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| import torch | |
| from .helper_functions import define_optimizer, predict, display_train, eval_test | |
| from tqdm import tqdm | |
| import matplotlib.pyplot as plt | |
| def save_model(model, optimizer, valid_loss, epoch, path='model.pt'): | |
| torch.save({'valid_loss': valid_loss, | |
| 'model_state_dict': model.state_dict(), | |
| 'epoch': epoch + 1, | |
| 'optimizer': optimizer.state_dict() | |
| }, path) | |
| tqdm.write(f'Model saved to ==> {path}') | |
| def save_metrics(train_loss_list, valid_loss_list, global_steps_list, path='metrics.pt'): | |
| torch.save({'train_loss_list': train_loss_list, | |
| 'valid_loss_list': valid_loss_list, | |
| 'global_steps_list': global_steps_list, | |
| }, path) | |
| def plot_losses(metrics_save_name='metrics', save_dir='./'): | |
| path = f'{save_dir}metrics_{metrics_save_name}.pt' | |
| state = torch.load(path) | |
| train_loss_list = state['train_loss_list'] | |
| valid_loss_list = state['valid_loss_list'] | |
| global_steps_list = state['global_steps_list'] | |
| plt.plot(global_steps_list, train_loss_list, label='Train') | |
| plt.plot(global_steps_list, valid_loss_list, label='Valid') | |
| plt.xlabel('Global Steps') | |
| plt.ylabel('Loss') | |
| plt.legend() | |
| plt.show() | |
| def trainer(model, train_loader, test_loader, valid_loader, num_epochs = 10, lr = 0.01, alpha = 0.99, eval_interval = 10, model_save_name='', save_dir='./'): | |
| # Use GPU if available, else use CPU | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| print(device) | |
| # History for train acc, test acc | |
| train_accs = [] | |
| valid_accs = [] | |
| global_step = 0 | |
| train_loss_list = [] | |
| valid_loss_list = [] | |
| global_steps_list = [] | |
| best_valid_loss = float("inf") | |
| # Define optimizer | |
| optimizer = define_optimizer(model, lr, alpha) | |
| # Training model | |
| for epoch in range(num_epochs): | |
| # Go trough all samples in train dataset | |
| model.train() | |
| running_loss = 0 | |
| correct = 0 | |
| total = 0 | |
| for i, (inputs, labels, notes) in enumerate(train_loader): | |
| # Get from dataloader and send to device | |
| inputs = inputs.transpose(1,2).float().to(device) | |
| # print(labels.shape) | |
| labels = labels.float().to(device) | |
| notes = notes.to(device) | |
| # print(labels.shape) | |
| # Forward pass | |
| outputs, predicted = predict(model, inputs, notes, device) | |
| # print(predicted.shape, labels.shape) | |
| # Check if predicted class matches label and count numbler of correct predictions | |
| total += labels.size(0) | |
| #TODO: change acc criteria | |
| # correct += torch.nn.functional.cosine_similarity(labels,predicted).sum().item() #(predicted == labels).sum().item() | |
| values, indices = torch.max(outputs,dim=1) | |
| correct += sum(1 for s, i in enumerate(indices) | |
| if labels[s][i] == 1) | |
| # Compute loss | |
| # we use outputs before softmax function to the cross_entropy loss | |
| loss = torch.nn.functional.binary_cross_entropy_with_logits(outputs, labels) | |
| running_loss += loss.item()*len(labels) | |
| global_step += 1*len(inputs) | |
| # Backward and optimize | |
| loss.backward() | |
| optimizer.step() | |
| optimizer.zero_grad() | |
| # Display losses over iterations and evaluate on validation set | |
| if (i+1) % eval_interval == 0: | |
| train_accuracy, valid_accuracy, valid_loss = display_train(epoch, num_epochs, i, model, \ | |
| correct, total, loss, \ | |
| train_loader, valid_loader, device) | |
| average_train_loss = running_loss / total | |
| # average_valid_loss = valid_loss | |
| train_loss_list.append(average_train_loss) | |
| valid_loss_list.append(valid_loss) | |
| global_steps_list.append(global_step) | |
| if valid_loss < best_valid_loss: | |
| best_valid_loss = valid_loss | |
| save_model(model, optimizer, best_valid_loss, epoch, path=f'{save_dir}model_{model_save_name}.pt') | |
| save_metrics(train_loss_list, valid_loss_list, global_steps_list, path=f'{save_dir}metrics_{model_save_name}.pt') | |
| # torch.save(model.state_dict(), f'./ckpt_mid/{model.name}_best_lr_{lr}.pt') | |
| if(len(train_loader)%eval_interval!=0): | |
| train_accuracy, valid_accuracy, valid_loss = display_train(epoch, num_epochs, i, model, \ | |
| correct, total, loss, \ | |
| train_loader, valid_loader, device) | |
| average_train_loss = running_loss / total | |
| # average_valid_loss = valid_loss/len(valid_loader.dataset) | |
| train_loss_list.append(average_train_loss) | |
| valid_loss_list.append(valid_loss) | |
| global_steps_list.append(global_step) | |
| if valid_loss < best_valid_loss: | |
| best_valid_loss = valid_loss | |
| save_model(model, optimizer, best_valid_loss, epoch, path=f'{save_dir}model_{model_save_name}.pt') | |
| save_metrics(train_loss_list, valid_loss_list, global_steps_list, path=f'{save_dir}metrics_{model_save_name}.pt') | |
| # torch.save(model.state_dict(), f'./ckpt_mid/{model.name}_best_lr_{lr}.pt') | |
| # Append accuracies to list at the end of each iteration | |
| train_accs.append(train_accuracy) | |
| valid_accs.append(valid_accuracy) | |
| # torch.save(model.state_dict(), f'./ckpt_mid/{model.name}_epoch_{epoch}_lr_{lr}.pt') | |
| save_metrics(train_loss_list, valid_loss_list, global_steps_list, | |
| path=f'{save_dir}metrics_{model_save_name}.pt') | |
| # Load best_model | |
| checkpoint = torch.load(f'{save_dir}model_{model_save_name}.pt') | |
| model.load_state_dict(checkpoint['model_state_dict']) | |
| # Evaluate on test after training has completed | |
| test_acc = eval_test(model, test_loader, device) | |
| # Return | |
| return train_accs, valid_accs, test_acc |