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 test_varnet_trainer(fastmri_mock_dataset, backend, tmp_path, monkeypatch):
knee_path, _, metadata = fastmri_mock_dataset
def retrieve_metadata_mock(a, fname):
return metadata[str(fname)]
monkeypatch.setattr(SliceDataset, "_retrieve_metadata",
retrieve_metadata_mock)
params = build_varnet_args(knee_path, tmp_path, backend)
params.fast_dev_run = True
params.backend = backend
mask = create_mask_for_mask_type(params.mask_type, params.center_fractions,
params.accelerations)
train_transform = VarNetDataTransform(mask_func=mask, use_seed=False)
val_transform = VarNetDataTransform(mask_func=mask)
test_transform = VarNetDataTransform()
data_module = FastMriDataModule(
data_path=params.data_path,
challenge=params.challenge,
train_transform=train_transform,
val_transform=val_transform,
test_transform=test_transform,
test_split=params.test_split,
sample_rate=params.sample_rate,
batch_size=params.batch_size,
num_workers=params.num_workers,
distributed_sampler=(params.accelerator == "ddp"),
use_dataset_cache_file=False,
)
model = VarNetModule(
num_cascades=params.num_cascades,
pools=params.pools,
chans=params.chans,
sens_pools=params.sens_pools,
sens_chans=params.sens_chans,
lr=params.lr,
lr_step_size=params.lr_step_size,
lr_gamma=params.lr_gamma,
weight_decay=params.weight_decay,
)
trainer = Trainer.from_argparse_args(params)
trainer.fit(model, data_module)
def build_args():
parser = ArgumentParser()
# basic args
path_config = pathlib.Path("../../fastmri_dirs.yaml")
backend = "ddp"
num_gpus = 2 if backend == "ddp" else 1
batch_size = 1
# set defaults based on optional directory config
data_path = fetch_dir("knee_path", path_config)
default_root_dir = fetch_dir("log_path",
path_config) / "varnet" / "varnet_demo"
# client arguments
parser.add_argument(
"--mode",
default="train",
choices=("train", "test"),
type=str,
help="Operation mode",
)
# data transform params
parser.add_argument(
"--mask_type",
choices=("random", "equispaced"),
default="equispaced",
type=str,
help="Type of k-space mask",
)
parser.add_argument(
"--center_fractions",
nargs="+",
default=[0.08],
type=float,
help="Number of center lines to use in mask",
)
parser.add_argument(
"--accelerations",
nargs="+",
default=[4],
type=int,
help="Acceleration rates to use for masks",
)
# data config
parser = FastMriDataModule.add_data_specific_args(parser)
parser.set_defaults(
data_path=data_path, # path to fastMRI data
mask_type="equispaced", # VarNet uses equispaced mask
challenge="multicoil", # only multicoil implemented for VarNet
batch_size=batch_size, # number of samples per batch
test_path=None, # path for test split, overwrites data_path
)
# module config
parser = VarNetModule.add_model_specific_args(parser)
parser.set_defaults(
num_cascades=8, # number of unrolled iterations
pools=4, # number of pooling layers for U-Net
chans=18, # number of top-level channels for U-Net
sens_pools=4, # number of pooling layers for sense est. U-Net
sens_chans=8, # number of top-level channels for sense est. U-Net
lr=0.001, # Adam learning rate
lr_step_size=40, # epoch at which to decrease learning rate
lr_gamma=0.1, # extent to which to decrease learning rate
weight_decay=0.0, # weight regularization strength
)
# trainer config
parser = pl.Trainer.add_argparse_args(parser)
parser.set_defaults(
gpus=num_gpus, # number of gpus to use
replace_sampler_ddp=
False, # this is necessary for volume dispatch during val
accelerator=backend, # what distributed version to use
seed=42, # random seed
deterministic=True, # makes things slower, but deterministic
default_root_dir=default_root_dir, # directory for logs and checkpoints
max_epochs=50, # max number of epochs
)
args = parser.parse_args()
# configure checkpointing in checkpoint_dir
checkpoint_dir = args.default_root_dir / "checkpoints"
if not checkpoint_dir.exists():
checkpoint_dir.mkdir(parents=True)
args.checkpoint_callback = pl.callbacks.ModelCheckpoint(
filepath=args.default_root_dir / "checkpoints",
save_top_k=True,
verbose=True,
monitor="validation_loss",
mode="min",
prefix="",
)
# set default checkpoint if one exists in our checkpoint directory
if args.resume_from_checkpoint is None:
ckpt_list = sorted(checkpoint_dir.glob("*.ckpt"), key=os.path.getmtime)
if ckpt_list:
args.resume_from_checkpoint = str(ckpt_list[-1])
return args
def build_varnet_args(data_path, logdir, backend):
parser = ArgumentParser()
num_gpus = 0
batch_size = 1
# data transform params
parser.add_argument(
"--mask_type",
choices=("random", "equispaced"),
default="equispaced",
type=str,
help="Type of k-space mask",
)
parser.add_argument(
"--center_fractions",
nargs="+",
default=[0.08],
type=float,
help="Number of center lines to use in mask",
)
parser.add_argument(
"--accelerations",
nargs="+",
default=[4],
type=int,
help="Acceleration rates to use for masks",
)
# data config
parser = FastMriDataModule.add_data_specific_args(parser)
parser.set_defaults(
data_path=data_path,
mask_type="equispaced",
challenge="multicoil",
batch_size=batch_size,
)
# module config
parser = VarNetModule.add_model_specific_args(parser)
parser.set_defaults(
num_cascades=4,
pools=2,
chans=8,
sens_pools=2,
sens_chans=4,
lr=0.001,
lr_step_size=40,
lr_gamma=0.1,
weight_decay=0.0,
)
# trainer config
parser = Trainer.add_argparse_args(parser)
parser.set_defaults(
gpus=num_gpus,
default_root_dir=logdir,
replace_sampler_ddp=(backend != "ddp"),
accelerator=backend,
)
parser.add_argument("--mode", default="train", type=str)
args = parser.parse_args([])
return args
