6e6f6c993efc6345f0582c32d96c778055f165cb,train_pt.py,,main,#,389

Before Change

        pretrained_model_file_path=args.resume.strip(),
        use_cuda=use_cuda)
    if hasattr(net, "module"):
        input_image_size = net.module.in_size[0] if hasattr(net.module, "in_size") else args.input_size
    else:
        input_image_size = net.in_size[0] if hasattr(net, "in_size") else args.input_size

After Change

    assert (hasattr(real_net, "num_classes"))
    num_classes = real_net.num_classes

    ds_metainfo = get_dataset_metainfo(dataset_name=args.dataset)
    ds_metainfo.update(args=args)

    train_data = get_train_data_source(
        ds_metainfo=ds_metainfo,
        batch_size=batch_size,
        num_workers=args.num_workers)
    val_data = get_val_data_source(
        ds_metainfo=ds_metainfo,
        batch_size=batch_size,
        num_workers=args.num_workers)

    optimizer, lr_scheduler, start_epoch = prepare_trainer(
        net=net,
        optimizer_name=args.optimizer_name,
        wd=args.wd,
        momentum=args.momentum,
        lr_mode=args.lr_mode,
        lr=args.lr,
        lr_decay_period=args.lr_decay_period,
        lr_decay_epoch=args.lr_decay_epoch,
        lr_decay=args.lr_decay,
        // warmup_epochs=args.warmup_epochs,
        // batch_size=batch_size,
        num_epochs=args.num_epochs,
        // num_training_samples=num_training_samples,
        state_file_path=args.resume_state)

    if args.save_dir and args.save_interval:
        param_names = ds_metainfo.val_metric_capts + ds_metainfo.train_metric_capts + ["Train.Loss", "LR"]
        lp_saver = TrainLogParamSaver(
            checkpoint_file_name_prefix="{}_{}".format(ds_metainfo.short_label, args.model),
            last_checkpoint_file_name_suffix="last",
            best_checkpoint_file_name_suffix=None,
            last_checkpoint_dir_path=args.save_dir,

Instances