def main(argv):
trainer = Trainer()
trainer.run()
lr = 0.001
momentum = 0.9
batch_size = 5
start_epoch = 1
end_epoch = 1
data_root = ''
# Preprocessing
transforms = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
datasets = dset.ImageFolder('../images/', transform=transforms)
train_loader = torch.utils.data.DataLoader(datasets,
batch_size=batch_size,
shuffle=True)
# Model Setting
model = models.vgg19(pretrained=True)
model.fc = nn.Linear(1000, num_classes)
if args.use_cuda:
model = model.cuda()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum)
trainer = Trainer(optimizer, criterion, model, 10, train_loader, args.use_cuda)
trained_model = trainer.run()
torch.save(trained_model.state_dict(), '../weights/vgg_weight.pth')