Created by: yeyupiaoling

环境

• PaddlePaddle
• Ubuntu 16.04
• Python 3.5

问题

我想用PaddlePaddle复现MTCNN模型的交叉熵损失函数，以下的tensorflow的代码

def cls_ohem(cls_prob, label):
    '''计算类别损失
    参数：
      cls_prob：预测类别，是否有人
      label：真实值
    返回值：
      损失
    '''
    zeros = tf.zeros_like(label)

    # 只把pos的label设定为1,其余都为0
    label_filter_invalid = tf.where(tf.less(label, 0), zeros, label)

    # 类别size[2*batch]
    num_cls_prob = tf.size(cls_prob)
    cls_prob_reshpae = tf.reshape(cls_prob, [num_cls_prob, -1])
    label_int = tf.cast(label_filter_invalid, tf.int32)

    # 获取batch数
    num_row = tf.to_int32(cls_prob.get_shape()[0])

    # 对应某一batch而言，batch*2为非人类别概率，batch*2+1为人概率类别,indices为对应 cls_prob_reshpae
    # 应该的真实值，后续用交叉熵计算损失
    row = tf.range(num_row) * 2
    indices_ = row + label_int

    # 真实标签对应的概率
    label_prob = tf.squeeze(tf.gather(cls_prob_reshpae, indices_))
    loss = -tf.log(label_prob + 1e-10)
    zeros = tf.zeros_like(label_prob, dtype=tf.float32)
    ones = tf.ones_like(label_prob, dtype=tf.float32)

    # 统计neg和pos的数量
    valid_inds = tf.where(label < zeros, zeros, ones)
    num_valid = tf.reduce_sum(valid_inds)

    # 选取70%的数据
    keep_num = tf.cast(num_valid * num_keep_radio, dtype=tf.int32)

    # 只选取neg，pos的70%损失
    loss = loss * valid_inds
    loss, _ = tf.nn.top_k(loss, k=keep_num)
    return tf.reduce_mean(loss)

我用PaddlePaddle大概复现了一下，逻辑也不知道对不对的。

def cls_ohem(cls_prob, label):
    '''计算类别损失
    参数：
      cls_prob：预测类别，是否有人
      label：真实值
    返回值：
      损失
    '''
    # 只把pos的label设定为1,其余都为0
    zeros = fluid.layers.zeros(shape=[int(i) for i in label.shape], dtype=label.dtype)
    cond = fluid.layers.less_than(x=label, y=fluid.layers.fill_constant(shape=[],dtype='float32', value=0))
    ie = fluid.layers.IfElse(cond)
    with ie.true_block():
        ie.output(zeros)

    with ie.false_block():
        ie.output(label)
    label_filter_invalid = ie()

    # 类别size[2*batch]
    num_cls_prob = sum(cls_prob.shape)
    cls_prob_reshpae = fluid.layers.reshape(cls_prob, [num_cls_prob, -1])
    label_int = fluid.layers.cast(label_filter_invalid[0], dtype='int32')

    # 获取batch数
    num_row = fluid.layers.fill_constant(shape=[],dtype='int32', value=cls_prob.shape[0])

    # 对应某一batch而言，batch*2为非人类别概率，batch*2+1为人概率类别,indices为对应 cls_prob_reshpae
    # 应该的真实值，后续用交叉熵计算损失
    indices_ = []
    i = fluid.layers.fill_constant(shape=[], dtype='int32', value=0)
    cond = fluid.layers.less_than(x=i, y=num_row)
    print(cond)
    while_op = fluid.layers.While(cond=cond)
    with while_op.block():
        indices_.append(i + label_int)
        i = i + 1
        fluid.layers.less_than(x=i, y=num_row, cond=cond)

    # 真实标签对应的概率
    indices_ = fluid.layers.cast(indices_, dtype='int32')
    label_prob = fluid.layers.squeeze(fluid.layers.gather(cls_prob_reshpae, indices_))
    loss = -fluid.layers.log(label_prob + 1e-10)
    zeros = fluid.layers.zeros(shape=[int(i) for i in label_prob.shape], dtype='float32')
    ones = fluid.layers.ones(shape=[int(i) for i in label_prob.shape], dtype='float32')

    # 统计neg和pos的数量
    cond2 = fluid.layers.less_than(x=label, y=zeros)
    ie2 = fluid.layers.IfElse(cond2)
    with ie2.true_block():
        ie2.output(zeros)
    with ie2.false_block():
        ie2.output(ones)
    valid_inds = ie2()
    num_valid = fluid.layers.reduce_sum(valid_inds)

    # 选取70%的数据
    keep_num = fluid.layers.cast(num_valid * num_keep_radio, dtype='int32')

    # 只选取neg，pos的70%损失
    loss = loss * valid_inds
    loss, _ = fluid.layers.nn.topk(loss, k=keep_num)
    return fluid.layers.reduce_mean(loss)

在使用的时候报错：

Traceback (most recent call last):
  File "/media/test/5C283BCA283BA1C6/yeyupiaoling/PyCharm/PaddlePaddle-MTCNN/train_PNet/train_PNet.py", line 12, in <module>
    image, label, bbox_target, landmark_target, label_cost, bbox_loss, landmark_loss, conv4_1, conv4_2, conv4_3 = P_Net()
  File "/media/test/5C283BCA283BA1C6/yeyupiaoling/PyCharm/PaddlePaddle-MTCNN/train_PNet/model.py", line 67, in P_Net
    label_cost = cls_ohem(cls_prob=cls_prob, label=label)
  File "/media/test/5C283BCA283BA1C6/yeyupiaoling/PyCharm/PaddlePaddle-MTCNN/train_PNet/model.py", line 114, in cls_ohem
    while_op = fluid.layers.While(cond=cond)
  File "/usr/local/lib/python3.5/dist-packages/paddle/fluid/layers/control_flow.py", line 693, in __init__
    raise TypeError("condition should be a bool scalar")
TypeError: condition should be a bool scalar

恳请各位老师帮忙复现一下，谢谢。