parse_query_tb_logs

tb_log_parser.py

import os
import pandas as pd
from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
from tqdm import tqdm
from pathlib import Path
import numpy as np
import matplotlib.pyplot as plt
import clipboard
import base64
from io import BytesIO
from PIL import Image

main_path = './runs/'

def get_event_files(main_path):
    """Return a list of event files under the given directory"""
    all_files = []
    for root, _, filenames in os.walk(main_path):
        for filename in filenames:
            if "events.out.tfevents" in filename:
                all_files.append(str(Path(root) / Path(filename)))
    return all_files

def process_event_acc(event_acc):
    """Process the EventAccumulator and return a dictionary of tag values"""
    all_tags = event_acc.Tags()
    temp_dict = {}
    for tag in all_tags.keys():
        if tag == 'scalars':
            for subtag in all_tags[tag]:
                temp_dict[subtag] = [tag[-1] for tag in event_acc.Scalars(tag=subtag)][-1]
        if tag == 'tensors':
            for subtag in all_tags[tag]:
                temp_dict[subtag.replace('/text_summary', "")] = [tag[-1] for tag in event_acc.Tensors(tag=subtag)][0].string_val[0].decode('ascii')
        if tag == 'images':
            for subtag in all_tags[tag]:
                temp_dict[subtag] = Image.open(BytesIO(event_acc.Images(subtag)[1].encoded_image_string))
    return temp_dict

def process_runs(main_path):
	""" Iterate over all the runs and return the dataframe"""
    all_files = get_event_files(main_path=main_path)
    all_dict = {}
    for files in tqdm(all_files, total = len(all_files)):
        event_acc = EventAccumulator(files)
        event_acc.Reload()
        temp_dict = process_event_acc(event_acc)
        all_dict[files] = temp_dict
    return pd.DataFrame.from_records(all_dict).T.reset_index()


combined_df = process_runs(main_path=main_path)

# Clean based on your columns
combined_df = combined_df[(~pd.isnull(combined_df['experiment_name'])) & (~pd.isnull(combined_df['Loss/Val']))]

# Verify images based on your columns
filtered_df = combined_df[(~pd.isnull(combined_df['converted_proxy'])) & (~pd.isnull(combined_df['original_images']))]
filtered_df.iloc[0].original_images