# Copyright 2022 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 onnx

from collections import OrderedDict

sys.path.append('./GMA/core')

from network import RAFTGMA

import argparse









def pth2onnx(input_file, output_file, args, opset_version=12):

    model = torch.nn.DataParallel(RAFTGMA(args))

    model.load_state_dict(torch.load(input_file, map_location=torch.device('cpu')))

    model = model.module

    model.eval()

    dummy_input_0 = torch.randn(args.batchsize, 3, args.image_size[0], args.image_size[1]) # 0 dimension --> bs

    dummy_input_1 = torch.randn(args.batchsize, 3, args.image_size[0], args.image_size[1]) # 0 dimension --> bs

    dummy_input = (dummy_input_0, dummy_input_1)

    input_names = ['image1', 'image2']

    output_names = ['out1']

    torch.onnx.export(model, dummy_input, output_file, input_names = input_names, \

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









if __name__ == "__main__":

    parser = argparse.ArgumentParser()

    parser.add_argument('--image_size', type=int, nargs='+', default=[440, 1024])

    parser.add_argument('--num_heads', default=1, type=int,

                        help='number of heads in attention and aggregation')

    parser.add_argument('--upsample-learn', action='store_true', default=False,

                        help='If True, use learned upsampling, otherwise, use bilinear upsampling.')

    parser.add_argument('--position_only', default=False, action='store_true',

                        help='only use position-wise attention')

    parser.add_argument('--position_and_content', default=False, action='store_true',

                        help='use position and content-wise attention')

    parser.add_argument('--output_file', type=str, default="gma.onnx", help='.onnx output file')

    parser.add_argument('--input_file', type=str, help='.pth input file')

    parser.add_argument('--mixed_precision', default=False, action='store_true',

                        help='use mixed precision')

    parser.add_argument('--batchsize', type=int)



    args = parser.parse_args()

    output_file = args.output_file

    input_file = args.input_file

    pth2onnx(input_file=input_file, output_file=output_file, args=args, opset_version=12)