# 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.

# ============================================================================

from CGAN import generator

import torch

import torch.onnx

import sys

from collections import OrderedDict

import argparse





def parse_args():

    desc = "Pytorch implementation of CGAN collections"

    parser = argparse.ArgumentParser(description=desc)

    parser.add_argument('--input_dim', type=int, default=62, help="The input_dim")

    parser.add_argument('--output_dim', type=int, default=3, help="The output_dim")

    parser.add_argument('--input_size', type=int, default=28, help="The image size of MNIST")

    parser.add_argument('--class_num', type=int, default=10, help="The num of classes of MNIST")

    parser.add_argument('--pth_path', type=str, default='CGAN_G.pth', help='pth model path')

    parser.add_argument('--onnx_path', type=str, default="CGAN.onnx", help='onnx model path')

    return parser.parse_args()





def proc_nodes_module(checkpoint):

    new_state_dict = OrderedDict()

    for k, v in checkpoint.items():

        if "module." in k:

            name = k.replace("module.", "")

        else:

            name = k

        new_state_dict[name] = v

    return new_state_dict



def pth2onnx():

    args = parse_args()

    net = generator(input_dim=args.input_dim, output_dim=args.output_dim,

                    input_size=args.input_size, class_num=args.class_num)

    model = net 

    checkpoint = torch.load(args.pth_path, map_location='cpu')

    checkpoint = proc_nodes_module(checkpoint)

    model.load_state_dict(checkpoint)

    model.eval()

    input_names = ["image"]

    output_names = ["output1"]

    #dynamic_axes = {'image': {0: '-1'}, 'output1': {0: '-1'}}

    dummy_input1 = torch.randn(100, 72)

    torch.onnx.export(model, dummy_input1, args.onnx_path, input_names=input_names,

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

    print("this model could generete pictures, specifically digits")

    print('onnx export done.')





if __name__ == "__main__":

    pth2onnx()