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Mihaela Rosca
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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"""Compute image metrics: IS, FID."""
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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_gan as tfgan
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def get_image_metrics_for_samples(
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real_images, generator, prior, data_processor, num_eval_samples):
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"""Compute inception score and FID."""
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max_classifier_batch = 10
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num_batches = num_eval_samples // max_classifier_batch
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def sample_fn(arg):
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del arg
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samples = generator(prior.sample(max_classifier_batch))
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# Ensure data is in [0, 1], as expected by TFGAN.
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# Resizing to appropriate size is done by TFGAN.
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return data_processor.postprocess(samples)
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fake_outputs = tfgan.eval.sample_and_run_inception(
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sample_fn,
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sample_inputs=[1.0] * num_batches) # Dummy inputs.
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fake_logits = fake_outputs['logits']
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inception_score = tfgan.eval.classifier_score_from_logits(fake_logits)
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real_outputs = tfgan.eval.run_inception(real_images, num_batches=num_batches)
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fid = tfgan.eval.frechet_classifier_distance_from_activations(
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real_outputs['pool_3'], fake_outputs['pool_3'])
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return {
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'inception_score': inception_score,
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'fid': fid}
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