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# Copyright 2019 Google LLC
#
# 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.
"""Provides the 'ExternalDataset' implementation of tf.Data.Dataset.
This wraps the generated op (in external_dataset_py_wrapper).
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from fcp.tensorflow import gen_external_dataset_py
_external_dataset_so = tf.load_op_library(
tf.compat.v1.resource_loader.get_path_to_datafile(
"./_external_dataset_op.so"))
class ExternalDataset(tf.data.Dataset):
"""An ExternalDataset is defined by whomever is running the graph.
To use an ExternalDataset, the graph must be fed a 'token' indicating what
external dataset to use. It also takes a 'selector' input - an opaque string,
to be interpreted by that external implementation.
"""
def __init__(self, token, selector):
token = tf.convert_to_tensor(token, dtype=tf.string, name="token")
selector = tf.convert_to_tensor(selector, dtype=tf.string, name="selector")
variant_tensor = gen_external_dataset_py.ExternalDataset(
token=token, selector=selector)
super(ExternalDataset, self).__init__(variant_tensor)
@property
def element_spec(self):
return tf.TensorSpec([], tf.string)
def _inputs(self):
return []
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