220 lines
7.7 KiB
Python
220 lines
7.7 KiB
Python
# This file is part of COAT, and is distributed under the
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# OSI-approved BSD 3-Clause License. See top-level LICENSE file or
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# https://github.com/Kitware/COAT/blob/master/LICENSE for details.
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from yacs.config import CfgNode as CN
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_C = CN()
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# -------------------------------------------------------- #
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# Input #
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# -------------------------------------------------------- #
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_C.INPUT = CN()
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_C.INPUT.DATASET = "CUHK-SYSU"
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_C.INPUT.DATA_ROOT = "data/CUHK-SYSU"
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# Size of the smallest side of the image
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_C.INPUT.MIN_SIZE = 900
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# Maximum size of the side of the image
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_C.INPUT.MAX_SIZE = 1500
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# Number of images per batch
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_C.INPUT.BATCH_SIZE_TRAIN = 5
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_C.INPUT.BATCH_SIZE_TEST = 1
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# Number of data loading threads
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_C.INPUT.NUM_WORKERS_TRAIN = 5
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_C.INPUT.NUM_WORKERS_TEST = 1
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# Image augmentation
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_C.INPUT.IMAGE_CUTOUT = False
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_C.INPUT.IMAGE_ERASE = False
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_C.INPUT.IMAGE_MIXUP = False
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# -------------------------------------------------------- #
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# GRID #
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# -------------------------------------------------------- #
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_C.INPUT.IMAGE_GRID = False
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_C.GRID = CN()
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_C.GRID.ROTATE = 1
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_C.GRID.OFFSET = 0
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_C.GRID.RATIO = 0.5
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_C.GRID.MODE = 1
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_C.GRID.PROB = 0.5
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# -------------------------------------------------------- #
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# Solver #
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# -------------------------------------------------------- #
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_C.SOLVER = CN()
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_C.SOLVER.MAX_EPOCHS = 13
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# Learning rate settings
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_C.SOLVER.BASE_LR = 0.003
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# The epoch milestones to decrease the learning rate by GAMMA
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_C.SOLVER.LR_DECAY_MILESTONES = [10, 14]
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_C.SOLVER.GAMMA = 0.1
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_C.SOLVER.WEIGHT_DECAY = 0.0005
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_C.SOLVER.SGD_MOMENTUM = 0.9
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# Loss weight of RPN regression
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_C.SOLVER.LW_RPN_REG = 1
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# Loss weight of RPN classification
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_C.SOLVER.LW_RPN_CLS = 1
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# Loss weight of Cascade R-CNN and Re-ID (OIM)
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_C.SOLVER.LW_RCNN_REG_1ST = 10
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_C.SOLVER.LW_RCNN_CLS_1ST = 1
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_C.SOLVER.LW_RCNN_REG_2ND = 10
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_C.SOLVER.LW_RCNN_CLS_2ND = 1
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_C.SOLVER.LW_RCNN_REG_3RD = 10
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_C.SOLVER.LW_RCNN_CLS_3RD = 1
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_C.SOLVER.LW_RCNN_REID_2ND = 0.5
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_C.SOLVER.LW_RCNN_REID_3RD = 0.5
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# Loss weight of box reid, softmax loss
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_C.SOLVER.LW_RCNN_SOFTMAX_2ND = 0.5
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_C.SOLVER.LW_RCNN_SOFTMAX_3RD = 0.5
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# Set to negative value to disable gradient clipping
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_C.SOLVER.CLIP_GRADIENTS = 10.0
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# -------------------------------------------------------- #
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# RPN #
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# -------------------------------------------------------- #
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_C.MODEL = CN()
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_C.MODEL.RPN = CN()
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# NMS threshold used on RoIs
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_C.MODEL.RPN.NMS_THRESH = 0.7
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# Number of anchors per image used to train RPN
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_C.MODEL.RPN.BATCH_SIZE_TRAIN = 256
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# Target fraction of foreground examples per RPN minibatch
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_C.MODEL.RPN.POS_FRAC_TRAIN = 0.5
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# Overlap threshold for an anchor to be considered foreground (if >= POS_THRESH_TRAIN)
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_C.MODEL.RPN.POS_THRESH_TRAIN = 0.7
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# Overlap threshold for an anchor to be considered background (if < NEG_THRESH_TRAIN)
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_C.MODEL.RPN.NEG_THRESH_TRAIN = 0.3
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# Number of top scoring RPN RoIs to keep before applying NMS
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_C.MODEL.RPN.PRE_NMS_TOPN_TRAIN = 12000
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_C.MODEL.RPN.PRE_NMS_TOPN_TEST = 6000
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# Number of top scoring RPN RoIs to keep after applying NMS
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_C.MODEL.RPN.POST_NMS_TOPN_TRAIN = 2000
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_C.MODEL.RPN.POST_NMS_TOPN_TEST = 300
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# -------------------------------------------------------- #
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# RoI head #
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# -------------------------------------------------------- #
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_C.MODEL.ROI_HEAD = CN()
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# Whether to use bn neck (i.e. batch normalization after linear)
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_C.MODEL.ROI_HEAD.BN_NECK = True
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# Number of RoIs per image used to train RoI head
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_C.MODEL.ROI_HEAD.BATCH_SIZE_TRAIN = 128
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# Target fraction of foreground examples per RoI minibatch
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_C.MODEL.ROI_HEAD.POS_FRAC_TRAIN = 0.25 # 0.5
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_C.MODEL.ROI_HEAD.USE_DIFF_THRESH = True
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# Overlap threshold for an RoI to be considered foreground (if >= POS_THRESH_TRAIN)
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_C.MODEL.ROI_HEAD.POS_THRESH_TRAIN = 0.5
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_C.MODEL.ROI_HEAD.POS_THRESH_TRAIN_2ND = 0.6
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_C.MODEL.ROI_HEAD.POS_THRESH_TRAIN_3RD = 0.7
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# Overlap threshold for an RoI to be considered background (if < NEG_THRESH_TRAIN)
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_C.MODEL.ROI_HEAD.NEG_THRESH_TRAIN = 0.5
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_C.MODEL.ROI_HEAD.NEG_THRESH_TRAIN_2ND = 0.6
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_C.MODEL.ROI_HEAD.NEG_THRESH_TRAIN_3RD = 0.7
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# Minimum score threshold
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_C.MODEL.ROI_HEAD.SCORE_THRESH_TEST = 0.5
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# NMS threshold used on boxes
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_C.MODEL.ROI_HEAD.NMS_THRESH_TEST = 0.4
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_C.MODEL.ROI_HEAD.NMS_THRESH_TEST_1ST = 0.4
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_C.MODEL.ROI_HEAD.NMS_THRESH_TEST_2ND = 0.4
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_C.MODEL.ROI_HEAD.NMS_THRESH_TEST_3RD = 0.5
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# Maximum number of detected objects
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_C.MODEL.ROI_HEAD.DETECTIONS_PER_IMAGE_TEST = 300
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# -------------------------------------------------------- #
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# Transformer head #
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# -------------------------------------------------------- #
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_C.MODEL.TRANSFORMER = CN()
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_C.MODEL.TRANSFORMER.DIM_MODEL = 512
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_C.MODEL.TRANSFORMER.ENCODER_LAYERS = 1
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_C.MODEL.TRANSFORMER.N_HEAD = 8
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_C.MODEL.TRANSFORMER.USE_OUTPUT_LAYER = False
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_C.MODEL.TRANSFORMER.DROPOUT = 0.
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_C.MODEL.TRANSFORMER.USE_LOCAL_SHORTCUT = True
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_C.MODEL.TRANSFORMER.USE_GLOBAL_SHORTCUT = True
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_C.MODEL.TRANSFORMER.USE_DIFF_SCALE = True
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_C.MODEL.TRANSFORMER.NAMES_1ST = ['scale1','scale2']
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_C.MODEL.TRANSFORMER.NAMES_2ND = ['scale1','scale2']
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_C.MODEL.TRANSFORMER.NAMES_3RD = ['scale1','scale2']
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_C.MODEL.TRANSFORMER.KERNEL_SIZE_1ST = [(1,1),(3,3)]
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_C.MODEL.TRANSFORMER.KERNEL_SIZE_2ND = [(1,1),(3,3)]
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_C.MODEL.TRANSFORMER.KERNEL_SIZE_3RD = [(1,1),(3,3)]
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_C.MODEL.TRANSFORMER.USE_MASK_1ST = False
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_C.MODEL.TRANSFORMER.USE_MASK_2ND = True
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_C.MODEL.TRANSFORMER.USE_MASK_3RD = True
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_C.MODEL.TRANSFORMER.USE_PATCH2VEC = True
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####
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_C.MODEL.USE_FEATURE_MASK = True
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_C.MODEL.FEATURE_AUG_TYPE = 'exchange_token' # 'exchange_token', 'jigsaw_token', 'cutout_patch', 'erase_patch', 'mixup_patch', 'jigsaw_patch'
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_C.MODEL.FEATURE_MASK_SIZE = 4
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_C.MODEL.MASK_SHAPE = 'stripe' # 'square', 'random'
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_C.MODEL.MASK_SIZE = 1
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_C.MODEL.MASK_MODE = 'random_direction' # 'horizontal', 'vertical' for stripe; 'random_size' for square
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_C.MODEL.MASK_PERCENT = 0.1
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####
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_C.MODEL.EMBEDDING_DIM = 256
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# -------------------------------------------------------- #
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# Loss #
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# -------------------------------------------------------- #
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_C.MODEL.LOSS = CN()
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# Size of the lookup table in OIM
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_C.MODEL.LOSS.LUT_SIZE = 5532
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# Size of the circular queue in OIM
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_C.MODEL.LOSS.CQ_SIZE = 5000
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_C.MODEL.LOSS.OIM_MOMENTUM = 0.5
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_C.MODEL.LOSS.OIM_SCALAR = 30.0
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_C.MODEL.LOSS.USE_SOFTMAX = True
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# -------------------------------------------------------- #
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# Evaluation #
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# -------------------------------------------------------- #
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# The period to evaluate the model during training
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_C.EVAL_PERIOD = 1
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# Evaluation with GT boxes to verify the upper bound of person search performance
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_C.EVAL_USE_GT = False
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# Fast evaluation with cached features
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_C.EVAL_USE_CACHE = False
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# Evaluation with Context Bipartite Graph Matching (CBGM) algorithm
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_C.EVAL_USE_CBGM = False
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# Gallery size in evaluation, only for CUHK-SYSU
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_C.EVAL_GALLERY_SIZE = 100
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# Feature used for evaluation
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_C.EVAL_FEATURE = 'concat' # 'stage2', 'stage3'
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# -------------------------------------------------------- #
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# Miscs #
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# -------------------------------------------------------- #
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# Save a checkpoint after every this number of epochs
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_C.CKPT_PERIOD = 1
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# The period (in terms of iterations) to display training losses
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_C.DISP_PERIOD = 10
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# Whether to use tensorboard for visualization
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_C.TF_BOARD = True
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# The device loading the model
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_C.DEVICE = "cuda:1"
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# Set seed to negative to fully randomize everything
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_C.SEED = 1
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# Directory where output files are written
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_C.OUTPUT_DIR = "./output"
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def get_default_cfg():
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"""
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Get a copy of the default config.
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"""
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return _C.clone()
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