|
12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576 |
- """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
-
- from utils.general import set_logging
- from utils.google_utils import attempt_download
-
- 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)
- set_logging()
-
- # 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.')
|