perceptual_model.py

from keras.models import Model
from keras.layers import Flatten, Concatenate
from keras.applications.vgg16 import VGG16, preprocess_input


class PerceptualModel(Model):

    def __init__(self, img_size, multi_layers=False):
        super().__init__()

        vgg = VGG16(include_top=False, input_shape=(img_size[0], img_size[1], 3))
        if multi_layers:
            layer_ids = [2, 5, 9, 13, 17]
            layer_outputs = [Flatten()(vgg.layers[layer_id].output) for layer_id in layer_ids]
            features = Concatenate(axis=-1)(layer_outputs)
        else:
            layer_ids = [13] #13 ==> conv4_3
            features = [Flatten()(vgg.layers[layer_id].output) for layer_id in layer_ids]

        self.__model = Model(inputs=vgg.input, outputs=features)

    def call(self, inputs, mask=None):
        return self.__model(preprocess_input(inputs))

    def compute_output_shape(self, input_shape):
        return self.__model.compute_output_shape(input_shape)