File size: 2,601 Bytes
69620d2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | 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 torch.multiprocessing import Process, set_start_method
try:
set_start_method('spawn')
except RuntimeError:
pass
# Cấu hình
BATCH_SIZE = 32
EPOCHS = 10
NUM_CLASSES = 2
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
DATA_ROOTS = [
'/home/ubuntu/vnet/TaoST/Data10kKaggle1',
'/home/ubuntu/vnet/TaoST/Data10kKaggle2'
]
MODEL_PATHS = [
'/home/ubuntu/vnet/FL/efficientnet_b0_kaggle1.pth',
'/home/ubuntu/vnet/FL/efficientnet_b0_kaggle2.pth'
]
def get_loaders(data_root):
train_transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
])
test_transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
])
train_set = datasets.ImageFolder(os.path.join(data_root, 'train'), transform=train_transform)
test_set = datasets.ImageFolder(os.path.join(data_root, 'test'), transform=test_transform)
train_loader = DataLoader(train_set, batch_size=BATCH_SIZE, shuffle=True, num_workers=4)
test_loader = DataLoader(test_set, batch_size=BATCH_SIZE, shuffle=False, num_workers=4)
return train_loader, test_loader
def train_model(data_root, model_path):
train_loader, test_loader = get_loaders(data_root)
model = models.efficientnet_b0(weights='IMAGENET1K_V1')
model.classifier[1] = nn.Linear(model.classifier[1].in_features, NUM_CLASSES)
model = model.to(DEVICE)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=1e-4)
for epoch in range(EPOCHS):
model.train()
running_loss = 0.0
for imgs, labels in train_loader:
imgs, labels = imgs.to(DEVICE), labels.to(DEVICE)
optimizer.zero_grad()
outputs = model(imgs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item() * imgs.size(0)
print(f"[{data_root}] Epoch {epoch+1}/{EPOCHS}, Loss: {running_loss/len(train_loader.dataset):.4f}")
torch.save(model.state_dict(), model_path)
print(f"Saved model to {model_path}")
def main():
p1 = Process(target=train_model, args=(DATA_ROOTS[0], MODEL_PATHS[0]))
p2 = Process(target=train_model, args=(DATA_ROOTS[1], MODEL_PATHS[1]))
p1.start()
p2.start()
p1.join()
p2.join()
if __name__ == "__main__":
main() |