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Created April 6, 2023 12:40
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0,1
Command Line Args: Namespace(config_file='configs/san_clip_vit_res4_coco.yaml', dist_url='tcp://127.0.0.1:52842', eval_only=False, machine_rank=0, num_gpus=2, num_machines=1, opts=['OUTPUT_DIR', './output'], resume=False)
Loading config configs/Base-coco-stuff-164K-171.yaml with yaml.unsafe_load. Your machine may be at risk if the file contains malicious content.
Loading config configs/Base-coco-stuff-164K-171.yaml with yaml.unsafe_load. Your machine may be at risk if the file contains malicious content.
[04/06 20:36:53 detectron2]: Rank of current process: 0. World size: 2
[04/06 20:36:55 detectron2]: Environment info:
------------------------------- ------------------------------------------------------------------------------------------------------
sys.platform linux
Python 3.8.3 (default, Jul 2 2020, 16:21:59) [GCC 7.3.0]
numpy 1.20.0
detectron2 0.6 @/gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/site-packages/detectron2
Compiler GCC 8.3
CUDA compiler not available
DETECTRON2_ENV_MODULE <not set>
PyTorch 1.12.1+cu113 @/gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/site-packages/torch
PyTorch debug build False
torch._C._GLIBCXX_USE_CXX11_ABI False
GPU available Yes
GPU 0,1 NVIDIA A100-PCIE-40GB (arch=8.0)
Driver version 470.57.02
CUDA_HOME /gs/home/statchao/cuda-11.3
Pillow 9.1.1
torchvision 0.13.1+cu113 @/gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/site-packages/torchvision
torchvision arch flags 3.5, 5.0, 6.0, 7.0, 7.5, 8.0, 8.6
fvcore 0.1.5.post20221221
iopath 0.1.9
cv2 4.5.1
------------------------------- ------------------------------------------------------------------------------------------------------
PyTorch built with:
- GCC 9.3
- C++ Version: 201402
- Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX2
- 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_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
- CuDNN 8.2.1
- Built with CuDNN 8.3.2
- Magma 2.5.2
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -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-unused-parameter -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,
[04/06 20:36:55 detectron2]: Command line arguments: Namespace(config_file='configs/san_clip_vit_res4_coco.yaml', dist_url='tcp://127.0.0.1:52842', eval_only=False, machine_rank=0, num_gpus=2, num_machines=1, opts=['OUTPUT_DIR', './output'], resume=False)
[04/06 20:36:55 detectron2]: Contents of args.config_file=configs/san_clip_vit_res4_coco.yaml:
_BASE_: Base-coco-stuff-164K-171.yaml
MODEL:
 META_ARCHITECTURE: "SAN"
SOLVER:
 BACKBONE_MULTIPLIER: 1.0
[04/06 20:36:55 detectron2]: Running with full config:
CUDNN_BENCHMARK: false
DATALOADER:
 ASPECT_RATIO_GROUPING: true
 FILTER_EMPTY_ANNOTATIONS: true
 NUM_WORKERS: 2
 REPEAT_THRESHOLD: 0.0
 SAMPLER_TRAIN: TrainingSampler
DATASETS:
 PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000
 PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000
 PROPOSAL_FILES_TEST: []
 PROPOSAL_FILES_TRAIN: []
 TEST:
 - coco_2017_test_stuff_sem_seg
 - pcontext_sem_seg_val
 TRAIN:
 - coco_2017_train_stuff_sem_seg
GLOBAL:
 HACK: 1.0
INPUT:
 COLOR_AUG_SSD: true
 CROP:
 ENABLED: true
 SINGLE_CATEGORY_MAX_AREA: 1.0
 SIZE:
 - 640
 - 640
 TYPE: absolute
 DATASET_MAPPER_NAME: mask_former_semantic
 FORMAT: RGB
 MASK_FORMAT: polygon
 MAX_SIZE_TEST: 2560
 MAX_SIZE_TRAIN: 2560
 MIN_SIZE_TEST: 640
 MIN_SIZE_TRAIN:
 - 320
 - 384
 - 448
 - 512
 - 576
 - 640
 - 704
 - 768
 - 832
 - 896
 - 960
 MIN_SIZE_TRAIN_SAMPLING: choice
 RANDOM_FLIP: horizontal
 SIZE_DIVISIBILITY: 640
MODEL:
 ANCHOR_GENERATOR:
 ANGLES:
 - - -90
 - 0
 - 90
 ASPECT_RATIOS:
 - - 0.5
 - 1.0
 - 2.0
 NAME: DefaultAnchorGenerator
 OFFSET: 0.0
 SIZES:
 - - 32
 - 64
 - 128
 - 256
 - 512
 BACKBONE:
 FREEZE_AT: 2
 NAME: build_resnet_backbone
 DEVICE: cuda
 FLASH: false
 FPN:
 FUSE_TYPE: sum
 IN_FEATURES: []
 NORM: ''
 OUT_CHANNELS: 256
 KEYPOINT_ON: false
 LOAD_PROPOSALS: false
 MASK_ON: false
 META_ARCHITECTURE: SAN
 PANOPTIC_FPN:
 COMBINE:
 ENABLED: true
 INSTANCES_CONFIDENCE_THRESH: 0.5
 OVERLAP_THRESH: 0.5
 STUFF_AREA_LIMIT: 4096
 INSTANCE_LOSS_WEIGHT: 1.0
 PIXEL_MEAN:
 - 103.53
 - 116.28
 - 123.675
 PIXEL_STD:
 - 1.0
 - 1.0
 - 1.0
 PROPOSAL_GENERATOR:
 MIN_SIZE: 0
 NAME: RPN
 RESNETS:
 DEFORM_MODULATED: false
 DEFORM_NUM_GROUPS: 1
 DEFORM_ON_PER_STAGE:
 - false
 - false
 - false
 - false
 DEPTH: 50
 NORM: FrozenBN
 NUM_GROUPS: 1
 OUT_FEATURES:
 - res4
 RES2_OUT_CHANNELS: 256
 RES4_DILATION: 1
 RES5_DILATION: 1
 RES5_MULTI_GRID:
 - 1
 - 2
 - 4
 STEM_OUT_CHANNELS: 64
 STEM_TYPE: deeplab
 STRIDE_IN_1X1: true
 WIDTH_PER_GROUP: 64
 RETINANET:
 BBOX_REG_LOSS_TYPE: smooth_l1
 BBOX_REG_WEIGHTS: &id002
 - 1.0
 - 1.0
 - 1.0
 - 1.0
 FOCAL_LOSS_ALPHA: 0.25
 FOCAL_LOSS_GAMMA: 2.0
 IN_FEATURES:
 - p3
 - p4
 - p5
 - p6
 - p7
 IOU_LABELS:
 - 0
 - -1
 - 1
 IOU_THRESHOLDS:
 - 0.4
 - 0.5
 NMS_THRESH_TEST: 0.5
 NORM: ''
 NUM_CLASSES: 80
 NUM_CONVS: 4
 PRIOR_PROB: 0.01
 SCORE_THRESH_TEST: 0.05
 SMOOTH_L1_LOSS_BETA: 0.1
 TOPK_CANDIDATES_TEST: 1000
 ROI_BOX_CASCADE_HEAD:
 BBOX_REG_WEIGHTS:
 - &id001
 - 10.0
 - 10.0
 - 5.0
 - 5.0
 - - 20.0
 - 20.0
 - 10.0
 - 10.0
 - - 30.0
 - 30.0
 - 15.0
 - 15.0
 IOUS:
 - 0.5
 - 0.6
 - 0.7
 ROI_BOX_HEAD:
 BBOX_REG_LOSS_TYPE: smooth_l1
 BBOX_REG_LOSS_WEIGHT: 1.0
 BBOX_REG_WEIGHTS: *id001
 CLS_AGNOSTIC_BBOX_REG: false
 CONV_DIM: 256
 FC_DIM: 1024
 FED_LOSS_FREQ_WEIGHT_POWER: 0.5
 FED_LOSS_NUM_CLASSES: 50
 NAME: ''
 NORM: ''
 NUM_CONV: 0
 NUM_FC: 0
 POOLER_RESOLUTION: 14
 POOLER_SAMPLING_RATIO: 0
 POOLER_TYPE: ROIAlignV2
 SMOOTH_L1_BETA: 0.0
 TRAIN_ON_PRED_BOXES: false
 USE_FED_LOSS: false
 USE_SIGMOID_CE: false
 ROI_HEADS:
 BATCH_SIZE_PER_IMAGE: 512
 IN_FEATURES:
 - res4
 IOU_LABELS:
 - 0
 - 1
 IOU_THRESHOLDS:
 - 0.5
 NAME: Res5ROIHeads
 NMS_THRESH_TEST: 0.5
 NUM_CLASSES: 80
 POSITIVE_FRACTION: 0.25
 PROPOSAL_APPEND_GT: true
 SCORE_THRESH_TEST: 0.05
 ROI_KEYPOINT_HEAD:
 CONV_DIMS:
 - 512
 - 512
 - 512
 - 512
 - 512
 - 512
 - 512
 - 512
 LOSS_WEIGHT: 1.0
 MIN_KEYPOINTS_PER_IMAGE: 1
 NAME: KRCNNConvDeconvUpsampleHead
 NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true
 NUM_KEYPOINTS: 17
 POOLER_RESOLUTION: 14
 POOLER_SAMPLING_RATIO: 0
 POOLER_TYPE: ROIAlignV2
 ROI_MASK_HEAD:
 CLS_AGNOSTIC_MASK: false
 CONV_DIM: 256
 NAME: MaskRCNNConvUpsampleHead
 NORM: ''
 NUM_CONV: 0
 POOLER_RESOLUTION: 14
 POOLER_SAMPLING_RATIO: 0
 POOLER_TYPE: ROIAlignV2
 RPN:
 BATCH_SIZE_PER_IMAGE: 256
 BBOX_REG_LOSS_TYPE: smooth_l1
 BBOX_REG_LOSS_WEIGHT: 1.0
 BBOX_REG_WEIGHTS: *id002
 BOUNDARY_THRESH: -1
 CONV_DIMS:
 - -1
 HEAD_NAME: StandardRPNHead
 IN_FEATURES:
 - res4
 IOU_LABELS:
 - 0
 - -1
 - 1
 IOU_THRESHOLDS:
 - 0.3
 - 0.7
 LOSS_WEIGHT: 1.0
 NMS_THRESH: 0.7
 POSITIVE_FRACTION: 0.5
 POST_NMS_TOPK_TEST: 1000
 POST_NMS_TOPK_TRAIN: 2000
 PRE_NMS_TOPK_TEST: 6000
 PRE_NMS_TOPK_TRAIN: 12000
 SMOOTH_L1_BETA: 0.0
 SAN:
 ASYMETRIC_INPUT: true
 CLASS_WEIGHT: 2.0
 CLIP_DEEPER_FROZEN_EXCLUDE: []
 CLIP_FROZEN_EXCLUDE:
 - positional_embedding
 CLIP_MODEL_NAME: ViT-B/16
 CLIP_PRETRAINED_NAME: openai
 CLIP_RESOLUTION: 0.5
 CLIP_TEMPLATE_SET: vild
 DICE_WEIGHT: 5.0
 FEATURE_LAST_LAYER_IDX: 9
 IMPORTANCE_SAMPLE_RATIO: 0.75
 MASK_WEIGHT: 5.0
 NO_OBJECT_WEIGHT: 0.1
 NUM_CLASSES: 171
 OVERSAMPLE_RATIO: 3.0
 REC_CROSS_ATTN: false
 REC_DOWNSAMPLE_METHOD: max
 SEM_SEG_POSTPROCESS_BEFORE_INFERENCE: true
 SIZE_DIVISIBILITY: 32
 SOS_TOKEN_FORMAT: cls_token
 TRAIN_NUM_POINTS: 12544
 SEM_SEG_HEAD:
 ASPP_CHANNELS: 256
 ASPP_DILATIONS:
 - 6
 - 12
 - 18
 ASPP_DROPOUT: 0.1
 COMMON_STRIDE: 4
 CONVS_DIM: 128
 IGNORE_VALUE: 255
 IN_FEATURES:
 - p2
 - p3
 - p4
 - p5
 LOSS_TYPE: hard_pixel_mining
 LOSS_WEIGHT: 1.0
 NAME: SemSegFPNHead
 NORM: GN
 NUM_CLASSES: 54
 PROJECT_CHANNELS:
 - 48
 PROJECT_FEATURES:
 - res2
 USE_DEPTHWISE_SEPARABLE_CONV: false
 SIDE_ADAPTER:
 ATTN_BIAS:
 EMBED_CHANNELS: 256
 MLP_CHANNELS: 256
 MLP_NUM_LAYERS: 3
 NUM_HEADS: 12
 NUM_LAYERS: 1
 RESCALE_ATTN_BIAS: true
 DEEP_SUPERVISION_IDXS:
 - 7
 - 8
 DROP_PATH_RATE: 0.0
 FUSION_MAP:
 - 0->0
 - 3->1
 - 6->2
 - 9->3
 FUSION_TYPE: add
 IMAGE_SIZE: 640
 NAME: RegionwiseSideAdapterNetwork
 NUM_QUERIES: 100
 PRETRAINED: false
 VIT_NAME: vit_w240n6d8_patch16
 WEIGHTS: ''
OUTPUT_DIR: ./output
SEED: -1
SOLVER:
 AMP:
 ENABLED: false
 BACKBONE_MULTIPLIER: 1.0
 BASE_LR: 0.0001
 BASE_LR_END: 0.0
 BIAS_LR_FACTOR: 1.0
 CHECKPOINT_PERIOD: 5000
 CLIP_GRADIENTS:
 CLIP_TYPE: full_model
 CLIP_VALUE: 0.01
 ENABLED: true
 NORM_TYPE: 2.0
 CLIP_MULTIPLIER: 1.0
 GAMMA: 0.1
 IMS_PER_BATCH: 4
 LR_SCHEDULER_NAME: WarmupPolyLR
 MAX_ITER: 60000
 MOMENTUM: 0.9
 NESTEROV: false
 NUM_DECAYS: 3
 OPTIMIZER: ADAMW
 POLY_LR_CONSTANT_ENDING: 0.0
 POLY_LR_POWER: 0.9
 REFERENCE_WORLD_SIZE: 0
 RESCALE_INTERVAL: false
 STEPS:
 - 30000
 TEST_IMS_PER_BATCH: 1
 WARMUP_FACTOR: 1.0
 WARMUP_ITERS: 0
 WARMUP_METHOD: linear
 WEIGHT_DECAY: 0.0001
 WEIGHT_DECAY_BIAS: null
 WEIGHT_DECAY_EMBED: 0.0
 WEIGHT_DECAY_EMBED_GROUP:
 - absolute_pos_embed
 - positional_embedding
 - pos_embed
 - query_embed
 - relative_position_bias_table
 WEIGHT_DECAY_NORM: 0.0
TEST:
 AUG:
 ENABLED: false
 FLIP: true
 MAX_SIZE: 4480
 MIN_SIZES:
 - 320
 - 480
 - 640
 - 800
 - 960
 - 1120
 DETECTIONS_PER_IMAGE: 100
 EVAL_PERIOD: 5000
 EXPECTED_RESULTS: []
 KEYPOINT_OKS_SIGMAS: []
 PRECISE_BN:
 ENABLED: false
 NUM_ITER: 200
VERSION: 2
VIS_PERIOD: 0
WANDB:
 NAME: null
 PROJECT: oseg_1111
[04/06 20:36:55 detectron2]: Full config saved to ./output/config.yaml
[04/06 20:36:56 d2.utils.env]: Using a generated random seed 56317096
WARNING:timm.models.helpers:No pretrained configuration specified for vit_tiny_patch16_224.augreg_in21k model. Using a default. Please add a config to the model pretrained_cfg registry or pass explicitly.
WARNING:timm.models.helpers:No pretrained configuration specified for vit_tiny_patch16_224.augreg_in21k model. Using a default. Please add a config to the model pretrained_cfg registry or pass explicitly.
[04/06 20:37:02 d2.engine.defaults]: Model:
SAN(
(criterion): Criterion SetCriterion
matcher: Matcher HungarianMatcher
cost_class: 2.0
cost_mask: 5.0
cost_dice: 5.0
losses: ['labels', 'masks']
weight_dict: {'loss_ce': 2.0, 'loss_mask': 5.0, 'loss_dice': 5.0, 'loss_ce_0': 2.0, 'loss_mask_0': 5.0, 'loss_dice_0': 5.0}
num_classes: 171
eos_coef: 0.1
num_points: 12544
oversample_ratio: 3.0
importance_sample_ratio: 0.75
(side_adapter_network): RegionwiseSideAdapterNetwork(
(vit_model): VisionTransformer(
(patch_embed): PatchEmbed(
(proj): Conv2d(3, 240, kernel_size=(16, 16), stride=(16, 16))
(norm): Identity()
)
(pos_drop): Dropout(p=0.0, inplace=False)
(norm_pre): Identity()
(blocks): Sequential(
(0): Block(
(norm1): LayerNorm((240,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(qkv): Linear(in_features=240, out_features=720, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=240, out_features=240, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
)
(ls1): Identity()
(drop_path1): Identity()
(norm2): LayerNorm((240,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=240, out_features=960, bias=True)
(act): GELU(approximate=none)
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=960, out_features=240, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(ls2): Identity()
(drop_path2): Identity()
)
(1): Block(
(norm1): LayerNorm((240,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(qkv): Linear(in_features=240, out_features=720, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=240, out_features=240, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
)
(ls1): Identity()
(drop_path1): Identity()
(norm2): LayerNorm((240,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=240, out_features=960, bias=True)
(act): GELU(approximate=none)
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=960, out_features=240, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(ls2): Identity()
(drop_path2): Identity()
)
(2): Block(
(norm1): LayerNorm((240,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(qkv): Linear(in_features=240, out_features=720, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=240, out_features=240, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
)
(ls1): Identity()
(drop_path1): Identity()
(norm2): LayerNorm((240,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=240, out_features=960, bias=True)
(act): GELU(approximate=none)
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=960, out_features=240, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(ls2): Identity()
(drop_path2): Identity()
)
(3): Block(
(norm1): LayerNorm((240,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(qkv): Linear(in_features=240, out_features=720, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=240, out_features=240, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
)
(ls1): Identity()
(drop_path1): Identity()
(norm2): LayerNorm((240,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=240, out_features=960, bias=True)
(act): GELU(approximate=none)
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=960, out_features=240, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(ls2): Identity()
(drop_path2): Identity()
)
(4): Block(
(norm1): LayerNorm((240,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(qkv): Linear(in_features=240, out_features=720, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=240, out_features=240, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
)
(ls1): Identity()
(drop_path1): Identity()
(norm2): LayerNorm((240,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=240, out_features=960, bias=True)
(act): GELU(approximate=none)
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=960, out_features=240, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(ls2): Identity()
(drop_path2): Identity()
)
(5): Block(
(norm1): LayerNorm((240,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(qkv): Linear(in_features=240, out_features=720, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=240, out_features=240, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
)
(ls1): Identity()
(drop_path1): Identity()
(norm2): LayerNorm((240,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=240, out_features=960, bias=True)
(act): GELU(approximate=none)
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=960, out_features=240, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(ls2): Identity()
(drop_path2): Identity()
)
(6): Block(
(norm1): LayerNorm((240,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(qkv): Linear(in_features=240, out_features=720, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=240, out_features=240, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
)
(ls1): Identity()
(drop_path1): Identity()
(norm2): LayerNorm((240,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=240, out_features=960, bias=True)
(act): GELU(approximate=none)
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=960, out_features=240, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(ls2): Identity()
(drop_path2): Identity()
)
(7): Block(
(norm1): LayerNorm((240,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(qkv): Linear(in_features=240, out_features=720, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=240, out_features=240, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
)
(ls1): Identity()
(drop_path1): Identity()
(norm2): LayerNorm((240,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=240, out_features=960, bias=True)
(act): GELU(approximate=none)
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=960, out_features=240, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(ls2): Identity()
(drop_path2): Identity()
)
)
(fc_norm): Identity()
(head): Identity()
(norm): Identity()
)
(fusion_layers): ModuleDict(
(layer_0): AddFusion(
(input_proj): Sequential(
(0): LayerNorm()
(1): Conv2d(768, 240, kernel_size=(1, 1), stride=(1, 1))
)
)
(layer_1): AddFusion(
(input_proj): Sequential(
(0): LayerNorm()
(1): Conv2d(768, 240, kernel_size=(1, 1), stride=(1, 1))
)
)
(layer_2): AddFusion(
(input_proj): Sequential(
(0): LayerNorm()
(1): Conv2d(768, 240, kernel_size=(1, 1), stride=(1, 1))
)
)
(layer_3): AddFusion(
(input_proj): Sequential(
(0): LayerNorm()
(1): Conv2d(768, 240, kernel_size=(1, 1), stride=(1, 1))
)
)
)
(mask_decoder): MLPMaskDecoder(
(query_mlp): MLP(
(layers): ModuleList(
(0): Linear(in_features=240, out_features=256, bias=True)
(1): Linear(in_features=256, out_features=256, bias=True)
(2): Linear(in_features=256, out_features=256, bias=True)
)
)
(pix_mlp): MLP(
(layers): ModuleList(
(0): Conv2d(240, 256, kernel_size=(1, 1), stride=(1, 1))
(1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
(2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
)
)
(attn_mlp): MLP(
(layers): ModuleList(
(0): Conv2d(240, 256, kernel_size=(1, 1), stride=(1, 1))
(1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
(2): Conv2d(256, 3072, kernel_size=(1, 1), stride=(1, 1))
)
)
(bias_scaling): Linear(in_features=1, out_features=1, bias=True)
)
)
(clip_visual_extractor): FeatureExtractor(
(patchnorm_pre_ln): Identity()
(conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False)
(patch_dropout): Identity()
(ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(resblocks): ModuleList(
(0): ResidualAttentionBlock(
(ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(ls_1): Identity()
(ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(c_fc): Linear(in_features=768, out_features=3072, bias=True)
(gelu): QuickGELU()
(c_proj): Linear(in_features=3072, out_features=768, bias=True)
)
(ls_2): Identity()
)
(1): ResidualAttentionBlock(
(ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(ls_1): Identity()
(ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(c_fc): Linear(in_features=768, out_features=3072, bias=True)
(gelu): QuickGELU()
(c_proj): Linear(in_features=3072, out_features=768, bias=True)
)
(ls_2): Identity()
)
(2): ResidualAttentionBlock(
(ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(ls_1): Identity()
(ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(c_fc): Linear(in_features=768, out_features=3072, bias=True)
(gelu): QuickGELU()
(c_proj): Linear(in_features=3072, out_features=768, bias=True)
)
(ls_2): Identity()
)
(3): ResidualAttentionBlock(
(ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(ls_1): Identity()
(ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(c_fc): Linear(in_features=768, out_features=3072, bias=True)
(gelu): QuickGELU()
(c_proj): Linear(in_features=3072, out_features=768, bias=True)
)
(ls_2): Identity()
)
(4): ResidualAttentionBlock(
(ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(ls_1): Identity()
(ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(c_fc): Linear(in_features=768, out_features=3072, bias=True)
(gelu): QuickGELU()
(c_proj): Linear(in_features=3072, out_features=768, bias=True)
)
(ls_2): Identity()
)
(5): ResidualAttentionBlock(
(ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(ls_1): Identity()
(ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(c_fc): Linear(in_features=768, out_features=3072, bias=True)
(gelu): QuickGELU()
(c_proj): Linear(in_features=3072, out_features=768, bias=True)
)
(ls_2): Identity()
)
(6): ResidualAttentionBlock(
(ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(ls_1): Identity()
(ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(c_fc): Linear(in_features=768, out_features=3072, bias=True)
(gelu): QuickGELU()
(c_proj): Linear(in_features=3072, out_features=768, bias=True)
)
(ls_2): Identity()
)
(7): ResidualAttentionBlock(
(ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(ls_1): Identity()
(ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(c_fc): Linear(in_features=768, out_features=3072, bias=True)
(gelu): QuickGELU()
(c_proj): Linear(in_features=3072, out_features=768, bias=True)
)
(ls_2): Identity()
)
(8): ResidualAttentionBlock(
(ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(ls_1): Identity()
(ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(c_fc): Linear(in_features=768, out_features=3072, bias=True)
(gelu): QuickGELU()
(c_proj): Linear(in_features=3072, out_features=768, bias=True)
)
(ls_2): Identity()
)
)
)
(clip_rec_head): RecWithAttnbiasHead(
(resblocks): ModuleList(
(0): ResidualAttentionBlock(
(ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(ls_1): Identity()
(ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(c_fc): Linear(in_features=768, out_features=3072, bias=True)
(gelu): QuickGELU()
(c_proj): Linear(in_features=3072, out_features=768, bias=True)
)
(ls_2): Identity()
)
(1): ResidualAttentionBlock(
(ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(ls_1): Identity()
(ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(c_fc): Linear(in_features=768, out_features=3072, bias=True)
(gelu): QuickGELU()
(c_proj): Linear(in_features=3072, out_features=768, bias=True)
)
(ls_2): Identity()
)
(2): ResidualAttentionBlock(
(ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(ls_1): Identity()
(ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(c_fc): Linear(in_features=768, out_features=3072, bias=True)
(gelu): QuickGELU()
(c_proj): Linear(in_features=3072, out_features=768, bias=True)
)
(ls_2): Identity()
)
)
(ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
(ov_classifier): LearnableBgOvClassifier(
(transformer): Transformer(
(resblocks): ModuleList(
(0): ResidualAttentionBlock(
(ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(ls_1): Identity()
(ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(c_fc): Linear(in_features=512, out_features=2048, bias=True)
(gelu): QuickGELU()
(c_proj): Linear(in_features=2048, out_features=512, bias=True)
)
(ls_2): Identity()
)
(1): ResidualAttentionBlock(
(ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(ls_1): Identity()
(ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(c_fc): Linear(in_features=512, out_features=2048, bias=True)
(gelu): QuickGELU()
(c_proj): Linear(in_features=2048, out_features=512, bias=True)
)
(ls_2): Identity()
)
(2): ResidualAttentionBlock(
(ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(ls_1): Identity()
(ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(c_fc): Linear(in_features=512, out_features=2048, bias=True)
(gelu): QuickGELU()
(c_proj): Linear(in_features=2048, out_features=512, bias=True)
)
(ls_2): Identity()
)
(3): ResidualAttentionBlock(
(ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(ls_1): Identity()
(ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(c_fc): Linear(in_features=512, out_features=2048, bias=True)
(gelu): QuickGELU()
(c_proj): Linear(in_features=2048, out_features=512, bias=True)
)
(ls_2): Identity()
)
(4): ResidualAttentionBlock(
(ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(ls_1): Identity()
(ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(c_fc): Linear(in_features=512, out_features=2048, bias=True)
(gelu): QuickGELU()
(c_proj): Linear(in_features=2048, out_features=512, bias=True)
)
(ls_2): Identity()
)
(5): ResidualAttentionBlock(
(ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(ls_1): Identity()
(ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(c_fc): Linear(in_features=512, out_features=2048, bias=True)
(gelu): QuickGELU()
(c_proj): Linear(in_features=2048, out_features=512, bias=True)
)
(ls_2): Identity()
)
(6): ResidualAttentionBlock(
(ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(ls_1): Identity()
(ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(c_fc): Linear(in_features=512, out_features=2048, bias=True)
(gelu): QuickGELU()
(c_proj): Linear(in_features=2048, out_features=512, bias=True)
)
(ls_2): Identity()
)
(7): ResidualAttentionBlock(
(ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(ls_1): Identity()
(ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(c_fc): Linear(in_features=512, out_features=2048, bias=True)
(gelu): QuickGELU()
(c_proj): Linear(in_features=2048, out_features=512, bias=True)
)
(ls_2): Identity()
)
(8): ResidualAttentionBlock(
(ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(ls_1): Identity()
(ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(c_fc): Linear(in_features=512, out_features=2048, bias=True)
(gelu): QuickGELU()
(c_proj): Linear(in_features=2048, out_features=512, bias=True)
)
(ls_2): Identity()
)
(9): ResidualAttentionBlock(
(ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(ls_1): Identity()
(ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(c_fc): Linear(in_features=512, out_features=2048, bias=True)
(gelu): QuickGELU()
(c_proj): Linear(in_features=2048, out_features=512, bias=True)
)
(ls_2): Identity()
)
(10): ResidualAttentionBlock(
(ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(ls_1): Identity()
(ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(c_fc): Linear(in_features=512, out_features=2048, bias=True)
(gelu): QuickGELU()
(c_proj): Linear(in_features=2048, out_features=512, bias=True)
)
(ls_2): Identity()
)
(11): ResidualAttentionBlock(
(ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(ls_1): Identity()
(ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(c_fc): Linear(in_features=512, out_features=2048, bias=True)
(gelu): QuickGELU()
(c_proj): Linear(in_features=2048, out_features=512, bias=True)
)
(ls_2): Identity()
)
)
)
(token_embedding): Embedding(49408, 512)
(ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
)
)
[04/06 20:37:02 san]: Optimizer Info:
+----------------------------------------------------------------+---------------+--------+--------+
| Param Name | Param Shape | Lr | Wd |
+================================================================+===============+========+========+
| side_adapter_network.query_embed | 1X100X240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.query_pos_embed | 1X100X240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.pos_embed | 1X1600X240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.patch_embed.proj.weight | 240X3X16X16 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.patch_embed.proj.bias | 240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.0.norm1.weight | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.0.norm1.bias | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.0.attn.qkv.weight | 720X240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.0.attn.qkv.bias | 720 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.0.attn.proj.weight | 240X240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.0.attn.proj.bias | 240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.0.norm2.weight | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.0.norm2.bias | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.0.mlp.fc1.weight | 960X240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.0.mlp.fc1.bias | 960 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.0.mlp.fc2.weight | 240X960 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.0.mlp.fc2.bias | 240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.1.norm1.weight | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.1.norm1.bias | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.1.attn.qkv.weight | 720X240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.1.attn.qkv.bias | 720 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.1.attn.proj.weight | 240X240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.1.attn.proj.bias | 240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.1.norm2.weight | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.1.norm2.bias | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.1.mlp.fc1.weight | 960X240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.1.mlp.fc1.bias | 960 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.1.mlp.fc2.weight | 240X960 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.1.mlp.fc2.bias | 240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.2.norm1.weight | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.2.norm1.bias | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.2.attn.qkv.weight | 720X240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.2.attn.qkv.bias | 720 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.2.attn.proj.weight | 240X240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.2.attn.proj.bias | 240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.2.norm2.weight | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.2.norm2.bias | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.2.mlp.fc1.weight | 960X240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.2.mlp.fc1.bias | 960 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.2.mlp.fc2.weight | 240X960 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.2.mlp.fc2.bias | 240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.3.norm1.weight | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.3.norm1.bias | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.3.attn.qkv.weight | 720X240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.3.attn.qkv.bias | 720 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.3.attn.proj.weight | 240X240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.3.attn.proj.bias | 240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.3.norm2.weight | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.3.norm2.bias | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.3.mlp.fc1.weight | 960X240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.3.mlp.fc1.bias | 960 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.3.mlp.fc2.weight | 240X960 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.3.mlp.fc2.bias | 240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.4.norm1.weight | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.4.norm1.bias | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.4.attn.qkv.weight | 720X240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.4.attn.qkv.bias | 720 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.4.attn.proj.weight | 240X240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.4.attn.proj.bias | 240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.4.norm2.weight | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.4.norm2.bias | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.4.mlp.fc1.weight | 960X240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.4.mlp.fc1.bias | 960 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.4.mlp.fc2.weight | 240X960 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.4.mlp.fc2.bias | 240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.5.norm1.weight | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.5.norm1.bias | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.5.attn.qkv.weight | 720X240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.5.attn.qkv.bias | 720 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.5.attn.proj.weight | 240X240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.5.attn.proj.bias | 240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.5.norm2.weight | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.5.norm2.bias | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.5.mlp.fc1.weight | 960X240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.5.mlp.fc1.bias | 960 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.5.mlp.fc2.weight | 240X960 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.5.mlp.fc2.bias | 240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.6.norm1.weight | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.6.norm1.bias | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.6.attn.qkv.weight | 720X240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.6.attn.qkv.bias | 720 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.6.attn.proj.weight | 240X240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.6.attn.proj.bias | 240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.6.norm2.weight | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.6.norm2.bias | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.6.mlp.fc1.weight | 960X240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.6.mlp.fc1.bias | 960 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.6.mlp.fc2.weight | 240X960 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.6.mlp.fc2.bias | 240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.7.norm1.weight | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.7.norm1.bias | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.7.attn.qkv.weight | 720X240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.7.attn.qkv.bias | 720 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.7.attn.proj.weight | 240X240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.7.attn.proj.bias | 240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.7.norm2.weight | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.7.norm2.bias | 240 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.7.mlp.fc1.weight | 960X240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.7.mlp.fc1.bias | 960 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.7.mlp.fc2.weight | 240X960 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.vit_model.blocks.7.mlp.fc2.bias | 240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.fusion_layers.layer_0.input_proj.0.weight | 768 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.fusion_layers.layer_0.input_proj.0.bias | 768 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.fusion_layers.layer_0.input_proj.1.weight | 240X768X1X1 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.fusion_layers.layer_0.input_proj.1.bias | 240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.fusion_layers.layer_1.input_proj.0.weight | 768 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.fusion_layers.layer_1.input_proj.0.bias | 768 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.fusion_layers.layer_1.input_proj.1.weight | 240X768X1X1 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.fusion_layers.layer_1.input_proj.1.bias | 240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.fusion_layers.layer_2.input_proj.0.weight | 768 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.fusion_layers.layer_2.input_proj.0.bias | 768 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.fusion_layers.layer_2.input_proj.1.weight | 240X768X1X1 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.fusion_layers.layer_2.input_proj.1.bias | 240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.fusion_layers.layer_3.input_proj.0.weight | 768 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.fusion_layers.layer_3.input_proj.0.bias | 768 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.fusion_layers.layer_3.input_proj.1.weight | 240X768X1X1 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.fusion_layers.layer_3.input_proj.1.bias | 240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.mask_decoder.query_mlp.layers.0.weight | 256X240 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.mask_decoder.query_mlp.layers.0.bias | 256 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.mask_decoder.query_mlp.layers.1.weight | 256X256 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.mask_decoder.query_mlp.layers.1.bias | 256 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.mask_decoder.query_mlp.layers.2.weight | 256X256 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.mask_decoder.query_mlp.layers.2.bias | 256 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.mask_decoder.pix_mlp.layers.0.weight | 256X240X1X1 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.mask_decoder.pix_mlp.layers.0.bias | 256 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.mask_decoder.pix_mlp.layers.1.weight | 256X256X1X1 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.mask_decoder.pix_mlp.layers.1.bias | 256 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.mask_decoder.pix_mlp.layers.2.weight | 256X256X1X1 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.mask_decoder.pix_mlp.layers.2.bias | 256 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.mask_decoder.attn_mlp.layers.0.weight | 256X240X1X1 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.mask_decoder.attn_mlp.layers.0.bias | 256 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.mask_decoder.attn_mlp.layers.1.weight | 256X256X1X1 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.mask_decoder.attn_mlp.layers.1.bias | 256 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.mask_decoder.attn_mlp.layers.2.weight | 3072X256X1X1 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.mask_decoder.attn_mlp.layers.2.bias | 3072 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.mask_decoder.bias_scaling.weight | 1X1 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| side_adapter_network.mask_decoder.bias_scaling.bias | 1 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| clip_visual_extractor.positional_embedding | 197X768 | 0.0001 | 0.0 |
+----------------------------------------------------------------+---------------+--------+--------+
| ov_classifier.bg_embed | 1X512 | 0.0001 | 0.0001 |
+----------------------------------------------------------------+---------------+--------+--------+
| Total | 8.37M | - | - |
+----------------------------------------------------------------+---------------+--------+--------+
[04/06 20:37:02 san.data.dataset_mappers.mask_former_semantic_dataset_mapper]: [MaskFormerSemanticDatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=(320, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960), max_size=2560, sample_style='choice'), RandomCrop_CategoryAreaConstraint(crop_type='absolute', crop_size=[640, 640], single_category_max_area=1.0, ignored_category=255), <detectron2.projects.point_rend.color_augmentation.ColorAugSSDTransform object at 0x7f58cb1dce20>, RandomFlip()]
WARNING [04/06 20:37:07 d2.data.datasets.coco]: Directory datasets/coco/train2017 and datasets/coco/stuffthingmaps_detectron2/train2017 has 118287 and 118280 files, respectively.
WARNING [04/06 20:37:08 d2.data.datasets.coco]: Will use their intersection of 118280 files.
[04/06 20:37:08 d2.data.datasets.coco]: Loaded 118280 images with semantic segmentation from datasets/coco/train2017
[04/06 20:37:08 san.data.build]: Using training sampler TrainingSampler
[04/06 20:37:08 d2.data.common]: Serializing the dataset using: <class 'detectron2.data.common._TorchSerializedList'>
[04/06 20:37:08 d2.data.common]: Serializing 118280 elements to byte tensors and concatenating them all ...
[04/06 20:37:08 d2.data.common]: Serialized dataset takes 24.48 MiB
[04/06 20:37:10 d2.checkpoint.detection_checkpoint]: [DetectionCheckpointer] Loading from ...
[04/06 20:37:10 fvcore.common.checkpoint]: No checkpoint found. Initializing model from scratch
[04/06 20:37:10 d2.engine.train_loop]: Starting training from iteration 0
*** Error in `/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python': double free or corruption (!prev): 0x0000562d2e7059a0 ***
======= Backtrace: =========
/lib64/libc.so.6(+0x733cf)[0x7ff00f8d43cf]
/lib64/libc.so.6(+0x78c3e)[0x7ff00f8d9c3e]
/lib64/libc.so.6(+0x79917)[0x7ff00f8da917]
/lib64/ld-linux-x86-64.so.2(_dl_deallocate_tls+0x58)[0x7ff01003f588]
/lib64/libpthread.so.0(+0x71f7)[0x7ff00fc141f7]
/lib64/libpthread.so.0(+0x730f)[0x7ff00fc1430f]
/lib64/libpthread.so.0(pthread_join+0xdb)[0x7ff00fc1665b]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so(pthreadpool_destroy+0x82)[0x7fefab7d80b2]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so(+0x344abe7)[0x7fefa9021be7]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so(_ZN2at15set_num_threadsEi+0x38)[0x7fefa735c3c8]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/site-packages/torch/lib/libtorch_python.so(+0x5e9d3a)[0x7feffb280d3a]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(+0x1401c5)[0x562d2b1471c5]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(+0xff58e)[0x562d2b10658e]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(_PyFunction_Vectorcall+0x10b)[0x562d2b19216b]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(PyVectorcall_Call+0x71)[0x562d2b143a41]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(_PyEval_EvalFrameDefault+0x207c)[0x562d2b1ca1dc]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(_PyFunction_Vectorcall+0x10b)[0x562d2b19216b]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(+0xff56d)[0x562d2b10656d]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(_PyEval_EvalCodeWithName+0x2d2)[0x562d2b1912a2]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(_PyFunction_Vectorcall+0x1e3)[0x562d2b192243]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(+0xff56d)[0x562d2b10656d]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(_PyFunction_Vectorcall+0x10b)[0x562d2b19216b]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(+0xff819)[0x562d2b106819]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(_PyEval_EvalCodeWithName+0x2d2)[0x562d2b1912a2]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(_PyFunction_Vectorcall+0x1e3)[0x562d2b192243]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(+0x10050b)[0x562d2b10750b]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(_PyEval_EvalCodeWithName+0x2d2)[0x562d2b1912a2]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(PyEval_EvalCodeEx+0x44)[0x562d2b192054]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(PyEval_EvalCode+0x1c)[0x562d2b2205bc]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(+0x219664)[0x562d2b220664]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(+0x24b874)[0x562d2b252874]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(PyRun_StringFlags+0x7d)[0x562d2b2550cd]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(PyRun_SimpleStringFlags+0x3f)[0x562d2b11c488]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(+0x11598b)[0x562d2b11c98b]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(Py_BytesMain+0x39)[0x562d2b255389]
/lib64/libc.so.6(__libc_start_main+0xf5)[0x7ff00f882a85]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(+0x1de553)[0x562d2b1e5553]
======= Memory map: ========
562d2b007000-562d2b066000 r--p 00000000 00:29 306124776 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python3.8
562d2b066000-562d2b256000 r-xp 0005f000 00:29 306124776 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python3.8
562d2b256000-562d2b33c000 r--p 0024f000 00:29 306124776 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python3.8
562d2b33c000-562d2b341000 r--p 00334000 00:29 306124776 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python3.8
562d2b341000-562d2b379000 rw-p 00339000 00:29 306124776 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python3.8
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7f3d35861000-7f3d35862000 r--p 00018000 fd:00 21539 /usr/lib64/libpthread-2.18.so
7f3d35862000-7f3d35863000 rw-p 00019000 fd:00 21539 /usr/lib64/libpthread-2.18.so
7f3d35863000-7f3d35867000 rw-p 00000000 00:00 0
7f3d35867000-7f3d35868000 r-xp 00000000 fd:00 106762810 /usr/local/lib/libproc.so
7f3d35868000-7f3d35a68000 ---p 00001000 fd:00 106762810 /usr/local/lib/libproc.so
7f3d35a68000-7f3d35a69000 r--p 00001000 fd:00 106762810 /usr/local/lib/libproc.so
7f3d35a69000-7f3d35a6a000 rw-p 00002000 fd:00 106762810 /usr/local/lib/libproc.so
7f3d35a6a000-7f3d35a8a000 r-xp 00000000 fd:00 755249 /usr/lib64/ld-2.18.so
7f3d35a8a000-7f3d35a8b000 rw-s 00000000 00:13 3335534432 /dev/shm/sem.mp-w81zbbok
7f3d35a8b000-7f3d35a8c000 rw-s 00000000 00:13 3335534443 /dev/shm/sem.mp-8gfqnspx
7f3d35a8c000-7f3d35a8d000 rw-s 00000000 00:13 3335534442 /dev/shm/sem.mp-glzuud1y
7f3d35a8d000-7f3d35a92000 r--p 00000000 00:29 550174832 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/lib-dynload/_socket.cpython-38-x86_64-linux-gnu.so
7f3d35a92000-7f3d35a9a000 r-xp 00005000 00:29 550174832 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/lib-dynload/_socket.cpython-38-x86_64-linux-gnu.so
7f3d35a9a000-7f3d35aa2000 r--p 0000d000 00:29 550174832 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/lib-dynload/_socket.cpython-38-x86_64-linux-gnu.so
7f3d35aa2000-7f3d35aa3000 r--p 00014000 00:29 550174832 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/lib-dynload/_socket.cpython-38-x86_64-linux-gnu.so
7f3d35aa3000-7f3d35aa4000 rw-p 00015000 00:29 550174832 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/lib-dynload/_socket.cpython-38-x86_64-linux-gnu.so
7f3d35aa4000-7f3d35aa7000 r--p 00000000 00:29 550174870 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/lib-dynload/_struct.cpython-38-x86_64-linux-gnu.so
7f3d35aa7000-7f3d35aad000 r-xp 00003000 00:29 550174870 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/lib-dynload/_struct.cpython-38-x86_64-linux-gnu.so
7f3d35aad000-7f3d35ab1000 r--p 00009000 00:29 550174870 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/lib-dynload/_struct.cpython-38-x86_64-linux-gnu.so
7f3d35ab1000-7f3d35ab2000 r--p 0000c000 00:29 550174870 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/lib-dynload/_struct.cpython-38-x86_64-linux-gnu.so
7f3d35ab2000-7f3d35ab3000 rw-p 0000d000 00:29 550174870 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/lib-dynload/_struct.cpython-38-x86_64-linux-gnu.so
7f3d35ab3000-7f3d35c78000 rw-p 00000000 00:00 0
7f3d35c78000-7f3d35c79000 rw-s 00000000 00:13 3335534441 /dev/shm/sem.mp-0g893wai
7f3d35c79000-7f3d35c7a000 r--p 00000000 00:29 550174871 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/lib-dynload/_heapq.cpython-38-x86_64-linux-gnu.so
7f3d35c7a000-7f3d35c7d000 r-xp 00001000 00:29 550174871 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/lib-dynload/_heapq.cpython-38-x86_64-linux-gnu.so
7f3d35c7d000-7f3d35c80000 r--p 00004000 00:29 550174871 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/lib-dynload/_heapq.cpython-38-x86_64-linux-gnu.so
7f3d35c80000-7f3d35c81000 r--p 00006000 00:29 550174871 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/lib-dynload/_heapq.cpython-38-x86_64-linux-gnu.so
7f3d35c81000-7f3d35c82000 rw-p 00007000 00:29 550174871 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/lib-dynload/_heapq.cpython-38-x86_64-linux-gnu.so
7f3d35c82000-7f3d35c89000 r--s 00000000 fd:00 268455426 /usr/lib64/gconv/gconv-modules.cache
7f3d35c89000-7f3d35c8a000 rw-p 00000000 00:00 0
7f3d35c8a000-7f3d35c8b000 r--p 00020000 fd:00 755249 /usr/lib64/ld-2.18.so
7f3d35c8b000-7f3d35c8c000 rw-p 00021000 fd:00 755249 /usr/lib64/ld-2.18.so
7f3d35c8c000-7f3d35c8d000 rw-p 00000000 00:00 0
7ffc88845000-7ffc88868000 rwxp 00000000 00:00 0 [stack]
7ffc88868000-7ffc8886c000 rw-p 00000000 00:00 0
7ffc889fb000-7ffc889fd000 r-xp 00000000 00:00 0 [vdso]
ffffffffff600000-ffffffffff601000 r-xp 00000000 00:00 0 [vsyscall]
*** Error in `/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python': double free or corruption (!prev): 0x00005630c147f9a0 ***
======= Backtrace: =========
/lib64/libc.so.6(+0x733cf)[0x7f42473143cf]
/lib64/libc.so.6(+0x78c3e)[0x7f4247319c3e]
/lib64/libc.so.6(+0x79917)[0x7f424731a917]
/lib64/ld-linux-x86-64.so.2(_dl_deallocate_tls+0x58)[0x7f4247a7f588]
/lib64/libpthread.so.0(+0x71f7)[0x7f42476541f7]
/lib64/libpthread.so.0(+0x730f)[0x7f424765430f]
/lib64/libpthread.so.0(pthread_join+0xdb)[0x7f424765665b]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so(pthreadpool_destroy+0x82)[0x7f41e32180b2]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so(+0x344abe7)[0x7f41e0a61be7]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so(_ZN2at15set_num_threadsEi+0x38)[0x7f41ded9c3c8]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/site-packages/torch/lib/libtorch_python.so(+0x5e9d3a)[0x7f4232cc0d3a]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(+0x1401c5)[0x5630bdf261c5]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(+0xff58e)[0x5630bdee558e]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(_PyFunction_Vectorcall+0x10b)[0x5630bdf7116b]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(PyVectorcall_Call+0x71)[0x5630bdf22a41]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(_PyEval_EvalFrameDefault+0x207c)[0x5630bdfa91dc]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(_PyFunction_Vectorcall+0x10b)[0x5630bdf7116b]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(+0xff56d)[0x5630bdee556d]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(_PyEval_EvalCodeWithName+0x2d2)[0x5630bdf702a2]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(_PyFunction_Vectorcall+0x1e3)[0x5630bdf71243]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(+0xff56d)[0x5630bdee556d]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(_PyFunction_Vectorcall+0x10b)[0x5630bdf7116b]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(+0xff819)[0x5630bdee5819]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(_PyEval_EvalCodeWithName+0x2d2)[0x5630bdf702a2]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(_PyFunction_Vectorcall+0x1e3)[0x5630bdf71243]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(+0x10050b)[0x5630bdee650b]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(_PyEval_EvalCodeWithName+0x2d2)[0x5630bdf702a2]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(PyEval_EvalCodeEx+0x44)[0x5630bdf71054]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(PyEval_EvalCode+0x1c)[0x5630bdfff5bc]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(+0x219664)[0x5630bdfff664]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(+0x24b874)[0x5630be031874]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(PyRun_StringFlags+0x7d)[0x5630be0340cd]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(PyRun_SimpleStringFlags+0x3f)[0x5630bdefb488]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(+0x11598b)[0x5630bdefb98b]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(Py_BytesMain+0x39)[0x5630be034389]
/lib64/libc.so.6(__libc_start_main+0xf5)[0x7f42472c2a85]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(+0x1de553)[0x5630bdfc4553]
======= Memory map: ========
5630bdde6000-5630bde45000 r--p 00000000 00:29 306124776 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python3.8
5630bde45000-5630be035000 r-xp 0005f000 00:29 306124776 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python3.8
5630be035000-5630be11b000 r--p 0024f000 00:29 306124776 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python3.8
5630be11b000-5630be120000 r--p 00334000 00:29 306124776 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python3.8
5630be120000-5630be158000 rw-p 00339000 00:29 306124776 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python3.8
5630be158000-5630be178000 rw-p 00000000 00:00 0
5630bf2fd000-5630c80ba000 rw-p 00000000 00:00 0 [heap]
7f418cce5000-7f418cce6000 ---p 00000000 00:00 0
7f418cce6000-7f418d4e6000 rwxp 00000000 00:00 0
7f418d4e6000-7f418d4e7000 ---p 00000000 00:00 0
7f418d4e7000-7f418dce7000 rwxp 00000000 00:00 0
7f418dce7000-7f418dce8000 ---p 00000000 00:00 0
7f418dce8000-7f418e4e8000 rwxp 00000000 00:00 0
7f418e4e8000-7f418e4e9000 ---p 00000000 00:00 0
7f418e4e9000-7f418ece9000 rwxp 00000000 00:00 0
7f418ece9000-7f418ecea000 ---p 00000000 00:00 0
7f418ecea000-7f418f4ea000 rwxp 00000000 00:00 0
7f418f4ea000-7f418f4eb000 ---p 00000000 00:00 0
7f418f4eb000-7f418fceb000 rwxp 00000000 00:00 0
7f418fceb000-7f418fcec000 ---p 00000000 00:00 0
7f418fcec000-7f41904ec000 rwxp 00000000 00:00 0
7f41904ec000-7f41904ed000 ---p 00000000 00:00 0
7f41904ed000-7f4190ced000 rwxp 00000000 00:00 0
7f4190ced000-7f4190cee000 ---p 00000000 00:00 0
7f4190cee000-7f41914ee000 rwxp 00000000 00:00 0
7f41914ee000-7f41914ef000 ---p 00000000 00:00 0
7f41914ef000-7f4191cef000 rwxp 00000000 00:00 0
7f4191cef000-7f4191cf0000 ---p 00000000 00:00 0
7f4191cf0000-7f41924f0000 rwxp 00000000 00:00 0
7f41924f0000-7f41924f1000 ---p 00000000 00:00 0
7f41924f1000-7f4192cf1000 rwxp 00000000 00:00 0
7f4192cf1000-7f4192cf2000 ---p 00000000 00:00 0
7f4192cf2000-7f41934f2000 rwxp 00000000 00:00 0
7f41934f2000-7f41934f3000 ---p 00000000 00:00 0
7f41934f3000-7f4193cf3000 rwxp 00000000 00:00 0
7f4193cf3000-7f4193cf4000 ---p 00000000 00:00 0
7f4193cf4000-7f41944f4000 rwxp 00000000 00:00 0
7f41944f4000-7f41944f5000 ---p 00000000 00:00 0
7f41944f5000-7f4194cf5000 rwxp 00000000 00:00 0
7f4194cf5000-7f4194cf6000 ---p 00000000 00:00 0
7f4194cf6000-7f41954f6000 rwxp 00000000 00:00 0
7f41954f6000-7f41954f7000 ---p 00000000 00:00 0
7f41954f7000-7f4195cf7000 rwxp 00000000 00:00 0
7f4195cf7000-7f4195cf8000 ---p 00000000 00:00 0
7f4195cf8000-7f41964f8000 rwxp 00000000 00:00 0
7f41964f8000-7f41964f9000 ---p 00000000 00:00 0
7f41964f9000-7f4196cf9000 rwxp 00000000 00:00 0
7f4196cf9000-7f4196cfa000 ---p 00000000 00:00 0
7f4196cfa000-7f41974fa000 rwxp 00000000 00:00 0
7f41974fa000-7f41974fb000 ---p 00000000 00:00 0
7f41974fb000-7f4197cfb000 rwxp 00000000 00:00 0
7f4197cfb000-7f4197cfc000 ---p 00000000 00:00 0
7f4197cfc000-7f41984fc000 rwxp 00000000 00:00 0
7f41984fc000-7f41984fd000 ---p 00000000 00:00 0
7f41984fd000-7f4198cfd000 rwxp 00000000 00:00 0
7f4198cfd000-7f4198cfe000 ---p 00000000 00:00 0
7f4198cfe000-7f41994fe000 rwxp 00000000 00:00 0
7f41994fe000-7f41994ff000 ---p 00000000 00:00 0
7f41994ff000-7f4199cff000 rwxp 00000000 00:00 0
7f4199cff000-7f4199d00000 ---p 00000000 00:00 0
7f4199d00000-7f419a500000 rwxp 00000000 00:00 0
7f419a500000-7f419a501000 ---p 00000000 00:00 0
7f419a501000-7f419ad01000 rwxp 00000000 00:00 0
7f419ad01000-7f419ad02000 ---p 00000000 00:00 0
7f419ad02000-7f419b502000 rwxp 00000000 00:00 0
7f419b502000-7f419b503000 ---p 00000000 00:00 0
7f419b503000-7f419bd03000 rwxp 00000000 00:00 0
7f419bd03000-7f419bd04000 ---p 00000000 00:00 0
7f419bd04000-7f419c504000 rwxp 00000000 00:00 0
7f419c504000-7f419c505000 ---p 00000000 00:00 0
7f419c505000-7f419cd05000 rwxp 00000000 00:00 0
7f419cd05000-7f419cd06000 ---p 00000000 00:00 0
7f419cd06000-7f419d506000 rwxp 00000000 00:00 0
7f419d506000-7f419d507000 ---p 00000000 00:00 0
7f419d507000-7f419dd07000 rwxp 00000000 00:00 0
7f419dd07000-7f419dd08000 ---p 00000000 00:00 0
7f419dd08000-7f419e508000 rwxp 00000000 00:00 0
7f419e508000-7f419e509000 ---p 00000000 00:00 0
7f419e509000-7f419ed09000 rwxp 00000000 00:00 0
7f419ed09000-7f419ed0a000 ---p 00000000 00:00 0
7f419ed0a000-7f419f50a000 rwxp 00000000 00:00 0
7f419f50a000-7f419f50b000 ---p 00000000 00:00 0
7f419f50b000-7f419fd0b000 rwxp 00000000 00:00 0
7f419fd0b000-7f419fd0c000 ---p 00000000 00:00 0
7f419fd0c000-7f41a050c000 rwxp 00000000 00:00 0
7f41a050c000-7f41a050d000 ---p 00000000 00:00 0
7f41a050d000-7f41a0d0d000 rwxp 00000000 00:00 0
7f41a0d0d000-7f41a0d0e000 ---p 00000000 00:00 0
7f41a0d0e000-7f41a150e000 rwxp 00000000 00:00 0
7f41a150e000-7f41a150f000 ---p 00000000 00:00 0
7f41a150f000-7f41a1d0f000 rwxp 00000000 00:00 0
7f41a1d0f000-7f41a1d10000 ---p 00000000 00:00 0
7f41a1d10000-7f41a2510000 rwxp 00000000 00:00 0
7f41a2510000-7f41a28d0000 rw-p 00000000 00:00 0
7f41a28d0000-7f41a29b8000 rw-s 00000000 00:13 3335756190 /dev/shm/torch_202626_2864713810_1 (deleted)
7f41a29b8000-7f41a4233000 rw-s 00000000 00:13 3335756189 /dev/shm/torch_202626_1217565150_0 (deleted)
7f41a4233000-7f41a42b3000 rw-p 00000000 00:00 0
7f41a42b3000-7f41a42bd000 r--p 00000000 00:29 419647377 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/site-packages/scipy/spatial/transform/rotation.cpython-38-x86_64-linux-gnu.so
7f41a42bd000-7f41a4318000 r-xp 0000a000 00:29 419647377 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/site-packages/scipy/spatial/transform/rotation.cpython-38-x86_64-linux-gnu.so
7f41a4318000-7f41a432e000 r--p 00065000 00:29 419647377 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/site-packages/scipy/spatial/transform/rotation.cpython-38-x86_64-linux-gnu.so
7f41a432e000-7f41a432f000 ---p 0007b000 00:29 419647377 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/site-pack*** Error in `/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python': double free or corruption (!prev): 0x000055c2d346a9a0 ***
ages/scipy/spatial/transform/rotation.cpython-38-x86_64-linux-gnu.so
7f41a432f000-7f41a4330000 r--p 0007b000 00:29 419647377 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/site-packages/scipy/spatial/transform/rotation.cpython-38-x86_64-linux-gnu.so
7f41a4330000-7f41a4340000 rw-p 0007c000 00:29 419647377 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/site-packages/scipy/spatial/transform/rotation.cpython-38-x86_64-linux-gnu.so
7f41a4340000-7f41a4381000 rw-p 00000000 00:00 0
7f41a4381000-7f41a4387000 r--p 00000000 00:29 419647432 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/site-packages/scipy/spatial/_hausdorff.cpython-38-x86_64-linux-gnu.so
7f41a4387000-7f41a43a0000 r-xp 00006000 00:29 419647432 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/site-packages/scipy/spatial/_hausdorff.cpython-38-x86_64-linux-gnu.so
7f41a43a0000-7f41a43a6000 r--p 0001f000 00:29 419647432 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/site-packages/scipy/spatial/_hausdorff.cpython-38-x86_64-linux-gnu.so
7f41a43a6000-7f41a43a7000 ---p 00025000 00:29 419647432 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/site-packages/scipy/spatial/_hausdorff.cpython-38-x86_64-linux-gnu.so
7f41a43a7000-7f41a43a8000 r--p 00025000 00:29 419647432 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/site-packages/scipy/spatial/_hausdorff.cpython-38-x86_64-linux-gnu.so
7f41a43a8000-7f41a43ab000 rw-p 00026000 00:29 419647432 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/site-packages/scipy/spatial/_hausdorff.cpython-38-x86_64-linux-gnu.so
7f41a43ab000-7f41a43ad000 r--p 00000000 00:29 419647356 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/site-packages/scipy/spatial/_distance_wrap.cpython-38-x86_64-linux-gnu.so
7f41a43ad000-7f41a43c1000 r-xp 00002000 00:29 419647356 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/site-packages/scipy/spatial/_distance_wrap.cpython-38-x86_64-linux-gnu.so
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7f41a49bc000-7f41a49bf000 r--p ======= Backtrace: =========
00000000 00:29 420172841 /gs/home/statchao/anaconda3/envs/guangrui_cuda11/lib/python3.8/site-packages/scipy/_lib/messagestream.cpython-38-x86_64-linux-gnu.so
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/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(+0x219664)[0x55c2cf4ec664]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(+0x24b874)[0x55c2cf51e874]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(PyRun_StringFlags+0x7d)[0x55c2cf5210cd]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(PyRun_SimpleStringFlags+0x3f)[0x55c2cf3e8488]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(+0x11598b)[0x55c2cf3e898b]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(Py_BytesMain+0x39)[0x55c2cf521389]
/lib64/libc.so.6(__libc_start_main+0xf5)[0x7fbf42a57a85]
/gs/home/statchao/anaconda3/envs/guangrui_cuda11/bin/python(+0x1de553)[0x55c2cf4b1553]
======= Memory map: ========
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7fbe9fd11000-7fbe9fd12
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