# 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

import os

from ssd300 import SSD300

import random

from argparse import ArgumentParser

from parse_config import parse_args



def pth2onnx(batch_size,input_file, output_file):

    model_options = {

        'use_nhwc' : False,

        'pad_input' : False,

        'bn_group' : 1,

    }

    ssd300_eval = SSD300(args, 81, **model_options)



    state_dict = torch.load(input_file, map_location="cpu")

    ssd300_eval.load_state_dict(state_dict['model'])



    ssd300_eval.eval()

    input_names = ["image"]

    output_names=["ploc","plabel"]

    dynamic_axes = {'image': {0: '-1'}, 'ploc': {0: '-1'}, 'plabel': {0: '-1'}}

    dummy_input = torch.randn(batch_size, 3, 300, 300)

    torch.onnx.export(ssd300_eval, dummy_input, output_file, input_names=input_names, dynamic_axes=dynamic_axes,

                      output_names=output_names, opset_version=11, verbose=False)



if __name__ == "__main__":

    args = parse_args()

    batch_size=args.bs

    input_file = args.pth_path

    output_file = args.onnx_path

    resnet_model=args.resnet34_model

    pth2onnx(batch_size,input_file, output_file)