Upload weights, notebooks, sample images

configs/decoder_pretrain.yaml

@@ -14,7 +14,6 @@ parameters:
         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
@@ -27,7 +26,7 @@ parameters:
           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: # <<<<<<<<<<<< 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: "weights/token_inpainter.pth"  # Path to pretrained token inpainter weights (optional)
@@ -226,6 +225,9 @@ parameters:
     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:
@@ -255,8 +257,6 @@ parameters:
   HLREG_FOCAL_GAMMA:
     value: 2.0      # Focal loss gamma parameter (0.0 = standard BCE, 1.0-2.0 helps with gradient vanishing)
   
-  TOKEN_INPAINT_LOSS_WEIGHT:
-    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:
@@ -302,5 +302,5 @@ parameters:
   WANDB_PROJECT:
     value: "UnReflectAnything"  # WandB project name
   NOTES:
-    value: "896 Decoder Pretraining"  # Notes/description for this training run
+    value: ""  # Notes/description for this training run
   

configs/finetune_e2e.yaml

@@ -0,0 +1,319 @@
+### 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:
+          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/diffuse_decoder.pt"  # 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: 1.0e-5 # 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:
+          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: 0.0  # 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: 0.05 # 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: 0.25  # 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: 15  # 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_OVERRIDE:
+    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: 16  # 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: 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   # DEPRECATED. SETTING LR FOR EACH MODULE ABOVE
+  WEIGHT_DECAY:   
+    value: 0.0  # L2 regularization weight (0.0 = no weight decay)
+  EPOCHS:
+    value: 20  # 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: 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: 1.0  # Weight for highlight mask regression loss
+  TOKEN_INPAINT_LOSS_WEIGHT:
+    value: 1.0  # 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)
+  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_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)
+  
+
+  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: 0.85  # 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: ""  # Notes/description for this training run

configs/pretrained_config.yaml

@@ -18,7 +18,7 @@ parameters:
           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)
+          # FROM_PRETRAINED: "weights/diffuse_decoder.pt"  # 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]

configs/tokeninp_preatrain.yaml

@@ -18,7 +18,7 @@ parameters:
           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)
+          FROM_PRETRAINED: "weights/diffuse_decoder.pt"  # 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]
@@ -48,11 +48,11 @@ parameters:
         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_WEIGHT: 0.25  # 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
+    value: 15  # 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:
@@ -158,7 +158,7 @@ parameters:
   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: 16  # Number of data loading worker processes (0 = main process only, "auto" = 90% of CPU affinity)
+    value: 4  # 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: