very_tiny_yolo.py 文件源码

def loss_func_yolo(output, exp):
  res = 0

  for i in range(BATCH_SIZE):
    for j in range(0, S*S*(B*5+CLASSES), B*5+CLASSES):
      res += COORD_W * tf.sign(exp[i][j+2]) * (tf.square(output[i][j] - exp[i][j]) + tf.square(output[i][j+1]-exp[i][j+1]) + 
                                               tf.square(tf.sqrt(tf.abs(output[i][j+2])) - tf.sqrt(exp[i][j+2])) + 
                                               tf.square(tf.sqrt(tf.abs(output[i][j+3])) - tf.sqrt(exp[i][j+3])))

      res += tf.sign(exp[i][j+2]) * (tf.square(output[i][j+4] - exp[i][j+4]))

      res += NOOBJ_W * tf.sign(tf.floor(exp[i][j])) * (tf.square(output[i][j+4] - exp[i][j+4]))

      res += COORD_W * tf.sign(exp[i][j+7]) * (tf.square(output[i][j+5] - exp[i][j+5]) + tf.square(output[i][j+6]-exp[i][j+6]) + 
                                               tf.square(tf.sqrt(tf.abs(output[i][j+7])) - tf.sqrt(exp[i][j+7])) + 
                                               tf.square(tf.sqrt(tf.abs(output[i][j+8])) - tf.sqrt(exp[i][j+8])))

      res += tf.sign(exp[i][j+7]) * (tf.square(output[i][j+9] - exp[i][j+9]))

      res += NOOBJ_W * tf.sign(tf.floor(exp[i][j+5])) * (tf.square(output[i][j+9] - exp[i][j+9]))

      res += tf.sign(exp[i][j+7]) * (tf.square(output[i][j+10] - exp[i][j+10]) + tf.square(output[i][j+11] - exp[i][j+11]))

  return res