from __future__ import print_function
import tensorflow as tf
import numpy as np
import logging
class VGG(object):
def __init__(self, name, include_top=False, weights='imagenet'):
with tf.variable_scope(name, reuse=tf.AUTO_REUSE) as scope:
if name.upper() == 'VGG19':
self.vgg = tf.keras.applications.VGG19(include_top=include_top,
weights=weights)
elif name.upper() == 'VGG16':
self.vgg = tf.keras.applications.VGG16(include_top=include_top,
weights=weights)
else:
raise TypeError('Not supported model: VGG{}'.format(name))
self.model = tf.keras.Model(inputs=self.vgg.input,
outputs = self.vgg.get_layer('block3_conv3').output)
self.model.trainable=False
print(" [*] ", name, " model was created")
def get_pair_feature(self, gen_img, real_img):
assert gen_img.shape.as_list() == real_img.shape.as_list()
batch_num = gen_img.shape.as_list()[0]
pair = tf.concat([gen_img, real_img], axis=0)
output = self.model(pair)
gen_feat, real_feat = output[:batch_num,:,:,:], output[batch_num:,:,:,:]
return gen_feat, real_feat
if __name__=='__main__':
model = VGG('vgg19')
vars = tf.trainable_variables()
for i, var in enumerate(vars):
print(i,"-th variable: ", var)
print(model.get_feature(np.ones([1,256,256,3])))
