def main(): start_time = time() net = Net().to(setting.device) net.train() print('The modle has been initialized.') # define loss and optimizer criterion = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate) train_dataloader = data_preprocess.get_train_dataloader() for epoch in tqdm(range(num_epochs)): start = time() # for train accuracy # total = setting.train_img_nums # correct = 0 for batch_idx, (imgs, labels) in enumerate(train_dataloader): # imgs = Variable(imgs) # label = Variable(labels) imgs, labels = imgs.to(setting.device), labels.to(setting.device) labels = labels.long() labels_ohe_predict = net(imgs) loss = 0 for i in range(setting.char_num): one_label = labels[:, i * setting.pool_length:(i + 1) * setting.pool_length] one_class = one_label.argmax(dim=1) one_predict_label = labels_ohe_predict[:, i * setting.pool_length: (i + 1) * setting.pool_length] one_loss = criterion(one_predict_label, one_class) loss += one_loss optimizer.zero_grad() loss.backward() optimizer.step() # for single in range(labels_ohe_predict.shape[0]): # single_labels_ohe_predict = labels_ohe_predict[single, :] # predict_label = '' # # get predict_label # for slice in range(setting.char_num): # char = ohe.num2char[np.argmax( # single_labels_ohe_predict[slice*setting.pool_length:(slice+1)*setting.pool_length].cpu().data.numpy())] # predict_label += char # # get true label # true_label = ohe.decode(labels[single, :].cpu().numpy()) # if predict_label == true_label: # correct += 1 end = time() print('epoch: {}, time: {:.2f}s loss: {:.04}'.format( epoch, end - start, loss.item())) # print('epoch: {}, time: {:.2f}s loss: {:.04} accuracy: {}/{} -- {:.4f}'.format( # epoch, end-start, loss.item(), correct, total, correct/total)) torch.save(net.state_dict(), './model.pt') finnal_time = time() print('End at {}, cost {:.0f}s'.format(finnal_time, finnal_time - start_time))
def train(loaders, save_path): """returns trained model""" # Initialize custom defined cnn model = Net() use_cuda = torch.cuda.is_available() if use_cuda: model.cuda() # cross entropy loss for classification task criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=config.lr) # initialize tracker for minimum validation loss valid_loss_min = np.Inf n_epochs = config.n_epochs for epoch in range(1, n_epochs + 1): # initialize variables to monitor training and validation loss train_loss = 0.0 valid_loss = 0.0 model.train() for batch_idx, (data, target) in enumerate(loaders['train']): # move to GPU if use_cuda: data, target = data.cuda(), target.cuda() optimizer.zero_grad() output = model(data) loss = criterion(output, target) loss.backward() optimizer.step() # average training loss train_loss += (1 / (batch_idx + 1)) * (loss.data - train_loss) # vaidation model.eval() for batch_idx, (data, target) in enumerate(loaders['valid']): # move to GPU if use_cuda: data, target = data.cuda(), target.cuda() ## update the average validation loss output = model(data) loss = criterion(output, target) valid_loss += (1 / (batch_idx + 1)) * (loss.data - valid_loss) # print training/validation statistics print('Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f}'. format(epoch, train_loss, valid_loss)) # save the model if validation loss has decreased if valid_loss <= valid_loss_min: torch.save(model.state_dict(), save_path) # Updating the validation loss minimum valid_loss_min = valid_loss # return trained model return model
help='whether to use dropout in network') parser.add_argument('--epochs', default=20, help='Number of Epochs') parser.add_argument('--lr', default=0.01, help='Default LR set to one') return parser.parse_args() if __name__ == '__main__': args = parse() # is cuda available? use_cuda = not args.no_cuda and torch.cuda.is_available() device = torch.device("cuda" if use_cuda else "cpu") # load the data train_loader, test_loader = LoadData().load_data() model = Net(dropout=args.dropout).to(device) logging.debug("Model Summary {}".format( summary(model, input_size=(3, 32, 32)))) model = Net().to(device) optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9, nesterov=False) # scheduler = StepLR(optimizer, step_size=8, gamma=0.1) for epoch in range(args.epochs): # print('Epoch:', epoch+1,'LR:', scheduler.get_lr()[0]) model.train(model, device, train_loader, optimizer) model.test(model, device, test_loader)