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layoutlmv3-publaynet
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[04/17 14:10:02 detectron2]: Rank of current process: 0. World size: 8
[04/17 14:10:20 detectron2]: Environment info:
----------------------  --------------------------------------------------------------------------------------------------------------------------
sys.platform            linux
Python                  3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]
numpy                   1.21.5
detectron2              0.6 @/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/detectron2
Compiler                GCC 7.3
CUDA compiler           CUDA 11.1
detectron2 arch flags   3.7, 5.0, 5.2, 6.0, 6.1, 7.0, 7.5, 8.0, 8.6
DETECTRON2_ENV_MODULE   <not set>
PyTorch                 1.10.0+cu111 @/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/torch
PyTorch debug build     False
GPU available           Yes
GPU 0,1,2,3,4,5,6,7     A100-SXM4-40GB (arch=8.0)
Driver version          450.142.00
CUDA_HOME               /usr/local/cuda
Pillow                  8.4.0
torchvision             0.11.1+cu111 @/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/torchvision
torchvision arch flags  3.5, 5.0, 6.0, 7.0, 7.5, 8.0, 8.6
fvcore                  0.1.5.post20211023
iopath                  0.1.9
cv2                     Not found
----------------------  --------------------------------------------------------------------------------------------------------------------------
PyTorch built with:
  - GCC 7.3
  - 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 v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 11.1
  - 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 8.0.5
  - Magma 2.5.2
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -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-sign-compare -Wno-unused-parameter -Wno-unused-variable -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 -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, 

[04/17 14:10:20 detectron2]: Command line arguments: Namespace(config_file='cascade_layoutlmv3.yaml', debug=False, dist_url='tcp://127.0.0.1:50156', eval_only=True, machine_rank=0, num_gpus=8, num_machines=1, opts=['MODEL.WEIGHTS', '/mnt/localdata/users/yupanhuang/models/layoutlmv3/fts/publaynet-base/model_final.pth', 'OUTPUT_DIR', '/mnt/localdata/users/yupanhuang/models/layoutlmv3/fts/publaynet-base/'], resume=False)
[04/17 14:10:20 detectron2]: Contents of args.config_file=cascade_layoutlmv3.yaml:
MODEL:
  MASK_ON: True
  MAX_LENGTH: 510
  IMAGE_ONLY: True
  META_ARCHITECTURE: "VLGeneralizedRCNN"
  PIXEL_MEAN: [ 127.5, 127.5, 127.5 ]
  PIXEL_STD: [ 127.5, 127.5, 127.5 ]
  WEIGHTS: "/mnt/localdata/users/yupanhuang/models/layoutlmv3/pts/layoutlmv3-base/pytorch_model.bin"
  BACKBONE:
    NAME: "build_vit_fpn_backbone"
  VIT:
    NAME: "layoutlmv3_base"
    OUT_FEATURES: [ "layer3", "layer5", "layer7", "layer11" ]
    DROP_PATH: 0.1
    IMG_SIZE: [ 224,224 ]
    POS_TYPE: "abs"
  ROI_HEADS:
    NAME: CascadeROIHeads
    IN_FEATURES: [ "p2", "p3", "p4", "p5" ]
    NUM_CLASSES: 5
  ROI_BOX_HEAD:
    CLS_AGNOSTIC_BBOX_REG: True
    NAME: "FastRCNNConvFCHead"
    NUM_FC: 2
    POOLER_RESOLUTION: 7
  ROI_MASK_HEAD:
    NAME: "MaskRCNNConvUpsampleHead"
    NUM_CONV: 4
    POOLER_RESOLUTION: 14
  FPN:
    IN_FEATURES: [ "layer3", "layer5", "layer7", "layer11" ]
  ANCHOR_GENERATOR:
    SIZES: [ [ 32 ], [ 64 ], [ 128 ], [ 256 ], [ 512 ] ]  # One size for each in feature map
    ASPECT_RATIOS: [ [ 0.5, 1.0, 2.0 ] ]  # Three aspect ratios (same for all in feature maps)
  RPN:
    IN_FEATURES: [ "p2", "p3", "p4", "p5", "p6" ]
    PRE_NMS_TOPK_TRAIN: 2000  # Per FPN level
    PRE_NMS_TOPK_TEST: 1000  # Per FPN level
    # Detectron1 uses 2000 proposals per-batch,
    # (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue)
    # which is approximately 1000 proposals per-image since the default batch size for FPN is 2.
    POST_NMS_TOPK_TRAIN: 2000
    POST_NMS_TOPK_TEST: 1000
DATASETS:
  TRAIN: ("publaynet_train",)
  TEST: ("publaynet_val",)
SOLVER:
  GRADIENT_ACCUMULATION_STEPS: 1
  BASE_LR: 0.0002
  WARMUP_ITERS: 1000
  IMS_PER_BATCH: 32
  MAX_ITER: 60000
  CHECKPOINT_PERIOD: 2000
  LR_SCHEDULER_NAME: "WarmupCosineLR"
  AMP:
    ENABLED: True
  OPTIMIZER: "ADAMW"
  BACKBONE_MULTIPLIER: 1.0
  CLIP_GRADIENTS:
    ENABLED: True
    CLIP_TYPE: "full_model"
    CLIP_VALUE: 1.0
    NORM_TYPE: 2.0
  WARMUP_FACTOR: 0.01
  WEIGHT_DECAY: 0.05
TEST:
  EVAL_PERIOD: 2000
INPUT:
  CROP:
    ENABLED: True
    TYPE: "absolute_range"
    SIZE: (384, 600)
  MIN_SIZE_TRAIN: (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800)
  FORMAT: "RGB"
DATALOADER:
  FILTER_EMPTY_ANNOTATIONS: False
VERSION: 2
AUG:
  DETR: True
SEED: 42
OUTPUT_DIR: "/mnt/localdata/users/yupanhuang/models/layoutlmv3/fts/publaynet/"
PUBLAYNET_DATA_DIR_TRAIN: "/mnt/localdata/users/yupanhuang/data/PubLayNet/publaynet/train"
PUBLAYNET_DATA_DIR_TEST: "/mnt/localdata/users/yupanhuang/data/PubLayNet/publaynet/val"
OCR_DATA_DIR_TRAIN: "/mnt/localdata/users/yupanhuang/data/PubLayNet/ocr/train"
OCR_DATA_DIR_TEST: "/mnt/localdata/users/yupanhuang/data/PubLayNet/ocr/val"
CACHE_DIR: "/mnt/localdata/users/yupanhuang/cache/huggingface"

[04/17 14:10:20 detectron2]: Running with full config:
AUG:
  DETR: true
CACHE_DIR: /mnt/localdata/users/yupanhuang/cache/huggingface
CUDNN_BENCHMARK: false
DATALOADER:
  ASPECT_RATIO_GROUPING: true
  FILTER_EMPTY_ANNOTATIONS: false
  NUM_WORKERS: 4
  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:
  - publaynet_val
  TRAIN:
  - publaynet_train
GLOBAL:
  HACK: 1.0
ICDAR_DATA_DIR_TEST: ''
ICDAR_DATA_DIR_TRAIN: ''
INPUT:
  CROP:
    ENABLED: true
    SIZE:
    - 384
    - 600
    TYPE: absolute_range
  FORMAT: RGB
  MASK_FORMAT: polygon
  MAX_SIZE_TEST: 1333
  MAX_SIZE_TRAIN: 1333
  MIN_SIZE_TEST: 800
  MIN_SIZE_TRAIN:
  - 480
  - 512
  - 544
  - 576
  - 608
  - 640
  - 672
  - 704
  - 736
  - 768
  - 800
  MIN_SIZE_TRAIN_SAMPLING: choice
  RANDOM_FLIP: horizontal
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_vit_fpn_backbone
  CONFIG_PATH: ''
  DEVICE: cuda
  FPN:
    FUSE_TYPE: sum
    IN_FEATURES:
    - layer3
    - layer5
    - layer7
    - layer11
    NORM: ''
    OUT_CHANNELS: 256
  IMAGE_ONLY: true
  KEYPOINT_ON: false
  LOAD_PROPOSALS: false
  MASK_ON: true
  MAX_LENGTH: 510
  META_ARCHITECTURE: VLGeneralizedRCNN
  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:
  - 127.5
  - 127.5
  - 127.5
  PIXEL_STD:
  - 127.5
  - 127.5
  - 127.5
  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
    RES5_DILATION: 1
    STEM_OUT_CHANNELS: 64
    STRIDE_IN_1X1: true
    WIDTH_PER_GROUP: 64
  RETINANET:
    BBOX_REG_LOSS_TYPE: smooth_l1
    BBOX_REG_WEIGHTS: &id001
    - 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:
    - - 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:
    - 10.0
    - 10.0
    - 5.0
    - 5.0
    CLS_AGNOSTIC_BBOX_REG: true
    CONV_DIM: 256
    FC_DIM: 1024
    NAME: FastRCNNConvFCHead
    NORM: ''
    NUM_CONV: 0
    NUM_FC: 2
    POOLER_RESOLUTION: 7
    POOLER_SAMPLING_RATIO: 0
    POOLER_TYPE: ROIAlignV2
    SMOOTH_L1_BETA: 0.0
    TRAIN_ON_PRED_BOXES: false
  ROI_HEADS:
    BATCH_SIZE_PER_IMAGE: 512
    IN_FEATURES:
    - p2
    - p3
    - p4
    - p5
    IOU_LABELS:
    - 0
    - 1
    IOU_THRESHOLDS:
    - 0.5
    NAME: CascadeROIHeads
    NMS_THRESH_TEST: 0.5
    NUM_CLASSES: 5
    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: 4
    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: *id001
    BOUNDARY_THRESH: -1
    CONV_DIMS:
    - -1
    HEAD_NAME: StandardRPNHead
    IN_FEATURES:
    - p2
    - p3
    - p4
    - p5
    - p6
    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: 1000
    PRE_NMS_TOPK_TRAIN: 2000
    SMOOTH_L1_BETA: 0.0
  SEM_SEG_HEAD:
    COMMON_STRIDE: 4
    CONVS_DIM: 128
    IGNORE_VALUE: 255
    IN_FEATURES:
    - p2
    - p3
    - p4
    - p5
    LOSS_WEIGHT: 1.0
    NAME: SemSegFPNHead
    NORM: GN
    NUM_CLASSES: 54
  VIT:
    DROP_PATH: 0.1
    IMG_SIZE:
    - 224
    - 224
    MODEL_KWARGS: '{}'
    NAME: layoutlmv3_base
    OUT_FEATURES:
    - layer3
    - layer5
    - layer7
    - layer11
    POS_TYPE: abs
  WEIGHTS: /mnt/localdata/users/yupanhuang/models/layoutlmv3/fts/publaynet-base/model_final.pth
OCR_DATA_DIR_TEST: /mnt/localdata/users/yupanhuang/data/PubLayNet/ocr/val
OCR_DATA_DIR_TRAIN: /mnt/localdata/users/yupanhuang/data/PubLayNet/ocr/train
OUTPUT_DIR: /mnt/localdata/users/yupanhuang/models/layoutlmv3/fts/publaynet-base/
PUBLAYNET_DATA_DIR_TEST: /mnt/localdata/users/yupanhuang/data/PubLayNet/publaynet/val
PUBLAYNET_DATA_DIR_TRAIN: /mnt/localdata/users/yupanhuang/data/PubLayNet/publaynet/train
SEED: 42
SOLVER:
  AMP:
    ENABLED: true
  BACKBONE_MULTIPLIER: 1.0
  BASE_LR: 0.0002
  BIAS_LR_FACTOR: 1.0
  CHECKPOINT_PERIOD: 2000
  CLIP_GRADIENTS:
    CLIP_TYPE: full_model
    CLIP_VALUE: 1.0
    ENABLED: true
    NORM_TYPE: 2.0
  GAMMA: 0.1
  GRADIENT_ACCUMULATION_STEPS: 1
  IMS_PER_BATCH: 32
  LR_SCHEDULER_NAME: WarmupCosineLR
  MAX_ITER: 60000
  MOMENTUM: 0.9
  NESTEROV: false
  OPTIMIZER: ADAMW
  REFERENCE_WORLD_SIZE: 0
  STEPS:
  - 30000
  WARMUP_FACTOR: 0.01
  WARMUP_ITERS: 1000
  WARMUP_METHOD: linear
  WEIGHT_DECAY: 0.05
  WEIGHT_DECAY_BIAS: null
  WEIGHT_DECAY_NORM: 0.0
TEST:
  AUG:
    ENABLED: false
    FLIP: true
    MAX_SIZE: 4000
    MIN_SIZES:
    - 400
    - 500
    - 600
    - 700
    - 800
    - 900
    - 1000
    - 1100
    - 1200
  DETECTIONS_PER_IMAGE: 100
  EVAL_PERIOD: 2000
  EXPECTED_RESULTS: []
  KEYPOINT_OKS_SIGMAS: []
  PRECISE_BN:
    ENABLED: false
    NUM_ITER: 200
VERSION: 2
VIS_PERIOD: 0

[04/17 14:10:20 detectron2]: Full config saved to /mnt/localdata/users/yupanhuang/models/layoutlmv3/fts/publaynet-base/config.yaml
[04/17 14:10:21 fvcore.common.checkpoint]: [Checkpointer] Loading from /mnt/localdata/users/yupanhuang/models/layoutlmv3/fts/publaynet-base/model_final.pth ...
[04/17 14:10:23 d2.data.datasets.coco]: Loading /mnt/localdata/users/yupanhuang/data/PubLayNet/publaynet/val.json takes 1.71 seconds.
[04/17 14:10:24 d2.data.datasets.coco]: Loaded 11245 images in COCO format from /mnt/localdata/users/yupanhuang/data/PubLayNet/publaynet/val.json
[04/17 14:10:25 d2.data.build]: Distribution of instances among all 5 categories:
|  category  | #instances   |  category  | #instances   |  category  | #instances   |
|:----------:|:-------------|:----------:|:-------------|:----------:|:-------------|
|    text    | 88625        |   title    | 18801        |    list    | 4239         |
|   table    | 4769         |   figure   | 4327         |            |              |
|   total    | 120761       |            |              |            |              |
[04/17 14:10:25 d2.data.common]: Serializing 11245 elements to byte tensors and concatenating them all ...
[04/17 14:10:25 d2.data.common]: Serialized dataset takes 55.80 MiB
/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/detectron2/structures/image_list.py:88: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  max_size = (max_size + (stride - 1)) // stride * stride
/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/torch/nn/functional.py:3635: UserWarning: Default upsampling behavior when mode=bicubic is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
  "See the documentation of nn.Upsample for details.".format(mode)
/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2157.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
[04/17 14:10:27 d2.evaluation.evaluator]: Start inference on 1406 batches
/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/detectron2/structures/image_list.py:88: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  max_size = (max_size + (stride - 1)) // stride * stride
/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/torch/nn/functional.py:3635: UserWarning: Default upsampling behavior when mode=bicubic is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
  "See the documentation of nn.Upsample for details.".format(mode)
/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2157.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/detectron2/structures/image_list.py:88: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  max_size = (max_size + (stride - 1)) // stride * stride
/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/detectron2/structures/image_list.py:88: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  max_size = (max_size + (stride - 1)) // stride * stride
/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/torch/nn/functional.py:3635: UserWarning: Default upsampling behavior when mode=bicubic is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
  "See the documentation of nn.Upsample for details.".format(mode)
/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/torch/nn/functional.py:3635: UserWarning: Default upsampling behavior when mode=bicubic is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
  "See the documentation of nn.Upsample for details.".format(mode)
/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/detectron2/structures/image_list.py:88: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  max_size = (max_size + (stride - 1)) // stride * stride
/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2157.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/torch/nn/functional.py:3635: UserWarning: Default upsampling behavior when mode=bicubic is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
  "See the documentation of nn.Upsample for details.".format(mode)
/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2157.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/detectron2/structures/image_list.py:88: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  max_size = (max_size + (stride - 1)) // stride * stride
/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/detectron2/structures/image_list.py:88: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  max_size = (max_size + (stride - 1)) // stride * stride
/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2157.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/torch/nn/functional.py:3635: UserWarning: Default upsampling behavior when mode=bicubic is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
  "See the documentation of nn.Upsample for details.".format(mode)
/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/torch/nn/functional.py:3635: UserWarning: Default upsampling behavior when mode=bicubic is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
  "See the documentation of nn.Upsample for details.".format(mode)
/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2157.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2157.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/detectron2/structures/image_list.py:88: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  max_size = (max_size + (stride - 1)) // stride * stride
/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/torch/nn/functional.py:3635: UserWarning: Default upsampling behavior when mode=bicubic is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
  "See the documentation of nn.Upsample for details.".format(mode)
/mnt/localdata/users/yupanhuang/Downloads/miniconda3/envs/layoutlmft/lib/python3.7/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2157.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
[04/17 14:10:39 d2.evaluation.evaluator]: Inference done 11/1406. Dataloading: 0.0029 s/iter. Inference: 0.1609 s/iter. Eval: 0.0212 s/iter. Total: 0.1850 s/iter. ETA=0:04:18
[04/17 14:10:44 d2.evaluation.evaluator]: Inference done 38/1406. Dataloading: 0.0036 s/iter. Inference: 0.1729 s/iter. Eval: 0.0140 s/iter. Total: 0.1909 s/iter. ETA=0:04:21
[04/17 14:10:50 d2.evaluation.evaluator]: Inference done 66/1406. Dataloading: 0.0027 s/iter. Inference: 0.1703 s/iter. Eval: 0.0149 s/iter. Total: 0.1882 s/iter. ETA=0:04:12
[04/17 14:10:55 d2.evaluation.evaluator]: Inference done 93/1406. Dataloading: 0.0035 s/iter. Inference: 0.1691 s/iter. Eval: 0.0146 s/iter. Total: 0.1874 s/iter. ETA=0:04:06
[04/17 14:11:00 d2.evaluation.evaluator]: Inference done 121/1406. Dataloading: 0.0034 s/iter. Inference: 0.1687 s/iter. Eval: 0.0141 s/iter. Total: 0.1864 s/iter. ETA=0:03:59
[04/17 14:11:05 d2.evaluation.evaluator]: Inference done 149/1406. Dataloading: 0.0031 s/iter. Inference: 0.1684 s/iter. Eval: 0.0137 s/iter. Total: 0.1853 s/iter. ETA=0:03:52
[04/17 14:11:10 d2.evaluation.evaluator]: Inference done 177/1406. Dataloading: 0.0029 s/iter. Inference: 0.1684 s/iter. Eval: 0.0134 s/iter. Total: 0.1849 s/iter. ETA=0:03:47
[04/17 14:11:15 d2.evaluation.evaluator]: Inference done 206/1406. Dataloading: 0.0030 s/iter. Inference: 0.1680 s/iter. Eval: 0.0127 s/iter. Total: 0.1838 s/iter. ETA=0:03:40
[04/17 14:11:20 d2.evaluation.evaluator]: Inference done 234/1406. Dataloading: 0.0032 s/iter. Inference: 0.1676 s/iter. Eval: 0.0125 s/iter. Total: 0.1835 s/iter. ETA=0:03:35
[04/17 14:11:25 d2.evaluation.evaluator]: Inference done 261/1406. Dataloading: 0.0031 s/iter. Inference: 0.1682 s/iter. Eval: 0.0124 s/iter. Total: 0.1838 s/iter. ETA=0:03:30
[04/17 14:11:30 d2.evaluation.evaluator]: Inference done 288/1406. Dataloading: 0.0031 s/iter. Inference: 0.1692 s/iter. Eval: 0.0122 s/iter. Total: 0.1846 s/iter. ETA=0:03:26
[04/17 14:11:35 d2.evaluation.evaluator]: Inference done 315/1406. Dataloading: 0.0030 s/iter. Inference: 0.1694 s/iter. Eval: 0.0121 s/iter. Total: 0.1846 s/iter. ETA=0:03:21
[04/17 14:11:40 d2.evaluation.evaluator]: Inference done 342/1406. Dataloading: 0.0030 s/iter. Inference: 0.1698 s/iter. Eval: 0.0121 s/iter. Total: 0.1850 s/iter. ETA=0:03:16
[04/17 14:11:46 d2.evaluation.evaluator]: Inference done 370/1406. Dataloading: 0.0030 s/iter. Inference: 0.1696 s/iter. Eval: 0.0118 s/iter. Total: 0.1846 s/iter. ETA=0:03:11
[04/17 14:11:51 d2.evaluation.evaluator]: Inference done 396/1406. Dataloading: 0.0030 s/iter. Inference: 0.1704 s/iter. Eval: 0.0117 s/iter. Total: 0.1852 s/iter. ETA=0:03:07
[04/17 14:11:56 d2.evaluation.evaluator]: Inference done 423/1406. Dataloading: 0.0029 s/iter. Inference: 0.1707 s/iter. Eval: 0.0118 s/iter. Total: 0.1856 s/iter. ETA=0:03:02
[04/17 14:12:01 d2.evaluation.evaluator]: Inference done 450/1406. Dataloading: 0.0030 s/iter. Inference: 0.1708 s/iter. Eval: 0.0120 s/iter. Total: 0.1859 s/iter. ETA=0:02:57
[04/17 14:12:06 d2.evaluation.evaluator]: Inference done 476/1406. Dataloading: 0.0029 s/iter. Inference: 0.1713 s/iter. Eval: 0.0120 s/iter. Total: 0.1863 s/iter. ETA=0:02:53
[04/17 14:12:11 d2.evaluation.evaluator]: Inference done 501/1406. Dataloading: 0.0029 s/iter. Inference: 0.1721 s/iter. Eval: 0.0119 s/iter. Total: 0.1871 s/iter. ETA=0:02:49
[04/17 14:12:16 d2.evaluation.evaluator]: Inference done 528/1406. Dataloading: 0.0030 s/iter. Inference: 0.1720 s/iter. Eval: 0.0120 s/iter. Total: 0.1871 s/iter. ETA=0:02:44
[04/17 14:12:21 d2.evaluation.evaluator]: Inference done 555/1406. Dataloading: 0.0030 s/iter. Inference: 0.1721 s/iter. Eval: 0.0121 s/iter. Total: 0.1873 s/iter. ETA=0:02:39
[04/17 14:12:26 d2.evaluation.evaluator]: Inference done 581/1406. Dataloading: 0.0031 s/iter. Inference: 0.1722 s/iter. Eval: 0.0123 s/iter. Total: 0.1876 s/iter. ETA=0:02:34
[04/17 14:12:31 d2.evaluation.evaluator]: Inference done 607/1406. Dataloading: 0.0031 s/iter. Inference: 0.1725 s/iter. Eval: 0.0123 s/iter. Total: 0.1880 s/iter. ETA=0:02:30
[04/17 14:12:36 d2.evaluation.evaluator]: Inference done 633/1406. Dataloading: 0.0031 s/iter. Inference: 0.1728 s/iter. Eval: 0.0122 s/iter. Total: 0.1882 s/iter. ETA=0:02:25
[04/17 14:12:41 d2.evaluation.evaluator]: Inference done 658/1406. Dataloading: 0.0031 s/iter. Inference: 0.1733 s/iter. Eval: 0.0123 s/iter. Total: 0.1888 s/iter. ETA=0:02:21
[04/17 14:12:47 d2.evaluation.evaluator]: Inference done 684/1406. Dataloading: 0.0031 s/iter. Inference: 0.1736 s/iter. Eval: 0.0123 s/iter. Total: 0.1891 s/iter. ETA=0:02:16
[04/17 14:12:52 d2.evaluation.evaluator]: Inference done 710/1406. Dataloading: 0.0031 s/iter. Inference: 0.1738 s/iter. Eval: 0.0124 s/iter. Total: 0.1894 s/iter. ETA=0:02:11
[04/17 14:12:57 d2.evaluation.evaluator]: Inference done 736/1406. Dataloading: 0.0031 s/iter. Inference: 0.1740 s/iter. Eval: 0.0124 s/iter. Total: 0.1897 s/iter. ETA=0:02:07
[04/17 14:13:02 d2.evaluation.evaluator]: Inference done 762/1406. Dataloading: 0.0031 s/iter. Inference: 0.1742 s/iter. Eval: 0.0124 s/iter. Total: 0.1898 s/iter. ETA=0:02:02
[04/17 14:13:07 d2.evaluation.evaluator]: Inference done 787/1406. Dataloading: 0.0031 s/iter. Inference: 0.1743 s/iter. Eval: 0.0126 s/iter. Total: 0.1902 s/iter. ETA=0:01:57
[04/17 14:13:12 d2.evaluation.evaluator]: Inference done 813/1406. Dataloading: 0.0031 s/iter. Inference: 0.1746 s/iter. Eval: 0.0126 s/iter. Total: 0.1904 s/iter. ETA=0:01:52
[04/17 14:13:17 d2.evaluation.evaluator]: Inference done 839/1406. Dataloading: 0.0031 s/iter. Inference: 0.1748 s/iter. Eval: 0.0125 s/iter. Total: 0.1905 s/iter. ETA=0:01:48
[04/17 14:13:22 d2.evaluation.evaluator]: Inference done 865/1406. Dataloading: 0.0031 s/iter. Inference: 0.1750 s/iter. Eval: 0.0125 s/iter. Total: 0.1907 s/iter. ETA=0:01:43
[04/17 14:13:27 d2.evaluation.evaluator]: Inference done 891/1406. Dataloading: 0.0031 s/iter. Inference: 0.1754 s/iter. Eval: 0.0124 s/iter. Total: 0.1910 s/iter. ETA=0:01:38
[04/17 14:13:32 d2.evaluation.evaluator]: Inference done 918/1406. Dataloading: 0.0031 s/iter. Inference: 0.1755 s/iter. Eval: 0.0123 s/iter. Total: 0.1910 s/iter. ETA=0:01:33
[04/17 14:13:37 d2.evaluation.evaluator]: Inference done 943/1406. Dataloading: 0.0030 s/iter. Inference: 0.1759 s/iter. Eval: 0.0121 s/iter. Total: 0.1912 s/iter. ETA=0:01:28
[04/17 14:13:43 d2.evaluation.evaluator]: Inference done 969/1406. Dataloading: 0.0030 s/iter. Inference: 0.1762 s/iter. Eval: 0.0121 s/iter. Total: 0.1914 s/iter. ETA=0:01:23
[04/17 14:13:48 d2.evaluation.evaluator]: Inference done 995/1406. Dataloading: 0.0030 s/iter. Inference: 0.1763 s/iter. Eval: 0.0121 s/iter. Total: 0.1915 s/iter. ETA=0:01:18
[04/17 14:13:53 d2.evaluation.evaluator]: Inference done 1021/1406. Dataloading: 0.0030 s/iter. Inference: 0.1763 s/iter. Eval: 0.0121 s/iter. Total: 0.1916 s/iter. ETA=0:01:13
[04/17 14:13:58 d2.evaluation.evaluator]: Inference done 1047/1406. Dataloading: 0.0031 s/iter. Inference: 0.1765 s/iter. Eval: 0.0120 s/iter. Total: 0.1917 s/iter. ETA=0:01:08
[04/17 14:14:03 d2.evaluation.evaluator]: Inference done 1073/1406. Dataloading: 0.0031 s/iter. Inference: 0.1766 s/iter. Eval: 0.0120 s/iter. Total: 0.1918 s/iter. ETA=0:01:03
[04/17 14:14:08 d2.evaluation.evaluator]: Inference done 1099/1406. Dataloading: 0.0031 s/iter. Inference: 0.1767 s/iter. Eval: 0.0120 s/iter. Total: 0.1919 s/iter. ETA=0:00:58
[04/17 14:14:13 d2.evaluation.evaluator]: Inference done 1125/1406. Dataloading: 0.0031 s/iter. Inference: 0.1768 s/iter. Eval: 0.0120 s/iter. Total: 0.1919 s/iter. ETA=0:00:53
[04/17 14:14:18 d2.evaluation.evaluator]: Inference done 1151/1406. Dataloading: 0.0031 s/iter. Inference: 0.1768 s/iter. Eval: 0.0120 s/iter. Total: 0.1920 s/iter. ETA=0:00:48
[04/17 14:14:23 d2.evaluation.evaluator]: Inference done 1177/1406. Dataloading: 0.0031 s/iter. Inference: 0.1769 s/iter. Eval: 0.0119 s/iter. Total: 0.1920 s/iter. ETA=0:00:43
[04/17 14:14:28 d2.evaluation.evaluator]: Inference done 1203/1406. Dataloading: 0.0031 s/iter. Inference: 0.1769 s/iter. Eval: 0.0120 s/iter. Total: 0.1921 s/iter. ETA=0:00:39
[04/17 14:14:33 d2.evaluation.evaluator]: Inference done 1228/1406. Dataloading: 0.0031 s/iter. Inference: 0.1770 s/iter. Eval: 0.0121 s/iter. Total: 0.1923 s/iter. ETA=0:00:34
[04/17 14:14:38 d2.evaluation.evaluator]: Inference done 1254/1406. Dataloading: 0.0031 s/iter. Inference: 0.1769 s/iter. Eval: 0.0122 s/iter. Total: 0.1924 s/iter. ETA=0:00:29
[04/17 14:14:43 d2.evaluation.evaluator]: Inference done 1279/1406. Dataloading: 0.0032 s/iter. Inference: 0.1770 s/iter. Eval: 0.0123 s/iter. Total: 0.1926 s/iter. ETA=0:00:24
[04/17 14:14:48 d2.evaluation.evaluator]: Inference done 1305/1406. Dataloading: 0.0031 s/iter. Inference: 0.1769 s/iter. Eval: 0.0124 s/iter. Total: 0.1926 s/iter. ETA=0:00:19
[04/17 14:14:54 d2.evaluation.evaluator]: Inference done 1331/1406. Dataloading: 0.0031 s/iter. Inference: 0.1770 s/iter. Eval: 0.0124 s/iter. Total: 0.1926 s/iter. ETA=0:00:14
[04/17 14:14:59 d2.evaluation.evaluator]: Inference done 1357/1406. Dataloading: 0.0031 s/iter. Inference: 0.1769 s/iter. Eval: 0.0126 s/iter. Total: 0.1927 s/iter. ETA=0:00:09
[04/17 14:15:04 d2.evaluation.evaluator]: Inference done 1385/1406. Dataloading: 0.0031 s/iter. Inference: 0.1767 s/iter. Eval: 0.0125 s/iter. Total: 0.1924 s/iter. ETA=0:00:04
[04/17 14:15:08 d2.evaluation.evaluator]: Total inference time: 0:04:29.845715 (0.192609 s / iter per device, on 8 devices)
[04/17 14:15:08 d2.evaluation.evaluator]: Total inference pure compute time: 0:04:07 (0.176466 s / iter per device, on 8 devices)
[04/17 14:15:17 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...
[04/17 14:15:17 d2.evaluation.coco_evaluation]: Saving results to /mnt/localdata/users/yupanhuang/models/layoutlmv3/fts/publaynet-base/inference/coco_instances_results.json
[04/17 14:15:18 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...
Loading and preparing results...
DONE (t=0.12s)
creating index...
index created!
[04/17 14:15:19 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*
[04/17 14:15:22 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 3.39 seconds.
[04/17 14:15:22 d2.evaluation.fast_eval_api]: Accumulating evaluation results...
[04/17 14:15:23 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.40 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.951
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.981
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.969
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.468
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.856
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.976
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.543
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.953
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.964
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.607
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.897
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.986
[04/17 14:15:23 d2.evaluation.coco_evaluation]: Evaluation results for bbox: 
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 95.088 | 98.066 | 96.933 | 46.800 | 85.592 | 97.626 |
[04/17 14:15:23 d2.evaluation.coco_evaluation]: Per-category bbox AP: 
| category   | AP     | category   | AP     | category   | AP     |
|:-----------|:-------|:-----------|:-------|:-----------|:-------|
| text       | 94.466 | title      | 90.569 | list       | 95.522 |
| table      | 97.883 | figure     | 97.001 |            |        |
Loading and preparing results...
DONE (t=2.05s)
creating index...
index created!
[04/17 14:15:28 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*
[04/17 14:15:38 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 10.92 seconds.
[04/17 14:15:39 d2.evaluation.fast_eval_api]: Accumulating evaluation results...
[04/17 14:15:39 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.43 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.928
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.981
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.967
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.506
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.824
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.959
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.535
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.938
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.949
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.632
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.879
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.973
[04/17 14:15:39 d2.evaluation.coco_evaluation]: Evaluation results for segm: 
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 92.819 | 98.070 | 96.719 | 50.628 | 82.397 | 95.917 |
[04/17 14:15:39 d2.evaluation.coco_evaluation]: Per-category segm AP: 
| category   | AP     | category   | AP     | category   | AP     |
|:-----------|:-------|:-----------|:-------|:-----------|:-------|
| text       | 93.433 | title      | 87.009 | list       | 88.864 |
| table      | 97.799 | figure     | 96.989 |            |        |
[04/17 14:15:40 d2.evaluation.testing]: copypaste: Task: bbox
[04/17 14:15:40 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl
[04/17 14:15:40 d2.evaluation.testing]: copypaste: 95.0883,98.0662,96.9331,46.8005,85.5919,97.6258
[04/17 14:15:40 d2.evaluation.testing]: copypaste: Task: segm
[04/17 14:15:40 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl
[04/17 14:15:40 d2.evaluation.testing]: copypaste: 92.8187,98.0704,96.7191,50.6278,82.3972,95.9172

Process finished with exit code 0