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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
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[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
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