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import os
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms, models
from torch.utils.data import DataLoader
from tqdm import tqdm
from pathlib import Path
# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Paths
root_dir = Path("/oxford_pet_dataset")
train_dir = root_dir / "train"
val_dir = root_dir / "val"
# Parameters
BATCH_SIZE = 32
EPOCHS = 10
NUM_CLASSES = len(os.listdir(train_dir)) # Assumes one folder per class
# Transforms
train_transforms = transforms.Compose([
transforms.Resize((224, 224)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.5]*3, [0.5]*3)
])
val_transforms = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize([0.5]*3, [0.5]*3)
])
# Datasets
train_dataset = datasets.ImageFolder(train_dir, transform=train_transforms)
val_dataset = datasets.ImageFolder(val_dir, transform=val_transforms)
# DataLoaders
train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE)
# Model
model = models.resnet18(pretrained=True)
model.fc = nn.Linear(model.fc.in_features, NUM_CLASSES)
model = model.to(device)
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=1e-4)
# Training loop
for epoch in range(EPOCHS):
model.train()
train_loss, train_correct = 0.0, 0
for inputs, labels in tqdm(train_loader, desc=f"Epoch {epoch+1}/{EPOCHS} [Train]"):
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
train_loss += loss.item() * inputs.size(0)
train_correct += (outputs.argmax(1) == labels).sum().item()
train_acc = train_correct / len(train_dataset)
# Validation
model.eval()
val_loss, val_correct = 0.0, 0
with torch.no_grad():
for inputs, labels in tqdm(val_loader, desc=f"Epoch {epoch+1}/{EPOCHS} [Val]"):
inputs, labels = inputs.to(device), labels.to(device)
outputs = model(inputs)
loss = criterion(outputs, labels)
val_loss += loss.item() * inputs.size(0)
val_correct += (outputs.argmax(1) == labels).sum().item()
val_acc = val_correct / len(val_dataset)
print(f"Epoch {epoch+1}: Train Acc: {train_acc:.4f}, Val Acc: {val_acc:.4f}")
# Save model
torch.save(model.state_dict(), "pet_classifier.pth")
print("Model saved as pet_classifier.pth")
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