def __init__(self, in_chans, out_chans, dropout, decoder_channels, lr,
lr_step_size, lr_gamma, weight_decay, data_path, batch_size,
mask_type, center_fractions, accelerations, optim_eps):
super().__init__()
self.save_hyperparameters()
self.in_chans = in_chans
self.out_chans = out_chans
self.decoder_channels = decoder_channels
self.lr = lr
self.lr_step_size = lr_step_size
self.lr_gamma = lr_gamma
self.weight_decay = weight_decay
self.optim_eps = optim_eps
self.net = ENet(in_channels=in_chans,
out_channels=out_chans,
decoder_channels=decoder_channels,
dropout=dropout)
mask = create_mask_for_mask_type(mask_type, center_fractions,
accelerations)
train_transform = UnetDataTransform('singlecoil',
mask_func=mask,
use_seed=False)
val_transform = UnetDataTransform('singlecoil', mask_func=mask)
test_transform = UnetDataTransform('singlecoil')
self.data_module = FastMriDataModule(data_path=pathlib.Path(data_path),
challenge='singlecoil',
train_transform=train_transform,
val_transform=val_transform,
test_transform=test_transform,
test_split='test',
test_path=None,
sample_rate=1.0,
batch_size=batch_size,
num_workers=4,
distributed_sampler=False)
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 __init__(self) -> None:
data_path = Path.cwd() / "data"
if data_path.is_dir():
shutil.rmtree(str(data_path))
data_path.mkdir(exist_ok=False, parents=True)
_, _, metadata = create_temp_data(data_path)
def retrieve_metadata_mock(a: Any, fname: Any) -> Any:
return metadata[str(fname)]
# That's a bit flaky, we should be un-doing that after, but there's no obvious place of doing so.
MonkeyPatch().setattr(SliceDataset, "_retrieve_metadata", retrieve_metadata_mock)
mask = create_mask_for_mask_type(mask_type_str="equispaced",
center_fractions=[0.08],
accelerations=[4])
# 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()
FastMriDataModule.__init__(self,
data_path=data_path / "knee_data",
challenge="multicoil",
train_transform=train_transform,
val_transform=val_transform,
test_transform=test_transform)
def test_mask_types(mask_type):
shape_list = ((4, 32, 32, 2), (2, 64, 32, 2), (1, 33, 24, 2))
center_fraction_list = ([0.08], [0.04], [0.04, 0.08])
acceleration_list = ([4], [8], [4, 8])
state = np.random.get_state()
for shape in shape_list:
for center_fractions, accelerations in zip(center_fraction_list,
acceleration_list):
mask_func = create_mask_for_mask_type(mask_type, center_fractions,
accelerations)
expected_mask, expected_num_low_frequencies = mask_func(shape,
seed=123)
x = create_input(shape)
output, mask, num_low_frequencies = transforms.apply_mask(
x, mask_func, seed=123)
assert (state[1] == np.random.get_state()[1]).all()
assert output.shape == x.shape
assert mask.shape == expected_mask.shape
assert np.all(expected_mask.numpy() == mask.numpy())
assert np.all(
np.where(mask.numpy() == 0, 0, output.numpy()) ==
output.numpy())
assert num_low_frequencies == expected_num_low_frequencies
def val_data_transform(self):
mask = create_mask_for_mask_type(
self.mask_type,
self.center_fractions,
self.accelerations,
)
return DataTransform(self.challenge, mask)
def train_data_transform(self):
mask = create_mask_for_mask_type(
self.mask_type,
self.center_fractions,
self.accelerations,
)
return DataTransform(self.challenge, mask, use_seed=False)
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 get_dataloaders_fastmri(mask_type = 'random',
center_fractions = [0.08],
accelerations = [4],
challenge = 'singlecoil',
batch_size = 8,
num_workers = 4,
distributed_bool = False,
dataset_dir = dataset_dir,
mri_dir = 'fastmri/knee/',
worker_init_fn = None,
include_test = False,
**kwargs):
data_path = Path(os.path.join(dataset_dir, mri_dir))
mask = create_mask_for_mask_type(mask_type_str = mask_type,
center_fractions = center_fractions,
accelerations = accelerations )
# use random masks for train transform, fixed masks for val transform
train_transform = UnetDataTransform(challenge, mask_func=mask, use_seed=False)
val_transform = UnetDataTransform(challenge, mask_func=mask)
test_transform = UnetDataTransform(challenge)
# ptl data module - this handles data loaders
data_module = FastMriDataModule(
data_path= data_path,
challenge= challenge,
train_transform=train_transform,
val_transform=val_transform,
test_transform=test_transform,
batch_size=batch_size,
num_workers=num_workers,
distributed_sampler = distributed_bool
)
if include_test:
dataloaders = {'train': data_module.train_dataloader() ,
'validation': data_module.val_dataloader(),
'test': data_module.test_dataloader()}
else:
dataloaders = {'train': data_module.train_dataloader() ,
'validation': data_module.val_dataloader()}
return dataloaders
def init_model(args):
# initialize model with given args
if torch.cuda.is_available():
device = torch.device("cuda")
print(f'There are {torch.cuda.device_count()} GPU(s) available.')
print('Device name:', torch.cuda.get_device_name(0))
else:
print('No GPU available, using the CPU instead.')
device = torch.device("cpu")
# 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('singlecoil', mask_func=mask, use_seed=False)
val_transform = UnetDataTransform('singlecoil', mask_func=mask)
test_transform = UnetDataTransform('singlecoil')
# Initialize Process Group
dist.init_process_group('gloo', init_method='file:///tmp/somefile', rank=0, world_size=1)
# define the data loaders
batch_size = args.batch_size
# create object for data module
data_module = FastMriDataModule(
data_path=args.data_path,
challenge='singlecoil',
train_transform=train_transform,
val_transform=val_transform,
test_transform=test_transform,
test_split='test',
test_path=args.data_path+'/singlecoil_test',
sample_rate=1,
batch_size=batch_size,
# may can use multiple workers here with linux?
num_workers=0,
distributed_sampler="ddp",
)
# save data to dataloader
dataloader_tr = data_module.train_dataloader()
dataloader_val = data_module.val_dataloader()
dataloader_test = data_module.test_dataloader()
return dataloader_tr, dataloader_val, dataloader_test, device
def get_fastmri_data_module(azure_dataset_id: str,
local_dataset: Optional[Path],
sample_rate: Optional[float],
test_path: str) -> LightningDataModule:
"""
Creates a LightningDataModule that consumes data from the FastMRI challenge. The type of challenge
(single/multicoil) is determined from the name of the dataset in Azure blob storage. The mask type is set to
equispaced, with 4x acceleration.
:param azure_dataset_id: The name of the dataset (folder name in blob storage).
:param local_dataset: The local folder at which the dataset has been mounted or downloaded.
:param sample_rate: Fraction of slices of the training data split to use. Set to a value <1.0 for rapid prototyping.
:param test_path: The name of the folder inside the dataset that contains the test data.
:return: A LightningDataModule object.
"""
if not azure_dataset_id:
raise ValueError("The azure_dataset_id argument must be provided.")
if not local_dataset:
raise ValueError("The local_dataset argument must be provided.")
for challenge in ["multicoil", "singlecoil"]:
if challenge in azure_dataset_id:
break
else:
raise ValueError(
f"Unable to determine the value for the challenge field for this "
f"dataset: {azure_dataset_id}")
mask = create_mask_for_mask_type(mask_type_str="equispaced",
center_fractions=[0.08],
accelerations=[4])
# 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()
return FastMriDataModule(data_path=local_dataset,
test_path=local_dataset / test_path,
challenge=challenge,
sample_rate=sample_rate,
train_transform=train_transform,
val_transform=val_transform,
test_transform=test_transform)
parser.add_argument(
"--out_chans",
default=1,
type=int,
help="number of output chanenls to U-Net",
)
parser.add_argument(
"--chans",
default=32,
type=int,
help="number of top-level U-Net channels",
)
# unet module arguments
parser.add_argument(
"--unet_module",
default="unet",
choices=("unet", "nestedunet"),
type=str,
help="Unet module to run with",
)
args = parser.parse_args()
mask = create_mask_for_mask_type(args.mask_type, args.center_fractions,
args.accelerations)
run_inference(args.challenge,
args.state_dict_file, args.data_path, args.output_path,
torch.device(args.device), mask, args.in_chans,
args.out_chans, args.chans)
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}")
device = 'cuda'
target = np.fft.fftshift(np.fft.ifft2(np.fft.ifftshift(kspace[slice], axes=(-2, -1)), axes=(-2, -1)), axes=(-2, -1))
target = target / np.max(np.abs(target))
target = np.sqrt(np.sum(T.center_crop(target, crop_size) ** 2, 0))
crop_size = (320, 320)
mask_func = create_mask_for_mask_type(mask_type_str="random", center_fractions=[0.08], accelerations=[4])
_kspace = T.to_tensor(kspace)[slice]
masked_kspace, mask = T.apply_mask(_kspace, mask_func)
linear_recon = masked_kspace[..., 0] + 1j * masked_kspace[..., 1]
linear_recon = np.fft.fftshift(np.fft.ifft2(np.fft.ifftshift(linear_recon, axes=(-2, -1)), axes=(-2, -1)),
axes=(-2, -1))
def test_data_transform(self):
mask = create_mask_for_mask_type(
self.mask_type, self.center_fractions, self.accelerations
)
return DataTransform(mask)
default="random",
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",
)
args = parser.parse_args()
run_inference(
args.challenge,
args.state_dict_file,
args.data_path,
args.output_path,
create_mask_for_mask_type(args.mask_type, args.center_fractions,
args.accelerations) if args.set == 'val' else None,
args.use_sens_net,
torch.device(args.device),
)
