forked from rcmalli/keras-vggface
-
Notifications
You must be signed in to change notification settings - Fork 13
/
Copy pathtest.py
61 lines (48 loc) · 2.15 KB
/
test.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
import numpy as np
from keras_vggface import VGGFace
from keras.preprocessing import image
from keras_vggface import utils
import keras
import unittest
class VGGFaceTests(unittest.TestCase):
def testVGG16(self):
keras.backend.set_image_dim_ordering('tf')
model = VGGFace(model='vgg16')
img = image.load_img('image/ajb.jpg', target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = utils.preprocess_input(x, version=1)
preds = model.predict(x)
#print ('\n', "VGG16")
#print('\n',preds)
#print('\n','Predicted:', utils.decode_predictions(preds))
self.assertIn('A.J._Buckley', utils.decode_predictions(preds)[0][0][0])
self.assertAlmostEqual(utils.decode_predictions(preds)[0][0][1], 0.9790116,places=3)
def testRESNET50(self):
keras.backend.set_image_dim_ordering('tf')
model = VGGFace(model='resnet50')
img = image.load_img('image/ajb.jpg', target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = utils.preprocess_input(x, version=2)
preds = model.predict(x)
#print ('\n',"RESNET50")
#print('\n',preds)
#print('\n','Predicted:', utils.decode_predictions(preds))
self.assertIn('A._J._Buckley', utils.decode_predictions(preds)[0][0][0])
self.assertAlmostEqual(utils.decode_predictions(preds)[0][0][1], 0.91819614,places=3)
# def testSENET50(self):
# keras.backend.set_image_dim_ordering('tf')
# model = VGGFace(model='senet50')
# img = image.load_img('image/ajb.jpg', target_size=(224, 224))
# x = image.img_to_array(img)
# x = np.expand_dims(x, axis=0)
# x = utils.preprocess_input(x, version=2)
# preds = model.predict(x)
# #print ('\n', "SENET50")
# #print('\n',preds)
# #print('\n','Predicted:', utils.decode_predictions(preds))
# self.assertIn(utils.decode_predictions(preds)[0][0][0], 'A._J._Buckley')
# self.assertAlmostEqual(utils.decode_predictions(preds)[0][0][1], 0.91819614)
if __name__ == '__main__':
unittest.main()