def main():
args = get_arguments()
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
## FOR REPRODUCIBILITY OF RESULTS
seed = 1777777
utils.reproducibility(args, seed)
utils.make_dirs(args.save)
utils.save_arguments(args, args.save)
training_generator, val_generator, full_volume, affine = medical_loaders.generate_datasets(
args, path='.././datasets')
model, optimizer = medzoo.create_model(args)
criterion = create_loss('CrossEntropyLoss')
criterion = DiceLoss(classes=args.classes,
weight=torch.tensor([0.1, 1, 1, 1]).cuda())
if args.cuda:
model = model.cuda()
print("Model transferred in GPU.....")
trainer = Trainer(args,
model,
criterion,
optimizer,
train_data_loader=training_generator,
valid_data_loader=val_generator,
lr_scheduler=None)
print("START TRAINING...")
trainer.training()
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()
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
## FOR REPRODUCIBILITY OF RESULTS
seed = 1777777
utils.reproducibility(args, seed)
utils.make_dirs(args.save)
name_model = args.model + "_" + args.dataset_name + "_" + utils.datestr()
# TODO visual3D_temp.Basewriter package
writer = SummaryWriter(log_dir='./runs/' + name_model, comment=name_model)
training_generator, val_generator, full_volume, affine = medical_loaders.generate_datasets(
args, path='.././datasets')
model, optimizer = medzoo.create_model(args)
if args.cuda:
model = model.cuda()
print("Model transferred in GPU.....")
print("START TRAINING...")
for epoch in range(1, args.nEpochs + 1):
train(args, model, training_generator, optimizer, epoch, writer)
val_metrics, confusion_matrix = validation(args, model, val_generator,
epoch, writer)
def ixi(self):
self.args.inChannels = 2
self.args.inModalities = 2
self.args.dim = (1, 1, 1)
self.args.dataset_name = "ixi"
generator, affine = medical_loaders.generate_datasets(self.args, path='.././datasets')
print("ixi OK!", len(generator))
def main():
args = get_arguments()
#os.environ["CUDA_VISIBLE_DEVICES"] = "0"
## FOR REPRODUCIBILITY OF RESULTS
seed = 1777777
utils.reproducibility(args, seed)
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)
#
# ## TODO LOAD PRETRAINED MODEL
print(affine.shape)
model.restore_checkpoint(args.pretrained)
if args.cuda:
model = model.cuda()
full_volume = full_volume.cuda()
print("Model transferred in GPU.....")
x = torch.randn(3, 156, 240, 240).cuda()
print(full_volume.shape)
output = non_overlap_padding(args,
full_volume,
model,
criterion,
kernel_dim=(32, 32, 32))
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 brats2018(self):
self.args.inChannels = 4
self.args.inModalities = 4
self.args.classes = 5
self.args.dataset_name = "brats2018"
training_generator, val_generator, full_volume, affine = medical_loaders.generate_datasets(self.args,
path='.././datasets')
print("brats2018 OK!", len(training_generator), len(val_generator))
def ISEG2017(self):
self.args.inChannels = 2
self.args.inModalities = 2
self.args.dataset_name = "iseg2017"
training_generator, val_generator, full_volume, affine = medical_loaders.generate_datasets(self.args,
path='.././datasets')
print("iseg OK! ", len(training_generator), len(val_generator))
def miccai2019(self):
self.args.dim = (64, 64)
self.args.inChannels = 3
self.args.inModalities = 1
self.args.classes = 7
self.args.dataset_name = "miccai2019"
training_generator, val_generator, full_volume, affine = medical_loaders.generate_datasets(self.args,
path='.././datasets')
print("miccai2019 OK!", len(training_generator), len(val_generator))
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()
def MRBRAINS_9_class(self):
self.args.classes = 9
training_generator, val_generator, full_volume, affine = medical_loaders.generate_datasets(self.args,
path='.././datasets')
print("mrbrains 8 OK!", len(training_generator), len(val_generator))
