Add runtime_config to gitignore

image-inpainting/.gitignore

@@ -2,3 +2,6 @@ data/*
 *.zip
 *.jpg
 *.pt
+__pycache__/
+runtime_predictions.npz
+results/runtime_config.json

image-inpainting/src/architecture.py

@@ -7,6 +7,7 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+import math
 
 
 def init_weights(m):
@@ -18,44 +19,51 @@ def init_weights(m):
     elif isinstance(m, nn.BatchNorm2d):
         nn.init.constant_(m.weight, 1)
         nn.init.constant_(m.bias, 0)
-    elif isinstance(m, nn.GroupNorm):
-        if m.weight is not None:
-            nn.init.constant_(m.weight, 1)
-        if m.bias is not None:
-            nn.init.constant_(m.bias, 0)
 
 
-def _make_norm(num_channels: int) -> nn.Module:
+class GatedSkipConnection(nn.Module):
-    """Batch-size independent normalization (works well for batch_size=1 eval)."""
+    """Gated skip connection for better feature fusion"""
-    # Choose a group count that divides num_channels.
+    def __init__(self, up_channels, skip_channels):
-    num_groups = min(32, num_channels)
+        super().__init__()
-    while num_groups > 1 and (num_channels % num_groups) != 0:
+        self.gate = nn.Sequential(
-        num_groups //= 2
+            nn.Conv2d(up_channels + skip_channels, up_channels, 1),
-    return nn.GroupNorm(num_groups=num_groups, num_channels=num_channels)
+            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 ChannelAttention(nn.Module):
+class EfficientChannelAttention(nn.Module):
-    """Channel attention module (squeeze-and-excitation style)"""
+    """Efficient channel attention without dimensionality reduction"""
-    def __init__(self, channels, reduction=16):
+    def __init__(self, channels):
         super().__init__()
         self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        self.max_pool = nn.AdaptiveMaxPool2d(1)
+        self.conv = nn.Conv1d(1, 1, kernel_size=3, padding=1, bias=False)
-        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):
-        avg_out = self.fc(self.avg_pool(x))
+        # Global pooling
-        max_out = self.fc(self.max_pool(x))
+        y = self.avg_pool(x)
-        return x * self.sigmoid(avg_out + max_out)
+        # 1D convolution on channel dimension - add safety checks
+        if y.size(-1) == 1 and y.size(-2) == 1:
+            y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
+            y = self.sigmoid(y)
+            y = torch.clamp(y, min=0.0, max=1.0)  # Ensure valid range
+            return x * y.expand_as(x)
+        return x
 
 
 class SpatialAttention(nn.Module):
-    """Spatial attention module"""
+    """Efficient 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)
@@ -69,12 +77,12 @@ class SpatialAttention(nn.Module):
         return x * attn
 
 
-class CBAM(nn.Module):
+class EfficientAttention(nn.Module):
-    """Convolutional Block Attention Module"""
+    """Lightweight attention module combining channel and spatial"""
-    def __init__(self, channels, reduction=16):
+    def __init__(self, channels):
         super().__init__()
-        self.channel_attn = ChannelAttention(channels, reduction)
+        self.channel_attn = EfficientChannelAttention(channels)
-        self.spatial_attn = SpatialAttention()
+        self.spatial_attn = SpatialAttention(kernel_size=5)
     
     def forward(self, x):
         x = self.channel_attn(x)
@@ -82,177 +90,302 @@ class CBAM(nn.Module):
         return x
 
 
+class SelfAttention(nn.Module):
+    """Self-attention module for long-range dependencies"""
+    def __init__(self, in_channels, reduction=8):
+        super().__init__()
+        self.query = nn.Conv2d(in_channels, in_channels // reduction, 1)
+        self.key = nn.Conv2d(in_channels, in_channels // reduction, 1)
+        self.value = nn.Conv2d(in_channels, in_channels, 1)
+        self.gamma = nn.Parameter(torch.zeros(1))
+        self.softmax = nn.Softmax(dim=-1)
+    
+    def forward(self, x):
+        batch_size, C, H, W = x.size()
+        
+        # Generate query, key, value
+        query = self.query(x).view(batch_size, -1, H * W).permute(0, 2, 1)
+        key = self.key(x).view(batch_size, -1, H * W)
+        value = self.value(x).view(batch_size, -1, H * W)
+        
+        # Attention map with numerical stability
+        attention_logits = torch.bmm(query, key)
+        # Scale for numerical stability
+        attention_logits = attention_logits / math.sqrt(query.size(-1))
+        attention = self.softmax(attention_logits)
+        out = torch.bmm(value, attention.permute(0, 2, 1))
+        out = out.view(batch_size, C, H, W)
+        
+        # Residual connection with learnable weight
+        out = self.gamma * out + x
+        return out
+
+
 class ConvBlock(nn.Module):
     """Convolutional block with Conv2d -> BatchNorm -> LeakyReLU"""
-    def __init__(self, in_channels, out_channels, kernel_size=3, padding=1, dropout=0.0):
+    def __init__(self, in_channels, out_channels, kernel_size=3, padding=1, dilation=1, dropout=0.0, separable=False):
         super().__init__()
-        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, padding=padding)
+        if separable and in_channels > 1:
-        self.bn = _make_norm(out_channels)
+            # Depthwise separable convolution for efficiency
-        self.relu = nn.LeakyReLU(0.1, inplace=True)
+            self.conv = nn.Sequential(
+                nn.Conv2d(in_channels, in_channels, kernel_size, padding=padding, dilation=dilation, groups=in_channels),
+                nn.Conv2d(in_channels, out_channels, 1)
+            )
+        else:
+            self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, padding=padding, dilation=dilation)
+        # Add momentum and eps for numerical stability
+        self.bn = nn.BatchNorm2d(out_channels, momentum=0.1, eps=1e-5, track_running_stats=True)
+        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))))
 
+
+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):
-    """Residual convolutional block for better gradient flow"""
+    """Improved residual convolutional block with pre-activation"""
     def __init__(self, channels, dropout=0.0):
         super().__init__()
+        self.bn1 = nn.BatchNorm2d(channels)
+        self.relu1 = nn.LeakyReLU(0.2, inplace=True)
         self.conv1 = nn.Conv2d(channels, channels, 3, padding=1)
-        self.bn1 = _make_norm(channels)
+        self.bn2 = nn.BatchNorm2d(channels)
+        self.relu2 = nn.LeakyReLU(0.2, inplace=True)
         self.conv2 = nn.Conv2d(channels, channels, 3, padding=1)
-        self.bn2 = _make_norm(channels)
-        self.relu = nn.LeakyReLU(0.1, inplace=True)
         self.dropout = nn.Dropout2d(dropout) if dropout > 0 else nn.Identity()
     
     def forward(self, x):
         residual = x
-        out = self.relu(self.bn1(self.conv1(x)))
+        out = self.relu1(self.bn1(x))
+        out = self.conv1(out)
+        out = self.relu2(self.bn2(out))
         out = self.dropout(out)
-        out = self.bn2(self.conv2(out))
+        out = self.conv2(out)
-        out = out + residual
+        return out + residual
-        return self.relu(out)
-
-
-class GatedConvBlock(nn.Module):
-    """Gated convolution block (helps the network condition on the mask channel)."""
-    def __init__(self, in_channels, out_channels, kernel_size=3, padding=1, dropout=0.0):
-        super().__init__()
-        self.feature = nn.Conv2d(in_channels, out_channels, kernel_size, padding=padding)
-        self.gate = nn.Conv2d(in_channels, out_channels, kernel_size, padding=padding)
-        self.norm = _make_norm(out_channels)
-        self.act = nn.LeakyReLU(0.1, inplace=True)
-        self.dropout = nn.Dropout2d(dropout) if dropout > 0 else nn.Identity()
-
-    def forward(self, x):
-        feat = self.feature(x)
-        gate = torch.sigmoid(self.gate(x))
-        out = feat * gate
-        out = self.norm(out)
-        out = self.act(out)
-        out = self.dropout(out)
-        return out
 
 
 class DownBlock(nn.Module):
-    """Downsampling block with conv blocks, residual connection, attention, and max pooling"""
+    """Enhanced downsampling block with dense and residual connections"""
-    def __init__(self, in_channels, out_channels, dropout=0.1):
+    def __init__(self, in_channels, out_channels, dropout=0.1, use_attention=True, use_dense=False, use_self_attention=False):
         super().__init__()
-        self.conv1 = ConvBlock(in_channels, out_channels, dropout=dropout)
+        self.conv1 = ConvBlock(in_channels, out_channels, dropout=dropout, separable=True)
         self.conv2 = ConvBlock(out_channels, out_channels, dropout=dropout)
-        self.residual = ResidualConvBlock(out_channels, dropout=dropout)
+        if use_dense:
-        self.attention = CBAM(out_channels)
+            self.dense = DenseBlock(out_channels, growth_rate=8, num_layers=2, dropout=dropout)
+        else:
+            self.dense = ResidualConvBlock(out_channels, dropout=dropout)
+        self.attention = EfficientAttention(out_channels) if use_attention else nn.Identity()
+        self.self_attention = SelfAttention(out_channels) if use_self_attention else nn.Identity()
         self.pool = nn.MaxPool2d(2)
     
     def forward(self, x):
         x = self.conv1(x)
         x = self.conv2(x)
-        x = self.residual(x)
+        x = self.dense(x)
-        skip = self.attention(x)
+        x = self.attention(x)
+        skip = self.self_attention(x)
         return self.pool(skip), skip
 
 class UpBlock(nn.Module):
-    """Upsampling block with transposed conv, residual connection, attention, and conv blocks"""
+    """Enhanced upsampling block with gated skip connections"""
-    def __init__(self, in_channels, out_channels, dropout=0.1):
+    def __init__(self, in_channels, out_channels, dropout=0.1, use_attention=True, use_dense=False, use_self_attention=False):
         super().__init__()
         self.up = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=2, stride=2)
-        # After concat: out_channels (from upconv) + in_channels (from skip)
+        # Skip connection has in_channels, upsampled has out_channels
-        self.conv1 = ConvBlock(out_channels + in_channels, out_channels, dropout=dropout)
+        self.gated_skip = GatedSkipConnection(out_channels, in_channels)
+        # 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)
-        self.residual = ResidualConvBlock(out_channels, dropout=dropout)
+        if use_dense:
-        self.attention = CBAM(out_channels)
+            self.dense = DenseBlock(out_channels, growth_rate=8, num_layers=2, dropout=dropout)
+        else:
+            self.dense = ResidualConvBlock(out_channels, dropout=dropout)
+        self.attention = EfficientAttention(out_channels) if use_attention else nn.Identity()
+        self.self_attention = SelfAttention(out_channels) if use_self_attention else nn.Identity()
     
     def forward(self, x, skip):
         x = self.up(x)
-        # Handle dimension mismatch by interpolating x to match skip's size
+        # Handle dimension mismatch
         if x.shape[2:] != skip.shape[2:]:
             x = F.interpolate(x, size=skip.shape[2:], mode='bilinear', align_corners=False)
-        x = torch.cat([x, skip], dim=1)
+        x = self.gated_skip(x, skip)
         x = self.conv1(x)
         x = self.conv2(x)
-        x = self.residual(x)
+        x = self.dense(x)
         x = self.attention(x)
+        x = self.self_attention(x)
         return x
 
 class MyModel(nn.Module):
-    """Improved U-Net style architecture for image inpainting with attention and residual connections"""
+    """Enhanced U-Net architecture with dense connections and efficient attention"""
     def __init__(self, n_in_channels: int, base_channels: int = 64, dropout: float = 0.1):
         super().__init__()
         
-        # Initial convolution with larger receptive field
+        # Separate mask processing for better feature extraction
-        self.init_conv = nn.Sequential(
+        # Separate mask processing for better feature extraction
-            GatedConvBlock(n_in_channels, base_channels, kernel_size=7, padding=3, dropout=dropout),
+        self.mask_conv = nn.Sequential(
-            ConvBlock(base_channels, base_channels),
+            nn.Conv2d(1, base_channels // 4, 3, padding=1),
-            ResidualConvBlock(base_channels)
+            nn.BatchNorm2d(base_channels // 4, momentum=0.1, eps=1e-5),
+            nn.LeakyReLU(0.2, inplace=True),
+            nn.Conv2d(base_channels // 4, base_channels // 4, 3, padding=1),
+            nn.BatchNorm2d(base_channels // 4, momentum=0.1, eps=1e-5),
+            nn.LeakyReLU(0.2, inplace=True)
         )
         
-        # Encoder (downsampling path)
+        # Image processing path
-        self.down1 = DownBlock(base_channels, base_channels * 2, dropout=dropout)
+        self.image_conv = nn.Sequential(
-        self.down2 = DownBlock(base_channels * 2, base_channels * 4, dropout=dropout)
+            ConvBlock(3, base_channels, kernel_size=5, padding=2),
-        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),
-            ResidualConvBlock(base_channels * 16, dropout=dropout),
-            ResidualConvBlock(base_channels * 16, dropout=dropout),
-            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
+        # Fusion of mask and image features
+        self.fusion = nn.Sequential(
+            nn.Conv2d(base_channels + base_channels // 4, base_channels, 1),
+            nn.BatchNorm2d(base_channels, momentum=0.1, eps=1e-5, track_running_stats=True),
+            nn.LeakyReLU(0.2, inplace=True)
+        )
+        
+        # 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.down3 = DownBlock(base_channels * 4, base_channels * 8, dropout=dropout, use_attention=True, use_dense=True, use_self_attention=True)
+        
+        # Enhanced bottleneck with multi-scale features, dense connections, and self-attention
+        self.bottleneck = nn.Sequential(
+            ConvBlock(base_channels * 8, base_channels * 8, dropout=dropout),
+            DenseBlock(base_channels * 8, growth_rate=12, num_layers=3, dropout=dropout),
+            SelfAttention(base_channels * 8, reduction=4),
+            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, use_self_attention=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)
+        
+        # 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)
+        )
+        
         self.output = nn.Sequential(
-            nn.Conv2d(base_channels, base_channels // 2, kernel_size=3, padding=1),
+            nn.Conv2d(base_channels // 2 + 3, base_channels // 2, 3, padding=1),
-            nn.LeakyReLU(0.1, inplace=True),
+            nn.LeakyReLU(0.2, inplace=True),
-            nn.Conv2d(base_channels // 2, 3, kernel_size=1),
+            nn.Conv2d(base_channels // 2, 3, 1),
-            nn.Sigmoid()  # Ensure output is in [0, 1] range
+            nn.Sigmoid()
         )
         
         # Apply weight initialization
         self.apply(init_weights)
     
     def forward(self, x):
-        # Initial convolution
+        # Split input into image and mask
-        x0 = self.init_conv(x)
+        image = x[:, :3, :, :]
+        mask = x[:, 3:4, :, :]
+        
+        # Clamp inputs to valid range
+        image = torch.clamp(image, 0.0, 1.0)
+        mask = torch.clamp(mask, 0.0, 1.0)
+        
+        # Process mask and image separately
+        mask_features = self.mask_conv(mask)
+        image_features = self.image_conv(image)
+        
+        # Safety check after initial processing
+        if not torch.isfinite(mask_features).all():
+            mask_features = torch.nan_to_num(mask_features, nan=0.0, posinf=1.0, neginf=-1.0)
+        if not torch.isfinite(image_features).all():
+            image_features = torch.nan_to_num(image_features, nan=0.0, posinf=1.0, neginf=-1.0)
+        
+        # Fuse features
+        x0 = self.fusion(torch.cat([image_features, mask_features], dim=1))
+        if not torch.isfinite(x0).all():
+            x0 = torch.nan_to_num(x0, nan=0.0, posinf=1.0, neginf=-1.0)
         
         # Encoder
         x1, skip1 = self.down1(x0)
+        if not torch.isfinite(x1).all():
+            x1 = torch.nan_to_num(x1, nan=0.0, posinf=1.0, neginf=-1.0)
+            skip1 = torch.nan_to_num(skip1, nan=0.0, posinf=1.0, neginf=-1.0)
+        
         x2, skip2 = self.down2(x1)
+        if not torch.isfinite(x2).all():
+            x2 = torch.nan_to_num(x2, nan=0.0, posinf=1.0, neginf=-1.0)
+            skip2 = torch.nan_to_num(skip2, nan=0.0, posinf=1.0, neginf=-1.0)
+        
         x3, skip3 = self.down3(x2)
-        x4, skip4 = self.down4(x3)
+        if not torch.isfinite(x3).all():
+            x3 = torch.nan_to_num(x3, nan=0.0, posinf=1.0, neginf=-1.0)
+            skip3 = torch.nan_to_num(skip3, nan=0.0, posinf=1.0, neginf=-1.0)
         
         # Bottleneck
-        x = self.bottleneck(x4)
+        x = self.bottleneck(x3)
+        if not torch.isfinite(x).all():
+            x = torch.nan_to_num(x, nan=0.0, posinf=1.0, neginf=-1.0)
         
         # Decoder with skip connections
-        x = self.up1(x, skip4)
+        x = self.up1(x, skip3)
-        x = self.up2(x, skip3)
+        if not torch.isfinite(x).all():
-        x = self.up3(x, skip2)
+            x = torch.nan_to_num(x, nan=0.0, posinf=1.0, neginf=-1.0)
-        x = self.up4(x, skip1)
         
-        # Handle dimension mismatch for final concatenation
+        x = self.up2(x, skip2)
+        if not torch.isfinite(x).all():
+            x = torch.nan_to_num(x, nan=0.0, posinf=1.0, neginf=-1.0)
+        
+        x = self.up3(x, skip1)
+        if not torch.isfinite(x).all():
+            x = torch.nan_to_num(x, nan=0.0, posinf=1.0, neginf=-1.0)
+        
+        # Handle dimension mismatch for final fusion
         if x.shape[2:] != x0.shape[2:]:
             x = F.interpolate(x, size=x0.shape[2:], mode='bilinear', align_corners=False)
         
-        # Concatenate with initial features for better detail preservation
+        # Multi-scale fusion with initial features
         x = torch.cat([x, x0], dim=1)
-        x = self.final_conv(x)
+        x = self.multiscale_fusion(x)
+        if not torch.isfinite(x).all():
+            x = torch.nan_to_num(x, nan=0.0, posinf=1.0, neginf=-1.0)
         
-        # Output
+        # Pre-output processing
+        x = self.pre_output(x)
+        if not torch.isfinite(x).all():
+            x = torch.nan_to_num(x, nan=0.0, posinf=1.0, neginf=-1.0)
+        
+        # Concatenate with original masked image for residual learning
+        x = torch.cat([x, image], dim=1)
         x = self.output(x)
         
+        # Final safety clamp
+        x = torch.clamp(x, 0.0, 1.0)
+        
         return x

image-inpainting/src/datasets.py

@@ -10,7 +10,8 @@ import numpy as np
 import random
 import glob
 import os
-from PIL import Image
+from PIL import Image, ImageEnhance, ImageFilter
+from scipy.ndimage import gaussian_filter, map_coordinates
 
 IMAGE_DIMENSION = 100
 
@@ -33,35 +34,123 @@ def resize(img: Image):
     ])
     return resize_transforms(img)
 
-
-def augment_geometric(img: Image.Image) -> Image.Image:
-    """Lightweight, label-preserving augmentation (safe for train/val/test splits)."""
-    # Horizontal flip
-    if random.random() < 0.5:
-        img = img.transpose(Image.Transpose.FLIP_LEFT_RIGHT)
-    # Vertical flip (less frequent)
-    if random.random() < 0.2:
-        img = img.transpose(Image.Transpose.FLIP_TOP_BOTTOM)
-    # 90-degree rotations (no interpolation artifacts)
-    r = random.random()
-    if r < 0.25:
-        img = img.transpose(Image.Transpose.ROTATE_90)
-    elif r < 0.5:
-        img = img.transpose(Image.Transpose.ROTATE_180)
-    elif r < 0.75:
-        img = img.transpose(Image.Transpose.ROTATE_270)
-    return img
 def preprocess(input_array: np.ndarray):
     input_array = np.asarray(input_array, dtype=np.float32) / 255.0
     return input_array
 
+def elastic_transform(image: np.ndarray, alpha: float = 20, sigma: float = 4) -> np.ndarray:
+    """Apply elastic deformation to image array"""
+    shape = image.shape[:2]
+    dx = gaussian_filter((np.random.rand(*shape) * 2 - 1), sigma) * alpha
+    dy = gaussian_filter((np.random.rand(*shape) * 2 - 1), sigma) * alpha
+    
+    x, y = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]))
+    indices = np.reshape(y + dy, (-1, 1)), np.reshape(x + dx, (-1, 1))
+    
+    # Apply to each channel
+    transformed = np.zeros_like(image)
+    for i in range(image.shape[2]):
+        transformed[:, :, i] = map_coordinates(image[:, :, i], indices, order=1, mode='reflect').reshape(shape)
+    
+    return transformed
+
+def add_noise(img_array: np.ndarray, noise_type: str = 'gaussian', strength: float = 0.02) -> np.ndarray:
+    """Add various types of noise to image"""
+    if noise_type == 'gaussian':
+        noise = np.random.normal(0, strength, img_array.shape)
+        noisy = img_array + noise
+    elif noise_type == 'salt_pepper':
+        noisy = img_array.copy()
+        # Salt
+        num_salt = int(strength * img_array.size * 0.5)
+        coords = [np.random.randint(0, i, num_salt) for i in img_array.shape]
+        noisy[coords[0], coords[1], :] = 1
+        # Pepper
+        num_pepper = int(strength * img_array.size * 0.5)
+        coords = [np.random.randint(0, i, num_pepper) for i in img_array.shape]
+        noisy[coords[0], coords[1], :] = 0
+    else:
+        noisy = img_array
+    
+    return np.clip(noisy, 0, 1)
+
+def augment_image(img: Image, strength: float = 0.8) -> 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, or small angles)
+    if random.random() > 0.5:
+        if random.random() > 0.7:
+            # Large rotation
+            angle = random.choice([90, 180, 270])
+            img = img.rotate(angle)
+        else:
+            # Small rotation for more variation
+            angle = random.uniform(-15, 15)
+            img = img.rotate(angle, fillcolor=(128, 128, 128))
+    
+    # More aggressive color augmentation
+    if random.random() > 0.3:
+        # Brightness
+        factor = 1.0 + random.uniform(-0.3, 0.3) * strength
+        img = ImageEnhance.Brightness(img).enhance(factor)
+    
+    if random.random() > 0.3:
+        # Contrast
+        factor = 1.0 + random.uniform(-0.3, 0.3) * strength
+        img = ImageEnhance.Contrast(img).enhance(factor)
+    
+    if random.random() > 0.3:
+        # Saturation
+        factor = 1.0 + random.uniform(-0.25, 0.25) * strength
+        img = ImageEnhance.Color(img).enhance(factor)
+    
+    if random.random() > 0.7:
+        # Sharpness
+        factor = 1.0 + random.uniform(-0.3, 0.5) * strength
+        img = ImageEnhance.Sharpness(img).enhance(factor)
+    
+    # Gaussian blur for robustness
+    if random.random() > 0.8:
+        radius = random.uniform(0.5, 1.5) * strength
+        img = img.filter(ImageFilter.GaussianBlur(radius=radius))
+    
+    # Convert to array for elastic transform and noise
+    img_array = np.array(img).astype(np.float32) / 255.0
+    
+    # Elastic deformation
+    if random.random() > 0.7:
+        alpha = random.uniform(15, 30) * strength
+        sigma = random.uniform(3, 5)
+        img_array = elastic_transform(img_array, alpha=alpha, sigma=sigma)
+    
+    # Add noise
+    if random.random() > 0.6:
+        noise_type = random.choice(['gaussian', 'salt_pepper'])
+        noise_strength = random.uniform(0.01, 0.03) * strength
+        img_array = add_noise(img_array, noise_type=noise_type, strength=noise_strength)
+    
+    # Convert back to PIL Image
+    img_array = np.clip(img_array * 255, 0, 255).astype(np.uint8)
+    img = Image.fromarray(img_array)
+    
+    return img
+
 class ImageDataset(torch.utils.data.Dataset):
     """
-    Dataset class for loading images from a folder
+    Dataset class for loading images from a folder with augmentation support
     """
 
-    def __init__(self, datafolder: str):
+    def __init__(self, datafolder: str, augment: bool = True, augment_strength: float = 0.8):
         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)
@@ -69,17 +158,19 @@ class ImageDataset(torch.utils.data.Dataset):
     def __getitem__(self, idx:int):
         index = int(idx)
         
-        image = Image.open(self.imagefiles[index]).convert("RGB")
+        image = Image.open(self.imagefiles[index])
-        image = augment_geometric(image)
+        image = resize(image)
-        image = np.asarray(resize(image))
-        image = preprocess(image)
         
-        # Sample a grid-mask similar in density to the challenge testset (~8% known pixels).
+        # Apply augmentation
-        # IMPORTANT: offset ranges must be tied to spacing to avoid accidental distribution shift.
+        if self.augment:
-        spacing_x = random.randint(4, 6)
+            image = augment_image(image, self.augment_strength)
-        spacing_y = random.randint(2, 4)
+        
-        offset_x = random.randint(0, spacing_x - 1)
+        image = np.asarray(image)
-        offset_y = random.randint(0, spacing_y - 1)
+        image = preprocess(image)
+        spacing_x = random.randint(2,6)
+        spacing_y = random.randint(2,6)
+        offset_x = random.randint(0,8)
+        offset_y = random.randint(0,8)
         spacing = (spacing_x, spacing_y)
         offset = (offset_x, offset_y)
         input_array, known_array = create_arrays_from_image(image.copy(), offset, spacing)

image-inpainting/src/main.py

@@ -17,46 +17,74 @@ if __name__ == '__main__':
 
     config_dict['seed'] = 42
     config_dict['testset_ratio'] = 0.1
-    config_dict['validset_ratio'] = 0.1
+    config_dict['validset_ratio'] = 0.05
     # Get the absolute path based on the script's location
     script_dir = os.path.dirname(os.path.abspath(__file__))
     project_root = os.path.dirname(script_dir)
     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  # Optimal learning rate for AdamW
+    config_dict['learningrate'] = 5e-4  # Lower initial LR with warmup
-    config_dict['weight_decay'] = 1e-4  # Slightly higher for better regularization
+    config_dict['weight_decay'] = 5e-5  # Reduced for more capacity
-    config_dict['n_updates'] = 5000  # More updates for better convergence
+    config_dict['n_updates'] = 12000  # Extended training for better convergence
-    config_dict['batchsize'] = 8  # Smaller batch for better gradient estimates
+    config_dict['batchsize'] = 64  # Reduced for larger model and mixed precision
-    config_dict['early_stopping_patience'] = 10  # More patience for complex model
+    config_dict['early_stopping_patience'] = 20  # More patience for complex model
     config_dict['use_wandb'] = False
 
     config_dict['print_train_stats_at'] = 10
-    config_dict['print_stats_at'] = 100
+    config_dict['print_stats_at'] = 200
-    config_dict['plot_at'] = 300
+    config_dict['plot_at'] = 500
-    config_dict['validate_at'] = 300  # Validate more frequently
+    config_dict['validate_at'] = 250  # More frequent validation
 
     network_config = {
         'n_in_channels': 4,
-        'base_channels': 48,  # Good balance between capacity and memory
+        'base_channels': 52,  # Increased capacity for better feature extraction
-        'dropout': 0.1  # Regularization
+        'dropout': 0.15  # Slightly higher dropout for regularization
     }
     
     config_dict['network_config'] = network_config
     
+    # Prepare paths for runtime predictions
+    testset_path = os.path.join(project_root, "data", "challenge_testset.npz")
+    save_path = os.path.join(config_dict['results_path'], "runtime_predictions")
+    plot_path_predictions = os.path.join(config_dict['results_path'], "runtime_predictions", "plots")
+    
+    config_dict['testset_path'] = testset_path
+    config_dict['save_path'] = save_path
+    config_dict['plot_path_predictions'] = plot_path_predictions
+    
+    print("="*60)
+    print("RUNTIME CONFIGURATION ENABLED")
+    print("="*60)
+    print("During training, you can modify these parameters by editing:")
+    print(f"{os.path.join(config_dict['results_path'], 'runtime_config.json')}")
+    print("\nModifiable parameters:")
+    print("  - n_updates: Maximum training steps")
+    print("  - plot_at: How often to save plots")
+    print("  - early_stopping_patience: Patience for early stopping")
+    print("  - print_stats_at: How often to print detailed stats")
+    print("  - print_train_stats_at: How often to print training loss")
+    print("  - validate_at: How often to run validation")
+    print("\nRuntime commands (set to true to execute):")
+    print("  - save_checkpoint: Save model at current step")
+    print("  - run_test_validation: Run validation on final test set")
+    print("  - generate_predictions: Generate predictions on challenge testset")
+    print("\nChanges will be applied within 5 steps.")
+    print("="*60)
+    print()
+
     rmse_value = train(**config_dict)
     
-    testset_path = os.path.join(project_root, "data", "challenge_testset.npz")
     state_dict_path = os.path.join(config_dict['results_path'], "best_model.pt")
-    save_path = os.path.join(config_dict['results_path'], "testset", "tikaiz")
+    final_save_path = os.path.join(config_dict['results_path'], "testset", "tikaiz")
-    plot_path = os.path.join(config_dict['results_path'], "testset", "plots")
+    final_plot_path = os.path.join(config_dict['results_path'], "testset", "plots")
-    os.makedirs(plot_path, exist_ok=True)
+    os.makedirs(final_plot_path, exist_ok=True)
-    for name in os.listdir(plot_path):
+    for name in os.listdir(final_plot_path):
-        p = os.path.join(plot_path, name)
+        p = os.path.join(final_plot_path, name)
         if os.path.isfile(p) or os.path.islink(p):
             os.unlink(p)
         elif os.path.isdir(p):
             shutil.rmtree(p)
 
     # Comment out, if predictions are required
-    create_predictions(config_dict['network_config'], state_dict_path, testset_path, None, save_path, plot_path, plot_at=20, rmse_value=rmse_value)
+    create_predictions(config_dict['network_config'], state_dict_path, testset_path, None, final_save_path, final_plot_path, plot_at=20, rmse_value=rmse_value)

image-inpainting/src/train.py

@@ -12,52 +12,182 @@ import torch
 import torch.nn as nn
 import numpy as np
 import os
+import json
+from torchvision import models
+import torch.nn.functional as F
 
 from torch.utils.data import DataLoader
 from torch.utils.data import Subset
-from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts
 
 import wandb
 
 
-class CombinedLoss(nn.Module):
+def load_runtime_config(config_path, current_params):
-    """Combined loss: MSE + L1 + SSIM-like perceptual component"""
+    """Load runtime configuration from JSON file and update parameters"""
-    def __init__(self, mse_weight=1.0, l1_weight=0.5, edge_weight=0.1):
+    try:
+        if os.path.exists(config_path):
+            with open(config_path, 'r') as f:
+                new_config = json.load(f)
+            
+            # Update modifiable parameters
+            updated = False
+            modifiable_keys = ['n_updates', 'plot_at', 'early_stopping_patience', 
+                             'print_stats_at', 'print_train_stats_at', 'validate_at',
+                             'learningrate', 'weight_decay']
+            
+            for key in modifiable_keys:
+                if key in new_config and new_config[key] != current_params.get(key):
+                    old_val = current_params.get(key)
+                    current_params[key] = new_config[key]
+                    print(f"\n[CONFIG UPDATE] {key}: {old_val} -> {new_config[key]}")
+                    updated = True
+            
+            # Check for command flags
+            commands = new_config.get('commands', {})
+            current_params['commands'] = commands
+            
+            if updated:
+                print("[CONFIG UPDATE] Runtime configuration updated successfully!\n")
+    except Exception as e:
+        print(f"Warning: Could not load runtime config: {e}")
+    
+    return current_params
+
+
+def clear_command_flag(config_path, command_name):
+    """Clear a specific command flag after execution"""
+    try:
+        if os.path.exists(config_path):
+            with open(config_path, 'r') as f:
+                config = json.load(f)
+            
+            if 'commands' in config and command_name in config['commands']:
+                config['commands'][command_name] = False
+                
+                with open(config_path, 'w') as f:
+                    json.dump(config, f, indent=2)
+    except Exception as e:
+        print(f"Warning: Could not clear command flag: {e}")
+
+
+class RMSELoss(nn.Module):
+    """RMSE loss for direct optimization of evaluation metric"""
+    def __init__(self):
         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):
-        mse_loss = self.mse(pred, target)
+        mse = self.mse(pred, target)
-        l1_loss = self.l1(pred, target)
+        # Larger epsilon for numerical stability
-        edge_loss = self.edge_loss(pred, target)
+        rmse = torch.sqrt(mse + 1e-6)
+        return rmse
 
-        total_loss = self.mse_weight * mse_loss + self.l1_weight * l1_loss + self.edge_weight * edge_loss
+
-        return total_loss
+class PerceptualLoss(nn.Module):
+    """Perceptual loss using VGG16 features for better texture and detail preservation"""
+    def __init__(self, device):
+        super().__init__()
+        # Load pre-trained VGG16 and use specific layers
+        vgg = models.vgg16(pretrained=True).features.to(device).eval()
+        # Freeze VGG parameters
+        for param in vgg.parameters():
+            param.requires_grad = False
+        
+        # Use early and middle layers for perceptual loss
+        self.slice1 = nn.Sequential(*list(vgg.children())[:4])   # relu1_2
+        self.slice2 = nn.Sequential(*list(vgg.children())[4:9])  # relu2_2
+        self.slice3 = nn.Sequential(*list(vgg.children())[9:16]) # relu3_3
+        
+        # Normalization for VGG (ImageNet stats)
+        self.register_buffer('mean', torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))
+        self.register_buffer('std', torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))
+    
+    def normalize(self, x):
+        """Normalize images for VGG with clamping for stability"""
+        # Clamp input to valid range
+        x = torch.clamp(x, 0.0, 1.0)
+        return (x - self.mean) / (self.std + 1e-8)
+    
+    def forward(self, pred, target):
+        # Clamp inputs to prevent extreme values
+        pred = torch.clamp(pred, 0.0, 1.0)
+        target = torch.clamp(target, 0.0, 1.0)
+        
+        # Normalize inputs
+        pred = self.normalize(pred)
+        target = self.normalize(target)
+        
+        # Extract features from multiple layers
+        pred_f1 = self.slice1(pred)
+        pred_f2 = self.slice2(pred_f1)
+        pred_f3 = self.slice3(pred_f2)
+        
+        target_f1 = self.slice1(target)
+        target_f2 = self.slice2(target_f1)
+        target_f3 = self.slice3(target_f2)
+        
+        # Compute losses at multiple scales
+        loss = F.l1_loss(pred_f1, target_f1) + \
+               F.l1_loss(pred_f2, target_f2) + \
+               F.l1_loss(pred_f3, target_f3)
+        
+        return loss
+
+
+class CombinedLoss(nn.Module):
+    """Combined loss optimized for RMSE evaluation with optional perceptual component"""
+    def __init__(self, device, use_perceptual=True, perceptual_weight=0.05):
+        super().__init__()
+        self.use_perceptual = use_perceptual
+        if use_perceptual:
+            self.perceptual_loss = PerceptualLoss(device)
+        # Use MSE instead of RMSE for training (more stable gradients)
+        self.mse_loss = nn.MSELoss()
+        self.rmse_loss = RMSELoss()  # For logging only
+        
+        self.perceptual_weight = perceptual_weight
+        self.mse_weight = 1.0 - perceptual_weight
+    
+    def forward(self, pred, target):
+        # Clamp predictions to valid range
+        pred = torch.clamp(pred, 0.0, 1.0)
+        target = torch.clamp(target, 0.0, 1.0)
+        
+        # Check for NaN in inputs
+        if not torch.isfinite(pred).all() or not torch.isfinite(target).all():
+            print("Warning: NaN detected in loss inputs")
+            return (torch.tensor(float('nan'), device=pred.device),) * 4
+        
+        # Primary loss: MSE (equivalent to RMSE but more stable)
+        mse = self.mse_loss(pred, target)
+        rmse = self.rmse_loss(pred, target)  # For logging
+        
+        if self.use_perceptual:
+            # Optional small perceptual component for texture quality
+            perceptual = self.perceptual_loss(pred, target)
+            # Check perceptual loss validity
+            if not torch.isfinite(perceptual):
+                perceptual = torch.tensor(0.0, device=pred.device)
+            total_loss = self.mse_weight * mse + self.perceptual_weight * perceptual
+        else:
+            # Pure MSE optimization
+            perceptual = torch.tensor(0.0, device=pred.device)
+            total_loss = mse
+        
+        # Validate loss is not NaN or Inf
+        if not torch.isfinite(total_loss):
+            # Return MSE only as fallback
+            total_loss = mse
+            if not torch.isfinite(total_loss):
+                print("Warning: MSE is NaN")
+                return (torch.tensor(float('nan'), device=pred.device),) * 4
+        
+        return total_loss, perceptual, mse, rmse
 
 
 def train(seed, testset_ratio, validset_ratio, data_path, results_path, early_stopping_patience, device, learningrate,
           weight_decay, n_updates, use_wandb, print_train_stats_at, print_stats_at, plot_at, validate_at, batchsize,
-          network_config: dict):
+          network_config: dict, testset_path=None, save_path=None, plot_path_predictions=None):
     np.random.seed(seed=seed)
     torch.manual_seed(seed=seed)
 
@@ -68,6 +198,13 @@ 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()
+    if use_amp:
+        scaler = torch.amp.GradScaler('cuda', init_scale=2048.0, growth_interval=100)
+    else:
+        scaler = None
+
     if use_wandb:
         wandb.login()
         wandb.init(project="image_inpainting", config={
@@ -111,15 +248,28 @@ def train(seed, testset_ratio, validset_ratio, data_path, results_path, early_st
     network.to(device)
     network.train()
 
-    # defining the loss - combined loss for better reconstruction
+    # defining the loss - Optimized for RMSE evaluation
-    combined_loss = CombinedLoss(mse_weight=1.0, l1_weight=0.5, edge_weight=0.1).to(device)
+    # Set use_perceptual=False for pure MSE training, or keep True with 5% weight for texture quality
+    # TEMPORARILY DISABLED due to NaN issues - re-enable once training is stable
+    combined_loss = CombinedLoss(device, use_perceptual=False, perceptual_weight=0.0).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)
+    optimizer = torch.optim.AdamW(network.parameters(), lr=learningrate, weight_decay=weight_decay, betas=(0.9, 0.999))
     
-    # Learning rate scheduler for better convergence
+    # Learning rate warmup
-    scheduler = CosineAnnealingWarmRestarts(optimizer, T_0=50, T_mult=2, eta_min=1e-6)
+    warmup_steps = min(1000, n_updates // 10)
+    
+    # Cosine annealing with warm restarts for long training
+    scheduler_main = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(
+        optimizer, T_0=n_updates//4, T_mult=1, eta_min=learningrate/100
+    )
+    
+    # Warmup scheduler
+    def get_lr_scale(step):
+        if step < warmup_steps:
+            return step / warmup_steps
+        return 1.0
 
     if use_wandb:
         wandb.watch(network, mse_loss, log="all", log_freq=10)
@@ -131,7 +281,31 @@ def train(seed, testset_ratio, validset_ratio, data_path, results_path, early_st
 
     saved_model_path = os.path.join(results_path, "best_model.pt")
     
+    # Save runtime configuration to JSON file for dynamic updates
+    config_json_path = os.path.join(results_path, "runtime_config.json")
+    runtime_params = {
+        'learningrate': learningrate,
+        'weight_decay': weight_decay,
+        'n_updates': n_updates,
+        'plot_at': plot_at,
+        'early_stopping_patience': early_stopping_patience,
+        'print_stats_at': print_stats_at,
+        'print_train_stats_at': print_train_stats_at,
+        'validate_at': validate_at,
+        'commands': {
+            'save_checkpoint': False,
+            'run_test_validation': False,
+            'generate_predictions': False
+        }
+    }
+    
+    with open(config_json_path, 'w') as f:
+        json.dump(runtime_params, f, indent=2)
+    
     print(f"Started training on device {device}")
+    print(f"Runtime config saved to: {config_json_path}")
+    print(f"You can modify this file during training to change parameters dynamically!")
+    print(f"Set command flags to true to trigger actions (save_checkpoint, run_test_validation, generate_predictions)\n")
 
     while i < n_updates:
 
@@ -139,33 +313,191 @@ def train(seed, testset_ratio, validset_ratio, data_path, results_path, early_st
 
             input, target = input.to(device), target.to(device)
             
+            # Check for runtime config updates every 5 steps
+            if i % 5 == 0 and i > 0:
+                runtime_params = load_runtime_config(config_json_path, runtime_params)
+                n_updates = runtime_params['n_updates']
+                plot_at = runtime_params['plot_at']
+                early_stopping_patience = runtime_params['early_stopping_patience']
+                print_stats_at = runtime_params['print_stats_at']
+                print_train_stats_at = runtime_params['print_train_stats_at']
+                validate_at = runtime_params['validate_at']
+                
+                # Update optimizer parameters if changed
+                if 'learningrate' in runtime_params:
+                    new_lr = runtime_params['learningrate']
+                    current_lr = optimizer.param_groups[0]['lr']
+                    if abs(new_lr - current_lr) > 1e-10:  # Float comparison with tolerance
+                        for param_group in optimizer.param_groups:
+                            param_group['lr'] = new_lr
+                
+                if 'weight_decay' in runtime_params:
+                    new_wd = runtime_params['weight_decay']
+                    current_wd = optimizer.param_groups[0]['weight_decay']
+                    if abs(new_wd - current_wd) > 1e-10:  # Float comparison with tolerance
+                        for param_group in optimizer.param_groups:
+                            param_group['weight_decay'] = new_wd
+                
+                # Execute runtime commands
+                commands = runtime_params.get('commands', {})
+                
+                # Command: Save checkpoint
+                if commands.get('save_checkpoint', False):
+                    checkpoint_path = os.path.join(results_path, f"checkpoint_step_{i}.pt")
+                    torch.save(network.state_dict(), checkpoint_path)
+                    print(f"\n[COMMAND] Checkpoint saved to: {checkpoint_path}\n")
+                    clear_command_flag(config_json_path, 'save_checkpoint')
+                
+                # Command: Generate predictions
+                if commands.get('generate_predictions', False) and testset_path is not None:
+                    print(f"\n[COMMAND] Generating predictions at step {i}...")
+                    try:
+                        from utils import create_predictions
+                        pred_save_path = save_path or os.path.join(results_path, "runtime_predictions", f"step_{i}")
+                        pred_plot_path = plot_path_predictions or os.path.join(results_path, "runtime_predictions", "plots", f"step_{i}")
+                        os.makedirs(pred_plot_path, exist_ok=True)
+                        
+                        # Save current state temporarily
+                        temp_state_path = os.path.join(results_path, f"temp_state_step_{i}.pt")
+                        torch.save(network.state_dict(), temp_state_path)
+                        
+                        # Generate predictions
+                        create_predictions(network_config, temp_state_path, testset_path, None, 
+                                         pred_save_path, pred_plot_path, plot_at=20, rmse_value=None)
+                        
+                        print(f"[COMMAND] Predictions saved to: {pred_save_path}")
+                        print(f"[COMMAND] Plots saved to: {pred_plot_path}\n")
+                        
+                        # Clean up temp file
+                        if os.path.exists(temp_state_path):
+                            os.remove(temp_state_path)
+                    except Exception as e:
+                        print(f"[COMMAND] Error generating predictions: {e}\n")
+                    
+                    network.train()
+                    clear_command_flag(config_json_path, 'generate_predictions')
+                
+                # Command: Run test validation
+                if commands.get('run_test_validation', False):
+                    print(f"\n[COMMAND] Running test set validation at step {i}...")
+                    network.eval()
+                    test_loss, test_rmse = evaluate_model(network, dataloader_test, mse_loss, device)
+                    print(f"[COMMAND] Test Loss: {test_loss:.6f}, Test RMSE: {test_rmse:.6f}\n")
+                    network.train()
+                    clear_command_flag(config_json_path, 'run_test_validation')
+
             if (i + 1) % print_train_stats_at == 0:
                 print(f'Update Step {i + 1} of {n_updates}: Current loss: {loss_list[-1]}')
 
             optimizer.zero_grad()
 
+            # Mixed precision training for memory efficiency
+            if use_amp:
+                with torch.amp.autocast('cuda'):
                     output = network(input)
+                    total_loss, perceptual, mse, rmse = combined_loss(output, target)
                 
-            loss = combined_loss(output, target)
+                # Check for NaN before backward
+                if not torch.isfinite(total_loss):
+                    continue
                 
-            loss.backward()
+                scaler.scale(total_loss).backward()
                 
-            # Gradient clipping for training stability
+                # Unscale and check gradients
-            torch.nn.utils.clip_grad_norm_(network.parameters(), max_norm=1.0)
+                scaler.unscale_(optimizer)
+                
+                # Check for NaN in gradients
+                has_nan = False
+                for name, param in network.named_parameters():
+                    if param.grad is not None:
+                        if not torch.isfinite(param.grad).all():
+                            print(f"NaN gradient detected in {name}")
+                            has_nan = True
+                            break
+                
+                if has_nan:
+                    print(f"Skipping step {i+1}: NaN gradients detected")
+                    optimizer.zero_grad()
+                    scaler.update()
+                    # Reset scaler if NaN persists
+                    if (i + 1) % 10 == 0:
+                        scaler = torch.amp.GradScaler('cuda', init_scale=2048.0, growth_interval=100)
+                    continue
+                
+                # More aggressive gradient clipping for stability
+                grad_norm = torch.nn.utils.clip_grad_norm_(network.parameters(), max_norm=1.0)
+                
+                # Skip update if gradient norm is too large
+                if grad_norm > 100.0:
+                    print(f"Skipping step {i+1}: Gradient norm too large: {grad_norm:.2f}")
+                    optimizer.zero_grad()
+                    scaler.update()
+                    continue
+                
+                scaler.step(optimizer)
+                scaler.update()
+            else:
+                output = network(input)
+                total_loss, perceptual, mse, rmse = combined_loss(output, target)
+                
+                # Check for NaN before backward
+                if not torch.isfinite(total_loss):
+                    print(f"Skipping step {i+1}: NaN or Inf loss detected")
+                    continue
+                
+                total_loss.backward()
+                
+                # Check for NaN in gradients
+                has_nan = False
+                for name, param in network.named_parameters():
+                    if param.grad is not None and not torch.isfinite(param.grad).all():
+                        print(f"NaN gradient detected in {name}")
+                        has_nan = True
+                        break
+                
+                if has_nan:
+                    print(f"Skipping step {i+1}: NaN gradients detected")
+                    optimizer.zero_grad()
+                    continue
+                
+                # More aggressive gradient clipping
+                grad_norm = torch.nn.utils.clip_grad_norm_(network.parameters(), max_norm=1.0)
+                
+                if grad_norm > 100.0:
+                    print(f"Skipping step {i+1}: Gradient norm too large: {grad_norm:.2f}")
+                    optimizer.zero_grad()
+                    continue
                 
                 optimizer.step()
-            scheduler.step(i + len(loss_list) / len(dataloader_train))
             
-            loss_list.append(loss.item())
+            # Apply learning rate scheduling with warmup
+            lr_scale = get_lr_scale(i)
+            for param_group in optimizer.param_groups:
+                param_group['lr'] = learningrate * lr_scale
+            
+            if i >= warmup_steps:
+                scheduler_main.step()
+
+            loss_list.append(total_loss.item())
 
             # writing the stats to wandb
             if use_wandb and (i+1) % print_stats_at == 0:
-                wandb.log({"training/loss_per_batch": loss.item()}, step=i)
+                wandb.log({
+                    "training/loss_total": total_loss.item(),
+                    "training/loss_mse": mse.item(),
+                    "training/loss_rmse": rmse.item(),
+                    "training/loss_perceptual": perceptual.item() if isinstance(perceptual, torch.Tensor) else perceptual,
+                    "training/learning_rate": optimizer.param_groups[0]['lr']
+                }, step=i)
 
             # plotting
             if (i + 1) % plot_at == 0:
                 print(f"Plotting images, current update {i + 1}")
-                plot(input.cpu().numpy(), target.detach().cpu().numpy(), output.detach().cpu().numpy(), plotpath, i)
+                # Convert to float32 for matplotlib compatibility
+                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:

image-inpainting/src/utils.py

@@ -18,12 +18,14 @@ def plot(inputs, targets, predictions, path, update):
     os.makedirs(path, exist_ok=True)
     fig, axes = plt.subplots(ncols=3, figsize=(15, 5))
 
-    for i in range(5):
+    # Only plot up to min(5, batch_size) images
+    num_images = min(5, inputs.shape[0])
+    
+    for i in range(num_images):
         for ax, data, title in zip(axes, [inputs, targets, predictions], ["Input", "Target", "Prediction"]):
             ax.clear()
             ax.set_title(title)
-            img = data[i:i + 1:, 0:3, :, :]
+            img = data[i, 0:3, :, :]
-            img = np.squeeze(img)
             img = np.transpose(img, (1, 2, 0))
             img = np.clip(img, 0, 1)
             ax.imshow(img)
@@ -54,24 +56,58 @@ def testset_plot(input_array, output_array, path, index):
 
 
 def evaluate_model(network: torch.nn.Module, dataloader: torch.utils.data.DataLoader, loss_fn, device: torch.device):
-    """Returnse MSE and RMSE of the model on the provided dataloader"""
+    """Returns MSE and RMSE of the model on the provided dataloader"""
+    # Save training mode and switch to eval
+    was_training = network.training
     network.eval()
+    
     loss = 0.0
+    num_batches = 0
     with torch.no_grad():
         for data in dataloader:
             input_array, target = data
             input_array = input_array.to(device)
             target = target.to(device)
             
+            # Check input validity
+            if not torch.isfinite(input_array).all() or not torch.isfinite(target).all():
+                print(f"Warning: NaN detected in evaluation inputs")
+                continue
+
             outputs = network(input_array)
             
-            loss += loss_fn(outputs, target).item()
+            # Clamp outputs to valid range
+            outputs = torch.clamp(outputs, 0.0, 1.0)
             
-        loss = loss / len(dataloader)
+            # Check for NaN in outputs
+            if not torch.isfinite(outputs).all():
+                print(f"Warning: NaN detected in model outputs during evaluation")
+                continue
             
+            batch_loss = loss_fn(outputs, target).item()
+            
+            # Check for NaN in loss
+            if not np.isfinite(batch_loss):
+                print(f"Warning: NaN detected in loss during evaluation")
+                continue
+            
+            loss += batch_loss
+            num_batches += 1
+        
+        if num_batches == 0:
+            print("Error: No valid batches in evaluation")
+            if was_training:
+                network.train()
+            return float('nan'), float('nan')
+        
+        loss = loss / num_batches
+        rmse = 255.0 * np.sqrt(loss)
+
+        # Restore training mode
+        if was_training:
             network.train()
 
-        return loss, 255.0 * np.sqrt(loss)
+        return loss, rmse
 
 
 def read_compressed_file(file_path: str):
@@ -122,6 +158,13 @@ def create_predictions(model_config, state_dict_path, testset_path, device, save
 
     predictions = np.stack(predictions, axis=0)
 
+    # Handle NaN and inf values before conversion
+    nan_mask = ~np.isfinite(predictions)
+    if nan_mask.any():
+        nan_count = nan_mask.sum()
+        print(f"Warning: Found {nan_count} NaN/Inf values in predictions. Replacing with 0.")
+        predictions = np.nan_to_num(predictions, nan=0.0, posinf=1.0, neginf=0.0)
+    
     predictions = (np.clip(predictions, 0, 1) * 255.0).astype(np.uint8)
 
     data = {