GitHub #292 Build the ML-examples using only ArmNN's public frontend API
* Refactoring of some of the public API headers
Signed-off-by: Matteo Martincigh <matteo.martincigh@arm.com>
Change-Id: I9006fe49945b10a6b83908b05aa4a6556639b491
diff --git a/include/armnn/Utils.hpp b/include/armnn/Utils.hpp
index d91cc79..26a27f4 100644
--- a/include/armnn/Utils.hpp
+++ b/include/armnn/Utils.hpp
@@ -6,8 +6,6 @@
#include "armnn/TypesUtils.hpp"
-#include <vector>
-
namespace armnn
{
diff --git a/src/armnn/NetworkQuantizer.cpp b/src/armnn/NetworkQuantizer.cpp
index f308d54..4a8bae5 100644
--- a/src/armnn/NetworkQuantizer.cpp
+++ b/src/armnn/NetworkQuantizer.cpp
@@ -3,36 +3,34 @@
// SPDX-License-Identifier: MIT
//
-#include <armnn/ILayerVisitor.hpp>
-#include <armnn/INetwork.hpp>
-#include <armnn/Tensor.hpp>
-#include <armnn/Types.hpp>
-#include <TensorUtils.hpp>
-#include <TensorIOUtils.hpp>
-
+#include "NetworkQuantizer.hpp"
+#include "NetworkQuantizerUtils.hpp"
#include "Graph.hpp"
#include "Layer.hpp"
#include "Network.hpp"
-#include "NetworkQuantizer.hpp"
-#include "NetworkQuantizerUtils.hpp"
-
#include "DynamicQuantizationVisitor.hpp"
#include "StaticRangeVisitor.hpp"
#include "QuantizerVisitor.hpp"
#include "OverrideInputRangeVisitor.hpp"
-#include <vector>
-#include <cmath>
+#include <armnn/ILayerVisitor.hpp>
+#include <armnn/INetwork.hpp>
+#include <armnn/Tensor.hpp>
+#include <armnn/Types.hpp>
+
+#include <TensorUtils.hpp>
+#include <TensorIOUtils.hpp>
#include <boost/variant.hpp>
+#include <vector>
+#include <cmath>
namespace armnn
{
using TContainer = boost::variant<std::vector<float>, std::vector<int>, std::vector<unsigned char>>;
-
INetworkQuantizer* INetworkQuantizer::CreateRaw(INetwork* inputNetwork, const QuantizerOptions& options)
{
return new NetworkQuantizer(inputNetwork, options);
diff --git a/src/armnnUtils/TensorIOUtils.hpp b/src/armnnUtils/TensorIOUtils.hpp
index 07f3723..1dc7f21 100644
--- a/src/armnnUtils/TensorIOUtils.hpp
+++ b/src/armnnUtils/TensorIOUtils.hpp
@@ -6,7 +6,6 @@
#pragma once
#include <armnn/Tensor.hpp>
-#include <vector>
#include <boost/format.hpp>
#include <boost/variant/apply_visitor.hpp>
@@ -15,17 +14,18 @@
{
template<typename TContainer>
-inline armnn::InputTensors MakeInputTensors(
- const std::vector<armnn::BindingPointInfo>& inputBindings,
- const std::vector<TContainer>& inputDataContainers)
+inline armnn::InputTensors MakeInputTensors(const std::vector<armnn::BindingPointInfo>& inputBindings,
+ const std::vector<TContainer>& inputDataContainers)
{
armnn::InputTensors inputTensors;
const size_t numInputs = inputBindings.size();
if (numInputs != inputDataContainers.size())
{
- throw armnn::Exception(boost::str(boost::format("Number of inputs does not match number of "
- "tensor data containers: %1% != %2%") % numInputs % inputDataContainers.size()));
+ throw armnn::Exception(boost::str(boost::format("The number of inputs does not match number of "
+ "tensor data containers: %1% != %2%")
+ % numInputs
+ % inputDataContainers.size()));
}
for (size_t i = 0; i < numInputs; i++)
@@ -34,28 +34,27 @@
const TContainer& inputData = inputDataContainers[i];
boost::apply_visitor([&](auto&& value)
- {
- if (value.size() != inputBinding.second.GetNumElements())
- {
- throw armnn::Exception(boost::str(boost::format("Input tensor has incorrect size "
- "(expected %1% got %2%)")
- % inputBinding.second.GetNumElements()
- % value.size()));
- }
+ {
+ if (value.size() != inputBinding.second.GetNumElements())
+ {
+ throw armnn::Exception(boost::str(boost::format("The input tensor has incorrect size "
+ "(expected %1% got %2%)")
+ % inputBinding.second.GetNumElements()
+ % value.size()));
+ }
- armnn::ConstTensor inputTensor(inputBinding.second, value.data());
- inputTensors.push_back(std::make_pair(inputBinding.first, inputTensor));
- },
- inputData);
+ armnn::ConstTensor inputTensor(inputBinding.second, value.data());
+ inputTensors.push_back(std::make_pair(inputBinding.first, inputTensor));
+ },
+ inputData);
}
return inputTensors;
}
template<typename TContainer>
-inline armnn::OutputTensors MakeOutputTensors(
- const std::vector<armnn::BindingPointInfo>& outputBindings,
- std::vector<TContainer>& outputDataContainers)
+inline armnn::OutputTensors MakeOutputTensors(const std::vector<armnn::BindingPointInfo>& outputBindings,
+ std::vector<TContainer>& outputDataContainers)
{
armnn::OutputTensors outputTensors;
@@ -63,7 +62,9 @@
if (numOutputs != outputDataContainers.size())
{
throw armnn::Exception(boost::str(boost::format("Number of outputs does not match number of "
- "tensor data containers: %1% != %2%") % numOutputs % outputDataContainers.size()));
+ "tensor data containers: %1% != %2%")
+ % numOutputs
+ % outputDataContainers.size()));
}
for (size_t i = 0; i < numOutputs; i++)
@@ -72,16 +73,16 @@
TContainer& outputData = outputDataContainers[i];
boost::apply_visitor([&](auto&& value)
- {
- if (value.size() != outputBinding.second.GetNumElements())
- {
- throw armnn::Exception("Output tensor has incorrect size");
- }
+ {
+ if (value.size() != outputBinding.second.GetNumElements())
+ {
+ throw armnn::Exception("Output tensor has incorrect size");
+ }
- armnn::Tensor outputTensor(outputBinding.second, value.data());
- outputTensors.push_back(std::make_pair(outputBinding.first, outputTensor));
- },
- outputData);
+ armnn::Tensor outputTensor(outputBinding.second, value.data());
+ outputTensors.push_back(std::make_pair(outputBinding.first, outputTensor));
+ },
+ outputData);
}
return outputTensors;
diff --git a/src/armnnUtils/TensorUtils.cpp b/src/armnnUtils/TensorUtils.cpp
index b4e8d5a..0dbb75c 100644
--- a/src/armnnUtils/TensorUtils.cpp
+++ b/src/armnnUtils/TensorUtils.cpp
@@ -4,55 +4,58 @@
//
#include "TensorUtils.hpp"
+
#include <backendsCommon/ITensorHandle.hpp>
#include <boost/assert.hpp>
#include <boost/format.hpp>
#include <boost/numeric/conversion/cast.hpp>
+using namespace armnn;
+
namespace armnnUtils
{
-armnn::TensorShape GetTensorShape(unsigned int numberOfBatches,
+TensorShape GetTensorShape(unsigned int numberOfBatches,
unsigned int numberOfChannels,
unsigned int height,
unsigned int width,
- const armnn::DataLayout dataLayout)
+ const DataLayout dataLayout)
{
switch (dataLayout)
{
- case armnn::DataLayout::NCHW:
- return armnn::TensorShape({numberOfBatches, numberOfChannels, height, width});
- case armnn::DataLayout::NHWC:
- return armnn::TensorShape({numberOfBatches, height, width, numberOfChannels});
+ case DataLayout::NCHW:
+ return TensorShape({numberOfBatches, numberOfChannels, height, width});
+ case DataLayout::NHWC:
+ return TensorShape({numberOfBatches, height, width, numberOfChannels});
default:
- throw armnn::InvalidArgumentException("Unknown data layout ["
+ throw InvalidArgumentException("Unknown data layout ["
+ std::to_string(static_cast<int>(dataLayout)) +
"]", CHECK_LOCATION());
}
}
-armnn::TensorInfo GetTensorInfo(unsigned int numberOfBatches,
+TensorInfo GetTensorInfo(unsigned int numberOfBatches,
unsigned int numberOfChannels,
unsigned int height,
unsigned int width,
- const armnn::DataLayout dataLayout,
- const armnn::DataType dataType)
+ const DataLayout dataLayout,
+ const DataType dataType)
{
switch (dataLayout)
{
- case armnn::DataLayout::NCHW:
- return armnn::TensorInfo({numberOfBatches, numberOfChannels, height, width}, dataType);
- case armnn::DataLayout::NHWC:
- return armnn::TensorInfo({numberOfBatches, height, width, numberOfChannels}, dataType);
+ case DataLayout::NCHW:
+ return TensorInfo({numberOfBatches, numberOfChannels, height, width}, dataType);
+ case DataLayout::NHWC:
+ return TensorInfo({numberOfBatches, height, width, numberOfChannels}, dataType);
default:
- throw armnn::InvalidArgumentException("Unknown data layout ["
+ throw InvalidArgumentException("Unknown data layout ["
+ std::to_string(static_cast<int>(dataLayout)) +
"]", CHECK_LOCATION());
}
}
-std::pair<float, float> FindMinMax(armnn::ITensorHandle* tensorHandle)
+std::pair<float, float> FindMinMax(ITensorHandle* tensorHandle)
{
auto tensor_data = static_cast<const float *>(tensorHandle->Map(true));
auto tensor_size = tensorHandle->GetShape().GetNumElements();
@@ -79,13 +82,13 @@
return std::make_pair(min, max);
}
-armnn::TensorShape ExpandDims(const armnn::TensorShape& tensorShape, int axis)
+TensorShape ExpandDims(const TensorShape& tensorShape, int axis)
{
unsigned int outputDim = tensorShape.GetNumDimensions() + 1;
if (axis < -boost::numeric_cast<int>(outputDim) || axis > boost::numeric_cast<int>(tensorShape.GetNumDimensions()))
{
- throw armnn::InvalidArgumentException(
+ throw InvalidArgumentException(
boost::str(boost::format("Invalid expansion axis %1% for %2%D input tensor. %3%") %
axis %
tensorShape.GetNumDimensions() %
@@ -104,10 +107,10 @@
}
outputShape.insert(outputShape.begin() + axis, 1);
- return armnn::TensorShape(outputDim, outputShape.data());
+ return TensorShape(outputDim, outputShape.data());
}
-unsigned int GetNumElementsBetween(const armnn::TensorShape& shape,
+unsigned int GetNumElementsBetween(const TensorShape& shape,
const unsigned int firstAxisInclusive,
const unsigned int lastAxisExclusive)
{
@@ -135,4 +138,4 @@
return uAxis;
}
-}
+} // namespace armnnUtils
diff --git a/tests/InferenceTestImage.hpp b/tests/InferenceTestImage.hpp
index 643d060..d15f263 100644
--- a/tests/InferenceTestImage.hpp
+++ b/tests/InferenceTestImage.hpp
@@ -128,15 +128,15 @@
// and now lie in the range [0,1]. Channel data is stored according to the ArmNN layout (CHW). The order in which
// channels appear in the resulting vector is defined by the provided layout.
std::vector<float> GetImageDataInArmNnLayoutAsNormalizedFloats(ImageChannelLayout layout,
- const InferenceTestImage& image);
+ const InferenceTestImage& image);
// Reads the contents of an inference test image as 3-channel pixels, whose value is the result of subtracting the mean
// from the values in the original image. Channel data is stored according to the ArmNN layout (CHW). The order in
// which channels appear in the resulting vector is defined by the provided layout. The order of the channels of the
// provided mean should also match the given layout.
std::vector<float> GetImageDataInArmNnLayoutAsFloatsSubtractingMean(ImageChannelLayout layout,
- const InferenceTestImage& image,
- const std::array<float, 3>& mean);
+ const InferenceTestImage& image,
+ const std::array<float, 3>& mean);
// Reads the contents of an inference test image as 3-channel pixels and returns the image data as normalized float
// values. The returned image stay in the original order (HWC) order. The C order may be changed according to the