def train_k2i(args):
# Maybe move this to args later.
train_method = 'W2I' # Weighted K-space to real-valued image.
# Creating checkpoint and logging directories, as well as the run name.
ckpt_path = Path(args.ckpt_root)
ckpt_path.mkdir(exist_ok=True)
ckpt_path = ckpt_path / train_method
ckpt_path.mkdir(exist_ok=True)
run_number, run_name = initialize(ckpt_path)
ckpt_path = ckpt_path / run_name
ckpt_path.mkdir(exist_ok=True)
log_path = Path(args.log_root)
log_path.mkdir(exist_ok=True)
log_path = log_path / train_method
log_path.mkdir(exist_ok=True)
log_path = log_path / run_name
log_path.mkdir(exist_ok=True)
logger = get_logger(name=__name__, save_file=log_path / run_name)
# Assignment inside running code appears to work.
if (args.gpu is not None) and torch.cuda.is_available():
device = torch.device(f'cuda:{args.gpu}')
logger.info(f'Using GPU {args.gpu} for {run_name}')
else:
device = torch.device('cpu')
logger.info(f'Using CPU for {run_name}')
# Saving peripheral variables and objects in args to reduce clutter and make the structure flexible.
args.run_number = run_number
args.run_name = run_name
args.ckpt_path = ckpt_path
args.log_path = log_path
args.device = device
save_dict_as_json(vars(args), log_dir=log_path, save_name=run_name)
# Input transforms. These are on a per-slice basis.
# UNET architecture requires that all inputs be dividable by some power of 2.
divisor = 2**args.num_pool_layers
if args.random_sampling:
mask_func = MaskFunc(args.center_fractions, args.accelerations)
else:
mask_func = UniformMaskFunc(args.center_fractions, args.accelerations)
# This is optimized for SSD storage.
# Sending to device should be inside the input transform for optimal performance on HDD.
data_prefetch = Prefetch2Device(device)
input_train_transform = WeightedPreProcessK(mask_func,
args.challenge,
device,
use_seed=False,
divisor=divisor)
input_val_transform = WeightedPreProcessK(mask_func,
args.challenge,
device,
use_seed=True,
divisor=divisor)
# DataLoaders
train_loader, val_loader = create_custom_data_loaders(
args, transform=data_prefetch)
losses = dict(img_loss=nn.L1Loss(reduction='mean')
# img_loss=L1CSSIM7(reduction='mean', alpha=args.alpha)
)
output_transform = WeightedReplacePostProcessK()
data_chans = 2 if args.challenge == 'singlecoil' else 30 # Multicoil has 15 coils with 2 for real/imag
model = UNetSkipGN(in_chans=data_chans,
out_chans=data_chans,
chans=args.chans,
num_pool_layers=args.num_pool_layers,
num_groups=args.num_groups,
pool_type=args.pool_type,
use_skip=args.use_skip,
use_att=args.use_att,
reduction=args.reduction,
use_gap=args.use_gap,
use_gmp=args.use_gmp).to(device)
optimizer = optim.Adam(model.parameters(), lr=args.init_lr)
scheduler = optim.lr_scheduler.MultiStepLR(optimizer,
milestones=args.lr_red_epochs,
gamma=args.lr_red_rate)
trainer = ModelTrainerK2I(args, model, optimizer, train_loader, val_loader,
input_train_transform, input_val_transform,
output_transform, losses, scheduler)
trainer.train_model()
def train_complex(args):
# Creating checkpoint and logging directories, as well as the run name.
ckpt_path = Path(args.ckpt_root)
ckpt_path.mkdir(exist_ok=True)
ckpt_path = ckpt_path / args.train_method
ckpt_path.mkdir(exist_ok=True)
run_number, run_name = initialize(ckpt_path)
ckpt_path = ckpt_path / run_name
ckpt_path.mkdir(exist_ok=True)
log_path = Path(args.log_root)
log_path.mkdir(exist_ok=True)
log_path = log_path / args.train_method
log_path.mkdir(exist_ok=True)
log_path = log_path / run_name
log_path.mkdir(exist_ok=True)
logger = get_logger(name=__name__, save_file=log_path / run_name)
# Assignment inside running code appears to work.
if (args.gpu is not None) and torch.cuda.is_available():
device = torch.device(f'cuda:{args.gpu}')
logger.info(f'Using GPU {args.gpu} for {run_name}')
else:
device = torch.device('cpu')
logger.info(f'Using CPU for {run_name}')
# Saving peripheral variables and objects in args to reduce clutter and make the structure flexible.
args.run_number = run_number
args.run_name = run_name
args.ckpt_path = ckpt_path
args.log_path = log_path
args.device = device
save_dict_as_json(vars(args), log_dir=log_path, save_name=run_name)
# Input transforms. These are on a per-slice basis.
# UNET architecture requires that all inputs be dividable by some power of 2.
divisor = 2**args.num_pool_layers
if args.random_sampling:
mask_func = RandomMaskFunc(args.center_fractions, args.accelerations)
else:
mask_func = UniformMaskFunc(args.center_fractions, args.accelerations)
data_prefetch = Prefetch2Device(device)
if args.train_method == 'WS2C': # Semi-k-space learning.
weight_func = SemiDistanceWeight(weight_type=args.weight_type)
input_train_transform = PreProcessWSK(mask_func,
weight_func,
args.challenge,
device,
use_seed=False,
divisor=divisor)
input_val_transform = PreProcessWSK(mask_func,
weight_func,
args.challenge,
device,
use_seed=True,
divisor=divisor)
output_transform = WeightedReplacePostProcessSemiK(
weighted=True, replace=args.replace)
elif args.train_method == 'WK2C': # k-space learning.
weight_func = TiltedDistanceWeight(weight_type=args.weight_type,
y_scale=args.y_scale)
input_train_transform = PreProcessWK(mask_func,
weight_func,
args.challenge,
device,
use_seed=False,
divisor=divisor)
input_val_transform = PreProcessWK(mask_func,
weight_func,
args.challenge,
device,
use_seed=True,
divisor=divisor)
output_transform = WeightedReplacePostProcessK(weighted=True,
replace=args.replace)
else:
raise NotImplementedError('Invalid train method!')
# DataLoaders
train_loader, val_loader = create_custom_data_loaders(
args, transform=data_prefetch)
losses = dict(cmg_loss=nn.MSELoss(reduction='mean'))
data_chans = 2 if args.challenge == 'singlecoil' else 30 # Multicoil has 15 coils with 2 for real/imag
# model = UNetModel(
# in_chans=data_chans, out_chans=data_chans, chans=args.chans, num_pool_layers=args.num_pool_layers,
# num_groups=args.num_groups, use_residual=args.use_residual, pool_type=args.pool_type, use_skip=args.use_skip,
# use_ca=args.use_ca, reduction=args.reduction, use_gap=args.use_gap, use_gmp=args.use_gmp,
# use_sa=args.use_sa, sa_kernel_size=args.sa_kernel_size, sa_dilation=args.sa_dilation, use_cap=args.use_cap,
# use_cmp=args.use_cmp).to(device)
model = UNetModelKSSE(in_chans=data_chans,
out_chans=data_chans,
chans=args.chans,
num_pool_layers=args.num_pool_layers,
num_groups=args.num_groups,
use_residual=args.use_residual,
pool_type=args.pool_type,
use_skip=args.use_skip,
min_ext_size=args.min_ext_size,
max_ext_size=args.max_ext_size,
ext_mode=args.ext_mode,
use_ca=args.use_ca,
reduction=args.reduction,
use_gap=args.use_gap,
use_gmp=args.use_gmp,
use_sa=args.use_sa,
sa_kernel_size=args.sa_kernel_size,
sa_dilation=args.sa_dilation,
use_cap=args.use_cap,
use_cmp=args.use_cmp).to(device)
optimizer = optim.Adam(model.parameters(), lr=args.init_lr)
scheduler = optim.lr_scheduler.MultiStepLR(optimizer,
milestones=args.lr_red_epochs,
gamma=args.lr_red_rate)
trainer = ModelTrainerCOMPLEX(args, model, optimizer, train_loader,
val_loader, input_train_transform,
input_val_transform, output_transform,
losses, scheduler)
trainer.train_model()
def train_img(args):
# Maybe move this to args later.
train_method = 'K2CI'
# Creating checkpoint and logging directories, as well as the run name.
ckpt_path = Path(args.ckpt_root)
ckpt_path.mkdir(exist_ok=True)
ckpt_path = ckpt_path / train_method
ckpt_path.mkdir(exist_ok=True)
run_number, run_name = initialize(ckpt_path)
ckpt_path = ckpt_path / run_name
ckpt_path.mkdir(exist_ok=True)
log_path = Path(args.log_root)
log_path.mkdir(exist_ok=True)
log_path = log_path / train_method
log_path.mkdir(exist_ok=True)
log_path = log_path / run_name
log_path.mkdir(exist_ok=True)
logger = get_logger(name=__name__, save_file=log_path / run_name)
# Assignment inside running code appears to work.
if (args.gpu is not None) and torch.cuda.is_available():
device = torch.device(f'cuda:{args.gpu}')
logger.info(f'Using GPU {args.gpu} for {run_name}')
else:
device = torch.device('cpu')
logger.info(f'Using CPU for {run_name}')
# Saving peripheral variables and objects in args to reduce clutter and make the structure flexible.
args.run_number = run_number
args.run_name = run_name
args.ckpt_path = ckpt_path
args.log_path = log_path
args.device = device
save_dict_as_json(vars(args), log_dir=log_path, save_name=run_name)
# Input transforms. These are on a per-slice basis.
# UNET architecture requires that all inputs be dividable by some power of 2.
divisor = 2**args.num_pool_layers
mask_func = MaskFunc(args.center_fractions, args.accelerations)
data_prefetch = Prefetch2Device(device)
input_train_transform = TrainPreProcessK(mask_func,
args.challenge,
args.device,
use_seed=False,
divisor=divisor)
input_val_transform = TrainPreProcessK(mask_func,
args.challenge,
args.device,
use_seed=True,
divisor=divisor)
# DataLoaders
train_loader, val_loader = create_custom_data_loaders(
args, transform=data_prefetch)
losses = dict(
cmg_loss=nn.MSELoss(reduction='mean'),
# img_loss=L1CSSIM7(reduction='mean', alpha=0.5)
img_loss=CSSIM(filter_size=7, reduction='mean'))
output_transform = OutputReplaceTransformK()
data_chans = 2 if args.challenge == 'singlecoil' else 30 # Multicoil has 15 coils with 2 for real/imag
model = UnetASE(in_chans=data_chans,
out_chans=data_chans,
ext_chans=args.chans,
chans=args.chans,
num_pool_layers=args.num_pool_layers,
min_ext_size=args.min_ext_size,
max_ext_size=args.max_ext_size,
use_ext_bias=args.use_ext_bias,
use_att=False).to(device)
optimizer = optim.Adam(model.parameters(), lr=args.init_lr)
scheduler = optim.lr_scheduler.StepLR(optimizer,
step_size=args.lr_red_epoch,
gamma=args.lr_red_rate)
trainer = ModelTrainerK2CI(args, model, optimizer, train_loader,
val_loader, input_train_transform,
input_val_transform, output_transform, losses,
scheduler)
trainer.train_model()