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Louise Deason
52 lines
1.8 KiB
Python
52 lines
1.8 KiB
Python
# Copyright 2021 DeepMind Technologies Limited.
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#
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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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"""Simple script to model evaluation on a checkpoint and dataset."""
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import ast
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from absl import app
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from absl import flags
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from absl import logging
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from galaxy_mergers import evaluator
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flags.DEFINE_string('checkpoint_path', '', 'Path to TF2 checkpoint to eval.')
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flags.DEFINE_string('data_path', '', 'Path to TFRecord(s) with data.')
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flags.DEFINE_string('filter_time_intervals', None,
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'Merger time intervals on which to perform regression.'
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'Specify None for the default time interval [-1,1], or'
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' a custom list of intervals, e.g. [[-0.2,0], [0.5,1]].')
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FLAGS = flags.FLAGS
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def main(_) -> None:
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if FLAGS.filter_time_intervals is not None:
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filter_time_intervals = ast.literal_eval(FLAGS.filter_time_intervals)
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else:
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filter_time_intervals = None
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config, ds, experiment = evaluator.get_config_dataset_evaluator(
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filter_time_intervals,
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FLAGS.checkpoint_path,
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config_override={
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'experiment_kwargs.data_config.dataset_path': FLAGS.data_path,
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})
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metrics, _, _ = evaluator.run_model_on_dataset(experiment, ds, config)
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logging.info('Evaluation complete. Metrics: %s', metrics)
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if __name__ == '__main__':
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app.run(main)
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