-
Notifications
You must be signed in to change notification settings - Fork 0
/
rico_baseline_old.py
85 lines (62 loc) · 3.48 KB
/
rico_baseline_old.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
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
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 20 09:01:06 2019
@author: dipu
"""
import numpy as np
import json
import pickle
from scipy.spatial.distance import cdist
from perform_tests import getBoundingBoxes
from eval_metrics_old.get_overall_IOU import get_overall_IOU
from eval_metrics_old.get_overall_Classwise_IOU import get_overall_Classwise_IOU
from eval_metrics_old.get_overall_pix_acc import get_overall_pix_acc
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
#boundingBoxes = getBoundingBoxes()
#def main():
path = '/mnt/amber/scratch/Dipu/RICO/ui_layout_vectors/ui_layout_vectors'
vectors = np.load('%s/ui_vectors.npy'%(path))
with open( '%s/ui_names.json'%(path), 'rb') as f:
ui_names_json = json.load(f)
ui_names = ui_names_json['ui_names']
ui_names = [x[:-4] for x in ui_names]
model_name = 'model_CAE_emb2688'
feat_file = 'features-{}.p'.format(model_name)
with open(feat_file, 'rb') as f:
features = pickle.load(f)
g_fnames = features['g_fnames']
q_fnames = features['q_fnames']
q_feat = features['q_feat']
qind = [ui_names.index(x) for x in q_fnames]
gnames = [x for x in g_fnames if (x in ui_names)]
print('Number of missing gallery images:', len(g_fnames) - len(gnames))
gind = [ui_names.index(x) for x in gnames]
q_feat = vectors[qind,:]
g_feat = vectors[gind,:]
distances = cdist(q_feat, g_feat, metric= 'euclidean')
sort_inds = np.argsort(distances)
# overallMeanClassIou, overallMeanWeightedClassIou, iouarray1 = get_overall_Classwise_IOU(boundingBoxes,sort_inds,g_fnames,q_fnames, topk = [1,5,10])
# overallMeanAvgPixAcc, overallMeanWeightedPixAcc, classPixAcc = get_overall_pix_acc(boundingBoxes,sort_inds,g_fnames,q_fnames, topk = [1,5,10])
#
# print('The overallMeanClassIou = ' + str([ '{:.3f}'.format(x) for x in overallMeanClassIou]) + '\n')
# print('The overallMeanWeightedClassIou = ' + str([ '{:.3f}'.format(x) for x in overallMeanWeightedClassIou]) + '\n')
# print('The overallMeanAvgPixAcc = ' + str([ '{:.3f}'.format(x) for x in overallMeanAvgPixAcc]) + '\n')
# print('The overallMeanWeightedPixAcc = ' + str([ '{:.3f}'.format(x) for x in overallMeanWeightedPixAcc]) + '\n')
#
#overallMeanIou, overallMeanWeightedIou, _ = get_overall_IOU(boundingBoxes,sort_inds,gnames,q_fnames)
overallMeanClassIou, overallMeanWeightedClassIou, iouarray2 = get_overall_Classwise_IOU(boundingBoxes,sort_inds,gnames,q_fnames)
overallMeanAvgPixAcc, overallMeanWeightedPixAcc, classpixAcc= get_overall_pix_acc(boundingBoxes,sort_inds,gnames,q_fnames)
overallMeanClassIou, overallMeanWeightedClassIou, classwiseClassIoU = get_overall_Classwise_IOU(boundingBoxes,sort_inds,g_fnames,q_fnames, topk = [1,5,10])
overallMeanAvgPixAcc, overallMeanWeightedPixAcc, classPixAcc = get_overall_pix_acc(boundingBoxes,sort_inds,g_fnames,q_fnames, topk = [1,5,10])
print('The baseline model using 64dim emb provided, UIST 2017')
print('\n\n Mean IOU/PixelAcc Values:')
#print('The overallMeanIou = {:.3f} '.format(overallMeanIou))
#print('The overallMeanWeightedIou = {:.3f}'.format(overallMeanWeightedIou))
print('The overallMeanClassIou = {:.3f})'.format(overallMeanClassIou))
print('The overallMeanWeightedClassIou = {:.3f})'.format(overallMeanWeightedClassIou))
print('The overallMeanAvgPixAcc = {:.3f}'.format(overallMeanAvgPixAcc))
print('The overallMeanWeightedPixAcc = {:.3f} '.format(overallMeanWeightedPixAcc))
#if __name__ == '__main__':
# main()