from tensorboard.backend.event_processing import event_accumulator
import argparse
import pandas as pd
from tqdm import tqdm
def main():
# load log data
parser = argparse.ArgumentParser(description='Export tensorboard data')
parser.add_argument('--in-path', type=str, default='/gpfs/home/xxy_1997032/wjl/log/googlenet/Test-Loss_Test:/', help='Tensorboard event files or a single tensorboard '
'file location')
parser.add_argument('--ex-path', type=str, default='/gpfs/home/xxy_1997032/wjl/log/googlenet/out/a.csv', help='location to save the exported data')
args = parser.parse_args()
event_data = event_accumulator.EventAccumulator(args.in_path) # a python interface for loading Event data
event_data.Reload() # synchronously loads all of the data written so far b
# print(event_data.Tags()) # print all tags
keys = event_data.scalars.Keys() # get all tags,save in a list
# print(keys)
df = pd.DataFrame(columns=keys[1:]) # my first column is training loss per iteration, so I abandon it
for key in tqdm(keys):
# print(key)
if key != 'train/total_loss_iter': # Other attributes' timestamp is epoch.Ignore it for the format of csv file
df[key] = pd.DataFrame(event_data.Scalars(key)).value
df.to_csv(args.ex_path)
print("Tensorboard data exported successfully")
if __name__ == '__main__':
main()
