In recent years we have seen a number of implementations with some additional attention to the task(classification/segmentation). This repo contains the 3D implementation of the commonly used attention mechanism for imaging.
Oktay, Ozan, et al. "Attention u-net: Learning where to look for the pancreas." arXiv preprint arXiv:1804.03999 (2018).
def Attention_mechanism(X,G,out_filters,kernel_size=1,strides=(1, 1, 1),use_bias=False,
kernel_initializer=tf.keras.initializers.VarianceScaling(distribution='uniform'),
bias_initializer=tf.zeros_initializer(),
kernel_regularizer=tf.keras.regularizers.l2(l=0.001),
bias_regularizer=None,
**kwargs):
conv_params={'padding': 'same',
'use_bias': use_bias,
'kernel_initializer': kernel_initializer,
'bias_initializer': bias_initializer,
'kernel_regularizer': kernel_regularizer,
'bias_regularizer': bias_regularizer}
###input from the resolution.
Original_x=G
###
X1=tf.keras.layers.Conv3D(filters=out_filters,kernel_size=1,strides=1,**conv_params)(X)
X1=tf.keras.layers.BatchNormalization()(X1)
G1=tf.keras.layers.Conv3D(filters=out_filters,kernel_size=1,strides=1,**conv_params)(G)
G1=tf.keras.layers.BatchNormalization()(G1)
##Adding
X1_G1=X1+G1
#Applying Relu
A1=tf.nn.relu6(X1_G1)
#Applying convolution again
A1=tf.keras.layers.Conv3D(filters=out_filters,kernel_size=1,strides=1,**conv_params)(A1)
#Sigmoid
A1=tf.keras.activations.sigmoid(A1)
final_attention=tf.math.multiply(Original_x,A1)
return final_attention
Fu, Jun, et al. "Dual attention network for scene segmentation."
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.
def Position_attention(postion_attention_input):
#--Getting the Shape of the inputs
in_shp = postion_attention_input.get_shape().as_list()
C1=tf.keras.layers.Conv3D(filters=int(in_shp[4]/8),kernel_size=1,strides=(1,1,1))(postion_attention_input)
C1_shp = C1.get_shape().as_list()
##--first-Batch
F1_HWDxC=tf.reshape(C1, [-1, C1_shp[1]*C1_shp[2]*C1_shp[3],C1_shp[4]])
print(F1_HWDxC.get_shape())
##--Seconr-Batch
F2_CxHWD=tf.transpose(F1_HWDxC,perm=[0, 2, 1])
F2_CxHWD=tf.matmul(F1_HWDxC,F2_CxHWD)
F2_CxHWD=tf.keras.activations.softmax(F2_CxHWD)
print(F2_CxHWD.get_shape())
##--thir-Batch
C2=tf.keras.layers.Conv3D(filters=in_shp[4],kernel_size=1,strides=(1,1,1))(postion_attention_input)
F3_HWDxC=tf.reshape(C2, [-1, in_shp[1]*in_shp[2]*in_shp[3],in_shp[4]])
F3xF2=tf.matmul(F2_CxHWD,F3_HWDxC)
F3=tf.reshape(F3xF2,[-1, in_shp[1],in_shp[2],in_shp[3],in_shp[4]])
print(F3.get_shape())
print(postion_attention_input.get_shape())
postion_attention_output=tf.keras.layers.Multiply()([postion_attention_input,F3])
postion_attention_output=tf.keras.layers.Conv3D(filters=in_shp[4],kernel_size=1,strides=(1,1,1))(postion_attention_output)
return postion_attention_outputFu, Jun, et al. "Dual attention network for scene segmentation."
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.
def Channel_attention(Channel_attention_input):
in_shp = Channel_attention_input.get_shape().as_list()
##--first-Batch
channel_C1=tf.reshape(Channel_attention_input, [-1, in_shp[1]*in_shp[2]*in_shp[3],in_shp[4]])
##--Seconr-Batch
channel_C2=tf.transpose(channel_C1,perm=[0, 2, 1])
channel_C2=tf.matmul(channel_C1,channel_C2)
channel_C2=tf.keras.activations.softmax(channel_C2)
channel_C3=tf.matmul(channel_C2,channel_C1)
channel_C3=tf.reshape(channel_C3,[-1, in_shp[1],in_shp[2],in_shp[3],in_shp[4]])
Channel_attention_output=tf.keras.layers.Multiply()([channel_C3,Channel_attention_input])
Channel_attention_output=tf.keras.layers.Conv3D(filters=in_shp[4],kernel_size=1,strides=(1,1,1))( Channel_attention_output)
return Channel_attention_output
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