The testing results of the whole dataset is empty.

[xal-zhang]

I
Issue
I tried to run The project in the tutorial that trained the model using balloon datasets, and by round 6, I had the error shown in the title: The testing results of the whole dataset is empty. And some indicators are 0 from the fifth epoch, and the loss are zero.
I hope to get some help. Thank you!

Code

新配置继承了基本配置，并做了必要的修改

base = 'D:/mmlab/mmdetection/configs/mask_rcnn/mask-rcnn_r50-caffe_fpn_1x_coco.py'

我们还需要更改 head 中的 num_classes 以匹配数据集中的类别数

model = dict(
roi_head=dict(
bbox_head=dict(num_classes=1), mask_head=dict(num_classes=1)))

修改数据集相关配置

data_root = 'D:/mmlab/mmdetection/mmdet/configs/balloon/balloon_dataset/'
metainfo = {
'classes': ('balloon', ),
'palette': [
(220, 20, 60),
]
}
train_dataloader = dict(
batch_size=1,
dataset=dict(
data_root=data_root,
metainfo=metainfo,
ann_file='annotation/train.json',
data_prefix=dict(img='train/')))
val_dataloader = dict(
dataset=dict(
data_root=data_root,
metainfo=metainfo,
ann_file='annotation/val.json',
data_prefix=dict(img='val/')))
test_dataloader = val_dataloader

修改评价指标相关配置

val_evaluator = dict(ann_file=data_root + 'annotation/val.json')
test_evaluator = val_evaluator

使用预训练的 Mask R-CNN 模型权重来做初始化，可以提高模型性能

load_from = 'https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth'

Environment
sys.platform: win32
Python: 3.11.4 | packaged by Anaconda, Inc. | (main, Jul 5 2023, 13:38:37) [MSC v.1916 64 bit (AMD64)]
CUDA available: True
MUSA available: False
numpy_random_seed: 2147483648
GPU 0: NVIDIA GeForce GTX 1650
CUDA_HOME: None
GCC: n/a
PyTorch: 2.0.1
PyTorch compiling details: PyTorch built with:

• C++ Version: 199711
• MSVC 193431937
• Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications
• Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)
• OpenMP 2019
• LAPACK is enabled (usually provided by MKL)
• CPU capability usage: AVX2
• CUDA Runtime 11.8
• 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=c
  ompute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90;-gencode;arch=compute_37,code=compute_37
• CuDNN 8.7
• Magma 2.5.4
• Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=C:/cb/pytorch_1000000000000/work/tmp_bin/sccache-cl.exe, CXX_FLAGS=/DWIN32 /D_WINDOWS /GR /EHsc /w /bigobj /FS -DUSE
  _PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISAB
  LE_GPU_ASSERTS=OFF, TORCH_VERSION=2.0.1, 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=OFF, USE_NNPACK=OFF, USE_OPENMP=ON, USE_ROCM=OFF,

TorchVision: 0.15.2
OpenCV: 4.7.0
MMEngine: 0.10.3
MMDetection: 3.3.0+cfd5d3a