Python run.train() Examples

The following are 3 code examples of run.train(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module run , or try the search function .
Example #1
Source File: imagenet.py    From MobileNetV3-pytorch with MIT License 6 votes vote down vote up 
def train_network(start_epoch, epochs, optim, model, train_loader, val_loader, criterion, mixup, device, dtype,
                  batch_size, log_interval, csv_logger, save_path, claimed_acc1, claimed_acc5, best_test, local_rank,
                  child):
    my_range = range if child else trange
    for epoch in my_range(start_epoch, epochs + 1):
        train_loss, train_accuracy1, train_accuracy5, = train(model, train_loader, mixup, epoch, optim, criterion,
                                                              device, dtype, batch_size, log_interval, child)
        test_loss, test_accuracy1, test_accuracy5 = test(model, val_loader, criterion, device, dtype, child)
        optim.epoch_step()

        csv_logger.write({'epoch': epoch + 1, 'val_error1': 1 - test_accuracy1, 'val_error5': 1 - test_accuracy5,
                          'val_loss': test_loss, 'train_error1': 1 - train_accuracy1,
                          'train_error5': 1 - train_accuracy5, 'train_loss': train_loss})
        save_checkpoint({'epoch': epoch + 1, 'state_dict': model.state_dict(), 'best_prec1': best_test,
                         'optimizer': optim.state_dict()}, test_accuracy1 > best_test, filepath=save_path,
                        local_rank=local_rank)
        # TODO: save on the end of the cycle

        csv_logger.plot_progress(claimed_acc1=claimed_acc1, claimed_acc5=claimed_acc5)

        if test_accuracy1 > best_test:
            best_test = test_accuracy1

    csv_logger.write_text('Best accuracy is {:.2f}% top-1'.format(best_test * 100.))
Example #2
Source File: imagenet.py    From MobileNetV2-pytorch with MIT License 6 votes vote down vote up 
def train_network(start_epoch, epochs, scheduler, model, train_loader, val_loader, optimizer, criterion, device, dtype,
                  batch_size, log_interval, csv_logger, save_path, claimed_acc1, claimed_acc5, best_test):
    for epoch in trange(start_epoch, epochs + 1):
        if not isinstance(scheduler, CyclicLR):
            scheduler.step()
        train_loss, train_accuracy1, train_accuracy5, = train(model, train_loader, epoch, optimizer, criterion, device,
                                                              dtype, batch_size, log_interval, scheduler)
        test_loss, test_accuracy1, test_accuracy5 = test(model, val_loader, criterion, device, dtype)
        csv_logger.write({'epoch': epoch + 1, 'val_error1': 1 - test_accuracy1, 'val_error5': 1 - test_accuracy5,
                          'val_loss': test_loss, 'train_error1': 1 - train_accuracy1,
                          'train_error5': 1 - train_accuracy5, 'train_loss': train_loss})
        save_checkpoint({'epoch': epoch + 1, 'state_dict': model.state_dict(), 'best_prec1': best_test,
                         'optimizer': optimizer.state_dict()}, test_accuracy1 > best_test, filepath=save_path)

        csv_logger.plot_progress(claimed_acc1=claimed_acc1, claimed_acc5=claimed_acc5)

        if test_accuracy1 > best_test:
            best_test = test_accuracy1

    csv_logger.write_text('Best accuracy is {:.2f}% top-1'.format(best_test * 100.))
Example #3
Source File: imagenet.py    From ShuffleNetV2-pytorch with MIT License 6 votes vote down vote up 
def train_network(start_epoch, epochs, scheduler, model, train_loader, val_loader, optimizer, criterion, device, dtype,
                  batch_size, log_interval, csv_logger, save_path, claimed_acc1, claimed_acc5, best_test):
    for epoch in trange(start_epoch, epochs + 1):
        if not isinstance(scheduler, CyclicLR):
            scheduler.step()
        train_loss, train_accuracy1, train_accuracy5, = train(model, train_loader, epoch, optimizer, criterion, device,
                                                              dtype, batch_size, log_interval, scheduler)
        test_loss, test_accuracy1, test_accuracy5 = test(model, val_loader, criterion, device, dtype)
        csv_logger.write({'epoch': epoch + 1, 'val_error1': 1 - test_accuracy1, 'val_error5': 1 - test_accuracy5,
                          'val_loss': test_loss, 'train_error1': 1 - train_accuracy1,
                          'train_error5': 1 - train_accuracy5, 'train_loss': train_loss})
        save_checkpoint({'epoch': epoch + 1, 'state_dict': model.state_dict(), 'best_prec1': best_test,
                         'optimizer': optimizer.state_dict()}, test_accuracy1 > best_test, filepath=save_path)

        csv_logger.plot_progress(claimed_acc1=claimed_acc1, claimed_acc5=claimed_acc5)

        if test_accuracy1 > best_test:
            best_test = test_accuracy1

    csv_logger.write_text('Best accuracy is {:.2f}% top-1'.format(best_test * 100.))