102 lines
3.1 KiB
Python
102 lines
3.1 KiB
Python
import os
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import sys
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import argparse
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import warnings
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import onnx
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import torch
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import torch.nn as nn
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from yolox.exp import get_exp
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from yolox.utils import replace_module
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from yolox.models.network_blocks import SiLU
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class DeepStreamOutput(nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x):
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boxes = x[:, :, :4]
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objectness = x[:, :, 4:5]
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scores, classes = torch.max(x[:, :, 5:], 2, keepdim=True)
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return torch.cat((boxes, scores * objectness, classes.float()), dim=2)
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def suppress_warnings():
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warnings.filterwarnings('ignore', category=torch.jit.TracerWarning)
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warnings.filterwarnings('ignore', category=UserWarning)
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warnings.filterwarnings('ignore', category=DeprecationWarning)
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def yolox_export(weights, exp_file):
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exp = get_exp(exp_file)
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model = exp.get_model()
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ckpt = torch.load(weights, map_location='cpu')
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model.eval()
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if 'model' in ckpt:
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ckpt = ckpt['model']
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model.load_state_dict(ckpt)
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model = replace_module(model, nn.SiLU, SiLU)
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model.head.decode_in_inference = True
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return model, exp
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def main(args):
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suppress_warnings()
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print('\nStarting: %s' % args.weights)
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print('Opening YOLOX model')
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device = torch.device('cpu')
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model, exp = yolox_export(args.weights, args.exp)
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model = nn.Sequential(model, DeepStreamOutput())
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img_size = [exp.input_size[1], exp.input_size[0]]
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onnx_input_im = torch.zeros(1, 3, *img_size).to(device)
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onnx_output_file = os.path.basename(args.weights).split('.pt')[0] + '.onnx'
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dynamic_axes = {
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'input': {
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0: 'batch'
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},
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'output': {
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0: 'batch'
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}
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}
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print('Exporting the model to ONNX')
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torch.onnx.export(model, onnx_input_im, onnx_output_file, verbose=False, opset_version=args.opset,
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do_constant_folding=True, input_names=['input'], output_names=['output'],
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dynamic_axes=dynamic_axes if args.dynamic else None)
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if args.simplify:
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print('Simplifying the ONNX model')
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import onnxsim
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model_onnx = onnx.load(onnx_output_file)
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model_onnx, _ = onnxsim.simplify(model_onnx)
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onnx.save(model_onnx, onnx_output_file)
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print('Done: %s\n' % onnx_output_file)
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def parse_args():
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parser = argparse.ArgumentParser(description='DeepStream YOLOX conversion')
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parser.add_argument('-w', '--weights', required=True, help='Input weights (.pth) file path (required)')
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parser.add_argument('-c', '--exp', required=True, help='Input exp (.py) file path (required)')
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parser.add_argument('--opset', type=int, default=11, help='ONNX opset version')
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parser.add_argument('--simplify', action='store_true', help='ONNX simplify model')
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parser.add_argument('--dynamic', action='store_true', help='Dynamic batch-size')
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args = parser.parse_args()
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if not os.path.isfile(args.weights):
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raise SystemExit('Invalid weights file')
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if not os.path.isfile(args.exp):
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raise SystemExit('Invalid exp file')
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return args
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if __name__ == '__main__':
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args = parse_args()
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sys.exit(main(args))
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