216 lines
7.2 KiB
Python
216 lines
7.2 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import os
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import re
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import matplotlib.pyplot as plt
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import numpy as np
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from mmcls.utils import load_json_log
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TEST_METRICS = ('precision', 'recall', 'f1_score', 'support', 'mAP', 'CP',
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'CR', 'CF1', 'OP', 'OR', 'OF1', 'accuracy')
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def cal_train_time(log_dicts, args):
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"""Compute the average time per training iteration."""
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for i, log_dict in enumerate(log_dicts):
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print(f'{"-" * 5}Analyze train time of {args.json_logs[i]}{"-" * 5}')
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all_times = []
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for epoch in log_dict.keys():
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if args.include_outliers:
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all_times.append(log_dict[epoch]['time'])
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else:
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all_times.append(log_dict[epoch]['time'][1:])
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all_times = np.array(all_times)
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epoch_ave_time = all_times.mean(-1)
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slowest_epoch = epoch_ave_time.argmax()
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fastest_epoch = epoch_ave_time.argmin()
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std_over_epoch = epoch_ave_time.std()
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print(f'slowest epoch {slowest_epoch + 1}, '
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f'average time is {epoch_ave_time[slowest_epoch]:.4f}')
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print(f'fastest epoch {fastest_epoch + 1}, '
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f'average time is {epoch_ave_time[fastest_epoch]:.4f}')
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print(f'time std over epochs is {std_over_epoch:.4f}')
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print(f'average iter time: {np.mean(all_times):.4f} s/iter')
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print()
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def get_legends(args):
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"""if legend is None, use {filename}_{key} as legend."""
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legend = args.legend
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if legend is None:
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legend = []
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for json_log in args.json_logs:
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for metric in args.keys:
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# remove '.json' in the end of log names
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basename = os.path.basename(json_log)[:-5]
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if basename.endswith('.log'):
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basename = basename[:-4]
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legend.append(f'{basename}_{metric}')
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assert len(legend) == (len(args.json_logs) * len(args.keys))
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return legend
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def plot_phase_train(metric, log_dict, epochs, curve_label, json_log):
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"""plot phase of train cruve."""
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if metric not in log_dict[epochs[0]]:
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raise KeyError(f'{json_log} does not contain metric {metric}'
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f' in train mode')
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xs, ys = [], []
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for epoch in epochs:
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iters = log_dict[epoch]['iter']
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if log_dict[epoch]['mode'][-1] == 'val':
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iters = iters[:-1]
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num_iters_per_epoch = iters[-1]
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assert len(iters) > 0, (
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'The training log is empty, please try to reduce the '
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'interval of log in config file.')
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xs.append(np.array(iters) / num_iters_per_epoch + (epoch - 1))
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ys.append(np.array(log_dict[epoch][metric][:len(iters)]))
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xs = np.concatenate(xs)
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ys = np.concatenate(ys)
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plt.xlabel('Epochs')
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plt.plot(xs, ys, label=curve_label, linewidth=0.75)
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def plot_phase_val(metric, log_dict, epochs, curve_label, json_log):
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"""plot phase of val cruves."""
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# some epoch may not have evaluation. as [(train, 5),(val, 1)]
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xs = [e for e in epochs if metric in log_dict[e]]
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ys = [log_dict[e][metric] for e in xs if metric in log_dict[e]]
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assert len(xs) > 0, (f'{json_log} does not contain metric {metric}')
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plt.xlabel('Epochs')
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plt.plot(xs, ys, label=curve_label, linewidth=0.75)
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def plot_curve_helper(log_dicts, metrics, args, legend):
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"""plot curves from log_dicts by metrics."""
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num_metrics = len(metrics)
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for i, log_dict in enumerate(log_dicts):
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epochs = list(log_dict.keys())
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for j, metric in enumerate(metrics):
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json_log = args.json_logs[i]
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print(f'plot curve of {json_log}, metric is {metric}')
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curve_label = legend[i * num_metrics + j]
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if any(m in metric for m in TEST_METRICS):
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plot_phase_val(metric, log_dict, epochs, curve_label, json_log)
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else:
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plot_phase_train(metric, log_dict, epochs, curve_label,
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json_log)
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plt.legend()
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def plot_curve(log_dicts, args):
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"""Plot train metric-iter graph."""
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# set backend and style
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if args.backend is not None:
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plt.switch_backend(args.backend)
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try:
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import seaborn as sns
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sns.set_style(args.style)
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except ImportError:
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print("Attention: The plot style won't be applied because 'seaborn' "
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'package is not installed, please install it if you want better '
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'show style.')
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# set plot window size
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wind_w, wind_h = args.window_size.split('*')
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wind_w, wind_h = int(wind_w), int(wind_h)
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plt.figure(figsize=(wind_w, wind_h))
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# get legends and metrics
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legends = get_legends(args)
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metrics = args.keys
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# plot curves from log_dicts by metrics
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plot_curve_helper(log_dicts, metrics, args, legends)
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# set title and show or save
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if args.title is not None:
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plt.title(args.title)
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if args.out is None:
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plt.show()
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else:
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print(f'save curve to: {args.out}')
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plt.savefig(args.out)
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plt.cla()
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def add_plot_parser(subparsers):
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parser_plt = subparsers.add_parser(
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'plot_curve', help='parser for plotting curves')
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parser_plt.add_argument(
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'json_logs',
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type=str,
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nargs='+',
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help='path of train log in json format')
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parser_plt.add_argument(
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'--keys',
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type=str,
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nargs='+',
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default=['loss'],
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help='the metric that you want to plot')
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parser_plt.add_argument('--title', type=str, help='title of figure')
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parser_plt.add_argument(
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'--legend',
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type=str,
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nargs='+',
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default=None,
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help='legend of each plot')
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parser_plt.add_argument(
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'--backend', type=str, default=None, help='backend of plt')
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parser_plt.add_argument(
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'--style', type=str, default='whitegrid', help='style of plt')
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parser_plt.add_argument('--out', type=str, default=None)
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parser_plt.add_argument(
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'--window-size',
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default='12*7',
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help='size of the window to display images, in format of "$W*$H".')
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def add_time_parser(subparsers):
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parser_time = subparsers.add_parser(
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'cal_train_time',
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help='parser for computing the average time per training iteration')
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parser_time.add_argument(
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'json_logs',
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type=str,
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nargs='+',
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help='path of train log in json format')
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parser_time.add_argument(
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'--include-outliers',
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action='store_true',
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help='include the first value of every epoch when computing '
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'the average time')
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def parse_args():
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parser = argparse.ArgumentParser(description='Analyze Json Log')
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# currently only support plot curve and calculate average train time
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subparsers = parser.add_subparsers(dest='task', help='task parser')
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add_plot_parser(subparsers)
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add_time_parser(subparsers)
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args = parser.parse_args()
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if args.window_size != '':
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assert re.match(r'\d+\*\d+', args.window_size), \
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"'window-size' must be in format 'W*H'."
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return args
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def main():
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args = parse_args()
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json_logs = args.json_logs
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for json_log in json_logs:
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assert json_log.endswith('.json')
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log_dicts = [load_json_log(json_log) for json_log in json_logs]
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eval(args.task)(log_dicts, args)
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if __name__ == '__main__':
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main()
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