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- """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 onnx
-
- 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
- _ = model(img) # dry run
-
- # Export to torchscript
- try:
- 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:
- print('Torchscript export failed.')
-
- # Export to ONNX
- try:
- f = opt.weights.replace('.pt', '.onnx') # filename
- model.fuse() # only for ONNX
- torch.onnx.export(model, img, f, verbose=False, opset_version=11, input_names=['images'],
- output_names=['output']) # output_names=['classes', 'boxes']
-
- # 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 representation of the graph
- print('ONNX export success, saved as %s\nView with https://github.com/lutzroeder/netron' % f)
- except:
- print('ONNX export failed.')
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