text: import keras.backend as K
  import numpy as np
  import tensorflow as tf
  from keras.backend.tensorflow_backend import set_session
  from keras.callbacks import (EarlyStopping, ModelCheckpoint, ReduceLROnPlateau,
                               TensorBoard)
  from keras.layers import Input, Lambda
  from keras.models import Model
  from keras.optimizers import Adam
  
  from nets.loss import yolo_loss
  from nets.yolo4_tiny import yolo_body
  from utils.utils import (WarmUpCosineDecayScheduler, get_random_data,
                           get_random_data_with_Mosaic, rand)
  
  
  #---------------------------------------------------#
  #   获得类和先验框
  #---------------------------------------------------#
  def get_classes(classes_path):
      '''loads the classes'''
      with open(classes_path) as f:
          class_names = f.readlines()
      class_names = [c.strip() for c in class_names]
      return class_names
  
  def get_anchors(anchors_path):
      '''loads the anchors from a file'''
      with open(anchors_path) as f:
          anchors = f.readline()
      anchors = [float(x) for x in anchors.split(',')]
      return np.array(anchors).reshape(-1, 2)
  
  #---------------------------------------------------#
  #   训练数据生成器
  #---------------------------------------------------#
  def data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes, mosaic=False, random=True):
      n = len(annotation_lines)
      i = 0
      flag = True
      while True:
          image_data = []
          box_data = []
          for b in range(batch_size):
              if i==0:
                  np.random.shuffle(annotation_lines)
              if mosaic:
                  if flag and (i+4) < n:
                      image, box = get_random_data_with_Mosaic(annotation_lines[i:i+4], input_shape)
                      i = (i+4) % n
                  else:
                      image, box = get_random_data(annotation_lines[i], input_shape, random=random)
                      i = (i+1) % n
                  flag = bool(1-flag)
              else:
                  image, box = get_random_data(annotation_lines[i], input_shape, random=random)
                  i = (i+1) % n
              image_data.append(image)
              box_data.append(box)
          image_data = np.array(image_data)
          box_data = np.array(box_data)
          y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes)
          yield [image_data, *y_true], np.zeros(batch_size)
  
  #---------------------------------------------------#
  #   读入xml文件，并输出y_true
  #---------------------------------------------------#
  def preprocess_true_boxes(true_boxes, input_shape, anchors, num_classes):
      assert (true_boxes[..., 4]<num_classes).all(), 'class id must be less than num_classes'
      # 一共有两个特征层数
      num_layers = len(anchors)//3
      #-----------------------------------------------------------#
      #   13x13的特征层对应的anchor是[81,82], [135,169], [344,319]
      #   26x26的特征层对应的anchor是[23,27], [37,58], [81,82]
      #-----------------------------------------------------------#
      anchor_mask = [[6,7,8], [3,4,5], [0,1,2]] if num_layers==3 else [[3,4,5], [1,2,3]]
  
      #-----------------------------------------------------------#
      #   获得框的坐标和图片的大小
      #-----------------------------------------------------------#
      true_boxes = np.array(true_boxes, dtype='float32')
      input_shape = np.array(input_shape, dtype='int32') 
      #-----------------------------------------------------------#
      #   通过计算获得真实框的中心和宽高
      #   中心点(m,n,2) 宽高(m,n,2)
      #-----------------------------------------------------------#
      boxes_xy = (true_boxes[..., 0:2] + true_boxes[..., 2:4]) // 2
      boxes_wh = true_boxes[..., 2:4] - true_boxes[..., 0:2]
      #-----------------------------------------------------------#
      #   将真实框归一化到小数形式
      #-----------------------------------------------------------#
      true_boxes[..., 0:2] = boxes_xy/input_shape[::-1]
      true_boxes[..., 2:4] = boxes_wh/input_shape[::-1]
  
      # m为图片数量，grid_shapes为网格的shape
      m = true_boxes.shape[0]
      grid_shapes = [input_shape//{0:32, 1:16, 2:8}[l] for l in range(num_layers)]
      #-----------------------------------------------------------#
      #   y_true的格式为(m,13,13,3,85)(m,26,26,3,85)(m,52,52,3,85)
      #-----------------------------------------------------------#
      y_true = [np.zeros((m,grid_shapes[l][0],grid_shapes[l][1],len(anchor_mask[l]),5+num_classes),
          dtype='float32') for l in range(num_layers)]
  
      #-----------------------------------------------------------#
      #   [6,2] -> [1,6,2]
      #-----------------------------------------------------------#
      anchors = np.expand_dims(anchors, 0)
      anchor_maxes = anchors / 2.
      anchor_mins = -anchor_maxes
  
      #-----------------------------------------------------------#
      #   长宽要大于0才有效
      #-----------------------------------------------------------#
      valid_mask = boxes_wh[..., 0]>0
  
      for b in range(m):
          # 对每一张图进行处理
          wh = boxes_wh[b, valid_mask[b]]
          if len(wh)==0: continue
          #-----------------------------------------------------------#
          #   [n,2] -> [n,1,2]
          #-----------------------------------------------------------#
          wh = np.expand_dims(wh, -2)
          box_maxes = wh / 2.
          box_mins = -box_maxes
  
          #-----------------------------------------------------------#
          #   计算所有真实框和先验框的交并比
          #   intersect_area  [n,6]
          #   box_area        [n,1]
          #   anchor_area     [1,6]
          #   iou             [n,6]
          #-----------------------------------------------------------#
          intersect_mins = np.maximum(box_mins, anchor_mins)
          intersect_maxes = np.minimum(box_maxes, anchor_maxes)
          intersect_wh = np.maximum(intersect_maxes - intersect_mins, 0.)
          intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]
  
          box_area = wh[..., 0] * wh[..., 1]
          anchor_area = anchors[..., 0] * anchors[..., 1]
  
          iou = intersect_area / (box_area + anchor_area - intersect_area)
          #-----------------------------------------------------------#
          #   维度是[n,] 感谢 消尽不死鸟 的提醒
          #-----------------------------------------------------------#
          best_anchor = np.argmax(iou, axis=-1)
  
          for t, n in enumerate(best_anchor):
              #-----------------------------------------------------------#
              #   找到每个真实框所属的特征层
              #-----------------------------------------------------------#
              for l in range(num_layers):
                  if n in anchor_mask[l]:
                      #-----------------------------------------------------------#
                      #   floor用于向下取整，找到真实框所属的特征层对应的x、y轴坐标
                      #-----------------------------------------------------------#
                      i = np.floor(true_boxes[b,t,0] * grid_shapes[l][1]).astype('int32')
                      j = np.floor(true_boxes[b,t,1] * grid_shapes[l][0]).astype('int32')
                      #-----------------------------------------------------------#
                      #   k指的的当前这个特征点的第k个先验框
                      #-----------------------------------------------------------#
                      k = anchor_mask[l].index(n)
                      #-----------------------------------------------------------#
                      #   c指的是当前这个真实框的种类
                      #-----------------------------------------------------------#
                      c = true_boxes[b, t, 4].astype('int32')
                      #-----------------------------------------------------------#
                      #   y_true的shape为(m,13,13,3,85)(m,26,26,3,85)(m,52,52,3,85)
                      #   最后的85可以拆分成4+1+80，4代表的是框的中心与宽高、
                      #   1代表的是置信度、80代表的是种类
                      #-----------------------------------------------------------#
                      y_true[l][b, j, i, k, 0:4] = true_boxes[b, t, 0:4]
                      y_true[l][b, j, i, k, 4] = 1
                      y_true[l][b, j, i, k, 5+c] = 1
  
      return y_true
  
  #----------------------------------------------------#
  #   检测精度mAP和pr曲线计算参考视频
  #   https://www.bilibili.com/video/BV1zE411u7Vw
  #----------------------------------------------------#
  if __name__ == "__main__":
      #----------------------------------------------------#
      #   获得图片路径和标签
      #----------------------------------------------------#
      annotation_path = '2007_train.txt'
      #------------------------------------------------------#
      #   训练后的模型保存的位置，保存在logs文件夹里面
      #------------------------------------------------------#
      log_dir = 'logs/'
      #----------------------------------------------------#
      #   classes和anchor的路径，非常重要
      #   训练前一定要修改classes_path，使其对应自己的数据集
      #----------------------------------------------------#
      classes_path = 'model_data/new_classes.txt'    
      anchors_path = 'model_data/yolo_anchors.txt'
      #------------------------------------------------------#
      #   权值文件请看README，百度网盘下载
      #   训练自己的数据集时提示维度不匹配正常
      #   预测的东西都不一样了自然维度不匹配
      #------------------------------------------------------#
      weights_path = 'model_data/yolov4_tiny_weights_voc.h5'
      #------------------------------------------------------#
      #   训练用图片大小
      #   一般在416x416和608x608选择
      #------------------------------------------------------#
      input_shape = (416,416)
      #------------------------------------------------------#
      #   是否对损失进行归一化，用于改变loss的大小
      #   用于决定计算最终loss是除上batch_size还是除上正样本数量
      #------------------------------------------------------#
      normalize = False
  
      #----------------------------------------------------#
      #   获取classes和anchor
      #----------------------------------------------------#
      class_names = get_classes(classes_path)
      anchors = get_anchors(anchors_path)
      #------------------------------------------------------#
      #   一共有多少类和多少先验框
      #------------------------------------------------------#
      num_classes = len(class_names)
      num_anchors = len(anchors)
      #------------------------------------------------------#
      #   Yolov4的tricks应用
      #   mosaic 马赛克数据增强 True or False 
      #   实际测试时mosaic数据增强并不稳定，所以默认为False
      #   Cosine_scheduler 余弦退火学习率 True or False
      #   label_smoothing 标签平滑 0.01以下一般 如0.01、0.005
      #------------------------------------------------------#
      mosaic = False
      Cosine_scheduler = False
      label_smoothing = 0
  
      K.clear_session()
      #------------------------------------------------------#
      #   创建yolo模型
      #------------------------------------------------------#
      image_input = Input(shape=(None, None, 3))
      h, w = input_shape
      print('Create YOLOv4-Tiny model with {} anchors and {} classes.'.format(num_anchors, num_classes))
      model_body = yolo_body(image_input, num_anchors//2, num_classes)
      
      #------------------------------------------------------#
      #   载入预训练权重
      #------------------------------------------------------#
      print('Load weights {}.'.format(weights_path))
      model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)
      
      #------------------------------------------------------#
      #   在这个地方设置损失，将网络的输出结果传入loss函数
      #   把整个模型的输出作为loss
      #------------------------------------------------------#
      y_true = [Input(shape=(h//{0:32, 1:16}[l], w//{0:32, 1:16}[l], num_anchors//2, num_classes+5)) for l in range(2)]
      loss_input = [*model_body.output, *y_true]
      model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',
          arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5, 'label_smoothing': label_smoothing, 'normalize':normalize})(loss_input)
  
      model = Model([model_body.input, *y_true], model_loss)
  
      #-------------------------------------------------------------------------------#
      #   训练参数的设置
      #   logging表示tensorboard的保存地址
      #   checkpoint用于设置权值保存的细节，period用于修改多少epoch保存一次
      #   reduce_lr用于设置学习率下降的方式
      #   early_stopping用于设定早停，val_loss多次不下降自动结束训练，表示模型基本收敛
      #-------------------------------------------------------------------------------#
      logging = TensorBoard(log_dir=log_dir)
      checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5',
          monitor='val_loss', save_weights_only=True, save_best_only=False, period=1)
      early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1)
  
      #----------------------------------------------------------------------#
      #   验证集的划分在train.py代码里面进行
      #   2007_test.txt和2007_val.txt里面没有内容是正常的。训练不会使用到。
      #   当前划分方式下，验证集和训练集的比例为1:9
      #----------------------------------------------------------------------#
      val_split = 0.1
      with open(annotation_path) as f:
          lines = f.readlines()
      np.random.seed(10101)
      np.random.shuffle(lines)
      np.random.seed(None)
      num_val = int(len(lines)*val_split)
      num_train = len(lines) - num_val
      
      #------------------------------------------------------#
      #   主干特征提取网络特征通用，冻结训练可以加快训练速度
      #   也可以在训练初期防止权值被破坏。
      #   Init_Epoch为起始世代
      #   Freeze_Epoch为冻结训练的世代
      #   Epoch总训练世代
      #   提示OOM或者显存不足请调小Batch_size
      #------------------------------------------------------#
      freeze_layers = 60
      for i in range(freeze_layers): model_body.layers[i].trainable = False
      print('Freeze the first {} layers of total {} layers.'.format(freeze_layers, len(model_body.layers)))
  
      # 调整非主干模型first
      if True:
          Init_epoch = 0
          Freeze_epoch = 30
          batch_size = 32
          learning_rate_base = 1e-3
  
          if Cosine_scheduler:
              # 预热期
              warmup_epoch = int((Freeze_epoch-Init_epoch)*0.2)
              # 总共的步长
              total_steps = int((Freeze_epoch-Init_epoch) * num_train / batch_size)
              # 预热步长
              warmup_steps = int(warmup_epoch * num_train / batch_size)
              # 学习率
              reduce_lr = WarmUpCosineDecayScheduler(learning_rate_base=learning_rate_base,
                                                          total_steps=total_steps,
                                                          warmup_learning_rate=1e-4,
                                                          warmup_steps=warmup_steps,
                                                          hold_base_rate_steps=num_train,
                                                          min_learn_rate=1e-6
                                                          )
              model.compile(optimizer=Adam(), loss={'yolo_loss': lambda y_true, y_pred: y_pred})
          else:
              reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, verbose=1)
              model.compile(optimizer=Adam(learning_rate_base), loss={'yolo_loss': lambda y_true, y_pred: y_pred})
  
          print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
          model.fit_generator(data_generator(lines[:num_train], batch_size, input_shape, anchors, num_classes, mosaic=mosaic),
                  steps_per_epoch=max(1, num_train//batch_size),
                  validation_data=data_generator(lines[num_train:], batch_size, input_shape, anchors, num_classes, mosaic=False),
                  validation_steps=max(1, num_val//batch_size),
                  epochs=Freeze_epoch,
                  initial_epoch=Init_epoch,
                  callbacks=[logging, checkpoint, reduce_lr, early_stopping])
          model.save_weights(log_dir + 'trained_weights_stage_1.h5')
  
      for i in range(freeze_layers): model_body.layers[i].trainable = True
  
      # 解冻后训练
      if True:
          Freeze_epoch = 30
          Epoch = 100
          batch_size = 16
          learning_rate_base = 1e-4
  
          if Cosine_scheduler:
              # 预热期
              warmup_epoch = int((Epoch-Freeze_epoch)*0.2)
              # 总共的步长
              total_steps = int((Epoch-Freeze_epoch) * num_train / batch_size)
              # 预热步长
              warmup_steps = int(warmup_epoch * num_train / batch_size)
              # 学习率
              reduce_lr = WarmUpCosineDecayScheduler(learning_rate_base=learning_rate_base,
                                                          total_steps=total_steps,
                                                          warmup_learning_rate=1e-5,
                                                          warmup_steps=warmup_steps,
                                                          hold_base_rate_steps=num_train//2,
                                                          min_learn_rate=1e-6
                                                          )
              model.compile(optimizer=Adam(), loss={'yolo_loss': lambda y_true, y_pred: y_pred})
          else:
              reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, verbose=1)
              model.compile(optimizer=Adam(learning_rate_base), loss={'yolo_loss': lambda y_true, y_pred: y_pred})
  
          print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
          model.fit_generator(data_generator(lines[:num_train], batch_size, input_shape, anchors, num_classes, mosaic=mosaic),
                  steps_per_epoch=max(1, num_train//batch_size),
                  validation_data=data_generator(lines[num_train:], batch_size, input_shape, anchors, num_classes, mosaic=False),
                  validation_steps=max(1, num_val//batch_size),
                  epochs=Epoch,
                  initial_epoch=Freeze_epoch,
                  callbacks=[logging, checkpoint, reduce_lr, early_stopping])
          model.save_weights(log_dir + 'last1.h5')
  