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improve baseline

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This commit is contained in:
Tim Kainz
2026-01-24 16:15:34 +01:00
parent 3b1d3c0497
commit 57026695d4
10 changed files with 228 additions and 63 deletions
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3
image-inpainting/.gitignore vendored
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@@ -1,3 +1,4 @@
data/*
*.zip
*.jpg
*.jpg
*.pt

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image-inpainting/results/best_model.pt
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image-inpainting/results/testset/my_submission_name.npz → image-inpainting/results/testset/tikaiz-29.3653.npz
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image-inpainting/src/__pycache__/architecture.cpython-313.pyc
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image-inpainting/src/__pycache__/train.cpython-313.pyc
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image-inpainting/src/__pycache__/utils.cpython-313.pyc
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201
image-inpainting/src/architecture.py
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@@ -6,78 +6,190 @@
import torch
import torch.nn as nn
import torch.nn.functional as F
def init_weights(m):
"""Initialize weights using Kaiming initialization for better training"""
if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
class ChannelAttention(nn.Module):
"""Channel attention module (squeeze-and-excitation style)"""
def __init__(self, channels, reduction=16):
super().__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
reduced = max(channels // reduction, 8)
self.fc = nn.Sequential(
nn.Conv2d(channels, reduced, 1, bias=False),
nn.ReLU(inplace=True),
nn.Conv2d(reduced, channels, 1, bias=False)
)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
avg_out = self.fc(self.avg_pool(x))
max_out = self.fc(self.max_pool(x))
return x * self.sigmoid(avg_out + max_out)
class SpatialAttention(nn.Module):
"""Spatial attention module"""
def __init__(self, kernel_size=7):
super().__init__()
self.conv = nn.Conv2d(2, 1, kernel_size, padding=kernel_size // 2, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
avg_out = torch.mean(x, dim=1, keepdim=True)
max_out, _ = torch.max(x, dim=1, keepdim=True)
attn = torch.cat([avg_out, max_out], dim=1)
attn = self.sigmoid(self.conv(attn))
return x * attn
class CBAM(nn.Module):
"""Convolutional Block Attention Module"""
def __init__(self, channels, reduction=16):
super().__init__()
self.channel_attn = ChannelAttention(channels, reduction)
self.spatial_attn = SpatialAttention()
def forward(self, x):
x = self.channel_attn(x)
x = self.spatial_attn(x)
return x
class ConvBlock(nn.Module):
"""Convolutional block with Conv2d -> BatchNorm -> ReLU"""
def __init__(self, in_channels, out_channels, kernel_size=3, padding=1):
"""Convolutional block with Conv2d -> BatchNorm -> LeakyReLU"""
def __init__(self, in_channels, out_channels, kernel_size=3, padding=1, dropout=0.0):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, padding=padding)
self.bn = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
self.relu = nn.LeakyReLU(0.1, inplace=True)
self.dropout = nn.Dropout2d(dropout) if dropout > 0 else nn.Identity()
def forward(self, x):
return self.relu(self.bn(self.conv(x)))
return self.dropout(self.relu(self.bn(self.conv(x))))
class ResidualConvBlock(nn.Module):
"""Residual convolutional block for better gradient flow"""
def __init__(self, channels, dropout=0.0):
super().__init__()
self.conv1 = nn.Conv2d(channels, channels, 3, padding=1)
self.bn1 = nn.BatchNorm2d(channels)
self.conv2 = nn.Conv2d(channels, channels, 3, padding=1)
self.bn2 = nn.BatchNorm2d(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.dropout(out)
out = self.bn2(self.conv2(out))
out = out + residual
return self.relu(out)
class DownBlock(nn.Module):
"""Downsampling block with two conv blocks and max pooling"""
def __init__(self, in_channels, out_channels):
"""Downsampling block with conv blocks, residual connection, attention, and max pooling"""
def __init__(self, in_channels, out_channels, dropout=0.1):
super().__init__()
self.conv1 = ConvBlock(in_channels, out_channels)
self.conv2 = ConvBlock(out_channels, out_channels)
self.conv1 = ConvBlock(in_channels, out_channels, dropout=dropout)
self.conv2 = ConvBlock(out_channels, out_channels, dropout=dropout)
self.residual = ResidualConvBlock(out_channels, dropout=dropout)
self.attention = CBAM(out_channels)
self.pool = nn.MaxPool2d(2)
def forward(self, x):
skip = self.conv2(self.conv1(x))
x = self.conv1(x)
x = self.conv2(x)
x = self.residual(x)
skip = self.attention(x)
return self.pool(skip), skip
class UpBlock(nn.Module):
"""Upsampling block with transposed conv and two conv blocks"""
def __init__(self, in_channels, out_channels):
"""Upsampling block with transposed conv, residual connection, attention, and conv blocks"""
def __init__(self, in_channels, out_channels, dropout=0.1):
super().__init__()
self.up = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=2, stride=2)
self.conv1 = ConvBlock(in_channels, out_channels) # in_channels because of concatenation
self.conv2 = ConvBlock(out_channels, out_channels)
# After concat: out_channels (from upconv) + in_channels (from skip)
self.conv1 = ConvBlock(out_channels + in_channels, out_channels, dropout=dropout)
self.conv2 = ConvBlock(out_channels, out_channels, dropout=dropout)
self.residual = ResidualConvBlock(out_channels, dropout=dropout)
self.attention = CBAM(out_channels)
def forward(self, x, skip):
x = self.up(x)
# Handle dimension mismatch by interpolating x to match skip's size
if x.shape[2:] != skip.shape[2:]:
x = nn.functional.interpolate(x, size=skip.shape[2:], mode='bilinear', align_corners=False)
x = F.interpolate(x, size=skip.shape[2:], mode='bilinear', align_corners=False)
x = torch.cat([x, skip], dim=1)
x = self.conv1(x)
x = self.conv2(x)
x = self.residual(x)
x = self.attention(x)
return x
class MyModel(nn.Module):
"""U-Net style architecture for image inpainting"""
def __init__(self, n_in_channels: int, base_channels: int = 64):
"""Improved U-Net style architecture for image inpainting with attention and residual connections"""
def __init__(self, n_in_channels: int, base_channels: int = 64, dropout: float = 0.1):
super().__init__()
# Initial convolution
self.init_conv = ConvBlock(n_in_channels, base_channels)
# Initial convolution with larger receptive field
self.init_conv = nn.Sequential(
ConvBlock(n_in_channels, base_channels, kernel_size=7, padding=3),
ConvBlock(base_channels, base_channels),
ResidualConvBlock(base_channels)
)
# Encoder (downsampling path)
self.down1 = DownBlock(base_channels, base_channels * 2)
self.down2 = DownBlock(base_channels * 2, base_channels * 4)
self.down3 = DownBlock(base_channels * 4, base_channels * 8)
self.down1 = DownBlock(base_channels, base_channels * 2, dropout=dropout)
self.down2 = DownBlock(base_channels * 2, base_channels * 4, dropout=dropout)
self.down3 = DownBlock(base_channels * 4, base_channels * 8, dropout=dropout)
self.down4 = DownBlock(base_channels * 8, base_channels * 16, dropout=dropout)
# Bottleneck
self.bottleneck1 = ConvBlock(base_channels * 8, base_channels * 16)
self.bottleneck2 = ConvBlock(base_channels * 16, base_channels * 16)
# 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)
self.up2 = UpBlock(base_channels * 8, base_channels * 4)
self.up3 = UpBlock(base_channels * 4, base_channels * 2)
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 upsampling and output
self.final_up = nn.ConvTranspose2d(base_channels * 2, base_channels, kernel_size=2, stride=2)
self.final_conv1 = ConvBlock(base_channels * 2, base_channels)
self.final_conv2 = ConvBlock(base_channels, base_channels)
# Final refinement layers
self.final_conv = nn.Sequential(
ConvBlock(base_channels * 2, base_channels),
ResidualConvBlock(base_channels),
ConvBlock(base_channels, base_channels)
)
# Output layer
self.output = nn.Conv2d(base_channels, 3, kernel_size=1)
self.sigmoid = nn.Sigmoid() # To ensure output is in [0, 1] range
# Output layer with smooth transition
self.output = nn.Sequential(
nn.Conv2d(base_channels, base_channels // 2, kernel_size=3, padding=1),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(base_channels // 2, 3, kernel_size=1),
nn.Sigmoid() # Ensure output is in [0, 1] range
)
# Apply weight initialization
self.apply(init_weights)
def forward(self, x):
# Initial convolution
@@ -87,27 +199,26 @@ class MyModel(nn.Module):
x1, skip1 = self.down1(x0)
x2, skip2 = self.down2(x1)
x3, skip3 = self.down3(x2)
x4, skip4 = self.down4(x3)
# Bottleneck
x = self.bottleneck1(x3)
x = self.bottleneck2(x)
x = self.bottleneck(x4)
# Decoder with skip connections
x = self.up1(x, skip3)
x = self.up2(x, skip2)
x = self.up3(x, skip1)
x = self.up1(x, skip4)
x = self.up2(x, skip3)
x = self.up3(x, skip2)
x = self.up4(x, skip1)
# Final layers
x = self.final_up(x)
# Handle dimension mismatch for final concatenation
if x.shape[2:] != x0.shape[2:]:
x = nn.functional.interpolate(x, size=x0.shape[2:], mode='bilinear', align_corners=False)
x = F.interpolate(x, size=x0.shape[2:], mode='bilinear', align_corners=False)
# Concatenate with initial features for better detail preservation
x = torch.cat([x, x0], dim=1)
x = self.final_conv1(x)
x = self.final_conv2(x)
x = self.final_conv(x)
# Output
x = self.output(x)
x = self.sigmoid(x)
return x

23
image-inpainting/src/main.py
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@@ -23,31 +23,32 @@ if __name__ == '__main__':
config_dict['results_path'] = os.path.join(project_root, "results")
config_dict['data_path'] = os.path.join(project_root, "data", "dataset")
config_dict['device'] = None
config_dict['learningrate'] = 5e-4 # Slightly lower for more stable training
config_dict['weight_decay'] = 1e-5 # default is 0
config_dict['n_updates'] = 200
config_dict['batchsize'] = 16 # Reduced due to larger model
config_dict['early_stopping_patience'] = 5 # More patience for complex model
config_dict['learningrate'] = 3e-4 # Optimal learning rate for AdamW
config_dict['weight_decay'] = 1e-4 # Slightly higher for better regularization
config_dict['n_updates'] = 300 # More updates for better convergence
config_dict['batchsize'] = 8 # Smaller batch for better gradient estimates
config_dict['early_stopping_patience'] = 10 # 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['plot_at'] = 100
config_dict['validate_at'] = 100
config_dict['plot_at'] = 300
config_dict['validate_at'] = 300 # Validate more frequently
network_config = {
'n_in_channels': 4,
'base_channels': 32 # Start with 32, can increase to 64 for even better results
'base_channels': 48, # Good balance between capacity and memory
'dropout': 0.1 # Regularization
}
config_dict['network_config'] = network_config
train(**config_dict)
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", "my_submission_name.npz")
save_path = os.path.join(config_dict['results_path'], "testset", "tikaiz")
plot_path = os.path.join(config_dict['results_path'], "testset", "plots")
# 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)
create_predictions(config_dict['network_config'], state_dict_path, testset_path, None, save_path, plot_path, plot_at=20, rmse_value=rmse_value)

57
image-inpainting/src/train.py
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@@ -9,15 +9,52 @@ from architecture import MyModel
from utils import plot, evaluate_model
import torch
import torch.nn as nn
import numpy as np
import os
from torch.utils.data import DataLoader
from torch.utils.data import Subset
from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts
import wandb
class CombinedLoss(nn.Module):
"""Combined loss: MSE + L1 + SSIM-like perceptual component"""
def __init__(self, mse_weight=1.0, l1_weight=0.5, edge_weight=0.1):
super().__init__()
self.mse_weight = mse_weight
self.l1_weight = l1_weight
self.edge_weight = edge_weight
self.mse = nn.MSELoss()
self.l1 = nn.L1Loss()
# Sobel filters for edge detection
sobel_x = torch.tensor([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], dtype=torch.float32).view(1, 1, 3, 3)
sobel_y = torch.tensor([[-1, -2, -1], [0, 0, 0], [1, 2, 1]], dtype=torch.float32).view(1, 1, 3, 3)
self.register_buffer('sobel_x', sobel_x.repeat(3, 1, 1, 1))
self.register_buffer('sobel_y', sobel_y.repeat(3, 1, 1, 1))
def edge_loss(self, pred, target):
"""Compute edge-aware loss using Sobel filters"""
pred_edge_x = torch.nn.functional.conv2d(pred, self.sobel_x, padding=1, groups=3)
pred_edge_y = torch.nn.functional.conv2d(pred, self.sobel_y, padding=1, groups=3)
target_edge_x = torch.nn.functional.conv2d(target, self.sobel_x, padding=1, groups=3)
target_edge_y = torch.nn.functional.conv2d(target, self.sobel_y, padding=1, groups=3)
edge_loss = self.l1(pred_edge_x, target_edge_x) + self.l1(pred_edge_y, target_edge_y)
return edge_loss
def forward(self, pred, target):
mse_loss = self.mse(pred, target)
l1_loss = self.l1(pred, target)
edge_loss = self.edge_loss(pred, target)
total_loss = self.mse_weight * mse_loss + self.l1_weight * l1_loss + self.edge_weight * edge_loss
return total_loss
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):
@@ -74,11 +111,15 @@ def train(seed, testset_ratio, validset_ratio, data_path, results_path, early_st
network.to(device)
network.train()
# defining the loss
mse_loss = torch.nn.MSELoss()
# defining the loss - combined loss for better reconstruction
combined_loss = CombinedLoss(mse_weight=1.0, l1_weight=0.5, edge_weight=0.1).to(device)
mse_loss = torch.nn.MSELoss() # Keep for evaluation
# defining the optimizer
optimizer = torch.optim.Adam(network.parameters(), lr=learningrate, weight_decay=weight_decay)
# defining the optimizer with AdamW for better weight decay handling
optimizer = torch.optim.AdamW(network.parameters(), lr=learningrate, weight_decay=weight_decay)
# Learning rate scheduler for better convergence
scheduler = CosineAnnealingWarmRestarts(optimizer, T_0=50, T_mult=2, eta_min=1e-6)
if use_wandb:
wandb.watch(network, mse_loss, log="all", log_freq=10)
@@ -105,11 +146,15 @@ def train(seed, testset_ratio, validset_ratio, data_path, results_path, early_st
output = network(input)
loss = mse_loss(output, target)
loss = combined_loss(output, target)
loss.backward()
# Gradient clipping for training stability
torch.nn.utils.clip_grad_norm_(network.parameters(), max_norm=1.0)
optimizer.step()
scheduler.step(i + len(loss_list) / len(dataloader_train))
loss_list.append(loss.item())
@@ -164,3 +209,5 @@ def train(seed, testset_ratio, validset_ratio, data_path, results_path, early_st
wandb.summary["testset/loss"] = testset_loss
wandb.summary["testset/RMSE"] = testset_rmse
wandb.finish()
return testset_rmse

7
image-inpainting/src/utils.py
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@@ -81,7 +81,7 @@ def read_compressed_file(file_path: str):
return input_arrays, known_arrays
def create_predictions(model_config, state_dict_path, testset_path, device, save_path, plot_path, plot_at=20):
def create_predictions(model_config, state_dict_path, testset_path, device, save_path, plot_path, plot_at=20, rmse_value=None):
"""
Here, one might needs to adjust the code based on the used preprocessing
"""
@@ -128,6 +128,11 @@ def create_predictions(model_config, state_dict_path, testset_path, device, save
"predictions": predictions
}
# Modify save_path to include RMSE value if provided
if rmse_value is not None:
base_path = save_path.rsplit('.npz', 1)[0]
save_path = f"{base_path}-{rmse_value:.4f}.npz"
np.savez_compressed(save_path, **data)
print(f"Predictions saved at {save_path}")