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Designing RESUNET.py
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Designing RESUNET.py
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# lets create model now
def resblock(X, f):
'''
function for creating res block
'''
X_copy = X #copy of input
# main path
X = Conv2D(f, kernel_size=(1,1), kernel_initializer='he_normal')(X)
X = BatchNormalization()(X)
X = Activation('relu')(X)
X = Conv2D(f, kernel_size=(3,3), padding='same', kernel_initializer='he_normal')(X)
X = BatchNormalization()(X)
# shortcut path
X_copy = Conv2D(f, kernel_size=(1,1), kernel_initializer='he_normal')(X_copy)
X_copy = BatchNormalization()(X_copy)
# Adding the output from main path and short path together
X = Add()([X, X_copy])
X = Activation('relu')(X)
return X
def upsample_concat(x, skip):
'''
funtion for upsampling image
'''
X = UpSampling2D((2,2))(x)
merge = Concatenate()([X, skip])
return merge
input_shape = (256,256,3)
X_input = Input(input_shape) #iniating tensor of input shape
# Stage 1
conv_1 = Conv2D(16, 3, activation='relu', padding='same', kernel_initializer='he_normal')(X_input)
conv_1 = BatchNormalization()(conv_1)
conv_1 = Conv2D(16, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv_1)
conv_1 = BatchNormalization()(conv_1)
pool_1 = MaxPool2D((2,2))(conv_1)
# stage 2
conv_2 = resblock(pool_1, 32)
pool_2 = MaxPool2D((2,2))(conv_2)
# Stage 3
conv_3 = resblock(pool_2, 64)
pool_3 = MaxPool2D((2,2))(conv_3)
# Stage 4
conv_4 = resblock(pool_3, 128)
pool_4 = MaxPool2D((2,2))(conv_4)
# Stage 5 (bottle neck)
conv_5 = resblock(pool_4, 256)
# Upsample Stage 1
up_1 = upsample_concat(conv_5, conv_4)
up_1 = resblock(up_1, 128)
# Upsample Stage 2
up_2 = upsample_concat(up_1, conv_3)
up_2 = resblock(up_2, 64)
# Upsample Stage 3
up_3 = upsample_concat(up_2, conv_2)
up_3 = resblock(up_3, 32)
# Upsample Stage 4
up_4 = upsample_concat(up_3, conv_1)
up_4 = resblock(up_4, 16)
# final output
out = Conv2D(1, (1,1), kernel_initializer='he_normal', padding='same', activation='sigmoid')(up_4)
seg_model = Model(X_input, out)
seg_model.summary()