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ucas-vg / P2BNet

ECCV2022, Point-to-Box Network for Accurate Object Detection via Single Point Supervision
MIT License
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OOM in evaluation #17

Open Luo-Z13 opened 1 year ago

Luo-Z13 commented 1 year ago

Environment ( I have tried pytorch 1.9.0 and pytorch 1.8.0 and their corresponding cudnn/ mmcv-full =1.14.0, both will cause memory increasing when training.)

sys.platform: linux Python: 3.8.17 (default, Jul 5 2023, 21:04:15) [GCC 11.2.0] CUDA available: True GPU 0: NVIDIA TITAN RTX CUDA_HOME: /usr/local/cuda-10.2 NVCC: Cuda compilation tools, release 10.2, V10.2.89 GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 PyTorch: 1.10.0 PyTorch compiling details: PyTorch built with:

  • GCC 7.3
  • C++ Version: 201402
  • Intel(R) oneAPI Math Kernel Library Version 2023.1-Product Build 20230303 for Intel(R) 64 architecture applications
  • Intel(R) 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 11.3
  • 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 2.5.2
  • Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, 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: 0.11.1 OpenCV: 4.7.0 MMCV: 1.4.0 MMCV Compiler: GCC 7.3 MMCV CUDA Compiler: 11.3 MMDetection: 2.13.0+c820f32

2023-09-05 21:09:00,423 - mmdet - INFO - Distributed training: False 2023-09-05 21:09:01,221 - mmdet - INFO - Config: checkpoint_config = dict(interval=1) log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')]) custom_hooks = [dict(type='NumClassCheckHook')] dist_params = dict(backend='nccl') log_level = 'INFO' load_from = None resume_from = None workflow = [('train', 1)] norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)

Bug Description

[>>> ] 426/3822, 16.0 task/s, elapsed: 27s, ETA: 213s [>>> ] 427/3822, 15.9 task/s, elapsed: 27s, ETA: 214s [>>> ] 428/3822, 15.9 task/s, elapsed: 27s, ETA: 213s [>>> ] 429/3822, 15.9 task/s, elapsed: 27s, ETA: 213s [>>> ] 430/3822, 15.9 task/s, elapsed: 27s, ETA: 213s [>>> ] 431/3822, 15.9 task/s, elapsed: 27s, ETA: 214s [>>> ] 432/3822, 15.9 task/s, elapsed: 27s, ETA: 213s [>>> ] 433/3822, 15.9 task/s, elapsed: 27s, ETA: 213s [>>> ] 434/3822, 15.9 task/s, elapsed: 27s, ETA: 213sTraceback (most recent call last): File "tools/train.py", line 192, in main() File "tools/train.py", line 181, in main train_detector( File "/media/dell/data1/ljw/code/test1/CPR/P2BNet/TOV_mmdetection/mmdet/apis/train.py", line 172, in train_detector runner.run(data_loaders, cfg.workflow) File "/media/dell/data1/miniconda3/envs/mmdetp2b/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 127, in run epoch_runner(data_loaders[i], kwargs) File "/media/dell/data1/miniconda3/envs/mmdetp2b/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 54, in train self.call_hook('after_train_epoch') File "/media/dell/data1/miniconda3/envs/mmdetp2b/lib/python3.8/site-packages/mmcv/runner/base_runner.py", line 307, in call_hook getattr(hook, fn_name)(self) File "/media/dell/data1/miniconda3/envs/mmdetp2b/lib/python3.8/site-packages/mmcv/runner/hooks/evaluation.py", line 267, in after_train_epoch self._do_evaluate(runner) File "/media/dell/data1/ljw/code/test1/CPR/P2BNet/TOV_mmdetection/mmdet/core/evaluation/eval_hooks.py", line 44, in _do_evaluate results = single_gpu_test(runner.model, self.dataloader, show=False) File "/media/dell/data1/ljw/code/test1/CPR/P2BNet/TOV_mmdetection/mmdet/apis/test.py", line 27, in single_gpu_test result = model(return_loss=False, rescale=True, data) File "/media/dell/data1/miniconda3/envs/mmdetp2b/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, kwargs) File "/media/dell/data1/miniconda3/envs/mmdetp2b/lib/python3.8/site-packages/mmcv/parallel/data_parallel.py", line 50, in forward return super().forward(*inputs, *kwargs) File "/media/dell/data1/miniconda3/envs/mmdetp2b/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 166, in forward return self.module(inputs[0], kwargs[0]) File "/media/dell/data1/miniconda3/envs/mmdetp2b/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, kwargs) File "/media/dell/data1/miniconda3/envs/mmdetp2b/lib/python3.8/site-packages/mmcv/runner/fp16_utils.py", line 98, in new_func return old_func(*args, kwargs) File "/media/dell/data1/ljw/code/test1/CPR/P2BNet/TOV_mmdetection/mmdet/models/detectors/base.py", line 177, in forward return self.forward_test(img, img_metas, kwargs) File "/media/dell/data1/ljw/code/test1/CPR/P2BNet/TOV_mmdetection/mmdet/models/detectors/base.py", line 150, in forward_test return self.simple_test(imgs[0], img_metas[0], *kwargs) File "/media/dell/data1/ljw/code/test1/CPR/P2BNet/TOV_mmdetection/mmdet/models/detectors/P2BNet.py", line 375, in simple_test test_result, pseudo_boxes = self.roi_head.simple_test(stage, File "/media/dell/data1/ljw/code/test1/CPR/P2BNet/TOV_mmdetection/mmdet/models/roi_heads/P2B_head.py", line 446, in simple_test det_bboxes, det_labels, pseudo_bboxes = self.simple_test_bboxes( File "/media/dell/data1/ljw/code/test1/CPR/P2BNet/TOV_mmdetection/mmdet/models/roi_heads/P2B_head.py", line 474, in simple_test_bboxes bbox_results = self._bbox_forward(x, rois, gt_bboxes, stage) File "/media/dell/data1/ljw/code/test1/CPR/P2BNet/TOV_mmdetection/mmdet/models/roi_heads/P2B_head.py", line 210, in _bbox_forward bbox_feats = self.bbox_roi_extractor( File "/media/dell/data1/miniconda3/envs/mmdetp2b/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(input, kwargs) File "/media/dell/data1/miniconda3/envs/mmdetp2b/lib/python3.8/site-packages/mmcv/runner/fp16_utils.py", line 186, in new_func return old_func(*args, *kwargs) File "/media/dell/data1/ljw/code/test1/CPR/P2BNet/TOV_mmdetection/mmdet/models/roi_heads/roi_extractors/single_level_roi_extractor.py", line 102, in forward roi_feats_t = self.roilayers[i](feats[i], rois) File "/media/dell/data1/miniconda3/envs/mmdetp2b/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(input, **kwargs) File "/media/dell/data1/miniconda3/envs/mmdetp2b/lib/python3.8/site-packages/mmcv/ops/roi_align.py", line 212, in forward return roi_align(input, rois, self.output_size, self.spatial_scale, File "/media/dell/data1/miniconda3/envs/mmdetp2b/lib/python3.8/site-packages/mmcv/ops/roi_align.py", line 84, in forward output = input.new_zeros(output_shape) RuntimeError: CUDA out of memory. Tried to allocate 2.28 GiB (GPU 0; 23.65 GiB total capacity; 14.99 GiB already allocated; 1.28 GiB free; 21.04 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

Luo-Z13 commented 1 year ago

@Darren-pfchen Could you give some advice about controlling the number of rois please?