Spaces:
Runtime error
Runtime error
| # 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])): | |
| plot_iters.append(epoch_logs['step'][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('step') | |
| 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 step, 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] | |
| prev_step = 0 | |
| for json_log, log_dict in zip(json_logs, log_dicts): | |
| with open(json_log) as log_file: | |
| for line in log_file: | |
| log = json.loads(line.strip()) | |
| # the final step in json file is 0. | |
| if 'step' in log and log['step'] != 0: | |
| step = log['step'] | |
| prev_step = step | |
| else: | |
| step = prev_step | |
| if step not in log_dict: | |
| log_dict[step] = defaultdict(list) | |
| for k, v in log.items(): | |
| log_dict[step][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() | |