Model fails (does not start) to classify custom image #200

BlueVelvetSackOfGoldPotatoes · 2023-09-21T12:00:26Z

❓ Questions and Help

Here's my system: docker image with gpu support ubuntu 18.04

(base) root@43a59b70d445:/app/scene-graph-benchmark# nvidia-smi
Thu Sep 21 11:57:45 2023       
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  NVIDIA GeForce RTX 3080        On  | 00000000:08:00.0  On |                  N/A |
| 53%   27C    P3              90W / 340W |    895MiB / 10240MiB |     14%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+
                                                                                         
+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
+---------------------------------------------------------------------------------------+
(base) root@43a59b70d445:/app/scene-graph-benchmark# conda list | grep 'cudatoolkit\|cudnn'

(base) root@43a59b70d445:/app/scene-graph-benchmark# python -c "import torch; print(torch.__version__)"
1.4.0
(base) root@43a59b70d445:/app/scene-graph-benchmark# python -c "import torch; print(torch.cuda.is_available())"
True
(base) root@43a59b70d445:/app/scene-graph-benchmark# nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Sun_Jul_28_19:07:16_PDT_2019
Cuda compilation tools, release 10.1, V10.1.243

This is what I get when tryin gto run --- SGDet, Original, MOTIFS Model, SUM Fusion or --- SGDet, Causal TDE, MOTIFS Model, SUM Fusion.

Error:
(base) root@43a59b70d445:/app/scene-graph-benchmark# CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --master_port 10027 --nproc_per_node=1 tools/relation_test_net.py --config-file "configs/e2e_relation_X_101_32_8_FPN_1x.yaml" MODEL.ROI_RELATION_HEAD.USE_GT_BOX False MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False MODEL.ROI_RELATION_HEAD.PREDICTOR CausalAnalysisPredictor MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_TYPE none MODEL.ROI_RELATION_HEAD.CAUSAL.FUSION_TYPE sum MODEL.ROI_RELATION_HEAD.CAUSAL.CONTEXT_LAYER motifs TEST.IMS_PER_BATCH 1 DTYPE "float16" GLOVE_DIR /app/scene-graph-benchmark/glove MODEL.PRETRAINED_DETECTOR_CKPT /home/kaihua/checkpoints/causal-motifs-sgdet OUTPUT_DIR /app/scene-graph-benchmark/upload_causal_motif_sgdet TEST.CUSTUM_EVAL True TEST.CUSTUM_PATH /app/scene-graph-benchmark/custom_images DETECTED_SGG_DIR /app/scene-graph-benchmark/custom_images

2023-09-21 11:58:35,785 maskrcnn_benchmark INFO: Using 1 GPUs
2023-09-21 11:58:35,785 maskrcnn_benchmark INFO: AMP_VERBOSE: False
DATALOADER:
  ASPECT_RATIO_GROUPING: True
  NUM_WORKERS: 4
  SIZE_DIVISIBILITY: 32
DATASETS:
  TEST: ('VG_stanford_filtered_with_attribute_test',)
  TO_TEST: None
  TRAIN: ('VG_stanford_filtered_with_attribute_train',)
  VAL: ('VG_stanford_filtered_with_attribute_val',)
DETECTED_SGG_DIR: /app/scene-graph-benchmark/custom_images
DTYPE: float16
GLOVE_DIR: /app/scene-graph-benchmark/glove
INPUT:
  BRIGHTNESS: 0.0
  CONTRAST: 0.0
  HUE: 0.0
  MAX_SIZE_TEST: 1000
  MAX_SIZE_TRAIN: 1000
  MIN_SIZE_TEST: 600
  MIN_SIZE_TRAIN: (600,)
  PIXEL_MEAN: [102.9801, 115.9465, 122.7717]
  PIXEL_STD: [1.0, 1.0, 1.0]
  SATURATION: 0.0
  TO_BGR255: True
  VERTICAL_FLIP_PROB_TRAIN: 0.0
MODEL:
  ATTRIBUTE_ON: False
  BACKBONE:
    CONV_BODY: R-101-FPN
    FREEZE_CONV_BODY_AT: 2
  CLS_AGNOSTIC_BBOX_REG: False
  DEVICE: cuda
  FBNET:
    ARCH: default
    ARCH_DEF: 
    BN_TYPE: bn
    DET_HEAD_BLOCKS: []
    DET_HEAD_LAST_SCALE: 1.0
    DET_HEAD_STRIDE: 0
    DW_CONV_SKIP_BN: True
    DW_CONV_SKIP_RELU: True
    KPTS_HEAD_BLOCKS: []
    KPTS_HEAD_LAST_SCALE: 0.0
    KPTS_HEAD_STRIDE: 0
    MASK_HEAD_BLOCKS: []
    MASK_HEAD_LAST_SCALE: 0.0
    MASK_HEAD_STRIDE: 0
    RPN_BN_TYPE: 
    RPN_HEAD_BLOCKS: 0
    SCALE_FACTOR: 1.0
    WIDTH_DIVISOR: 1
  FLIP_AUG: False
  FPN:
    USE_GN: False
    USE_RELU: False
  GROUP_NORM:
    DIM_PER_GP: -1
    EPSILON: 1e-05
    NUM_GROUPS: 32
  KEYPOINT_ON: False
  MASK_ON: False
  META_ARCHITECTURE: GeneralizedRCNN
  PRETRAINED_DETECTOR_CKPT: /home/kaihua/checkpoints/causal-motifs-sgdet
  RELATION_ON: True
  RESNETS:
    BACKBONE_OUT_CHANNELS: 256
    DEFORMABLE_GROUPS: 1
    NUM_GROUPS: 32
    RES2_OUT_CHANNELS: 256
    RES5_DILATION: 1
    STAGE_WITH_DCN: (False, False, False, False)
    STEM_FUNC: StemWithFixedBatchNorm
    STEM_OUT_CHANNELS: 64
    STRIDE_IN_1X1: False
    TRANS_FUNC: BottleneckWithFixedBatchNorm
    WIDTH_PER_GROUP: 8
    WITH_MODULATED_DCN: False
  RETINANET:
    ANCHOR_SIZES: (32, 64, 128, 256, 512)
    ANCHOR_STRIDES: (8, 16, 32, 64, 128)
    ASPECT_RATIOS: (0.5, 1.0, 2.0)
    BBOX_REG_BETA: 0.11
    BBOX_REG_WEIGHT: 4.0
    BG_IOU_THRESHOLD: 0.4
    FG_IOU_THRESHOLD: 0.5
    INFERENCE_TH: 0.05
    LOSS_ALPHA: 0.25
    LOSS_GAMMA: 2.0
    NMS_TH: 0.4
    NUM_CLASSES: 81
    NUM_CONVS: 4
    OCTAVE: 2.0
    PRE_NMS_TOP_N: 1000
    PRIOR_PROB: 0.01
    SCALES_PER_OCTAVE: 3
    STRADDLE_THRESH: 0
    USE_C5: True
  RETINANET_ON: False
  ROI_ATTRIBUTE_HEAD:
    ATTRIBUTE_BGFG_RATIO: 3
    ATTRIBUTE_BGFG_SAMPLE: True
    ATTRIBUTE_LOSS_WEIGHT: 1.0
    FEATURE_EXTRACTOR: FPN2MLPFeatureExtractor
    MAX_ATTRIBUTES: 10
    NUM_ATTRIBUTES: 201
    POS_WEIGHT: 50.0
    PREDICTOR: FPNPredictor
    SHARE_BOX_FEATURE_EXTRACTOR: True
    USE_BINARY_LOSS: True
  ROI_BOX_HEAD:
    CONV_HEAD_DIM: 256
    DILATION: 1
    FEATURE_EXTRACTOR: FPN2MLPFeatureExtractor
    MLP_HEAD_DIM: 4096
    NUM_CLASSES: 151
    NUM_STACKED_CONVS: 4
    POOLER_RESOLUTION: 7
    POOLER_SAMPLING_RATIO: 2
    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
    PREDICTOR: FPNPredictor
    USE_GN: False
  ROI_HEADS:
    BATCH_SIZE_PER_IMAGE: 256
    BBOX_REG_WEIGHTS: (10.0, 10.0, 5.0, 5.0)
    BG_IOU_THRESHOLD: 0.3
    DETECTIONS_PER_IMG: 80
    FG_IOU_THRESHOLD: 0.5
    NMS: 0.3
    NMS_FILTER_DUPLICATES: True
    POSITIVE_FRACTION: 0.5
    POST_NMS_PER_CLS_TOPN: 300
    SCORE_THRESH: 0.01
    USE_FPN: True
  ROI_KEYPOINT_HEAD:
    CONV_LAYERS: (512, 512, 512, 512, 512, 512, 512, 512)
    FEATURE_EXTRACTOR: KeypointRCNNFeatureExtractor
    MLP_HEAD_DIM: 1024
    NUM_CLASSES: 17
    POOLER_RESOLUTION: 14
    POOLER_SAMPLING_RATIO: 0
    POOLER_SCALES: (0.0625,)
    PREDICTOR: KeypointRCNNPredictor
    RESOLUTION: 14
    SHARE_BOX_FEATURE_EXTRACTOR: True
  ROI_MASK_HEAD:
    CONV_LAYERS: (256, 256, 256, 256)
    DILATION: 1
    FEATURE_EXTRACTOR: ResNet50Conv5ROIFeatureExtractor
    MLP_HEAD_DIM: 1024
    POOLER_RESOLUTION: 14
    POOLER_SAMPLING_RATIO: 0
    POOLER_SCALES: (0.0625,)
    POSTPROCESS_MASKS: False
    POSTPROCESS_MASKS_THRESHOLD: 0.5
    PREDICTOR: MaskRCNNC4Predictor
    RESOLUTION: 14
    SHARE_BOX_FEATURE_EXTRACTOR: True
    USE_GN: False
  ROI_RELATION_HEAD:
    ADD_GTBOX_TO_PROPOSAL_IN_TRAIN: True
    BATCH_SIZE_PER_IMAGE: 1024
    CAUSAL:
      CONTEXT_LAYER: motifs
      EFFECT_ANALYSIS: True
      EFFECT_TYPE: none
      FUSION_TYPE: sum
      SEPARATE_SPATIAL: False
      SPATIAL_FOR_VISION: True
    CONTEXT_DROPOUT_RATE: 0.2
    CONTEXT_HIDDEN_DIM: 512
    CONTEXT_OBJ_LAYER: 1
    CONTEXT_POOLING_DIM: 4096
    CONTEXT_REL_LAYER: 1
    EMBED_DIM: 200
    FEATURE_EXTRACTOR: RelationFeatureExtractor
    LABEL_SMOOTHING_LOSS: False
    NUM_CLASSES: 51
    NUM_SAMPLE_PER_GT_REL: 4
    POOLING_ALL_LEVELS: True
    POSITIVE_FRACTION: 0.25
    PREDICTOR: CausalAnalysisPredictor
    PREDICT_USE_BIAS: True
    PREDICT_USE_VISION: True
    REL_PROP: [0.01858, 0.00057, 0.00051, 0.00109, 0.0015, 0.00489, 0.00432, 0.02913, 0.00245, 0.00121, 0.00404, 0.0011, 0.00132, 0.00172, 5e-05, 0.00242, 0.0005, 0.00048, 0.00208, 0.15608, 0.0265, 0.06091, 0.009, 0.00183, 0.00225, 0.0009, 0.00028, 0.00077, 0.04844, 0.08645, 0.31621, 0.00088, 0.00301, 0.00042, 0.00186, 0.001, 0.00027, 0.01012, 0.0001, 0.01286, 0.00647, 0.00084, 0.01077, 0.00132, 0.00069, 0.00376, 0.00214, 0.11424, 0.01205, 0.02958]
    REQUIRE_BOX_OVERLAP: False
    TRANSFORMER:
      DROPOUT_RATE: 0.1
      INNER_DIM: 2048
      KEY_DIM: 64
      NUM_HEAD: 8
      OBJ_LAYER: 4
      REL_LAYER: 2
      VAL_DIM: 64
    USE_GT_BOX: False
    USE_GT_OBJECT_LABEL: False
  RPN:
    ANCHOR_SIZES: (32, 64, 128, 256, 512)
    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
    ASPECT_RATIOS: (0.23232838, 0.63365731, 1.28478321, 3.15089189)
    BATCH_SIZE_PER_IMAGE: 256
    BG_IOU_THRESHOLD: 0.3
    FG_IOU_THRESHOLD: 0.7
    FPN_POST_NMS_PER_BATCH: False
    FPN_POST_NMS_TOP_N_TEST: 1000
    FPN_POST_NMS_TOP_N_TRAIN: 1000
    MIN_SIZE: 0
    NMS_THRESH: 0.7
    POSITIVE_FRACTION: 0.5
    POST_NMS_TOP_N_TEST: 1000
    POST_NMS_TOP_N_TRAIN: 1000
    PRE_NMS_TOP_N_TEST: 6000
    PRE_NMS_TOP_N_TRAIN: 6000
    RPN_HEAD: SingleConvRPNHead
    RPN_MID_CHANNEL: 256
    STRADDLE_THRESH: 0
    USE_FPN: True
  RPN_ONLY: False
  VGG:
    VGG16_OUT_CHANNELS: 512
  WEIGHT: catalog://ImageNetPretrained/FAIR/20171220/X-101-32x8d
OUTPUT_DIR: /app/scene-graph-benchmark/upload_causal_motif_sgdet
PATHS_CATALOG: /app/scene-graph-benchmark/maskrcnn_benchmark/config/paths_catalog.py
PATHS_DATA: /app/scene-graph-benchmark/maskrcnn_benchmark/config/../data/datasets
SOLVER:
  BASE_LR: 0.01
  BIAS_LR_FACTOR: 1
  CHECKPOINT_PERIOD: 2000
  CLIP_NORM: 5.0
  GAMMA: 0.1
  GRAD_NORM_CLIP: 5.0
  IMS_PER_BATCH: 16
  MAX_ITER: 40000
  MOMENTUM: 0.9
  PRE_VAL: True
  PRINT_GRAD_FREQ: 4000
  SCHEDULE:
    COOLDOWN: 0
    FACTOR: 0.1
    MAX_DECAY_STEP: 3
    PATIENCE: 2
    THRESHOLD: 0.001
    TYPE: WarmupReduceLROnPlateau
  STEPS: (10000, 16000)
  TO_VAL: True
  UPDATE_SCHEDULE_DURING_LOAD: False
  VAL_PERIOD: 2000
  WARMUP_FACTOR: 0.1
  WARMUP_ITERS: 500
  WARMUP_METHOD: linear
  WEIGHT_DECAY: 0.0001
  WEIGHT_DECAY_BIAS: 0.0
TEST:
  ALLOW_LOAD_FROM_CACHE: False
  BBOX_AUG:
    ENABLED: False
    H_FLIP: False
    MAX_SIZE: 4000
    SCALES: ()
    SCALE_H_FLIP: False
  CUSTUM_EVAL: True
  CUSTUM_PATH: /app/scene-graph-benchmark/custom_images
  DETECTIONS_PER_IMG: 100
  EXPECTED_RESULTS: []
  EXPECTED_RESULTS_SIGMA_TOL: 4
  IMS_PER_BATCH: 1
  RELATION:
    IOU_THRESHOLD: 0.5
    LATER_NMS_PREDICTION_THRES: 0.5
    MULTIPLE_PREDS: False
    REQUIRE_OVERLAP: False
    SYNC_GATHER: True
  SAVE_PROPOSALS: False
2023-09-21 11:58:35,785 maskrcnn_benchmark INFO: Collecting env info (might take some time)
2023-09-21 11:58:37,647 maskrcnn_benchmark INFO: 
PyTorch version: 1.4.0
Is debug build: No
CUDA used to build PyTorch: 10.1

OS: Ubuntu 18.04.6 LTS
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
CMake version: version 3.10.2

Python version: 3.8
Is CUDA available: Yes
CUDA runtime version: 10.1.243
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3080
Nvidia driver version: 535.104.05
cuDNN version: Could not collect

Versions of relevant libraries:
[pip3] numpy==1.20.1
[pip3] numpydoc==1.1.0
[pip3] torch==1.4.0
[pip3] torchvision==0.5.0
[conda] blas                      1.0                         mkl  
[conda] mkl                       2021.2.0           h06a4308_296  
[conda] mkl-service               2.3.0            py38h27cfd23_1  
[conda] mkl_fft                   1.3.0            py38h42c9631_2  
[conda] mkl_random                1.2.1            py38ha9443f7_2  
[conda] torch                     1.4.0                    pypi_0    pypi
[conda] torchvision               0.5.0                    pypi_0    pypi
        Pillow (8.2.0)
2023-09-21 11:58:39,524 maskrcnn_benchmark.data.build INFO: ----------------------------------------------------------------------------------------------------
2023-09-21 11:58:39,524 maskrcnn_benchmark.data.build INFO: get dataset statistics...
2023-09-21 11:58:39,524 maskrcnn_benchmark.data.build INFO: Loading data statistics from: /app/scene-graph-benchmark/upload_causal_motif_sgdet/VG_stanford_filtered_with_attribute_train_statistics.cache
2023-09-21 11:58:39,524 maskrcnn_benchmark.data.build INFO: ----------------------------------------------------------------------------------------------------
loading word vectors from /app/scene-graph-benchmark/glove/glove.6B.200d.pt
__background__ -> __background__ 
fail on __background__
loading word vectors from /app/scene-graph-benchmark/glove/glove.6B.200d.pt
__background__ -> __background__ 
fail on __background__
INIT SAVE DIR /app/scene-graph-benchmark/upload_causal_motif_sgdet
get_checkpoint_file /app/scene-graph-benchmark/upload_causal_motif_sgdet/last_checkpoint
last_saved /app/scene-graph-benchmark/upload_causal_motif_sgdet/model_0028000.pth
2023-09-21 11:58:41,861 maskrcnn_benchmark.utils.checkpoint INFO: Loading checkpoint from /app/scene-graph-benchmark/upload_causal_motif_sgdet/model_0028000.pth
 50%|█████████████████████████████████████████████████████████████████▌                                                                 | 1/2 [00:00<00:00,  9.66it/s]Skipping non-image file: custom_data_info.json
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 19.29it/s]
=====> /app/scene-graph-benchmark/custom_images/custom_data_info.json SAVED !
2023-09-21 11:58:42,846 maskrcnn_benchmark.inference INFO: Start evaluation on VG_stanford_filtered_with_attribute_test dataset(1 images).
  0%|                                                                                                                                           | 0/1 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "tools/relation_test_net.py", line 123, in <module>
    main()
  File "tools/relation_test_net.py", line 107, in main
    inference(
  File "/app/scene-graph-benchmark/maskrcnn_benchmark/engine/inference.py", line 110, in inference
    predictions = compute_on_dataset(model, data_loader, device, synchronize_gather=cfg.TEST.RELATION.SYNC_GATHER, timer=inference_timer)
  File "/app/scene-graph-benchmark/maskrcnn_benchmark/engine/inference.py", line 34, in compute_on_dataset
    output = model(images.to(device), targets)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "/app/scene-graph-benchmark/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py", line 49, in forward
    features = self.backbone(images.tensors)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/container.py", line 100, in forward
    input = module(input)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "/app/scene-graph-benchmark/maskrcnn_benchmark/modeling/backbone/resnet.py", line 149, in forward
    x = getattr(self, stage_name)(x)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/container.py", line 100, in forward
    input = module(input)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "/app/scene-graph-benchmark/maskrcnn_benchmark/modeling/backbone/resnet.py", line 331, in forward
    out = self.conv2(out)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "/app/scene-graph-benchmark/maskrcnn_benchmark/layers/misc.py", line 33, in forward
    return super(Conv2d, self).forward(x)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 345, in forward
    return self.conv2d_forward(input, self.weight)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 341, in conv2d_forward
    return F.conv2d(input, weight, self.bias, self.stride,
  File "/opt/conda/lib/python3.8/site-packages/apex/amp/wrap.py", line 28, in wrapper
    return orig_fn(*new_args, **kwargs)
RuntimeError: cuDNN error: CUDNN_STATUS_MAPPING_ERROR
Traceback (most recent call last):
  File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 263, in <module>
    main()
  File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 258, in main
    raise subprocess.CalledProcessError(returncode=process.returncode,
subprocess.CalledProcessError: Command '['/opt/conda/bin/python', '-u', 'tools/relation_test_net.py', '--local_rank=0', '--config-file', 'configs/e2e_relation_X_101_32_8_FPN_1x.yaml', 'MODEL.ROI_RELATION_HEAD.USE_GT_BOX', 'False', 'MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL', 'False', 'MODEL.ROI_RELATION_HEAD.PREDICTOR', 'CausalAnalysisPredictor', 'MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_TYPE', 'none', 'MODEL.ROI_RELATION_HEAD.CAUSAL.FUSION_TYPE', 'sum', 'MODEL.ROI_RELATION_HEAD.CAUSAL.CONTEXT_LAYER', 'motifs', 'TEST.IMS_PER_BATCH', '1', 'DTYPE', 'float16', 'GLOVE_DIR', '/app/scene-graph-benchmark/glove', 'MODEL.PRETRAINED_DETECTOR_CKPT', '/home/kaihua/checkpoints/causal-motifs-sgdet', 'OUTPUT_DIR', '/app/scene-graph-benchmark/upload_causal_motif_sgdet', 'TEST.CUSTUM_EVAL', 'True', 'TEST.CUSTUM_PATH', '/app/scene-graph-benchmark/custom_images', 'DETECTED_SGG_DIR', '/app/scene-graph-benchmark/custom_images']' returned non-zero exit status 1.

Can someone point me in the right direction?

The text was updated successfully, but these errors were encountered:

GalaxDust · 2023-10-19T02:22:27Z

可能是你的cuda 版本太高了项目代码的cuda用的10.1 3080应该不适配

majianbo3 · 2024-01-11T14:44:33Z

请问你解决了吗我也碰到这个问题

GalaxDust · 2024-01-11T15:31:42Z

可以看看这几个报错主要：
cuDNN error: CUDNN_STATUS_MAPPING_ERROR: 这是主要错误，表明在使用 CUDA Deep Neural Network (cuDNN) 库时出现了映射错误。cuDNN 是 NVIDIA 提供的用于深度神经网络的库，这个错误通常与 GPU 资源分配或兼容性有关。
应该是安装的 cuDNN 版本可能与 CUDA 版本不兼容导致的报错，可能你用的显卡是30系或者更高版本的显卡，但是这个项目的代码是在低版本的cuda下运行的 30系以上的显卡只支持cuda 11以上的版本了我用的是2080ti 可以避免这个问题

GalaxDust · 2024-01-11T15:37:53Z

❓ Questions and Help

Here's my system: docker image with gpu support ubuntu 18.04

(base) root@43a59b70d445:/app/scene-graph-benchmark# nvidia-smi
Thu Sep 21 11:57:45 2023       
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  NVIDIA GeForce RTX 3080        On  | 00000000:08:00.0  On |                  N/A |
| 53%   27C    P3              90W / 340W |    895MiB / 10240MiB |     14%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+
                                                                                         
+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
+---------------------------------------------------------------------------------------+
(base) root@43a59b70d445:/app/scene-graph-benchmark# conda list | grep 'cudatoolkit\|cudnn'

(base) root@43a59b70d445:/app/scene-graph-benchmark# python -c "import torch; print(torch.__version__)"
1.4.0
(base) root@43a59b70d445:/app/scene-graph-benchmark# python -c "import torch; print(torch.cuda.is_available())"
True
(base) root@43a59b70d445:/app/scene-graph-benchmark# nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Sun_Jul_28_19:07:16_PDT_2019
Cuda compilation tools, release 10.1, V10.1.243

This is what I get when tryin gto run --- SGDet, Original, MOTIFS Model, SUM Fusion or --- SGDet, Causal TDE, MOTIFS Model, SUM Fusion.

Error:
(base) root@43a59b70d445:/app/scene-graph-benchmark# CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --master_port 10027 --nproc_per_node=1 tools/relation_test_net.py --config-file "configs/e2e_relation_X_101_32_8_FPN_1x.yaml" MODEL.ROI_RELATION_HEAD.USE_GT_BOX False MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False MODEL.ROI_RELATION_HEAD.PREDICTOR CausalAnalysisPredictor MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_TYPE none MODEL.ROI_RELATION_HEAD.CAUSAL.FUSION_TYPE sum MODEL.ROI_RELATION_HEAD.CAUSAL.CONTEXT_LAYER motifs TEST.IMS_PER_BATCH 1 DTYPE "float16" GLOVE_DIR /app/scene-graph-benchmark/glove MODEL.PRETRAINED_DETECTOR_CKPT /home/kaihua/checkpoints/causal-motifs-sgdet OUTPUT_DIR /app/scene-graph-benchmark/upload_causal_motif_sgdet TEST.CUSTUM_EVAL True TEST.CUSTUM_PATH /app/scene-graph-benchmark/custom_images DETECTED_SGG_DIR /app/scene-graph-benchmark/custom_images

2023-09-21 11:58:35,785 maskrcnn_benchmark INFO: Using 1 GPUs
2023-09-21 11:58:35,785 maskrcnn_benchmark INFO: AMP_VERBOSE: False
DATALOADER:
  ASPECT_RATIO_GROUPING: True
  NUM_WORKERS: 4
  SIZE_DIVISIBILITY: 32
DATASETS:
  TEST: ('VG_stanford_filtered_with_attribute_test',)
  TO_TEST: None
  TRAIN: ('VG_stanford_filtered_with_attribute_train',)
  VAL: ('VG_stanford_filtered_with_attribute_val',)
DETECTED_SGG_DIR: /app/scene-graph-benchmark/custom_images
DTYPE: float16
GLOVE_DIR: /app/scene-graph-benchmark/glove
INPUT:
  BRIGHTNESS: 0.0
  CONTRAST: 0.0
  HUE: 0.0
  MAX_SIZE_TEST: 1000
  MAX_SIZE_TRAIN: 1000
  MIN_SIZE_TEST: 600
  MIN_SIZE_TRAIN: (600,)
  PIXEL_MEAN: [102.9801, 115.9465, 122.7717]
  PIXEL_STD: [1.0, 1.0, 1.0]
  SATURATION: 0.0
  TO_BGR255: True
  VERTICAL_FLIP_PROB_TRAIN: 0.0
MODEL:
  ATTRIBUTE_ON: False
  BACKBONE:
    CONV_BODY: R-101-FPN
    FREEZE_CONV_BODY_AT: 2
  CLS_AGNOSTIC_BBOX_REG: False
  DEVICE: cuda
  FBNET:
    ARCH: default
    ARCH_DEF: 
    BN_TYPE: bn
    DET_HEAD_BLOCKS: []
    DET_HEAD_LAST_SCALE: 1.0
    DET_HEAD_STRIDE: 0
    DW_CONV_SKIP_BN: True
    DW_CONV_SKIP_RELU: True
    KPTS_HEAD_BLOCKS: []
    KPTS_HEAD_LAST_SCALE: 0.0
    KPTS_HEAD_STRIDE: 0
    MASK_HEAD_BLOCKS: []
    MASK_HEAD_LAST_SCALE: 0.0
    MASK_HEAD_STRIDE: 0
    RPN_BN_TYPE: 
    RPN_HEAD_BLOCKS: 0
    SCALE_FACTOR: 1.0
    WIDTH_DIVISOR: 1
  FLIP_AUG: False
  FPN:
    USE_GN: False
    USE_RELU: False
  GROUP_NORM:
    DIM_PER_GP: -1
    EPSILON: 1e-05
    NUM_GROUPS: 32
  KEYPOINT_ON: False
  MASK_ON: False
  META_ARCHITECTURE: GeneralizedRCNN
  PRETRAINED_DETECTOR_CKPT: /home/kaihua/checkpoints/causal-motifs-sgdet
  RELATION_ON: True
  RESNETS:
    BACKBONE_OUT_CHANNELS: 256
    DEFORMABLE_GROUPS: 1
    NUM_GROUPS: 32
    RES2_OUT_CHANNELS: 256
    RES5_DILATION: 1
    STAGE_WITH_DCN: (False, False, False, False)
    STEM_FUNC: StemWithFixedBatchNorm
    STEM_OUT_CHANNELS: 64
    STRIDE_IN_1X1: False
    TRANS_FUNC: BottleneckWithFixedBatchNorm
    WIDTH_PER_GROUP: 8
    WITH_MODULATED_DCN: False
  RETINANET:
    ANCHOR_SIZES: (32, 64, 128, 256, 512)
    ANCHOR_STRIDES: (8, 16, 32, 64, 128)
    ASPECT_RATIOS: (0.5, 1.0, 2.0)
    BBOX_REG_BETA: 0.11
    BBOX_REG_WEIGHT: 4.0
    BG_IOU_THRESHOLD: 0.4
    FG_IOU_THRESHOLD: 0.5
    INFERENCE_TH: 0.05
    LOSS_ALPHA: 0.25
    LOSS_GAMMA: 2.0
    NMS_TH: 0.4
    NUM_CLASSES: 81
    NUM_CONVS: 4
    OCTAVE: 2.0
    PRE_NMS_TOP_N: 1000
    PRIOR_PROB: 0.01
    SCALES_PER_OCTAVE: 3
    STRADDLE_THRESH: 0
    USE_C5: True
  RETINANET_ON: False
  ROI_ATTRIBUTE_HEAD:
    ATTRIBUTE_BGFG_RATIO: 3
    ATTRIBUTE_BGFG_SAMPLE: True
    ATTRIBUTE_LOSS_WEIGHT: 1.0
    FEATURE_EXTRACTOR: FPN2MLPFeatureExtractor
    MAX_ATTRIBUTES: 10
    NUM_ATTRIBUTES: 201
    POS_WEIGHT: 50.0
    PREDICTOR: FPNPredictor
    SHARE_BOX_FEATURE_EXTRACTOR: True
    USE_BINARY_LOSS: True
  ROI_BOX_HEAD:
    CONV_HEAD_DIM: 256
    DILATION: 1
    FEATURE_EXTRACTOR: FPN2MLPFeatureExtractor
    MLP_HEAD_DIM: 4096
    NUM_CLASSES: 151
    NUM_STACKED_CONVS: 4
    POOLER_RESOLUTION: 7
    POOLER_SAMPLING_RATIO: 2
    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
    PREDICTOR: FPNPredictor
    USE_GN: False
  ROI_HEADS:
    BATCH_SIZE_PER_IMAGE: 256
    BBOX_REG_WEIGHTS: (10.0, 10.0, 5.0, 5.0)
    BG_IOU_THRESHOLD: 0.3
    DETECTIONS_PER_IMG: 80
    FG_IOU_THRESHOLD: 0.5
    NMS: 0.3
    NMS_FILTER_DUPLICATES: True
    POSITIVE_FRACTION: 0.5
    POST_NMS_PER_CLS_TOPN: 300
    SCORE_THRESH: 0.01
    USE_FPN: True
  ROI_KEYPOINT_HEAD:
    CONV_LAYERS: (512, 512, 512, 512, 512, 512, 512, 512)
    FEATURE_EXTRACTOR: KeypointRCNNFeatureExtractor
    MLP_HEAD_DIM: 1024
    NUM_CLASSES: 17
    POOLER_RESOLUTION: 14
    POOLER_SAMPLING_RATIO: 0
    POOLER_SCALES: (0.0625,)
    PREDICTOR: KeypointRCNNPredictor
    RESOLUTION: 14
    SHARE_BOX_FEATURE_EXTRACTOR: True
  ROI_MASK_HEAD:
    CONV_LAYERS: (256, 256, 256, 256)
    DILATION: 1
    FEATURE_EXTRACTOR: ResNet50Conv5ROIFeatureExtractor
    MLP_HEAD_DIM: 1024
    POOLER_RESOLUTION: 14
    POOLER_SAMPLING_RATIO: 0
    POOLER_SCALES: (0.0625,)
    POSTPROCESS_MASKS: False
    POSTPROCESS_MASKS_THRESHOLD: 0.5
    PREDICTOR: MaskRCNNC4Predictor
    RESOLUTION: 14
    SHARE_BOX_FEATURE_EXTRACTOR: True
    USE_GN: False
  ROI_RELATION_HEAD:
    ADD_GTBOX_TO_PROPOSAL_IN_TRAIN: True
    BATCH_SIZE_PER_IMAGE: 1024
    CAUSAL:
      CONTEXT_LAYER: motifs
      EFFECT_ANALYSIS: True
      EFFECT_TYPE: none
      FUSION_TYPE: sum
      SEPARATE_SPATIAL: False
      SPATIAL_FOR_VISION: True
    CONTEXT_DROPOUT_RATE: 0.2
    CONTEXT_HIDDEN_DIM: 512
    CONTEXT_OBJ_LAYER: 1
    CONTEXT_POOLING_DIM: 4096
    CONTEXT_REL_LAYER: 1
    EMBED_DIM: 200
    FEATURE_EXTRACTOR: RelationFeatureExtractor
    LABEL_SMOOTHING_LOSS: False
    NUM_CLASSES: 51
    NUM_SAMPLE_PER_GT_REL: 4
    POOLING_ALL_LEVELS: True
    POSITIVE_FRACTION: 0.25
    PREDICTOR: CausalAnalysisPredictor
    PREDICT_USE_BIAS: True
    PREDICT_USE_VISION: True
    REL_PROP: [0.01858, 0.00057, 0.00051, 0.00109, 0.0015, 0.00489, 0.00432, 0.02913, 0.00245, 0.00121, 0.00404, 0.0011, 0.00132, 0.00172, 5e-05, 0.00242, 0.0005, 0.00048, 0.00208, 0.15608, 0.0265, 0.06091, 0.009, 0.00183, 0.00225, 0.0009, 0.00028, 0.00077, 0.04844, 0.08645, 0.31621, 0.00088, 0.00301, 0.00042, 0.00186, 0.001, 0.00027, 0.01012, 0.0001, 0.01286, 0.00647, 0.00084, 0.01077, 0.00132, 0.00069, 0.00376, 0.00214, 0.11424, 0.01205, 0.02958]
    REQUIRE_BOX_OVERLAP: False
    TRANSFORMER:
      DROPOUT_RATE: 0.1
      INNER_DIM: 2048
      KEY_DIM: 64
      NUM_HEAD: 8
      OBJ_LAYER: 4
      REL_LAYER: 2
      VAL_DIM: 64
    USE_GT_BOX: False
    USE_GT_OBJECT_LABEL: False
  RPN:
    ANCHOR_SIZES: (32, 64, 128, 256, 512)
    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
    ASPECT_RATIOS: (0.23232838, 0.63365731, 1.28478321, 3.15089189)
    BATCH_SIZE_PER_IMAGE: 256
    BG_IOU_THRESHOLD: 0.3
    FG_IOU_THRESHOLD: 0.7
    FPN_POST_NMS_PER_BATCH: False
    FPN_POST_NMS_TOP_N_TEST: 1000
    FPN_POST_NMS_TOP_N_TRAIN: 1000
    MIN_SIZE: 0
    NMS_THRESH: 0.7
    POSITIVE_FRACTION: 0.5
    POST_NMS_TOP_N_TEST: 1000
    POST_NMS_TOP_N_TRAIN: 1000
    PRE_NMS_TOP_N_TEST: 6000
    PRE_NMS_TOP_N_TRAIN: 6000
    RPN_HEAD: SingleConvRPNHead
    RPN_MID_CHANNEL: 256
    STRADDLE_THRESH: 0
    USE_FPN: True
  RPN_ONLY: False
  VGG:
    VGG16_OUT_CHANNELS: 512
  WEIGHT: catalog://ImageNetPretrained/FAIR/20171220/X-101-32x8d
OUTPUT_DIR: /app/scene-graph-benchmark/upload_causal_motif_sgdet
PATHS_CATALOG: /app/scene-graph-benchmark/maskrcnn_benchmark/config/paths_catalog.py
PATHS_DATA: /app/scene-graph-benchmark/maskrcnn_benchmark/config/../data/datasets
SOLVER:
  BASE_LR: 0.01
  BIAS_LR_FACTOR: 1
  CHECKPOINT_PERIOD: 2000
  CLIP_NORM: 5.0
  GAMMA: 0.1
  GRAD_NORM_CLIP: 5.0
  IMS_PER_BATCH: 16
  MAX_ITER: 40000
  MOMENTUM: 0.9
  PRE_VAL: True
  PRINT_GRAD_FREQ: 4000
  SCHEDULE:
    COOLDOWN: 0
    FACTOR: 0.1
    MAX_DECAY_STEP: 3
    PATIENCE: 2
    THRESHOLD: 0.001
    TYPE: WarmupReduceLROnPlateau
  STEPS: (10000, 16000)
  TO_VAL: True
  UPDATE_SCHEDULE_DURING_LOAD: False
  VAL_PERIOD: 2000
  WARMUP_FACTOR: 0.1
  WARMUP_ITERS: 500
  WARMUP_METHOD: linear
  WEIGHT_DECAY: 0.0001
  WEIGHT_DECAY_BIAS: 0.0
TEST:
  ALLOW_LOAD_FROM_CACHE: False
  BBOX_AUG:
    ENABLED: False
    H_FLIP: False
    MAX_SIZE: 4000
    SCALES: ()
    SCALE_H_FLIP: False
  CUSTUM_EVAL: True
  CUSTUM_PATH: /app/scene-graph-benchmark/custom_images
  DETECTIONS_PER_IMG: 100
  EXPECTED_RESULTS: []
  EXPECTED_RESULTS_SIGMA_TOL: 4
  IMS_PER_BATCH: 1
  RELATION:
    IOU_THRESHOLD: 0.5
    LATER_NMS_PREDICTION_THRES: 0.5
    MULTIPLE_PREDS: False
    REQUIRE_OVERLAP: False
    SYNC_GATHER: True
  SAVE_PROPOSALS: False
2023-09-21 11:58:35,785 maskrcnn_benchmark INFO: Collecting env info (might take some time)
2023-09-21 11:58:37,647 maskrcnn_benchmark INFO: 
PyTorch version: 1.4.0
Is debug build: No
CUDA used to build PyTorch: 10.1

OS: Ubuntu 18.04.6 LTS
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
CMake version: version 3.10.2

Python version: 3.8
Is CUDA available: Yes
CUDA runtime version: 10.1.243
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3080
Nvidia driver version: 535.104.05
cuDNN version: Could not collect

Versions of relevant libraries:
[pip3] numpy==1.20.1
[pip3] numpydoc==1.1.0
[pip3] torch==1.4.0
[pip3] torchvision==0.5.0
[conda] blas                      1.0                         mkl  
[conda] mkl                       2021.2.0           h06a4308_296  
[conda] mkl-service               2.3.0            py38h27cfd23_1  
[conda] mkl_fft                   1.3.0            py38h42c9631_2  
[conda] mkl_random                1.2.1            py38ha9443f7_2  
[conda] torch                     1.4.0                    pypi_0    pypi
[conda] torchvision               0.5.0                    pypi_0    pypi
        Pillow (8.2.0)
2023-09-21 11:58:39,524 maskrcnn_benchmark.data.build INFO: ----------------------------------------------------------------------------------------------------
2023-09-21 11:58:39,524 maskrcnn_benchmark.data.build INFO: get dataset statistics...
2023-09-21 11:58:39,524 maskrcnn_benchmark.data.build INFO: Loading data statistics from: /app/scene-graph-benchmark/upload_causal_motif_sgdet/VG_stanford_filtered_with_attribute_train_statistics.cache
2023-09-21 11:58:39,524 maskrcnn_benchmark.data.build INFO: ----------------------------------------------------------------------------------------------------
loading word vectors from /app/scene-graph-benchmark/glove/glove.6B.200d.pt
__background__ -> __background__ 
fail on __background__
loading word vectors from /app/scene-graph-benchmark/glove/glove.6B.200d.pt
__background__ -> __background__ 
fail on __background__
INIT SAVE DIR /app/scene-graph-benchmark/upload_causal_motif_sgdet
get_checkpoint_file /app/scene-graph-benchmark/upload_causal_motif_sgdet/last_checkpoint
last_saved /app/scene-graph-benchmark/upload_causal_motif_sgdet/model_0028000.pth
2023-09-21 11:58:41,861 maskrcnn_benchmark.utils.checkpoint INFO: Loading checkpoint from /app/scene-graph-benchmark/upload_causal_motif_sgdet/model_0028000.pth
 50%|█████████████████████████████████████████████████████████████████▌                                                                 | 1/2 [00:00<00:00,  9.66it/s]Skipping non-image file: custom_data_info.json
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 19.29it/s]
=====> /app/scene-graph-benchmark/custom_images/custom_data_info.json SAVED !
2023-09-21 11:58:42,846 maskrcnn_benchmark.inference INFO: Start evaluation on VG_stanford_filtered_with_attribute_test dataset(1 images).
  0%|                                                                                                                                           | 0/1 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "tools/relation_test_net.py", line 123, in <module>
    main()
  File "tools/relation_test_net.py", line 107, in main
    inference(
  File "/app/scene-graph-benchmark/maskrcnn_benchmark/engine/inference.py", line 110, in inference
    predictions = compute_on_dataset(model, data_loader, device, synchronize_gather=cfg.TEST.RELATION.SYNC_GATHER, timer=inference_timer)
  File "/app/scene-graph-benchmark/maskrcnn_benchmark/engine/inference.py", line 34, in compute_on_dataset
    output = model(images.to(device), targets)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "/app/scene-graph-benchmark/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py", line 49, in forward
    features = self.backbone(images.tensors)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/container.py", line 100, in forward
    input = module(input)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "/app/scene-graph-benchmark/maskrcnn_benchmark/modeling/backbone/resnet.py", line 149, in forward
    x = getattr(self, stage_name)(x)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/container.py", line 100, in forward
    input = module(input)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "/app/scene-graph-benchmark/maskrcnn_benchmark/modeling/backbone/resnet.py", line 331, in forward
    out = self.conv2(out)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "/app/scene-graph-benchmark/maskrcnn_benchmark/layers/misc.py", line 33, in forward
    return super(Conv2d, self).forward(x)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 345, in forward
    return self.conv2d_forward(input, self.weight)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 341, in conv2d_forward
    return F.conv2d(input, weight, self.bias, self.stride,
  File "/opt/conda/lib/python3.8/site-packages/apex/amp/wrap.py", line 28, in wrapper
    return orig_fn(*new_args, **kwargs)
RuntimeError: cuDNN error: CUDNN_STATUS_MAPPING_ERROR
Traceback (most recent call last):
  File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 263, in <module>
    main()
  File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 258, in main
    raise subprocess.CalledProcessError(returncode=process.returncode,
subprocess.CalledProcessError: Command '['/opt/conda/bin/python', '-u', 'tools/relation_test_net.py', '--local_rank=0', '--config-file', 'configs/e2e_relation_X_101_32_8_FPN_1x.yaml', 'MODEL.ROI_RELATION_HEAD.USE_GT_BOX', 'False', 'MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL', 'False', 'MODEL.ROI_RELATION_HEAD.PREDICTOR', 'CausalAnalysisPredictor', 'MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_TYPE', 'none', 'MODEL.ROI_RELATION_HEAD.CAUSAL.FUSION_TYPE', 'sum', 'MODEL.ROI_RELATION_HEAD.CAUSAL.CONTEXT_LAYER', 'motifs', 'TEST.IMS_PER_BATCH', '1', 'DTYPE', 'float16', 'GLOVE_DIR', '/app/scene-graph-benchmark/glove', 'MODEL.PRETRAINED_DETECTOR_CKPT', '/home/kaihua/checkpoints/causal-motifs-sgdet', 'OUTPUT_DIR', '/app/scene-graph-benchmark/upload_causal_motif_sgdet', 'TEST.CUSTUM_EVAL', 'True', 'TEST.CUSTUM_PATH', '/app/scene-graph-benchmark/custom_images', 'DETECTED_SGG_DIR', '/app/scene-graph-benchmark/custom_images']' returned non-zero exit status 1.

Can someone point me in the right direction?
These are the main errors identified:

cuDNN error: CUDNN_STATUS_MAPPING_ERROR: This is the primary error, indicating a mapping error when using the CUDA Deep Neural Network (cuDNN) library. cuDNN, provided by NVIDIA for deep neural networks, often encounters this error due to GPU resource allocation or compatibility issues.
The issue might stem from an incompatibility between the installed version of cuDNN and the CUDA version. If you're using a 30 series or higher version graphics card, but the project's code is running on a lower version of CUDA, this could cause a problem. Graphics cards from the 30 series and above only support CUDA 11 and higher versions. I am using a 2080ti, which can avoid this problem.

majianbo3 · 2024-01-11T15:49:25Z

你好可以加个联系方式细聊嘛我这个配的问题有点多

majianbo3 · 2024-01-12T06:12:11Z

您好，下载的预训练文件VG_stanford_filtered_with_attribute_train_statistics.cache我出现unable load 您知道怎么解决吗

GalaxDust · 2024-01-12T06:25:35Z

我之前你也遇到过这个问题看看你的配置路径有没有写对比如我的VG_stanford_filtered_with_attribute_train_statistics.cache是在/root/autodl-tmp/checkpoints/relation_motif_SGDet下在命令行中要指定OUTPUT_DIR /root/autodl-tmp/checkpoints/relation_motif_SGDet 再看一下你的maskracc_benchmark/config/paths_catalog.py文件中DatasetCatalog类的 DATA_DIR 有没有配置好

majianbo3 · 2024-01-12T06:57:47Z

非常感谢您的帮助，但在这个问题解决后，它出现AttributeError: 'tqdm' object has no attribute 'disable'

GalaxDust · 2024-01-12T07:04:17Z

这是tqdm库的问题可能是你装的库不对

majianbo3 · 2024-01-12T07:56:36Z

非常感谢，现在出现
File "/home/handofgod/anaconda3/envs/scene_graph/lib/python3.6/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/handofgod/Scene-Graph-Benchmark.pytorch-master/maskrcnn_benchmark/modeling/rpn/inference.py", line 142, in forward
sampled_boxes.append(self.forward_for_single_feature_map(a, o, b))
File "/home/handofgod/Scene-Graph-Benchmark.pytorch-master/maskrcnn_benchmark/modeling/rpn/inference.py", line 122, in forward_for_single_feature_map
score_field="objectness",
File "/home/handofgod/Scene-Graph-Benchmark.pytorch-master/maskrcnn_benchmark/structures/boxlist_ops.py", line 28, in boxlist_nms
keep = _box_nms(boxes, score, nms_thresh)
File "/home/handofgod/anaconda3/envs/scene_graph/lib/python3.6/site-packages/apex-0.1-py3.6.egg/apex/amp/amp.py", line 22, in wrapper
return orig_fn(*args, **kwargs)
RuntimeError: Not compiled with GPU support

GalaxDust · 2024-01-12T08:08:33Z

其实你可以使用Google

majianbo3 · 2024-01-12T08:10:49Z

抱歉，我刚刚学习sgg，不太清楚具体要怎么弄

GalaxDust · 2024-01-12T08:22:37Z

看你的报错应该是amp编译后和你的GPU版本不适配 PyTorch 版本或apex没有安装 GPU 支持版本这个项目的代码很老了

majianbo3 · 2024-01-12T08:24:36Z

你好，有什么较新的SGG项目推荐嘛

GalaxDust · 2024-01-12T08:32:28Z

KaiHua大佬的框架应该是整理的比较好的了你可以在网上搜一下CSDN上有人在3080上部署过这个项目应该可以解决你的问题 http://t.csdnimg.cn/aUaqv

majianbo3 · 2024-01-12T08:44:55Z

非常感谢！！！

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Model fails (does not start) to classify custom image #200

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