def check_livecell_images():
patch_shape = (512, 512)
loader = get_livecell_loader(PATH,
patch_shape,
"train",
download=True,
batch_size=1)
check_loader(loader, 10, instance_labels=True)
def check(args, train=True, val=True, n_images=2):
from torch_em.util.debug import check_loader
if train:
print("Check train loader")
loader = get_loader(args.input, is_train=True, n_samples=100)
check_loader(loader, n_images)
if val:
print("Check val loader")
loader = get_loader(args.input, is_train=False, n_samples=100)
check_loader(loader, n_images)
def check(args, train=True, val=True, n_images=2):
from torch_em.util.debug import check_loader
patch_shape = [32, 320, 320]
if train:
print("Check train loader")
loader = get_loader(args.input, True, patch_shape)
check_loader(loader, n_images)
if val:
print("Check val loader")
loader = get_loader(args.input, False, patch_shape)
check_loader(loader, n_images)
def check(input_path, samples, train=True, val=True, n_images=5):
from torch_em.util.debug import check_loader
patch_shape = [32, 256, 256]
if train:
print("Check train loader")
loader = get_loader(input_path, samples, splits=["train"], patch_shape=patch_shape)
check_loader(loader, n_images)
if val:
print("Check val loader")
loader = get_loader(input_path, samples, splits=["val"], patch_shape=patch_shape)
check_loader(loader, n_images)
def check(train=True, val=True, n_images=5):
from torch_em.util.debug import check_loader
patch_shape = [1, 512, 512]
if train:
print("Check train loader")
loader = get_loader('train', patch_shape, batch_size=1)
check_loader(loader, n_images)
if val:
print("Check val loader")
loader = get_loader('val', patch_shape, batch_size=1)
check_loader(loader, n_images)
def check(args, train=True, val=True, n_images=2):
from torch_em.util.debug import check_loader
patch_shape = [32, 256, 256]
samples, _ = normalize_samples(args.samples)
if train:
print("Check train loader")
loader = get_loader(args.input, samples, True, patch_shape)
check_loader(loader, n_images)
if val:
print("Check val loader")
loader = get_loader(args.input, samples, False, patch_shape)
check_loader(loader, n_images)
def check(datasets, train=True, val=True, n_images=5):
from torch_em.util.debug import check_loader
patch_shape = [32, 256, 256]
if train:
print("Check train loader")
dsets = [f'{ds}_train' for ds in datasets]
loader = get_loader(dsets, patch_shape)
check_loader(loader, n_images)
if val:
print("Check val loader")
dsets = [f'{ds}_val' for ds in datasets]
loader = get_loader(dsets, patch_shape)
check_loader(loader, n_images)
def check_loader(args, n=4):
from torch_em.util.debug import check_loader
loader = get_isbi_loader(args, "train", "./checkpoints/isbi2d/rfs")
check_loader(loader, n)
trainer = torch_em.default_segmentation_trainer(name,
model,
train_loader,
val_loader,
loss=dice_loss,
metric=dice_loss,
learning_rate=1.0e-4,
device=args.device,
log_image_interval=50)
trainer.fit(args.n_iterations)
def check_loader(args, n=4):
from torch_em.util.debug import check_loader
loader = get_isbi_loader(args, "train", "./checkpoints/isbi2d/rfs")
check_loader(loader, n)
if __name__ == "__main__":
parser = torch_em.util.parser_helper()
parser.add_argument("-p", "--pseudo_label", type=int, default=0)
parser.add_argument("--n_rfs", type=int, default=500)
parser.add_argument("--n_threads", type=int, default=32)
args = parser.parse_args()
if args.check:
check_loader(args)
elif args.pseudo_label:
train_pseudo_label(args)
else:
train_shallow2deep(args)
def check():
from torch_em.util.debug import check_loader
loader = get_loader('train')
check_loader(loader, 4)