# Copyright (c) Soumith Chintala 2016,
# All rights reserved
#
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the BSD 3-Clause License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://spdx.org/licenses/BSD-3-Clause.html
#
# 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
import sys
from models import DnCNN
from collections import OrderedDict

def proc_nodes_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(pth_file, onnx_file):
    pretrained_net = torch.load(pth_file, map_location='cpu')
    pretrained_net['state_dict'] = proc_nodes_module(pretrained_net)

    model = DnCNN(channels=1, num_of_layers=17)
    model.load_state_dict(pretrained_net['state_dict'])
    model.eval()
    input_names = ["actual_input_1"]
    dummy_input = torch.randn(1, 1, 481, 481)
    #torch.onnx.export(model, dummy_input, onnx_file, input_names = input_names, opset_version=11, verbose=True)

    dynamic_axes = {'actual_input_1': {0: '-1'}}
    torch.onnx.export(model, dummy_input, onnx_file, dynamic_axes=dynamic_axes, input_names=input_names, opset_version=11)

if __name__ == "__main__":
    
    pth_file = sys.argv[1]
    onnx_file = sys.argv[2]

    convert(pth_file, onnx_file)