GitHub #719 Set quantization parameter scale to 1.0, instead of 0.0.

* Arm NN does not account for int8 or uint8 not quantized types, Tensorflow does.
Not quantized int8 and uint8 is the same as quantized int8 and uint8 with scale = 1.0 and offset= 0
Default offset/zero_point was already 0, this review sets the default scale to 1.0.

Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com>
Change-Id: Ibc3eecc281de516c2cc706e17bde01c64ff9556e

2 files changed, 12 insertions, 12 deletions

diff --git a/src/armnnSerializer/test/LstmSerializationTests.cpp b/src/armnnSerializer/test/LstmSerializationTests.cpp
index ae2d813fc0..ff96a4bb85 100644
--- a/src/armnnSerializer/test/LstmSerializationTests.cpp
+++ b/src/armnnSerializer/test/LstmSerializationTests.cpp
@@ -1,5 +1,5 @@
 //
-// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
@@ -1318,10 +1318,10 @@ TEST_CASE("EnsureLstmLayersBackwardCompatibility")
     params.m_CellToOutputWeights      = &cellToOutputWeights;
 
     const std::string layerName("lstm");
-    armnn::TensorInfo inputTensorInfo({ batchSize, inputSize }, armnn::DataType::Float32);
-    armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits}, armnn::DataType::Float32);
-    armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
-    armnn::TensorInfo lstmTensorInfoScratchBuff({ batchSize, numUnits * 4 }, armnn::DataType::Float32);
+    armnn::TensorInfo inputTensorInfo({ batchSize, inputSize }, armnn::DataType::Float32, 0.0f , 0);
+    armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits}, armnn::DataType::Float32, 0.0f , 0);
+    armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32, 0.0f , 0);
+    armnn::TensorInfo lstmTensorInfoScratchBuff({ batchSize, numUnits * 4 }, armnn::DataType::Float32, 0.0f , 0);
 
     VerifyLstmLayer<armnn::LstmDescriptor> checker(
             layerName,
diff --git a/src/armnnSerializer/test/SerializerTests.cpp b/src/armnnSerializer/test/SerializerTests.cpp
index 3998ee730d..90d778991b 100644
--- a/src/armnnSerializer/test/SerializerTests.cpp
+++ b/src/armnnSerializer/test/SerializerTests.cpp
@@ -1553,7 +1553,7 @@ TEST_CASE("EnsureL2NormalizationBackwardCompatibility")
     CHECK(deserializedNetwork);
 
     const std::string layerName("l2Normalization");
-    const armnn::TensorInfo inputInfo = armnn::TensorInfo({1, 2, 1, 5}, armnn::DataType::Float32);
+    const armnn::TensorInfo inputInfo = armnn::TensorInfo({1, 2, 1, 5}, armnn::DataType::Float32, 0.0f, 0);
 
     armnn::L2NormalizationDescriptor desc;
     desc.m_DataLayout = armnn::DataLayout::NCHW;
@@ -1805,8 +1805,8 @@ TEST_CASE("EnsureMergerLayerBackwardCompatibility")
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(std::string(mergerModel.begin(), mergerModel.end()));
     CHECK(deserializedNetwork);
 
-    const armnn::TensorInfo inputInfo  = armnn::TensorInfo({ 2, 3, 2, 2 }, armnn::DataType::Float32);
-    const armnn::TensorInfo outputInfo = armnn::TensorInfo({ 4, 3, 2, 2 }, armnn::DataType::Float32);
+    const armnn::TensorInfo inputInfo  = armnn::TensorInfo({ 2, 3, 2, 2 }, armnn::DataType::Float32, 0.0f, 0);
+    const armnn::TensorInfo outputInfo = armnn::TensorInfo({ 4, 3, 2, 2 }, armnn::DataType::Float32, 0.0f, 0);
 
     const std::vector<armnn::TensorShape> shapes({inputInfo.GetShape(), inputInfo.GetShape()});
 
@@ -2071,8 +2071,8 @@ TEST_CASE("EnsurePadBackwardCompatibility")
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(std::string(padModel.begin(), padModel.end()));
     CHECK(deserializedNetwork);
 
-    const armnn::TensorInfo inputInfo  = armnn::TensorInfo({ 1, 2, 3, 4 }, armnn::DataType::Float32);
-    const armnn::TensorInfo outputInfo = armnn::TensorInfo({ 1, 3, 5, 7 }, armnn::DataType::Float32);
+    const armnn::TensorInfo inputInfo  = armnn::TensorInfo({ 1, 2, 3, 4 }, armnn::DataType::Float32, 0.0f, 0);
+    const armnn::TensorInfo outputInfo = armnn::TensorInfo({ 1, 3, 5, 7 }, armnn::DataType::Float32, 0.0f, 0);
 
     armnn::PadDescriptor descriptor({{ 0, 0 }, { 1, 0 }, { 1, 1 }, { 1, 2 }});
 
@@ -2441,8 +2441,8 @@ TEST_CASE("EnsureResizeBilinearBackwardCompatibility")
         DeserializeNetwork(std::string(resizeBilinearModel.begin(), resizeBilinearModel.end()));
     CHECK(deserializedNetwork);
 
-    const armnn::TensorInfo inputInfo  = armnn::TensorInfo({1, 3, 5, 5}, armnn::DataType::Float32);
-    const armnn::TensorInfo outputInfo = armnn::TensorInfo({1, 3, 2, 4}, armnn::DataType::Float32);
+    const armnn::TensorInfo inputInfo  = armnn::TensorInfo({1, 3, 5, 5}, armnn::DataType::Float32, 0.0f, 0);
+    const armnn::TensorInfo outputInfo = armnn::TensorInfo({1, 3, 2, 4}, armnn::DataType::Float32, 0.0f, 0);
 
     armnn::ResizeDescriptor descriptor;
     descriptor.m_TargetWidth  = 4u;