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:
