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