| [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 | |
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| [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 | |
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| [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 | |