Deepstream-YOLO模型转换笔记

1、使用Deepstream-YOLO

https://github.com/marcoslucianops/DeepStream-Yolo

2、使用集群服务器

conda activate yolov11

3、模型转换

使用yolov5-6.0，一定使用6.0,其他版本，使用下面的脚本会有些问题

import os
import onnx
import torch
import torch.nn as nn

from models.experimental import attempt_load


class DeepStreamOutput(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        x = x[0]
        boxes = x[:, :, :4]
        convert_matrix = torch.tensor(
            [[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
        )
        boxes @= convert_matrix
        objectness = x[:, :, 4:5]
        scores, labels = torch.max(x[:, :, 5:], dim=-1, keepdim=True)
        scores *= objectness
        return torch.cat([boxes, scores, labels.to(boxes.dtype)], dim=-1)


def yolov5_export(weights, device, inplace=True, fuse=True):
    model = attempt_load(weights, inplace=inplace, fuse=fuse)  # , device=device
    model.eval()
    for k, m in model.named_modules():
        if m.__class__.__name__ == 'Detect':
            m.inplace = False
            m.dynamic = False
            m.export = True
    return model


def suppress_warnings():
    import warnings
    warnings.filterwarnings('ignore', category=torch.jit.TracerWarning)
    warnings.filterwarnings('ignore', category=UserWarning)
    warnings.filterwarnings('ignore', category=DeprecationWarning)
    warnings.filterwarnings('ignore', category=FutureWarning)
    warnings.filterwarnings('ignore', category=ResourceWarning)


def main(args):
    suppress_warnings()

    print(f'\nStarting: {args.weights}')

    print('Opening YOLOv5 model')

    device = torch.device('cpu')
    model = yolov5_export(args.weights, device)
    print()
    if len(model.names) > 0:
        print('Creating labels.txt file')
        with open('labels.txt', 'w', encoding='utf-8') as f:
            for name in model.names:
                f.write(f'{name}\n')

    model = nn.Sequential(model, DeepStreamOutput())

    img_size = args.size * 2 if len(args.size) == 1 else args.size

    if img_size == [640, 640] and args.p6:
        img_size = [1280] * 2

    onnx_input_im = torch.zeros(args.batch, 3, *img_size).to(device)
    onnx_output_file = f'{args.weights}.onnx'

    dynamic_axes = {
        'input': {
            0: 'batch'
        },
        'output': {
            0: 'batch'
        }
    }

    print('Exporting the model to ONNX')
    torch.onnx.export(
        model, onnx_input_im, onnx_output_file, verbose=False, opset_version=args.opset, do_constant_folding=True,
        input_names=['input'], output_names=['output'], dynamic_axes=dynamic_axes if args.dynamic else None
    )

    if args.simplify:
        print('Simplifying the ONNX model')
        import onnxslim
        model_onnx = onnx.load(onnx_output_file)
        model_onnx = onnxslim.slim(model_onnx)
        onnx.save(model_onnx, onnx_output_file)

    print(f'Done: {onnx_output_file}\n')


def parse_args():
    import argparse
    parser = argparse.ArgumentParser(description='DeepStream YOLOv5 conversion')
    parser.add_argument('-w', '--weights', required=True, type=str, help='Input weights (.pt) file path (required)')
    parser.add_argument('-s', '--size', nargs='+', type=int, default=[640], help='Inference size [H,W] (default [640])')
    parser.add_argument('--p6', action='store_true', help='P6 model')
    parser.add_argument('--opset', type=int, default=12, help='ONNX opset version')
    parser.add_argument('--simplify', action='store_true', help='ONNX simplify model')
    parser.add_argument('--dynamic', action='store_true', help='Dynamic batch-size')
    parser.add_argument('--batch', type=int, default=1, help='Static batch-size')
    args = parser.parse_args()
    if not os.path.isfile(args.weights):
        raise SystemExit('Invalid weights file')
    if args.dynamic and args.batch > 1:
        raise SystemExit('Cannot set dynamic batch-size and static batch-size at same time')
    return args


if __name__ == '__main__':
    args = parse_args()
    main(args)

4、提示silu激活函数相关的错误

解决参考：

YOLOV5 | AttributeError: ‘Upsample‘ object has no attribute ‘recompute_scale_factor‘ 问题解决 亲测有效_yolov5-lite export.py attributeerror: 'upsample' o-优快云博客

错误：nvbufsurface: Failed to create EGLImage.

 

解决：

$ export DISPLAY=:0
$ xhost + 

5、在NX上完成模型格式转换即可

 config_infer_primary_yoloV5.txt

[property]
gpu-id=0
net-scale-factor=0.0039215697906911373
model-color-format=0
onnx-file=best0116-2.onnx
# custom-network-config=yolov5_best.cfg
# model-file=yolov5_best111.wts
model-engine-file=model_b1_gpu0_fp32.engine
#int8-calib-file=calib.table
labelfile-path=labels1.txt
batch-size=1
network-mode=0
num-detected-classes=4
interval=0
gie-unique-id=1
process-mode=1
network-type=0
cluster-mode=2
maintain-aspect-ratio=1
symmetric-padding=1
#workspace-size=2000
parse-bbox-func-name=NvDsInferParseYolo
#parse-bbox-func-name=NvDsInferParseYoloCuda
custom-lib-path=nvdsinfer_custom_impl_Yolo/libnvdsinfer_custom_impl_Yolo.so
engine-create-func-name=NvDsInferYoloCudaEngineGet

[class-attrs-all]
nms-iou-threshold=0.45
pre-cluster-threshold=0.25
topk=300

deepstream-app -C config_infer_primary_yoloV5.txt