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 = UnetDataTransform(args.challenge,
mask_func=mask,
use_seed=False)
val_transform = UnetDataTransform(args.challenge, mask_func=mask)
test_transform = UnetDataTransform(args.challenge)
# 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,
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 in ("ddp", "ddp_cpu")),
proportion=args.proportion,
)
# ------------
# model
# ------------
model = UnetModule(
in_chans=args.in_chans,
out_chans=args.out_chans,
chans=args.chans,
num_pool_layers=args.num_pool_layers,
drop_prob=args.drop_prob,
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_unet_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_unet_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 = UnetDataTransform(params.challenge,
mask_func=mask,
use_seed=False)
val_transform = UnetDataTransform(params.challenge, mask_func=mask)
test_transform = UnetDataTransform(params.challenge)
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 = UnetModule(
in_chans=params.in_chans,
out_chans=params.out_chans,
chans=params.chans,
num_pool_layers=params.num_pool_layers,
drop_prob=params.drop_prob,
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 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 = UnetDataTransform(args.challenge,
mask_func=mask,
use_seed=False)
val_transform = UnetDataTransform(args.challenge, mask_func=mask)
test_transform = UnetDataTransform(args.challenge)
# 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,
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 in ("ddp", "ddp_cpu")),
)
# ------------
# model
# ------------
model = None
if args.unet_module == "unet":
model = UnetModule(
in_chans=args.in_chans,
out_chans=args.out_chans,
chans=int(args.chans),
num_pool_layers=args.num_pool_layers,
drop_prob=args.drop_prob,
lr=args.lr,
lr_step_size=args.lr_step_size,
lr_gamma=args.lr_gamma,
weight_decay=args.weight_decay,
optimizer=args.optmizer,
)
elif args.unet_module == "nestedunet":
model = NestedUnetModule(
in_chans=args.in_chans,
out_chans=args.out_chans,
chans=args.chans,
num_pool_layers=args.num_pool_layers,
drop_prob=args.drop_prob,
lr=args.lr,
lr_step_size=args.lr_step_size,
lr_gamma=args.lr_gamma,
weight_decay=args.weight_decay,
optimizer=args.optmizer,
)
if args.device == "cuda" and not torch.cuda.is_available():
raise ValueError(
"The requested cuda device isn't available please set --device cpu"
)
pretrained_dict = torch.load(args.state_dict_file,
map_location=args.device)
model_dict = model.unet.state_dict()
if args.unet_module == "unet":
model_dict = {
k: pretrained_dict["classy_state_dict"]["base_model"]["model"]
["trunk"]["_feature_blocks.unetblock." + k]
for k, _ in model_dict.items()
}
elif args.unet_module == "nestedunet":
model_dict = {
k: pretrained_dict["classy_state_dict"]["base_model"]["model"]
["trunk"]["_feature_blocks.nublock." + k]
for k, v in model_dict.items()
}
model.unet.load_state_dict(model_dict)
# ------------
# trainer
# ------------
trainer = pl.Trainer.from_argparse_args(args)
# ------------
# run
# ------------
output_filename = f"fine_tuned_{args.unet_module}.torch"
output_model_filepath = f"{args.output_path}/{output_filename}"
if args.mode == "train":
trainer.fit(model, datamodule=data_module)
print(f"Saving model: {output_model_filepath}")
torch.save(model.state_dict(), output_model_filepath)
print("DONE!")
elif args.mode == "test":
trainer.test(model, datamodule=data_module)
else:
raise ValueError(f"unrecognized mode {args.mode}")