有没有人为keras写过weldon pooling？

Question

是否在Keras中实现了Weldon pooling 1？

我可以看到它已经由作者2在pytorch中实现了，但是找不到与keras等效的。

1 T. Durand，N. Thome和M. Cord.韦尔登:深度卷积神经网络的弱监督学习。在CVPR，2016年。2

Answer 1

这是一个基于lua版本的(有一个pytorch impl，但我认为取max+min的平均值是错误的)。我假设lua版本的最大和最小值的平均值仍然是正确的。我还没有测试整个自定义的层方面，但是足够近了，可以让一些东西运行起来，欢迎评论。

托尼

class WeldonPooling(Layer):
    """Class to implement Weldon selective spacial pooling with negative evidence
    """

    #@interfaces.legacy_global_pooling_support
    def __init__(self, kmax, kmin=-1, data_format=None, **kwargs):
        super(WeldonPooling, self).__init__(**kwargs)
        self.data_format = conv_utils.normalize_data_format(data_format)
        self.input_spec = InputSpec(ndim=4)
        self.kmax=kmax
        self.kmin=kmin

    def compute_output_shape(self, input_shape):
        if self.data_format == 'channels_last':
            return (input_shape[0], input_shape[3])
        else:
            return (input_shape[0], input_shape[1])

    def get_config(self):
        config = {'data_format': self.data_format}
        base_config = super(_GlobalPooling2D, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))

    def call(self, inputs):
        if self.data_format == "channels_last":
            inputs = tf.transpose(inputs, [0, 3, 1, 2])
        kmax=self.kmax
        kmin=self.kmin
        shape=tf.shape(inputs)
        batch_size = shape[0]
        num_channels = shape[1]
        h = shape[2]
        w = shape[3]
        n = h * w
        view = tf.reshape(inputs, [batch_size, num_channels, n])
        sorted, indices = tf.nn.top_k(view, n, sorted=True)
        #indices_max = tf.slice(indices,[0,0,0],[batch_size, num_channels, kmax])
        output = tf.div(tf.reduce_sum(tf.slice(sorted,[0,0,0],[batch_size, num_channels, kmax]),2),kmax)

        if kmin > 0:
            #indices_min = tf.slice(indices,[0,0, n-kmin],[batch_size, num_channels, kmin])
            output=tf.add(output,tf.div(tf.reduce_sum(tf.slice(sorted,[0,0,n-kmin],[batch_size, num_channels, kmin]),2),kmin))

        return tf.reshape(output,[batch_size, num_channels])