attension.py

from keras.applications.vgg16 import VGG16 as PTModel
from keras.applications.inception_resnet_v2 import InceptionResNetV2 as PTModel
from keras.applications.inception_v3 import InceptionV3 as PTModel
from keras.layers import GlobalAveragePooling2D, Dense, Dropout, Flatten, Input, Conv2D, multiply, LocallyConnected2D, Lambda
from keras.models import Model

in_lay = Input(t_x.shape[1:])
base_pretrained_model = PTModel(input_shape =  t_x.shape[1:], include_top = False, weights = 'imagenet')
base_pretrained_model.trainable = False
pt_depth = base_pretrained_model.get_output_shape_at(0)[-1]
pt_features = base_pretrained_model(in_lay)
from keras.layers import BatchNormalization
bn_features = BatchNormalization()(pt_features)

# here we do an attention mechanism to turn pixels in the GAP on an off

attn_layer = Conv2D(64, kernel_size = (1,1), padding = 'same', activation = 'relu')(Dropout(0.5)(bn_features))
attn_layer = Conv2D(16, kernel_size = (1,1), padding = 'same', activation = 'relu')(attn_layer)
attn_layer = Conv2D(8, kernel_size = (1,1), padding = 'same', activation = 'relu')(attn_layer)
attn_layer = Conv2D(1, 
                    kernel_size = (1,1), 
                    padding = 'valid', 
                    activation = 'sigmoid')(attn_layer)
# fan it out to all of the channels
up_c2_w = np.ones((1, 1, 1, pt_depth))
up_c2 = Conv2D(pt_depth, kernel_size = (1,1), padding = 'same', 
               activation = 'linear', use_bias = False, weights = [up_c2_w])
up_c2.trainable = False
attn_layer = up_c2(attn_layer)

mask_features = multiply([attn_layer, bn_features])
gap_features = GlobalAveragePooling2D()(mask_features)
gap_mask = GlobalAveragePooling2D()(attn_layer)

# to account for missing values from the attention model
gap = Lambda(lambda x: x[0]/x[1], name = 'RescaleGAP')([gap_features, gap_mask])
gap_dr = Dropout(0.25)(gap)
dr_steps = Dropout(0.25)(Dense(128, activation = 'relu')(gap_dr))
out_layer = Dense(t_y.shape[-1], activation = 'softmax')(dr_steps)
retina_model = Model(inputs = [in_lay], outputs = [out_layer])
from keras.metrics import top_k_categorical_accuracy
def top_2_accuracy(in_gt, in_pred):
    return top_k_categorical_accuracy(in_gt, in_pred, k=2)

retina_model.compile(optimizer = 'adam', loss = 'categorical_crossentropy',
                           metrics = ['categorical_accuracy', top_2_accuracy])
retina_model.summary()