--- a
+++ b/tools/analysis/analyze_logs.py
@@ -0,0 +1,167 @@
+# 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:])
+        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[0]]:
+                raise KeyError(
+                    f'{args.json_logs[i]} does not contain metric {metric}')
+            xs = []
+            ys = []
+            num_iters_per_epoch = log_dict[epochs[0]]['iter'][-1]
+            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=['top1_acc'],
+        help='the metric that you want to plot')
+    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, top1_acc
+    # 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 line in log_file:
+                log = json.loads(line.strip())
+                # 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()