fastmri_examples/varnet/varnet_reproduce_20201111/varnet_brain_leaderboard.py [19:87]:
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def cli_main(args):
    pl.seed_everything(args.seed)

    # ------------
    # data
    # ------------
    # this creates a k-space mask for transforming input data
    mask = create_mask_for_mask_type(
        args.mask_type, args.center_fractions, args.accelerations
    )
    # use random masks for train transform, fixed masks for val transform
    train_transform = VarNetDataTransform(mask_func=mask, use_seed=False)
    val_transform = VarNetDataTransform(mask_func=mask)
    test_transform = VarNetDataTransform()
    # ptl data module - this handles data loaders
    data_module = FastMriDataModule(
        data_path=args.data_path,
        challenge=args.challenge,
        train_transform=train_transform,
        val_transform=val_transform,
        test_transform=test_transform,
        combine_train_val=True,
        test_split=args.test_split,
        test_path=args.test_path,
        sample_rate=args.sample_rate,
        batch_size=args.batch_size,
        num_workers=args.num_workers,
        distributed_sampler=(args.accelerator == "ddp"),
    )

    # ------------
    # model
    # ------------
    model = VarNetModule(
        num_cascades=args.num_cascades,
        pools=args.pools,
        chans=args.chans,
        sens_pools=args.sens_pools,
        sens_chans=args.sens_chans,
        lr=args.lr,
        lr_step_size=args.lr_step_size,
        lr_gamma=args.lr_gamma,
        weight_decay=args.weight_decay,
    )

    # ------------
    # trainer
    # ------------
    trainer = pl.Trainer.from_argparse_args(args)

    # ------------
    # run
    # ------------
    if args.mode == "train":
        trainer.fit(model, datamodule=data_module)
    elif args.mode == "test":
        trainer.test(model, datamodule=data_module)
    else:
        raise ValueError(f"unrecognized mode {args.mode}")


def build_args():
    parser = ArgumentParser()

    # basic args
    path_config = pathlib.Path("../../fastmri_dirs.yaml")
    backend = "ddp"
    num_gpus = 32
    batch_size = 1
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fastmri_examples/varnet/varnet_reproduce_20201111/varnet_knee_leaderboard.py [19:87]:
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def cli_main(args):
    pl.seed_everything(args.seed)

    # ------------
    # data
    # ------------
    # this creates a k-space mask for transforming input data
    mask = create_mask_for_mask_type(
        args.mask_type, args.center_fractions, args.accelerations
    )
    # use random masks for train transform, fixed masks for val transform
    train_transform = VarNetDataTransform(mask_func=mask, use_seed=False)
    val_transform = VarNetDataTransform(mask_func=mask)
    test_transform = VarNetDataTransform()
    # ptl data module - this handles data loaders
    data_module = FastMriDataModule(
        data_path=args.data_path,
        challenge=args.challenge,
        train_transform=train_transform,
        val_transform=val_transform,
        test_transform=test_transform,
        combine_train_val=True,
        test_split=args.test_split,
        test_path=args.test_path,
        sample_rate=args.sample_rate,
        batch_size=args.batch_size,
        num_workers=args.num_workers,
        distributed_sampler=(args.accelerator == "ddp"),
    )

    # ------------
    # model
    # ------------
    model = VarNetModule(
        num_cascades=args.num_cascades,
        pools=args.pools,
        chans=args.chans,
        sens_pools=args.sens_pools,
        sens_chans=args.sens_chans,
        lr=args.lr,
        lr_step_size=args.lr_step_size,
        lr_gamma=args.lr_gamma,
        weight_decay=args.weight_decay,
    )

    # ------------
    # trainer
    # ------------
    trainer = pl.Trainer.from_argparse_args(args)

    # ------------
    # run
    # ------------
    if args.mode == "train":
        trainer.fit(model, datamodule=data_module)
    elif args.mode == "test":
        trainer.test(model, datamodule=data_module)
    else:
        raise ValueError(f"unrecognized mode {args.mode}")


def build_args():
    parser = ArgumentParser()

    # basic args
    path_config = pathlib.Path("../../fastmri_dirs.yaml")
    backend = "ddp"
    num_gpus = 32
    batch_size = 1
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