# 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 argparse



import torch



from utils.google_utils import attempt_download



if __name__ == '__main__':

    parser = argparse.ArgumentParser()

    parser.add_argument('--weights', type=str, default='./yolov4.pt', help='weights path')

    parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size')

    parser.add_argument('--batch-size', type=int, default=1, help='batch size')

    opt = parser.parse_args()

    opt.img_size *= 2 if len(opt.img_size) == 1 else 1  # expand

    print(opt)



    # Input

    img = torch.zeros((opt.batch_size, 3, *opt.img_size))  # image size(1,3,320,192) iDetection



    # Load PyTorch model

    attempt_download(opt.weights)

    model = torch.load(opt.weights, map_location=torch.device('cpu'))['model'].float()

    model.eval()

    model.model[-1].export = True  # set Detect() layer export=True

    y = model(img)  # dry run



    # TorchScript export

    try:

        print('\nStarting TorchScript export with torch %s...' % torch.__version__)

        f = opt.weights.replace('.pt', '.torchscript.pt')  # filename

        ts = torch.jit.trace(model, img)

        ts.save(f)

        print('TorchScript export success, saved as %s' % f)

    except Exception as e:

        print('TorchScript export failure: %s' % e)



    # ONNX export

    try:

        import onnx



        print('\nStarting ONNX export with onnx %s...' % onnx.__version__)

        f = opt.weights.replace('.pt', '.onnx')  # filename

        model.fuse()  # only for ONNX

        torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'],

                          output_names=['classes', 'boxes'] if y is None else ['output'])



        # Checks

        onnx_model = onnx.load(f)  # load onnx model

        onnx.checker.check_model(onnx_model)  # check onnx model

        print(onnx.helper.printable_graph(onnx_model.graph))  # print a human readable model

        print('ONNX export success, saved as %s' % f)

    except Exception as e:

        print('ONNX export failure: %s' % e)



    # CoreML export

    try:

        import coremltools as ct



        print('\nStarting CoreML export with coremltools %s...' % ct.__version__)

        # convert model from torchscript and apply pixel scaling as per detect.py

        model = ct.convert(ts, inputs=[ct.ImageType(name='images', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])])

        f = opt.weights.replace('.pt', '.mlmodel')  # filename

        model.save(f)

        print('CoreML export success, saved as %s' % f)

    except Exception as e:

        print('CoreML export failure: %s' % e)



    # Finish

    print('\nExport complete. Visualize with https://github.com/lutzroeder/netron.')