基于python+pytorch的车道线检测源码+项目部署教程.zip

# 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.start_epoch) - 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 = np.arange( int(args.start_epoch), max(epochs) + 1, int(args.eval_interval)) ys = [] for epoch in epochs: ys += log_dict[epoch][metric] ax = plt.gca() ax.set_xticks(xs) 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()