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import os
import torch
import torch.nn as nn
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from torchvision.transforms import ToTensor
import time
from datetime import datetime
import multiprocessing
def get_optimal_threads():
"""Calculate optimal number of threads based on CPU cores"""
return max(1, multiprocessing.cpu_count() - 1) # Leave one core free for system
# Simple UNet-style denoising model
class DenoisingModel(nn.Module):
def __init__(self):
super(DenoisingModel, self).__init__()
# Encoder
self.enc1 = nn.Sequential(
nn.Conv2d(3, 64, 3, padding=1),
nn.ReLU(),
nn.Conv2d(64, 64, 3, padding=1),
nn.ReLU()
)
self.pool1 = nn.MaxPool2d(2, 2)
# Decoder
self.up1 = nn.ConvTranspose2d(64, 64, 2, stride=2)
self.dec1 = nn.Sequential(
nn.Conv2d(64, 64, 3, padding=1),
nn.ReLU(),
nn.Conv2d(64, 3, 3, padding=1)
)
def forward(self, x):
# Encoder
e1 = self.enc1(x)
p1 = self.pool1(e1)
# Decoder
u1 = self.up1(p1)
d1 = self.dec1(u1)
return d1
class DenoiseDataset(Dataset):
def __init__(self, noisy_folder, target_folder, patch_size=256):
self.noisy_folder = noisy_folder
self.target_folder = target_folder
self.patch_size = patch_size
self.image_pairs = [
(os.path.join(noisy_folder, f), os.path.join(target_folder, f.replace("_noisy", "_target")))
for f in os.listdir(noisy_folder) if "_noisy" in f
]
self.transform = ToTensor()
print(f"Dataset initialization:")
print(f"- Noisy folder: {noisy_folder}")
print(f"- Target folder: {target_folder}")
print(f"- Patch size: {patch_size}")
print(f"- Found {len(self.image_pairs)} image pairs")
if not self.image_pairs:
raise ValueError("No image pairs found. Check if noisy and target images are correctly named.")
# Precalculate number of patches per image for better performance
self.patches_per_image = {}
for noisy_path, _ in self.image_pairs:
try:
self.patches_per_image[noisy_path] = self._get_num_patches_per_image(noisy_path)
except Exception as e:
print(f"Error calculating patches for {noisy_path}: {e}. Skipping this image pair.")
self.image_pairs = [(n, t) for n, t in self.image_pairs if n != noisy_path]
self.total_patches = sum(self.patches_per_image.values())
def __len__(self):
return self.total_patches
def __getitem__(self, idx):
image_idx = 0
cumulative_patches = 0
for i, (noisy_path, _) in enumerate(self.image_pairs):
num_patches = self.patches_per_image[noisy_path]
if cumulative_patches + num_patches > idx:
image_idx = i
break
cumulative_patches += num_patches
patch_idx = idx - cumulative_patches
noisy_path, target_path = self.image_pairs[image_idx]
try:
noisy_image = self._load_image(noisy_path)
target_image = self._load_image(target_path)
except Exception as e:
print(f"Error loading image pair ({noisy_path}, {target_path}): {e}. Returning default values.")
return torch.zeros((3, self.patch_size, self.patch_size)), torch.zeros((3, self.patch_size, self.patch_size))
try:
noisy_patch = self._get_patch(noisy_image, patch_idx)
target_patch = self._get_patch(target_image, patch_idx)
except Exception as e:
print(f"Error getting patch from image pair ({noisy_path}, {target_path}): {e}. Returning default values.")
return torch.zeros((3, self.patch_size, self.patch_size)), torch.zeros((3, self.patch_size, self.patch_size))
return noisy_patch, target_patch
def _load_image(self, image_path):
try:
image = Image.open(image_path).convert("RGB")
return self.transform(image)
except Exception as e:
raise Exception(f"Error loading image {image_path}: {e}")
def _get_num_patches_per_image(self, image_path):
try:
image = Image.open(image_path)
width, height = image.size
num_patches = (width // self.patch_size) * (height // self.patch_size)
return num_patches
except Exception as e:
raise Exception(f"Error calculating patches for {image_path}: {e}")
def _get_patch(self, image, patch_idx):
width, height = image.shape[2], image.shape[1]
patches_per_row = width // self.patch_size
row = patch_idx // patches_per_row
col = patch_idx % patches_per_row
x_start = col * self.patch_size
y_start = row * self.patch_size
return image[:, y_start:y_start+self.patch_size, x_start:x_start+self.patch_size]
def train_model(noisy_dir, target_dir, epochs, batch_size, learning_rate, save_interval, num_workers):
# Set up CUDA if available
if torch.cuda.is_available():
torch.backends.cudnn.benchmark = True # Enable cuDNN auto-tuner
device = torch.device("cuda")
print(f"\nUsing GPU: {torch.cuda.get_device_name(0)}")
print(f"CUDA version: {torch.version.cuda}")
else:
device = torch.device("cpu")
print("\nNo GPU detected, using CPU")
# Create output directory for models
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
output_dir = f"model_checkpoints_{timestamp}"
os.makedirs(output_dir, exist_ok=True)
print("\nTraining Configuration:")
print(f"- Number of epochs: {epochs}")
print(f"- Batch size: {batch_size}")
print(f"- Learning rate: {learning_rate}")
print(f"- Number of worker threads: {num_workers}")
print(f"- Model checkpoint directory: {output_dir}")
# Initialize dataset and dataloader with specified number of workers
dataset = DenoiseDataset(noisy_dir, target_dir)
dataloader = DataLoader(
dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
pin_memory=True if torch.cuda.is_available() else False
)
# Initialize model, loss function, and optimizer
model = DenoisingModel().to(device)
if torch.cuda.device_count() > 1:
print(f"Using {torch.cuda.device_count()} GPUs!")
model = nn.DataParallel(model)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# Training loop
total_batches = len(dataloader)
start_time = time.time()
print("\nStarting training...")
for epoch in range(epochs):
epoch_loss = 0.0
for batch_idx, (noisy_patches, target_patches) in enumerate(dataloader):
# Move data to device
noisy_patches = noisy_patches.to(device, non_blocking=True)
target_patches = target_patches.to(device, non_blocking=True)
# Forward pass
outputs = model(noisy_patches)
loss = criterion(outputs, target_patches)
# Backward pass and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Update epoch loss
epoch_loss += loss.item()
# Print progress
if (batch_idx + 1) % 100 == 0:
elapsed_time = time.time() - start_time
print(f"Epoch [{epoch+1}/{epochs}], "
f"Batch [{batch_idx+1}/{total_batches}], "
f"Loss: {loss.item():.6f}, "
f"Time: {elapsed_time:.2f}s")
# Save model checkpoint
if (batch_idx + 1) % save_interval == 0:
checkpoint_path = os.path.join(output_dir,
f"denoising_model_epoch{epoch+1}_batch{batch_idx+1}.pth")
torch.save({
'epoch': epoch,
'batch': batch_idx,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': loss.item(),
}, checkpoint_path)
print(f"\nCheckpoint saved: {checkpoint_path}")
# End of epoch summary
avg_epoch_loss = epoch_loss / total_batches
print(f"\nEpoch [{epoch+1}/{epochs}] completed. "
f"Average loss: {avg_epoch_loss:.6f}")
# Save epoch checkpoint
checkpoint_path = os.path.join(output_dir, f"denoising_model_epoch{epoch+1}.pth")
torch.save({
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': avg_epoch_loss,
}, checkpoint_path)
print(f"Epoch checkpoint saved: {checkpoint_path}")
print("\nTraining completed!")
print(f"Total training time: {time.time() - start_time:.2f} seconds")
# Save final model
final_model_path = os.path.join(output_dir, "denoising_model_final.pth")
torch.save(model.state_dict(), final_model_path)
print(f"Final model saved: {final_model_path}")
def main():
noisy_dir = 'noisy_images'
target_dir = 'target_images'
epochs = 10
batch_size = 4
learning_rate = 0.001
save_interval = 1000
num_workers = get_optimal_threads()
train_model(noisy_dir, target_dir, epochs, batch_size, learning_rate, save_interval, num_workers)
if __name__ == "__main__":
main() |