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()
#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 __init__(self,
model,
exp_name,
device=0,
class_num=2,
optim=torch.optim.Adam,
optim_args={},
loss_args={},
model_name='arcnet',
labels=None,
num_epochs=10,
log_nth=5,
lr_scheduler_step_size=3,
lr_scheduler_gamma=0.5,
use_last_checkpoint=True,
exp_dir='experiments',
log_dir='logs'):
self.device = device
self.model = model
self.model_name = model_name
self.num_epochs = num_epochs
self.vae = loss_args["vae_loss"]
# get the customized loss function
if self.vae:
loss_func = CombinedLoss(k1=loss_args["loss_k1_weight"],
k2=loss_args["loss_k2_weight"],
classes=class_num)
else:
loss_func = DiceLoss(classes=class_num)
if torch.cuda.is_available():
self.loss_func = loss_func.cuda(device)
else:
self.loss_func = loss_func
self.optim = optim(model.parameters(), **optim_args)
self.scheduler = lr_scheduler.StepLR(self.optim,
step_size=lr_scheduler_step_size,
gamma=lr_scheduler_gamma)
exp_dir_path = os.path.join(exp_dir, exp_name)
common_utils.create_if_not(exp_dir_path)
common_utils.create_if_not(os.path.join(exp_dir_path, CHECKPOINT_DIR))
self.exp_dir_path = exp_dir_path
self.log_nth = log_nth
self.logWriter = LogWriter(class_num, log_dir, exp_name,
use_last_checkpoint, labels)
self.use_last_checkpoint = use_last_checkpoint
self.start_epoch = 1
self.start_iteration = 1
self.best_ds_mean = 0
self.best_ds_mean_epoch = 1
if use_last_checkpoint:
self.load_checkpoint()
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()
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)
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.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)
criterion = DiceLoss(classes=args.classes)
if args.cuda:
torch.cuda.manual_seed(seed)
model = model.cuda()
print("Model transferred in GPU.....")
print("START TRAINING...")
for epoch in range(1, args.nEpochs + 1):
train_stats = train.train_dice(args, epoch, model, training_generator,
optimizer, criterion)
val_stats = train.test_dice(args, epoch, model, val_generator,
criterion)
#old
utils.write_train_val_score(writer, epoch, train_stats, val_stats)
model.save_checkpoint(args.save,
epoch,
val_stats[0],
optimizer=optimizer)
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"] = "1"
## FOR REPRODUCIBILITY OF RESULTS
seed = 1777777
torch.manual_seed(seed)
if args.cuda:
torch.cuda.manual_seed(seed)
np.random.seed(seed)
cudnn.deterministic = True
# FOR FASTER GPU TRAINING WHEN INPUT SIZE DOESN'T VARY
# cudnn.benchmark = True
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 = 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)
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()
utils.make_dirs(args.save)
train_f, val_f = utils.create_stats_files(args.save)
name_model = args.model + "_" + args.dataset_name + "_" + utils.datestr()
writer = SummaryWriter(log_dir='../runs/' + name_model, comment=name_model)
best_prec1 = 100.
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:
torch.cuda.manual_seed(seed)
model = model.cuda()
print("Model transferred in GPU.....")
print("START TRAINING...")
for epoch in range(1, args.nEpochs + 1):
train_stats = train.train_dice(args, epoch, model, training_generator,
optimizer, criterion, train_f, writer)
val_stats = train.test_dice(args, epoch, model, val_generator,
criterion, val_f, writer)
utils.write_train_val_score(writer, epoch, train_stats, val_stats)
model.save_checkpoint(args.save,
epoch,
val_stats[0],
optimizer=optimizer)
# if epoch % 5 == 0:
# utils.visualize_no_overlap(args, full_volume, affine, model, epoch, DIM, writer)
#utils.save_model(model, args, val_stats[0], epoch, best_prec1)
train_f.close()
val_f.close()
def __init__(self, k1=0.1, k2=0.1, classes=2):
super(CombinedLoss, self).__init__()
self.k1 = k2 #need or not
self.k2 = k2
self.dice_loss = DiceLoss(classes=classes)
