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import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms, datasets
from torch.utils.data import DataLoader
import segmentation_models_pytorch as smp
# Define U-Net model for cloth fold segmentation
class ClothFoldUNet(nn.Module):
def __init__(self):
super(ClothFoldUNet, self).__init__()
self.model = smp.Unet(
encoder_name="resnet34", # Pre-trained backbone
encoder_weights="imagenet",
in_channels=3,
classes=1, # Single channel output for segmentation
)
def forward(self, x):
return self.model(x)
# Load dataset (placeholder, replace with real dataset)
def get_dataloader(batch_size=8):
transform = transforms.Compose([
transforms.Resize((256, 256)),
transforms.ToTensor(),
])
dataset = datasets.FakeData(transform=transform)
return DataLoader(dataset, batch_size=batch_size, shuffle=True)
# Train function
def train_model():
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = ClothFoldUNet().to(device)
optimizer = optim.Adam(model.parameters(), lr=1e-4)
criterion = nn.BCEWithLogitsLoss()
dataloader = get_dataloader()
for epoch in range(10): # Placeholder epoch count
for images, _ in dataloader:
images = images.to(device)
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, torch.ones_like(outputs)) # Placeholder loss
loss.backward()
optimizer.step()
print(f"Epoch {epoch+1}: Loss {loss.item():.4f}")
# Run training
if __name__ == "__main__":
train_model() |