diff options
Diffstat (limited to 'runtimes/pure_arm_compute/src/internal/arm_compute/Cast.h')
| -rw-r--r-- | runtimes/pure_arm_compute/src/internal/arm_compute/Cast.h | 170 |
1 files changed, 82 insertions, 88 deletions
diff --git a/runtimes/pure_arm_compute/src/internal/arm_compute/Cast.h b/runtimes/pure_arm_compute/src/internal/arm_compute/Cast.h index e2ceb8fef..42b547feb 100644 --- a/runtimes/pure_arm_compute/src/internal/arm_compute/Cast.h +++ b/runtimes/pure_arm_compute/src/internal/arm_compute/Cast.h @@ -14,104 +14,98 @@ * limitations under the License. */ +/** + * @file Cast.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines casting functions from internal object to arm compute object + */ #ifndef __ARM_COMPUTE_CAST_H__ +#define __ARM_COMPUTE_CAST_H__ +#include <arm_compute/core/Coordinates.h> +#include <arm_compute/core/TensorInfo.h> #include <arm_compute/core/TensorShape.h> +#include <arm_compute/core/Types.h> -#include "internal/Swizzle.h" -#include "internal/Model.h" - -inline ::arm_compute::Coordinates getARMComputeAxises(uint32_t rank) -{ - ::arm_compute::Coordinates res{}; - - res.set_num_dimensions(rank); - - for (uint32_t axis = 0; axis < rank; ++axis) - { - res.set(axis, ToARMComputeAxis(rank, axis).value()); - } - - return res; -} - -inline ::arm_compute::TensorShape asTensorShape(const internal::tflite::operand::Shape &shape, - bool apply_dim_correction = true) -{ - const uint32_t rank = shape.rank(); +#include <NeuralNetworks.h> - ::arm_compute::TensorShape res{}; - - res.set_num_dimensions(rank); - - for (uint32_t axis = 0; axis < rank; ++axis) - { - // NOTE In some cases, in incorrect dimensions is required. - // For example, intput_size is 1 in LSTM. The input-to-input weights([num_units, input_size]) of - // LSTM is used as the weight of the FullyConnected. - // The FullyConnected's weight must be greater or equal than 2-dimensions. - // However, if the dimension correction is applied to input_to_input_weights with input_size - // equal to 1, it will be changed to 1-D. - // So input_to_input_weights is not used by the weight of FullyConnected. - res.set(ToARMComputeAxis(rank, axis).value(), shape.dim(axis), apply_dim_correction); - } - - return res; -} +#include "internal/Model.h" -::arm_compute::DataType asDataType(const int32_t type) -{ - switch (type) - { - case ANEURALNETWORKS_FLOAT32: - case ANEURALNETWORKS_TENSOR_FLOAT32: - return ::arm_compute::DataType::F32; - case ANEURALNETWORKS_INT32: - case ANEURALNETWORKS_TENSOR_INT32: - return ::arm_compute::DataType::S32; - case ANEURALNETWORKS_UINT32: - return ::arm_compute::DataType::U32; - case ANEURALNETWORKS_TENSOR_QUANT8_ASYMM: - return ::arm_compute::DataType::QASYMM8; - default: - throw std::runtime_error("Not supported, yet"); - break; - } -} +/** + * @brief Generate arm compute coordinate object from rank + * @param[in] rank Rank number + * @return Coordinate object + */ +::arm_compute::Coordinates getARMComputeAxises(uint32_t rank); + +/** + * @brief Generate arm compute coordinate object from runtime coordinate object + * @param[in] runtime_coord Runtime coordinates object + * @param[in] axises Coordinates for axises to map runtime-coordinates to + * arm_compute-coordinates + * @return Arm_compute coordinate object + */ +::arm_compute::Coordinates asARMComputeCoordinates(const ::arm_compute::Coordinates &runtime_coord, + const ::arm_compute::Coordinates &axises); + +/** +* @brief Generate arm compute permutation vector from runtime permutation vector +* @param[in] rank Rank number supported upto 4 +* @param[in] runtime_pv Integer array for runtime permutation vector +* @return Permutation vector of arm compute +*/ +::arm_compute::PermutationVector getARMComputePermutationVector(uint32_t rank, + const int32_t *runtime_pv); +/** + * @brief Cast from shape of internal to arm compute + * @param[in] shape Internal shape object + * @param[in] apply_dim_correction Flag to state whether apply dimension correction after setting + * one dimension in arm compute + * @return TensorShape object of arm compute + */ +::arm_compute::TensorShape asTensorShape(const internal::tflite::operand::Shape &shape, + bool apply_dim_correction = true); -::arm_compute::ActivationLayerInfo asActivationInfo(FuseCode code) -{ - switch (code) - { - case ANEURALNETWORKS_FUSED_NONE: - return ::arm_compute::ActivationLayerInfo{}; - case ANEURALNETWORKS_FUSED_RELU: - return ::arm_compute::ActivationLayerInfo{ - ::arm_compute::ActivationLayerInfo::ActivationFunction::RELU}; - case ANEURALNETWORKS_FUSED_RELU1: - return ::arm_compute::ActivationLayerInfo{ - ::arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 1.0f, -1.0f}; - case ANEURALNETWORKS_FUSED_RELU6: - return ::arm_compute::ActivationLayerInfo{ - ::arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.0f, 0.0f}; - default: - throw std::runtime_error("Not supported, yet"); - break; - } -} +/** + * @brief Cast from data type enum of NNAPI to arm compute + * @param[in] type NNAPI data type + * @return Data type of arm compute + */ +::arm_compute::DataType asDataType(const int32_t type); -::arm_compute::QuantizationInfo asQuantizationInfo(const float scale, const int32_t offset) -{ - return ::arm_compute::QuantizationInfo(scale, offset); -} +/** + * @brief Cast from NNAPI activation type enum to activation object of arm compute + * @param[in] code NNAPI activation type + * @return ActivationLayerInfo object of arm compute + */ +::arm_compute::ActivationLayerInfo asActivationInfo(FuseCode code); +/** + * @brief Generate quantization info object of arm compute + * @param[in] scale Scale of quantization + * @param[in] offset Offset of quantization + * @return QuantizationInfo object of arm compute + */ +::arm_compute::QuantizationInfo asQuantizationInfo(const float scale, const int32_t offset); + +/** + * @brief Cast from internal tensor info to tensor info object of arm compute + * @param[in] shape Tensor shape + * @param[in] type Tensor type + * @param[in] scale Scale of tensor quantization + * @param[in] zeroPoint Zeropoint of tensor quantization + * @return TensorInfo object of arm compute + */ ::arm_compute::TensorInfo asTensorInfo(const ::arm_compute::TensorShape &shape, const int32_t type, - const float scale = 0.0f, const int32_t zeroPoint = 0) -{ - return ::arm_compute::TensorInfo(shape, 1, asDataType(type), - asQuantizationInfo(scale, zeroPoint)); -} - + const float scale = 0.0f, const int32_t zeroPoint = 0); + +/** + * @brief Set value to arm compute tensor with casting + * @param[in] value Value to set + * @param[out] to Target tensor of arm compute + * @param[in] id Position of element + * @return N/A + */ template <typename FromT> void copyCast(const FromT value, ::arm_compute::ITensor *to, const ::arm_compute::Coordinates &id) { |