def GetModel(model_file):
net = RefineNet(4, 5)
state_dict = torch.load(model_file)
rename_state_dict = {}
for key, value in state_dict.items():
rename_state_dict['.'.join(key.split('.')[1:])] = value
net.load_state_dict(rename_state_dict)
return net
else:
train_dataset = None
if need_val and len(val_folders) > 0:
val_dataset = ISLESDataset(val_folders, is_train=False)
else:
val_dataset = None
return train_dataset, val_dataset
if __name__ == '__main__':
fold = int(sys.argv[1])
train_dataset, val_dataset = GetDataset(fold, num_fold=6)
print('number of training %d' % len(train_dataset))
if val_dataset is not None:
print('number of validation %d' % len(val_dataset))
net = RefineNet(9, 2, dropout=False)
output_dir = './output/isles_%d' % fold
try:
os.makedirs(os.path.join(output_dir, 'model'))
except:
pass
try:
os.makedirs(os.path.join(output_dir, 'tensorboard'))
except:
pass
Train(train_dataset,
val_dataset,
net,
num_epoch=2000,
lr=0.0001,
else:
train_dataset = None
if need_val and len(val_folders)>0:
val_dataset = BRATSDataset(val_folders, is_train=False)
else:
val_dataset = None
return train_dataset, val_dataset
if __name__ == '__main__':
fold = int(sys.argv[1])
train_dataset, val_dataset = GetDataset(fold, num_fold=5)
print('number of training %d' % len(train_dataset))
if val_dataset is not None:
print('number of validation %d' % len(val_dataset))
#net = VoxResNet_V0(4, 5)
net = RefineNet(4,5)
#net = VoxResNet_V1(4, 5)
output_dir = './output/brast_%d' % fold
try:
os.makedirs(os.path.join(output_dir, 'model'))
except:
pass
try:
os.makedirs(os.path.join(output_dir, 'tensorboard'))
except:
pass
Train(train_dataset, val_dataset, net,
num_epoch=3000, lr=0.0001, output_dir=output_dir)
def GetModel(model_file_path):
net = RefineNet(4, 5)
state_dict = torch.load(model_file_path)
net.load_state_dict(state_dict)
return net
def GetModel(model_file=None):
net = RefineNet(4, 5)
return net
# val
if i_epoch % 100 == 0:
eval_dict_val = Evaluate(net, val_set, 'val')
for key, value in eval_dict_val.items():
solver.writer.add_scalar(key, value, i_epoch)
if __name__ == '__main__':
fold = int(sys.argv[1])
train_set, val_set = GetDataset(fold, num_fold=5)
print('Size of train set: %d' % len(train_set))
if val_set is not None:
print('Size of val set: %d' % len(val_set))
net = RefineNet(in_channels=4, num_classes=5)
output_dir = './output/brast_%d' % fold
try:
os.makedirs(os.path.join(output_dir, 'model'))
except:
pass
try:
os.makedirs(os.path.join(output_dir, 'tensorboard'))
except:
pass
Train(train_set,
val_set,
net,
num_epoch=1000,
lr=0.0001,