def train():
# args = brats2019_arguments()
utils.reproducibility(args, seed)
utils.make_dirs(args.save)
(
training_generator,
val_generator,
full_volume,
affine,
) = medical_loaders.generate_datasets(args)
model, optimizer = medzoo.create_model(args)
val_criterion = DiceLoss(classes=11, skip_index_after=args.classes)
# criterion = DiceLoss(classes=3, skip_index_after=args.classes)
# criterion = DiceLoss(classes=args.classes)
criterion = torch.nn.CrossEntropyLoss()
if args.cuda:
model = model.cuda()
print("Model transferred in GPU.....")
trainer = train_module.Trainer(
args,
model,
criterion,
optimizer,
val_criterion=val_criterion,
train_data_loader=training_generator,
valid_data_loader=val_generator,
lr_scheduler=None,
)
print("START TRAINING...")
trainer.training()
def main():
args = get_arguments()
utils.reproducibility(args, seed)
utils.make_dirs(args.save)
training_generator, val_generator, full_volume, affine = medical_loaders.generate_datasets(
args, path='.././datasets')
model, optimizer = medzoo.create_model(args)
criterion = DiceLoss(classes=args.classes)
if args.cuda:
model = model.cuda()
print("Model transferred in GPU.....")
trainer = train.Trainer(args,
model,
criterion,
optimizer,
train_data_loader=training_generator,
valid_data_loader=val_generator,
lr_scheduler=None)
print("START TRAINING...")
trainer.training()
visualize_3D_no_overlap_new(args, full_volume, affine, model, 10, args.dim)
def main():
args = get_arguments()
utils.reproducibility(args, seed)
utils.make_dirs(args.save)
training_generator, val_generator, full_volume, affine = medical_loaders.generate_datasets(args,
path='.././datasets')
model, optimizer = medzoo.create_model(args)
criterion = DiceLoss(classes=args.classes)
if args.cuda:
model = model.cuda()
trainer = train.Trainer(args, model, criterion, optimizer, train_data_loader=training_generator,
valid_data_loader=val_generator)
trainer.training()
def main():
args = get_arguments()
utils.reproducibility(args, seed)
utils.make_dirs(args.save)
training_generator, val_generator, full_volume, affine = medical_loaders.generate_datasets(
args, path='.././datasets')
model, optimizer = medzoo.create_model(args)
criterion = DiceLoss(
classes=args.classes
) # ,skip_index_after=2,weight=torch.tensor([0.00001,1,1,1]).cuda())
if args.cuda:
model = model.cuda()
print("Model transferred in GPU.....")
trainer = train.Trainer(args,
model,
criterion,
optimizer,
train_data_loader=training_generator,
valid_data_loader=val_generator)
print("START TRAINING...")
trainer.training()