layers.py 文件源码

def channel_wise_fc_layer(bottom, name, bias=True):
    """
    channel-wise fully connected layer
    """
    _, width, height, n_feat_map = bottom.get_shape().as_list()
    input_reshape = tf.reshape( bottom, [-1, width*height, n_feat_map] )  # order='C'
    input_transpose = tf.transpose( input_reshape, [2,0,1] )  # n_feat_map * batch * d

    with tf.variable_scope(name):
        W = tf.get_variable(
                "W",
                shape=[n_feat_map,width*height, width*height], # n_feat_map * d * d_filter
                initializer=tf.truncated_normal_initializer(0., 0.005))
        output = tf.batch_matmul(input_transpose, W)  # n_feat_map * batch * d_filter

        if bias == True:
            b = tf.get_variable(
                "b",
                shape=width*height,
                initializer=tf.constant_initializer(0.))
            output = tf.nn.bias_add(output, b)

    output_transpose = tf.transpose(output, [1,2,0])  # batch * d_filter * n_feat_map
    output_reshape = tf.reshape( output_transpose, [-1, width, height, n_feat_map] )
    return output_reshape