Spaces:
Runtime error
Runtime error
File size: 6,324 Bytes
2f8b452 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 |
#importing libraries
from keras.models import Model
from keras.layers import Input
from keras.layers import MaxPooling2D
from keras.utils import plot_model
from keras.layers import BatchNormalization
from keras.layers import AveragePooling2D
import tensorflow as tf
from keras.layers import GlobalAveragePooling2D, GlobalMaxPooling2D, Reshape, Dense, Input
from keras.layers import Activation, Concatenate, Conv2D, Multiply
Img_input = Input(shape= (299,299,3))
channel_axis = 3
# In inception v3 architecture, Every convolutin layer had batch normalization and relu activation function
def conv2d_bn(x, filters, num_row, num_col, padding='same', strides=(1,1)):
x=Conv2D(filters, (num_row, num_col), strides=strides, padding=padding)(x)
x=BatchNormalization(axis=3, scale=False)(x)
x=Activation('relu')(x)
return x
#Inception Block-A
def inc_block_a(x):
branch1x1 = conv2d_bn(x,64,1,1)
branch3x3 = conv2d_bn(x,48,1,1)
branch3x3 = conv2d_bn(branch3x3,64,3,3)
branch3x3db1 = conv2d_bn(x,64,1,1)
branch3x3db1 = conv2d_bn(branch3x3db1,96,3,3)
branch3x3db1 = conv2d_bn(branch3x3db1,96,3,3)
branch_pool = AveragePooling2D((3,3), strides=(1,1), padding='same')(x)
branch_pool = conv2d_bn(branch_pool,32,1,1)
x = Concatenate(axis= channel_axis)([branch1x1,branch3x3,branch3x3db1,branch_pool])
return x
#Reduction Block-A
def reduction_block_a(x):
branch3x3 = conv2d_bn(x,384,3,3,strides=(2,2),padding='valid')
branch3x3db1 = conv2d_bn(x,64,1,1)
branch3x3db1 = conv2d_bn(branch3x3db1,96,3,3)
branch3x3db1 = conv2d_bn(branch3x3db1, 96,3,3,strides=(2,2),padding='valid')
branch_pool = MaxPooling2D((3,3),strides=(2,2))(x)
x=Concatenate(axis=channel_axis)([branch3x3,branch3x3db1,branch_pool])
return x
#Inception Block-B
def inc_block_b(x):
branch1x1 = conv2d_bn(x,192,1,1)
branch7x7 = conv2d_bn(x,128,1,1)
branch7x7 = conv2d_bn(branch7x7,128,1,7)
branch7x7 = conv2d_bn(branch7x7,192,7,1)
branch7x7db1 = conv2d_bn(x,128,1,1)
branch7x7db1 = conv2d_bn(branch7x7db1,128,7,1)
branch7x7db1 = conv2d_bn(branch7x7db1,128,1,7)
branch7x7db1 = conv2d_bn(branch7x7db1,128,7,1)
branch7x7db1 = conv2d_bn(branch7x7db1,192,1,7)
branch_pool = AveragePooling2D((3,3),strides=(1,1),padding='same')(x)
branch_pool = conv2d_bn(branch_pool,192,1,1)
x = Concatenate(axis = channel_axis)([branch1x1,branch7x7,branch7x7db1,branch_pool])
return x
#Reduction Block-B
def reduction_block_b(x):
branch3x3 = conv2d_bn(x,192,1,1)
branch3x3 = conv2d_bn(branch3x3, 320,3,3,strides=(2,2),padding='valid')
branch7x7x3 = conv2d_bn(x,192,1,1)
branch7x7x3 = conv2d_bn(branch7x7x3,192,1,7)
branch7x7x3 = conv2d_bn(branch7x7x3,192,7,1)
branch7x7x3 = conv2d_bn(branch7x7x3,192,3,3,strides=(2,2),padding='valid')
branch_pool = MaxPooling2D((3,3),strides=(2,2))(x)
x = Concatenate( axis=channel_axis)([branch3x3,branch7x7x3,branch_pool])
return x
#Inception Block-C
def inc_block_c(x):
branch1x1 = conv2d_bn(x,320,1,1)
branch3x3 = conv2d_bn(x,384,1,1)
branch3x3_1 = conv2d_bn(branch3x3,384,1,3)
branch3x3_2 = conv2d_bn(branch3x3,384,3,1)
branch3x3 = Concatenate(axis=channel_axis)([branch3x3_1,branch3x3_2])
branch3x3db1 = conv2d_bn(x,448,1,1)
branch3x3db1 = conv2d_bn(branch3x3db1,384,3,3)
branch3x3db1_1 = conv2d_bn(branch3x3db1,384,1,3)
branch3x3db1_2 = conv2d_bn(branch3x3db1,384,3,1)
branch3x3db1 = Concatenate(axis= channel_axis)([branch3x3db1_1,branch3x3db1_2])
branch_pool = AveragePooling2D((3,3),strides=(1,1),padding='same')(x)
branch_pool = conv2d_bn(branch_pool,192,1,1)
x= Concatenate(axis=channel_axis)([branch1x1, branch3x3, branch3x3db1,branch_pool])
return x
# Channel Attention Module
# Channel Attention Module
def channel_attention_module(x, ratio=8):
batch,_,_,channel=x.shape
# shared layers
l1 = Dense(channel//ratio, activation="relu", use_bias=False)
l2 = Dense(channel, use_bias= False)
x1 = GlobalAveragePooling2D()(x)
x1 = l1(x1)
x1 = l2(x1)
x2 = GlobalMaxPooling2D()(x)
x2 = l1(x2)
x2 = l2(x2)
feats = x1 + x2
feats = Activation("sigmoid")(feats)
feats = Multiply()([x,feats])
return feats
# Spatial Attention Module
# spatical attention module
def spatial_attention_module(x):
# Average Pooling
x1 = tf.reduce_mean(x,axis = -1)
x1 = tf.expand_dims(x1,axis = -1)
# max pooling
x2 = tf.reduce_max(x, axis = -1)
x2 = tf.expand_dims(x2,axis=-1)
feats = Concatenate()([x1,x2])
feats = Conv2D(1,kernel_size=7, padding="same",activation="sigmoid")(feats)
feats = Multiply()([x,feats])
return feats
# CBAM (Convolutional Block Attention Mechanism) Modules
def cbam(x):
x = channel_attention_module(x)
x = spatial_attention_module(x)
return x
# Building model layer by layer integrating CBAM
def inception_cbam_model(Img_input):
x = conv2d_bn(Img_input, 32,3,3,strides=(2,2),padding='valid')
x = conv2d_bn(x,32,3,3,padding='valid')
x = conv2d_bn(x,64,3,3)
x = MaxPooling2D((3,3), strides=(2,2))(x)
x = conv2d_bn(x,80,1,1,padding='valid')
x = conv2d_bn(x,192,3,3,padding='valid')
x=MaxPooling2D((3,3),strides=(2,2))(x)
x = inc_block_a(x)
x = inc_block_a(x)
x = inc_block_a(x)
x = reduction_block_a(x)
x = inc_block_b(x)
x = inc_block_b(x)
x = inc_block_b(x)
x = inc_block_b(x)
x = reduction_block_b(x)
x = inc_block_c(x)
x = inc_block_c(x)
x = AveragePooling2D()(x)
base_model = x
model = cbam(base_model)
# Add additional layers
# model.add(layers.Flatten())
# model.add(layers.Dense(256, activation='relu'))
# model.add(layers.Dropout(0.5))
# model.add(layers.Dense(1, activation='linear')) # regression task for bone age
# create model
inputs = Img_input
model = Model(inputs,model,name='inception_v3')
return model
image_model = inception_cbam_model(Img_input)
print(image_model.summary())
# Compile the model
image_model.compile(optimizer='adam', loss='mean_squared_error', metrics=['mae'])
|