# Copyright 2020 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 torch.onnx
from collections import OrderedDict
from network import MGN
from opt import opt


def proc_node_module(checkpoint):
    new_state_dict = OrderedDict()
    for k, v in checkpoint.items():
        if(k[0:7] == "module."):
            name = k[7:]
        else:
            name = k[0:]
        new_state_dict[name] = v
    return new_state_dict


def convert():
    checkpoint = torch.load(opt.weight, map_location='cpu')
    checkpoint = proc_node_module(checkpoint)
    model = MGN()
    model.load_state_dict(checkpoint)
    model.eval()
    print(model)

    input_names = ["actual_input_1"]
    output_names = ["output1"]
    dummy_input = torch.randn(64, 3, 384, 128)
    torch.onnx.export(model, dummy_input, "mgn.onnx", input_names=input_names, output_names=output_names,
                      opset_version=11)
    print("export onnx done! save to mgn.onnx")


if __name__ == "__main__":
    convert()