[Reimplementation] IndexError :argmax() in refine_bboxes function of sparse-rcnn (latest version (3.x).)

Prerequisite

• I have searched Issues and Discussions but cannot get the expected help.
• I have read the FAQ documentation but cannot get the expected help.
• The bug has not been fixed in the latest version (master) or latest version (3.x).

💬 Describe the reimplementation questions

When I try to train the sparse-rcnn series in the latest version (3. x) mmdet with my personal dataset (only 1 class), the following errors always occur:

  File "I:\mmdetection-3.x\mmdetection\mmdet\models\detectors\base.py", line 92, in forward
    return self.loss(inputs, data_samples)
  File "I:\mmdetection-3.x\mmdetection\mmdet\models\detectors\two_stage.py", line 187, in loss
    roi_losses = self.roi_head.loss(x, rpn_results_list,
  File "I:\mmdetection-3.x\mmdetection\mmdet\models\roi_heads\sparse_roi_head.py", line 344, in loss
    bbox_results = self.bbox_loss(
  File "I:\mmdetection-3.x\mmdetection\mmdet\models\roi_heads\sparse_roi_head.py", line 122, in bbox_loss
    bbox_results = self._bbox_forward(stage, x, rois, object_feats,
  File "I:\mmdetection-3.x\mmdetection\mmdet\models\roi_heads\sparse_roi_head.py", line 230, in _bbox_forward
    results_list = bbox_head.refine_bboxes(
  File "I:\mmdetection-3.x\mmdetection\mmdet\models\roi_heads\bbox_heads\bbox_head.py", line 645, in refine_bboxes
    cls_scores[:, :-1].argmax(1), labels)
IndexError: argmax(): Expected reduction dim 1 to have non-zero size.

Process finished with exit code 1

And the class_scores’s shape is (100,1).100 is num_proposals. I guess this 1 should be 2. Because there are two categories: foreground and background.

In the config file, I only modified the num_classes option in the head part and dataset address.

Environment

sys.platform: win32
Python: 3.8.12 (default, Oct 12 2021, 03:01:40) [MSC v.1916 64 bit (AMD64)]
CUDA available: True
numpy_random_seed: 2147483648
GPU 0: NVIDIA GeForce RTX 3090
CUDA_HOME: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.4
NVCC: Cuda compilation tools, release 11.4, V11.4.152
GCC: n/a
PyTorch: 1.11.0
PyTorch compiling details: PyTorch built with:
  - C++ Version: 199711
  - MSVC 192829337
  - Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)
  - OpenMP 2019
  - LAPACK is enabled (usually provided by MKL)
  - CPU capability usage: AVX512
  - CUDA Runtime 11.3
  - 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_61,code=sm_61;-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;-gencode;arch=compute_37,code=compute_37
  - CuDNN 8.2
  - Magma 2.5.4
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=C:/cb/pytorch_1000000000000/work/tmp_bin/sccache-cl.exe, CXX_FLAGS=/DWIN32 /D_WINDOWS /GR /EHsc /w /bigobj -DUSE_PTHREADPOOL -openmp:experimental -IC:/cb/pytorch_1000000000000/work/mkl/include -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DUSE_FBGEMM -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=OFF, USE_OPENMP=ON, USE_ROCM=OFF,

TorchVision: 0.12.0
OpenCV: 4.1.2
MMEngine: 0.3.1
MMDetection: 3.0.0rc3+5b0d5b4

Expected results

No response

Additional information

1.In config:

• changed the data and annotation path
• added metainfo to add my ‘CLASSES’
• changed ‘num_classes’ from 80 to 1.

2.I also test mmdet3.0 with my dataset in cascade rcnn.And the second dim of cls_scores is 2. So I suppose is there some problems in DIIHead? Or just I use the wrong way to train sparse rcnn?

[RangiLyu]

fixed in https://github.com/open-mmlab/mmdetection/pull/9358

[laCorse]

@RangiLyu Thanks for your reply. BTW, I love your nanodet!