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()
output = non_overlap_padding(args,
x,
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='/home/mulns/My_project/VV/MedicalZooPytorch/datasets')
model, optimizer = medzoo.create_model(args)
criterion = DiceLoss(classes=args.classes)
if args.cuda:
# model=torch.nn.DataParallel(model)
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()
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 main():
args = get_arguments()
utils.reproducibility(args, seed)
# utils.make_dirs(args.save)
if not os.path.exists(args.save):
os.makedirs(args.save)
# training_generator, val_generator, full_volume, affine = medical_loaders.generate_datasets(args,
training_generator, val_generator, full_volume, affine, dataset = medical_loaders.generate_datasets(args,
path='/data/hejy/MedicalZooPytorch_2cls/datasets')
model, optimizer = medzoo.create_model(args)
criterion = DiceLoss(classes=2, skip_index_after=args.classes, weight = torch.tensor([1, 1]).cuda(), sigmoid_normalization=True)
# criterion = WeightedCrossEntropyLoss()
if args.cuda:
model = model.cuda()
# model.restore_checkpoint(args.pretrained)
dataloader_train = MICCAI2020_RIBFRAC_DataLoader3D(dataset, args.batchSz, args.dim, num_threads_in_multithreaded=2)
tr_transforms = get_train_transform(args.dim)
training_generator_aug = SingleThreadedAugmenter(dataloader_train, tr_transforms,)
trainer = train.Trainer(args, model, criterion, optimizer, train_data_loader=training_generator,
valid_data_loader=val_generator, lr_scheduler=None, dataset = dataset, train_data_loader_aug=training_generator_aug)
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')
def main():
args = get_arguments()
utils.reproducibility(args, seed)
# utils.make_dirs(args.save)
if not os.path.exists(args.save):
os.makedirs(args.save)
training_generator, val_generator, full_volume, affine, dataset = medical_loaders.generate_datasets(
args, path='/data/hejy/MedicalZooPytorch_2cls/datasets')
model, optimizer = medzoo.create_model(args)
criterion = DiceLoss(classes=2,
skip_index_after=args.classes,
weight=torch.tensor([1, 1]).cuda(),
sigmoid_normalization=True)
# criterion = WeightedCrossEntropyLoss()
if args.cuda:
model = model.cuda()
# model.restore_checkpoint(args.pretrained)
trainer = train.Trainer(args,
model,
criterion,
optimizer,
train_data_loader=training_generator,
valid_data_loader=val_generator,
lr_scheduler=None)
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()
os.environ["CUDA_VISIBLE_DEVICES"] = "0,2"
## 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 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 main():
args = get_arguments()
if args.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend='nccl',
init_method='env://')
args.world_size = torch.distributed.get_world_size()
assert torch.backends.cudnn.enabled, "Amp requires cudnn backend to be enabled."
torch.backends.cudnn.benchmark = True
utils.reproducibility(args, seed)
# utils.make_dirs(args.save)
if not os.path.exists(args.save):
os.makedirs(args.save)
training_generator, val_generator, full_volume, affine, dataset = medical_loaders.generate_datasets(
args, path='/data/hejy/MedicalZooPytorch_2cls/datasets')
model, optimizer = medzoo.create_model(args)
if args.sync_bn:
model = apex.parallel.convert_syncbn_model(model)
criterion = DiceLoss(classes=2,
skip_index_after=args.classes,
weight=torch.tensor([1, 1]).cuda(),
sigmoid_normalization=True)
# criterion = WeightedCrossEntropyLoss()
if args.cuda:
model = model.cuda()
if args.distributed:
model = DDP(model, delay_allreduce=True)
# model.restore_checkpoint(args.pretrained)
trainer = train.Trainer(args,
model,
criterion,
optimizer,
train_data_loader=training_generator,
valid_data_loader=val_generator,
lr_scheduler=None)
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)
# print("training_generator shape:", training_generator.dim())
# print("val_generator shape:", val_generator.dim())
if args.cuda:
model = model.cuda()
print("start training...")
# torch.save(training_generator, "training_generator.tch")
# torch.save(val_generator, "val_generator.tch")
trainer = train.Trainer(args, model, criterion, optimizer, train_data_loader=training_generator,
valid_data_loader=val_generator)
trainer.training()
def main():
args = get_arguments()
if args.distributed:
torch.distributed.init_process_group(backend='nccl',
init_method='env://')
args.world_size = torch.distributed.get_world_size()
assert torch.backends.cudnn.enabled, "Amp requires cudnn backend to be enabled." #1
torch.backends.cudnn.benchmark = True
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=11, skip_index_after=args.classes)
if args.sync_bn:
model = apex.parallel.convert_syncbn_model(model)
if args.cuda:
model = model.cuda()
print("Model transferred in GPU.....")
if args.distributed:
model = DDP(model, delay_allreduce=True)
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()
