def main():
parser = argparse.ArgumentParser()
parser.add_argument('model_name',
choices=('resnet50', 'resnet101', 'resnet152'))
parser.add_argument('caffemodel')
parser.add_argument('output', nargs='?', default=None)
args = parser.parse_args()
caffemodel = CaffeFunction(args.caffemodel)
if args.model_name == 'resnet50':
model = ResNet50(pretrained_model=None, n_class=1000, mode='he')
model(np.zeros((1, 3, 224, 224), dtype=np.float32))
_transfer_resnet50(caffemodel, model)
elif args.model_name == 'resnet101':
model = ResNet101(pretrained_model=None, n_class=1000, mode='he')
model(np.zeros((1, 3, 224, 224), dtype=np.float32))
_transfer_resnet101(caffemodel, model)
elif args.model_name == 'resnet152':
model = ResNet152(pretrained_model=None, n_class=1000, mode='he')
model(np.zeros((1, 3, 224, 224), dtype=np.float32))
_transfer_resnet152(caffemodel, model)
if args.output is None:
output = '{}_imagenet_convert.npz'.format(args.model_name)
else:
output = args.output
chainer.serializers.save_npz(output, model)
def main():
parser = argparse.ArgumentParser(
description='Learning convnet from ILSVRC2012 dataset')
parser.add_argument('val', help='Path to root of the validation dataset')
parser.add_argument(
'--model', choices=('vgg16', 'resnet50', 'resnet101', 'resnet152'))
parser.add_argument('--pretrained_model', default='imagenet')
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--batchsize', type=int, default=32)
parser.add_argument('--crop', choices=('center', '10'), default='center')
parser.add_argument('--resnet_mode', default='he')
args = parser.parse_args()
dataset = DirectoryParsingLabelDataset(args.val)
label_names = directory_parsing_label_names(args.val)
n_class = len(label_names)
iterator = iterators.MultiprocessIterator(
dataset, args.batchsize, repeat=False, shuffle=False,
n_processes=6, shared_mem=300000000)
if args.model == 'vgg16':
extractor = VGG16(n_class, args.pretrained_model)
elif args.model == 'resnet50':
extractor = ResNet50(
n_class, args.pretrained_model, mode=args.resnet_mode)
elif args.model == 'resnet101':
extractor = ResNet101(
n_class, args.pretrained_model, mode=args.resnet_mode)
elif args.model == 'resnet152':
extractor = ResNet152(
n_class, args.pretrained_model, mode=args.resnet_mode)
model = FeaturePredictor(
extractor, crop_size=224, scale_size=256, crop=args.crop)
if args.gpu >= 0:
chainer.cuda.get_device(args.gpu).use()
model.to_gpu()
print('Model has been prepared. Evaluation starts.')
in_values, out_values, rest_values = apply_to_iterator(
model.predict, iterator, hook=ProgressHook(len(dataset)))
del in_values
pred_probs, = out_values
gt_labels, = rest_values
accuracy = F.accuracy(
np.array(list(pred_probs)), np.array(list(gt_labels))).data
print()
print('Top 1 Error {}'.format(1. - accuracy))