1. 出力 mAP 曲線
コンソールのコードは次のとおりです。
python tools/analysis_tools/analyze_logs.py plot_curve configs/lad_log_lad_r50_paa_r101_fpn_coco_1x/20230105_221053.log.json --keys mAP --legend mAP --out mAP4.jpg
「analyze_logs」のコードは次のとおりです。
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import json
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
def cal_train_time(log_dicts, args):
for i, log_dict in enumerate(log_dicts):
print(f'{
"-" * 5}Analyze train time of {
args.json_logs[i]}{
"-" * 5}')
all_times = []
for epoch in log_dict.keys():
if args.include_outliers:
all_times.append(log_dict[epoch]['time'])
else:
all_times.append(log_dict[epoch]['time'][1:])
if not all_times:
raise KeyError(
'Please reduce the log interval in the config so that'
'interval is less than iterations of one epoch.')
all_times = np.array(all_times)
epoch_ave_time = all_times.mean(-1)
slowest_epoch = epoch_ave_time.argmax()
fastest_epoch = epoch_ave_time.argmin()
std_over_epoch = epoch_ave_time.std()
print(f'slowest epoch {
slowest_epoch + 1}, '
f'average time is {
epoch_ave_time[slowest_epoch]:.4f}')
print(f'fastest epoch {
fastest_epoch + 1}, '
f'average time is {
epoch_ave_time[fastest_epoch]:.4f}')
print(f'time std over epochs is {
std_over_epoch:.4f}')
print(f'average iter time: {
np.mean(all_times):.4f} s/iter')
print()
def plot_curve(log_dicts, args):
if args.backend is not None:
plt.switch_backend(args.backend)
sns.set_style(args.style)
# if legend is None, use {filename}_{key} as legend
legend = args.legend
if legend is None:
legend = []
for json_log in args.json_logs:
for metric in args.keys:
legend.append(f'{
json_log}_{
metric}')
assert len(legend) == (len(args.json_logs) * len(args.keys))
metrics = args.keys
num_metrics = len(metrics)
for i, log_dict in enumerate(log_dicts):
epochs = list(log_dict.keys())
for j, metric in enumerate(metrics):
print(f'plot curve of {
args.json_logs[i]}, metric is {
metric}')
if metric not in log_dict[epochs[int(args.eval_interval) - 1]]:
if 'mAP' in metric:
raise KeyError(
f'{
args.json_logs[i]} does not contain metric '
f'{
metric}. Please check if "--no-validate" is '
'specified when you trained the model.')
raise KeyError(
f'{
args.json_logs[i]} does not contain metric {
metric}. '
'Please reduce the log interval in the config so that '
'interval is less than iterations of one epoch.')
if 'mAP' in metric:
xs = []
ys = []
for epoch in epochs:
ys += log_dict[epoch][metric]
if 'val' in log_dict[epoch]['mode']:
xs.append(epoch)
plt.xlabel('epoch')
plt.plot(xs, ys, label=legend[i * num_metrics + j], marker='o')
else:
xs = []
ys = []
num_iters_per_epoch = log_dict[epochs[0]]['iter'][-2]
for epoch in epochs:
iters = log_dict[epoch]['iter']
if log_dict[epoch]['mode'][-1] == 'val':
iters = iters[:-1]
xs.append(
np.array(iters) + (epoch - 1) * num_iters_per_epoch)
ys.append(np.array(log_dict[epoch][metric][:len(iters)]))
xs = np.concatenate(xs)
ys = np.concatenate(ys)
plt.xlabel('iter')
plt.plot(
xs, ys, label=legend[i * num_metrics + j], linewidth=0.5)
plt.legend()
if args.title is not None:
plt.title(args.title)
if args.out is None:
plt.show()
else:
print(f'save curve to: {
args.out}')
plt.savefig(args.out)
plt.cla()
def add_plot_parser(subparsers):
parser_plt = subparsers.add_parser(
'plot_curve', help='parser for plotting curves')
parser_plt.add_argument(
'json_logs',
type=str,
nargs='+',
help='path of train log in json format')
parser_plt.add_argument(
'--keys',
type=str,
nargs='+',
default=['bbox_mAP'],
help='the metric that you want to plot')
parser_plt.add_argument(
'--start-epoch',
type=str,
default='1',
help='the epoch that you want to start')
parser_plt.add_argument(
'--eval-interval',
type=str,
default='1',
help='the eval interval when training')
parser_plt.add_argument('--title', type=str, help='title of figure')
parser_plt.add_argument(
'--legend',
type=str,
nargs='+',
default=None,
help='legend of each plot')
parser_plt.add_argument(
'--backend', type=str, default=None, help='backend of plt')
parser_plt.add_argument(
'--style', type=str, default='dark', help='style of plt')
parser_plt.add_argument('--out', type=str, default=None)
def add_time_parser(subparsers):
parser_time = subparsers.add_parser(
'cal_train_time',
help='parser for computing the average time per training iteration')
parser_time.add_argument(
'json_logs',
type=str,
nargs='+',
help='path of train log in json format')
parser_time.add_argument(
'--include-outliers',
action='store_true',
help='include the first value of every epoch when computing '
'the average time')
def parse_args():
parser = argparse.ArgumentParser(description='Analyze Json Log')
# currently only support plot curve and calculate average train time
subparsers = parser.add_subparsers(dest='task', help='task parser')
add_plot_parser(subparsers)
add_time_parser(subparsers)
args = parser.parse_args()
return args
def load_json_logs(json_logs):
# load and convert json_logs to log_dict, key is epoch, value is a sub dict
# keys of sub dict is different metrics, e.g. memory, bbox_mAP
# value of sub dict is a list of corresponding values of all iterations
log_dicts = [dict() for _ in json_logs]
for json_log, log_dict in zip(json_logs, log_dicts):
with open(json_log, 'r') as log_file:
for i, line in enumerate(log_file):
log = json.loads(line.strip())
# skip the first training info line
if i == 0:
continue
# skip lines without `epoch` field
if 'epoch' not in log:
continue
epoch = log.pop('epoch')
if epoch not in log_dict:
log_dict[epoch] = defaultdict(list)
for k, v in log.items():
log_dict[epoch][k].append(v)
return log_dicts
def main():
args = parse_args()
json_logs = args.json_logs
for json_log in json_logs:
assert json_log.endswith('.json')
log_dicts = load_json_logs(json_logs)
eval(args.task)(log_dicts, args)
if __name__ == '__main__':
main()
2.「loss_cls」を出力します。
コンソール入力:
python tools/analysis_tools/analyze_logs.py plot_curve configs/lad_log_lad_r50_paa_r101_fpn_coco_1x/20230105_221053.log.json --keys loss_cls --legend loss_cls
または直接保存
python tools/analysis_tools/analyze_logs.py plot_curve configs/lad_log_lad_r50_paa_r101_fpn_coco_1x/20230105_221053.log.json --keys loss_cls --legend loss_cls --out loss_cls.jpg
IR曲線
python tools/analysis_tools/analyze_logs.py plot_curve configs/lad_log_lad_r50_paa_r101_fpn_coco_1x/20230105_221053.log.json --keys lr --legend lr --out lr.jpg
- 2 つの mAP 曲線を比較し、1 つのグラフに描画します
python tools/analysis_tools/analyze_logs.py plot_curve configs/lad_log_lad_r50_paa_r101_fpn_coco_1x/202301
05_221053.log.json configs/lad_log_lad_r50_paa_r101_fpn_coco_1x2/20230322_205043.log.json --keys mAP --legend run1 run2
複数の曲線が 1 つのグラフに配置されており、コンソール コードは次のとおりです。
python tools/analysis_tools/analyze_logs.py plot_curve configs/lad_log_lad_r50_paa_r101_fpn_coco_1x/20230105_221053.log.json configs/mask_rcnn_log_mask_rcnn_r50_fpn_1x_coco/20230116_132706.log.json configs/lad_log_lad_r50_paa_r101_fpn_coco_1x2/20230322_205043.log.json --keys mAP --legend run1 run2 run3
参考文献: https://zhuanlan.zhihu.com/p/486190295?utm_id=0