problem in reproduce ICDAR2015

[HotaekHan]

Hello, Thanks for your amazing work :)

I tried to reproduce ICDAR 2015 result from paper.
But I can't get the result from paper with pre-trained weights.

I'm not changing any code. download dataset and pre-trained weights.
train with pre-trained weight. but I got loss almost 30.0~
it looks like not converge.

below is my log.

[07/25 14:21:07] detectron2 INFO: Rank of current process: 0. World size: 8
[07/25 14:21:11] detectron2 INFO: Environment info:

---

sys.platform linux
Python 3.8.10 (default, Jun 22 2022, 20:18:18) [GCC 9.4.0]
numpy 1.23.4
detectron2 0.6 @/usr/local/lib/python3.8/dist-packages/detectron2
Compiler GCC 9.4
CUDA compiler CUDA 11.3
detectron2 arch flags 8.6
DETECTRON2_ENV_MODULE
PyTorch 1.12.1+cu113 @/usr/local/lib/python3.8/dist-packages/torch
PyTorch debug build False
torch._C._GLIBCXX_USE_CXX11_ABI False
GPU available Yes
GPU 0,1,2,3,4,5,6,7 Tesla T4 (arch=7.5)
Driver version 450.80.02
CUDA_HOME /usr/local/cuda
Pillow 9.2.0
torchvision 0.13.1+cu113 @/usr/local/lib/python3.8/dist-packages/torchvision
torchvision arch flags 3.5, 5.0, 6.0, 7.0, 7.5, 8.0, 8.6
fvcore 0.1.5.post20221221
iopath 0.1.9
cv2 4.1.2

---

PyTorch built with:

• GCC 9.3
• C++ Version: 201402
• Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
• Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)
• OpenMP 201511 (a.k.a. OpenMP 4.5)
• LAPACK is enabled (usually provided by MKL)
• NNPACK is enabled
• CPU capability usage: AVX2
• CUDA Runtime 11.3
• NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
• CuDNN 8.3.2 (built against CUDA 11.5)
• Magma 2.5.2
• Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,

[07/25 14:21:11] detectron2 INFO: Command line arguments: Namespace(config_file='configs/TESTR/ICDAR15/TESTR_R_50_Polygon.yaml', dist_url='tcp://127.0.0.1:59588', eval_only=False, machine_rank=0, num_gpus=8, num_machines=1, opts=[], resume=False)
[07/25 14:21:11] detectron2 INFO: Contents of args.config_file=configs/TESTR/ICDAR15/TESTR_R_50_Polygon.yaml:
BASE: "Base-ICDAR15-Polygon.yaml"
MODEL:
WEIGHTS: "weights/TESTR/pretrain_testr_R_50_polygon.pth"
RESNETS:
DEPTH: 50
TRANSFORMER:
NUM_FEATURE_LEVELS: 4
INFERENCE_TH_TEST: 0.3
ENC_LAYERS: 6
DEC_LAYERS: 6
DIM_FEEDFORWARD: 1024
HIDDEN_DIM: 256
DROPOUT: 0.1
NHEADS: 8
NUM_QUERIES: 100
ENC_N_POINTS: 4
DEC_N_POINTS: 4
SOLVER:
IMS_PER_BATCH: 8
BASE_LR: 1e-5
LR_BACKBONE: 1e-6
WARMUP_ITERS: 0

STEPS: (200000,)

MAX_ITER: 200000
CHECKPOINT_PERIOD: 10000
TEST:
EVAL_PERIOD: 10000
OUTPUT_DIR: "output/TESTR/icdar15/TESTR_R_50_Polygon"

[07/25 14:21:11] detectron2 INFO: Running with full config:
CUDNN_BENCHMARK: false
DATALOADER:
ASPECT_RATIO_GROUPING: true
FILTER_EMPTY_ANNOTATIONS: true
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:

• icdar2015_test
  TRAIN:
• icdar2015_train
  GLOBAL:
  HACK: 1.0
  INPUT:
  CROP:
  CROP_INSTANCE: false
  ENABLED: true
  SIZE:
  • 0.1
  • 0.1
    TYPE: relative_range
    FORMAT: RGB
    HFLIP_TRAIN: false
    MASK_FORMAT: polygon
    MAX_SIZE_TEST: 4000
    MAX_SIZE_TRAIN: 2333
    MIN_SIZE_TEST: 1440
    MIN_SIZE_TRAIN:
• 800
• 832
• 864
• 896
• 1000
• 1200
• 1400
  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:
      ANTI_ALIAS: false
      FREEZE_AT: 2
      NAME: build_resnet_backbone
      BASIS_MODULE:
      ANN_SET: coco
      COMMON_STRIDE: 8
      CONVS_DIM: 128
      IN_FEATURES:
  • p3
  • p4
  • p5
    LOSS_ON: false
    LOSS_WEIGHT: 0.3
    NAME: ProtoNet
    NORM: SyncBN
    NUM_BASES: 4
    NUM_CLASSES: 80
    NUM_CONVS: 3
    BATEXT:
    CANONICAL_SIZE: 96
    CONV_DIM: 256
    CUSTOM_DICT: ''
    IN_FEATURES:
  • p2
  • p3
  • p4
    NUM_CHARS: 25
    NUM_CONV: 2
    POOLER_RESOLUTION:
  • 8
  • 32
    POOLER_SCALES:
  • 0.25
  • 0.125
  • 0.0625
    RECOGNITION_LOSS: ctc
    RECOGNIZER: attn
    SAMPLING_RATIO: 1
    USE_AET: false
    USE_COORDCONV: false
    VOC_SIZE: 96
    BLENDMASK:
    ATTN_SIZE: 14
    BOTTOM_RESOLUTION: 56
    INSTANCE_LOSS_WEIGHT: 1.0
    POOLER_SAMPLING_RATIO: 1
    POOLER_SCALES:
  • 0.25
    POOLER_TYPE: ROIAlignV2
    TOP_INTERP: bilinear
    VISUALIZE: false
    BOXINST:
    BOTTOM_PIXELS_REMOVED: 10
    ENABLED: false
    PAIRWISE:
    COLOR_THRESH: 0.3
    DILATION: 2
    SIZE: 3
    WARMUP_ITERS: 10000
    BiFPN:
    IN_FEATURES:
  • res2
  • res3
  • res4
  • res5
    NORM: ''
    NUM_REPEATS: 6
    OUT_CHANNELS: 160
    CONDINST:
    BOTTOM_PIXELS_REMOVED: -1
    MASK_BRANCH:
    CHANNELS: 128
    IN_FEATURES:
    • p3
    • p4
    • p5
      NORM: BN
      NUM_CONVS: 4
      OUT_CHANNELS: 8
      SEMANTIC_LOSS_ON: false
      MASK_HEAD:
      CHANNELS: 8
      DISABLE_REL_COORDS: false
      NUM_LAYERS: 3
      USE_FP16: false
      MASK_OUT_STRIDE: 4
      MAX_PROPOSALS: -1
      TOPK_PROPOSALS_PER_IM: -1
      DEVICE: cuda
      DLA:
      CONV_BODY: DLA34
      NORM: FrozenBN
      OUT_FEATURES:
  • stage2
  • stage3
  • stage4
  • stage5
    FCOS:
    BOX_QUALITY: ctrness
    CENTER_SAMPLE: true
    FPN_STRIDES:
  • 8
  • 16
  • 32
  • 64
  • 128
    INFERENCE_TH_TEST: 0.05
    INFERENCE_TH_TRAIN: 0.05
    IN_FEATURES:
  • p3
  • p4
  • p5
  • p6
  • p7
    LOC_LOSS_TYPE: giou
    LOSS_ALPHA: 0.25
    LOSS_GAMMA: 2.0
    LOSS_NORMALIZER_CLS: fg
    LOSS_WEIGHT_CLS: 1.0
    NMS_TH: 0.6
    NORM: GN
    NUM_BOX_CONVS: 4
    NUM_CLASSES: 80
    NUM_CLS_CONVS: 4
    NUM_SHARE_CONVS: 0
    POST_NMS_TOPK_TEST: 100
    POST_NMS_TOPK_TRAIN: 100
    POS_RADIUS: 1.5
    PRE_NMS_TOPK_TEST: 1000
    PRE_NMS_TOPK_TRAIN: 1000
    PRIOR_PROB: 0.01
    SIZES_OF_INTEREST:
  • 64
  • 128
  • 256
  • 512
    THRESH_WITH_CTR: false
    TOP_LEVELS: 2
    USE_DEFORMABLE: false
    USE_RELU: true
    USE_SCALE: true
    YIELD_BOX_FEATURES: false
    YIELD_PROPOSAL: false
    FPN:
    FUSE_TYPE: sum
    IN_FEATURES: []
    NORM: ''
    OUT_CHANNELS: 256
    KEYPOINT_ON: false
    LOAD_PROPOSALS: false
    MASK_ON: false
    MEInst:
    AGNOSTIC: true
    CENTER_SAMPLE: true
    DIM_MASK: 60
    FLAG_PARAMETERS: false
    FPN_STRIDES:
  • 8
  • 16
  • 32
  • 64
  • 128
    GCN_KERNEL_SIZE: 9
    INFERENCE_TH_TEST: 0.05
    INFERENCE_TH_TRAIN: 0.05
    IN_FEATURES:
  • p3
  • p4
  • p5
  • p6
  • p7
    IOU_LABELS:
  • 0
  • 1
    IOU_THRESHOLDS:
  • 0.5
    LAST_DEFORMABLE: false
    LOC_LOSS_TYPE: giou
    LOSS_ALPHA: 0.25
    LOSS_GAMMA: 2.0
    LOSS_ON_MASK: false
    MASK_LOSS_TYPE: mse
    MASK_ON: true
    MASK_SIZE: 28
    NMS_TH: 0.6
    NORM: GN
    NUM_BOX_CONVS: 4
    NUM_CLASSES: 80
    NUM_CLS_CONVS: 4
    NUM_MASK_CONVS: 4
    NUM_SHARE_CONVS: 0
    PATH_COMPONENTS: datasets/coco/components/coco_2017_train_class_agnosticTrue_whitenTrue_sigmoidTrue_60.npz
    POST_NMS_TOPK_TEST: 100
    POST_NMS_TOPK_TRAIN: 100
    POS_RADIUS: 1.5
    PRE_NMS_TOPK_TEST: 1000
    PRE_NMS_TOPK_TRAIN: 1000
    PRIOR_PROB: 0.01
    SIGMOID: true
    SIZES_OF_INTEREST:
  • 64
  • 128
  • 256
  • 512
    THRESH_WITH_CTR: false
    TOP_LEVELS: 2
    TYPE_DEFORMABLE: DCNv1
    USE_DEFORMABLE: false
    USE_GCN_IN_MASK: false
    USE_RELU: true
    USE_SCALE: true
    WHITEN: true
    META_ARCHITECTURE: TransformerDetector
    MOBILENET: false
    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:
• 123.675
• 116.28
• 103.53
  PIXEL_STD:
• 58.395
• 57.12
• 57.375
  PROPOSAL_GENERATOR:
  MIN_SIZE: 0
  NAME: RPN
  RESNETS:
  DEFORM_INTERVAL: 1
  DEFORM_MODULATED: false
  DEFORM_NUM_GROUPS: 1
  DEFORM_ON_PER_STAGE:
  • false
  • false
  • false
  • false
    DEPTH: 50
    NORM: FrozenBN
    NUM_GROUPS: 1
    OUT_FEATURES:
  • res3
  • res4
  • res5
    RES2_OUT_CHANNELS: 256
    RES5_DILATION: 1
    STEM_OUT_CHANNELS: 64
    STRIDE_IN_1X1: false
    WIDTH_PER_GROUP: 64
    RETINANET:
    BBOX_REG_LOSS_TYPE: smooth_l1
    BBOX_REG_WEIGHTS: &id002
  • 1.0
  • 1.0
  • 1.0
  • 1.0
    FOCAL_LOSS_ALPHA: 0.25
    FOCAL_LOSS_GAMMA: 2.0
    IN_FEATURES:
  • p3
  • p4
  • p5
  • p6
  • p7
    IOU_LABELS:
  • 0
  • -1
  • 1
    IOU_THRESHOLDS:
  • 0.4
  • 0.5
    NMS_THRESH_TEST: 0.5
    NORM: ''
    NUM_CLASSES: 80
    NUM_CONVS: 4
    PRIOR_PROB: 0.01
    SCORE_THRESH_TEST: 0.05
    SMOOTH_L1_LOSS_BETA: 0.1
    TOPK_CANDIDATES_TEST: 1000
    ROI_BOX_CASCADE_HEAD:
    BBOX_REG_WEIGHTS:
  • &id001
    • 10.0
    • 10.0
    • 5.0
    • 5.0
  • • 20.0
    • 20.0
    • 10.0
    • 10.0
  • • 30.0
    • 30.0
    • 15.0
    • 15.0
      IOUS:
  • 0.5
  • 0.6
  • 0.7
    ROI_BOX_HEAD:
    BBOX_REG_LOSS_TYPE: smooth_l1
    BBOX_REG_LOSS_WEIGHT: 1.0
    BBOX_REG_WEIGHTS: *id001
    CLS_AGNOSTIC_BBOX_REG: false
    CONV_DIM: 256
    FC_DIM: 1024
    FED_LOSS_FREQ_WEIGHT_POWER: 0.5
    FED_LOSS_NUM_CLASSES: 50
    NAME: ''
    NORM: ''
    NUM_CONV: 0
    NUM_FC: 0
    POOLER_RESOLUTION: 14
    POOLER_SAMPLING_RATIO: 0
    POOLER_TYPE: ROIAlignV2
    SMOOTH_L1_BETA: 0.0
    TRAIN_ON_PRED_BOXES: false
    USE_FED_LOSS: false
    USE_SIGMOID_CE: false
    ROI_HEADS:
    BATCH_SIZE_PER_IMAGE: 512
    IN_FEATURES:
  • res4
    IOU_LABELS:
  • 0
  • 1
    IOU_THRESHOLDS:
  • 0.5
    NAME: Res5ROIHeads
    NMS_THRESH_TEST: 0.5
    NUM_CLASSES: 80
    POSITIVE_FRACTION: 0.25
    PROPOSAL_APPEND_GT: true
    SCORE_THRESH_TEST: 0.05
    ROI_KEYPOINT_HEAD:
    CONV_DIMS:
  • 512
  • 512
  • 512
  • 512
  • 512
  • 512
  • 512
  • 512
    LOSS_WEIGHT: 1.0
    MIN_KEYPOINTS_PER_IMAGE: 1
    NAME: KRCNNConvDeconvUpsampleHead
    NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true
    NUM_KEYPOINTS: 17
    POOLER_RESOLUTION: 14
    POOLER_SAMPLING_RATIO: 0
    POOLER_TYPE: ROIAlignV2
    ROI_MASK_HEAD:
    CLS_AGNOSTIC_MASK: false
    CONV_DIM: 256
    NAME: MaskRCNNConvUpsampleHead
    NORM: ''
    NUM_CONV: 0
    POOLER_RESOLUTION: 14
    POOLER_SAMPLING_RATIO: 0
    POOLER_TYPE: ROIAlignV2
    RPN:
    BATCH_SIZE_PER_IMAGE: 256
    BBOX_REG_LOSS_TYPE: smooth_l1
    BBOX_REG_LOSS_WEIGHT: 1.0
    BBOX_REG_WEIGHTS: *id002
    BOUNDARY_THRESH: -1
    CONV_DIMS:
  • -1
    HEAD_NAME: StandardRPNHead
    IN_FEATURES:
  • res4
    IOU_LABELS:
  • 0
  • -1
  • 1
    IOU_THRESHOLDS:
  • 0.3
  • 0.7
    LOSS_WEIGHT: 1.0
    NMS_THRESH: 0.7
    POSITIVE_FRACTION: 0.5
    POST_NMS_TOPK_TEST: 1000
    POST_NMS_TOPK_TRAIN: 2000
    PRE_NMS_TOPK_TEST: 6000
    PRE_NMS_TOPK_TRAIN: 12000
    SMOOTH_L1_BETA: 0.0
    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
    SOLOV2:
    FPN_INSTANCE_STRIDES:
  • 8
  • 8
  • 16
  • 32
  • 32
    FPN_SCALE_RANGES:
  • • 1
    • 96
  • • 48
    • 192
  • • 96
    • 384
  • • 192
    • 768
  • • 384
    • 2048
      INSTANCE_CHANNELS: 512
      INSTANCE_IN_CHANNELS: 256
      INSTANCE_IN_FEATURES:
  • p2
  • p3
  • p4
  • p5
  • p6
    LOSS:
    DICE_WEIGHT: 3.0
    FOCAL_ALPHA: 0.25
    FOCAL_GAMMA: 2.0
    FOCAL_USE_SIGMOID: true
    FOCAL_WEIGHT: 1.0
    MASK_CHANNELS: 128
    MASK_IN_CHANNELS: 256
    MASK_IN_FEATURES:
  • p2
  • p3
  • p4
  • p5
    MASK_THR: 0.5
    MAX_PER_IMG: 100
    NMS_KERNEL: gaussian
    NMS_PRE: 500
    NMS_SIGMA: 2
    NMS_TYPE: matrix
    NORM: GN
    NUM_CLASSES: 80
    NUM_GRIDS:
  • 40
  • 36
  • 24
  • 16
  • 12
    NUM_INSTANCE_CONVS: 4
    NUM_KERNELS: 256
    NUM_MASKS: 256
    PRIOR_PROB: 0.01
    SCORE_THR: 0.1
    SIGMA: 0.2
    TYPE_DCN: DCN
    UPDATE_THR: 0.05
    USE_COORD_CONV: true
    USE_DCN_IN_INSTANCE: false
    TOP_MODULE:
    DIM: 16
    NAME: conv
    TRANSFORMER:
    AUX_LOSS: true
    DEC_LAYERS: 6
    DEC_N_POINTS: 4
    DIM_FEEDFORWARD: 1024
    DROPOUT: 0.1
    ENABLED: true
    ENC_LAYERS: 6
    ENC_N_POINTS: 4
    HIDDEN_DIM: 256
    INFERENCE_TH_TEST: 0.3
    LOSS:
    AUX_LOSS: true
    BOX_CLASS_WEIGHT: 2.0
    BOX_COORD_WEIGHT: 5.0
    BOX_GIOU_WEIGHT: 2.0
    FOCAL_ALPHA: 0.25
    FOCAL_GAMMA: 2.0
    POINT_CLASS_WEIGHT: 2.0
    POINT_COORD_WEIGHT: 5.0
    POINT_TEXT_WEIGHT: 4.0
    NHEADS: 8
    NUM_CHARS: 25
    NUM_CTRL_POINTS: 16
    NUM_FEATURE_LEVELS: 4
    NUM_QUERIES: 100
    POSITION_EMBEDDING_SCALE: 6.283185307179586
    USE_POLYGON: true
    VOC_SIZE: 96
    VOVNET:
    BACKBONE_OUT_CHANNELS: 256
    CONV_BODY: V-39-eSE
    NORM: FrozenBN
    OUT_CHANNELS: 256
    OUT_FEATURES:
  • stage2
  • stage3
  • stage4
  • stage5
    WEIGHTS: weights/TESTR/pretrain_testr_R_50_polygon.pth
    OUTPUT_DIR: output/TESTR/icdar15/TESTR_R_50_Polygon
    SEED: -1
    SOLVER:
    AMP:
    ENABLED: false
    BASE_LR: 1.0e-05
    BASE_LR_END: 0.0
    BIAS_LR_FACTOR: 1.0
    CHECKPOINT_PERIOD: 10000
    CLIP_GRADIENTS:
    CLIP_TYPE: full_model
    CLIP_VALUE: 0.1
    ENABLED: true
    NORM_TYPE: 2.0
    GAMMA: 0.1
    IMS_PER_BATCH: 8
    LR_BACKBONE: 1.0e-06
    LR_BACKBONE_NAMES:
• backbone.0
  LR_LINEAR_PROJ_MULT: 0.1
  LR_LINEAR_PROJ_NAMES:
• reference_points
• sampling_offsets
  LR_SCHEDULER_NAME: WarmupMultiStepLR
  MAX_ITER: 200000
  MOMENTUM: 0.9
  NESTEROV: false
  NUM_DECAYS: 3
  OPTIMIZER: ADAMW
  REFERENCE_WORLD_SIZE: 0
  RESCALE_INTERVAL: false
  STEPS:
• 30000
  WARMUP_FACTOR: 0.001
  WARMUP_ITERS: 0
  WARMUP_METHOD: linear
  WEIGHT_DECAY: 0.0001
  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: 10000
    EXPECTED_RESULTS: []
    KEYPOINT_OKS_SIGMAS: []
    LEXICON_TYPE: 3
    PRECISE_BN:
    ENABLED: false
    NUM_ITER: 200
    USE_LEXICON: true
    WEIGHTED_EDIT_DIST: true
    VERSION: 2
    VIS_PERIOD: 0

[07/25 14:21:11] detectron2 INFO: Full config saved to output/TESTR/icdar15/TESTR_R_50_Polygon/config.yaml
[07/25 14:21:11] d2.utils.env INFO: Using a generated random seed 11819301
[07/25 14:21:13] d2.engine.defaults INFO: Model:
TransformerDetector(
(testr): TESTR(
(backbone): Joiner(
(0): MaskedBackbone(
(backbone): ResNet(
(stem): BasicStem(
(conv1): Conv2d(
3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False
(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
)
)
(res2): Sequential(
(0): BottleneckBlock(
(shortcut): Conv2d(
64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv1): Conv2d(
64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
)
(conv2): Conv2d(
64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
)
(conv3): Conv2d(
64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
)
(1): BottleneckBlock(
(conv1): Conv2d(
256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
)
(conv2): Conv2d(
64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
)
(conv3): Conv2d(
64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
)
(2): BottleneckBlock(
(conv1): Conv2d(
256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
)
(conv2): Conv2d(
64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
)
(conv3): Conv2d(
64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
)
)
(res3): Sequential(
(0): BottleneckBlock(
(shortcut): Conv2d(
256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
(conv1): Conv2d(
256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
)
(conv2): Conv2d(
128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
)
(conv3): Conv2d(
128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
)
(1): BottleneckBlock(
(conv1): Conv2d(
512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
)
(conv2): Conv2d(
128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
)
(conv3): Conv2d(
128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
)
(2): BottleneckBlock(
(conv1): Conv2d(
512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
)
(conv2): Conv2d(
128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
)
(conv3): Conv2d(
128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
)
(3): BottleneckBlock(
(conv1): Conv2d(
512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
)
(conv2): Conv2d(
128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
)
(conv3): Conv2d(
128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
)
)
(res4): Sequential(
(0): BottleneckBlock(
(shortcut): Conv2d(
512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False
(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
)
(conv1): Conv2d(
512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
)
)
(1): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
)
)
(2): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
)
)
(3): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
)
)
(4): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
)
)
(5): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
)
)
)
(res5): Sequential(
(0): BottleneckBlock(
(shortcut): Conv2d(
1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False
(norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
)
(conv1): Conv2d(
1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
(conv2): Conv2d(
512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
(conv3): Conv2d(
512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
)
)
(1): BottleneckBlock(
(conv1): Conv2d(
2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
(conv2): Conv2d(
512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
(conv3): Conv2d(
512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
)
)
(2): BottleneckBlock(
(conv1): Conv2d(
2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
(conv2): Conv2d(
512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
(conv3): Conv2d(
512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
)
)
)
)
)
(1): PositionalEncoding2D()
)
(text_pos_embed): PositionalEncoding1D()
(transformer): DeformableTransformer(
(encoder): DeformableTransformerEncoder(
(layers): ModuleList(
(0): DeformableTransformerEncoderLayer(
(self_attn): MSDeformAttn(
(sampling_offsets): Linear(in_features=256, out_features=256, bias=True)
(attention_weights): Linear(in_features=256, out_features=128, bias=True)
(value_proj): Linear(in_features=256, out_features=256, bias=True)
(output_proj): Linear(in_features=256, out_features=256, bias=True)
)
(dropout1): Dropout(p=0.1, inplace=False)
(norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(linear1): Linear(in_features=256, out_features=1024, bias=True)
(dropout2): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=1024, out_features=256, bias=True)
(dropout3): Dropout(p=0.1, inplace=False)
(norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
)
(1): DeformableTransformerEncoderLayer(
(self_attn): MSDeformAttn(
(sampling_offsets): Linear(in_features=256, out_features=256, bias=True)
(attention_weights): Linear(in_features=256, out_features=128, bias=True)
(value_proj): Linear(in_features=256, out_features=256, bias=True)
(output_proj): Linear(in_features=256, out_features=256, bias=True)
)
(dropout1): Dropout(p=0.1, inplace=False)
(norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(linear1): Linear(in_features=256, out_features=1024, bias=True)
(dropout2): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=1024, out_features=256, bias=True)
(dropout3): Dropout(p=0.1, inplace=False)
(norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
)
(2): DeformableTransformerEncoderLayer(
(self_attn): MSDeformAttn(
(sampling_offsets): Linear(in_features=256, out_features=256, bias=True)
(attention_weights): Linear(in_features=256, out_features=128, bias=True)
(value_proj): Linear(in_features=256, out_features=256, bias=True)
(output_proj): Linear(in_features=256, out_features=256, bias=True)
)
(dropout1): Dropout(p=0.1, inplace=False)
(norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(linear1): Linear(in_features=256, out_features=1024, bias=True)
(dropout2): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=1024, out_features=256, bias=True)
(dropout3): Dropout(p=0.1, inplace=False)
(norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
)
(3): DeformableTransformerEncoderLayer(
(self_attn): MSDeformAttn(
(sampling_offsets): Linear(in_features=256, out_features=256, bias=True)
(attention_weights): Linear(in_features=256, out_features=128, bias=True)
(value_proj): Linear(in_features=256, out_features=256, bias=True)
(output_proj): Linear(in_features=256, out_features=256, bias=True)
)
(dropout1): Dropout(p=0.1, inplace=False)
(norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(linear1): Linear(in_features=256, out_features=1024, bias=True)
(dropout2): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=1024, out_features=256, bias=True)
(dropout3): Dropout(p=0.1, inplace=False)
(norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
)
(4): DeformableTransformerEncoderLayer(
(self_attn): MSDeformAttn(
(sampling_offsets): Linear(in_features=256, out_features=256, bias=True)
(attention_weights): Linear(in_features=256, out_features=128, bias=True)
(value_proj): Linear(in_features=256, out_features=256, bias=True)
(output_proj): Linear(in_features=256, out_features=256, bias=True)
)
(dropout1): Dropout(p=0.1, inplace=False)
(norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(linear1): Linear(in_features=256, out_features=1024, bias=True)
(dropout2): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=1024, out_features=256, bias=True)
(dropout3): Dropout(p=0.1, inplace=False)
(norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
)
(5): DeformableTransformerEncoderLayer(
(self_attn): MSDeformAttn(
(sampling_offsets): Linear(in_features=256, out_features=256, bias=True)
(attention_weights): Linear(in_features=256, out_features=128, bias=True)
(value_proj): Linear(in_features=256, out_features=256, bias=True)
(output_proj): Linear(in_features=256, out_features=256, bias=True)
)
(dropout1): Dropout(p=0.1, inplace=False)
(norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(linear1): Linear(in_features=256, out_features=1024, bias=True)
(dropout2): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=1024, out_features=256, bias=True)
(dropout3): Dropout(p=0.1, inplace=False)
(norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
)
)
)
(decoder): DeformableCompositeTransformerDecoder(
(layers): ModuleList(
(0): DeformableCompositeTransformerDecoderLayer(
(attn_cross): MSDeformAttn(
(sampling_offsets): Linear(in_features=256, out_features=256, bias=True)
(attention_weights): Linear(in_features=256, out_features=128, bias=True)
(value_proj): Linear(in_features=256, out_features=256, bias=True)
(output_proj): Linear(in_features=256, out_features=256, bias=True)
)
(dropout_cross): Dropout(p=0.1, inplace=False)
(norm_cross): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_intra): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
)
(dropout_intra): Dropout(p=0.1, inplace=False)
(norm_intra): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_inter): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
)
(dropout_inter): Dropout(p=0.1, inplace=False)
(norm_inter): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(linear1): Linear(in_features=256, out_features=1024, bias=True)
(dropout3): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=1024, out_features=256, bias=True)
(dropout4): Dropout(p=0.1, inplace=False)
(norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_intra_text): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
)
(dropout_intra_text): Dropout(p=0.1, inplace=False)
(norm_intra_text): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_inter_text): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
)
(dropout_inter_text): Dropout(p=0.1, inplace=False)
(norm_inter_text): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_cross_text): MSDeformAttn(
(sampling_offsets): Linear(in_features=256, out_features=256, bias=True)
(attention_weights): Linear(in_features=256, out_features=128, bias=True)
(value_proj): Linear(in_features=256, out_features=256, bias=True)
(output_proj): Linear(in_features=256, out_features=256, bias=True)
)
(dropout_cross_text): Dropout(p=0.1, inplace=False)
(norm_cross_text): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(linear1_text): Linear(in_features=256, out_features=1024, bias=True)
(dropout3_text): Dropout(p=0.1, inplace=False)
(linear2_text): Linear(in_features=1024, out_features=256, bias=True)
(dropout4_text): Dropout(p=0.1, inplace=False)
(norm3_text): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
)
(1): DeformableCompositeTransformerDecoderLayer(
(attn_cross): MSDeformAttn(
(sampling_offsets): Linear(in_features=256, out_features=256, bias=True)
(attention_weights): Linear(in_features=256, out_features=128, bias=True)
(value_proj): Linear(in_features=256, out_features=256, bias=True)
(output_proj): Linear(in_features=256, out_features=256, bias=True)
)
(dropout_cross): Dropout(p=0.1, inplace=False)
(norm_cross): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_intra): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
)
(dropout_intra): Dropout(p=0.1, inplace=False)
(norm_intra): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_inter): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
)
(dropout_inter): Dropout(p=0.1, inplace=False)
(norm_inter): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(linear1): Linear(in_features=256, out_features=1024, bias=True)
(dropout3): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=1024, out_features=256, bias=True)
(dropout4): Dropout(p=0.1, inplace=False)
(norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_intra_text): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
)
(dropout_intra_text): Dropout(p=0.1, inplace=False)
(norm_intra_text): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_inter_text): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
)
(dropout_inter_text): Dropout(p=0.1, inplace=False)
(norm_inter_text): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_cross_text): MSDeformAttn(
(sampling_offsets): Linear(in_features=256, out_features=256, bias=True)
(attention_weights): Linear(in_features=256, out_features=128, bias=True)
(value_proj): Linear(in_features=256, out_features=256, bias=True)
(output_proj): Linear(in_features=256, out_features=256, bias=True)
)
(dropout_cross_text): Dropout(p=0.1, inplace=False)
(norm_cross_text): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(linear1_text): Linear(in_features=256, out_features=1024, bias=True)
(dropout3_text): Dropout(p=0.1, inplace=False)
(linear2_text): Linear(in_features=1024, out_features=256, bias=True)
(dropout4_text): Dropout(p=0.1, inplace=False)
(norm3_text): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
)
(2): DeformableCompositeTransformerDecoderLayer(
(attn_cross): MSDeformAttn(
(sampling_offsets): Linear(in_features=256, out_features=256, bias=True)
(attention_weights): Linear(in_features=256, out_features=128, bias=True)
(value_proj): Linear(in_features=256, out_features=256, bias=True)
(output_proj): Linear(in_features=256, out_features=256, bias=True)
)
(dropout_cross): Dropout(p=0.1, inplace=False)
(norm_cross): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_intra): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
)
(dropout_intra): Dropout(p=0.1, inplace=False)
(norm_intra): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_inter): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
)
(dropout_inter): Dropout(p=0.1, inplace=False)
(norm_inter): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(linear1): Linear(in_features=256, out_features=1024, bias=True)
(dropout3): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=1024, out_features=256, bias=True)
(dropout4): Dropout(p=0.1, inplace=False)
(norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_intra_text): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
)
(dropout_intra_text): Dropout(p=0.1, inplace=False)
(norm_intra_text): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_inter_text): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
)
(dropout_inter_text): Dropout(p=0.1, inplace=False)
(norm_inter_text): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_cross_text): MSDeformAttn(
(sampling_offsets): Linear(in_features=256, out_features=256, bias=True)
(attention_weights): Linear(in_features=256, out_features=128, bias=True)
(value_proj): Linear(in_features=256, out_features=256, bias=True)
(output_proj): Linear(in_features=256, out_features=256, bias=True)
)
(dropout_cross_text): Dropout(p=0.1, inplace=False)
(norm_cross_text): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(linear1_text): Linear(in_features=256, out_features=1024, bias=True)
(dropout3_text): Dropout(p=0.1, inplace=False)
(linear2_text): Linear(in_features=1024, out_features=256, bias=True)
(dropout4_text): Dropout(p=0.1, inplace=False)
(norm3_text): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
)
(3): DeformableCompositeTransformerDecoderLayer(
(attn_cross): MSDeformAttn(
(sampling_offsets): Linear(in_features=256, out_features=256, bias=True)
(attention_weights): Linear(in_features=256, out_features=128, bias=True)
(value_proj): Linear(in_features=256, out_features=256, bias=True)
(output_proj): Linear(in_features=256, out_features=256, bias=True)
)
(dropout_cross): Dropout(p=0.1, inplace=False)
(norm_cross): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_intra): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
)
(dropout_intra): Dropout(p=0.1, inplace=False)
(norm_intra): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_inter): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
)
(dropout_inter): Dropout(p=0.1, inplace=False)
(norm_inter): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(linear1): Linear(in_features=256, out_features=1024, bias=True)
(dropout3): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=1024, out_features=256, bias=True)
(dropout4): Dropout(p=0.1, inplace=False)
(norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_intra_text): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
)
(dropout_intra_text): Dropout(p=0.1, inplace=False)
(norm_intra_text): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_inter_text): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
)
(dropout_inter_text): Dropout(p=0.1, inplace=False)
(norm_inter_text): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_cross_text): MSDeformAttn(
(sampling_offsets): Linear(in_features=256, out_features=256, bias=True)
(attention_weights): Linear(in_features=256, out_features=128, bias=True)
(value_proj): Linear(in_features=256, out_features=256, bias=True)
(output_proj): Linear(in_features=256, out_features=256, bias=True)
)
(dropout_cross_text): Dropout(p=0.1, inplace=False)
(norm_cross_text): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(linear1_text): Linear(in_features=256, out_features=1024, bias=True)
(dropout3_text): Dropout(p=0.1, inplace=False)
(linear2_text): Linear(in_features=1024, out_features=256, bias=True)
(dropout4_text): Dropout(p=0.1, inplace=False)
(norm3_text): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
)
(4): DeformableCompositeTransformerDecoderLayer(
(attn_cross): MSDeformAttn(
(sampling_offsets): Linear(in_features=256, out_features=256, bias=True)
(attention_weights): Linear(in_features=256, out_features=128, bias=True)
(value_proj): Linear(in_features=256, out_features=256, bias=True)
(output_proj): Linear(in_features=256, out_features=256, bias=True)
)
(dropout_cross): Dropout(p=0.1, inplace=False)
(norm_cross): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_intra): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
)
(dropout_intra): Dropout(p=0.1, inplace=False)
(norm_intra): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_inter): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
)
(dropout_inter): Dropout(p=0.1, inplace=False)
(norm_inter): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(linear1): Linear(in_features=256, out_features=1024, bias=True)
(dropout3): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=1024, out_features=256, bias=True)
(dropout4): Dropout(p=0.1, inplace=False)
(norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_intra_text): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
)
(dropout_intra_text): Dropout(p=0.1, inplace=False)
(norm_intra_text): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_inter_text): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
)
(dropout_inter_text): Dropout(p=0.1, inplace=False)
(norm_inter_text): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_cross_text): MSDeformAttn(
(sampling_offsets): Linear(in_features=256, out_features=256, bias=True)
(attention_weights): Linear(in_features=256, out_features=128, bias=True)
(value_proj): Linear(in_features=256, out_features=256, bias=True)
(output_proj): Linear(in_features=256, out_features=256, bias=True)
)
(dropout_cross_text): Dropout(p=0.1, inplace=False)
(norm_cross_text): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(linear1_text): Linear(in_features=256, out_features=1024, bias=True)
(dropout3_text): Dropout(p=0.1, inplace=False)
(linear2_text): Linear(in_features=1024, out_features=256, bias=True)
(dropout4_text): Dropout(p=0.1, inplace=False)
(norm3_text): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
)
(5): DeformableCompositeTransformerDecoderLayer(
(attn_cross): MSDeformAttn(
(sampling_offsets): Linear(in_features=256, out_features=256, bias=True)
(attention_weights): Linear(in_features=256, out_features=128, bias=True)
(value_proj): Linear(in_features=256, out_features=256, bias=True)
(output_proj): Linear(in_features=256, out_features=256, bias=True)
)
(dropout_cross): Dropout(p=0.1, inplace=False)
(norm_cross): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_intra): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
)
(dropout_intra): Dropout(p=0.1, inplace=False)
(norm_intra): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_inter): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
)
(dropout_inter): Dropout(p=0.1, inplace=False)
(norm_inter): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(linear1): Linear(in_features=256, out_features=1024, bias=True)
(dropout3): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=1024, out_features=256, bias=True)
(dropout4): Dropout(p=0.1, inplace=False)
(norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_intra_text): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
)
(dropout_intra_text): Dropout(p=0.1, inplace=False)
(norm_intra_text): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_inter_text): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
)
(dropout_inter_text): Dropout(p=0.1, inplace=False)
(norm_inter_text): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn_cross_text): MSDeformAttn(
(sampling_offsets): Linear(in_features=256, out_features=256, bias=True)
(attention_weights): Linear(in_features=256, out_features=128, bias=True)
(value_proj): Linear(in_features=256, out_features=256, bias=True)
(output_proj): Linear(in_features=256, out_features=256, bias=True)
)
(dropout_cross_text): Dropout(p=0.1, inplace=False)
(norm_cross_text): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(linear1_text): Linear(in_features=256, out_features=1024, bias=True)
(dropout3_text): Dropout(p=0.1, inplace=False)
(linear2_text): Linear(in_features=1024, out_features=256, bias=True)
(dropout4_text): Dropout(p=0.1, inplace=False)
(norm3_text): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
)
)
)
(enc_output): Linear(in_features=256, out_features=256, bias=True)
(enc_output_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(pos_trans): Linear(in_features=256, out_features=256, bias=True)
(pos_trans_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(bbox_class_embed): Linear(in_features=256, out_features=1, bias=True)
(bbox_embed): MLP(
(layers): ModuleList(
(0): Linear(in_features=256, out_features=256, bias=True)
(1): Linear(in_features=256, out_features=256, bias=True)
(2): Linear(in_features=256, out_features=4, bias=True)
)
)
)
(ctrl_point_class): ModuleList(
(0): Linear(in_features=256, out_features=1, bias=True)
(1): Linear(in_features=256, out_features=1, bias=True)
(2): Linear(in_features=256, out_features=1, bias=True)
(3): Linear(in_features=256, out_features=1, bias=True)
(4): Linear(in_features=256, out_features=1, bias=True)
(5): Linear(in_features=256, out_features=1, bias=True)
)
(ctrl_point_coord): ModuleList(
(0): MLP(
(layers): ModuleList(
(0): Linear(in_features=256, out_features=256, bias=True)
(1): Linear(in_features=256, out_features=256, bias=True)
(2): Linear(in_features=256, out_features=2, bias=True)
)
)
(1): MLP(
(layers): ModuleList(
(0): Linear(in_features=256, out_features=256, bias=True)
(1): Linear(in_features=256, out_features=256, bias=True)
(2): Linear(in_features=256, out_features=2, bias=True)
)
)
(2): MLP(
(layers): ModuleList(
(0): Linear(in_features=256, out_features=256, bias=True)
(1): Linear(in_features=256, out_features=256, bias=True)
(2): Linear(in_features=256, out_features=2, bias=True)
)
)
(3): MLP(
(layers): ModuleList(
(0): Linear(in_features=256, out_features=256, bias=True)
(1): Linear(in_features=256, out_features=256, bias=True)
(2): Linear(in_features=256, out_features=2, bias=True)
)
)
(4): MLP(
(layers): ModuleList(
(0): Linear(in_features=256, out_features=256, bias=True)
(1): Linear(in_features=256, out_features=256, bias=True)
(2): Linear(in_features=256, out_features=2, bias=True)
)
)
(5): MLP(
(layers): ModuleList(
(0): Linear(in_features=256, out_features=256, bias=True)
(1): Linear(in_features=256, out_features=256, bias=True)
(2): Linear(in_features=256, out_features=2, bias=True)
)
)
)
(bbox_coord): MLP(
(layers): ModuleList(
(0): Linear(in_features=256, out_features=256, bias=True)
(1): Linear(in_features=256, out_features=256, bias=True)
(2): Linear(in_features=256, out_features=4, bias=True)
)
)
(bbox_class): Linear(in_features=256, out_features=1, bias=True)
(text_class): Linear(in_features=256, out_features=97, bias=True)
(ctrl_point_embed): Embedding(16, 256)
(text_embed): Embedding(25, 256)
(input_proj): ModuleList(
(0): Sequential(
(0): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
(1): GroupNorm(32, 256, eps=1e-05, affine=True)
)
(1): Sequential(
(0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
(1): GroupNorm(32, 256, eps=1e-05, affine=True)
)
(2): Sequential(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
(1): GroupNorm(32, 256, eps=1e-05, affine=True)
)
(3): Sequential(
(0): Conv2d(2048, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(1): GroupNorm(32, 256, eps=1e-05, affine=True)
)
)
)
(criterion): SetCriterion(
(enc_matcher): BoxHungarianMatcher()
(dec_matcher): CtrlPointHungarianMatcher()
)
)
[07/25 14:21:13] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in training: [RandomCrop(crop_type='relative_range', crop_size=[0.1, 0.1]), ResizeShortestEdge(short_edge_length=(800, 832, 864, 896, 1000, 1200, 1400), max_size=2333, sample_style='choice'), RandomFlip()]
[07/25 14:21:13] adet.data.dataset_mapper INFO: Rebuilding the augmentations. The previous augmentations will be overridden.
[07/25 14:21:13] adet.data.detection_utils INFO: Augmentations used in training: [ResizeShortestEdge(short_edge_length=(800, 832, 864, 896, 1000, 1200, 1400), max_size=2333, sample_style='choice')]
[07/25 14:21:13] adet.data.dataset_mapper INFO: Cropping used in training: RandomCropWithInstance(crop_type='relative_range', crop_size=[0.1, 0.1], crop_instance=False)
[07/25 14:21:13] adet.data.datasets.text INFO: Loaded 1000 images in COCO format from datasets/icdar2015/train_poly.json
[07/25 14:21:13] d2.data.build INFO: Removed 21 images with no usable annotations. 979 images left.
[07/25 14:21:13] d2.data.build INFO: Distribution of instances among all 1 categories:
�[36m| category | #instances |
|:----------:|:-------------|
| text | 4468 |
| | |�[0m
[07/25 14:21:13] d2.data.build INFO: Using training sampler TrainingSampler
[07/25 14:21:13] d2.data.common INFO: Serializing the dataset using: <class 'detectron2.data.common.TorchSerializedList'>
[07/25 14:21:13] d2.data.common INFO: Serializing 979 elements to byte tensors and concatenating them all ...
[07/25 14:21:13] d2.data.common INFO: Serialized dataset takes 1.64 MiB
[07/25 14:21:13] d2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from weights/TESTR/pretrain_testr_R_50_polygon.pth ...
[07/25 14:21:13] fvcore.common.checkpoint INFO: [Checkpointer] Loading from weights/TESTR/pretrain_testr_R_50_polygon.pth ...
[07/25 14:21:14] adet.trainer INFO: Starting training from iteration 0
[07/25 17:20:06] d2.utils.events INFO: eta: 2 days, 13:01:22 iter: 9359 total_loss: 44.08 loss_ce: 0.783 loss_ctrl_points: 2.31 loss_texts: 3.764 loss_ce_0: 0.8143 loss_ctrl_points_0: 2.423 loss_texts_0: 3.801 loss_ce_1: 0.8142 loss_ctrl_points_1: 2.4 loss_texts_1: 3.759 loss_ce_2: 0.8032 loss_ctrl_points_2: 2.351 loss_texts_2: 3.756 loss_ce_3: 0.7866 loss_ctrl_points_3: 2.334 loss_texts_3: 3.758 loss_ce_4: 0.7786 loss_ctrl_points_4: 2.311 loss_texts_4: 3.77 loss_ce_enc: 0.8066 loss_bbox_enc: 0.3008 loss_giou_enc: 0.7569 time: 1.1431 last_time: 0.8115 data_time: 0.0088 last_data_time: 0.0066 lr: 1e-05 max_mem: 12183M
[07/25 17:20:28] d2.utils.events INFO: eta: 2 days, 13:02:11 iter: 9379 total_loss: 42.63 loss_ce: 0.7653 loss_ctrl_points: 2.407 loss_texts: 3.758 loss_ce_0: 0.8062 loss_ctrl_points_0: 2.635 loss_texts_0: 3.792 loss_ce_1: 0.7863 loss_ctrl_points_1: 2.568 loss_texts_1: 3.736 loss_ce_2: 0.7788 loss_ctrl_points_2: 2.537 loss_texts_2: 3.737 loss_ce_3: 0.77 loss_ctrl_points_3: 2.508 loss_texts_3: 3.748 loss_ce_4: 0.7641 loss_ctrl_points_4: 2.456 loss_texts_4: 3.748 loss_ce_enc: 0.7962 loss_bbox_enc: 0.2918 loss_giou_enc: 0.73 time: 1.1431 last_time: 0.9134 data_time: 0.0084 last_data_time: 0.0075 lr: 1e-05 max_mem: 12183M
[07/25 17:20:51] d2.utils.events INFO: eta: 2 days, 13:05:45 iter: 9399 total_loss: 44.09 loss_ce: 0.7944 loss_ctrl_points: 2.32 loss_texts: 3.633 loss_ce_0: 0.8154 loss_ctrl_points_0: 2.634 loss_texts_0: 3.668 loss_ce_1: 0.802 loss_ctrl_points_1: 2.506 loss_texts_1: 3.633 loss_ce_2: 0.8023 loss_ctrl_points_2: 2.369 loss_texts_2: 3.626 loss_ce_3: 0.7987 loss_ctrl_points_3: 2.281 loss_texts_3: 3.624 loss_ce_4: 0.7966 loss_ctrl_points_4: 2.309 loss_texts_4: 3.62 loss_ce_enc: 0.8003 loss_bbox_enc: 0.2937 loss_giou_enc: 0.7454 time: 1.1431 last_time: 1.1894 data_time: 0.0081 last_data_time: 0.0227 lr: 1e-05 max