def model_creator(in_shape, class_nb, weight_path):
in_lay = Input(in_shape)
base_pretrained_model = PTModel(input_shape=in_shape, include_top=False,
weights=None)
base_pretrained_model.trainable = False
pt_depth = base_pretrained_model.get_output_shape_at(0)[-1]
pt_features = base_pretrained_model(in_lay)
bn_features = BatchNormalization()(pt_features)
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)
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], name='outcnn')
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)
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(class_nb, activation='softmax')(dr_steps)
retina_model = Model(inputs=[in_lay], outputs=[out_layer])
retina_model.load_weights(weight_path)
return retina_model
def get_model(X, y):
in_lay = Input(X.shape[1:])
base_pretrained_model = PTModel(input_shape=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)
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(y.shape[-1], activation='softmax')(dr_steps)
retina_model = Model(inputs=[in_lay], outputs=[out_layer])
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])
return retina_model
