parsing training log and plot

save loss data to csv

scripts/log_parser/log_parser.py

@@ -0,0 +1,105 @@
+# -*- coding: utf-8 -*-
+# @Time    : 2018/4/25 20:28
+# @Author  : Adesun
+# @Site    : https://github.com/Adesun
+# @File    : log_parser.py
+
+import argparse
+import logging
+import os
+import platform
+import re
+import sys
+
+import matplotlib.pyplot as plt
+from matplotlib.ticker import MultipleLocator, FormatStrFormatter
+
+
+def get_file_name_and_ext(filename):
+    (file_path, temp_filename) = os.path.split(filename)
+    (file_name, file_ext) = os.path.splitext(temp_filename)
+    return file_name, file_ext
+
+
+def show_message(message, stop=False):
+    print(message)
+    if stop:
+        sys.exit(0)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="training log parser by DeepKeeper ")
+    parser.add_argument('--source-dir', dest='source_dir', type=str, default='./',
+                        help='the log source directory')
+    parser.add_argument('--save-dir', dest='save_dir', type=str, default='./',
+                        help='the directory to be saved')
+    parser.add_argument('--csv-file', dest='csv_file', type=str, default="",
+                        help='training log file')
+    parser.add_argument('--log-file', dest='log_file', type=str, default="",
+                        help='training log file')
+    parser.add_argument('--show', dest='show_plot', type=bool, default=False,
+                        help='whether to show')
+    return parser.parse_args()
+
+
+def log_parser(args):
+    if not args.log_file:
+        show_message('log file must be specified.', True)
+
+    log_path = os.path.join(args.source_dir, args.log_file)
+    if not os.path.exists(log_path):
+        show_message('log file does not exist.', True)
+
+    file_name, _ = get_file_name_and_ext(log_path)
+    log_content = open(log_path).read()
+
+    iterations = []
+    losses = []
+    fig, ax = plt.subplots()
+
+    major_locator = MultipleLocator(1)
+    minor_locator = MultipleLocator(0.5)
+
+    ax.yaxis.set_major_locator(major_locator)
+    ax.yaxis.set_minor_locator(minor_locator)
+
+    ax.yaxis.grid(True, which='minor')
+
+    pattern = re.compile(r"([\d]*): .*?, (.*?) avg,")
+    matches = pattern.findall(log_content)
+    counter = 0
+    log_count = len(matches)
+
+    if args.csv_file != '':
+        csv_path = os.path.join(args.save_dir, args.csv_file)
+        out_file = open(csv_path, 'w')
+    else:
+        csv_path = os.path.join(args.save_dir, file_name + '.csv')
+        out_file = open(csv_path, 'w')
+
+    for match in matches:
+        counter += 1
+        if log_count > 200:
+            if counter % 200 == 0:
+                print('parsing {}/{}'.format(counter, log_count))
+        else:
+            print('parsing {}/{}'.format(counter, log_count))
+        iteration, loss = match
+        iterations.append(int(iteration))
+        losses.append(float(loss))
+        out_file.write(iteration + ',' + loss + '\n')
+
+    ax.plot(iterations, losses)
+    plt.xlabel('Iteration')
+    plt.ylabel('Loss')
+    plt.grid()
+
+    save_path = os.path.join(args.save_dir, file_name + '.png')
+    plt.savefig(save_path, dpi=300)
+    if args.show_plot:
+        plt.show()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    log_parser(args)