98 lines
3.8 KiB
Python
98 lines
3.8 KiB
Python
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"""Exports a YOLOv5 *.pt model to ONNX and TorchScript formats
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Usage:
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$ export PYTHONPATH="$PWD" && python models/export.py --weights ./weights/yolov5s.pt --img 640 --batch 1
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"""
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import argparse
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import sys
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import time
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sys.path.append('./') # to run '$ python *.py' files in subdirectories
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import torch
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import torch.nn as nn
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import models
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from models.experimental import attempt_load
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from utils.activations import Hardswish, SiLU
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from utils.general import set_logging, check_img_size
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path') # from yolov5/models/
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parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') # height, width
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parser.add_argument('--batch-size', type=int, default=1, help='batch size')
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opt = parser.parse_args()
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opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand
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print(opt)
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set_logging()
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t = time.time()
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# Load PyTorch model
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model = attempt_load(opt.weights, map_location=torch.device('cpu')) # load FP32 model
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labels = model.names
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# Checks
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gs = int(max(model.stride)) # grid size (max stride)
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opt.img_size = [check_img_size(x, gs) for x in opt.img_size] # verify img_size are gs-multiples
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# Input
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img = torch.zeros(opt.batch_size, 3, *opt.img_size) # image size(1,3,320,192) iDetection
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# Update model
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for k, m in model.named_modules():
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m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
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if isinstance(m, models.common.Conv): # assign export-friendly activations
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if isinstance(m.act, nn.Hardswish):
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m.act = Hardswish()
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elif isinstance(m.act, nn.SiLU):
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m.act = SiLU()
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# elif isinstance(m, models.yolo.Detect):
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# m.forward = m.forward_export # assign forward (optional)
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model.model[-1].export = True # set Detect() layer export=True
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y = model(img) # dry run
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# TorchScript export
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try:
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print('\nStarting TorchScript export with torch %s...' % torch.__version__)
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f = opt.weights.replace('.pt', '.torchscript.pt') # filename
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ts = torch.jit.trace(model, img)
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ts.save(f)
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print('TorchScript export success, saved as %s' % f)
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except Exception as e:
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print('TorchScript export failure: %s' % e)
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# ONNX export
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try:
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import onnx
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print('\nStarting ONNX export with onnx %s...' % onnx.__version__)
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f = opt.weights.replace('.pt', '.onnx') # filename
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torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'],
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output_names=['classes', 'boxes'] if y is None else ['output'])
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# Checks
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onnx_model = onnx.load(f) # load onnx model
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onnx.checker.check_model(onnx_model) # check onnx model
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# print(onnx.helper.printable_graph(onnx_model.graph)) # print a human readable model
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print('ONNX export success, saved as %s' % f)
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except Exception as e:
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print('ONNX export failure: %s' % e)
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# CoreML export
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try:
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import coremltools as ct
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print('\nStarting CoreML export with coremltools %s...' % ct.__version__)
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# convert model from torchscript and apply pixel scaling as per detect.py
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model = ct.convert(ts, inputs=[ct.ImageType(name='image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])])
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f = opt.weights.replace('.pt', '.mlmodel') # filename
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model.save(f)
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print('CoreML export success, saved as %s' % f)
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except Exception as e:
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print('CoreML export failure: %s' % e)
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# Finish
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print('\nExport complete (%.2fs). Visualize with https://github.com/lutzroeder/netron.' % (time.time() - t))
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