correct += (pred == labels).sum().item()
print('Test set: Average loss: {:.4f}, Accuracy:{}/{} ({:.2f}%)'.format(
test_loss, correct, len(test_loader.dataset), 100. * correct/len(test_loader.dataset)))
outfile.write('Test set: Average loss: {:.4f}, Accuracy:{}/{} ({:.2f}%)'.format(
test_loss, correct, len(test_loader.dataset), 100. * correct/len(test_loader.dataset)))
outfile.write('\n')
return 100. * correct/len(test_loader.dataset)
transform = tr.Compose([
tr.RandomHorizontalFlip(p=0.5),
#tr.ToTensor()
])
train_dataset = dataloader.RetinopathyLoader('data/', 'train', transform=transform)
test_dataset = dataloader.RetinopathyLoader('data/', 'test')
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=False)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)
#import sys
if __name__ == '__main__':
# start training
#model = Resnet18()
print(resnet_model)
train_acc, test_acc = train(resnet_model, optimizer)
torch.save({ 'state_dict': resnet_model.state_dict()}, 'resnet50_with_pretrain_5.tar')
help="weight_decay(L2 penalty)")
parser.add_argument('--epochs',
default=10,
type=int,
help="number of total epochs to run")
parser.add_argument('--lr',
'--learning-rate',
default=1e-3,
type=float,
help="initial learning rate")
parser.add_argument('--checkpoint', type=str, help="name of checkpoint file")
parser.add_argument('--resume', type=str, help="name of checkpoint file")
args = parser.parse_args()
# DataLoader
train_dataset = dataloader.RetinopathyLoader("./data/", 'train')
train_dataloader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True)
test_dataset = dataloader.RetinopathyLoader("./data/", 'test')
test_dataloader = DataLoader(test_dataset, batch_size=args.batch_size)
# Model
model = None
if args.model == "ResNet18":
print('Use ResNet18')
# model = resnet.resnet18(num_classes=5)
model = torchvision.models.resnet18()
num_ftrs = model.fc.in_features
model.avgpool = nn.AdaptiveAvgPool2d(1)
model.fc = nn.Linear(num_ftrs, num_classes)
print('Test set: Average loss: {:.4f}, Accuracy:{}/{} ({:.2f}%)'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
outfile.write(
'Test set: Average loss: {:.4f}, Accuracy:{}/{} ({:.2f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
cm.plot_confusion_matrix(np.array(preds),
np.array(test_dataset.label),
np.array(['0', '1', '2', '3', '4']),
normalize=True)
return 100. * correct / len(test_loader.dataset)
train_dataset = dataloader.RetinopathyLoader('data/', 'train')
test_dataset = dataloader.RetinopathyLoader('data/', 'test')
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=False)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=batch_size,
shuffle=False)
load_model = models.ResNet().to(device)
#import sys
if __name__ == '__main__':
# start training