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import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms, models
from torch.utils.data import DataLoader
import os
import copy
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torchvision.models import resnet50, ResNet50_Weights
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
# data transformations with augmentation
train_transform = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(10),
transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
val_test_transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
class ResNetLungCancer(nn.Module):
def __init__(self, num_classes, use_pretrained=True):
super(ResNetLungCancer, self).__init__()
if use_pretrained:
weights = ResNet50_Weights.IMAGENET1K_V1
else:
weights = None
self.resnet = resnet50(weights=weights)
num_ftrs = self.resnet.fc.in_features
self.resnet.fc = nn.Identity() # remove the final fully connected layer
self.fc = nn.Sequential(
nn.Linear(num_ftrs, 256),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(256, num_classes)
)
def forward(self, x):
x = self.resnet(x)
return self.fc(x)
# train function
def train_model(model, train_loader, valid_loader, criterion, optimizer, scheduler, num_epochs=50, device='cuda'):
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0
for epoch in range(num_epochs):
print(f'Epoch {epoch}/{num_epochs - 1}')
print('-' * 10)
for phase in ['train', 'valid']:
if phase == 'train':
model.train()
dataloader = train_loader
else:
model.eval()
dataloader = valid_loader
running_loss = 0.0
running_corrects = 0
for inputs, labels in dataloader:
inputs = inputs.to(device)
labels = labels.to(device)
optimizer.zero_grad()
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
if phase == 'train':
loss.backward()
optimizer.step()
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
epoch_loss = running_loss / len(dataloader.dataset)
epoch_acc = running_corrects.double() / len(dataloader.dataset)
print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')
if phase == 'valid':
scheduler.step(epoch_acc)
current_lr = optimizer.param_groups[0]['lr']
print(f'Learning rate: {current_lr}')
if epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = copy.deepcopy(model.state_dict())
print()
print(f'Best val Acc: {best_acc:.4f}')
model.load_state_dict(best_model_wts)
return model
# eval the model
def evaluate_model(model, test_loader, device='cuda'):
model.eval()
running_corrects = 0
with torch.no_grad():
for inputs, labels in test_loader:
inputs = inputs.to(device)
labels = labels.to(device)
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
running_corrects += torch.sum(preds == labels.data)
test_acc = running_corrects.double() / len(test_loader.dataset)
print(f'Test Acc: {test_acc:.4f}')
if __name__ == "__main__":
# device
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")
# data
data_dir = 'Processed_Data'
train_dataset = datasets.ImageFolder(os.path.join(data_dir, 'train'), transform=train_transform)
valid_dataset = datasets.ImageFolder(os.path.join(data_dir, 'valid'), transform=val_test_transform)
test_dataset = datasets.ImageFolder(os.path.join(data_dir, 'test'), transform=val_test_transform)
# dataloaders
batch_size = 32
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)
valid_loader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False, num_workers=4)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=4)
print(f"Number of training images: {len(train_dataset)}")
print(f"Number of validation images: {len(valid_dataset)}")
print(f"Number of test images: {len(test_dataset)}")
# initialize model, loss, and optimizer
num_classes = len(train_dataset.classes)
model = ResNetLungCancer(num_classes)
model = model.to(device)
criterion = nn.CrossEntropyLoss()
pretrained_params = list(model.resnet.parameters())
new_params = list(model.fc.parameters())
optimizer = optim.Adam([
{'params': pretrained_params, 'lr': 1e-5},
{'params': new_params, 'lr': 1e-4}
], weight_decay=1e-6)
scheduler = ReduceLROnPlateau(optimizer, mode='max', factor=0.5, patience=7)
# train the model
trained_model = train_model(model, train_loader, valid_loader, criterion, optimizer, scheduler, num_epochs=50, device=device)
# eval the model
evaluate_model(trained_model, test_loader, device=device)
# save the model weights
torch.save(trained_model.state_dict(), 'lung_cancer_detection_model.pth')
# save the model in ONNX format
dummy_input = torch.randn(1, 3, 224, 224).to(device)
torch.onnx.export(trained_model, dummy_input, "lung_cancer_detection_model.onnx", input_names=['input'], output_names=['output'])
print("Training completed. Model saved.") |