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"""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 = []
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['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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