CUDA error: out of memory

[yux326]

我正在使用两块2080ti训练hrnet，出现了 Out of memory报错 每块显卡有11GB可用显存 我的配置文件是 " experiments/coco/hrnet/w32_256x192_adam_lr1e-3.yaml" 输出报错信息为： I‘m running the train.py with two 2080ti but got a "Out of memery" error. Each of the two gpus has 11GB memory available. My config file is " experiments/coco/hrnet/w32_256x192_adam_lr1e-3.yaml" The output infomation is:

=> creating output/coco/pose_hrnet/w32_256x192_adam_lr1e-3 => creating log/coco/pose_hrnet/w32_256x192_adam_lr1e-3_2022-04-23-08-07 Namespace(cfg='experiments/coco/hrnet/w32_256x192_adam_lr1e-3.yaml', dataDir='', logDir='', modelDir='', opts=[], prevModelDir='') AUTO_RESUME: True CUDNN: BENCHMARK: True DETERMINISTIC: False ENABLED: True DATASET: COLOR_RGB: True DATASET: coco DATA_FORMAT: jpg FLIP: True HYBRID_JOINTS_TYPE: NUM_JOINTS_HALF_BODY: 8 PROB_HALF_BODY: 0.3 ROOT: data/coco/ ROT_FACTOR: 45 SCALE_FACTOR: 0.35 SELECT_DATA: False TEST_SET: val2017 TRAIN_SET: train2017 DATA_DIR: DEBUG: DEBUG: True SAVE_BATCH_IMAGES_GT: True SAVE_BATCH_IMAGES_PRED: True SAVE_HEATMAPS_GT: True SAVE_HEATMAPS_PRED: True GPUS: (2, 5) LOG_DIR: log LOSS: TOPK: 8 USE_DIFFERENT_JOINTS_WEIGHT: False USE_OHKM: False USE_TARGET_WEIGHT: True MODEL: EXTRA: FINAL_CONV_KERNEL: 1 PRETRAINED_LAYERS: ['conv1', 'bn1', 'conv2', 'bn2', 'layer1', 'transition1', 'stage2', 'transition2', 'stage3', 'transition3', 'stage4'] STAGE2: BLOCK: BASIC FUSE_METHOD: SUM NUM_BLOCKS: [4, 4] NUM_BRANCHES: 2 NUM_CHANNELS: [32, 64] NUM_MODULES: 1 STAGE3: BLOCK: BASIC FUSE_METHOD: SUM NUM_BLOCKS: [4, 4, 4] NUM_BRANCHES: 3 NUM_CHANNELS: [32, 64, 128] NUM_MODULES: 4 STAGE4: BLOCK: BASIC FUSE_METHOD: SUM NUM_BLOCKS: [4, 4, 4, 4] NUM_BRANCHES: 4 NUM_CHANNELS: [32, 64, 128, 256] NUM_MODULES: 3 HEATMAP_SIZE: [48, 64] IMAGE_SIZE: [192, 256] INIT_WEIGHTS: True NAME: pose_hrnet NUM_JOINTS: 17 PRETRAINED: models/pytorch/imagenet/hrnet_w32-36af842e.pth SIGMA: 2 TAG_PER_JOINT: True TARGET_TYPE: gaussian OUTPUT_DIR: output PIN_MEMORY: True PRINT_FREQ: 100 RANK: 0 TEST: BATCH_SIZE_PER_GPU: 32 BBOX_THRE: 1.0 COCO_BBOX_FILE: data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json FLIP_TEST: True IMAGE_THRE: 0.0 IN_VIS_THRE: 0.2 MODEL_FILE: NMS_THRE: 1.0 OKS_THRE: 0.9 POST_PROCESS: True SHIFT_HEATMAP: True SOFT_NMS: False USE_GT_BBOX: True TRAIN: BATCH_SIZE_PER_GPU: 2 BEGIN_EPOCH: 0 CHECKPOINT: END_EPOCH: 210 GAMMA1: 0.99 GAMMA2: 0.0 LR: 0.001 LR_FACTOR: 0.1 LR_STEP: [170, 200] MOMENTUM: 0.9 NESTEROV: False OPTIMIZER: adam RESUME: False SHUFFLE: True WD: 0.0001 WORKERS: 24 => init weights from normal distribution Traceback (most recent call last): File "tools/train.py", line 223, in main() File "tools/train.py", line 92, in main cfg, is_train=True File "/home/deep-high-resolution-net.pytorch/tools/../lib/models/pose_hrnet.py", line 499, in get_pose_net model.init_weights(cfg['MODEL']['PRETRAINED']) File "/home/deep-high-resolution-net.pytorch/tools/../lib/models/pose_hrnet.py", line 481, in init_weights pretrained_state_dict = torch.load(pretrained) File "/opt/conda/envs/py36torch/lib/python3.6/site-packages/torch/serialization.py", line 608, in load return _legacy_load(opened_file, map_location, pickle_module, *pickle_load_args) File "/opt/conda/envs/py36torch/lib/python3.6/site-packages/torch/serialization.py", line 787, in _legacy_load result = unpickler.load() File "/opt/conda/envs/py36torch/lib/python3.6/site-packages/torch/serialization.py", line 743, in persistent_load deserialized_objects[root_key] = restore_location(obj, location) File "/opt/conda/envs/py36torch/lib/python3.6/site-packages/torch/serialization.py", line 175, in default_restore_location result = fn(storage, location) File "/opt/conda/envs/py36torch/lib/python3.6/site-packages/torch/serialization.py", line 155, in _cuda_deserialize return storage_type(obj.size()) File "/opt/conda/envs/py36torch/lib/python3.6/site-packages/torch/cuda/init.py", line 606, in _lazy_new return super(_CudaBase, cls).new(cls, args, **kwargs) RuntimeError: CUDA error: out of memory CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1.

[Masum06]

@yanyixuan2000 did you find a solution? I have the same error with CUDA version 11.7 and pytoch cuda 11.6.

[anas-zafar]

Trying using batch size BATCH_SIZE_PER_GPU: 16 or 8

[yanbiaogit]

batch_size调低一点，在yaml文件里面