# Copyright 2021 Huawei Technologies Co., Ltd

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#     http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.



import torch

from data.config import cfg

from pyramidbox import build_net

import numpy as np

import os

import sys



def main(onnx_path,path):

    input_names=["image"]

    output_names = ["output"]

    net = build_net('test',2)

    net.eval()

    net.load_state_dict(torch.load(path,map_location='cpu'))

    # dynamic_axes = {'image': {0: '-1'}, 'output': {0: '-1'}}

    dummy_input = torch.randn(1,3,1000,1000)

    torch.onnx.export(net,dummy_input,onnx_path,input_names = input_names,output_names=output_names,verbose=True,enable_onnx_checker=False,opset_version=9) 

    

if __name__ =="__main__":

    onnx_path = os.path.abspath(sys.argv[1])

    path = os.path.abspath(sys.argv[2])

    main(onnx_path,path)