mmdetection各种评价指标的曲线图

1.输出mAP曲线图

控制台代码如下：

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”代码如下：

# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import json
from collections import defaultdictimport matplotlib.pyplot as plt
import numpy as np
import seaborn as snsdef 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 legendlegend = args.legendif 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.keysnum_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 timesubparsers = parser.add_subparsers(dest='task', help='task parser')add_plot_parser(subparsers)add_time_parser(subparsers)args = parser.parse_args()return argsdef 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 iterationslog_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 lineif i == 0:continue# skip lines without `epoch` fieldif 'epoch' not in log:continueepoch = 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_dictsdef main():args = parse_args()json_logs = args.json_logsfor 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.输出“loss_cls”

控制台输入：

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

或者直接保存

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

lr曲线

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

3. 对比两个mAP曲线，画在一幅图当中

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

多个曲线放在一个图当中，控制台的代码如下：

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