# 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 sys

import torch

import torch.onnx

from collections import OrderedDict



sys.path.append(r"./pycls")

from pycls.models.effnet import EffNet

import pycls.core.config as config

from pycls.core.config import cfg





def proc_node_module(checkpoint, attr_name):

    new_model_state = OrderedDict()

    for k, v in checkpoint[attr_name].items():

        if (k[0: 7] == "module."):

            name = k[7:]

        else:

            name = k[0:]

        new_model_state[name] = v

    return new_model_state





def convert(input_file_, yaml_file_, output_file_):

    config.load_cfg(yaml_file_)

    cfg.freeze()



    model = EffNet()

    checkpoint = torch.load(input_file_, map_location='cpu')

    checkpoint['model_state'] = proc_node_module(checkpoint, 'model_state')

    model.load_state_dict(checkpoint["model_state"])

    model.eval()



    input_names = ["image"]

    output_names = ["class"]

    dynamic_axes = {'image': {0: '-1'}, 'class': {0: '-1'}}

    dummy_input = torch.randn(1, 3, 240, 240)

    torch.onnx.export(model, dummy_input, output_file_, input_names=input_names, dynamic_axes=dynamic_axes,

                      output_names=output_names, opset_version=11, verbose=True)





if __name__ == "__main__":

    input_file = sys.argv[1]

    yaml_file = sys.argv[2]

    output_file = sys.argv[3]

    convert(input_file, yaml_file, output_file)