def generate_pose(image_size, pose_rep_type, num_landmarks):
inps = Input(list(image_size) + [3], name='inps')
cf_model = content_features_model(inps.get_shape().as_list(),
'block3_conv1')
for layer in cf_model.layers:
layer.trainable = False
reference = cf_model(inps)
feature = Dense(
64, input_shape=(reference.get_shape().as_list()[-1], ))(reference)
weight = Flatten(name='flatten')(reference)
weight = Dense(128)(weight)
weight = LeakyReLU(0.2)(weight)
weight = Dense(64)(weight)
weight = Reshape((1, 1, 64), input_shape=(64, ))(weight)
out = Multiply()([feature, weight])
out = SizeTransformLayer(tuple(inps.get_shape().as_list()[:-1] +
[64]))(out)
out = Dense(num_landmarks, input_shape=(64, ))(out)
return Model(inputs=[inps], outputs=[out])
