conf['in_channels'] = args.in_channels
conf['inter_channels'] = args.inter_channels
conf['num_iter'] = args.num_iter
conf['blur'] = args.blur
conf['num_iter'] = args.num_iter
conf['trainable'] = args.trainable
conf['manual_seed'] = args.manual_seed
conf['loss'] = {}
conf['loss']['name'] = args.loss
conf['eval_metric'] = {}
conf['eval_metric']['name'] = args.eval_metric
conf['eval_metric']['skip_channels'] = args.skip_channels
logger = get_logger('Trainer')
# Load and log experiment configuration
logger.info('The configurations: ')
for k, v in conf.items():
print('%s: %s' % (k, v))
manual_seed = conf.get('manual_seed', None)
if manual_seed is not None:
logger.info(f'Seed the RNG for all devices with {manual_seed}')
torch.manual_seed(manual_seed)
# see https://pytorch.org/docs/stable/notes/randomness.html
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
# Create the model
def main():
start_time = time.time()
mean = 466.0
std = 379.0
parser = get_parser()
args = parser.parse_args()
foldInd = args.fold_ind
dataDir = args.data_dir
foldIndData = np.load(os.path.join(dataDir, 'split_ind_fold' + str(foldInd) + '.npz'))
train_ind = foldIndData['train_ind']
val_ind = foldIndData['val_ind']
test_ind = foldIndData['test_ind']
conf = get_default_conf()
conf['manual_seed'] = args.seed
conf['device'] = args.device
conf['model']['name'] = args.model
conf['model']['gcn_mode'] = args.gcn_mode
conf['loss'] = {}
conf['loss']['name'] = args.loss
conf['loss']['lamda'] = args.lamda
conf['eval_metric'] = {}
conf['eval_metric']['name'] = args.eval_metric
conf['eval_metric']['skip_channels'] = args.skip_channels
conf['optimizer']['name'] = args.optimizer
conf['optimizer']['learning_rate'] = args.learning_rate
conf['lr_scheduler']['milestones'] = [args.epochs // 3, args.epochs // 1.5]
conf['trainer']['resume'] = args.resume
conf['trainer']['pre_trained'] = args.pre_trained
conf['trainer']['validate_after_iters'] = args.validate_after_iters
conf['trainer']['log_after_iters'] = args.log_after_iters
conf['trainer']['epochs'] = args.epochs
conf['trainer']['ds_weight'] = args.ds_weight
train_foldDir = os.path.join(dataDir, 'model', 'fold' + str(foldInd))
if not os.path.exists(train_foldDir):
os.makedirs(train_foldDir)
if args.loss == 'FPFNLoss':
return_weight = True
if 'gcn' in args.model:
if args.gcn_mode == 2:
identifier = args.model + '_gcn_mode_' + str(args.gcn_mode) + '_ds_weight_' + str(args.ds_weight) + \
'_loss_' + args.loss + '_lamda_' + str(
args.lamda) + '_' + args.optimizer + '_lr_' + str(args.learning_rate)
else:
identifier = args.model + '_gcn_mode_' + str(args.gcn_mode) + '_loss_' + args.loss + '_lamda_' + str(
args.lamda) \
+ '_' + args.optimizer + '_lr_' + str(args.learning_rate)
else:
identifier = args.model + '_loss_' + args.loss + '_lamda_' + str(
args.lamda) + '_' + args.optimizer + '_lr_' + str(args.learning_rate)
elif args.loss == 'PixelWiseCrossEntropyLoss':
return_weight = True
if 'gcn' in args.model:
if args.gcn_mode == 2:
identifier = args.model + '_gcn_mode_' + str(args.gcn_mode) + '_ds_weight_' + str(args.ds_weight) + \
'_loss_' + args.loss + '_' + args.optimizer + \
'_lr_' + str(args.learning_rate)
else:
identifier = args.model + '_gcn_mode_' + str(
args.gcn_mode) + '_loss_' + args.loss + '_' + args.optimizer + \
'_lr_' + str(args.learning_rate)
else:
identifier = args.model + '_loss_' + args.loss + '_' + args.optimizer + '_lr_' + str(args.learning_rate)
else:
return_weight = False
if 'gcn' in args.model:
if args.gcn_mode == 2:
identifier = args.model + '_gcn_mode_' + str(args.gcn_mode) + '_ds_weight_' + str(args.ds_weight) + \
'_loss_' + args.loss + '_' + args.optimizer + \
'_lr_' + str(args.learning_rate)
else:
identifier = args.model + '_gcn_mode_' + str(
args.gcn_mode) + '_loss_' + args.loss + '_' + args.optimizer + \
'_lr_' + str(args.learning_rate)
else:
identifier = args.model + '_loss_' + args.loss + '_' + args.optimizer + '_lr_' + str(args.learning_rate)
if args.seed != 0:
identifier = identifier + '_seed_' + str(args.seed)
if args.resume:
identifier = identifier + '_resume'
elif args.pre_trained:
identifier = identifier + '_pretrained'
out_dir = os.path.join(dataDir, 'out', 'fold' + str(foldInd), identifier)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
checkpoint_dir = os.path.join(train_foldDir, identifier)
# model_path = os.path.join(checkpoint_dir, 'best_checkpoint.pytorch')
model_path = os.path.join(checkpoint_dir, 'last_checkpoint.pytorch')
conf['trainer']['checkpoint_dir'] = checkpoint_dir
# Create main logger
logger = get_logger('UNet3DTrainer')
# Load and log experiment configuration
logger.info('The configurations: ')
for k, v in conf.items():
print('%s: %s' % (k, v))
manual_seed = conf.get('manual_seed', None)
if manual_seed is not None:
logger.info(f'Seed the RNG for all devices with {manual_seed}')
torch.manual_seed(manual_seed)
# see https://pytorch.org/docs/stable/notes/randomness.html
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
# Create the model
if args.model == 'DeepLabv3_plus_skipconnection_3d':
model = DeepLabv3_plus_skipconnection_3d(nInputChannels=conf['model']['in_channels'],
n_classes=conf['model']['out_channels'],
os=16, pretrained=False, _print=True,
final_sigmoid=conf['model']['final_sigmoid'],
normalization='bn',
num_groups=8, devices=conf['device'])
elif args.model == 'DeepLabv3_plus_gcn_skipconnection_3d':
model = DeepLabv3_plus_gcn_skipconnection_3d(nInputChannels=conf['model']['in_channels'], n_classes=conf['model']['out_channels'],
os=16, pretrained=False, _print=True, final_sigmoid=conf['model']['final_sigmoid'],
hidden_layers=conf['model']['hidden_layers'], devices=conf['device'],
gcn_mode=conf['model']['gcn_mode'])
elif args.model == 'UNet3D':
model = UNet3D(in_channels=conf['model']['in_channels'], out_channels=conf['model']['out_channels'],
final_sigmoid=conf['model']['final_sigmoid'], f_maps=32, layer_order='cbr')
elif args.model == 'ResidualUNet3D':
model = ResidualUNet3D(in_channels=conf['model']['in_channels'], out_channels=conf['model']['out_channels'],
final_sigmoid=conf['model']['final_sigmoid'], f_maps=32, conv_layer_order='cbr')
utils.load_checkpoint(model_path, model)
# put the model on GPUs
logger.info(f"Sending the model to '{conf['device']}'")
# Log the number of learnable parameters
logger.info(f'Number of learnable params {get_number_of_learnable_parameters(model)}')
# Create evaluation metric
eval_criterion = get_evaluation_metric(conf)
model.eval()
eval_scores = []
for i in test_ind:
raw_path = os.path.join(dataDir,'in', 'nii', 'original_mr', 'Case' + str(i) + '.nii.gz')
label_path = os.path.join(dataDir, 'in', 'nii', 'mask', 'mask_case' + str(i) + '.nii.gz')
raw_nii = nib.load(raw_path)
raw = raw_nii.get_data()
H, W, D = raw.shape # [H, W, D]
transform_scale = [256./(H/2.), 512./W, 1.0]
transform_raw, crop_h_start, crop_h_end = transform_volume(raw, is_crop=True, is_expand_dim=False,
scale=transform_scale, interpolation='cubic')
flag = False
new_d = 18
if D < new_d:
transform_raw, start_d, end_d = pad_d(transform_raw, new_d=new_d)
D = new_d
flag = True
else:
start_d = 0
end_d = 0
original_shape = [H, W, D]
transform_raw -= mean
transform_raw /= std
transform_raw = np.expand_dims(transform_raw, axis=0)
transform_raw = np.expand_dims(transform_raw, axis=0)
transform_raw = torch.from_numpy(transform_raw.astype(np.float32))
label_nii = nib.load(label_path)
label = label_nii.get_data() # numpy
label = label.transpose((2, 0, 1)) # [D, H, W]
label = np.expand_dims(label, axis=0) # [1, D, H, W]
label_tensor = torch.from_numpy(label.astype(np.int64))
with torch.no_grad():
data = transform_raw.to(conf['device'][0])
output = model(data) # batch, classes, d, h, w
prob = output[0]
prob_tensor = inv_transform_volume_tensor(input=prob, crop_h_start=crop_h_start, crop_h_end=crop_h_end,
original_shape=original_shape,
is_crop_d=flag, start_d=start_d, end_d=end_d)
eval_score = eval_criterion(prob_tensor, label_tensor)
if args.eval_metric == 'DiceCoefficient':
print('Case %d, dice = %f' % (i, eval_score))
elif args.eval_metric == 'MeanIoU':
print('Case %d, IoU = %f' % (i, eval_score))
eval_scores.append(eval_score)
prob = prob_tensor.to('cpu').numpy()
prob = np.squeeze(prob, axis=0) # numpy, [C, D, H, W]
seg = np.argmax(prob, axis=0).astype(np.uint8) # numpy, [D, H, W]
seg = seg.transpose((1,2,0)) # numpy, [H, W, D]
path_split = raw_path.split(os.sep)
seg_path = os.sep.join([out_dir, 'seg_' + path_split[-1]])
segNii = nib.Nifti1Image(seg.astype(np.uint8), affine=raw_nii.affine)
nib.save(segNii, seg_path)
if args.eval_metric == 'DiceCoefficient':
print('mean dice = %f' % np.mean(eval_scores))
np.savez(os.path.join(out_dir, 'eval_scores.npz'), dice=eval_scores, mean_dice=np.mean(eval_scores))
elif args.eval_metric == 'MeanIoU':
print('mean IOU = %f' % np.mean(eval_scores))
np.savez(os.path.join(out_dir, 'eval_scores.npz'), iou=eval_scores, mean_iou=np.mean(eval_scores))
end_time = time.time()
total_time = end_time - start_time
mean_time = total_time / test_ind.size
print('mean time for segmenting one volume: %.2f seconds' % mean_time)
def main():
parser = get_parser()
args = parser.parse_args()
foldInd = args.fold_ind
dataDir = args.data_dir
filePath = os.path.join(dataDir, 'in', 'h5py', 'data_fold' + str(foldInd) + '.h5')
batch_size = args.batch_size
conf = get_default_conf()
conf['manual_seed'] = args.seed
conf['device'] = args.device
conf['model']['name'] = args.model
conf['model']['gcn_mode'] = args.gcn_mode
conf['loss'] = {}
conf['loss']['name'] = args.loss
conf['loss']['lamda'] = args.lamda
conf['eval_metric'] = {}
conf['eval_metric']['name'] = args.eval_metric
conf['eval_metric']['skip_channels'] = args.skip_channels
conf['optimizer']['name'] = args.optimizer
conf['optimizer']['learning_rate'] = args.learning_rate
conf['lr_scheduler']['milestones'] = [args.epochs // 3, args.epochs // 1.5]
# conf['trainer']['resume'] = args.resume
# conf['trainer']['pre_trained'] = args.pre_trained
conf['trainer']['validate_after_iters'] = args.validate_after_iters
conf['trainer']['log_after_iters'] = args.log_after_iters
conf['trainer']['epochs'] = args.epochs
conf['trainer']['ds_weight'] = args.ds_weight
foldDir = os.path.join(dataDir, 'model', 'fold' + str(foldInd))
if not os.path.exists(foldDir):
os.makedirs(foldDir)
if args.loss == 'FPFNLoss':
return_weight = True
if 'gcn' in args.model:
if args.gcn_mode == 2:
identifier = args.model + '_gcn_mode_' + str(args.gcn_mode) + '_ds_weight_' + str(args.ds_weight) + \
'_loss_' + args.loss + '_lamda_' + str(
args.lamda) + '_' + args.optimizer + '_lr_' + str(args.learning_rate)
else:
identifier = args.model + '_gcn_mode_' + str(args.gcn_mode) + '_loss_' + args.loss + '_lamda_' + str(args.lamda)\
+ '_' + args.optimizer + '_lr_' + str(args.learning_rate)
else:
identifier = args.model + '_loss_' + args.loss + '_lamda_' + str(
args.lamda) + '_' + args.optimizer + '_lr_' + str(args.learning_rate)
elif args.loss == 'PixelWiseCrossEntropyLoss':
return_weight = True
if 'gcn' in args.model:
if args.gcn_mode == 2:
identifier = args.model + '_gcn_mode_' + str(args.gcn_mode) + '_ds_weight_' + str(args.ds_weight) + \
'_loss_' + args.loss + '_' + args.optimizer + \
'_lr_' + str(args.learning_rate)
else:
identifier = args.model + '_gcn_mode_' + str(
args.gcn_mode) + '_loss_' + args.loss + '_' + args.optimizer + \
'_lr_' + str(args.learning_rate)
else:
identifier = args.model + '_loss_' + args.loss + '_' + args.optimizer + '_lr_' + str(args.learning_rate)
else:
return_weight = False
if 'gcn' in args.model:
if args.gcn_mode == 2:
identifier = args.model + '_gcn_mode_' + str(args.gcn_mode) + '_ds_weight_' + str(args.ds_weight) + \
'_loss_' + args.loss + '_' + args.optimizer + \
'_lr_' + str(args.learning_rate)
else:
identifier = args.model + '_gcn_mode_' + str(args.gcn_mode) + '_loss_' + args.loss + '_' + args.optimizer + \
'_lr_' + str(args.learning_rate)
else:
identifier = args.model + '_loss_' + args.loss + '_' + args.optimizer + '_lr_' + str(args.learning_rate)
if args.seed != 0:
identifier = identifier + '_seed_' + str(args.seed)
if args.resume:
identifier = identifier + '_resume'
conf['trainer']['resume'] = os.path.join(foldDir, identifier,'best_checkpoint.pytorch')
if not os.path.exists(os.path.join(foldDir, identifier)):
if args.seed == 0:
src = os.path.join(foldDir, 'DeepLabv3_plus_skipconnection_3d' +
'_loss_' + args.loss + '_' + args.optimizer + '_lr_' + str(args.learning_rate))
else:
src = os.path.join(foldDir, 'DeepLabv3_plus_skipconnection_3d' +
'_loss_' + args.loss + '_' + args.optimizer + '_lr_' + str(args.learning_rate)
+ '_seed_' + str(args.seed))
shutil.copytree(src=src, dst=os.path.join(foldDir, identifier))
elif args.pre_trained:
identifier = identifier + '_pretrained'
conf['trainer']['pre_trained'] = os.path.join(foldDir, identifier, 'best_checkpoint.pytorch')
if not os.path.exists(os.path.join(foldDir, identifier)):
if args.seed == 0:
# src = os.path.join(foldDir, 'DeepLabv3_plus_skipconnection_3d' +
# '_loss_' + args.loss + '_' + args.optimizer + '_lr_' + str(args.learning_rate))
src = os.path.join(foldDir, 'DeepLabv3_plus_skipconnection_3d' +
'_loss_' + args.loss + '_' + args.optimizer + '_lr_0.001')
else:
# src = os.path.join(foldDir, 'DeepLabv3_plus_skipconnection_3d' +
# '_loss_' + args.loss + '_' + args.optimizer + '_lr_' + str(args.learning_rate)
# + '_seed_' + str(args.seed))
src = os.path.join(foldDir, 'DeepLabv3_plus_skipconnection_3d' +
'_loss_' + args.loss + '_' + args.optimizer + '_lr_0.001'
+ '_seed_' + str(args.seed))
shutil.copytree(src=src, dst=os.path.join(foldDir, identifier))
checkpoint_dir = os.path.join(foldDir, identifier)
conf['trainer']['checkpoint_dir'] = checkpoint_dir
# Create main logger
logger = get_logger('UNet3DTrainer')
# Load and log experiment configuration
logger.info('The configurations: ')
for k, v in conf.items():
print('%s: %s' % (k, v))
manual_seed = conf.get('manual_seed', None)
if manual_seed is not None:
logger.info(f'Seed the RNG for all devices with {manual_seed}')
torch.manual_seed(manual_seed)
# see https://pytorch.org/docs/stable/notes/randomness.html
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
# Create the model
if args.model == 'DeepLabv3_plus_skipconnection_3d':
model = DeepLabv3_plus_skipconnection_3d(nInputChannels=conf['model']['in_channels'],
n_classes=conf['model']['out_channels'],
os=16, pretrained=False, _print=True,
final_sigmoid=conf['model']['final_sigmoid'],
normalization='bn',
num_groups=8, devices=conf['device'])
elif args.model == 'DeepLabv3_plus_gcn_skipconnection_3d':
model = DeepLabv3_plus_gcn_skipconnection_3d(nInputChannels=conf['model']['in_channels'], n_classes=conf['model']['out_channels'],
os=16, pretrained=False, _print=True, final_sigmoid=conf['model']['final_sigmoid'],
hidden_layers=conf['model']['hidden_layers'], devices=conf['device'],
gcn_mode=conf['model']['gcn_mode'])
elif args.model == 'UNet3D':
model = UNet3D(in_channels=conf['model']['in_channels'], out_channels=conf['model']['out_channels'],
final_sigmoid=conf['model']['final_sigmoid'], f_maps=32, layer_order='cbr')
elif args.model == 'ResidualUNet3D':
model = ResidualUNet3D(in_channels=conf['model']['in_channels'], out_channels=conf['model']['out_channels'],
final_sigmoid=conf['model']['final_sigmoid'], f_maps=32, conv_layer_order='cbr')
# put the model on GPUs
logger.info(f"Sending the model to '{conf['device']}'")
# model = model.to(conf['device'])
# Log the number of learnable parameters
logger.info(f'Number of learnable params {get_number_of_learnable_parameters(model)}')
# Create loss criterion
loss_criterion = get_loss_criterion(conf)
# Create evaluation metric
eval_criterion = get_evaluation_metric(conf)
# Create data loaders
train_data_loader, val_data_loader, test_data_loader, f = load_data(filePath=filePath, return_weight=return_weight,
transformer_config=conf['transformer'],
batch_size=batch_size)
loaders = {'train': train_data_loader, 'val': val_data_loader}
# Create the optimizer
optimizer = _create_optimizer(conf, model)
# Create learning rate adjustment strategy
lr_scheduler = _create_lr_scheduler(conf, optimizer)
# Create model trainer
trainer = _create_trainer(conf, model=model, optimizer=optimizer, lr_scheduler=lr_scheduler,
loss_criterion=loss_criterion, eval_criterion=eval_criterion, loaders=loaders,
logger=logger)
# Start training
trainer.fit()
def main():
parser = get_parser()
args = parser.parse_args()
data_dir = args.data_dir
fold_ind = args.fold_ind
batch_size = args.batch_size
if args.coarse_identifier == 'DeepLabv3_plus_gcn_skipconnection_3d_gcn_mode_2_ds_weight_0.3_loss_' \
'CrossEntropyLoss_Adam_lr_0.001_pretrained':
data_path = os.path.join(data_dir,
'in/h5py/fold' + str(fold_ind) + '_data.h5')
else:
data_path = os.path.join(
data_dir, 'in/h5py/fold' + str(fold_ind) + '_data_' +
args.coarse_identifier + '.h5')
if args.coarse_identifier == 'DeepLabv3_plus_gcn_skipconnection_3d_gcn_mode_2_ds_weight_0.3_loss_' \
'CrossEntropyLoss_Adam_lr_0.001_pretrained':
fold_dir = os.path.join(data_dir, 'model', 'fold' + str(fold_ind))
else:
fold_dir = os.path.join(
data_dir, 'model',
'fold' + str(fold_ind) + '_' + args.coarse_identifier)
if not os.path.exists(fold_dir):
os.makedirs(fold_dir)
if args.model == 'non_local_crf':
identifier = args.model + '_' + args.optimizer + '_lr_' + str(
args.learning_rate) + '_num_iter_' + str(args.num_iter)
elif args.model == 'DeepLabv3_plus_gcn_skipconnection_2d':
if args.gcn_mode == 2:
identifier = args.model + '_gcn_mode_' + str(args.gcn_mode) + '_ds_weight_' + str(args.ds_weight) +\
'_' + args.optimizer + '_lr_' + str(
args.learning_rate) + '_weight_decay_' + str(
args.weight_decay)
else:
identifier = args.model + '_gcn_mode_' + str(
args.gcn_mode) + '_' + args.optimizer + '_lr_' + str(
args.learning_rate) + '_weight_decay_' + str(
args.weight_decay)
if args.use_unary is False:
identifier = identifier + '_noUnary'
in_channels = 1
else:
in_channels = 21
else:
identifier = args.model + '_' + args.optimizer + '_lr_' + str(
args.learning_rate) + '_weight_decay_' + str(args.weight_decay)
if args.use_unary is False:
identifier = identifier + '_noUnary'
in_channels = 1
else:
in_channels = 21
if args.augment:
identifier = identifier + '_augment'
if args.loss != 'CrossEntropyLoss':
identifier = identifier + '_loss_' + args.loss
conf = get_default_conf()
conf['device'] = args.device
conf['manual_seed'] = args.manual_seed
conf['loss'] = {}
conf['loss']['name'] = args.loss
conf['eval_metric'] = {}
conf['eval_metric']['name'] = args.eval_metric
conf['eval_metric']['skip_channels'] = args.skip_channels
conf['optimizer'] = {}
conf['optimizer']['name'] = args.optimizer
conf['optimizer']['learning_rate'] = args.learning_rate
conf['optimizer']['weight_decay'] = args.weight_decay
conf['optimizer']['momentum'] = args.momentum
conf['optimizer']['nesterov'] = args.nesterov
conf['lr_scheduler'] = {}
conf['lr_scheduler']['name'] = 'MultiStepLR'
conf['lr_scheduler']['milestones'] = [args.epochs // 3, args.epochs // 1.5]
conf['lr_scheduler']['gamma'] = args.gamma
conf['trainer'] = {}
conf['trainer']['batch_size'] = batch_size
conf['trainer']['epochs'] = args.epochs
conf['trainer']['iters'] = args.iters
conf['trainer'][
'eval_score_higher_is_better'] = args.eval_score_higher_is_better
conf['trainer']['ds_weight'] = args.ds_weight
if args.loss == 'PixelWiseCrossEntropyLoss':
return_weight = True
else:
return_weight = False
if args.resume:
identifier = identifier + '_resume'
conf['trainer']['resume'] = os.path.join(fold_dir, identifier,
'best_checkpoint.pytorch')
if not os.path.exists(os.path.join(fold_dir, identifier)):
src = os.path.join(
fold_dir, 'DeepLabv3_plus_skipconnection_2d' + '_Adam_lr_' +
str(args.learning_rate) + '_weight_decay_' +
str(args.weight_decay) + '_noUnary_augment')
shutil.copytree(src=src, dst=os.path.join(fold_dir, identifier))
elif args.pre_trained:
identifier = identifier + '_pretrained'
conf['trainer']['pre_trained'] = os.path.join(
fold_dir, identifier, 'best_checkpoint.pytorch')
if not os.path.exists(os.path.join(fold_dir, identifier)):
src = os.path.join(
fold_dir, 'DeepLabv3_plus_skipconnection_2d' + '_Adam_lr_' +
str(args.learning_rate) + '_weight_decay_' +
str(args.weight_decay) + '_noUnary_augment')
shutil.copytree(src=src, dst=os.path.join(fold_dir, identifier))
checkpoint_dir = os.path.join(fold_dir, identifier)
conf['trainer']['checkpoint_dir'] = checkpoint_dir
logger = get_logger('Trainer')
# Load and log experiment configuration
logger.info('The configurations: ')
for k, v in conf.items():
print('%s: %s' % (k, v))
manual_seed = conf.get('manual_seed', None)
if manual_seed is not None:
logger.info(f'Seed the RNG for all devices with {manual_seed}')
torch.manual_seed(manual_seed)
# see https://pytorch.org/docs/stable/notes/randomness.html
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
# Create the model
if args.model == 'UNet2D':
model = UNet2D(in_channels=in_channels,
out_channels=20,
final_sigmoid=False,
f_maps=32,
layer_order='cbr',
num_groups=8)
elif args.model == 'ResidualUNet2D':
model = ResidualUNet2D(in_channels=in_channels,
out_channels=20,
final_sigmoid=False,
f_maps=32,
conv_layer_order='cbr',
num_groups=8)
elif args.model == 'DeepLabv3_plus_skipconnection_2d':
model = DeepLabv3_plus_skipconnection_2d(nInputChannels=in_channels,
n_classes=20,
os=16,
pretrained=False,
_print=True,
final_sigmoid=False)
elif args.model == 'DeepLabv3_plus_gcn_skipconnection_2d':
model = DeepLabv3_plus_gcn_skipconnection_2d(
nInputChannels=in_channels,
n_classes=20,
os=16,
pretrained=False,
_print=True,
final_sigmoid=False,
hidden_layers=128,
gcn_mode=args.gcn_mode,
device=conf['device'])
# put the model on GPUs
logger.info(f"Sending the model to '{conf['device']}'")
model = model.to(conf['device'])
# Log the number of learnable parameters
logger.info(
f'Number of learnable params {get_number_of_learnable_parameters(model)}'
)
# Create loss criterion
loss_criterion = get_loss_criterion(conf)
# Create evaluation metric
eval_criterion = get_evaluation_metric(conf)
try:
if args.augment:
train_data_loader, val_data_loader, test_data_loader, f = load_data(
data_path,
batch_size=batch_size,
transformer_config=conf['transformer'],
return_weight=return_weight)
else:
train_data_loader, val_data_loader, test_data_loader, f = load_data(
data_path, batch_size=batch_size, return_weight=return_weight)
conf['trainer']['validate_after_iters'] = len(train_data_loader)
conf['trainer']['log_after_iters'] = len(train_data_loader)
# Create the optimizer
optimizer = _create_optimizer(conf, model)
# Create learning rate adjustment strategy
lr_scheduler = _create_lr_scheduler(conf, optimizer)
# Create model trainer
trainer = _create_trainer(conf,
model=model,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
loss_criterion=loss_criterion,
eval_criterion=eval_criterion,
train_loader=train_data_loader,
val_loader=val_data_loader,
logger=logger)
# Start training
trainer.fit()
finally:
f.close()
import importlib
import numpy as np
import torch
import torch.nn.functional as F
from skimage import measure
from networks.losses import compute_per_channel_dice, expand_as_one_hot
from networks.utils import get_logger, adapted_rand
LOGGER = get_logger('EvalMetric')
SUPPORTED_METRICS = [
'dice', 'iou', 'boundary_ap', 'dt_ap', 'quantized_dt_ap', 'angle',
'inverse_angular'
]
class DiceCoefficient:
"""Computes Dice Coefficient.
Generalized to multiple channels by computing per-channel Dice Score
(as described in https://arxiv.org/pdf/1707.03237.pdf) and theTn simply taking the average.
Input is expected to be probabilities instead of logits.
This metric is mostly useful when channels contain the same semantic class (e.g. affinities computed with different offsets).
DO NOT USE this metric when training with DiceLoss, otherwise the results will be biased towards the loss.
"""
def __init__(self,
epsilon=1e-5,
ignore_index=None,
skip_channels=None,
**kwargs):
