Spaces:
Runtime error
Runtime error
File size: 4,478 Bytes
412c852 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 |
# 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()
|