added result, 16.1240

image-inpainting/results/runtime_config.json

@@ -0,0 +1,16 @@
+{
+  "learningrate": 0.0003,
+  "weight_decay": 1e-05,
+  "n_updates": 150000,
+  "plot_at": 400,
+  "early_stopping_patience": 40,
+  "print_stats_at": 200,
+  "print_train_stats_at": 50,
+  "validate_at": 200,
+  "accumulation_steps": 1,
+  "commands": {
+    "save_checkpoint": false,
+    "run_test_validation": false,
+    "generate_predictions": false
+  }
+}

image-inpainting/src/architecture.py

@@ -223,8 +223,8 @@ class MyModel(nn.Module):
         )
         
         # Encoder with progressive feature extraction
-        self.down1 = DownBlock(base_channels, base_channels * 2, dropout=dropout, use_attention=False, use_dense=False)
-        self.down2 = DownBlock(base_channels * 2, base_channels * 4, dropout=dropout, use_attention=True, use_dense=True)
+        self.down1 = DownBlock(base_channels, base_channels * 2, dropout=dropout*0.5, use_attention=False, use_dense=False)
+        self.down2 = DownBlock(base_channels * 2, base_channels * 4, dropout=dropout*0.7, use_attention=True, use_dense=True)
         self.down3 = DownBlock(base_channels * 4, base_channels * 8, dropout=dropout, use_attention=True, use_dense=True)
         
         # Enhanced bottleneck with multi-scale features and dense connections
@@ -238,8 +238,8 @@ class MyModel(nn.Module):
         
         # Decoder with progressive reconstruction
         self.up1 = UpBlock(base_channels * 8, base_channels * 4, dropout=dropout, use_attention=True, use_dense=True)
-        self.up2 = UpBlock(base_channels * 4, base_channels * 2, dropout=dropout, use_attention=True, use_dense=True)
-        self.up3 = UpBlock(base_channels * 2, base_channels, dropout=dropout, use_attention=False, use_dense=False)
+        self.up2 = UpBlock(base_channels * 4, base_channels * 2, dropout=dropout*0.7, use_attention=True, use_dense=True)
+        self.up3 = UpBlock(base_channels * 2, base_channels, dropout=dropout*0.5, use_attention=False, use_dense=False)
         
         # Multi-scale feature fusion with dense connections
         self.multiscale_fusion = nn.Sequential(

image-inpainting/src/main.py

@@ -24,11 +24,11 @@ if __name__ == '__main__':
     config_dict['results_path'] = os.path.join(project_root, "results")
     config_dict['data_path'] = os.path.join(project_root, "data", "dataset")
     config_dict['device'] = None
-    config_dict['learningrate'] = 8e-4  # Optimized for long training
-    config_dict['weight_decay'] = 1e-4  # Better regularization for long training
-    config_dict['n_updates'] = 30000  # Full day of training (~24 hours)
-    config_dict['batchsize'] = 64  # Balanced for memory and quality
-    config_dict['early_stopping_patience'] = 15  # More patience for better convergence
+    config_dict['learningrate'] = 3e-4  # More stable learning rate
+    config_dict['weight_decay'] = 1e-4  # Proper regularization
+    config_dict['n_updates'] = 40000  # Extended training
+    config_dict['batchsize'] = 96  # Maximize batch size for better gradients
+    config_dict['early_stopping_patience'] = 20  # More patience for convergence
     config_dict['use_wandb'] = False
 
     config_dict['print_train_stats_at'] = 50
@@ -38,8 +38,8 @@ if __name__ == '__main__':
 
     network_config = {
         'n_in_channels': 4,
-        'base_channels': 44,  # Optimal capacity for 16GB VRAM
-        'dropout': 0.12  # Higher dropout for longer training
+        'base_channels': 64,
+        'dropout': 0.1  # Proper dropout for regularization
     }
     
     config_dict['network_config'] = network_config

image-inpainting/src/train.py

@@ -10,6 +10,7 @@ from utils import plot, evaluate_model
 
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 import numpy as np
 import os
 
@@ -19,15 +20,25 @@ from torch.utils.data import Subset
 import wandb
 
 
-class RMSELoss(nn.Module):
-    """RMSE loss for direct optimization of evaluation metric"""
+class EnhancedRMSELoss(nn.Module):
+    """Enhanced RMSE loss with edge weighting for sharper predictions"""
     def __init__(self):
         super().__init__()
-        self.mse = nn.MSELoss()
     
     def forward(self, pred, target):
-        mse = self.mse(pred, target)
-        rmse = torch.sqrt(mse + 1e-8)  # Add epsilon for numerical stability
+        # Compute per-pixel squared error
+        se = (pred - target) ** 2
+        
+        # Weight edges more heavily for sharper results
+        edge_weight = 1.0 + 0.3 * torch.abs(target[:, :, 1:, :] - target[:, :, :-1, :]).mean(dim=1, keepdim=True)
+        edge_weight = F.pad(edge_weight, (0, 0, 0, 1), value=1.0)
+        
+        # Apply weighting
+        weighted_se = se * edge_weight
+        
+        # Compute RMSE
+        mse = weighted_se.mean()
+        rmse = torch.sqrt(mse + 1e-8)
         return rmse
 
 
@@ -91,14 +102,14 @@ def train(seed, testset_ratio, validset_ratio, data_path, results_path, early_st
     network.to(device)
     network.train()
 
-    # defining the loss - RMSE for direct optimization of evaluation metric
-    rmse_loss = RMSELoss().to(device)
+    # defining the loss - Enhanced RMSE for sharper predictions
+    rmse_loss = EnhancedRMSELoss().to(device)
     mse_loss = torch.nn.MSELoss()  # Keep for evaluation
 
     # defining the optimizer with AdamW for better weight decay handling
-    optimizer = torch.optim.AdamW(network.parameters(), lr=learningrate, weight_decay=weight_decay, betas=(0.9, 0.999))
+    optimizer = torch.optim.AdamW(network.parameters(), lr=learningrate, weight_decay=weight_decay, betas=(0.9, 0.999), eps=1e-8)
     
-    # Cosine annealing with warm restarts for long training
+    # Cosine annealing with warm restarts for gradual learning rate decay
     scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(
         optimizer, T_0=n_updates//4, T_mult=1, eta_min=learningrate/100
     )