namespace armcomputetensorutils
{
-/// Utility function to map an armnn::DataType to corresponding arm_compute::DataType
+/// Utility function to map an armnn::DataType to corresponding arm_compute::DataType.
arm_compute::DataType GetArmComputeDataType(armnn::DataType dataType);
-/// Utility function used to setup an arm_compute::TensorShape object from an armnn::TensorShape
+/// Utility function used to setup an arm_compute::TensorShape object from an armnn::TensorShape.
arm_compute::TensorShape BuildArmComputeTensorShape(const armnn::TensorShape& tensorShape);
/// Utility function used to setup an arm_compute::ITensorInfo object whose dimensions are based on the given
-/// armnn::ITensorInfo
+/// armnn::ITensorInfo.
arm_compute::TensorInfo BuildArmComputeTensorInfo(const armnn::TensorInfo& tensorInfo);
-/// Utility function used to setup an arm_compute::PoolingLayerInfo object from an armnn::Pooling2dDescriptor
+/// Utility function used to setup an arm_compute::PoolingLayerInfo object from an armnn::Pooling2dDescriptor.
arm_compute::PoolingLayerInfo BuildArmComputePoolingLayerInfo(const Pooling2dDescriptor& descriptor);
-/// Utility function to setup an arm_compute::NormalizationLayerInfo object from an armnn::NormalizationDescriptor
+/// Utility function to setup an arm_compute::NormalizationLayerInfo object from an armnn::NormalizationDescriptor.
arm_compute::NormalizationLayerInfo BuildArmComputeNormalizationLayerInfo(const NormalizationDescriptor& desc);
-/// Utility function used to setup an arm_compute::PermutationVector object from an armnn::PermutationVector
+/// Utility function used to setup an arm_compute::PermutationVector object from an armnn::PermutationVector.
arm_compute::PermutationVector BuildArmComputePermutationVector(const armnn::PermutationVector& vector);
-/// Utility function used to setup an arm_compute::PadStrideInfo object from an armnn layer descriptor
+/// Utility function used to setup an arm_compute::PadStrideInfo object from an armnn layer descriptor.
template <typename Descriptor>
arm_compute::PadStrideInfo BuildArmComputePadStrideInfo(const Descriptor &descriptor)
{
tensor.allocator()->allocate();
}
+/// Utility function to free unused tensors after a workload is configured and prepared
+template <typename Tensor>
+void FreeTensorIfUnused(std::unique_ptr<Tensor>& tensor)
+{
+ if (tensor && !tensor->is_used())
+ {
+ tensor.reset(nullptr);
+ }
+}
+
// Helper function to obtain byte offset into tensor data
inline size_t GetTensorOffset(const arm_compute::ITensorInfo& info,
uint32_t batchIndex,
uint32_t x)
{
arm_compute::Coordinates coords;
- coords.set(3, boost::numeric_cast<int>(batchIndex));
- coords.set(2, boost::numeric_cast<int>(channelIndex));
- coords.set(1, boost::numeric_cast<int>(y));
- coords.set(0, boost::numeric_cast<int>(x));
+ coords.set(3, static_cast<int>(batchIndex));
+ coords.set(2, static_cast<int>(channelIndex));
+ coords.set(1, static_cast<int>(y));
+ coords.set(0, static_cast<int>(x));
return info.offset_element_in_bytes(coords);
}
-// Helper function to obtain element offset into data buffer representing tensor data (assuming no strides)
+// Helper function to obtain element offset into data buffer representing tensor data (assuming no strides).
inline size_t GetLinearBufferOffset(const arm_compute::ITensorInfo& info,
uint32_t batchIndex,
uint32_t channelIndex,
uint32_t x)
{
const arm_compute::TensorShape& shape = info.tensor_shape();
- uint32_t width = boost::numeric_cast<uint32_t>(shape[0]);
- uint32_t height = boost::numeric_cast<uint32_t>(shape[1]);
- uint32_t numChannels = boost::numeric_cast<uint32_t>(shape[2]);
+ uint32_t width = static_cast<uint32_t>(shape[0]);
+ uint32_t height = static_cast<uint32_t>(shape[1]);
+ uint32_t numChannels = static_cast<uint32_t>(shape[2]);
return ((batchIndex * numChannels + channelIndex) * height + y) * width + x;
}
template <typename T>
void CopyArmComputeITensorData(const arm_compute::ITensor& srcTensor, T* dstData)
{
- // if MaxNumOfTensorDimensions is increased, this loop will need fixing
+ // If MaxNumOfTensorDimensions is increased, this loop will need fixing.
static_assert(MaxNumOfTensorDimensions == 4, "Please update CopyArmComputeITensorData");
{
const arm_compute::ITensorInfo& info = *srcTensor.info();
const arm_compute::TensorShape& shape = info.tensor_shape();
const uint8_t* const bufferPtr = srcTensor.buffer();
- uint32_t width = boost::numeric_cast<uint32_t>(shape[0]);
- uint32_t height = boost::numeric_cast<uint32_t>(shape[1]);
- uint32_t numChannels = boost::numeric_cast<uint32_t>(shape[2]);
- uint32_t numBatches = boost::numeric_cast<uint32_t>(shape[3]);
+ uint32_t width = static_cast<uint32_t>(shape[0]);
+ uint32_t height = static_cast<uint32_t>(shape[1]);
+ uint32_t numChannels = static_cast<uint32_t>(shape[2]);
+ uint32_t numBatches = static_cast<uint32_t>(shape[3]);
for (unsigned int batchIndex = 0; batchIndex < numBatches; ++batchIndex)
{
{
for (unsigned int y = 0; y < height; ++y)
{
- // Copy one row from arm_compute tensor buffer to linear memory buffer
- // A row is the largest contiguous region we can copy, as the tensor data may be using strides
+ // Copies one row from arm_compute tensor buffer to linear memory buffer.
+ // A row is the largest contiguous region we can copy, as the tensor data may be using strides.
memcpy(dstData + GetLinearBufferOffset(info, batchIndex, channelIndex, y, 0),
bufferPtr + GetTensorOffset(info, batchIndex, channelIndex, y, 0),
width * sizeof(T));
template <typename T>
void CopyArmComputeITensorData(const T* srcData, arm_compute::ITensor& dstTensor)
{
- // if MaxNumOfTensorDimensions is increased, this loop will need fixing
+ // If MaxNumOfTensorDimensions is increased, this loop will need fixing.
static_assert(MaxNumOfTensorDimensions == 4, "Please update CopyArmComputeITensorData");
{
const arm_compute::ITensorInfo& info = *dstTensor.info();
const arm_compute::TensorShape& shape = info.tensor_shape();
uint8_t* const bufferPtr = dstTensor.buffer();
- uint32_t width = boost::numeric_cast<uint32_t>(shape[0]);
- uint32_t height = boost::numeric_cast<uint32_t>(shape[1]);
- uint32_t numChannels = boost::numeric_cast<uint32_t>(shape[2]);
- uint32_t numBatches = boost::numeric_cast<uint32_t>(shape[3]);
+ uint32_t width = static_cast<uint32_t>(shape[0]);
+ uint32_t height = static_cast<uint32_t>(shape[1]);
+ uint32_t numChannels = static_cast<uint32_t>(shape[2]);
+ uint32_t numBatches = static_cast<uint32_t>(shape[3]);
for (unsigned int batchIndex = 0; batchIndex < numBatches; ++batchIndex)
{
{
for (unsigned int y = 0; y < height; ++y)
{
- // Copy one row from linear memory buffer to arm_compute tensor buffer
- // A row is the largest contiguous region we can copy, as the tensor data may be using strides
+ // Copies one row from linear memory buffer to arm_compute tensor buffer.
+ // A row is the largest contiguous region we can copy, as the tensor data may be using strides.
memcpy(bufferPtr + GetTensorOffset(info, batchIndex, channelIndex, y, 0),
srcData + GetLinearBufferOffset(info, batchIndex, channelIndex, y, 0),
width * sizeof(T));
}
}
+/// Construct a TensorShape object from an ArmCompute object based on arm_compute::Dimensions.
+/// \tparam ArmComputeType Any type that implements the Dimensions interface
+/// \tparam T Shape value type
+/// \param shapelike An ArmCompute object that implements the Dimensions interface
+/// \param initial A default value to initialise the shape with
+/// \return A TensorShape object filled from the Acl shapelike object.
+template<typename ArmComputeType, typename T>
+TensorShape GetTensorShape(const ArmComputeType& shapelike, T initial)
+{
+ std::vector<unsigned int> s(MaxNumOfTensorDimensions, initial);
+ for (unsigned int i=0; i < shapelike.num_dimensions(); ++i)
+ {
+ s[(shapelike.num_dimensions()-1)-i] = boost::numeric_cast<unsigned int>(shapelike[i]);
+ }
+ return TensorShape(boost::numeric_cast<unsigned int>(shapelike.num_dimensions()), s.data());
+};
+
+/// Get the strides from an ACL strides object
+inline TensorShape GetStrides(const arm_compute::Strides& strides)
+{
+ return GetTensorShape(strides, 0U);
+}
+
+/// Get the shape from an ACL shape object
+inline TensorShape GetShape(const arm_compute::TensorShape& shape)
+{
+ return GetTensorShape(shape, 1U);
+}
+
} // namespace armcomputetensorutils
} // namespace armnn
projects / platform / upstream / armnn.git / blobdiff