tb2csv.py

import os
import numpy as np
import pandas as pd

from collections import defaultdict
from tensorboard.backend.event_processing.event_accumulator import EventAccumulator


def tabulate_events(dpath):
    summary_iterators = [EventAccumulator(os.path.join(dpath, dname)).Reload() for dname in os.listdir(dpath)]

    tags = summary_iterators[0].Tags()['scalars']

    for it in summary_iterators:
        assert it.Tags()['scalars'] == tags

    out = defaultdict(list)
    steps = []

    for tag in tags:
        steps = [e.step for e in summary_iterators[1].Scalars(tag)]

        for events in zip(*[acc.Scalars(tag) for acc in summary_iterators]):
            assert len(set(e.step for e in events)) == 1

            out[tag].append([e.value for e in events])

    return out, steps


def to_csv(dpath):
    dirs = os.listdir(dpath)

    d, steps = tabulate_events(dpath)
    tags, values = zip(*d.items())
    np_values = np.array(values)

    for index, tag in enumerate(tags):
        df = pd.DataFrame(np_values[index], index=steps, columns=dirs)
        df.to_csv(get_file_path(dpath, tag))


def get_file_path(dpath, tag):
    file_name = tag.replace("/", "_") + '.csv'
    folder_path = os.path.join(dpath, 'csv')
    if not os.path.exists(folder_path):
        os.makedirs(folder_path)
    return os.path.join(folder_path, file_name)


if __name__ == '__main__':
    path = "./logdir"
    to_csv(path)