model.py 文件源码

def prepare_label(self, input_batch, new_size):
        """Resize masks and perform one-hot encoding.

        Args:
          input_batch: input tensor of shape [batch_size H W 1].
          new_size: a tensor with new height and width.

        Returns:
          Outputs a tensor of shape [batch_size h w 21]
          with last dimension comprised of 0's and 1's only.
        """
        with tf.name_scope('label_encode'):
            input_batch = tf.image.resize_nearest_neighbor(input_batch, new_size) # As labels are integer numbers, need to use NN interp.
            input_batch = tf.squeeze(input_batch, axis=[3]) # Reducing the channel dimension.
            input_batch = tf.one_hot(input_batch, depth=21)
        return input_batch