Harzva
commented
4 years ago (base) root@dgx2:~/data/gvision# /opt/conda/bin/python /root/data/gvision/detectron2-master/projects/repulsion-loss/my_maskrcnn_reploss.py
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: ()
TRAIN: ()
GLOBAL:
HACK: 1.0
INPUT:
CROP:
ENABLED: False
SIZE: [0.9, 0.9]
TYPE: relative_range
FORMAT: BGR
MASK_FORMAT: polygon
MAX_SIZE_TEST: 1333
MAX_SIZE_TRAIN: 1333
MIN_SIZE_TEST: 800
MIN_SIZE_TRAIN: (800,)
MIN_SIZE_TRAIN_SAMPLING: choice
MODEL:
ANCHOR_GENERATOR:
ANGLES: [[-90, 0, 90]]
ASPECT_RATIOS: [[0.5, 1.0, 2.0]]
NAME: DefaultAnchorGenerator
OFFSET: 0.0
SIZES: [[32, 64, 128, 256, 512]]
BACKBONE:
FREEZE_AT: 2
NAME: build_resnet_backbone
DETR:
BBOX_LOSS_COEFF: 5.0
COST_BBOX: 5.0
COST_CLASS: 1.0
COST_GIOU: 2.0
DICE_LOSS_COEFF: 1.0
EOS_COEFF: 0.1
GIOU_LOSS_COEFF: 2.0
MASK_LOSS_COEFF: 1.0
NO_AUX_LOSS: False
NUM_CLASSES: 80
NUM_QUERIES: 100
POSITION_EMBEDDING: sine
TRANSFORMER:
ACTIVATION: relu
DIM_FFN: 2048
DROPOUT_RATE: 0.1
D_MODEL: 256
NUM_DEC_LAYERS: 6
NUM_ENC_LAYERS: 6
N_HEAD: 8
PRE_NORM: False
RETURN_INTERMEDIATE_DEC: True
DEVICE: cuda
FPN:
FUSE_TYPE: sum
IN_FEATURES: []
NORM:
OUT_CHANNELS: 256
KEYPOINT_ON: False
LOAD_PROPOSALS: False
MASK_ON: False
META_ARCHITECTURE: GeneralizedRCNN
PANOPTIC_FPN:
COMBINE:
ENABLED: True
INSTANCES_CONFIDENCE_THRESH: 0.5
OVERLAP_THRESH: 0.5
STUFF_AREA_LIMIT: 4096
INSTANCE_LOSS_WEIGHT: 1.0
PIXEL_MEAN: [103.53, 116.28, 123.675]
PIXEL_STD: [1.0, 1.0, 1.0]
PROPOSAL_GENERATOR:
MIN_SIZE: 0
NAME: RPN
RESNETS:
DEFORM_MODULATED: False
DEFORM_NUM_GROUPS: 1
DEFORM_ON_PER_STAGE: [False, False, False, False]
DEPTH: 50
NORM: FrozenBN
NUM_GROUPS: 1
OUT_FEATURES: ['res4']
RES2_OUT_CHANNELS: 256
RES5_DILATION: 1
STEM_OUT_CHANNELS: 64
STRIDE_IN_1X1: True
WIDTH_PER_GROUP: 64
RETINANET:
BBOX_REG_WEIGHTS: (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
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: False
CONV_DIM: 256
FC_DIM: 1024
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
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
REPULSION_LOSS:
D2_NORMALIZE: True
REP_BOX_FACTOR: 0.5
REP_BOX_SIGMA: 0.1
REP_GT_FACTOR: 0.5
REP_GT_SIGMA: 0.9
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_WEIGHTS: (1.0, 1.0, 1.0, 1.0)
BOUNDARY_THRESH: -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
WEIGHTS:
OUTPUT_DIR: ./output
SEED: -1
SOLVER:
BASE_LR: 0.001
BASE_LR_BACKBONE: 0.001
BIAS_LR_FACTOR: 1.0
CHECKPOINT_PERIOD: 5000
CLIP_GRADIENTS:
CLIP_TYPE: value
CLIP_VALUE: 1.0
ENABLED: False
NORM_TYPE: 2.0
GAMMA: 0.1
IMS_PER_BATCH: 16
LR_SCHEDULER_NAME: WarmupMultiStepLR
MAX_ITER: 40000
MOMENTUM: 0.9
NESTEROV: False
OPTIMIZER_NAME: SGD
STEPS: (30000,)
WARMUP_FACTOR: 0.001
WARMUP_ITERS: 1000
WARMUP_METHOD: linear
WEIGHT_DECAY: 0.0001
WEIGHT_DECAY_BIAS: 0.0001
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: 0
EXPECTED_RESULTS: []
KEYPOINT_OKS_SIGMAS: []
PRECISE_BN:
ENABLED: False
NUM_ITER: 200
VERSION: 2
VIS_PERIOD: 0
cfg_filename:/root/data/gvision/detectron2-master/projects/repulsion-loss/configs/COCO-Detection/my_mask_rcnn_R_50_FPN_Reploss_3x.yaml
self._obj_map {'SemanticSegmentor': <class 'detectron2.modeling.meta_arch.semantic_seg.SemanticSegmentor'>, 'PanopticFPN': <class 'detectron2.modeling.meta_arch.panoptic_fpn.PanopticFPN'>, 'GeneralizedRCNN': <class 'detectron2.modeling.meta_arch.rcnn.GeneralizedRCNN'>, 'ProposalNetwork': <class 'detectron2.modeling.meta_arch.rcnn.ProposalNetwork'>, 'RetinaNet': <class 'detectron2.modeling.meta_arch.retinanet.RetinaNet'>}
self._obj_map {'build_resnet_backbone': <function build_resnet_backbone at 0x7f1c228ded08>, 'build_resnet_fpn_backbone': <function build_resnet_fpn_backbone at 0x7f1c228ef400>, 'build_retinanet_resnet_fpn_backbone': <function build_retinanet_resnet_fpn_backbone at 0x7f1c228ef488>}
self._obj_map {'RPN': <class 'detectron2.modeling.proposal_generator.rpn.RPN'>, 'RRPN': <class 'detectron2.modeling.proposal_generator.rrpn.RRPN'>}
self._obj_map {'DefaultAnchorGenerator': <class 'detectron2.modeling.anchor_generator.DefaultAnchorGenerator'>, 'RotatedAnchorGenerator': <class 'detectron2.modeling.anchor_generator.RotatedAnchorGenerator'>}
self._obj_map {'StandardRPNHead': <class 'detectron2.modeling.proposal_generator.rpn.StandardRPNHead'>}
self._obj_map {'DefaultAnchorGenerator': <class 'detectron2.modeling.anchor_generator.DefaultAnchorGenerator'>, 'RotatedAnchorGenerator': <class 'detectron2.modeling.anchor_generator.RotatedAnchorGenerator'>}
RepLossROIHeads
self._obj_map {'Res5ROIHeads': <class 'detectron2.modeling.roi_heads.roi_heads.Res5ROIHeads'>, 'StandardROIHeads': <class 'detectron2.modeling.roi_heads.roi_heads.StandardROIHeads'>, 'RROIHeads': <class 'detectron2.modeling.roi_heads.rotated_fast_rcnn.RROIHeads'>, 'CascadeROIHeads': <class 'detectron2.modeling.roi_heads.cascade_rcnn.CascadeROIHeads'>}
Traceback (most recent call last):
File "/root/data/gvision/detectron2-master/projects/repulsion-loss/my_maskrcnn_reploss.py", line 417, in
justinkay
"No object named 'RepLossROIHeads' found in 'ROI_HEADS' registry!"