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"""
Export TorchScript model of MODNet
Arguments:
--ckpt-path: path of the checkpoint that will be converted
--output-path: path for saving the TorchScript model
Example:
python export_torchscript.py \
--ckpt-path=modnet_photographic_portrait_matting.ckpt \
--output-path=modnet_photographic_portrait_matting.torchscript
"""
import os
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import modnet_torchscript
if __name__ == '__main__':
# define cmd arguments
parser = argparse.ArgumentParser()
parser.add_argument('--ckpt-path', type=str, required=True, help='path of the checkpoint that will be converted')
parser.add_argument('--output-path', type=str, required=True, help='path for saving the TorchScript model')
args = parser.parse_args()
# check input arguments
if not os.path.exists(args.ckpt_path):
print(args.ckpt_path)
print('Cannot find checkpoint path: {0}'.format(args.ckpt_path))
exit()
# create MODNet and load the pre-trained ckpt
modnet = modnet_torchscript.MODNet(backbone_pretrained=False)
modnet = nn.DataParallel(modnet).cuda()
state_dict = torch.load(args.ckpt_path)
modnet.load_state_dict(state_dict)
modnet.eval()
# export to TorchScript model
scripted_model = torch.jit.script(modnet.module)
torch.jit.save(scripted_model, os.path.join(args.output_path))
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