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| #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']) | |