Tencent
yolov8s-pose转ncnn
error log | 日志或报错信息 | ログ
model | 模型 | モデル
- original model：yolov8s-pose.pt
how to reproduce | 复现步骤 | 再現方法
1.yolov8s-pose.pt转onnx后，再使用ncnn2onnx时报错： Unsupported slice step! ScatterND not supported yet！
2.然后尝试先转为torchscript 转torchscript代码如下： from ultralytics import YOLO model = YOLO("yolov8s-pose.pt") # load a pretrained model (recommended for training) success = model.export(format="torchscript", simplify=True) # export the model to onnx format assert success
再使用pnnx工具转模型，输入命令如下 ./pnnx yolov8s-pose.torchscript inputshape=[1,3,640,640] 报错如下 pnnxparam = yolov8s-pose.pnnx.param pnnxbin = yolov8s-pose.pnnx.bin pnnxpy = yolov8s-pose_pnnx.py pnnxonnx = yolov8s-pose.pnnx.onnx ncnnparam = yolov8s-pose.ncnn.param ncnnbin = yolov8s-pose.ncnn.bin ncnnpy = yolov8s-pose_ncnn.py fp16 = 1 optlevel = 2 device = cpu inputshape = [1,3,640,640]f32 inputshape2 = customop = moduleop = ############# pass_level0 inline module = ultralytics.nn.modules.Bottleneck inline module = ultralytics.nn.modules.C2f inline module = ultralytics.nn.modules.Concat inline module = ultralytics.nn.modules.Conv inline module = ultralytics.nn.modules.DFL inline module = ultralytics.nn.modules.Pose inline module = ultralytics.nn.modules.SPPF inline module = ultralytics.nn.modules.Bottleneck inline module = ultralytics.nn.modules.C2f inline module = ultralytics.nn.modules.Concat inline module = ultralytics.nn.modules.Conv inline module = ultralytics.nn.modules.DFL inline module = ultralytics.nn.modules.Pose inline module = ultralytics.nn.modules.SPPF
############# pass_level1 unknown Parameter value kind prim::Constant of TensorType, t.dim = 2 unknown Parameter value kind prim::Constant of TensorType, t.dim = 2 unknown Parameter value kind prim::Constant of TensorType, t.dim = 2 unknown Parameter value kind prim::Constant of TensorType, t.dim = 2 unknown Parameter value kind prim::Constant of TensorType, t.dim = 2 unknown Parameter value kind prim::Constant of TensorType, t.dim = 2 ############# pass_level2 ############# pass_level3 ############# pass_level4 ############# pass_level5 make_slice_expression input 206 make_slice_expression self 207 make_slice_expression src 209 make_slice_expression input 210 make_slice_expression self 214 make_slice_expression src 216 make_slice_expression input 217 make_slice_expression self 221 make_slice_expression src 223 ############# pass_ncnn force batch axis 233 for operand 212 force batch axis 233 for operand 219 force batch axis 233 for operand pnnx_expr_35_sub(212,5.000000e-01) force batch axis 233 for operand pnnx_expr_9_sub(219,5.000000e-01) binaryop broadcast across batch axis 0 and 233 is not supported binaryop broadcast across batch axis 0 and 233 is not supported binaryop broadcast across batch axis 0 and 233 is not supported binaryop broadcast across batch axis 0 and 233 is not supported binaryop broadcast across batch axis 0 and 233 is not supported select along batch axis 0 is not supported select along batch axis 0 is not supported slice with step 3 is not supported slice with step 3 is not supported slice with step 3 is not supported slice_copy with step 3 is not supported slice_copy with step 3 is not supported slice_copy with step 3 is not supported ignore Crop select_0 param dim=0 ignore Crop select_0 param index=0 ignore Crop select_1 param dim=0 ignore Crop select_1 param index=1
技穷了，请大神赐教！！！
问题已解决。 YOLOv8-pose在ncnn框架下部署过程记录（包含ncnn、pnnx安装以及模型结构更改及转换） https://github.com/Rachel-liuqr/yolov8s-pose-ncnn 如果有帮助的话，麻烦star，谢谢！
@Rachel-liuqr I have great news 😃! I've recently added official support for Ultralytics YOLOv8 NCNN export ✅ in PR https://github.com/ultralytics/ultralytics/pull/3529 with the help of @nihui which is part of ultralytics==8.0.129. NCNN works for all tasks including Detect, Segment, Pose and Classify.
You can now export with CLI:
yolo export model=yolov8n.pt format=ncnn
or Python:
from ultralytics import YOLO
# Create a model
model = YOLO('yolov8n.pt')
# Export the model to NCNN with arguments
model.export(format='ncnn', half=True, imgsz=640)
Output is a yolov8n_ncnn_model/ directory containing model.bin, model.param and metadata.yaml, along with extra PNNX files. For details see https://github.com/pnnx/pnnx README.
To get this update:
-
Git – Run
git pullfrom within yourultralytics/directory or rungit clone https://github.com/ultralytics/ultralyticsagain -
Pip – Update with
pip install -U ultralytics -
Notebooks – Check out the updated notebooks
-
Docker – Run
sudo docker pull ultralytics/ultralytics:latestto update your image
Please let us know if NCNN export is working correctly for you, and don't hesitate to report any other issues you find or feature requests you may have. Happy training with YOLOv8 🚀!
@glenn-jocher Although ncnn has been converted, there is a lack of examples of using c++ to call ncnn. Can this be provided, such as how to display the results predicted by ncnn?
@triple-Mu The following is the inference time of ncnn-gpu, ncnn-cpu and pt-ncnn that I tested. Is this normal? This is tested by yolo prediction code. https://github.com/ultralytics/ultralytics/issues/3917
针对onnx模型转换的各种问题，推荐使用最新的pnnx工具转换到ncnn In view of various problems in onnx model conversion, it is recommended to use the latest pnnx tool to convert your model to ncnn
pip install pnnx
pnnx model.onnx inputshape=[1,3,224,224]
详细参考文档 Detailed reference documentation https://github.com/pnnx/pnnx https://github.com/Tencent/ncnn/wiki/use-ncnn-with-pytorch-or-onnx#how-to-use-pnnx
