118 lines
3.8 KiB
Python
118 lines
3.8 KiB
Python
import os
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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 models.experimental import attempt_load
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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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x = x[0]
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boxes = x[:, :, :4]
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convert_matrix = torch.tensor(
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[[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]], dtype=boxes.dtype, device=boxes.device
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)
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boxes @= convert_matrix
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objectness = x[:, :, 4:5]
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scores, labels = torch.max(x[:, :, 5:], dim=-1, keepdim=True)
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scores *= objectness
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return torch.cat([boxes, scores, labels.to(boxes.dtype)], dim=-1)
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def yolov5_export(weights, device, inplace=True, fuse=True):
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model = attempt_load(weights, device=device, inplace=inplace, fuse=fuse)
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model.eval()
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for k, m in model.named_modules():
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if m.__class__.__name__ == 'Detect':
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m.inplace = False
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m.dynamic = False
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m.export = True
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return model
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def suppress_warnings():
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import 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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warnings.filterwarnings('ignore', category=FutureWarning)
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warnings.filterwarnings('ignore', category=ResourceWarning)
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def main(args):
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suppress_warnings()
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print(f'\nStarting: {args.weights}')
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print('Opening YOLOv5 model')
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device = torch.device('cpu')
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model = yolov5_export(args.weights, device)
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if len(model.names.keys()) > 0:
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print('Creating labels.txt file')
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with open('labels.txt', 'w', encoding='utf-8') as f:
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for name in model.names.values():
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f.write(f'{name}\n')
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model = nn.Sequential(model, DeepStreamOutput())
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img_size = args.size * 2 if len(args.size) == 1 else args.size
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if img_size == [640, 640] and args.p6:
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img_size = [1280] * 2
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onnx_input_im = torch.zeros(args.batch, 3, *img_size).to(device)
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onnx_output_file = f'{args.weights}.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(
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model, onnx_input_im, onnx_output_file, verbose=False, opset_version=args.opset, do_constant_folding=True,
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input_names=['input'], output_names=['output'], dynamic_axes=dynamic_axes if args.dynamic else None
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)
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if args.simplify:
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print('Simplifying the ONNX model')
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import onnxslim
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model_onnx = onnx.load(onnx_output_file)
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model_onnx = onnxslim.slim(model_onnx)
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onnx.save(model_onnx, onnx_output_file)
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print(f'Done: {onnx_output_file}\n')
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def parse_args():
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import argparse
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parser = argparse.ArgumentParser(description='DeepStream YOLOv5 conversion')
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parser.add_argument('-w', '--weights', required=True, type=str, help='Input weights (.pt) file path (required)')
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parser.add_argument('-s', '--size', nargs='+', type=int, default=[640], help='Inference size [H,W] (default [640])')
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parser.add_argument('--p6', action='store_true', help='P6 model')
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parser.add_argument('--opset', type=int, default=17, 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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parser.add_argument('--batch', type=int, default=1, help='Static 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 args.dynamic and args.batch > 1:
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raise SystemExit('Cannot set dynamic batch-size and static batch-size at same time')
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return args
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if __name__ == '__main__':
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args = parse_args()
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main(args)
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