(self, nets_list)
return net
Max pool across all nets in spatial dimensions.

    Args:
      nets_list: A list of 4D tensors with identical size.

    Returns:
      A tensor with the same size as any input tensors.
Max pool across all nets in spatial dimensions.
Max pool across all nets in spatial dimensions .
def max_pool_views(self, nets_list):
    """Max pool across all nets in spatial dimensions.

    Args:
      nets_list: A list of 4D tensors with identical size.

    Returns:
      A tensor with the same size as any input tensors.
    """
    batch_size, height, width, num_features = [
      d.value for d in nets_list[0].get_shape().dims
    ]
    xy_flat_shape = (batch_size, 1, height * width, num_features)
    nets_for_merge = []
    with tf.variable_scope('max_pool_views', values=nets_list):
      for net in nets_list:
        nets_for_merge.append(tf.reshape(net, xy_flat_shape))
      merged_net = tf.concat(nets_for_merge, 1)
      net = slim.max_pool2d(
        merged_net, kernel_size=[len(nets_list), 1], stride=1)
      net = tf.reshape(net, (batch_size, height, width, num_features))
    return net
def max_pool_views ( self , nets_list ) : batch_size , height , width , num_features = [ d . value for d in nets_list [ 0 ] . get_shape ( ) . dims ] xy_flat_shape = ( batch_size , 1 , height * width , num_features ) nets_for_merge = [ ] with tf . variable_scope ( 'max_pool_views' , values = nets_list ) : for net in nets_list : nets_for_merge . append ( tf . reshape ( net , xy_flat_shape ) ) merged_net = tf . concat ( nets_for_merge , 1 ) net = slim . max_pool2d ( merged_net , kernel_size = [ len ( nets_list ) , 1 ] , stride = 1 ) net = tf . reshape ( net , ( batch_size , height , width , num_features ) ) return net