def create_dataloader():
train_joint_transform = joint_augment.Compose([
joint_augment.To_PIL_Image(),
joint_augment.RandomAffine(0, translate=(0.125, 0.125)),
joint_augment.RandomRotate((-180, 180)),
joint_augment.FixResize(256)
])
transform = standard_augment.Compose([
standard_augment.to_Tensor(),
# standard_augment.normalize([cfg.DATASET.MEAN], [cfg.DATASET.STD])])
standard_augment.normalize_meanstd()
])
target_transform = standard_augment.Compose([standard_augment.to_Tensor()])
if cfg.DATASET.NAME == 'acdc':
train_set = AcdcDataset(data_list=cfg.DATASET.TRAIN_LIST,
joint_augment=train_joint_transform,
augment=transform,
target_augment=target_transform)
# train_sampler = torch.utils.data.distributed.DistributedSampler(train_set,
# num_replicas=dist.get_world_size(), rank=dist.get_rank())
train_loader = DataLoader(
train_set,
batch_size=args.batch_size,
pin_memory=True,
num_workers=args.workers,
shuffle=False,
)
if args.train_with_eval:
eval_transform = joint_augment.Compose([
joint_augment.To_PIL_Image(),
joint_augment.FixResize(256), # divided by 32
joint_augment.To_Tensor()
])
evalImg_transform = standard_augment.Compose([
# standard_augment.normalize([cfg.DATASET.MEAN], [cfg.DATASET.STD])],)
standard_augment.normalize_meanstd()
])
if cfg.DATASET.NAME == 'acdc':
test_set = AcdcDataset(data_list=cfg.DATASET.TEST_LIST,
joint_augment=eval_transform,
augment=evalImg_transform)
test_loader = DataLoader(
test_set,
batch_size=args.batch_size,
pin_memory=True,
num_workers=args.workers,
shuffle=False,
)
else:
test_loader = None
return train_loader, test_loader
def transform(imgs):
mean = 63.19523533061758
std = 70.74166957523165
trans = standard_augment.Compose([
standard_augment.To_PIL_Image(),
# joint_augment.RandomAffine(0,translate=(0.125, 0.125)),
# joint_augment.RandomRotate((-180,180)),
# joint_augment.FixResize(224),
standard_augment.to_Tensor(),
standard_augment.normalize([mean], [std]),
])
return trans(imgs)
def create_dataloader():
eval_transform = joint_augment.Compose([
joint_augment.To_PIL_Image(),
joint_augment.FixResize(256),
joint_augment.To_Tensor()
])
evalImg_transform = standard_augment.Compose(
[standard_augment.normalize([cfg.DATASET.MEAN], [cfg.DATASET.STD])])
if cfg.DATASET.NAME == "acdc":
test_set = AcdcDataset(cfg.DATASET.TEST_LIST,
df_used=True,
joint_augment=eval_transform,
augment=evalImg_transform)
test_loader = DataLoader(test_set,
batch_size=1,
pin_memory=True,
num_workers=args.workers,
shuffle=False,
collate_fn=BatchCollator(size_divisible=32,
df_used=True))
return test_loader, test_set
def create_dataloader_Insensee(do_elastic_transform=True,
alpha=(100., 350.),
sigma=(14., 17.),
do_rotation=True,
a_x=(0., 2 * np.pi),
a_y=(-0.000001, 0.00001),
a_z=(-0.000001, 0.00001),
do_scale=True,
scale_range=(0.7, 1.3)):
transform = SpatialTransform((352, 352),
list(np.array((352, 352)) // 2),
do_elastic_transform,
alpha,
sigma,
do_rotation,
a_x,
a_y,
a_z,
do_scale,
scale_range,
'constant',
0,
3,
'constant',
0,
0,
random_crop=False)
train_set_Isensee = AcdcDataset_Isensee(data_list=cfg.DATASET.TRAIN_LIST,
Isensee_augment=transform)
train_loader_Isensee = DataLoader(train_set_Isensee,
batch_size=args.batch_size,
pin_memory=True,
num_workers=1,
shuffle=False)
if args.train_with_eval:
eval_transform = joint_augment.Compose([
joint_augment.To_PIL_Image(),
joint_augment.FixResize(352), # divided by 32
joint_augment.To_Tensor()
])
evalImg_transform = standard_augment.Compose(
[standard_augment.normalize_meanstd()])
if cfg.DATASET.NAME == 'acdc':
test_set = AcdcDataset(data_list=cfg.DATASET.TEST_LIST,
joint_augment=eval_transform,
augment=evalImg_transform)
test_loader = DataLoader(
test_set,
batch_size=args.batch_size,
pin_memory=True,
num_workers=args.workers,
shuffle=False,
)
else:
test_loader = None
return train_loader_Isensee, test_loader
for i in np.unique(lbls)] # calculate every region's number
largest_region = np.argmax(
lbls_sizes[1:]) + 1 # from 1 because need excluding the background
print('labls:', np.unique(lbls), 'largest_region:', largest_region)
seg[lbls !=
largest_region] = 0 # only allow one pred region,set others to zero
return seg
eval_transform = joint_augment.Compose([
joint_augment.To_PIL_Image(),
joint_augment.FixResize(256),
joint_augment.To_Tensor()
])
evalImg_transform = standard_augment.Compose(
[standard_augment.normalize_meanstd()])
if cfg.DATASET.NAME == 'acdc':
test_set = AcdcDataset_Upload(data_list=cfg.DATASET.TEST_UPLOAD,
joint_augment=eval_transform,
augment=evalImg_transform)
test_loader = DataLoader(test_set,
batch_size=1,
pin_memory=True,
num_workers=args.workers,
shuffle=False)
model = CleanU_Net()
nii_numpy_data = []
nii_numpy_lab = []
def transform(imgs, cfg):
trans = standard_augment.Compose([
standard_augment.normalize([cfg.DATASET.MEAN], [cfg.DATASET.STD]),
])
return trans(imgs)
for i in np.unique(lbls)] # calculate every region's number
largest_region = np.argmax(
lbls_sizes[1:]) + 1 # from 1 because need excluding the background
seg[lbls !=
largest_region] = 0 # only allow one pred region,set others to zero
return seg
train_joint_transform = joint_augment.Compose([
joint_augment.To_PIL_Image(),
joint_augment.RandomAffine(0, translate=(0.125, 0.125)),
joint_augment.RandomRotate((-180, 180)),
joint_augment.FixResize(256)
])
transform = standard_augment.Compose(
[standard_augment.to_Tensor(),
standard_augment.normalize_meanstd()])
target_transform = standard_augment.Compose([standard_augment.to_Tensor()])
if cfg.DATASET.NAME == 'acdc':
train_set = AcdcDataset(data_list=cfg.DATASET.TRAIN_LIST,
joint_augment=train_joint_transform,
augment=transform,
target_augment=target_transform)
train_loader = DataLoader(train_set,
batch_size=args.batch_size,
pin_memory=True,
num_workers=1,
shuffle=False)
eval_transform = joint_augment.Compose([
def create_dataloader(logger):
train_joint_transform = joint_augment.Compose([
joint_augment.To_PIL_Image(),
joint_augment.RandomAffine(0, translate=(0.125, 0.125)),
joint_augment.RandomRotate((-180, 180)),
joint_augment.FixResize(256)
])
transform = standard_augment.Compose([
standard_augment.to_Tensor(),
standard_augment.normalize([cfg.DATASET.MEAN], [cfg.DATASET.STD])
])
target_transform = standard_augment.Compose([standard_augment.to_Tensor()])
if cfg.DATASET.NAME == 'acdc':
train_set = AcdcDataset(data_list=cfg.DATASET.TRAIN_LIST,
df_used=cfg.DATASET.DF_USED,
df_norm=cfg.DATASET.DF_NORM,
boundary=cfg.DATASET.BOUNDARY,
joint_augment=train_joint_transform,
augment=transform,
target_augment=target_transform)
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_set, num_replicas=dist.get_world_size(), rank=dist.get_rank())
train_loader = DataLoader(train_set,
batch_size=args.batch_size,
pin_memory=True,
num_workers=args.workers,
shuffle=False,
sampler=train_sampler,
collate_fn=BatchCollator(
size_divisible=32,
df_used=cfg.DATASET.DF_USED,
boundary=cfg.DATASET.BOUNDARY))
if args.train_with_eval:
eval_transform = joint_augment.Compose([
joint_augment.To_PIL_Image(),
joint_augment.FixResize(256),
joint_augment.To_Tensor()
])
evalImg_transform = standard_augment.Compose([
standard_augment.normalize([cfg.DATASET.MEAN], [cfg.DATASET.STD])
])
if cfg.DATASET.NAME == 'acdc':
test_set = AcdcDataset(data_list=cfg.DATASET.TEST_LIST,
df_used=cfg.DATASET.DF_USED,
df_norm=cfg.DATASET.DF_NORM,
boundary=cfg.DATASET.BOUNDARY,
joint_augment=eval_transform,
augment=evalImg_transform)
test_sampler = torch.utils.data.distributed.DistributedSampler(
test_set, num_replicas=dist.get_world_size(), rank=dist.get_rank())
test_loader = DataLoader(test_set,
batch_size=args.batch_size,
pin_memory=True,
num_workers=args.workers,
shuffle=False,
sampler=test_sampler,
collate_fn=BatchCollator(
size_divisible=32,
df_used=cfg.DATASET.DF_USED,
boundary=cfg.DATASET.BOUNDARY))
else:
test_loader = None
return train_loader, test_loader