config 设置的两种方式

1. easydict 模块来设置config

config.py  （双下划线会重写属性名称，会在该属性的前面加上_class__attribute,直接调用class.__attribute 会报错，以下代码的双下划线应该也是为了避免命名冲突吧！）

from easydict import EasyDict as edict


__C                             = edict()  
# Consumers can get config by: from config import cfg

cfg                             = __C


# YOLO options
__C.YOLO                        = edict()

__C.YOLO.CLASSES                = "./data/classes/coco.names"
__C.YOLO.ANCHORS                = "./data/anchors/basline_anchors.txt"
__C.YOLO.MOVING_AVE_DECAY       = 0.9995
__C.YOLO.STRIDES                = [8, 16, 32]
__C.YOLO.ANCHOR_PER_SCALE       = 3
__C.YOLO.IOU_LOSS_THRESH        = 0.5
__C.YOLO.UPSAMPLE_METHOD        = "resize"
__C.YOLO.ORIGINAL_WEIGHT        = "./checkpoint/yolov3_coco.ckpt"
__C.YOLO.DEMO_WEIGHT            = "./checkpoint/yolov3_coco_demo.ckpt"


# Train options
__C.TRAIN                       = edict()

__C.TRAIN.ANNOT_PATH            = "./data/dataset/voc_train.txt"
__C.TRAIN.BATCH_SIZE            = 6
__C.TRAIN.INPUT_SIZE            = [320, 352, 384, 416, 448, 480, 512, 544, 576, 608]
__C.TRAIN.DATA_AUG              = True
__C.TRAIN.LEARN_RATE_INIT       = 1e-4
__C.TRAIN.LEARN_RATE_END        = 1e-6
__C.TRAIN.WARMUP_EPOCHS         = 2
__C.TRAIN.FISRT_STAGE_EPOCHS    = 20
__C.TRAIN.SECOND_STAGE_EPOCHS   = 30
__C.TRAIN.INITIAL_WEIGHT        = "./checkpoint/yolov3_coco_demo.ckpt"


# TEST options
__C.TEST                        = edict()

__C.TEST.ANNOT_PATH             = "./data/dataset/voc_test.txt"
__C.TEST.BATCH_SIZE             = 2
__C.TEST.INPUT_SIZE             = 544
__C.TEST.DATA_AUG               = False
__C.TEST.WRITE_IMAGE            = True
__C.TEST.WRITE_IMAGE_PATH       = "./data/detection/"
__C.TEST.WRITE_IMAGE_SHOW_LABEL = True
__C.TEST.WEIGHT_FILE            = "./checkpoint/yolov3_test_loss=9.2099.ckpt-5"
__C.TEST.SHOW_LABEL             = True
__C.TEST.SCORE_THRESHOLD        = 0.3
__C.TEST.IOU_THRESHOLD          = 0.45

　train.py　

from config import cfg


class YoloTrain(object):
    def __init__(self):
        self.anchor_per_scale    = cfg.YOLO.ANCHOR_PER_SCALE
        self.classes             = utils.read_class_names(cfg.YOLO.CLASSES)
        self.num_classes         = len(self.classes)
        self.learn_rate_init     = cfg.TRAIN.LEARN_RATE_INIT
        self.learn_rate_end      = cfg.TRAIN.LEARN_RATE_END
        self.first_stage_epochs  = cfg.TRAIN.FISRT_STAGE_EPOCHS
        self.second_stage_epochs = cfg.TRAIN.SECOND_STAGE_EPOCHS
        self.warmup_periods      = cfg.TRAIN.WARMUP_EPOCHS
        self.initial_weight      = cfg.TRAIN.INITIAL_WEIGHT
        self.time                = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime(time.time()))
        self.moving_ave_decay    = cfg.YOLO.MOVING_AVE_DECAY

2. 通过configobj 来设置config

config

[param]
# coco dataset json file
datasetFile= "D:\coco\person_keypoints_val2014.json"
new_datasetFile="D:\coco\new_person_keypoints_val2014.json"
new_val2014="D:\coco\new_val2014\"
val2014="D:\coco\val2014\"
image_height=552
blank=2
crop_ratio = 1
bbox_ratio = 1
num_keypoints=4
area=32*32

config_reader.py 

from configobj import ConfigObj

def config_reader():
    config = ConfigObj('config')
    param = config['param']
    datasetFile = param['datasetFile']
    new_datasetFile = param['new_datasetFile']
    area=param['area']
    num_keypoints=param['num_keypoints']
    new_val2014 = param['new_val2014']
    val2014=param['val2014']
    image_height=param['image_height']
    blank=param['blank']
    return param
if __name__ == "__main__":
    config_reader()