text: """Exports a YOLOv3 *.pt model to ONNX and TorchScript formats
  
  Usage:
      $ export PYTHONPATH="$PWD" && python models/export.py --weights ./weights/yolov3.pt --img 640 --batch 1
  """
  
  import argparse
  import sys
  import time
  
  sys.path.append('./')  # to run '$ python *.py' files in subdirectories
  
  import torch
  import torch.nn as nn
  
  import models
  from models.experimental import attempt_load
  from utils.activations import Hardswish, SiLU
  from utils.general import set_logging, check_img_size
  from utils.torch_utils import select_device
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser()
      parser.add_argument('--weights', type=str, default='./yolov3.pt', help='weights path')  # from yolov3/models/
      parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size')  # height, width
      parser.add_argument('--batch-size', type=int, default=1, help='batch size')
      parser.add_argument('--dynamic', action='store_true', help='dynamic ONNX axes')
      parser.add_argument('--grid', action='store_true', help='export Detect() layer grid')
      parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
      opt = parser.parse_args()
      opt.img_size *= 2 if len(opt.img_size) == 1 else 1  # expand
      print(opt)
      set_logging()
      t = time.time()
  
      # Load PyTorch model
      device = select_device(opt.device)
      model = attempt_load(opt.weights, map_location=device)  # load FP32 model
      labels = model.names
  
      # Checks
      gs = int(max(model.stride))  # grid size (max stride)
      opt.img_size = [check_img_size(x, gs) for x in opt.img_size]  # verify img_size are gs-multiples
  
      # Input
      img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device)  # image size(1,3,320,192) iDetection
  
      # Update model
      for k, m in model.named_modules():
          m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
          if isinstance(m, models.common.Conv):  # assign export-friendly activations
              if isinstance(m.act, nn.Hardswish):
                  m.act = Hardswish()
              elif isinstance(m.act, nn.SiLU):
                  m.act = SiLU()
          # elif isinstance(m, models.yolo.Detect):
          #     m.forward = m.forward_export  # assign forward (optional)
      model.model[-1].export = not opt.grid  # set Detect() layer grid export
      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, strict=False)
          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
          torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'],
                            output_names=['classes', 'boxes'] if y is None else ['output'],
                            dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'},  # size(1,3,640,640)
                                          'output': {0: 'batch', 2: 'y', 3: 'x'}} if opt.dynamic else None)
  
          # 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='image', 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 (%.2fs). Visualize with https://github.com/lutzroeder/netron.' % (time.time() - t))
  