import os

'''
path and dataset parameter
配置文件
'''

DATA_PATH = 'data'
PASCAL_PATH = os.path.join(DATA_PATH, 'pascal_voc')
CACHE_PATH = os.path.join(PASCAL_PATH, 'cache')
OUTPUT_DIR = os.path.join(PASCAL_PATH, 'output')        # 存放输出文件的地方，data/pascal_voc/output
WEIGHTS_DIR = os.path.join(PASCAL_PATH, 'weights')      # weights_dir, 路径为data/pascal_voc/weights
WEIGHTS_FILE = None   # weights file
# WEIGHTS_FILE = os.path.join(DATA_PATH, 'weights', 'YOLO_small.ckpt')

# PASCAL VOC数据集的20个类别
CLASSES = ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus',
           'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
           'motorbike', 'person', 'pottedplant', 'sheep', 'sofa',
           'train', 'tvmonitor']

FLIPPED = True

"""
model parameter
"""
IMAGE_SIZE = 448        # 输入图片的大小
CELL_SIZE = 7           # grid cell大小（cell_size * cell_size的大小）
BOXES_PER_CELL = 2      # 每个cell负责预测两个bounding box
ALPHA = 0.1             # Leaky Relu的泄露参数
DISP_CONSOLE = False

"""
下面这几个是论文中涉及的参数
"""
OBJECT_SCALE = 1.0
NOOBJECT_SCALE = 1.0
CLASS_SCALE = 2.0
COORD_SCALE = 5.0


"""
hyper-parameter
"""
GPU = ''
LEARNING_RATE = 0.0001              # 学习率
DECAY_STEPS = 30000
DECAY_RATE = 0.1
STAIRCASE = True
BATCH_SIZE = 64                     # batch size
MAX_ITER = 135                      # 迭代次数135，论文中为135个迭代，可自定义
SUMMARY_ITER = 10
SAVE_ITER = 1000
MOMENTUM = 0.9                      # 角动量

"""
test parameter
"""
THRESHOLD = 0.2
IOU_THRESHOLD = 0.5                # IOU阈值0.5
import os

'''
path and dataset parameter
配置文件
'''

DATA_PATH = 'data'
PASCAL_PATH = os.path.join(DATA_PATH, 'pascal_voc')
CACHE_PATH = os.path.join(PASCAL_PATH, 'cache')
OUTPUT_DIR = os.path.join(PASCAL_PATH, 'output')        # 存放输出文件的地方，data/pascal_voc/output
WEIGHTS_DIR = os.path.join(PASCAL_PATH, 'weights')      # weights_dir, 路径为data/pascal_voc/weights
WEIGHTS_FILE = None   # weights file
# WEIGHTS_FILE = os.path.join(DATA_PATH, 'weights', 'YOLO_small.ckpt')

# PASCAL VOC数据集的20个类别
CLASSES = ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus',
           'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
           'motorbike', 'person', 'pottedplant', 'sheep', 'sofa',
           'train', 'tvmonitor']

FLIPPED = True

"""
model parameter
"""
IMAGE_SIZE = 448        # 输入图片的大小
CELL_SIZE = 7           # grid cell大小（cell_size * cell_size的大小）
BOXES_PER_CELL = 2      # 每个cell负责预测两个bounding box
ALPHA = 0.1             # Leaky Relu的泄露参数
DISP_CONSOLE = False

"""
下面这几个是论文中涉及的参数
"""
OBJECT_SCALE = 1.0
NOOBJECT_SCALE = 1.0
CLASS_SCALE = 2.0
COORD_SCALE = 5.0


"""
hyper-parameter
"""
GPU = ''
LEARNING_RATE = 0.0001              # 学习率
DECAY_STEPS = 30000
DECAY_RATE = 0.1
STAIRCASE = True
BATCH_SIZE = 64                     # batch size
MAX_ITER = 135                      # 迭代次数135，论文中为135个迭代，可自定义
SUMMARY_ITER = 10
SAVE_ITER = 1000
MOMENTUM = 0.9                      # 角动量

"""
test parameter
"""
THRESHOLD = 0.2
IOU_THRESHOLD = 0.5                # IOU阈值0.5
path and dataset parameter 配置文件 # 存放输出文件的地方，data/pascal_voc/output # weights_dir, 路径为data/pascal_voc/weights # weights file # WEIGHTS_FILE = os.path.join(DATA_PATH, 'weights', 'YOLO_small.ckpt') # PASCAL VOC数据集的20个类别 model parameter # 输入图片的大小 # grid cell大小（cell_size * cell_size的大小） # 每个cell负责预测两个bounding box # Leaky Relu的泄露参数 下面这几个是论文中涉及的参数 hyper-parameter # 学习率 # batch size # 迭代次数135，论文中为135个迭代，可自定义 # 角动量 test parameter # IOU阈值0.5