62 lines
2.2 KiB
Python
62 lines
2.2 KiB
Python
# Copyright The PyTorch Lightning team.
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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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# http://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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import os
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import matplotlib.pylab as plt
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import pandas as pd
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from benchmarks.test_basic_parity import measure_loops
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from tests.base.models import ParityModuleMNIST, ParityModuleRNN
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NUM_EPOCHS = 20
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NUM_RUNS = 50
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MODEL_CLASSES = (ParityModuleRNN, ParityModuleMNIST)
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PATH_HERE = os.path.dirname(__file__)
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FIGURE_EXTENSION = '.png'
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def _main():
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fig, axarr = plt.subplots(nrows=len(MODEL_CLASSES))
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for i, cls_model in enumerate(MODEL_CLASSES):
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path_csv = os.path.join(PATH_HERE, f'dump-times_{cls_model.__name__}.csv')
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if os.path.isfile(path_csv):
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df_time = pd.read_csv(path_csv, index_col=0)
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else:
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# todo: kind="Vanilla PT" -> use_lightning=False
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vanilla = measure_loops(cls_model, kind="Vanilla PT", num_epochs=NUM_EPOCHS, num_runs=NUM_RUNS)
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lightning = measure_loops(cls_model, kind="PT Lightning", num_epochs=NUM_EPOCHS, num_runs=NUM_RUNS)
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df_time = pd.DataFrame({'vanilla PT': vanilla['durations'][1:], 'PT Lightning': lightning['durations'][1:]})
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df_time /= NUM_RUNS
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df_time.to_csv(os.path.join(PATH_HERE, f'dump-times_{cls_model.__name__}.csv'))
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# todo: add also relative X-axis ticks to see both: relative and absolute time differences
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df_time.plot.hist(
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ax=axarr[i],
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bins=20,
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alpha=0.5,
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title=cls_model.__name__,
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legend=True,
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grid=True,
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)
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axarr[i].set(xlabel='time [seconds]')
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path_fig = os.path.join(PATH_HERE, f'figure-parity-times{FIGURE_EXTENSION}')
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fig.tight_layout()
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fig.savefig(path_fig)
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if __name__ == '__main__':
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_main()
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