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- # Copyright (c) OpenMMLab. All rights reserved.
- """Modified from https://github.com/open-
- mmlab/mmdetection/blob/master/tools/analysis_tools/analyze_logs.py."""
- import argparse
- import json
- from collections import defaultdict
-
- import matplotlib.pyplot as plt
- import seaborn as sns
-
-
- 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}')
- plot_epochs = []
- plot_iters = []
- plot_values = []
- # In some log files exist lines of validation,
- # `mode` list is used to only collect iter number
- # of training line.
- for epoch in epochs:
- epoch_logs = log_dict[epoch]
- if metric not in epoch_logs.keys():
- continue
- if metric in ['mIoU', 'mAcc', 'aAcc']:
- plot_epochs.append(epoch)
- plot_values.append(epoch_logs[metric][0])
- else:
- for idx in range(len(epoch_logs[metric])):
- if epoch_logs['mode'][idx] == 'train':
- plot_iters.append(epoch_logs['iter'][idx])
- plot_values.append(epoch_logs[metric][idx])
- ax = plt.gca()
- label = legend[i * num_metrics + j]
- if metric in ['mIoU', 'mAcc', 'aAcc']:
- ax.set_xticks(plot_epochs)
- plt.xlabel('epoch')
- plt.plot(plot_epochs, plot_values, label=label, marker='o')
- else:
- plt.xlabel('iter')
- plt.plot(plot_iters, plot_values, label=label, 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 parse_args():
- parser = argparse.ArgumentParser(description='Analyze Json Log')
- parser.add_argument(
- 'json_logs',
- type=str,
- nargs='+',
- help='path of train log in json format')
- parser.add_argument(
- '--keys',
- type=str,
- nargs='+',
- default=['mIoU'],
- help='the metric that you want to plot')
- parser.add_argument('--title', type=str, help='title of figure')
- parser.add_argument(
- '--legend',
- type=str,
- nargs='+',
- default=None,
- help='legend of each plot')
- parser.add_argument(
- '--backend', type=str, default=None, help='backend of plt')
- parser.add_argument(
- '--style', type=str, default='dark', help='style of plt')
- parser.add_argument('--out', type=str, default=None)
- 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
- # 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)
- plot_curve(log_dicts, args)
-
-
- if __name__ == '__main__':
- main()
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