239 lines
10 KiB
Python
239 lines
10 KiB
Python
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#==============================================================================
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"""Lookup operations."""
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from tensorflow.python.data.experimental.ops.cardinality import assert_cardinality
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import tensor_spec
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from tensorflow.python.ops import gen_experimental_dataset_ops as ged_ops
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from tensorflow.python.ops import lookup_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.util.tf_export import tf_export
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def _check_table_initializer_element_spec(element_spec):
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"""Raises an error if the given table initializer element spec is invalid."""
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base_error = ("Datasets used to initialize lookup tables must "
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"produce elements in the form (key, value), where "
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"the keys and values are scalar tensors. ")
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specific_error = None
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if len(element_spec) != 2:
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raise ValueError(base_error + "However, the given dataset produces "
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f"{len(element_spec)} components instead of two "
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"(key, value) components. Full dataset element spec: "
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f"{element_spec}.")
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if not isinstance(element_spec[0], tensor_spec.TensorSpec):
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raise ValueError(base_error + "However, the given dataset produces "
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f"non-Tensor keys of type {type(element_spec[0])}.")
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if not isinstance(element_spec[1], tensor_spec.TensorSpec):
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raise ValueError(base_error + "However, the given dataset produces "
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f"non-Tensor values of type {type(element_spec[1])}.")
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if element_spec[0].shape.rank not in (None, 0):
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raise ValueError(
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base_error + "However, the given dataset produces "
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f"non-scalar key Tensors of rank {element_spec[0].shape.rank}.")
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if element_spec[1].shape.rank not in (None, 0):
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raise ValueError(
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base_error + "However, the given dataset produces "
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f"non-scalar value Tensors of rank {element_spec[1].shape.rank}.")
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@tf_export("data.experimental.DatasetInitializer")
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class DatasetInitializer(lookup_ops.TableInitializerBase):
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"""Creates a table initializer from a `tf.data.Dataset`.
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Sample usage:
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>>> keys = tf.data.Dataset.range(100)
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>>> values = tf.data.Dataset.range(100).map(
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... lambda x: tf.strings.as_string(x * 2))
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>>> ds = tf.data.Dataset.zip((keys, values))
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>>> init = tf.data.experimental.DatasetInitializer(ds)
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>>> table = tf.lookup.StaticHashTable(init, "")
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>>> table.lookup(tf.constant([0, 1, 2], dtype=tf.int64)).numpy()
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array([b'0', b'2', b'4'], dtype=object)
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Attributes:
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dataset: A `tf.data.Dataset` object that produces tuples of scalars. The
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first scalar is treated as a key and the second as value.
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Raises: ValueError if `dataset` doesn't conform to specifications.
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"""
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def __init__(self, dataset):
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"""Creates a table initializer from a `tf.data.Dataset`.
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Args:
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dataset: A `tf.data.Dataset` object that produces tuples of scalars. The
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first scalar is treated as a key and the second as value.
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Raises: ValueError if `dataset` doesn't conform to specifications.
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Returns: A `DatasetInitializer` object
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"""
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# Assert that the dataset element spec is a tuple of TensorSpecs where
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# each tensor is a scalar.
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self.dataset = dataset
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elem_spec = self.dataset.element_spec
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_check_table_initializer_element_spec(elem_spec)
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key_type = elem_spec[0].dtype
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value_type = elem_spec[1].dtype
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super(DatasetInitializer, self).__init__(key_type, value_type)
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def initialize(self, table):
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lookup_ops.check_table_dtypes(table, self._key_dtype, self._value_dtype)
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init_op = ged_ops.initialize_table_from_dataset(
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table.resource_handle, self.dataset._variant_tensor) # pylint: disable=protected-access
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ops.add_to_collection(ops.GraphKeys.TABLE_INITIALIZERS, init_op)
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return init_op
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@tf_export("data.experimental.table_from_dataset")
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def table_from_dataset(dataset=None,
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num_oov_buckets=0,
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vocab_size=None,
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default_value=None,
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hasher_spec=lookup_ops.FastHashSpec,
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key_dtype=dtypes.string,
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name=None):
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"""Returns a lookup table based on the given dataset.
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This operation constructs a lookup table based on the given dataset of pairs
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of (key, value).
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Any lookup of an out-of-vocabulary token will return a bucket ID based on its
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hash if `num_oov_buckets` is greater than zero. Otherwise it is assigned the
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`default_value`.
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The bucket ID range is
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`[vocabulary size, vocabulary size + num_oov_buckets - 1]`.
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Sample Usages:
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>>> keys = tf.data.Dataset.range(100)
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>>> values = tf.data.Dataset.range(100).map(
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... lambda x: tf.strings.as_string(x * 2))
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>>> ds = tf.data.Dataset.zip((keys, values))
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>>> table = tf.data.experimental.table_from_dataset(
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... ds, default_value='n/a', key_dtype=tf.int64)
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>>> table.lookup(tf.constant([0, 1, 2], dtype=tf.int64)).numpy()
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array([b'0', b'2', b'4'], dtype=object)
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Args:
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dataset: A dataset containing (key, value) pairs.
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num_oov_buckets: The number of out-of-vocabulary buckets.
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vocab_size: Number of the elements in the vocabulary, if known.
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default_value: The value to use for out-of-vocabulary feature values.
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Defaults to -1.
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hasher_spec: A `HasherSpec` to specify the hash function to use for
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assignation of out-of-vocabulary buckets.
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key_dtype: The `key` data type.
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name: A name for this op (optional).
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Returns:
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The lookup table based on the given dataset.
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Raises:
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ValueError: If
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* `dataset` does not contain pairs
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* The 2nd item in the `dataset` pairs has a dtype which is incompatible
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with `default_value`
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* `num_oov_buckets` is negative
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* `vocab_size` is not greater than zero
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* The `key_dtype` is not integer or string
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"""
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elem_spec = dataset.element_spec
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_check_table_initializer_element_spec(elem_spec)
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if default_value is None:
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default_value = -1
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if not (elem_spec[1].dtype.is_integer or elem_spec[1].dtype.is_floating):
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raise ValueError("`default_value` must be specified when creating a "
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"table from a dataset that produces values of type "
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f"{elem_spec[1].dtype}.")
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if num_oov_buckets < 0:
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raise ValueError("`num_oov_buckets` must be greater than or equal to 0, "
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f"got {num_oov_buckets}.")
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if (not isinstance(vocab_size, ops.Tensor) and vocab_size is not None and
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vocab_size < 1):
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raise ValueError(f"`vocab_size` must be greater than 0, got {vocab_size}.")
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if (not key_dtype.is_integer) and (dtypes.string != key_dtype.base_dtype):
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raise TypeError("`key_dtype` must be either an integer or string type, "
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f"but got {key_dtype}")
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if vocab_size is not None:
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if isinstance(vocab_size, ops.Tensor):
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vocab_size = math_ops.cast(vocab_size, dtypes.int64)
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dataset = dataset.take(vocab_size)
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dataset = dataset.apply(assert_cardinality(vocab_size))
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with ops.name_scope(name, "string_to_index"):
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initializer = DatasetInitializer(dataset)
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with ops.name_scope(None, "hash_table"):
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table = lookup_ops.StaticHashTableV1(initializer, default_value)
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if num_oov_buckets:
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table = lookup_ops.IdTableWithHashBuckets(
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table,
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num_oov_buckets=num_oov_buckets,
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hasher_spec=hasher_spec,
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key_dtype=key_dtype)
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return table
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@tf_export("data.experimental.index_table_from_dataset")
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def index_table_from_dataset(dataset=None,
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num_oov_buckets=0,
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vocab_size=None,
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default_value=-1,
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hasher_spec=lookup_ops.FastHashSpec,
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key_dtype=dtypes.string,
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name=None):
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"""Returns an index lookup table based on the given dataset.
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This operation constructs a lookup table based on the given dataset of keys.
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Any lookup of an out-of-vocabulary token will return a bucket ID based on its
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hash if `num_oov_buckets` is greater than zero. Otherwise it is assigned the
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`default_value`.
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The bucket ID range is
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`[vocabulary size, vocabulary size + num_oov_buckets - 1]`.
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Sample Usages:
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>>> ds = tf.data.Dataset.range(100).map(lambda x: tf.strings.as_string(x * 2))
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>>> table = tf.data.experimental.index_table_from_dataset(
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... ds, key_dtype=dtypes.int64)
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>>> table.lookup(tf.constant(['0', '2', '4'], dtype=tf.string)).numpy()
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array([0, 1, 2])
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Args:
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dataset: A dataset of keys.
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num_oov_buckets: The number of out-of-vocabulary buckets.
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vocab_size: Number of the elements in the vocabulary, if known.
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default_value: The value to use for out-of-vocabulary feature values.
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Defaults to -1.
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hasher_spec: A `HasherSpec` to specify the hash function to use for
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assignation of out-of-vocabulary buckets.
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key_dtype: The `key` data type.
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name: A name for this op (optional).
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Returns:
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The lookup table based on the given dataset.
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Raises:
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ValueError: If
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* `num_oov_buckets` is negative
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* `vocab_size` is not greater than zero
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* The `key_dtype` is not integer or string
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"""
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return table_from_dataset(dataset.enumerate().map(lambda v, k: (k, v)),
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num_oov_buckets, vocab_size, default_value,
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hasher_spec, key_dtype, name)
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