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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include <armnnUtils/TensorUtils.hpp>
#include <armnn/backends/ITensorHandle.hpp>
#include <armnn/utility/Assert.hpp>
#include <armnn/utility/NumericCast.hpp>
#include <fmt/format.h>
using namespace armnn;
namespace armnnUtils
{
TensorShape GetTensorShape(unsigned int numberOfBatches,
unsigned int numberOfChannels,
unsigned int height,
unsigned int width,
const DataLayout dataLayout)
{
switch (dataLayout)
{
case DataLayout::NCHW:
return TensorShape({numberOfBatches, numberOfChannels, height, width});
case DataLayout::NHWC:
return TensorShape({numberOfBatches, height, width, numberOfChannels});
default:
throw InvalidArgumentException("Unknown data layout ["
+ std::to_string(static_cast<int>(dataLayout)) +
"]", CHECK_LOCATION());
}
}
TensorInfo GetTensorInfo(unsigned int numberOfBatches,
unsigned int numberOfChannels,
unsigned int height,
unsigned int width,
const DataLayout dataLayout,
const DataType dataType)
{
switch (dataLayout)
{
case DataLayout::NCHW:
return TensorInfo({numberOfBatches, numberOfChannels, height, width}, dataType);
case DataLayout::NHWC:
return TensorInfo({numberOfBatches, height, width, numberOfChannels}, dataType);
default:
throw InvalidArgumentException("Unknown data layout ["
+ std::to_string(static_cast<int>(dataLayout)) +
"]", CHECK_LOCATION());
}
}
TensorInfo GetTensorInfo(unsigned int numberOfBatches,
unsigned int numberOfChannels,
unsigned int depth,
unsigned int height,
unsigned int width,
const DataLayout dataLayout,
const DataType dataType)
{
switch (dataLayout)
{
case DataLayout::NDHWC:
return TensorInfo({numberOfBatches, depth, height, width, numberOfChannels}, dataType);
case DataLayout::NCDHW:
return TensorInfo({numberOfBatches, numberOfChannels, depth, height, width}, dataType);
default:
throw InvalidArgumentException("Unknown data layout ["
+ std::to_string(static_cast<int>(dataLayout)) +
"]", CHECK_LOCATION());
}
}
std::pair<float, float> FindMinMax(ITensorHandle* tensorHandle)
{
auto tensor_data = static_cast<const float *>(tensorHandle->Map(true));
auto tensor_size = tensorHandle->GetShape().GetNumElements();
// Set min/max initially to first value in tensor
float min = tensor_data[0];
float max = tensor_data[0];
// Loop over rest of tensor and update min/max if necessary
for (unsigned int val = 1; val < tensor_size; val++)
{
if (tensor_data[val] < min)
{
min = tensor_data[val];
}
else if (tensor_data[val] > max)
{
max = tensor_data[val];
}
}
tensorHandle->Unmap();
return std::make_pair(min, max);
}
TensorShape ExpandDims(const TensorShape& tensorShape, int axis)
{
unsigned int outputDim = tensorShape.GetNumDimensions() + 1;
if (axis < -armnn::numeric_cast<int>(outputDim) || axis > armnn::numeric_cast<int>(tensorShape.GetNumDimensions()))
{
throw InvalidArgumentException(fmt::format("Invalid expansion axis {} for {}D input tensor. {}",
axis,
tensorShape.GetNumDimensions(),
CHECK_LOCATION().AsString()));
}
if (axis < 0)
{
axis = armnn::numeric_cast<int>(outputDim) + axis;
}
std::vector<unsigned int> outputShape;
outputShape.reserve(tensorShape.GetNumDimensions());
for (unsigned int i = 0; i < tensorShape.GetNumDimensions(); ++i)
{
outputShape.push_back(tensorShape[i]);
}
outputShape.insert(outputShape.begin() + axis, 1);
return TensorShape(outputDim, outputShape.data());
}
std::vector<unsigned int> SqueezeDims(const TensorShape& tensorShape)
{
unsigned int outputDimSize = 0;
std::vector<unsigned int> squeezedDims;
for (unsigned int i = 0; i < tensorShape.GetNumDimensions(); ++i)
{
if (tensorShape[i] != 1)
{
squeezedDims.push_back(tensorShape[i]);
++outputDimSize;
}
}
return squeezedDims;
}
unsigned int GetNumElementsBetween(const TensorShape& shape,
const unsigned int firstAxisInclusive,
const unsigned int lastAxisExclusive)
{
ARMNN_ASSERT(firstAxisInclusive <= lastAxisExclusive);
ARMNN_ASSERT(lastAxisExclusive <= shape.GetNumDimensions());
unsigned int count = 1;
for (unsigned int i = firstAxisInclusive; i < lastAxisExclusive; i++)
{
count *= shape[i];
}
return count;
}
unsigned int GetUnsignedAxis(const unsigned int inputDimension, const int axis)
{
ARMNN_ASSERT_MSG(axis < armnn::numeric_cast<int>(inputDimension),
"Required axis index greater than number of dimensions.");
ARMNN_ASSERT_MSG(axis >= -armnn::numeric_cast<int>(inputDimension),
"Required axis index lower than negative of the number of dimensions");
unsigned int uAxis = axis < 0 ?
inputDimension - armnn::numeric_cast<unsigned int>(abs(axis))
: armnn::numeric_cast<unsigned int>(axis);
return uAxis;
}
unsigned int GetNumElementsAfter(const armnn::TensorShape& shape, unsigned int axis)
{
unsigned int numDim = shape.GetNumDimensions();
ARMNN_ASSERT(axis <= numDim - 1);
unsigned int count = 1;
for (unsigned int i = axis+1; i < numDim; i++)
{
count *= shape[i];
}
return count;
}
std::pair<unsigned int, std::vector<float>> GetPerAxisParams(const armnn::TensorInfo& info)
{
const std::vector<float>& scales = info.GetQuantizationScales();
armnn::Optional<unsigned int> quantizationDim = info.GetQuantizationDim();
if (!info.HasPerAxisQuantization())
{
throw armnn::InvalidArgumentException(
std::string("Per-axis quantization params not set for tensor of type ") +
armnn::GetDataTypeName(info.GetDataType()), CHECK_LOCATION());
}
unsigned int axisFactor = GetNumElementsAfter(info.GetShape(), quantizationDim.value()) ;
return { axisFactor, scales };
}
} // namespace armnnUtils
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