aa538de146783494dfcdfc278d32aa8d1de2d844,imagenet.py,,main,#,32

Before Change

    gpus = list(range(torch.cuda.device_count()))
    se_resnet = nn.DataParallel(se_resnet50(num_classes=1000),
                                device_ids=gpus)
    optimizer = optim.SGD(params=se_resnet.parameters(), lr=0.6 / 1024 * batch_size, momentum=0.9, weight_decay=1e-4)
    scheduler = optim.lr_scheduler.StepLR(optimizer, 30, gamma=0.1)
    trainer = Trainer(se_resnet, optimizer, F.cross_entropy, save_dir=".")
    trainer.loop(100, train_loader, test_loader, scheduler)

After Change

    optimizer = optim.SGD(lr=0.6 / 1024 * args.batch_size, momentum=0.9, weight_decay=1e-4)
    scheduler = lr_scheduler.StepLR(30, gamma=0.1)
    weight_saver = callbacks.WeightSave("checkpoints")
    tqdm_rep = reporter.TQDMReporter(range(args.epochs), callbacks=[callbacks.LossCallback(),
                                                                    callbacks.AccuracyCallback])

    trainer = Trainer(se_resnet, optimizer, F.cross_entropy, scheduler=scheduler,
                      callbacks=callbacks.CallbackList(weight_saver, tqdm_rep))
    for _ in tqdm_rep:
        trainer.train(train_loader)
        trainer.test(test_loader)


if __name__ == "__main__":
    import argparse

    p = argparse.ArgumentParser()

Instances