def create_datasets(args):
train_mask = MaskFunc(args.center_fractions, args.accelerations)
dev_mask = MaskFunc(args.center_fractions, args.accelerations)
train_data = SliceData(
root=args.data_path / f'{args.challenge}_train',
transform=DataTransform(train_mask, args.resolution, args.challenge),
sample_rate=args.sample_rate,
challenge=args.challenge
)
if not args.overfit:
dev_data = SliceData(
root=args.data_path / f'{args.challenge}_val',
transform=DataTransform(dev_mask, args.resolution, args.challenge, use_seed=True),
sample_rate=args.sample_rate,
challenge=args.challenge,
)
else:
dev_data = SliceData(
root=args.data_path / f'{args.challenge}_train',
transform=DataTransform(dev_mask, args.resolution, args.challenge, use_seed=True),
sample_rate=args.sample_rate,
challenge=args.challenge,
)
if args.use_dicom:
dicom_data = SliceDICOM(root=args.data_path,
transform=DICOMTransform(args.resolution),
sample_rate=args.sample_rate,
)
return dev_data, train_data, dicom_data
return dev_data, train_data
def create_datasets(args):
train_data = SliceData(
root=args.data_path / 'multicoil_train',
transform=DataTransform(args.resolution),
sample_rate=args.sample_rate
)
dev_data = SliceData(
root=args.data_path / 'multicoil_val',
transform=DataTransform(args.resolution),
sample_rate=args.sample_rate
)
return dev_data, train_data
def create_data_loader(args):
dev_mask = MaskFunc(args.center_fractions, args.accelerations)
data = SliceData(root=args.data_path / f'{args.challenge}_val',
transform=DataTransform(dev_mask),
challenge=args.challenge,
sample_rate=args.sample_rate)
return data
def _create_data_loader(self,
data_transform,
data_partition,
sample_rate=None):
sample_rate = sample_rate or self.hparams.sample_rate
dataset = SliceData(root=self.hparams.data_path /
f'{self.hparams.challenge}_{data_partition}',
transform=data_transform,
sample_rate=sample_rate,
challenge=self.hparams.challenge)
is_train = (data_partition == 'train')
if is_train:
sampler = DistributedSampler(dataset)
else:
sampler = VolumeSampler(dataset)
return DataLoader(
dataset=dataset,
batch_size=self.hparams.batch_size,
num_workers=4,
pin_memory=False,
drop_last=is_train,
sampler=sampler,
)
def create_datasets(args):
train_mask = MaskFunc(args.center_fractions, args.accelerations)
dev_mask = MaskFunc(args.center_fractions, args.accelerations)
train_data = SliceData(
root=args.data_path / f'{args.challenge}_train',
transform=DataTransform(train_mask, args.resolution, args.challenge,use_aug=args.aug),
sample_rate=args.sample_rate,
challenge=args.challenge
)
dev_data = SliceData(
root=args.data_path / f'{args.challenge}_val',
transform=DataTransform(dev_mask, args.resolution, args.challenge, use_seed=True, use_aug=False),
sample_rate=args.sample_rate,
challenge=args.challenge
)
return dev_data, train_data
def create_datasets_multi(args):
train_mask = arc_masking_func
dev_mask = arc_masking_func
train_data = SliceData(
root=args.data_path / f'{args.challenge}_train',
transform=SquareDataTransformC3_multi(train_mask, args.resolution, args.challenge),
sample_rate=args.sample_rate,
challenge=args.challenge
)
dev_data = SliceData(
root=args.data_path / f'{args.challenge}_val',
transform=SquareDataTransformC3_multi(dev_mask, args.resolution, args.challenge, use_seed=True),
sample_rate=args.sample_rate,
challenge=args.challenge,
)
return dev_data, train_data
def create_datasets(args):
train_mask = create_mask_for_mask_type(args.mask_type, args.center_fractions, args.accelerations)
dev_mask = create_mask_for_mask_type(args.mask_type, args.center_fractions, args.accelerations)
train_data = SliceData(
root=args.data_path / f'{args.challenge}_train',
transform=DataTransform(train_mask, args.resolution, args.challenge),
sample_rate=args.sample_rate,
challenge=args.challenge,
bbox_root=args.bbox_root,
)
dev_data = SliceData(
root=args.data_path / f'{args.challenge}_val',
transform=DataTransform(dev_mask, args.resolution, args.challenge, use_seed=True),
sample_rate=args.sample_rate,
challenge=args.challenge,
)
return dev_data, train_data
def create_datasets(args):
train_mask = subsample.MaskFunc(args.center_fractions, args.accelerations)
dev_mask = subsample.MaskFunc(args.center_fractions, args.accelerations)
train_data = SliceData(
root=args.data_path + '{}_train'.format(args.challenge),
transform=DataTransform(train_mask, args.resolution, args.challenge),
sample_rate=args.sample_rate,
challenge=args.challenge)
dev_data = SliceData(
root=args.data_path + '{}_val'.format(args.challenge),
transform=DataTransform(dev_mask,
args.resolution,
args.challenge,
use_seed=True),
sample_rate=args.sample_rate,
challenge=args.challenge,
)
return dev_data, train_data
def load_data(args):
print("Staring load_data()")
train_dataset = SliceData(
root=args.data_path + '/singlecoil_train',
transform=DataTransform(resolution=args.resolution),
challenge=args.challenge,
sample_rate=args.sample_rate)
train_data_loader = DataLoader(
dataset=train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True,
)
val_dataset = SliceData(
root=args.data_path + '/singlecoil_val',
transform=DataTransform(resolution=args.resolution),
challenge=args.challenge,
sample_rate=args.sample_rate)
val_data_loader = DataLoader(
dataset=val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True,
)
display_dataset = [
val_dataset[i] for i in range(0, len(val_dataset),
len(val_dataset) // args.display_images)
]
display_data_loader = DataLoader(
dataset=display_dataset,
batch_size=args.display_images,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True,
)
print("Ended load_data()")
return train_data_loader, val_data_loader, display_data_loader
def create_data_loaders(args):
data = SliceData(root=args.data_path / f'multicoil_{args.data_split}',
transform=DataTransform(args.resolution),
sample_rate=args.sample_rate)
data_loader = DataLoader(
dataset=data,
batch_size=args.batch_size,
num_workers=4,
pin_memory=True,
)
return data_loader
def create_data_loaders(args):
data = SliceData(root=args.origin_file,
transform=DataTransform(args.resolution, args.challenge),
sample_rate=1.,
challenge=args.challenge)
data_loader = DataLoader(
dataset=data,
batch_size=args.batch_size,
num_workers=4,
pin_memory=True,
)
return data_loader
def create_data_loaders(args):
mask_func = None
if args.mask_kspace:
mask_func = MaskFunc(args.center_fractions, args.accelerations)
data = SliceData(
root=args.data_path / f'{args.challenge}_{args.data_split}',
transform=DataTransform(args.resolution, args.challenge, mask_func),
sample_rate=1.,
challenge=args.challenge)
data_loader = DataLoader(
dataset=data,
batch_size=args.batch_size,
num_workers=4,
pin_memory=True,
)
return data_loader
def create_dataset(args, transform, split):
if args.model == 'unet_volumes':
return MultiSliceData(root=args.data_path /
f'{args.challenge}_{split}',
transform=transform,
sample_rate=args.sample_rate,
overfit=args.overfit,
challenge=args.challenge,
num_volumes=args.num_volumes)
return SliceData(
root=args.data_path / f'{args.challenge}_{split}',
transform=transform,
sample_rate=args.sample_rate,
overfit=args.overfit,
challenge=args.challenge,
)
def _create_data_loader(self,
data_transform,
data_partition,
sample_rate=None):
sample_rate = sample_rate or self.hparams.sample_rate
dataset = SliceData(root=self.hparams.data_path /
f'{self.hparams.challenge}_{data_partition}',
transform=data_transform,
sample_rate=sample_rate,
challenge=self.hparams.challenge)
sampler = DistributedSampler(dataset)
return DataLoader(
dataset=dataset,
batch_size=self.hparams.batch_size,
num_workers=8,
pin_memory=True,
sampler=sampler,
)
def create_data_loader(args):
data = SliceData(root=args.data_path / f'{args.challenge}_test',
transform=data_transform,
challenge=args.challenge,
sample_rate=args.sample_rate)
return data
def main():
logger.info("Logger is set - training start")
# set default gpu device id
torch.cuda.set_device(config.gpus[0])
# set seed
np.random.seed(config.seed)
torch.manual_seed(config.seed)
torch.cuda.manual_seed_all(config.seed)
torch.backends.cudnn.benchmark = True
# get data with meta info
# input_size, input_channels, n_classes, train_data = utils.get_data(
# config.dataset, config.data_path, cutout_length=0, validation=False)
input_size = 320
input_channels = 2
n_classes = 2
train_mask = MaskFunc([0.08, 0.04], [4, 8])
train_data = SliceData(
root=config.dataset + 'train',
transform=DataTransform(train_mask, input_size, 'singlecoil'),
challenge='singlecoil'
)
net_crit = nn.L1Loss().to(device)
model = SearchCNNController(input_channels, config.init_channels, n_classes, config.layers,
net_crit, device_ids=config.gpus)
model = model.to(device)
# weights optimizer
w_optim = torch.optim.SGD(model.weights(), config.w_lr, momentum=config.w_momentum,
weight_decay=config.w_weight_decay)
# alphas optimizer
alpha_optim = torch.optim.Adam(model.alphas(), config.alpha_lr, betas=(0.5, 0.999),
weight_decay=config.alpha_weight_decay)
# split data to train/validation
n_train = len(train_data)
split = n_train // 2
indices = list(range(n_train))
train_sampler = torch.utils.data.sampler.SubsetRandomSampler(indices[:split])
valid_sampler = torch.utils.data.sampler.SubsetRandomSampler(indices[split:])
train_loader = torch.utils.data.DataLoader(train_data,
batch_size=config.batch_size,
sampler=train_sampler,
num_workers=config.workers,
pin_memory=True)
valid_loader = torch.utils.data.DataLoader(train_data,
batch_size=config.batch_size,
sampler=valid_sampler,
num_workers=config.workers,
pin_memory=True)
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
w_optim, config.epochs, eta_min=config.w_lr_min)
architect = Architect(model, config.w_momentum, config.w_weight_decay)
# training loop
best_top1 = 0.
for epoch in range(config.epochs):
lr_scheduler.step()
lr = lr_scheduler.get_lr()[0]
model.print_alphas(logger)
# training
train(train_loader, valid_loader, model, architect, w_optim, alpha_optim, lr, epoch)
# validation
cur_step = (epoch+1) * len(train_loader)
top1 = validate(valid_loader, model, epoch, cur_step)
# log
# genotype
genotype = model.genotype()
logger.info("genotype = {}".format(genotype))
# genotype as a image
plot_path = os.path.join(config.plot_path, "EP{:02d}".format(epoch+1))
caption = "Epoch {}".format(epoch+1)
plot(genotype.normal, plot_path + "-normal", caption)
plot(genotype.reduce, plot_path + "-reduce", caption)
# save
if best_top1 < top1:
best_top1 = top1
best_genotype = genotype
is_best = True
else:
is_best = False
utils.save_checkpoint(model, config.path, is_best)
print("")
logger.info("Final best PSNR = {:.4%}".format(best_top1))
logger.info("Best Genotype = {}".format(best_genotype))
def create_datasets(args):
train_data = SliceData(args.train_path, args.acceleration)
dev_data = SliceDataDev(args.val_path, args.acceleration)
return dev_data, train_data
