from easydict import EasyDict as edict import numpy as np __C = edict() cfg = __C # 0. basic config __C.TAG = 'default' __C.CLASSES = 'Car' __C.INCLUDE_SIMILAR_TYPE = False # config of augmentation __C.AUG_DATA = True __C.AUG_METHOD_LIST = ['rotation', 'scaling', 'flip'] __C.AUG_METHOD_PROB = [0.5, 0.5, 0.5] __C.AUG_ROT_RANGE = 18 __C.GT_AUG_ENABLED = False __C.GT_EXTRA_NUM = 15 __C.GT_AUG_RAND_NUM = False __C.GT_AUG_APPLY_PROB = 0.75 __C.GT_AUG_HARD_RATIO = 0.6 __C.PC_REDUCE_BY_RANGE = True __C.PC_AREA_SCOPE = np.array([[-40, 40], [-1, 3], [0, 70.4]]) # x, y, z scope in rect camera coords __C.CLS_MEAN_SIZE = np.array([[1.52, 1.63, 3.88]], dtype=np.float32) # 1. config of rpn network __C.RPN = edict() __C.RPN.ENABLED = True __C.RPN.FIXED = False __C.RPN.USE_INTENSITY = True # config of bin-based loss __C.RPN.LOC_XZ_FINE = False __C.RPN.LOC_SCOPE = 3.0 __C.RPN.LOC_BIN_SIZE = 0.5 __C.RPN.NUM_HEAD_BIN = 12 # config of network structure __C.RPN.BACKBONE = 'pointnet2_msg' __C.RPN.USE_BN = True __C.RPN.NUM_POINTS = 16384 __C.RPN.SA_CONFIG = edict() __C.RPN.SA_CONFIG.NPOINTS = [4096, 1024, 256, 64] __C.RPN.SA_CONFIG.RADIUS = [[0.1, 0.5], [0.5, 1.0], [1.0, 2.0], [2.0, 4.0]] __C.RPN.SA_CONFIG.NSAMPLE = [[16, 32], [16, 32], [16, 32], [16, 32]] __C.RPN.SA_CONFIG.MLPS = [[[16, 16, 32], [32, 32, 64]], [[64, 64, 128], [64, 96, 128]], [[128, 196, 256], [128, 196, 256]], [[256, 256, 512], [256, 384, 512]]] __C.RPN.FP_MLPS = [[128, 128], [256, 256], [512, 512], [512, 512]] __C.RPN.CLS_FC = [128] __C.RPN.REG_FC = [128] __C.RPN.DP_RATIO = 0.5 # config of training __C.RPN.LOSS_CLS = 'DiceLoss' __C.RPN.FG_WEIGHT = 15 __C.RPN.FOCAL_ALPHA = [0.25, 0.75] __C.RPN.FOCAL_GAMMA = 2.0 __C.RPN.REG_LOSS_WEIGHT = [1.0, 1.0, 1.0, 1.0] __C.RPN.LOSS_WEIGHT = [1.0, 1.0] __C.RPN.NMS_TYPE = 'normal' # normal, rotate # config of testing __C.RPN.SCORE_THRESH = 0.3 # 2. config of rcnn network __C.RCNN = edict() __C.RCNN.ENABLED = False # config of input __C.RCNN.USE_RPN_FEATURES = True __C.RCNN.USE_MASK = True __C.RCNN.MASK_TYPE = 'seg' __C.RCNN.USE_INTENSITY = False __C.RCNN.USE_DEPTH = True __C.RCNN.USE_SEG_SCORE = False __C.RCNN.ROI_SAMPLE_JIT = False __C.RCNN.ROI_FG_AUG_TIMES = 10 __C.RCNN.REG_AUG_METHOD = 'multiple' # multiple, single, normal __C.RCNN.POOL_EXTRA_WIDTH = 1.0 # config of bin-based loss __C.RCNN.LOC_SCOPE = 1.5 __C.RCNN.LOC_BIN_SIZE = 0.5 __C.RCNN.NUM_HEAD_BIN = 9 __C.RCNN.LOC_Y_BY_BIN = False __C.RCNN.LOC_Y_SCOPE = 0.5 __C.RCNN.LOC_Y_BIN_SIZE = 0.25 __C.RCNN.SIZE_RES_ON_ROI = False # config of network structure __C.RCNN.USE_BN = False __C.RCNN.DP_RATIO = 0.0 __C.RCNN.BACKBONE = 'pointnet' # pointnet, pointsift __C.RCNN.XYZ_UP_LAYER = [128, 128] __C.RCNN.NUM_POINTS = 512 __C.RCNN.SA_CONFIG = edict() __C.RCNN.SA_CONFIG.NPOINTS = [128, 32, -1] __C.RCNN.SA_CONFIG.RADIUS = [0.2, 0.4, 100] __C.RCNN.SA_CONFIG.NSAMPLE = [64, 64, 64] __C.RCNN.SA_CONFIG.MLPS = [[128, 128, 128], [128, 128, 256], [256, 256, 512]] __C.RCNN.CLS_FC = [256, 256] __C.RCNN.REG_FC = [256, 256] # config of training __C.RCNN.LOSS_CLS = 'BinaryCrossEntropy' __C.RCNN.FOCAL_ALPHA = [0.25, 0.75] __C.RCNN.FOCAL_GAMMA = 2.0 __C.RCNN.CLS_WEIGHT = np.array([1.0, 1.0, 1.0], dtype=np.float32) __C.RCNN.CLS_FG_THRESH = 0.6 __C.RCNN.CLS_BG_THRESH = 0.45 __C.RCNN.CLS_BG_THRESH_LO = 0.05 __C.RCNN.REG_FG_THRESH = 0.55 __C.RCNN.FG_RATIO = 0.5 __C.RCNN.ROI_PER_IMAGE = 64 __C.RCNN.HARD_BG_RATIO = 0.6 # config of testing __C.RCNN.SCORE_THRESH = 0.3 __C.RCNN.NMS_THRESH = 0.1 # general training config __C.TRAIN = edict() __C.TRAIN.SPLIT = 'train' __C.TRAIN.VAL_SPLIT = 'smallval' __C.TRAIN.LR = 0.002 __C.TRAIN.LR_CLIP = 0.00001 __C.TRAIN.LR_DECAY = 0.5 __C.TRAIN.DECAY_STEP_LIST = [50, 100, 150, 200, 250, 300] __C.TRAIN.LR_WARMUP = False __C.TRAIN.WARMUP_MIN = 0.0002 __C.TRAIN.WARMUP_EPOCH = 5 __C.TRAIN.BN_MOMENTUM = 0.9 __C.TRAIN.BN_DECAY = 0.5 __C.TRAIN.BNM_CLIP = 0.01 __C.TRAIN.BN_DECAY_STEP_LIST = [50, 100, 150, 200, 250, 300] __C.TRAIN.OPTIMIZER = 'adam' __C.TRAIN.WEIGHT_DECAY = 0.0 # "L2 regularization coeff [default: 0.0]" __C.TRAIN.MOMENTUM = 0.9 __C.TRAIN.MOMS = [0.95, 0.85] __C.TRAIN.DIV_FACTOR = 10.0 __C.TRAIN.PCT_START = 0.4 __C.TRAIN.GRAD_NORM_CLIP = 1.0 __C.TRAIN.RPN_PRE_NMS_TOP_N = 12000 __C.TRAIN.RPN_POST_NMS_TOP_N = 2048 __C.TRAIN.RPN_NMS_THRESH = 0.85 __C.TRAIN.RPN_DISTANCE_BASED_PROPOSE = True __C.TEST = edict() __C.TEST.SPLIT = 'val' __C.TEST.RPN_PRE_NMS_TOP_N = 9000 __C.TEST.RPN_POST_NMS_TOP_N = 300 __C.TEST.RPN_NMS_THRESH = 0.7 __C.TEST.RPN_DISTANCE_BASED_PROPOSE = True def cfg_from_file(filename): """Load a config file and merge it into the default options.""" import yaml with open(filename, 'r') as f: yaml_cfg = edict(yaml.load(f)) _merge_a_into_b(yaml_cfg, __C) def _merge_a_into_b(a, b): """Merge config dictionary a into config dictionary b, clobbering the options in b whenever they are also specified in a. """ if type(a) is not edict: return for k, v in a.items(): # a must specify keys that are in b if k not in b: raise KeyError('{} is not a valid config key'.format(k)) # the types must match, too old_type = type(b[k]) if old_type is not type(v): if isinstance(b[k], np.ndarray): v = np.array(v, dtype=b[k].dtype) else: raise ValueError(('Type mismatch ({} vs. {}) ' 'for config key: {}').format(type(b[k]), type(v), k)) # recursively merge dicts if type(v) is edict: try: _merge_a_into_b(a[k], b[k]) except: print(('Error under config key: {}'.format(k))) raise else: b[k] = v def cfg_from_list(cfg_list): """Set config keys via list (e.g., from command line).""" from ast import literal_eval assert len(cfg_list) % 2 == 0 for k, v in zip(cfg_list[0::2], cfg_list[1::2]): key_list = k.split('.') d = __C for subkey in key_list[:-1]: assert subkey in d d = d[subkey] subkey = key_list[-1] assert subkey in d try: value = literal_eval(v) except: # handle the case when v is a string literal value = v assert type(value) == type(d[subkey]), \ 'type {} does not match original type {}'.format(type(value), type(d[subkey])) d[subkey] = value def save_config_to_file(cfg, pre='cfg', logger=None): for key, val in cfg.items(): if isinstance(cfg[key], edict): if logger is not None: logger.info('\n%s.%s = edict()' % (pre, key)) else: print('\n%s.%s = edict()' % (pre, key)) save_config_to_file(cfg[key], pre=pre + '.' + key, logger=logger) continue if logger is not None: logger.info('%s.%s: %s' % (pre, key, val)) else: print('%s.%s: %s' % (pre, key, val))