# Copyright 2021 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://opensource.org/licenses/BSD-3-Clause
#
# 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 argparse
import os.path as osp

import numpy as np
import torch

from mmdet.core import (build_model_from_cfg, generate_inputs_and_wrap_model,
                        preprocess_example_input)


def pytorch2onnx(config_path,
                 checkpoint_path,
                 input_img,
                 input_shape,
                 opset_version=11,
                 show=False,
                 output_file='tmp.onnx',
                 verify=False,
                 normalize_cfg=None):

    input_config = {
        'input_shape': input_shape,
        'input_path': input_img,
        'normalize_cfg': normalize_cfg
    }

    # prepare original model and meta for verifying the onnx model
    orig_model = build_model_from_cfg(config_path, checkpoint_path)
    one_img, one_meta = preprocess_example_input(input_config)

    model, tensor_data = generate_inputs_and_wrap_model(
        config_path, checkpoint_path, input_config)

    torch.onnx.export(
        model,
        tensor_data,
        output_file,
        export_params=True,
        keep_initializers_as_inputs=True,
        verbose=show,
        opset_version=opset_version)

    print(f'Successfully exported ONNX model: {output_file}')
    if verify:
        import onnx
        import onnxruntime as rt
        
        # check by onnx
        onnx_model = onnx.load(output_file)
        onnx.checker.check_model(onnx_model)

        # check the numerical value
        # get pytorch output
        model = orig_model.npu()
        pytorch_result = model(tensor_data, [[one_meta]], return_loss=False)

        # get onnx output
        input_all = [node.name for node in onnx_model.graph.input]
        input_initializer = [
            node.name for node in onnx_model.graph.initializer
        ]
        net_feed_input = list(set(input_all) - set(input_initializer))
        assert (len(net_feed_input) == 1)
        sess = rt.InferenceSession(output_file)
        from mmdet.core import bbox2result
        det_bboxes, det_labels = sess.run(
            None, {net_feed_input[0]: one_img.detach().numpy()})
        # only compare a part of result
        bbox_results = bbox2result(det_bboxes, det_labels, 1)
        onnx_results = bbox_results[0]
        assert np.allclose(
            pytorch_result[0][0][0][:4], onnx_results[0][:4], 
            rtol=1.e-3), 'The outputs are different between Pytorch and ONNX'
        print('The numerical values are the same between Pytorch and ONNX')


def parse_args():
    parser = argparse.ArgumentParser(
        description='Convert MMDetection models to ONNX')
    parser.add_argument(
        '--config',
        default='configs/ssd/ssd300_coco_npu.py',
        help='test config file path')
    parser.add_argument(
        '--checkpoint',
        default='work_dirs/ssd300_coco_npu_8p/latest.pth',
        help='checkpoint file')
    parser.add_argument('--input-img', type=str, help='Images for input')
    parser.add_argument('--show', action='store_true', help='show onnx graph')
    parser.add_argument('--output-file', type=str, default='ssd300.onnx')
    parser.add_argument('--opset-version', type=int, default=11)
    parser.add_argument(
        '--verify',
        action='store_true',
        help='verify the onnx model output against pytorch output')
    parser.add_argument(
        '--shape',
        type=int,
        nargs='+',
        default=[800, 1216],
        help='input image size')
    parser.add_argument(
        '--mean',
        type=float,
        nargs='+',
        default=[123.675, 116.28, 103.53],
        help='mean value used for preprocess input data')
    parser.add_argument(
        '--std',
        type=float,
        nargs='+',
        default=[58.395, 57.12, 57.375],
        help='variance value used for preprocess input data')
    args = parser.parse_args()
    return args


if __name__ == '__main__':
    args = parse_args()

    assert args.opset_version == 11, 'MMDet only support opset 11 now'

    if not args.input_img:
        args.input_img = osp.join(
            osp.dirname(__file__), 'tests/data/color.jpg')

    if len(args.shape) == 1:
        input_shape = (1, 3, args.shape[0], args.shape[0])
    elif len(args.shape) == 2:
        input_shape = (1, 3) + tuple(args.shape)
    else:
        raise ValueError('invalid input shape')

    assert len(args.mean) == 3
    assert len(args.std) == 3

    normalize_cfg = {'mean': args.mean, 'std': args.std}

    # convert model to onnx file
    pytorch2onnx(
        args.config,
        args.checkpoint,
        args.input_img,
        input_shape,
        opset_version=args.opset_version,
        show=args.show,
        output_file=args.output_file,
        verify=args.verify,
        normalize_cfg=normalize_cfg)