args = vars(parser.parse_args())
# enforce CPU processing if necessary
if args['cpu']:
print('using CPU, hiding all CUDA_VISIBLE_DEVICES')
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
del args['cpu']
# limit the number of threads to be used if running on CPU
import tensorflow as tf
tf.config.threading.set_intra_op_parallelism_threads(args['threads'])
del args['threads']
# default parameters
path_label_list = os.path.join(
synthseg_home,
'data/labels_classes_priors/SynthSeg_segmentation_labels.npy')
path_names_list = os.path.join(
synthseg_home,
'data/labels_classes_priors/SynthSeg_segmentation_names.npy')
path_model = os.path.join(synthseg_home, 'models/SynthSeg.h5')
args['segmentation_label_list'] = path_label_list
args['segmentation_names_list'] = path_names_list
args['path_model'] = path_model
args['sigma_smoothing'] = 0.5
args['keep_biggest_component'] = True
args['aff_ref'] = 'FS'
# call predict
predict(**args)
default=2,
help="conv par level")
parser.add_argument("--unet_feat",
type=int,
dest="unet_feat_count",
default=24,
help="number of features of Unet's first layer")
parser.add_argument("--feat_mult",
type=int,
dest="feat_multiplier",
default=2,
help="factor of new feature maps per level")
parser.add_argument("--no_batch_norm",
action='store_true',
dest="no_batch_norm",
help="deactivate batch norm")
# Evaluation parameters
parser.add_argument(
"--gt",
type=str,
default=None,
dest="gt_folder",
help=
"folder containing ground truth segmentations, evaluation is performed only if this is "
"specified. Evaluation results will be preferably stored in out_seg folder, or else in "
"out_posteriors folder")
args = parser.parse_args()
predict(**vars(args))