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#!/usr/bin/env python

# -*- coding: utf-8 -*-

import unittest

try:

from tests.unittests_helper import CustomTestCase

except ImportError:

from unittests_helper import CustomTestCase

import tensorflow as tf

import tensorlayer as tl

class VGG_Model_Test(CustomTestCase):

@classmethod

def setUpClass(cls):

with tf.Graph().as_default():

# - Classify ImageNet classes with VGG16, see `tutorial_models_vgg16.py <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_models_vgg16.py>__`

x = tf.placeholder(tf.float32, [None, 224, 224, 3])

# get the whole model

vgg1 = tl.models.VGG16(x)

# restore pre-trained VGG parameters

# sess = tf.InteractiveSession()

# vgg.restore_params(sess)

# use for inferencing

# probs = tf.nn.softmax(vgg1.outputs)

cls.vgg1_layers = vgg1.all_layers

cls.vgg1_params = vgg1.all_params

with tf.Graph().as_default():

# - Extract features with VGG16 and Train a classifier with 100 classes

x = tf.placeholder(tf.float32, [None, 224, 224, 3])

# get VGG without the last layer

vgg2 = tl.models.VGG16(x, end_with='fc2_relu')

cls.vgg2_layers = vgg2.all_layers

cls.vgg2_params = vgg2.all_params

# add one more layer

_ = tl.layers.DenseLayer(vgg2, n_units=100, name='out')

# initialize all parameters

# sess = tf.InteractiveSession()

# tl.layers.initialize_global_variables(sess)

# restore pre-trained VGG parameters

# vgg.restore_params(sess)

# train your own classifier (only update the last layer)

cls.vgg2_train_params = tl.layers.get_variables_with_name('out')

with tf.Graph().as_default() as graph:

# - Reuse model

x = tf.placeholder(tf.float32, [None, 224, 224, 3])

# get VGG without the last layer

vgg3 = tl.models.VGG16(x, end_with='fc2_relu')

# reuse the parameters of vgg1 with different input

# restore pre-trained VGG parameters (as they share parameters, we don’t need to restore vgg2)

# sess = tf.InteractiveSession()

# vgg1.restore_params(sess)

cls.vgg3_layers = vgg3.all_layers

cls.vgg3_params = vgg3.all_params

cls.vgg3_graph = graph

@classmethod

def tearDownClass(cls):

tf.reset_default_graph()

def test_vgg1_layers(self):

self.assertEqual(len(self.vgg1_layers), 22)

def test_vgg2_layers(self):

self.assertEqual(len(self.vgg2_layers), 21)

def test_vgg3_layers(self):

self.assertEqual(len(self.vgg3_layers), 21)

def test_vgg1_params(self):

self.assertEqual(len(self.vgg1_params), 32)

def test_vgg2_params(self):

self.assertEqual(len(self.vgg2_params), 30)

def test_vgg3_params(self):

self.assertEqual(len(self.vgg3_params), 30)

def test_vgg2_train_params(self):

self.assertEqual(len(self.vgg2_train_params), 2)

def test_reuse_vgg(self):

with self.assertNotRaises(Exception):

with self.vgg3_graph.as_default():

x = tf.placeholder(tf.float32, [None, 224, 224, 3])

_ = tl.models.VGG16(x, end_with='fc2_relu', reuse=True)

if __name__ == '__main__':

# tf.logging.set_verbosity(tf.logging.INFO)

tf.logging.set_verbosity(tf.logging.DEBUG)

unittest.main()