Segmentation fault (core dumped)

[Y1nFaChu]

❓ Questions and Help

skrcnn-benchmark-main/maskrcnn_benchmark/myconfig/e2e_faster_rcnn_fbnet.yaml"
2022-02-24 13:03:48,689 maskrcnn_benchmark INFO: Using 1 GPUs
2022-02-24 13:03:48,689 maskrcnn_benchmark INFO: Namespace(config_file='/opt/data/private/yfc/maskrcnn-benchmark-main/maskrcnn_benchmark/myconfig/e2e_faster_rcnn_fbnet.yaml', distributed=False, local_rank=0, opts=[], skip_test=False)
2022-02-24 13:03:48,690 maskrcnn_benchmark INFO: Collecting env info (might take some time)
2022-02-24 13:03:51,904 maskrcnn_benchmark INFO:
PyTorch version: 1.6.0+cu101
Is debug build: No
CUDA used to build PyTorch: 10.1

OS: Ubuntu 16.04.6 LTS
GCC version: (Ubuntu 5.4.0-6ubuntu1~16.04.11) 5.4.0 20160609
CMake version: version 3.5.1

Python version: 3.6
Is CUDA available: Yes
CUDA runtime version: 10.1.243
GPU models and configuration: GPU 0: GeForce RTX 2080 Ti
Nvidia driver version: 430.40
cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.5.1

Versions of relevant libraries:
[pip3] numpy==1.19.5
[pip3] torch==1.6.0+cu101
[pip3] torchvision==0.7.0+cu101
[conda] blas 1.0 mkl https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
[conda] cpuonly 1.0 0 pytorch
[conda] cuda90 1.0 h6433d27_0 pytorch
[conda] cudatoolkit 9.0 h13b8566_0
[conda] mkl 2022.0.1 h06a4308_117
[conda] numpy 1.19.5 pypi_0 pypi
[conda] torch 1.4.0 pypi_0 pypi
[conda] torchvision 0.7.0+cu101 pypi_0 pypi
Pillow (4.2.1)
2022-02-24 13:03:51,904 maskrcnn_benchmark INFO: Loaded configuration file /opt/data/private/yfc/maskrcnn-benchmark-main/maskrcnn_benchmark/myconfig/e2e_faster_rcnn_fbnet.yaml
2022-02-24 13:03:51,905 maskrcnn_benchmark INFO:
MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
BACKBONE:
CONV_BODY: FBNet
FBNET:
ARCH: "default"
BN_TYPE: "bn"
WIDTH_DIVISOR: 8
DW_CONV_SKIP_BN: True
DW_CONV_SKIP_RELU: True
RPN:
ANCHOR_SIZES: (16, 32, 64, 128, 256)
ANCHOR_STRIDE: (16, )
BATCH_SIZE_PER_IMAGE: 256
PRE_NMS_TOP_N_TRAIN: 6000
PRE_NMS_TOP_N_TEST: 6000
POST_NMS_TOP_N_TRAIN: 2000
POST_NMS_TOP_N_TEST: 100
RPN_HEAD: FBNet.rpn_head
ROI_HEADS:
BATCH_SIZE_PER_IMAGE: 512
ROI_BOX_HEAD:
POOLER_RESOLUTION: 6
FEATURE_EXTRACTOR: FBNet.roi_head
NUM_CLASSES: 2
DATASETS:
TRAIN: ("coco_2017_train", )
TEST: ("coco_2017_val",)
SOLVER:
BASE_LR: 0.06
WARMUP_FACTOR: 0.1
WEIGHT_DECAY: 0.0001
STEPS: (60000, 80000)
MAX_ITER: 90000
IMS_PER_BATCH: 128 # for 8GPUs

TEST:

IMS_PER_BATCH: 8

INPUT:
MIN_SIZE_TRAIN: (320, )
MAX_SIZE_TRAIN: 640
MIN_SIZE_TEST: 320
MAX_SIZE_TEST: 640
PIXEL_MEAN: [103.53, 116.28, 123.675]
PIXEL_STD: [57.375, 57.12, 58.395]
OUTPUT_DIR: "/opt/data/private/yfc/maskrcnn-benchmark-main/maskrcnn_benchmark/weight"
PATHS_CATALOG: "/opt/data/private/yfc/maskrcnn-benchmark-main/maskrcnn_benchmark/myconfig/paths_catalog.py"
2022-02-24 13:03:51,906 maskrcnn_benchmark INFO: Running with config:
AMP_VERBOSE: False
DATALOADER:
ASPECT_RATIO_GROUPING: True
NUM_WORKERS: 4
SIZE_DIVISIBILITY: 0
DATASETS:
TEST: ('coco_2017_val',)
TRAIN: ('coco_2017_train',)
DTYPE: float32
INPUT:
BRIGHTNESS: 0.0
CONTRAST: 0.0
HORIZONTAL_FLIP_PROB_TRAIN: 0.5
HUE: 0.0
MAX_SIZE_TEST: 640
MAX_SIZE_TRAIN: 640
MIN_SIZE_TEST: 320
MIN_SIZE_TRAIN: (320,)
PIXEL_MEAN: [103.53, 116.28, 123.675]
PIXEL_STD: [57.375, 57.12, 58.395]
SATURATION: 0.0
TO_BGR255: True
VERTICAL_FLIP_PROB_TRAIN: 0.0
MODEL:
BACKBONE:
CONV_BODY: FBNet
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: 8
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
RESNETS:
BACKBONE_OUT_CHANNELS: 1024
DEFORMABLE_GROUPS: 1
NUM_GROUPS: 1
RES2_OUT_CHANNELS: 256
RES5_DILATION: 1
STAGE_WITH_DCN: (False, False, False, False)
STEM_FUNC: StemWithFixedBatchNorm
STEM_OUT_CHANNELS: 64
STRIDE_IN_1X1: True
TRANS_FUNC: BottleneckWithFixedBatchNorm
WIDTH_PER_GROUP: 64
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_BOX_HEAD:
CONV_HEAD_DIM: 256
DILATION: 1
FEATURE_EXTRACTOR: FBNet.roi_head
MLP_HEAD_DIM: 1024
NUM_CLASSES: 2
NUM_STACKED_CONVS: 4
POOLER_RESOLUTION: 6
POOLER_SAMPLING_RATIO: 0
POOLER_SCALES: (0.0625,)
PREDICTOR: FastRCNNPredictor
USE_GN: False
ROI_HEADS:
BATCH_SIZE_PER_IMAGE: 512
BBOX_REG_WEIGHTS: (10.0, 10.0, 5.0, 5.0)
BG_IOU_THRESHOLD: 0.5
DETECTIONS_PER_IMG: 100
FG_IOU_THRESHOLD: 0.5
NMS: 0.5
POSITIVE_FRACTION: 0.25
SCORE_THRESH: 0.05
USE_FPN: False
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
RPN:
ANCHOR_SIZES: (16, 32, 64, 128, 256)
ANCHOR_STRIDE: (16,)
ASPECT_RATIOS: (0.5, 1.0, 2.0)
BATCH_SIZE_PER_IMAGE: 256
BG_IOU_THRESHOLD: 0.3
FG_IOU_THRESHOLD: 0.7
FPN_POST_NMS_PER_BATCH: True
FPN_POST_NMS_TOP_N_TEST: 2000
FPN_POST_NMS_TOP_N_TRAIN: 2000
MIN_SIZE: 0
NMS_THRESH: 0.7
POSITIVE_FRACTION: 0.5
POST_NMS_TOP_N_TEST: 100
POST_NMS_TOP_N_TRAIN: 2000
PRE_NMS_TOP_N_TEST: 6000
PRE_NMS_TOP_N_TRAIN: 6000
RPN_HEAD: FBNet.rpn_head
STRADDLE_THRESH: 0
USE_FPN: False
RPN_ONLY: False
WEIGHT:
OUTPUT_DIR: /opt/data/private/yfc/maskrcnn-benchmark-main/maskrcnn_benchmark/weight
PATHS_CATALOG: /opt/data/private/yfc/maskrcnn-benchmark-main/maskrcnn_benchmark/myconfig/paths_catalog.py
SOLVER:
BASE_LR: 0.06
BIAS_LR_FACTOR: 2
CHECKPOINT_PERIOD: 2500
GAMMA: 0.1
IMS_PER_BATCH: 128
MAX_ITER: 90000
MOMENTUM: 0.9
STEPS: (60000, 80000)
TEST_PERIOD: 0
WARMUP_FACTOR: 0.1
WARMUP_ITERS: 500
WARMUP_METHOD: linear
WEIGHT_DECAY: 0.0001
WEIGHT_DECAY_BIAS: 0
TEST:
BBOX_AUG:
ENABLED: False
H_FLIP: False
MAX_SIZE: 4000
SCALES: ()
SCALE_H_FLIP: False
DETECTIONS_PER_IMG: 100
EXPECTED_RESULTS: []
EXPECTED_RESULTS_SIGMA_TOL: 4
IMS_PER_BATCH: 8
2022-02-24 13:03:51,906 maskrcnn_benchmark INFO: Saving config into: /opt/data/private/yfc/maskrcnn-benchmark-main/maskrcnn_benchmark/weight/config.yml
2022-02-24 13:03:51,955 maskrcnn_benchmark.modeling.backbone.fbnet INFO: Building fbnet model with arch default (without scaling):
....
Segmentation fault (core dumped)