mmdetection: Error exporting YoloX model to ONNX

I was trying to do onnx export of yolox model - but i got an error.

The script that I used is: python tools/deployment/pytorch2onnx.py
configs/yolox/yolox_s_8x8_300e_coco.py
work_dirs/yolox_s_lite/latest.pth
–output-file output.onnx
–input-img demo/demo.jpg
–dynamic-export
–show
–verify
–simplify

But then I get this error: File “/data/ssd/files/a0393608/work/code/github/openmmlab/mmdetection/tools/deployment/pytorch2onnx.py”, line 330, in <module> normalize_cfg = parse_normalize_cfg(cfg.test_pipeline) File “/data/ssd/files/a0393608/work/code/github/openmmlab/mmdetection/tools/deployment/pytorch2onnx.py”, line 218, in parse_normalize_cfg assert len(norm_config_li) == 1, ‘norm_config should only have one’ AssertionError: norm_config should only have one

This is because yolox doesn’t have input Normalization. I worked around that by providing a dummy normalization. But then I got another error.

File “/data/ssd/files/a0393608/work/code/github/openmmlab/mmdetection/mmdet/models/detectors/base.py”, line 169, in forward return self.onnx_export(img[0], img_metas[0]) File “/data/ssd/files/a0393608/work/code/github/openmmlab/mmdetection/mmdet/models/detectors/single_stage.py”, line 169, in onnx_export *outs, img_metas, with_nms=with_nms) File “/user/a0393608/work/apps/miniconda3/envs/edgeai-mmdetection/lib/python3.7/site-packages/mmcv/runner/fp16_utils.py”, line 186, in new_func return old_func(*args, **kwargs) File “/data/ssd/files/a0393608/work/code/github/openmmlab/mmdetection/mmdet/models/dense_heads/base_dense_head.py”, line 492, in onnx_export bboxes = self.bbox_coder.decode( File “/user/a0393608/work/apps/miniconda3/envs/edgeai-mmdetection/lib/python3.7/site-packages/torch/nn/modules/module.py”, line 1178, in getattr type(self).name, name)) AttributeError: ‘YOLOXHead’ object has no attribute ‘bbox_coder’

Environment sys.platform: linux Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] CUDA available: True GPU 0,1,2,3: RTX A4000 CUDA_HOME: /usr/local/cuda NVCC: Build cuda_11.1.TC455_06.29190527_0 GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 PyTorch: 1.10.0+cu111 PyTorch compiling details: PyTorch built with:

GCC 7.3
C++ Version: 201402
Intel® Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel® 64 architecture applications
Intel® MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
OpenMP 201511 (a.k.a. OpenMP 4.5)
LAPACK is enabled (usually provided by MKL)
NNPACK is enabled
CPU capability usage: AVX512
CUDA Runtime 11.1
NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
CuDNN 8.0.5
Magma 2.5.2
Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,

TorchVision: 0.11.0a0+972e9af OpenCV: 4.5.4 MMCV: 1.4.2 MMCV Compiler: GCC 7.5 MMCV CUDA Compiler: 11.1 MMDetection: 2.20.0+ff9bc39

About this issue

Original URL
State: closed
Created 2 years ago
Comments: 25

Most upvoted comments

YOLOX does not use image normalization, so another minor change is needed in tools/deployment/pytorch2onnx.py in function parse_normalize_cfg() as shown below.

def parse_normalize_cfg(test_pipeline):
    transforms = None
    for pipeline in test_pipeline:
        if 'transforms' in pipeline:
            transforms = pipeline['transforms']
            break
    assert transforms is not None, 'Failed to find `transforms`'
    norm_config_li = [_ for _ in transforms if _['type'] == 'Normalize']
    assert len(norm_config_li) <= 1, '`norm_config` should only have one'
    norm_config = norm_config_li[0] if len(norm_config_li)>0 else dict(mean=0.0, std=1.0)
    return norm_config

With these two changes, onnx export of YOLOX should work.

mathmanu on Jan 19, 2022

very thank u share u code,I successfully used it to export the onnx model today. I wish you a happy Chinese New Year in advance.

请问您是用的哪个版本的mmdetection，我在最新版本上按照上面的代码修改了yolox.py和pytorch2onnx.py文件，还是会报错AttributeError: ‘YOLOXHead’ object has no attribute ‘bbox_coder’。麻烦看一下是不是还有其他地方需要修改。谢谢

VJoer on Feb 11, 2022

Adding the following to the class YOLOX in mmdetection/mmdet/models/detectors/yolox.py enables the onnx export. The following code was inspired by mmdeploy:

    def onnx_export(self, img, img_metas=None, **kwargs):
        img_metas = img_metas[0] if isinstance(img_metas, (list,tuple)) else img_metas
        assert isinstance(img_metas, dict)
        assert isinstance(img, torch.Tensor)

        is_dynamic_flag = False
        # get origin input shape as tensor to support onnx dynamic shape
        img_shape = torch._shape_as_tensor(img)[2:]
        if not is_dynamic_flag:
            img_shape = [int(val) for val in img_shape]
        img_metas['img_shape'] = img_shape
        img_metas = [img_metas]
        return self.simple_test(img, img_metas, **kwargs)

    def simple_test(self, img, img_metas, **kwargs):
        if torch.onnx.is_in_onnx_export():
            feat = self.extract_feat(img)
            return self.bbox_head.simple_test(feat, img_metas, **kwargs)
        else:
            return super().simple_test(img, img_metas, **kwargs)

It’s good to add it since it’s a popular model.

mathmanu on Jan 18, 2022

Hi @RunningLeon, I tried mmdeploy and the onnx export worked. However it would be nice if it works from mmdetection itself - the other single stage models work. It would be good to avoid this additional dependency in our use case.

mathmanu on Jan 13, 2022