yolov5 导出LibTorch模型（CPU和GPU)

 官方给出的是CPU：

  

   

    

     

    
    

     

      """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
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      import torch
     
    
   

    

     

    
    

     

      import torch.nn as nn
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      import models
     
    
   

    

     

    
    

     

      from models.experimental import attempt_load
     
    
   

    

     

    
    

     

      from utils.activations import Hardswish
     
    
   

    

     

    
    

     

      from utils.general import set_logging
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      if __name__ == '__main__':
     
    
   

    

     

    
    

     

          parser = argparse.ArgumentParser()
     
    
   

    

     

    
    

     

          parser.add_argument('--weights', type=str, default='../runs/exp7/weights/best.pt', help='weights path')  # from yolov5/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')
     
    
   

    

     

    
    

     

          opt = parser.parse_args()
     
    
   

    

     

    
    

     

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

    

     

    
    

     
    print(opt)
     
    
   

    

     

    
    

     

          set_logging()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
    # Input
     
    
   

    

     

    
    

     

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

    

     

    
    

     
 
     
    
   

    

     

    
    

     
    # Load PyTorch model
     
    
   

    

     

    
    

     

          model = attempt_load(opt.weights, map_location=torch.device('cpu'))  # load FP32 model
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
    # Update model
     
    
   

    

     

    
    

     
    for k, m in model.named_modules():
     
    
   

    

     

    
    

     

              m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatability
     
    
   

    

     

    
    

     
        if isinstance(m, models.common.Conv) and isinstance(m.act, nn.Hardswish):
     
    
   

    

     

    
    

     

                  m.act = Hardswish()  # assign activation
     
    
   

    

     

    
    

     
        # if isinstance(m, models.yolo.Detect):
     
    
   

    

     

    
    

     
        # m.forward = m.forward_export # assign forward (optional)
     
    
   

    

     

    
    

     

          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)
     
    
  
 

 GPU

  

   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      import argparse
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      import torch
     
    
   

    

     

    
    

     

      import torch.nn as nn
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      import models
     
    
   

    

     

    
    

     

      from models.experimental import attempt_load
     
    
   

    

     

    
    

     

      from utils.activations import Hardswish
     
    
   

    

     

    
    

     

      from utils.general import set_logging
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      if __name__ == '__main__':
     
    
   

    

     

    
    

     

          parser = argparse.ArgumentParser()
     
    
   

    

     

    
    

     

          parser.add_argument('--weights', type=str, default='../runs/exp7/weights/best.pt', help='weights path')  # from yolov5/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')
     
    
   

    

     

    
    

     

          opt = parser.parse_args()
     
    
   

    

     

    
    

     

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

    

     

    
    

     
    print(opt)
     
    
   

    

     

    
    

     

          set_logging()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
    # Input
     
    
   

    

     

    
    

     

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

    

     

    
    

     
 
     
    
   

    

     

    
    

     
    # Load PyTorch model
     
    
   

    

     

    
    

     

          model = attempt_load(opt.weights, map_location=torch.device('cuda'))  # load FP32 model
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
    # Update model
     
    
   

    

     

    
    

     
    for k, m in model.named_modules():
     
    
   

    

     

    
    

     

              m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatability
     
    
   

    

     

    
    

     
        if isinstance(m, models.common.Conv) and isinstance(m.act, nn.Hardswish):
     
    
   

    

     

    
    

     

                  m.act = Hardswish()  # assign activation
     
    
   

    

     

    
    

     
        # if isinstance(m, models.yolo.Detect):
     
    
   

    

     

    
    

     
        # m.forward = m.forward_export # assign forward (optional)
     
    
   

    

     

    
    

     

          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)
     
    
  
 

文章来源: wanghao.blog.csdn.net，作者：AI浩，版权归原作者所有，如需转载，请联系作者。

原文链接：wanghao.blog.csdn.net/article/details/116117544