-
Notifications
You must be signed in to change notification settings - Fork 221
/
visualize_metrics.py
54 lines (41 loc) · 1.7 KB
/
visualize_metrics.py
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
# Copyright 2020 Magic Leap, Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Originating Author: Zak Murez (zak.murez.com)
import argparse
import json
import numpy as np
import os
def visualize(fname):
key_names = ['AbsRel', 'AbsDiff', 'SqRel', 'RMSE', 'LogRMSE', 'r1', 'r2', 'r3', 'complete', 'dist1', 'dist2', 'prec', 'recal', 'fscore', 'l1']
metrics = json.load(open(fname, 'r'))
metrics = sorted([(scene, metric) for scene, metric in metrics.items()], key=lambda x: x[0])
scenes = [m[0] for m in metrics]
metrics = [m[1] for m in metrics]
keys = metrics[0].keys()
metrics1 = {m:[] for m in keys}
for m in metrics:
for k in keys:
metrics1[k].append(m[k])
for k in key_names:
if k in metrics1:
v = np.nanmean(np.array(metrics1[k]))
else:
v = np.nan
print('%10s %0.3f'%(k, v))
def main():
parser = argparse.ArgumentParser(description="Atlas Testing")
parser.add_argument("--model", required=True, metavar="FILE",
help="path to metrics file")
args = parser.parse_args()
rslt_file = os.path.join(args.model, 'metrics.json')
visualize(rslt_file)
if __name__ == "__main__":
main()