import numpy as np from easydict import EasyDict __C = EasyDict() config = __C __C.USE_GPU_NMS = True __C.GPU_ID = 0 __C.ANCHOR_SCALES = [8, 16, 32] __C.N_CLASSES = 21 __C.TARGET_SIZE = 1000 __C.TRAIN = EasyDict() __C.TRAIN.LEARNING_RATE = 1e-3 __C.TRAIN.MOMENTUM = 0.9 __C.TRAIN.GAMMA = 0.1 __C.TRAIN.STEPSIZE = 200000 __C.TRAIN.RPN_NEGATIVE_OVERLAP = 0.3 __C.TRAIN.RPN_POSITIVE_OVERLAP = 0.7 __C.TRAIN.RPN_PRE_NMS_TOP_N = 12000 __C.TRAIN.RPN_POST_NMS_TOP_N = 2000 __C.TRAIN.RPN_NMS_THRESHOLD = 0.7 __C.TRAIN.RPN_MIN_SIZE = 16
__C.TRAIN.WEIGHT_DECAY = 0.0005
# Factor for reducing the learning rate
__C.TRAIN.GAMMA = 0.1
# Whether to double the learning rate for bias
__C.TRAIN.DOUBLE_BIAS = True
# Whether to initialize the weights with truncated normal distribution
__C.TRAIN.TRUNCATED = False
# Whether to have weight decay on bias as well
__C.TRAIN.BIAS_DECAY = False
##### RPN OPTION ####
__C.ANCHOR_SCALES = np.array([8, 16, 32])
__C.ANCHOR_RATIOS = [0.5, 1, 2]
# Down-sampling ratio
__C.FEAT_STRIDE = [
16,
]
__C.TRAIN.RPN_CLOBBER_POSITIVES = False
__C.TRAIN.RPN_POSITIVE_OVERLAP = 0.7
__C.TRAIN.RPN_NEGATIVE_OVERLAP = 0.3
__C.TRAIN.RPN_BATCHSIZE = 256
