added result, 21.9691

image-inpainting/.gitignore

@@ -1,5 +1,4 @@
 data/*
 *.zip
 *.jpg
-*.pt
+*.pt
-__pycache__/

image-inpainting/results/runtime_config.json

@@ -1,16 +0,0 @@
-{
-  "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

@@ -15,51 +15,35 @@ def init_weights(m):
         nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
         if m.bias is not None:
             nn.init.constant_(m.bias, 0)
-    elif isinstance(m, nn.BatchNorm2d):
+    elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d)):
-        nn.init.constant_(m.weight, 1)
+        if m.weight is not None:
-        nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1)
+        if m.bias is not None:
+            nn.init.constant_(m.bias, 0)
 
 
-class GatedSkipConnection(nn.Module):
+class ChannelAttention(nn.Module):
-    """Gated skip connection for better feature fusion"""
+    """Channel attention module (squeeze-and-excitation style)"""
-    def __init__(self, up_channels, skip_channels):
+    def __init__(self, channels, reduction=16):
-        super().__init__()
-        self.gate = nn.Sequential(
-            nn.Conv2d(up_channels + skip_channels, up_channels, 1),
-            nn.Sigmoid()
-        )
-        # Project skip to match up_channels if they differ
-        if skip_channels != up_channels:
-            self.skip_proj = nn.Conv2d(skip_channels, up_channels, 1)
-        else:
-            self.skip_proj = nn.Identity()
-    
-    def forward(self, x, skip):
-        skip_proj = self.skip_proj(skip)
-        combined = torch.cat([x, skip], dim=1)
-        gate = self.gate(combined)
-        return x * gate + skip_proj * (1 - gate)
-
-
-class EfficientChannelAttention(nn.Module):
-    """Efficient channel attention without dimensionality reduction"""
-    def __init__(self, channels):
         super().__init__()
         self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        self.conv = nn.Conv1d(1, 1, kernel_size=3, padding=1, bias=False)
+        self.max_pool = nn.AdaptiveMaxPool2d(1)
+        reduced = max(channels // reduction, 8)
+        self.fc = nn.Sequential(
+            nn.Conv2d(channels, reduced, 1, bias=False),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(reduced, channels, 1, bias=False)
+        )
         self.sigmoid = nn.Sigmoid()
     
     def forward(self, x):
-        # Global pooling
+        avg_out = self.fc(self.avg_pool(x))
-        y = self.avg_pool(x)
+        max_out = self.fc(self.max_pool(x))
-        # 1D convolution on channel dimension
+        return x * self.sigmoid(avg_out + max_out)
-        y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
-        y = self.sigmoid(y)
-        return x * y.expand_as(x)
 
 
 class SpatialAttention(nn.Module):
-    """Efficient spatial attention module"""
+    """Spatial attention module"""
     def __init__(self, kernel_size=7):
         super().__init__()
         self.conv = nn.Conv2d(2, 1, kernel_size, padding=kernel_size // 2, bias=False)
@@ -73,12 +57,12 @@ class SpatialAttention(nn.Module):
         return x * attn
 
 
-class EfficientAttention(nn.Module):
+class CBAM(nn.Module):
-    """Lightweight attention module combining channel and spatial"""
+    """Convolutional Block Attention Module"""
-    def __init__(self, channels):
+    def __init__(self, channels, reduction=16):
         super().__init__()
-        self.channel_attn = EfficientChannelAttention(channels)
+        self.channel_attn = ChannelAttention(channels, reduction)
-        self.spatial_attn = SpatialAttention(kernel_size=5)
+        self.spatial_attn = SpatialAttention()
     
     def forward(self, x):
         x = self.channel_attn(x)
@@ -87,221 +71,157 @@ class EfficientAttention(nn.Module):
 
 
 class ConvBlock(nn.Module):
-    """Convolutional block with Conv2d -> BatchNorm -> LeakyReLU"""
+    """Convolutional block with Conv2d -> InstanceNorm2d -> GELU"""
-    def __init__(self, in_channels, out_channels, kernel_size=3, padding=1, dilation=1, dropout=0.0, separable=False):
+    def __init__(self, in_channels, out_channels, kernel_size=3, padding=1, dropout=0.0, dilation=1):
         super().__init__()
-        if separable and in_channels > 1:
+        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, padding=padding, dilation=dilation)
-            # Depthwise separable convolution for efficiency
+        # InstanceNorm is preferred for style/inpainting tasks
-            self.conv = nn.Sequential(
+        self.bn = nn.InstanceNorm2d(out_channels, affine=True)
-                nn.Conv2d(in_channels, in_channels, kernel_size, padding=padding, dilation=dilation, groups=in_channels),
+        self.act = nn.GELU()
-                nn.Conv2d(in_channels, out_channels, 1)
-            )
-        else:
-            self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, padding=padding, dilation=dilation)
-        self.bn = nn.BatchNorm2d(out_channels)
-        self.relu = nn.LeakyReLU(0.2, inplace=True)
         self.dropout = nn.Dropout2d(dropout) if dropout > 0 else nn.Identity()
     
     def forward(self, x):
-        return self.dropout(self.relu(self.bn(self.conv(x))))
+        return self.dropout(self.act(self.bn(self.conv(x))))
-
-
-class DenseBlock(nn.Module):
-    """Lightweight dense block for better gradient flow"""
-    def __init__(self, channels, growth_rate=8, num_layers=2, dropout=0.0):
-        super().__init__()
-        self.layers = nn.ModuleList()
-        for i in range(num_layers):
-            self.layers.append(ConvBlock(channels + i * growth_rate, growth_rate, dropout=dropout))
-        self.fusion = nn.Conv2d(channels + num_layers * growth_rate, channels, 1)
-        self.bn = nn.BatchNorm2d(channels)
-        self.relu = nn.LeakyReLU(0.2, inplace=True)
-    
-    def forward(self, x):
-        features = [x]
-        for layer in self.layers:
-            out = layer(torch.cat(features, dim=1))
-            features.append(out)
-        out = self.fusion(torch.cat(features, dim=1))
-        out = self.relu(self.bn(out))
-        return out + x  # Residual connection
 
 class ResidualConvBlock(nn.Module):
-    """Improved residual convolutional block with pre-activation"""
+    """Residual convolutional block for better gradient flow"""
-    def __init__(self, channels, dropout=0.0):
+    def __init__(self, channels, dropout=0.0, dilation=1):
         super().__init__()
-        self.bn1 = nn.BatchNorm2d(channels)
+        self.conv1 = nn.Conv2d(channels, channels, 3, padding=dilation, dilation=dilation)
-        self.relu1 = nn.LeakyReLU(0.2, inplace=True)
+        self.bn1 = nn.InstanceNorm2d(channels, affine=True)
-        self.conv1 = nn.Conv2d(channels, channels, 3, padding=1)
+        self.conv2 = nn.Conv2d(channels, channels, 3, padding=dilation, dilation=dilation)
-        self.bn2 = nn.BatchNorm2d(channels)
+        self.bn2 = nn.InstanceNorm2d(channels, affine=True)
-        self.relu2 = nn.LeakyReLU(0.2, inplace=True)
+        self.act = nn.GELU()
-        self.conv2 = nn.Conv2d(channels, channels, 3, padding=1)
         self.dropout = nn.Dropout2d(dropout) if dropout > 0 else nn.Identity()
     
     def forward(self, x):
         residual = x
-        out = self.relu1(self.bn1(x))
+        out = self.act(self.bn1(self.conv1(x)))
-        out = self.conv1(out)
-        out = self.relu2(self.bn2(out))
         out = self.dropout(out)
-        out = self.conv2(out)
+        out = self.bn2(self.conv2(out))
-        return out + residual
+        out = out + residual
+        return self.act(out)
 
 
 class DownBlock(nn.Module):
-    """Enhanced downsampling block with dense and residual connections"""
+    """Downsampling block with conv blocks, residual connection, attention, and max pooling"""
-    def __init__(self, in_channels, out_channels, dropout=0.1, use_attention=True, use_dense=False):
+    def __init__(self, in_channels, out_channels, dropout=0.1):
         super().__init__()
-        self.conv1 = ConvBlock(in_channels, out_channels, dropout=dropout, separable=True)
+        self.conv1 = ConvBlock(in_channels, out_channels, dropout=dropout)
         self.conv2 = ConvBlock(out_channels, out_channels, dropout=dropout)
-        if use_dense:
+        self.residual = ResidualConvBlock(out_channels, dropout=dropout)
-            self.dense = DenseBlock(out_channels, growth_rate=8, num_layers=2, dropout=dropout)
+        self.attention = CBAM(out_channels)
-        else:
-            self.dense = ResidualConvBlock(out_channels, dropout=dropout)
-        self.attention = EfficientAttention(out_channels) if use_attention else nn.Identity()
         self.pool = nn.MaxPool2d(2)
     
     def forward(self, x):
         x = self.conv1(x)
         x = self.conv2(x)
-        x = self.dense(x)
+        x = self.residual(x)
         skip = self.attention(x)
         return self.pool(skip), skip
 
 class UpBlock(nn.Module):
-    """Enhanced upsampling block with gated skip connections"""
+    """Upsampling block with transposed conv, residual connection, attention, and conv blocks"""
-    def __init__(self, in_channels, out_channels, dropout=0.1, use_attention=True, use_dense=False):
+    def __init__(self, in_channels, out_channels, dropout=0.1):
         super().__init__()
         self.up = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=2, stride=2)
-        # Skip connection has in_channels, upsampled has out_channels
+        # After concat: out_channels (from upconv) + in_channels (from skip)
-        self.gated_skip = GatedSkipConnection(out_channels, in_channels)
+        self.conv1 = ConvBlock(out_channels + in_channels, out_channels, dropout=dropout)
-        # After gated skip: out_channels
-        self.conv1 = ConvBlock(out_channels, out_channels, dropout=dropout, separable=True)
         self.conv2 = ConvBlock(out_channels, out_channels, dropout=dropout)
-        if use_dense:
+        self.residual = ResidualConvBlock(out_channels, dropout=dropout)
-            self.dense = DenseBlock(out_channels, growth_rate=8, num_layers=2, dropout=dropout)
+        self.attention = CBAM(out_channels)
-        else:
-            self.dense = ResidualConvBlock(out_channels, dropout=dropout)
-        self.attention = EfficientAttention(out_channels) if use_attention else nn.Identity()
     
     def forward(self, x, skip):
         x = self.up(x)
-        # Handle dimension mismatch
+        # Handle dimension mismatch by interpolating x to match skip's size
         if x.shape[2:] != skip.shape[2:]:
             x = F.interpolate(x, size=skip.shape[2:], mode='bilinear', align_corners=False)
-        x = self.gated_skip(x, skip)
+        x = torch.cat([x, skip], dim=1)
         x = self.conv1(x)
         x = self.conv2(x)
-        x = self.dense(x)
+        x = self.residual(x)
         x = self.attention(x)
         return x
 
 class MyModel(nn.Module):
-    """Enhanced U-Net architecture with dense connections and efficient attention"""
+    """Improved U-Net style architecture for image inpainting with attention and residual connections"""
     def __init__(self, n_in_channels: int, base_channels: int = 64, dropout: float = 0.1):
         super().__init__()
         
-        # Separate mask processing for better feature extraction
+        # Initial convolution with larger receptive field
-        self.mask_conv = nn.Sequential(
+        self.init_conv = nn.Sequential(
-            nn.Conv2d(1, base_channels // 4, 3, padding=1),
+            ConvBlock(n_in_channels, base_channels, kernel_size=7, padding=3),
-            nn.LeakyReLU(0.2, inplace=True),
-            nn.Conv2d(base_channels // 4, base_channels // 4, 3, padding=1),
-            nn.LeakyReLU(0.2, inplace=True)
-        )
-        
-        # Image processing path
-        self.image_conv = nn.Sequential(
-            ConvBlock(3, base_channels, kernel_size=5, padding=2),
-            ConvBlock(base_channels, base_channels)
-        )
-        
-        # Fusion of mask and image features
-        self.fusion = nn.Sequential(
-            nn.Conv2d(base_channels + base_channels // 4, base_channels, 1),
-            nn.BatchNorm2d(base_channels),
-            nn.LeakyReLU(0.2, inplace=True)
-        )
-        
-        # Encoder with progressive feature extraction
-        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
-        self.bottleneck = nn.Sequential(
-            ConvBlock(base_channels * 8, base_channels * 8, dropout=dropout),
-            DenseBlock(base_channels * 8, growth_rate=10, num_layers=3, dropout=dropout),
-            ConvBlock(base_channels * 8, base_channels * 8, dilation=2, padding=2, dropout=dropout),
-            ResidualConvBlock(base_channels * 8, dropout=dropout),
-            EfficientAttention(base_channels * 8)
-        )
-        
-        # 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*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(
-            ConvBlock(base_channels * 2, base_channels),
-            DenseBlock(base_channels, growth_rate=8, num_layers=2, dropout=dropout//2),
-            ConvBlock(base_channels, base_channels)
-        )
-        
-        # Output with residual connection to input
-        self.pre_output = nn.Sequential(
             ConvBlock(base_channels, base_channels),
-            ConvBlock(base_channels, base_channels // 2)
+            ResidualConvBlock(base_channels)
         )
         
+        # Encoder (downsampling path)
+        self.down1 = DownBlock(base_channels, base_channels * 2, dropout=dropout)
+        self.down2 = DownBlock(base_channels * 2, base_channels * 4, dropout=dropout)
+        self.down3 = DownBlock(base_channels * 4, base_channels * 8, dropout=dropout)
+        self.down4 = DownBlock(base_channels * 8, base_channels * 16, dropout=dropout)
+        
+        # Bottleneck with multiple residual blocks
+        self.bottleneck = nn.Sequential(
+            ConvBlock(base_channels * 16, base_channels * 16, dropout=dropout),
+            ResidualConvBlock(base_channels * 16, dropout=dropout, dilation=2),
+            ResidualConvBlock(base_channels * 16, dropout=dropout, dilation=4),
+            ResidualConvBlock(base_channels * 16, dropout=dropout, dilation=8),
+            CBAM(base_channels * 16)
+        )
+        
+        # Decoder (upsampling path)
+        self.up1 = UpBlock(base_channels * 16, base_channels * 8, dropout=dropout)
+        self.up2 = UpBlock(base_channels * 8, base_channels * 4, dropout=dropout)
+        self.up3 = UpBlock(base_channels * 4, base_channels * 2, dropout=dropout)
+        self.up4 = UpBlock(base_channels * 2, base_channels, dropout=dropout)
+        
+        # Final refinement layers
+        self.final_conv = nn.Sequential(
+            ConvBlock(base_channels * 2, base_channels),
+            ResidualConvBlock(base_channels),
+            ConvBlock(base_channels, base_channels)
+        )
+        
+        # Output layer with smooth transition
         self.output = nn.Sequential(
-            nn.Conv2d(base_channels // 2 + 3, base_channels // 2, 3, padding=1),
+            nn.Conv2d(base_channels, base_channels // 2, kernel_size=3, padding=1),
-            nn.LeakyReLU(0.2, inplace=True),
+            nn.GELU(),
-            nn.Conv2d(base_channels // 2, 3, 1),
+            nn.Conv2d(base_channels // 2, 3, kernel_size=1),
-            nn.Sigmoid()
+            nn.Sigmoid()  # Ensure output is in [0, 1] range
         )
         
         # Apply weight initialization
         self.apply(init_weights)
     
     def forward(self, x):
-        # Split input into image and mask
+        # Initial convolution
-        image = x[:, :3, :, :]
+        x0 = self.init_conv(x)
-        mask = x[:, 3:4, :, :]
-        
-        # Process mask and image separately
-        mask_features = self.mask_conv(mask)
-        image_features = self.image_conv(image)
-        
-        # Fuse features
-        x0 = self.fusion(torch.cat([image_features, mask_features], dim=1))
         
         # Encoder
         x1, skip1 = self.down1(x0)
         x2, skip2 = self.down2(x1)
         x3, skip3 = self.down3(x2)
+        x4, skip4 = self.down4(x3)
         
         # Bottleneck
-        x = self.bottleneck(x3)
+        x = self.bottleneck(x4)
         
         # Decoder with skip connections
-        x = self.up1(x, skip3)
+        x = self.up1(x, skip4)
-        x = self.up2(x, skip2)
+        x = self.up2(x, skip3)
-        x = self.up3(x, skip1)
+        x = self.up3(x, skip2)
+        x = self.up4(x, skip1)
         
-        # Handle dimension mismatch for final fusion
+        # Handle dimension mismatch for final concatenation
         if x.shape[2:] != x0.shape[2:]:
             x = F.interpolate(x, size=x0.shape[2:], mode='bilinear', align_corners=False)
         
-        # Multi-scale fusion with initial features
+        # Concatenate with initial features for better detail preservation
         x = torch.cat([x, x0], dim=1)
-        x = self.multiscale_fusion(x)
+        x = self.final_conv(x)
         
-        # Pre-output processing
+        # Output
-        x = self.pre_output(x)
-        
-        # Concatenate with original masked image for residual learning
-        x = torch.cat([x, image], dim=1)
         x = self.output(x)
         
         return x

image-inpainting/src/datasets.py

@@ -10,7 +10,7 @@ import numpy as np
 import random
 import glob
 import os
-from PIL import Image, ImageEnhance
+from PIL import Image
 
 IMAGE_DIMENSION = 100
 
@@ -26,58 +26,34 @@ def create_arrays_from_image(image_array: np.ndarray, offset: tuple, spacing: tu
 
     return image_array, known_array
 
-def resize(img: Image):
+def resize(img: Image, augment: bool = False):
-    resize_transforms = transforms.Compose([
+    transforms_list = [
         transforms.Resize((IMAGE_DIMENSION, IMAGE_DIMENSION)),
         transforms.CenterCrop((IMAGE_DIMENSION, IMAGE_DIMENSION))
-    ])
+    ]
+    
+    if augment:
+        transforms_list = [
+            transforms.RandomHorizontalFlip(),
+            transforms.RandomVerticalFlip(),
+            transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.05),
+            transforms.RandomRotation(10),
+        ] + transforms_list
+        
+    resize_transforms = transforms.Compose(transforms_list)
     return resize_transforms(img)
-
 def preprocess(input_array: np.ndarray):
     input_array = np.asarray(input_array, dtype=np.float32) / 255.0
     return input_array
 
-def augment_image(img: Image, strength: float = 0.7) -> Image:
-    """Apply comprehensive data augmentation for better generalization"""
-    # Random horizontal flip
-    if random.random() > 0.5:
-        img = img.transpose(Image.FLIP_LEFT_RIGHT)
-    
-    # Random vertical flip
-    if random.random() > 0.5:
-        img = img.transpose(Image.FLIP_TOP_BOTTOM)
-    
-    # Random rotation (90, 180, 270 degrees)
-    if random.random() > 0.5:
-        angle = random.choice([90, 180, 270])
-        img = img.rotate(angle)
-    
-    # Color augmentation - more aggressive for long training
-    rand = random.random()
-    if rand > 0.75:
-        # Brightness
-        factor = 1.0 + random.uniform(-0.2, 0.2) * strength
-        img = ImageEnhance.Brightness(img).enhance(factor)
-    elif rand > 0.5:
-        # Contrast
-        factor = 1.0 + random.uniform(-0.2, 0.2) * strength
-        img = ImageEnhance.Contrast(img).enhance(factor)
-    elif rand > 0.25:
-        # Saturation
-        factor = 1.0 + random.uniform(-0.15, 0.15) * strength
-        img = ImageEnhance.Color(img).enhance(factor)
-    
-    return img
-
 class ImageDataset(torch.utils.data.Dataset):
     """
-    Dataset class for loading images from a folder with augmentation support
+    Dataset class for loading images from a folder
     """
 
-    def __init__(self, datafolder: str, augment: bool = True, augment_strength: float = 0.7):
+    def __init__(self, datafolder: str, augment: bool = False):
         self.imagefiles = sorted(glob.glob(os.path.join(datafolder,"**","*.jpg"),recursive=True))
         self.augment = augment
-        self.augment_strength = augment_strength
 
     def __len__(self):
         return len(self.imagefiles)
@@ -86,13 +62,7 @@ class ImageDataset(torch.utils.data.Dataset):
         index = int(idx)
         
         image = Image.open(self.imagefiles[index])
-        image = resize(image)
+        image = np.asarray(resize(image, self.augment))
-        
-        # Apply augmentation
-        if self.augment:
-            image = augment_image(image, self.augment_strength)
-        
-        image = np.asarray(image)
         image = preprocess(image)
         spacing_x = random.randint(2,6)
         spacing_y = random.randint(2,6)

image-inpainting/src/main.py

@@ -24,22 +24,22 @@ 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'] = 3e-4  # More stable learning rate
+    config_dict['learningrate'] = 3e-4  # Optimal learning rate for AdamW
-    config_dict['weight_decay'] = 1e-4  # Proper regularization
+    config_dict['weight_decay'] = 1e-4  # Slightly higher for better regularization
-    config_dict['n_updates'] = 40000  # Extended training
+    config_dict['n_updates'] = 5000  # More updates for better convergence
-    config_dict['batchsize'] = 96  # Maximize batch size for better gradients
+    config_dict['batchsize'] = 8  # Smaller batch for better gradient estimates
-    config_dict['early_stopping_patience'] = 20  # More patience for convergence
+    config_dict['early_stopping_patience'] = 10  # More patience for complex model
     config_dict['use_wandb'] = False
 
-    config_dict['print_train_stats_at'] = 50
+    config_dict['print_train_stats_at'] = 10
-    config_dict['print_stats_at'] = 200
+    config_dict['print_stats_at'] = 100
-    config_dict['plot_at'] = 500
+    config_dict['plot_at'] = 300
-    config_dict['validate_at'] = 500  # Regular validation
+    config_dict['validate_at'] = 300  # Validate more frequently
 
     network_config = {
         'n_in_channels': 4,
-        'base_channels': 64,
+        'base_channels': 48,  # Good balance between capacity and memory
-        'dropout': 0.1  # Proper dropout for regularization
+        'dropout': 0.1  # Regularization
     }
     
     config_dict['network_config'] = network_config

image-inpainting/src/train.py

@@ -10,36 +10,49 @@ 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
 
 from torch.utils.data import DataLoader
 from torch.utils.data import Subset
+from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts
 
 import wandb
 
 
-class EnhancedRMSELoss(nn.Module):
+class CombinedLoss(nn.Module):
-    """Enhanced RMSE loss with edge weighting for sharper predictions"""
+    """Combined loss: MSE + L1 + SSIM-like perceptual component"""
-    def __init__(self):
+    def __init__(self, mse_weight=1.0, l1_weight=0.5, edge_weight=0.1):
         super().__init__()
+        self.mse_weight = mse_weight
+        self.l1_weight = l1_weight
+        self.edge_weight = edge_weight
+        self.mse = nn.MSELoss()
+        self.l1 = nn.L1Loss()
+        
+        # Sobel filters for edge detection
+        sobel_x = torch.tensor([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], dtype=torch.float32).view(1, 1, 3, 3)
+        sobel_y = torch.tensor([[-1, -2, -1], [0, 0, 0], [1, 2, 1]], dtype=torch.float32).view(1, 1, 3, 3)
+        self.register_buffer('sobel_x', sobel_x.repeat(3, 1, 1, 1))
+        self.register_buffer('sobel_y', sobel_y.repeat(3, 1, 1, 1))
+    
+    def edge_loss(self, pred, target):
+        """Compute edge-aware loss using Sobel filters"""
+        pred_edge_x = torch.nn.functional.conv2d(pred, self.sobel_x, padding=1, groups=3)
+        pred_edge_y = torch.nn.functional.conv2d(pred, self.sobel_y, padding=1, groups=3)
+        target_edge_x = torch.nn.functional.conv2d(target, self.sobel_x, padding=1, groups=3)
+        target_edge_y = torch.nn.functional.conv2d(target, self.sobel_y, padding=1, groups=3)
+        
+        edge_loss = self.l1(pred_edge_x, target_edge_x) + self.l1(pred_edge_y, target_edge_y)
+        return edge_loss
     
     def forward(self, pred, target):
-        # Compute per-pixel squared error
+        mse_loss = self.mse(pred, target)
-        se = (pred - target) ** 2
+        l1_loss = self.l1(pred, target)
+        edge_loss = self.edge_loss(pred, target)
         
-        # Weight edges more heavily for sharper results
+        total_loss = self.mse_weight * mse_loss + self.l1_weight * l1_loss + self.edge_weight * edge_loss
-        edge_weight = 1.0 + 0.3 * torch.abs(target[:, :, 1:, :] - target[:, :, :-1, :]).mean(dim=1, keepdim=True)
+        return total_loss
-        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
 
 
 def train(seed, testset_ratio, validset_ratio, data_path, results_path, early_stopping_patience, device, learningrate,
@@ -54,10 +67,6 @@ def train(seed, testset_ratio, validset_ratio, data_path, results_path, early_st
 
     if isinstance(device, str):
         device = torch.device(device)
-    
-    # Enable mixed precision training for memory efficiency
-    use_amp = torch.cuda.is_available()
-    scaler = torch.amp.GradScaler('cuda') if use_amp else None
 
     if use_wandb:
         wandb.login()
@@ -75,16 +84,21 @@ def train(seed, testset_ratio, validset_ratio, data_path, results_path, early_st
     plotpath = os.path.join(results_path, "plots")
     os.makedirs(plotpath, exist_ok=True)
 
-    image_dataset = datasets.ImageDataset(datafolder=data_path)
+    image_dataset = datasets.ImageDataset(datafolder=data_path, augment=False)
 
     n_total = len(image_dataset)
     n_test = int(n_total * testset_ratio)
     n_valid = int(n_total * validset_ratio)
     n_train = n_total - n_test - n_valid
     indices = np.random.permutation(n_total)
-    dataset_train = Subset(image_dataset, indices=indices[0:n_train])
+    
-    dataset_valid = Subset(image_dataset, indices=indices[n_train:n_train + n_valid])
+    # Create datasets with and without augmentation
-    dataset_test = Subset(image_dataset, indices=indices[n_train + n_valid:n_total])
+    train_dataset_source = datasets.ImageDataset(datafolder=data_path, augment=True)
+    val_test_dataset_source = datasets.ImageDataset(datafolder=data_path, augment=False)
+    
+    dataset_train = Subset(train_dataset_source, indices=indices[0:n_train])
+    dataset_valid = Subset(val_test_dataset_source, indices=indices[n_train:n_train + n_valid])
+    dataset_test = Subset(val_test_dataset_source, indices=indices[n_train + n_valid:n_total])
 
     assert len(image_dataset) == len(dataset_train) + len(dataset_test) + len(dataset_valid)
 
@@ -102,17 +116,15 @@ def train(seed, testset_ratio, validset_ratio, data_path, results_path, early_st
     network.to(device)
     network.train()
 
-    # defining the loss - Enhanced RMSE for sharper predictions
+    # defining the loss - combined loss for better reconstruction
-    rmse_loss = EnhancedRMSELoss().to(device)
+    combined_loss = CombinedLoss(mse_weight=1.0, l1_weight=0.5, edge_weight=0.1).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), eps=1e-8)
+    optimizer = torch.optim.AdamW(network.parameters(), lr=learningrate, weight_decay=weight_decay)
     
-    # Cosine annealing with warm restarts for gradual learning rate decay
+    # Learning rate scheduler for better convergence
-    scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(
+    scheduler = CosineAnnealingWarmRestarts(optimizer, T_0=50, T_mult=2, eta_min=1e-6)
-        optimizer, T_0=n_updates//4, T_mult=1, eta_min=learningrate/100
-    )
 
     if use_wandb:
         wandb.watch(network, mse_loss, log="all", log_freq=10)
@@ -137,31 +149,17 @@ def train(seed, testset_ratio, validset_ratio, data_path, results_path, early_st
 
             optimizer.zero_grad()
 
-            # Mixed precision training for memory efficiency
+            output = network(input)
-            if use_amp:
+
-                with torch.amp.autocast('cuda'):
+            loss = combined_loss(output, target)
-                    output = network(input)
+
-                    loss = rmse_loss(output, target)
+            loss.backward()
-                
-                scaler.scale(loss).backward()
-                
-                # Gradient clipping for training stability
-                scaler.unscale_(optimizer)
-                torch.nn.utils.clip_grad_norm_(network.parameters(), max_norm=1.0)
-                
-                scaler.step(optimizer)
-                scaler.update()
-            else:
-                output = network(input)
-                loss = rmse_loss(output, target)
-                loss.backward()
-                
-                # Gradient clipping for training stability
-                torch.nn.utils.clip_grad_norm_(network.parameters(), max_norm=1.0)
-                
-                optimizer.step()
             
-            scheduler.step()
+            # Gradient clipping for training stability
+            torch.nn.utils.clip_grad_norm_(network.parameters(), max_norm=1.0)
+
+            optimizer.step()
+            scheduler.step(i + len(loss_list) / len(dataloader_train))
 
             loss_list.append(loss.item())
 
@@ -172,11 +170,7 @@ def train(seed, testset_ratio, validset_ratio, data_path, results_path, early_st
             # plotting
             if (i + 1) % plot_at == 0:
                 print(f"Plotting images, current update {i + 1}")
-                # Convert to float32 for matplotlib compatibility
+                plot(input.cpu().numpy(), target.detach().cpu().numpy(), output.detach().cpu().numpy(), plotpath, i)
-                plot(input.float().cpu().numpy(), 
-                     target.detach().float().cpu().numpy(), 
-                     output.detach().float().cpu().numpy(), 
-                     plotpath, i)
 
             # evaluating model every validate_at sample
             if (i + 1) % validate_at == 0: