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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 torch | |
| import torch.nn as nn | |
| from models.common import Conv | |
| 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='./yolov5s.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, Conv) and isinstance(m.act, nn.Hardswish): | |
| m.act = Hardswish() # assign activation | |
| # if isinstance(m, 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) | |
| # 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']) | |
| # 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.') | |