def __init__(self, model_path, gpu=-1, batchsize=32): # Define a model logger.info('Define a HyperFace model using {}'.format(model_path)) model = models.HyperFaceModel() chainer.serializers.load_npz(model_path, model) model.train = False model.report = False model.backward = False # GPU if gpu >= 0: chainer.cuda.check_cuda_available() chainer.cuda.get_device(gpu).use() model.to_gpu() xp = chainer.cuda.cupy else: xp = np # Variables self.model = model self.xp = xp self.batchsize = batchsize
parser.add_argument('--config', '-c', default='config.json', help='Load config from given json file') parser.add_argument('--model', required=True, help='Trained model path') parser.add_argument('--img', required=True, help='Input image path') args = parser.parse_args() logger.info('HyperFace Evaluation') # Load config config.load(args.config) # Define a model logger.info('Define a HyperFace model') model = models.HyperFaceModel() model.train = False model.report = False model.backward = False # Initialize model logger.info('Initialize a model using model "{}"'.format(args.model)) chainer.serializers.load_npz(args.model, model) # Setup GPU if config.gpu >= 0: chainer.cuda.check_cuda_available() chainer.cuda.get_device(config.gpu).use() model.to_gpu() xp = chainer.cuda.cupy else: