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KaihuaTang / Scene-Graph-Benchmark.pytorch

A new codebase for popular Scene Graph Generation methods (2020). Visualization & Scene Graph Extraction on custom images/datasets are provided. It's also a PyTorch implementation of paper “Unbiased Scene Graph Generation from Biased Training CVPR 2020”
MIT License
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cudaErrorAssert: device-side assert triggered #207

Open M1091027 opened 3 months ago

M1091027 commented 3 months ago

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2024-03-08 21:33:06,932 maskrcnn_benchmark INFO: Using 1 GPUs 2024-03-08 21:33:06,932 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: TRAIN: ('VG_stanford_filtered_with_attribute_train',) VAL: ('VG_stanford_filtered_with_attribute_val',) DETECTED_SGG_DIR: /mnt/d/zm/paper/Scene/detected DTYPE: float16 GLOVE_DIR: /mnt/d/zm/paper/Scene/checkpoints/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: /mnt/d/zm/paper/Scene/checkpoints/causal_motif_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: TDE 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: /mnt/d/zm/paper/Scene/checkpoints/causal_motif_sgdet PATHS_CATALOG: /mnt/d/zm/paper/Scene/maskrcnn_benchmark/config/paths_catalog.py PATHS_DATA: /mnt/d/zm/paper/Scene/maskrcnn_benchmark/config/../data/datasets SOLVER: BASE_LR: 0.0025 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: 1e-07 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: /mnt/d/zm/paper/Scene/myImage 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 2024-03-08 21:33:06,933 maskrcnn_benchmark INFO: Collecting env info (might take some time) 2024-03-08 21:33:09,224 maskrcnn_benchmark INFO: PyTorch version: 1.4.0 Is debug build: No CUDA used to build PyTorch: 10.1

OS: Ubuntu 22.04.3 LTS GCC version: (Ubuntu 7.5.0-6ubuntu2) 7.5.0 CMake version: Could not collect

Python version: 3.7 Is CUDA available: Yes CUDA runtime version: 10.1.243 GPU models and configuration: GPU 0: NVIDIA RTX A6000 Nvidia driver version: 537.99 cuDNN version: Could not collect

Versions of relevant libraries: [pip3] numpy==1.21.6 [pip3] torch==1.4.0 [pip3] torchvision==0.5.0 [conda] _pytorch_select 0.2 gpu_0 [conda] blas 1.0 mkl [conda] mkl 2020.2 256 [conda] mkl-service 2.3.0 py37h8f50634_2 conda-forge [conda] pytorch 1.4.0 cuda101py37h02f0884_0 [conda] torchvision 0.5.0 pypi_0 pypi Pillow (9.5.0) 2024-03-08 21:33:11,388 maskrcnn_benchmark.data.build INFO: ---------------------------------------------------------------------------------------------------- 2024-03-08 21:33:11,389 maskrcnn_benchmark.data.build INFO: get dataset statistics... 2024-03-08 21:33:11,394 maskrcnn_benchmark.data.build INFO: Loading data statistics from: /mnt/d/zm/paper/Scene/checkpoints/causal_motif_sgdet/VG_stanford_filtered_with_attribute_train_statistics.cache 2024-03-08 21:33:11,394 maskrcnn_benchmark.data.build INFO: ---------------------------------------------------------------------------------------------------- loading word vectors from /mnt/d/zm/paper/Scene/checkpoints/glove/glove.6B.200d.pt background -> background fail on background loading word vectors from /mnt/d/zm/paper/Scene/checkpoints/glove/glove.6B.200d.pt background -> background fail on background INIT SAVE DIR /mnt/d/zm/paper/Scene/checkpoints/causal_motif_sgdet get_checkpoint_file /mnt/d/zm/paper/Scene/checkpoints/causal_motif_sgdet/last_checkpoint last_saved /mnt/d/zm/paper/Scene/checkpoints/causal_motif_sgdet/model_0028000.pth 2024-03-08 21:53:56,362 maskrcnn_benchmark.utils.checkpoint INFO: Loading checkpoint from /mnt/d/zm/paper/Scene/checkpoints/causal_motif_sgdet/model_0028000.pth 100%|██████████████████████████████████████████████████████████████████████████████| 3468/3468 [00:13<00:00, 264.41it/s]=====> /mnt/d/zm/paper/Scene/detected/custom_data_info.json SAVED ! 2024-03-08 21:54:12,525 maskrcnn_benchmark.inference INFO: Start evaluation on VG_stanford_filtered_with_attribute_test dataset(3468 images). 0%| | 0/3468 [00:00<?, 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/tmp/pip-req-build-ufslq_a9/aten/src/ATen/native/cuda/IndexKernel.cu:60: lambda ->auto::operator()(int)->auto: block: [9,0,0], thread: [27,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. /tmp/pip-req-build-ufslq_a9/aten/src/ATen/native/cuda/IndexKernel.cu:60: lambda ->auto::operator()(int)->auto: block: [9,0,0], thread: [28,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. /tmp/pip-req-build-ufslq_a9/aten/src/ATen/native/cuda/IndexKernel.cu:60: lambda ->auto::operator()(int)->auto: block: [9,0,0], thread: [29,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. /tmp/pip-req-build-ufslq_a9/aten/src/ATen/native/cuda/IndexKernel.cu:60: lambda ->auto::operator()(int)->auto: block: [9,0,0], thread: [30,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. /tmp/pip-req-build-ufslq_a9/aten/src/ATen/native/cuda/IndexKernel.cu:60: lambda ->auto::operator()(int)->auto: block: [9,0,0], thread: [31,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. 0%| | 0/3468 [00:00<?, ?it/s]Traceback (most recent call last): File "tools/relation_test_net.py", line 124, in main() File "tools/relation_test_net.py", line 118, in main output_folder=output_folder, File "/mnt/d/zm/paper/Scene/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 "/mnt/d/zm/paper/Scene/maskrcnn_benchmark/engine/inference.py", line 34, in compute_on_dataset output = model(images.to(device), targets) File "/home/zm/anaconda3/envs/pytorch14/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in call result = self.forward(*input, kwargs) File "/mnt/d/zm/paper/Scene/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py", line 50, in forward proposals, proposal_losses = self.rpn(images, features, targets) File "/home/zm/anaconda3/envs/pytorch14/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in call result = self.forward(*input, *kwargs) File "/mnt/d/zm/paper/Scene/maskrcnn_benchmark/modeling/rpn/rpn.py", line 161, in forward return self._forward_test(anchors, objectness, rpn_box_regression) File "/mnt/d/zm/paper/Scene/maskrcnn_benchmark/modeling/rpn/rpn.py", line 187, in _forward_test boxes = self.box_selector_test(anchors, objectness, rpn_box_regression) File "/home/zm/anaconda3/envs/pytorch14/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in call result = self.forward(input, kwargs) File "/mnt/d/zm/paper/Scene/maskrcnn_benchmark/modeling/rpn/inference.py", line 142, in forward sampled_boxes.append(self.forward_for_single_feature_map(a, o, b)) File "/mnt/d/zm/paper/Scene/maskrcnn_benchmark/modeling/rpn/inference.py", line 117, in forward_for_single_feature_map boxlist = remove_small_boxes(boxlist, self.min_size) File "/mnt/d/zm/paper/Scene/maskrcnn_benchmark/structures/boxlist_ops.py", line 47, in remove_small_boxes (ws >= min_size) & (hs >= min_size) RuntimeError: copy_if failed to synchronize: cudaErrorAssert: device-side assert triggered`

运行readme中SGDet on Custom Images里的第一条指令： 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 TDE 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 /mnt/d/zm/paper/Scene/checkpoints/glove MODEL.PRETRAINED_DETECTOR_CKPT /mnt/d/zm/paper/Scene/checkpoints/causal_motif_sgdet OUTPUT_DIR /mnt/d/zm/paper/Scene/checkpoints/causal_motif_sgdet TEST.CUSTUM_EVAL True TEST.CUSTUM_PATH /mnt/d/zm/paper/Scene/myImage DETECTED_SGG_DIR /mnt/d/zm/paper/Scene/detected 出现了上面的错误，请问应该如何解决呢？