448,448]  # 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: [448,448]  # 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: [448,448]  # 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: [448,448]  # 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 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
-
-      CHOLEC80:
-        VAL_SCENES: ["val"]  # Validation scene names
-        TARGET_SIZE: [448,448]  # 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
-
-      # POLARGB:
-      #   TRAIN_SCENES: "train"
-      #   VAL_SCENES: "test"
-      #   TARGET_SIZE: [448,448]
-      #   RESIZE_MODE: "resize+crop"
-      #   SAMPLE_EVERY_N: 1
-      #   LOAD_RGB_ONLY: True
-
-  BATCH_SIZE: # Max batch size with img size 448 is 32
-    value: 32 # Number of samples per batch (adjust based on GPU memory)
-  NUM_WORKERS:
-    value: 8  # Number of data loading worker processes (0 = main process only)
-  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  # Dilation 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                                                                      
-  LEARNING_RATE:
-    value: 1.0e-3  # Base learning rate for optimizer
-  WEIGHT_DECAY:   
-    value: 0.0  # L2 regularization weight (0.0 = no weight decay)
-  EPOCHS:
-    value: 25  # Maximum number of training epochs
-  GRADIENT_ACCUMULATION_STEPS:
-    value: 1  # Number of steps to accumulate gradients before optimizer step (1 = no accumulation)
-  WARMUP:
-    value: 200  # 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: 5  # Number of cosine periods over training
-      STEPWISE:  # StepLR scheduler (reduces LR at fixed step intervals)
-        N_STEPS: 4  # Number of times to reduce LR during training
-        GAMMA: 0.5  # 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: 10  # 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
-  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: 1.0  # 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_TOKEN_INPAINT:
-    value: 0.0  # Weight for token-space inpainting loss (L1 + cosine similarity in feature space)
-  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: 0.5  # 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: False  # 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: "Final - TKI Learns, Decoder Learns faster"  # Notes/description for this training run

configs/pretrain_tki.yaml

@@ -1,306 +0,0 @@
-### BASELINE: CONVERGES AFTER LONG
-
-parameters:
-
-  ### MODEL ARCHITECTURE                                                       
-  MODEL:
-    value:
-      MODEL_CLASS: "UnReflect_Model_TokenInpainter"  # Main model class name (must match class in models.py)
-      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:
-          USE_FILM: False  # Enable FiLM (Feature-wise Linear Modulation) conditioning in decoder
-          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: "weights/decoder_896.pth"  # Path to pretrained decoder weights (optional)
-          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: 0.0 # Custom learning rate for decoder (0.0 = frozen, 1.0 = same as base LR)
-          NUM_FUSION_BLOCKS_TRAINABLE: 1  # 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)
-        highlight:
-          USE_FILM: False  # Enable FiLM conditioning in highlight decoder
-          FEATURE_DIM: 1024  # Feature dimension for highlight decoder
-          REASSEMBLE_OUT_CHANNELS: [96,192,384,768]  # Output channels for each decoder stage
-          REASSEMBLE_FACTORS: [4.0, 2.0, 1.0, 0.5]  # Spatial upsampling factors for each stage
-          READOUT_TYPE: "ignore"  # Readout type for DPT decoder
-          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: 1  # Number of output channels (1 for highlight mask)
-          DECODER_LR: 5.0e-4  # 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)
-      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: "weights/token_inpainter.pth"  # Path to pretrained token inpainter weights (optional)
-        TOKEN_INPAINTER_LR: 1.0e-4  # 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: 1  # 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
-
-  ### 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 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
-
-      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
-
-      # POLARGB:
-      #   TRAIN_SCENES: "train"
-      #   VAL_SCENES: "test"
-      #   TARGET_SIZE: [896,896]
-      #   RESIZE_MODE: "resize+crop"
-      #   SAMPLE_EVERY_N: 1
-      #   LOAD_RGB_ONLY: True
-
-  BATCH_SIZE: # Max batch size with img size 896 is 32
-    value: 16 # Number of samples per batch (adjust based on GPU memory)
-  NUM_WORKERS:
-    value: 8  # Number of data loading worker processes (0 = main process only)
-  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: 2.0  # 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  # Dilation 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                                                                      
-  LEARNING_RATE:
-    value: 1.0e-3  # Base learning rate for optimizer
-  WEIGHT_DECAY:   
-    value: 0.0  # L2 regularization weight (0.0 = no weight decay)
-  EPOCHS:
-    value: 25  # Maximum number of training epochs
-  GRADIENT_ACCUMULATION_STEPS:
-    value: 1  # Number of steps to accumulate gradients before optimizer step (1 = no accumulation)
-  WARMUP:
-    value: 200  # 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: 5  # Number of cosine periods over training
-      STEPWISE:  # StepLR scheduler (reduces LR at fixed step intervals)
-        N_STEPS: 4  # Number of times to reduce LR during training
-        GAMMA: 0.5  # 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: 10  # 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: 0.0  # Weight for diffuse component reconstruction loss
-  HIGHLIGHT_LOSS_WEIGHT:
-    value: 0.0  # Weight for highlight mask regression loss
-  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: 1.0  # 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_TOKEN_INPAINT:
-    value: 1.0  # Weight for token-space inpainting loss (L1 + cosine similarity in feature space)
-  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: 0.5  # 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: 0.1  # 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: False  # 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: "Final - TKI Learns, Decoder Learns faster"  # Notes/description for this training run