I train eqlv2 using 8 gpus, howerer, the AP is only 0.192

[CRuJia]

Describe the issue

I used your training command, but it doesn't works. But i use your pretrained model to test, i can get a expected result.
Tkanks.

Reproduction

1. What command or script did you run?

./tools/dist_test.sh configs/end2end/eqlv2_r50_8x2_1x.py data/pretrain_models/eqlv2_1x.pth 8 --out results.pkl --eval bbox segm

2. What config dir you run?

configs/end2end/eqlv2_r50_8x2_1x.py 

3. Did you make any modifications on the code or config? Did you understand what you have modified?
   No.

4. What dataset did you use?
   LVIS

Environment

1. Please run python mmdet/utils/collect_env.py to collect necessary environment information and paste it here.

Python: 3.7.0 (default, Oct  9 2018, 10:31:47) [GCC 7.3.0]
CUDA available: True
CUDA_HOME: None
GPU 0,1,2,3,4,5,6,7: TITAN Xp
GCC: gcc (Ubuntu 7.4.0-1ubuntu1~18.04.1) 7.4.0
PyTorch: 1.5.0
PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v0.21.1 (Git Hash 7d2fd500bc78936d1d648ca713b901012f470dbc)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 10.2
  - 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_37,code=compute_37
  - CuDNN 7.6.5
  - Magma 2.5.2
  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_INTERNAL_THREADPOOL_IMPL -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-strict-overflow -Wno-strict-aliasing
-Wno-error=deprecated-declarations -Wno-stringop-overflow -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, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=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, USE_STATIC_DISPATCH=OFF,

TorchVision: 0.6.0a0+82fd1c8
OpenCV: 4.5.1
MMCV: 1.0.5
MMDetection: ('2.3.0',)
MMDetection Compiler: GCC 7.3
MMDetection CUDA Compiler: 10.2

Results

i wish get your result in readme file, howerer, the really result is this.

Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=300 catIds=all] = 0.192
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=300 catIds=all] = 0.310
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=300 catIds=all] = 0.206
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=     s | maxDets=300 catIds=all] = 0.153
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=     m | maxDets=300 catIds=all] = 0.259
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=     l | maxDets=300 catIds=all] = 0.318
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=300 catIds=  r] = 0.021
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=300 catIds=  c] = 0.169
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=300 catIds=  f] = 0.293
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 catIds=all] = 0.268
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=     s | maxDets=300 catIds=all] = 0.196
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=     m | maxDets=300 catIds=all] = 0.354
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=     l | maxDets=300 catIds=all] = 0.449
2021-04-13 23:22:31,913 - mmdet - INFO - Evaluating segm...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=300 catIds=all] = 0.184
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=300 catIds=all] = 0.288
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=300 catIds=all] = 0.194
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=     s | maxDets=300 catIds=all] = 0.140
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=     m | maxDets=300 catIds=all] = 0.254
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=     l | maxDets=300 catIds=all] = 0.317
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=300 catIds=  r] = 0.018
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=300 catIds=  c] = 0.169
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=300 catIds=  f] = 0.274
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 catIds=all] = 0.257
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=     s | maxDets=300 catIds=all] = 0.180
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=     m | maxDets=300 catIds=all] = 0.345
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=     l | maxDets=300 catIds=all] = 0.442
2021-04-13 23:38:18,184 - mmdet - INFO - Epoch [12][6213/6213]  lr: 2.000e-04, bbox_AP: 0.1920, bbox_AP50: 0.3100, bbox_AP75: 0.2060, bbox_APs: 0.1530, bbox_APm: 0.2590,
 bbox_APl: 0.3180, bbox_APr: 0.0210, bbox_APc: 0.1690, bbox_APf: 0.2930, bbox_mAP_copypaste: AP:0.192 AP50:0.310 AP75:0.206 APs:0.153 APm:0.259 APl:0.318 APr:0.021 APc:0
.169 APf:0.293, segm_AP: 0.1840, segm_AP50: 0.2880, segm_AP75: 0.1940, segm_APs: 0.1400, segm_APm: 0.2540, segm_APl: 0.3170, segm_APr: 0.0180, segm_APc: 0.1690, segm_APf
: 0.2740, segm_mAP_copypaste: AP:0.184 AP50:0.288 AP75:0.194 APs:0.140 APm:0.254 APl:0.317 APr:0.018 APc:0.169 APf:0.274

Issue fix

If you have already identified the reason, you can provide the information here. If you are willing to create a PR to fix it, please also leave a comment here and that would be much appreciated!

[CRuJia]

i'm sorry, i train model with eqlv1

[GitDu6]

@CRuJia Do you solve this problem?