/
new_r2unet.py
121 lines (100 loc) · 4.62 KB
/
new_r2unet.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
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 7 00:35:36 2021
@author: kaushik.dutta
"""
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Conv2D, Conv2DTranspose, MaxPooling2D, concatenate, Input, Dropout, Add, Activation, UpSampling2D, Concatenate
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler
from tensorflow.keras import backend as K
import numpy as np
from tensorflow.keras.metrics import Precision, Recall, AUC, Accuracy
K.set_image_data_format('channels_last')
smooth = 1
def dice_coef(y_true, y_pred):
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)
def dice_loss(y_true, y_pred):
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
return (1 -(2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth))
"""Recurrent Layer"""
def rec_layer(layer, filters):
reconv1 = Conv2D(filters, (3, 3), activation='relu', padding='same')(layer)
#drop_inter = Dropout(0.3)(reconc1)
reconv1 = Conv2D(filters, (3, 3), activation='relu', padding='same')(reconv1)
return reconv1
########## Initialization of Parameters #######################
image_row = 128
image_col = 128
image_depth = 2
def r2unet():
inputs = inputs = Input((image_row, image_col, image_depth))
conv1 = rec_layer(inputs,32)
conv1 = rec_layer(conv1,32)
conv1add = Conv2D(32, kernel_size=(1, 1), padding='same')(inputs)
add1 = Add()([conv1add, conv1])
#dense1 = concatenate([add1, conv1], axis=3)
pool1 = MaxPooling2D(pool_size=(2, 2))(add1)
conv2 = rec_layer(pool1, 64)
conv2 = rec_layer(conv2, 64)
conv2add = Conv2D(64, kernel_size=(1, 1), padding='same')(pool1)
add2 = Add()([conv2add, conv2])
#dense2 = concatenate([add2, conv2], axis=3)
pool2 = MaxPooling2D(pool_size=(2, 2))(add2)
conv3 = rec_layer(pool2, 128)
conv3 = rec_layer(conv3, 128)
conv3add = Conv2D(128, kernel_size=(1, 1), padding='same')(pool2)
add3 = Add()([conv3add, conv3])
#dense3 = concatenate([add3, conv3], axis=3)
pool3 = MaxPooling2D(pool_size=(2, 2))(add3)
conv4 = rec_layer(pool3, 256)
conv4 = rec_layer(conv4, 256)
conv4add = Conv2D(256, kernel_size=(1, 1), padding='same')(pool3)
add4 = Add()([conv4add, conv4])
#dense4 = concatenate([add4, conv4], axis=3)
#drop4 = Dropout(0.4)(dense4)
pool4 = MaxPooling2D(pool_size=(2, 2))(add4)
conv5 = rec_layer(pool4, 512)
conv5 = rec_layer(conv5, 512)
conv5add = Conv2D(512, kernel_size=(1, 1), padding='same')(pool4)
add5 = Add()([conv5add, conv5])
#dense5 = concatenate([add5, conv5], axis=3)
drop5 = Dropout(0.4)(add5)
up6 = concatenate([Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(drop5), conv4], axis=3)
conv6 = rec_layer(up6, 256)
conv6 = rec_layer(conv6, 256)
conv6add = Conv2D(256, kernel_size=(1, 1), padding='same')(up6)
add6 = Add()([conv6add, conv6])
#dense6 = concatenate([add6, conv6], axis=3)
up7 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(add6), conv3], axis=3)
conv7 = rec_layer(up7, 128)
conv7 = rec_layer(conv7, 128)
conv7add = Conv2D(128, kernel_size=(1, 1), padding='same')(up7)
add7 = Add()([conv7add, conv7])
#dense7 = concatenate([add7, conv7], axis=3)
up8 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(add7), conv2], axis=3)
conv8 = rec_layer(up8, 64)
conv8 = rec_layer(conv8, 64)
conv8add = Conv2D(64, kernel_size=(1, 1), padding='same')(up8)
add8 = Add()([conv8add, conv8])
#dense8 = concatenate([add8, conv8], axis=3)
up9 = concatenate([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(add8), conv1], axis=3)
conv9 = rec_layer(up9, 64)
conv9 = rec_layer(conv9, 64)
conv9add = Conv2D(64, kernel_size=(1, 1), padding='same')(up9)
add9 = Add()([conv9add, conv9])
#dense9 = concatenate([add9, conv9], axis=3)
conv10 = Conv2D(1, (1, 1), activation='sigmoid')(add9)
model = Model(inputs=[inputs], outputs=[conv10])
model.summary()
model.compile(optimizer=Adam(lr=1e-5), loss= dice_loss, metrics=[dice_coef, Precision(), Recall(), AUC(), Accuracy()])
pretrained_weights = None
if(pretrained_weights):
model.load_weights(pretrained_weights)
return model