remove pycache

image-inpainting/.gitignore

@@ -3,5 +3,3 @@ data/*
 *.jpg
 *.pt
 __pycache__/
-runtime_predictions.npz
-results/runtime_config.json

image-inpainting/results/runtime_config.json

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

image-inpainting/src/architecture.py

@@ -7,7 +7,6 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-import math
 
 
 def init_weights(m):
@@ -53,13 +52,10 @@ class EfficientChannelAttention(nn.Module):
     def forward(self, x):
         # Global pooling
         y = self.avg_pool(x)
-        # 1D convolution on channel dimension - add safety checks
+        # 1D convolution on channel dimension
-        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):
@@ -90,37 +86,6 @@ class EfficientAttention(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, dilation=1, dropout=0.0, separable=False):
@@ -133,8 +98,7 @@ class ConvBlock(nn.Module):
             )
         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)
-        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()
     
@@ -186,7 +150,7 @@ class ResidualConvBlock(nn.Module):
 
 class DownBlock(nn.Module):
     """Enhanced downsampling block with dense and residual connections"""
-    def __init__(self, in_channels, out_channels, dropout=0.1, use_attention=True, use_dense=False, use_self_attention=False):
+    def __init__(self, in_channels, out_channels, dropout=0.1, use_attention=True, use_dense=False):
         super().__init__()
         self.conv1 = ConvBlock(in_channels, out_channels, dropout=dropout, separable=True)
         self.conv2 = ConvBlock(out_channels, out_channels, dropout=dropout)
@@ -195,20 +159,18 @@ class DownBlock(nn.Module):
         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.dense(x)
-        x = self.attention(x)
+        skip = self.attention(x)
-        skip = self.self_attention(x)
         return self.pool(skip), skip
 
 class UpBlock(nn.Module):
     """Enhanced upsampling block with gated skip connections"""
-    def __init__(self, in_channels, out_channels, dropout=0.1, use_attention=True, use_dense=False, use_self_attention=False):
+    def __init__(self, in_channels, out_channels, dropout=0.1, use_attention=True, use_dense=False):
         super().__init__()
         self.up = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=2, stride=2)
         # Skip connection has in_channels, upsampled has out_channels
@@ -221,7 +183,6 @@ class UpBlock(nn.Module):
         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)
@@ -233,7 +194,6 @@ class UpBlock(nn.Module):
         x = self.conv2(x)
         x = self.dense(x)
         x = self.attention(x)
-        x = self.self_attention(x)
         return x
 
 class MyModel(nn.Module):
@@ -241,14 +201,11 @@ class MyModel(nn.Module):
     def __init__(self, n_in_channels: int, base_channels: int = 64, dropout: float = 0.1):
         super().__init__()
         
-        # Separate mask processing for better feature extraction
         # Separate mask processing for better feature extraction
         self.mask_conv = nn.Sequential(
             nn.Conv2d(1, base_channels // 4, 3, padding=1),
-            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)
         )
         
@@ -261,29 +218,28 @@ class MyModel(nn.Module):
         # 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.BatchNorm2d(base_channels),
             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.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, use_attention=True, use_dense=True)
+        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, use_self_attention=True)
+        self.down3 = DownBlock(base_channels * 4, base_channels * 8, dropout=dropout, use_attention=True, use_dense=True)
         
-        # Enhanced bottleneck with multi-scale features, dense connections, and self-attention
+        # 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=12, num_layers=3, dropout=dropout),
+            DenseBlock(base_channels * 8, growth_rate=10, 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.up1 = UpBlock(base_channels * 8, base_channels * 4, dropout=dropout, use_attention=True, use_dense=True)
-        self.up2 = UpBlock(base_channels * 4, base_channels * 2, dropout=dropout, use_attention=True, use_dense=True)
+        self.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, use_attention=False, use_dense=False)
+        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(
@@ -313,58 +269,25 @@ class MyModel(nn.Module):
         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)
-        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(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, skip3)
-        if not torch.isfinite(x).all():
-            x = torch.nan_to_num(x, nan=0.0, posinf=1.0, neginf=-1.0)
-        
         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:]:
@@ -373,19 +296,12 @@ class MyModel(nn.Module):
         # Multi-scale fusion with initial features
         x = torch.cat([x, x0], dim=1)
         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)
         
         # 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

@@ -10,8 +10,7 @@ import numpy as np
 import random
 import glob
 import os
-from PIL import Image, ImageEnhance, ImageFilter
+from PIL import Image, ImageEnhance
-from scipy.ndimage import gaussian_filter, map_coordinates
 
 IMAGE_DIMENSION = 100
 
@@ -38,43 +37,7 @@ 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:
+def augment_image(img: Image, strength: float = 0.7) -> Image:
-    """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:
@@ -84,62 +47,26 @@ def augment_image(img: Image, strength: float = 0.8) -> Image:
     if random.random() > 0.5:
         img = img.transpose(Image.FLIP_TOP_BOTTOM)
     
-    # Random rotation (90, 180, 270 degrees, or small angles)
+    # Random rotation (90, 180, 270 degrees)
     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
+    # Color augmentation - more aggressive for long training
-    if random.random() > 0.3:
+    rand = random.random()
+    if rand > 0.75:
         # Brightness
-        factor = 1.0 + random.uniform(-0.3, 0.3) * strength
+        factor = 1.0 + random.uniform(-0.2, 0.2) * strength
         img = ImageEnhance.Brightness(img).enhance(factor)
-    
+    elif rand > 0.5:
-    if random.random() > 0.3:
         # Contrast
-        factor = 1.0 + random.uniform(-0.3, 0.3) * strength
+        factor = 1.0 + random.uniform(-0.2, 0.2) * strength
         img = ImageEnhance.Contrast(img).enhance(factor)
-    
+    elif rand > 0.25:
-    if random.random() > 0.3:
         # Saturation
-        factor = 1.0 + random.uniform(-0.25, 0.25) * strength
+        factor = 1.0 + random.uniform(-0.15, 0.15) * 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):
@@ -147,7 +74,7 @@ class ImageDataset(torch.utils.data.Dataset):
     Dataset class for loading images from a folder with augmentation support
     """
 
-    def __init__(self, datafolder: str, augment: bool = True, augment_strength: float = 0.8):
+    def __init__(self, datafolder: str, augment: bool = True, augment_strength: float = 0.7):
         self.imagefiles = sorted(glob.glob(os.path.join(datafolder,"**","*.jpg"),recursive=True))
         self.augment = augment
         self.augment_strength = augment_strength

image-inpainting/src/main.py

@@ -17,74 +17,46 @@ if __name__ == '__main__':
 
     config_dict['seed'] = 42
     config_dict['testset_ratio'] = 0.1
-    config_dict['validset_ratio'] = 0.05
+    config_dict['validset_ratio'] = 0.1
     # 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'] = 5e-4  # Lower initial LR with warmup
+    config_dict['learningrate'] = 3e-4  # More stable learning rate
-    config_dict['weight_decay'] = 5e-5  # Reduced for more capacity
+    config_dict['weight_decay'] = 1e-4  # Proper regularization
-    config_dict['n_updates'] = 12000  # Extended training for better convergence
+    config_dict['n_updates'] = 40000  # Extended training
-    config_dict['batchsize'] = 64  # Reduced for larger model and mixed precision
+    config_dict['batchsize'] = 96  # Maximize batch size for better gradients
-    config_dict['early_stopping_patience'] = 20  # More patience for complex model
+    config_dict['early_stopping_patience'] = 20  # More patience for convergence
     config_dict['use_wandb'] = False
 
-    config_dict['print_train_stats_at'] = 10
+    config_dict['print_train_stats_at'] = 50
     config_dict['print_stats_at'] = 200
     config_dict['plot_at'] = 500
-    config_dict['validate_at'] = 250  # More frequent validation
+    config_dict['validate_at'] = 500  # Regular validation
 
     network_config = {
         'n_in_channels': 4,
-        'base_channels': 52,  # Increased capacity for better feature extraction
+        'base_channels': 64,
-        'dropout': 0.15  # Slightly higher dropout for regularization
+        'dropout': 0.1  # Proper 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")
-    final_save_path = os.path.join(config_dict['results_path'], "testset", "tikaiz")
+    save_path = os.path.join(config_dict['results_path'], "testset", "tikaiz")
-    final_plot_path = os.path.join(config_dict['results_path'], "testset", "plots")
+    plot_path = os.path.join(config_dict['results_path'], "testset", "plots")
-    os.makedirs(final_plot_path, exist_ok=True)
+    os.makedirs(plot_path, exist_ok=True)
-    for name in os.listdir(final_plot_path):
+    for name in os.listdir(plot_path):
-        p = os.path.join(final_plot_path, name)
+        p = os.path.join(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, final_save_path, final_plot_path, plot_at=20, rmse_value=rmse_value)
+    create_predictions(config_dict['network_config'], state_dict_path, testset_path, None, save_path, plot_path, plot_at=20, rmse_value=rmse_value)

image-inpainting/src/train.py

@@ -10,11 +10,9 @@ 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
-import json
-from torchvision import models
-import torch.nn.functional as F
 
 from torch.utils.data import DataLoader
 from torch.utils.data import Subset
@@ -22,172 +20,31 @@ from torch.utils.data import Subset
 import wandb
 
 
-def load_runtime_config(config_path, current_params):
+class EnhancedRMSELoss(nn.Module):
-    """Load runtime configuration from JSON file and update parameters"""
+    """Enhanced RMSE loss with edge weighting for sharper predictions"""
-    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 = nn.MSELoss()
     
     def forward(self, pred, target):
-        mse = self.mse(pred, target)
+        # Compute per-pixel squared error
-        # Larger epsilon for numerical stability
+        se = (pred - target) ** 2
-        rmse = torch.sqrt(mse + 1e-6)
+        
+        # Weight edges more heavily for sharper results
+        edge_weight = 1.0 + 0.3 * torch.abs(target[:, :, 1:, :] - target[:, :, :-1, :]).mean(dim=1, keepdim=True)
+        edge_weight = F.pad(edge_weight, (0, 0, 0, 1), value=1.0)
+        
+        # Apply weighting
+        weighted_se = se * edge_weight
+        
+        # Compute RMSE
+        mse = weighted_se.mean()
+        rmse = torch.sqrt(mse + 1e-8)
         return rmse
 
 
-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, testset_path=None, save_path=None, plot_path_predictions=None):
+          network_config: dict):
     np.random.seed(seed=seed)
     torch.manual_seed(seed=seed)
 
@@ -200,10 +57,7 @@ def train(seed, testset_ratio, validset_ratio, data_path, results_path, early_st
     
     # Enable mixed precision training for memory efficiency
     use_amp = torch.cuda.is_available()
-    if use_amp:
+    scaler = torch.amp.GradScaler('cuda') if use_amp else None
-        scaler = torch.amp.GradScaler('cuda', init_scale=2048.0, growth_interval=100)
-    else:
-        scaler = None
 
     if use_wandb:
         wandb.login()
@@ -248,29 +102,18 @@ def train(seed, testset_ratio, validset_ratio, data_path, results_path, early_st
     network.to(device)
     network.train()
 
-    # defining the loss - Optimized for RMSE evaluation
+    # defining the loss - Enhanced RMSE for sharper predictions
-    # Set use_perceptual=False for pure MSE training, or keep True with 5% weight for texture quality
+    rmse_loss = EnhancedRMSELoss().to(device)
-    # 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, betas=(0.9, 0.999))
+    optimizer = torch.optim.AdamW(network.parameters(), lr=learningrate, weight_decay=weight_decay, betas=(0.9, 0.999), eps=1e-8)
     
-    # Learning rate warmup
+    # Cosine annealing with warm restarts for gradual learning rate decay
-    warmup_steps = min(1000, n_updates // 10)
+    scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(
-    
-    # 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)
 
@@ -281,31 +124,7 @@ 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:
 
@@ -313,79 +132,6 @@ 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]}')
 
@@ -395,100 +141,33 @@ def train(seed, testset_ratio, validset_ratio, data_path, results_path, early_st
             if use_amp:
                 with torch.amp.autocast('cuda'):
                     output = network(input)
-                    total_loss, perceptual, mse, rmse = combined_loss(output, target)
+                    loss = rmse_loss(output, target)
                 
-                # Check for NaN before backward
+                scaler.scale(loss).backward()
-                if not torch.isfinite(total_loss):
-                    continue
                 
-                scaler.scale(total_loss).backward()
+                # Gradient clipping for training stability
-                
-                # Unscale and check gradients
                 scaler.unscale_(optimizer)
-                
+                torch.nn.utils.clip_grad_norm_(network.parameters(), max_norm=1.0)
-                # 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)
+                loss = rmse_loss(output, target)
+                loss.backward()
                 
-                # Check for NaN before backward
+                # Gradient clipping for training stability
-                if not torch.isfinite(total_loss):
+                torch.nn.utils.clip_grad_norm_(network.parameters(), max_norm=1.0)
-                    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()
             
-            # Apply learning rate scheduling with warmup
+            scheduler.step()
-            lr_scale = get_lr_scale(i)
-            for param_group in optimizer.param_groups:
-                param_group['lr'] = learningrate * lr_scale
 
-            if i >= warmup_steps:
+            loss_list.append(loss.item())
-                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({
+                wandb.log({"training/loss_per_batch": loss.item()}, step=i)
-                    "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:

image-inpainting/src/utils.py

@@ -18,14 +18,12 @@ def plot(inputs, targets, predictions, path, update):
     os.makedirs(path, exist_ok=True)
     fig, axes = plt.subplots(ncols=3, figsize=(15, 5))
 
-    # Only plot up to min(5, batch_size) images
+    for i in range(5):
-    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, 0:3, :, :]
+            img = data[i:i + 1:, 0:3, :, :]
+            img = np.squeeze(img)
             img = np.transpose(img, (1, 2, 0))
             img = np.clip(img, 0, 1)
             ax.imshow(img)
@@ -56,58 +54,24 @@ 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):
-    """Returns MSE and RMSE of the model on the provided dataloader"""
+    """Returnse 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)
 
-            # Clamp outputs to valid range
+            loss += loss_fn(outputs, target).item()
-            outputs = torch.clamp(outputs, 0.0, 1.0)
 
-            # Check for NaN in outputs
+        loss = loss / len(dataloader)
-            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, rmse
+        return loss, 255.0 * np.sqrt(loss)
 
 
 def read_compressed_file(file_path: str):
@@ -158,13 +122,6 @@ 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 = {