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Cyprien de Masson d'Autume
49 lines
1.7 KiB
Python
49 lines
1.7 KiB
Python
# Copyright 2019 DeepMind Technologies Limited and Google LLC
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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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# https://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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"""Evaluation metrics."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tensorflow as tf
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import tensorflow_gan as tfgan
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import tensorflow_hub as hub
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def fid(generated_sentences, real_sentences):
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"""Compute FID rn sentences using pretrained universal sentence encoder.
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Args:
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generated_sentences: list of N strings.
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real_sentences: list of N strings.
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Returns:
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Frechet distance between activations.
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"""
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embed = hub.Module("https://tfhub.dev/google/universal-sentence-encoder/2")
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real_embed = embed(real_sentences)
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generated_embed = embed(generated_sentences)
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distance = tfgan.eval.frechet_classifier_distance_from_activations(
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real_embed, generated_embed)
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# Restrict the thread pool size to prevent excessive CPU usage.
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config = tf.ConfigProto()
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config.intra_op_parallelism_threads = 16
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config.inter_op_parallelism_threads = 16
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with tf.Session(config=config) as session:
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session.run(tf.global_variables_initializer())
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session.run(tf.tables_initializer())
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distance_np = session.run(distance)
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return distance_np
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