text: """Exports a YOLOv5 *.pt model to ONNX and TorchScript formats
  
  Usage:
      $ export PYTHONPATH="$PWD" && python models/export.py --weights ./weights/yolov5s.pt --img 640 --batch 1
  """
  
  import argparse
  
  from models.common import *
  from utils import google_utils
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser()
      parser.add_argument('--weights', type=str, default='./yolov5s.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
      google_utils.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')  # 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.')
  