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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Abstract base class for all predictors."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
import six
@six.add_metaclass(abc.ABCMeta)
class Predictor(object):
"""Abstract base class for all predictors."""
@property
def graph(self):
return self._graph
@property
def session(self):
return self._session
@property
def feed_tensors(self):
return self._feed_tensors
@property
def fetch_tensors(self):
return self._fetch_tensors
def __repr__(self):
return '{} with feed tensors {} and fetch_tensors {}'.format(
type(self).__name__, self._feed_tensors, self._fetch_tensors)
def __call__(self, input_dict):
"""Returns predictions based on `input_dict`.
Args:
input_dict: a `dict` mapping strings to numpy arrays. These keys
must match `self._feed_tensors.keys()`.
Returns:
A `dict` mapping strings to numpy arrays. The keys match
`self.fetch_tensors.keys()`.
Raises:
ValueError: `input_dict` does not match `feed_tensors`.
"""
# TODO(jamieas): make validation optional?
input_keys = set(input_dict.keys())
expected_keys = set(self.feed_tensors.keys())
unexpected_keys = input_keys - expected_keys
if unexpected_keys:
raise ValueError(
'Got unexpected keys in input_dict: {}\nexpected: {}'.format(
unexpected_keys, expected_keys))
feed_dict = {}
for key in self.feed_tensors.keys():
value = input_dict.get(key)
if value is not None:
feed_dict[self.feed_tensors[key]] = value
return self._session.run(fetches=self.fetch_tensors, feed_dict=feed_dict)
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