From c577f2c6a3b4ddb6ba87a882723c53a248afbeba Mon Sep 17 00:00:00 2001
From: telsoa01 <telmo.soares@arm.com>
Date: Fri, 31 Aug 2018 09:22:23 +0100
Subject: Release 18.08

---
 src/armnnTfParser/test/FullyConnected.cpp | 38 +++++++++++++++----------------
 1 file changed, 19 insertions(+), 19 deletions(-)

(limited to 'src/armnnTfParser/test/FullyConnected.cpp')

diff --git a/src/armnnTfParser/test/FullyConnected.cpp b/src/armnnTfParser/test/FullyConnected.cpp
index 2a7b4951b7..e7f040e784 100644
--- a/src/armnnTfParser/test/FullyConnected.cpp
+++ b/src/armnnTfParser/test/FullyConnected.cpp
@@ -14,15 +14,15 @@ BOOST_AUTO_TEST_SUITE(TensorflowParser)
 
 // In Tensorflow fully connected layers are expressed as a MatMul followed by an Add.
 // The TfParser must detect this case and convert them to a FullyConnected layer.
-struct FullyConnectedFixture : public ParserPrototxtFixture<armnnTfParser::ITfParser>
+struct FullyConnectedFixture : public armnnUtils::ParserPrototxtFixture<armnnTfParser::ITfParser>
 {
     FullyConnectedFixture()
     {
-        // input = tf.placeholder(tf.float32, [1, 1], "input")
-        // weights = tf.constant([2], tf.float32, [1, 1])
-        // matmul = tf.matmul(input, weights)
-        // bias = tf.constant([1], tf.float32)
-        // output = tf.add(matmul, bias, name="output")
+        // Input = tf.placeholder(tf.float32, [1, 1], "input")
+        // Weights = tf.constant([2], tf.float32, [1, 1])
+        // Matmul = tf.matmul(input, weights)
+        // Bias = tf.constant([1], tf.float32)
+        // Output = tf.add(matmul, bias, name="output")
         m_Prototext = R"(
 node {
   name: "input"
@@ -153,7 +153,7 @@ BOOST_FIXTURE_TEST_CASE(FullyConnected, FullyConnectedFixture)
 // C-- A  A -- C
 //     \ /
 //      A
-struct MatMulUsedInTwoFcFixture : public ParserPrototxtFixture<armnnTfParser::ITfParser>
+struct MatMulUsedInTwoFcFixture : public armnnUtils::ParserPrototxtFixture<armnnTfParser::ITfParser>
 {
     MatMulUsedInTwoFcFixture()
     {
@@ -326,7 +326,7 @@ BOOST_FIXTURE_TEST_CASE(MatMulUsedInTwoFc, MatMulUsedInTwoFcFixture)
     RunTest<1>({ 3 }, { 32 });
     // Ideally we would check here that the armnn network has 5 layers:
     //  Input, 2 x FullyConnected (biased), Add and Output.
-    // This would make sure the parser hasn't incorrectly added some unconnected layers corresponding to the MatMul
+    // This would make sure the parser hasn't incorrectly added some unconnected layers corresponding to the MatMul.
 }
 
 // Similar to MatMulUsedInTwoFc, but this time the Adds are 'staggered' (see diagram), which means that only one
@@ -338,16 +338,16 @@ BOOST_FIXTURE_TEST_CASE(MatMulUsedInTwoFc, MatMulUsedInTwoFcFixture)
 // C2 -- A  |
 //       \ /
 //        A
-struct MatMulUsedInTwoFcStaggeredFixture : public ParserPrototxtFixture<armnnTfParser::ITfParser>
+struct MatMulUsedInTwoFcStaggeredFixture : public armnnUtils::ParserPrototxtFixture<armnnTfParser::ITfParser>
 {
     MatMulUsedInTwoFcStaggeredFixture()
     {
-        // input = tf.placeholder(tf.float32, shape=[1,1], name = "input")
-        // const1 = tf.constant([17], tf.float32, [1,1])
-        // mul = tf.matmul(input, const1)
-        // const2 = tf.constant([7], tf.float32, [1])
-        // fc = tf.add(mul, const2)
-        // output = tf.add(mul, fc, name="output")
+        // Input = tf.placeholder(tf.float32, shape=[1,1], name = "input")
+        // Const1 = tf.constant([17], tf.float32, [1,1])
+        // Mul = tf.matmul(input, const1)
+        // Monst2 = tf.constant([7], tf.float32, [1])
+        // Fc = tf.add(mul, const2)
+        // Output = tf.add(mul, fc, name="output")
         m_Prototext = R"(
 node {
   name: "input"
@@ -484,13 +484,13 @@ BOOST_FIXTURE_TEST_CASE(MatMulUsedInTwoFcStaggered, MatMulUsedInTwoFcStaggeredFi
 }
 
 // A MatMul in isolation, not connected to an add. Should result in a non-biased FullyConnected layer.
-struct MatMulFixture : public ParserPrototxtFixture<armnnTfParser::ITfParser>
+struct MatMulFixture : public armnnUtils::ParserPrototxtFixture<armnnTfParser::ITfParser>
 {
     MatMulFixture()
     {
-        // input = tf.placeholder(tf.float32, shape = [1, 1], name = "input")
-        // const = tf.constant([17], tf.float32, [1, 1])
-        //  output = tf.matmul(input, const, name = "output")
+        // Input = tf.placeholder(tf.float32, shape = [1, 1], name = "input")
+        // Const = tf.constant([17], tf.float32, [1, 1])
+        //  Output = tf.matmul(input, const, name = "output")
         m_Prototext = R"(
 node {
   name: "input"
-- 
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