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//
// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "SerializerTestUtils.hpp"
#include "../Serializer.hpp"
#include <doctest/doctest.h>
using armnnDeserializer::IDeserializer;
LayerVerifierBase::LayerVerifierBase(const std::string& layerName,
const std::vector<armnn::TensorInfo>& inputInfos,
const std::vector<armnn::TensorInfo>& outputInfos)
: m_LayerName(layerName)
, m_InputTensorInfos(inputInfos)
, m_OutputTensorInfos(outputInfos)
{}
void LayerVerifierBase::ExecuteStrategy(const armnn::IConnectableLayer* layer,
const armnn::BaseDescriptor& descriptor,
const std::vector<armnn::ConstTensor>& constants,
const char* name,
const armnn::LayerBindingId id)
{
armnn::IgnoreUnused(descriptor, constants, id);
switch (layer->GetType())
{
case armnn::LayerType::Input: break;
case armnn::LayerType::Output: break;
default:
{
VerifyNameAndConnections(layer, name);
}
}
}
void LayerVerifierBase::VerifyNameAndConnections(const armnn::IConnectableLayer* layer, const char* name)
{
CHECK(std::string(name) == m_LayerName.c_str());
CHECK(layer->GetNumInputSlots() == m_InputTensorInfos.size());
CHECK(layer->GetNumOutputSlots() == m_OutputTensorInfos.size());
for (unsigned int i = 0; i < m_InputTensorInfos.size(); i++)
{
const armnn::IOutputSlot* connectedOutput = layer->GetInputSlot(i).GetConnection();
CHECK(connectedOutput);
const armnn::TensorInfo& connectedInfo = connectedOutput->GetTensorInfo();
CHECK(connectedInfo.GetShape() == m_InputTensorInfos[i].GetShape());
CHECK(
GetDataTypeName(connectedInfo.GetDataType()) == GetDataTypeName(m_InputTensorInfos[i].GetDataType()));
// If weights and bias are connected to DepthwiseConvolution2d via Constant Layer we do not check.
// Constant Layer already disabled in SerializerTestUtils.hpp from entering function.
if (layer->GetType() == armnn::LayerType::DepthwiseConvolution2d &&
connectedOutput->GetOwningIConnectableLayer().GetType() != armnn::LayerType::Constant)
{
CHECK(connectedInfo.GetQuantizationScale() == m_InputTensorInfos[i].GetQuantizationScale());
CHECK(connectedInfo.GetQuantizationOffset() == m_InputTensorInfos[i].GetQuantizationOffset());
}
}
for (unsigned int i = 0; i < m_OutputTensorInfos.size(); i++)
{
const armnn::TensorInfo& outputInfo = layer->GetOutputSlot(i).GetTensorInfo();
CHECK(outputInfo.GetShape() == m_OutputTensorInfos[i].GetShape());
CHECK(GetDataTypeName(outputInfo.GetDataType()) == GetDataTypeName(m_OutputTensorInfos[i].GetDataType()));
CHECK(outputInfo.GetQuantizationScale() == m_OutputTensorInfos[i].GetQuantizationScale());
CHECK(outputInfo.GetQuantizationOffset() == m_OutputTensorInfos[i].GetQuantizationOffset());
}
}
void LayerVerifierBase::VerifyConstTensors(const std::string& tensorName,
const armnn::ConstTensor* expectedPtr,
const armnn::ConstTensor* actualPtr)
{
if (expectedPtr == nullptr)
{
CHECK_MESSAGE(actualPtr == nullptr, (tensorName + " should not exist"));
}
else
{
CHECK_MESSAGE(actualPtr != nullptr, (tensorName + " should have been set"));
if (actualPtr != nullptr)
{
const armnn::TensorInfo& expectedInfo = expectedPtr->GetInfo();
const armnn::TensorInfo& actualInfo = actualPtr->GetInfo();
CHECK_MESSAGE(expectedInfo.GetShape() == actualInfo.GetShape(),
(tensorName + " shapes don't match"));
CHECK_MESSAGE(
GetDataTypeName(expectedInfo.GetDataType()) == GetDataTypeName(actualInfo.GetDataType()),
(tensorName + " data types don't match"));
CHECK_MESSAGE(expectedPtr->GetNumBytes() == actualPtr->GetNumBytes(),
(tensorName + " (GetNumBytes) data sizes do not match"));
if (expectedPtr->GetNumBytes() == actualPtr->GetNumBytes())
{
//check the data is identical
const char* expectedData = static_cast<const char*>(expectedPtr->GetMemoryArea());
const char* actualData = static_cast<const char*>(actualPtr->GetMemoryArea());
bool same = true;
for (unsigned int i = 0; i < expectedPtr->GetNumBytes(); ++i)
{
same = expectedData[i] == actualData[i];
if (!same)
{
break;
}
}
CHECK_MESSAGE(same, (tensorName + " data does not match"));
}
}
}
}
void CompareConstTensor(const armnn::ConstTensor& tensor1, const armnn::ConstTensor& tensor2)
{
CHECK(tensor1.GetShape() == tensor2.GetShape());
CHECK(GetDataTypeName(tensor1.GetDataType()) == GetDataTypeName(tensor2.GetDataType()));
switch (tensor1.GetDataType())
{
case armnn::DataType::Float32:
CompareConstTensorData<const float*>(
tensor1.GetMemoryArea(), tensor2.GetMemoryArea(), tensor1.GetNumElements());
break;
case armnn::DataType::QAsymmU8:
case armnn::DataType::Boolean:
CompareConstTensorData<const uint8_t*>(
tensor1.GetMemoryArea(), tensor2.GetMemoryArea(), tensor1.GetNumElements());
break;
case armnn::DataType::QSymmS8:
CompareConstTensorData<const int8_t*>(
tensor1.GetMemoryArea(), tensor2.GetMemoryArea(), tensor1.GetNumElements());
break;
case armnn::DataType::Signed32:
CompareConstTensorData<const int32_t*>(
tensor1.GetMemoryArea(), tensor2.GetMemoryArea(), tensor1.GetNumElements());
break;
default:
// Note that Float16 is not yet implemented
MESSAGE("Unexpected datatype");
CHECK(false);
}
}
armnn::INetworkPtr DeserializeNetwork(const std::string& serializerString)
{
std::vector<std::uint8_t> const serializerVector{serializerString.begin(), serializerString.end()};
return IDeserializer::Create()->CreateNetworkFromBinary(serializerVector);
}
std::string SerializeNetwork(const armnn::INetwork& network)
{
armnnSerializer::ISerializerPtr serializer = armnnSerializer::ISerializer::Create();
serializer->Serialize(network);
std::stringstream stream;
serializer->SaveSerializedToStream(stream);
std::string serializerString{stream.str()};
return serializerString;
}
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