# Copyright 2017 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A decoder that splits a string into tokens and returns the
individual tokens and the length.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import tensorflow as tf
from tensorflow.contrib.slim.python.slim.data import data_decoder
class SplitTokensDecoder(data_decoder.DataDecoder):
"""A DataProvider that splits a string tensor into individual tokens and
returns the tokens and the length.
Optionally prepends or appends special tokens.
Args:
delimiter: Delimiter to split on. Must be a single character.
tokens_feature_name: A descriptive feature name for the token values
length_feature_name: A descriptive feature name for the length value
"""
def __init__(self,
delimiter=" ",
tokens_feature_name="tokens",
length_feature_name="length",
prepend_token=None,
append_token=None):
self.delimiter = delimiter
self.tokens_feature_name = tokens_feature_name
self.length_feature_name = length_feature_name
self.prepend_token = prepend_token
self.append_token = append_token
def decode(self, data, items):
decoded_items = {}
# Split tokens
tokens = tf.string_split([data], delimiter=self.delimiter).values
# Optionally prepend a special token
if self.prepend_token is not None:
tokens = tf.concat([[self.prepend_token], tokens], 0)
# Optionally append a special token
if self.append_token is not None:
tokens = tf.concat([tokens, [self.append_token]], 0)
decoded_items[self.length_feature_name] = tf.size(tokens)
decoded_items[self.tokens_feature_name] = tokens
return [decoded_items[_] for _ in items]
def list_items(self):
return [self.tokens_feature_name, self.length_feature_name]
