Finetuning X3D model on Hagrid dataset

pytorchvideo/lightning_logs/HaGrid/aggregated_logs/tflogs2pandas.py

@@ -0,0 +1,128 @@
+#!/usr/bin/env python3
+
+import glob
+import os
+import pprint
+import traceback
+
+import click
+import pandas as pd
+from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
+
+
+# Extraction function
+def tflog2pandas(path: str) -> pd.DataFrame:
+    """convert single tensorflow log file to pandas DataFrame
+
+    Parameters
+    ----------
+    path : str
+        path to tensorflow log file
+
+    Returns
+    -------
+    pd.DataFrame
+        converted dataframe
+    """
+    DEFAULT_SIZE_GUIDANCE = {
+        "compressedHistograms": 1,
+        "images": 1,
+        "scalars": 0,  # 0 means load all
+        "histograms": 1,
+    }
+    runlog_data = pd.DataFrame({"metric": [], "value": [], "step": []})
+    try:
+        event_acc = EventAccumulator(path, DEFAULT_SIZE_GUIDANCE)
+        event_acc.Reload()
+        tags = event_acc.Tags()["scalars"]
+        for tag in tags:
+            event_list = event_acc.Scalars(tag)
+            values = list(map(lambda x: x.value, event_list))
+            step = list(map(lambda x: x.step, event_list))
+            r = {"metric": [tag] * len(step), "value": values, "step": step}
+            r = pd.DataFrame(r)
+            runlog_data = pd.concat([runlog_data, r])
+    # Dirty catch of DataLossError
+    except Exception:
+        print("Event file possibly corrupt: {}".format(path))
+        traceback.print_exc()
+    return runlog_data
+
+
+def many_logs2pandas(event_paths):
+    all_logs = pd.DataFrame()
+    for path in event_paths:
+        log = tflog2pandas(path)
+        if log is not None:
+            if all_logs.shape[0] == 0:
+                all_logs = log
+            else:
+                all_logs = all_logs.append(log, ignore_index=True)
+    return all_logs
+
+
+@click.command()
+@click.argument("logdir-or-logfile")
+@click.option(
+    "--write-pkl/--no-write-pkl", help="save to pickle file or not", default=False
+)
+@click.option(
+    "--write-csv/--no-write-csv", help="save to csv file or not", default=True
+)
+@click.option("--out-dir", "-o", help="output directory", default=".")
+def main(logdir_or_logfile: str, write_pkl: bool, write_csv: bool, out_dir: str):
+    """This is a enhanced version of
+    https://gist.github.com/ptschandl/ef67bbaa93ec67aba2cab0a7af47700b
+
+    This script exctracts variables from all logs from tensorflow event
+    files ("event*"),
+    writes them to Pandas and finally stores them a csv-file or
+    pickle-file including all (readable) runs of the logging directory.
+
+    Example usage:
+
+    # create csv file from all tensorflow logs in provided directory (.)
+    # and write it to folder "./converted"
+    tflogs2pandas.py . --write-csv --no-write-pkl --o converted
+
+    # creaste csv file from tensorflow logfile only and write into
+    # and write it to folder "./converted"
+    tflogs2pandas.py tflog.hostname.12345 --write-csv --no-write-pkl --o converted
+    """
+    pp = pprint.PrettyPrinter(indent=4)
+    if os.path.isdir(logdir_or_logfile):
+        # Get all event* runs from logging_dir subdirectories
+        event_paths = glob.glob(os.path.join(logdir_or_logfile, "event*"))
+    elif os.path.isfile(logdir_or_logfile):
+        event_paths = [logdir_or_logfile]
+    else:
+        raise ValueError(
+            "input argument {} has to be a file or a directory".format(
+                logdir_or_logfile
+            )
+        )
+    # Call & append
+    if event_paths:
+        pp.pprint("Found tensorflow logs to process:")
+        pp.pprint(event_paths)
+        all_logs = many_logs2pandas(event_paths)
+        pp.pprint("Head of created dataframe")
+        pp.pprint(all_logs.head())
+
+        os.makedirs(out_dir, exist_ok=True)
+        if write_csv:
+            print("saving to csv file")
+            out_file = os.path.join(out_dir, "all_training_logs_in_one_file.csv")
+            print(out_file)
+            all_logs.to_csv(out_file, index=None)
+        if write_pkl:
+            print("saving to pickle file")
+            out_file = os.path.join(out_dir, "all_training_logs_in_one_file.pkl")
+            print(out_file)
+            all_logs.to_pickle(out_file)
+    else:
+        print("No event paths have been found.")
+
+
+if __name__ == "__main__":
+    main()