blob: aa70cac548c43147c57cb5a63032c83a769178e4 [file] [log] [blame]
from __future__ import print_function
import argparse
import tools.find_mxnet
import mxnet as mx
import os
import importlib
import sys
from symbol.symbol_factory import get_symbol
def parse_args():
parser = argparse.ArgumentParser(description='Convert a trained model to deploy model')
parser.add_argument('--network', dest='network', type=str, default='vgg16_reduced',
help='which network to use')
parser.add_argument('--epoch', dest='epoch', help='epoch of trained model',
default=0, type=int)
parser.add_argument('--prefix', dest='prefix', help='trained model prefix',
default=os.path.join(os.getcwd(), 'model', 'ssd_'), type=str)
parser.add_argument('--data-shape', dest='data_shape', type=int, default=300,
help='data shape')
parser.add_argument('--num-class', dest='num_classes', help='number of classes',
default=20, type=int)
parser.add_argument('--nms', dest='nms_thresh', type=float, default=0.5,
help='non-maximum suppression threshold, default 0.5')
parser.add_argument('--force', dest='force_nms', type=bool, default=True,
help='force non-maximum suppression on different class')
parser.add_argument('--topk', dest='nms_topk', type=int, default=400,
help='apply nms only to top k detections based on scores.')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
net = get_symbol(args.network, args.data_shape,
num_classes=args.num_classes, nms_thresh=args.nms_thresh,
force_suppress=args.force_nms, nms_topk=args.nms_topk)
if args.prefix.endswith('_'):
prefix = args.prefix + args.network + '_' + str(args.data_shape)
else:
prefix = args.prefix
_, arg_params, aux_params = mx.model.load_checkpoint(prefix, args.epoch)
# new name
tmp = prefix.rsplit('/', 1)
save_prefix = '/deploy_'.join(tmp)
mx.model.save_checkpoint(save_prefix, args.epoch, net, arg_params, aux_params)
print("Saved model: {}-{:04d}.param".format(save_prefix, args.epoch))
print("Saved symbol: {}-symbol.json".format(save_prefix))