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#!/usr/bin/env python

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

import unittest

import tensorflow as tf

import tensorlayer as tl

def model(x, is_train=True, reuse=False):

with tf.variable_scope("model", reuse=reuse):

n = tl.layers.InputLayer(x, name='in')

n = tl.layers.Conv2d(n, n_filter=80, name='conv2d_1')

n = tl.layers.BatchNormLayer(n, is_train=is_train, name='norm_batch')

n = tl.layers.Conv2d(n, n_filter=80, name='conv2d_2')

n = tl.layers.LocalResponseNormLayer(n, name='norm_local')

n = tl.layers.LayerNormLayer(n, reuse=reuse, name='norm_layer')

n = tl.layers.InstanceNormLayer(n, name='norm_instance')

return n

class Layer_Normalization_Test(unittest.TestCase):

@classmethod

def setUpClass(cls):

x = tf.placeholder(tf.float32, [None, 100, 100, 3])

net_train = model(x, is_train=True, reuse=False)

net_eval = model(x, is_train=False, reuse=True)

net_train.print_layers()

net_train.print_params(False)

cls.data = dict()

cls.data["train_network"] = dict()

cls.data["eval_network"] = dict()

cls.data["train_network"]["layers"] = net_train.all_layers

cls.data["eval_network"]["layers"] = net_eval.all_layers

cls.data["train_network"]["params"] = net_train.all_params

cls.data["train_network"]["n_params"] = net_train.count_params()

@classmethod

def tearDownClass(cls):

tf.reset_default_graph()

def test_all_layers(self):

self.assertEqual(len(self.data["train_network"]["layers"]), 6)

self.assertEqual(len(self.data["eval_network"]["layers"]), 6)

def test_all_params(self):

self.assertEqual(len(self.data["train_network"]["params"]), 12)

def test_n_params(self):

self.assertEqual(self.data["train_network"]["n_params"], 60560)

if __name__ == '__main__':

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

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

unittest.main()