-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathunet_cifar10.py
198 lines (165 loc) · 7.02 KB
/
unet_cifar10.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
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
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
import numpy as np
from keras import models, backend
from keras import datasets, utils
import matplotlib.pyplot as plt
from keras.layers import Input, Conv2D, MaxPooling2D, Dropout, \
UpSampling2D, BatchNormalization, Concatenate, Activation
from sklearn.preprocessing import minmax_scale
from keraspp.skeras import plot_loss
import argparse
from distutils import util
class UNET(models.Model):
def __init__(self, org_shape, n_ch):
ic = 3 if backend.image_data_format() == 'channels_last' else 1
def conv(x, n_f, mp_flag=True):
x = MaxPooling2D((2, 2), padding='same')(x) if mp_flag else x
x = Conv2D(n_f, (3, 3), padding='same')(x)
x = BatchNormalization()(x)
x = Activation('tanh')(x)
x = Dropout(0.05)(x)
x = Conv2D(n_f, (3, 3), padding='same')(x)
x = BatchNormalization()(x)
x = Activation('tanh')(x)
return x
def deconv_unet(x, e, n_f):
x = UpSampling2D((2, 2))(x)
x = Concatenate(axis=ic)([x, e])
x = Conv2D(n_f, (3, 3), padding='same')(x)
x = BatchNormalization()(x)
x = Activation('tanh')(x)
x = Conv2D(n_f, (3, 3), padding='same')(x)
x = BatchNormalization()(x)
x = Activation('tanh')(x)
return x
original = Input(shape=org_shape)
c1 = conv(original, 16, mp_flag=False)
c2 = conv(c1, 32)
encoded = conv(c2, 64)
x = deconv_unet(encoded, c2, 32)
x = deconv_unet(x, c1, 16)
decoded = Conv2D(n_ch, (3, 3), activation='sigmoid', padding='same')(x)
super().__init__(original, decoded)
self.compile(optimizer='adadelta', loss='mse')
class DATA():
def __init__(self, in_ch=None):
(x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data()
if x_train.ndim == 4:
if backend.image_data_format() == 'channels_first':
n_ch, img_rows, img_cols = x_train.shape[1:]
else:
img_rows, img_cols, n_ch = x_train.shape[1:]
else:
img_rows, img_cols = x_train.shape[1:]
n_ch = 1
in_ch = n_ch if in_ch is None else in_ch
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
def RGB2Gray(X, fmt):
if fmt == 'channels_first':
R = X[:, 0:1]
G = X[:, 1:2]
B = X[:, 2:3]
else:
R = X[..., 0:1]
G = X[..., 1:2]
B = X[..., 2:3]
return 0.299 * R + 0.587 * G + 0.114 * B
def RGB2RG(x_train_out, x_test_out, fmt):
if fmt == 'channels_first':
x_train_in = x_train_out[:, :2]
x_test_in = x_test_out[:, :2]
else:
x_train_in = x_train_out[..., :2]
x_test_in = x_test_out[..., :2]
return x_train_in, x_test_in
if backend.image_data_format() == 'channels_first':
x_train_out = x_train.reshape(x_train.shape[0], n_ch, img_rows, img_cols)
x_test_out = x_test.reshape(x_test.shape[0], n_ch, img_rows, img_cols)
input_shape = (in_ch, img_rows, img_cols)
else:
x_train_out = x_train.reshape(x_train.shape[0], img_rows, img_cols, n_ch)
x_test_out = x_test.reshape(x_test.shape[0], img_rows, img_cols, n_ch)
input_shape = (img_rows, img_cols, in_ch)
if in_ch == 1 and n_ch == 3:
x_train_in = RGB2Gray(x_train_out, backend.image_data_format())
x_test_in = RGB2Gray(x_test_out, backend.image_data_format())
elif in_ch == 2 and n_ch == 3:
x_train_in, x_test_in = RGB2RG(x_train_out, x_test_out, backend.image_data_format())
else:
x_train_in = x_train_out
x_test_in = x_test_out
self.input_shape = input_shape
self.x_train_in, self.x_train_out = x_train_in, x_train_out
self.x_test_in, self.x_test_out = x_test_in, x_test_out
self.n_ch = n_ch
self.in_ch = in_ch
def show_images(data, unet):
x_test_in = data.x_test_in
x_test_out = data.x_test_out
decoded_imgs_org = unet.predict(x_test_in)
decoded_imgs = decoded_imgs_org
if backend.image_data_format() == 'channels_first':
print(x_test_out.shape)
x_test_out = x_test_out.swapaxes(1, 3).swapaxes(1, 2)
print(x_test_out.shape)
decoded_imgs = decoded_imgs.swapaxes(1, 3).swapaxes(1, 2)
if data.in_ch == 1:
x_test_in = x_test_in[:, 0, ...]
elif data.in_ch == 2:
print(x_test_out.shape)
x_test_in_tmp = np.zeros_like(x_test_out)
x_test_in = x_test_in.swapaxes(1, 3).swapaxes(1, 2)
x_test_in_tmp[..., :2] = x_test_in
x_test_in = x_test_in_tmp
else:
x_test_in = x_test_in.swapaxes(1, 3).swapaxes(1, 2)
else:
if data.in_ch == 1:
x_test_in = x_test_in[..., 0]
elif data.in_ch == 2:
x_test_in_tmp = np.zeros_like(x_test_out)
x_test_in_tmp[..., :2] = x_test_in
x_test_in = x_test_in_tmp
n = 10
plt.figure(figsize=(20, 6))
for i in range(n):
ax = plt.subplot(3, n, i + 1)
if x_test_in.ndim < 4:
plt.imshow(x_test_in[i], cmap='gray')
else:
plt.imshow(x_test_in[i])
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax = plt.subplot(3, n, i + 1 + n)
plt.imshow(decoded_imgs[i])
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax = plt.subplot(3, n, i + 1 + n * 2)
plt.imshow(x_test_out[i])
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
def main(in_ch=1, epochs=10, batch_size=512, fig=True):
data = DATA(in_ch=in_ch)
print(data.input_shape, data.x_train_in.shape)
unet = UNET(data.input_shape, data.n_ch)
history = unet.fit(data.x_train_in, data.x_train_out,
epochs=epochs,
batch_size=batch_size,
shuffle=True,
validation_split=0.2)
if fig:
plot_loss(history)
show_images(data, unet)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='UNET for Cifar-10: Gray to RGB')
parser.add_argument('--input_channels', type=int, default=1, help='input channels (default: 1)')
parser.add_argument('--epochs', type=int, default=10, help='training epochs (default: 10)')
parser.add_argument('--batch_size', type=int, default=512, help='batch size (default: 1000)')
parser.add_argument('--fig', type=lambda x: bool(util.strtobool(x)), default=True, help='flag to show figures (default: True)')
args = parser.parse_args()
print("Aargs:", args)
print(args.fig)
main(args.input_channels, args.epochs, args.batch_size, args.fig)