* rhs_info.k0: same as lhs_info.k0
* @param[in] gemm_info GEMM information used to retrieve the original dimensions of the input matrices
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
const GEMMReshapeInfo &gemm_info);
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpMatrixMultiplyNativeKernel
*
*
* @note lhs_info.k0 must be equal to rhs_info.k0
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
const GEMMReshapeInfo &gemm_info);
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpMatrixMultiplyReshapedKernel
*
* @param[in] output_shifts (Optional) Output shifts tensor. In case of per-channel quantization, the number of multipliers must be equal to the number of filters (OFM).
* Supported data types: S32.
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, const GEMMKernelInfo &gemm_info, const ICLTensor *vector_sum_col = nullptr,
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, const GEMMKernelInfo &gemm_info, const ICLTensor *vector_sum_col = nullptr,
const ICLTensor *vector_sum_row = nullptr, const ICLTensor *bias = nullptr, const ICLTensor *output_multipliers = nullptr, const ICLTensor *output_shifts = nullptr);
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel
*
* @param[in] a_offset Offset to be added to each element of the matrix A.
* @param[in] b_offset Offset to be added to each element of the matrix B.
*/
- void configure(CLCompileContext &compile_context, ICLTensor *mm_result, const ICLTensor *vector_sum_col, const ICLTensor *vector_sum_row, const ICLTensor *bias, int32_t k, int32_t a_offset,
+ void configure(const CLCompileContext &compile_context, ICLTensor *mm_result, const ICLTensor *vector_sum_col, const ICLTensor *vector_sum_row, const ICLTensor *bias, int32_t k, int32_t a_offset,
int32_t b_offset);
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpOffsetContributionKernel
*
* @param[in] output_shifts Output shifts tensor. In case of per-channel quantization, the number of multipliers must be equal to the number of filters (OFM).
* Supported data types: S32
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *mm_result, const ICLTensor *vector_sum_col, const ICLTensor *vector_sum_row, const ICLTensor *bias, ICLTensor *output, int32_t k,
+ void configure(const CLCompileContext &compile_context, const ICLTensor *mm_result, const ICLTensor *vector_sum_col, const ICLTensor *vector_sum_row, const ICLTensor *bias, ICLTensor *output, int32_t k,
int32_t a_offset, int32_t b_offset,
const GEMMLowpOutputStageInfo &output_stage, const ICLTensor *output_multipliers, const ICLTensor *output_shifts);
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpOffsetContributionKernel
* @param[out] output Output tensor. Data type supported: Data type supported: QASYMM8/QASYMM8_SIGNED
* @param[in] info Output stage info. Used to pass the quantized output data type
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const GEMMLowpOutputStageInfo *info);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const GEMMLowpOutputStageInfo *info);
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel
*
* @param[in] input Input tensor. Data type supported: S32
* @param[out] output Output tensor. Data type supported: Data type supported: QASYMM8/QASYMM8_SIGNED
* @param[in] output_stage GEMMLowp output stage metadata.
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const GEMMLowpOutputStageInfo *output_stage);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const GEMMLowpOutputStageInfo *output_stage);
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpQuantizeDownInt32ScaleKernel
*
* @param[in] input Input tensor. Data type supported: S32
* @param[in] max (Optional) Max value used to saturate up the output result before converting back to QSYMM16.
* Along with @p min, this value can be used to implement "rectified linear unit" activation functions. Defaults to 0.
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift, int min = 0, int max = 0);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift, int min = 0, int max = 0);
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel
*
* @param[in] input Input tensor info. Data type supported: S32
* @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8_SIGNED. Defaults to 0
* Along with @p min, this value can be used to implement "rectified linear unit" activation functions
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift,
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift,
int min = 0, int max = 0);
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel
*
* @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
* Along with @p min, this value can be used to implement "rectified linear unit" activation functions
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift,
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift,
int min = 0, int max = 0);
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel
*
* - scalar Scalar value to multiply each reduced column/row by.
* - mul_byscalar True if each reduced column/row must be multiplied by a scalar value.
*/
- virtual void configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const GEMMLowpReductionKernelInfo &info) = 0;
+ virtual void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const GEMMLowpReductionKernelInfo &info) = 0;
protected:
const ICLTensor *_input;
* - scalar Scalar value to multiply each reduced column/row by.
* - mul_byscalar True if each reduced column/row must be multiplied by a scalar value.
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *mtx_a, ICLTensor *vector_sum_row, const GEMMLowpReductionKernelInfo &info) override;
+ void configure(const CLCompileContext &compile_context, const ICLTensor *mtx_a, ICLTensor *vector_sum_row, const GEMMLowpReductionKernelInfo &info) override;
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpMatrixAReductionKernel
*
* @param[in] mtx_a Input tensor. Data type supported: QASYMM8/QASYMM8_SIGNED
* - scalar Scalar value to multiply each reduced column/row by.
* - mul_byscalar True if each reduced column/row must be multiplied by a scalar value.
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *mtx_b, ICLTensor *vector_sum_col, const GEMMLowpReductionKernelInfo &info) override;
+ void configure(const CLCompileContext &compile_context, const ICLTensor *mtx_b, ICLTensor *vector_sum_col, const GEMMLowpReductionKernelInfo &info) override;
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpMatrixBReductionKernel
*
* @param[in] mtx_b Input tensor. Data type supported: Data type supported: QASYMM8/QASYMM8_SIGNED
* @param[in, out] accum The accumulate tensor to convert. Data types supported: F16/F32
* @param[in] biases The shared biases tensor to append. It must be 1D tensor. Data types supported: Same as @p input
*/
- void configure(CLCompileContext &compile_context, ICLTensor *accum, const ICLTensor *biases);
+ void configure(const CLCompileContext &compile_context, ICLTensor *accum, const ICLTensor *biases);
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMMatrixAccumulateBiasesKernel
*
* @param[in] accum The accumulate tensor to convert. Data types supported: F16/F32
* @param[in] activation_info (Optional) Activation to apply after the matrix multiplication
*
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta = 0.f,
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta = 0.f,
bool is_interleaved_transposed = true, const GEMMReshapeInfo &reshape_info = GEMMReshapeInfo(), bool fp_mixed_precision = false, const ActivationLayerInfo &activation_info = ActivationLayerInfo());
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMMatrixMultiplyKernel
*
* rhs_info.k0: same of lhs_info.k0
* @param[in] gemm_info GEMM information used to retrieve the original dimensions of the input matrices
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta,
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta,
const GEMMLHSMatrixInfo &lhs_info,
const GEMMRHSMatrixInfo &rhs_info,
const GEMMKernelInfo &gemm_info);
*
* @note lhs_info.k0 must be equal to rhs_info.k0
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta,
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta,
const GEMMLHSMatrixInfo &lhs_info,
const GEMMRHSMatrixInfo &rhs_info,
const GEMMKernelInfo &gemm_info);
* rhs_info.transpose: true,false
* @param[in] gemm_info GEMM information used to retrieve the original dimensions of the input matrices
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta,
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta,
const GEMMLHSMatrixInfo &lhs_info,
const GEMMRHSMatrixInfo &rhs_info,
const GEMMKernelInfo &gemm_info);
* @param[in] input1 The 2D reshaped weights tensor. Data type supported: Same as @p input.
* @param[out] output The output 2D tensor. Data types supported: Same as @p input, S32 for QASYMM8/QASYMM8_SIGNED.
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output);
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMMatrixVectorMultiplyKernel
*
* @param[in] input0 The reshaped input tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32
* lhs_info.interleave: true, false
* @param[in] reinterpret_input_as_3d (Optional) True if the input has to be reinterpreted as 3D tensor
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d = false);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d = false);
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMReshapeLHSMatrixKernel
*
* @param[in] input Input tensor info. Data types supported: All
* rhs_info.transpose: true, false
* rhs_info.interleave: true, false
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const GEMMRHSMatrixInfo &rhs_info);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const GEMMRHSMatrixInfo &rhs_info);
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMReshapeRHSMatrixKernel
*
* @param[in] input Input tensor info. Data types supported: All
* @param[out] output Destination tensor. Data type supported: Same as @p input
* @param[in] axis (Optional) The axis in @p input to gather @p indices from. Negative values wrap around. Defaults to 0
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *indices, ICLTensor *output, int axis = 0);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *indices, ICLTensor *output, int axis = 0);
/** Static function to check if given info will lead to a valid configuration of @ref CLGatherKernel
*
* @param[out] output The output tensor. Data types supported: U8.
* @param[in] border_undefined True if the border mode is undefined. False if it's replicate or constant.
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, bool border_undefined);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, bool border_undefined);
// Inherited methods overridden:
BorderSize border_size() const override;
* @param[out] output Destination tensor. Data types supported: S16.
* @param[in] border_undefined True if the border mode is undefined. False if it's replicate or constant.
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, bool border_undefined);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, bool border_undefined);
private:
//Make the configure method of the parent class private
* @param[out] output Destination tensor. Data types supported: U8.
* @param[in] border_undefined True if the border mode is undefined. False if it's replicate or constant.
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, bool border_undefined);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, bool border_undefined);
private:
//Make the configure method of the parent class private
* @param[in] input Source tensor. Data types supported: U8.
* @param[out] output Destination tensor. Output should have half the input width. Data types supported: U16.
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output);
// Inherited methods overridden:
void run(const Window &window, cl::CommandQueue &queue) override;
* @param[in] input Source tensor. Data types supported: U16.
* @param[out] output Destination tensor. Output should have half the input height. Data types supported: U8.
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output);
// Inherited methods overridden:
void run(const Window &window, cl::CommandQueue &queue) override;
* @param[in] info Contains Compute Anchors operation information described in @ref ComputeAnchorsInfo
*
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *anchors, ICLTensor *all_anchors, const ComputeAnchorsInfo &info);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *anchors, ICLTensor *all_anchors, const ComputeAnchorsInfo &info);
/** Static function to check if given info will lead to a valid configuration of @ref CLComputeAllAnchorsKernel
*
* @param[out] output Output tensor which stores the local HOG for each cell. DataType supported: F32. Number of channels supported: equal to the number of histogram bins per cell
* @param[in] hog_info HOG's metadata
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input_magnitude, const ICLTensor *input_phase, ICLTensor *output, const HOGInfo *hog_info);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input_magnitude, const ICLTensor *input_phase, ICLTensor *output, const HOGInfo *hog_info);
// Inherited methods overridden:
void run(const Window &window, cl::CommandQueue &queue) override;
* @param[out] output Output tensor which stores the normalised blocks. Data type supported: F32. Number of channels supported: equal to the number of histogram bins per block
* @param[in] hog_info HOG's metadata
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const HOGInfo *hog_info);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const HOGInfo *hog_info);
// Inherited methods overridden:
void run(const Window &window, cl::CommandQueue &queue) override;
* @param[in] threshold (Optional) Threshold for the distance between features and SVM classifying plane
* @param[in] idx_class (Optional) Index of the class used for evaluating which class the detection window belongs to
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, const ICLHOG *hog, ICLDetectionWindowArray *detection_windows, cl::Buffer *num_detection_windows,
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLHOG *hog, ICLDetectionWindowArray *detection_windows, cl::Buffer *num_detection_windows,
const Size2D &detection_window_stride, float threshold = 0.0f,
uint16_t idx_class = 0);
* @param[in] sensitivity Sensitivity threshold k from the Harris-Stephens equation.
* @param[in] border_undefined True if the border mode is undefined. False if it's replicate or constant.
*/
- void configure(CLCompileContext &compile_context, const ICLImage *input1, const ICLImage *input2, ICLImage *output,
+ void configure(const CLCompileContext &compile_context, const ICLImage *input1, const ICLImage *input2, ICLImage *output,
int32_t block_size, float norm_factor, float strength_thresh, float sensitivity,
bool border_undefined);
* @param[out] output Output tensor. Data types supported: Same as @p input.
*
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, unsigned int height_offset, ICLTensor *output);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, unsigned int height_offset, ICLTensor *output);
/** Static function to check if given info will lead to a valid configuration of @ref CLHeightConcatenateLayerKernel
*
* @param[in] input Input tensor info. Data types supported: All.
* @param[in] input Source image. Data types supported: U8.
* @param[out] output Destination distribution.
*/
- void configure(CLCompileContext &compile_context, const ICLImage *input, ICLDistribution1D *output);
+ void configure(const CLCompileContext &compile_context, const ICLImage *input, ICLDistribution1D *output);
// Inherited methods overridden:
void run(const Window &window, cl::CommandQueue &queue) override;
* @param[in] input Source image. Data types supported: U8.
* @param[out] output Destination distribution.
*/
- void configure(CLCompileContext &compile_context, const ICLImage *input, ICLDistribution1D *output);
+ void configure(const CLCompileContext &compile_context, const ICLImage *input, ICLDistribution1D *output);
// Inherited methods overridden:
void run(const Window &window, cl::CommandQueue &queue) override;
* @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
* @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias,
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias,
const Size2D &dilation = Size2D(1U, 1U),
unsigned int num_groups = 1);
/** Static function to check if given info will lead to a valid configuration of @ref CLIm2ColKernel
* @param[out] output Destination tensor. Data types and data layouts supported: same as @p input.
* @param[in] info Kernel meta-data descriptor
*/
- void configure(CLCompileContext &compile_context, ICLTensor *input, ICLTensor *output, const InstanceNormalizationLayerKernelInfo &info);
+ void configure(const CLCompileContext &compile_context, ICLTensor *input, ICLTensor *output, const InstanceNormalizationLayerKernelInfo &info);
/** Static function to check if given info will lead to a valid configuration of @ref CLInstanceNormalizationLayer.
*
* @param[in] input An input tensor. Data types supported: U8
* @param[out] output Destination tensor, Data types supported: U32.
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output);
};
/** Interface to run the vertical pass of the integral image kernel. */
* @param[in] compile_context The compile context to be used.
* @param[in,out] in_out The input/output tensor. Data types supported: U32
*/
- void configure(CLCompileContext &compile_context, ICLTensor *in_out);
+ void configure(const CLCompileContext &compile_context, ICLTensor *in_out);
// Inherited methods overridden:
void run(const Window &window, cl::CommandQueue &queue) override;
* @param[in] axis Axis along which to reduce. Negative values wrap around. Maximum supported actual reduction axis : 2
* @param[in] epsilon Lower bound value for the normalization.
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *sum, ICLTensor *output, int axis, float epsilon);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *sum, ICLTensor *output, int axis, float epsilon);
/** Static function to check if given info will lead to a valid configuration of @ref CLL2NormalizeLayerKernel.
*
* @param[in] num_levels The number of pyramid levels
* @param[in] pyramid_scale Scale factor used for generating the pyramid
*/
- void configure(CLCompileContext &compile_context, const ICLKeyPointArray *old_points, const ICLKeyPointArray *new_points_estimates,
+ void configure(const CLCompileContext &compile_context, const ICLKeyPointArray *old_points, const ICLKeyPointArray *new_points_estimates,
ICLLKInternalKeypointArray *old_points_internal, ICLLKInternalKeypointArray *new_points_internal,
bool use_initial_estimate, size_t level, size_t num_levels, float pyramid_scale);
* @param[in] new_points_internal Pointer to the array of internal @ref CLLKInternalKeypoint new points
* @param[out] new_points Pointer to the @ref ICLKeyPointArray storing new key points
*/
- void configure(CLCompileContext &compile_context, ICLLKInternalKeypointArray *new_points_internal, ICLKeyPointArray *new_points);
+ void configure(const CLCompileContext &compile_context, ICLLKInternalKeypointArray *new_points_internal, ICLKeyPointArray *new_points);
// Inherited methods overridden:
void run(const Window &window, cl::CommandQueue &queue) override;
* @param[in] window_dimension The size of the window on which to perform the algorithm
* @param[in] level The pyramid level
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *old_input, const ICLTensor *old_scharr_gx, const ICLTensor *old_scharr_gy,
+ void configure(const CLCompileContext &compile_context, const ICLTensor *old_input, const ICLTensor *old_scharr_gx, const ICLTensor *old_scharr_gy,
ICLLKInternalKeypointArray *old_points_internal, ICLLKInternalKeypointArray *new_points_internal,
ICLCoefficientTableArray *coeff_table, ICLOldValArray *old_ival,
size_t window_dimension, size_t level);
* @param[in] window_dimension The size of the window on which to perform the algorithm
* @param[in] level The pyramid level
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *new_input, ICLLKInternalKeypointArray *new_points_internal, ICLCoefficientTableArray *coeff_table, ICLOldValArray *old_ival,
+ void configure(const CLCompileContext &compile_context, const ICLTensor *new_input, ICLLKInternalKeypointArray *new_points_internal, ICLCoefficientTableArray *coeff_table, ICLOldValArray *old_ival,
Termination termination, float epsilon, size_t num_iterations, size_t window_dimension, size_t level);
// Inherited methods overridden:
* @param[in] input1 Second input tensor. Data type supported: same as @p input0
* @param[out] output Output tensor to store the result. Data type supported: same as @p input0
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output);
/** Static function to check if given info will lead to a valid configuration of @ref CLLocallyConnectedMatrixMultiplyKernel
*
* @param[in] input0 First input tensor info. Data types supported: F32
* @param[in] mag_type (Optional) Magnitude calculation type. Default: L2NORM.
* @param[in] phase_type (Optional) Phase calculation type. Default: SIGNED.
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *gx, const ICLTensor *gy, ICLTensor *magnitude, ICLTensor *phase,
+ void configure(const CLCompileContext &compile_context, const ICLTensor *gx, const ICLTensor *gy, ICLTensor *magnitude, ICLTensor *phase,
MagnitudeType mag_type = MagnitudeType::L2NORM, PhaseType phase_type = PhaseType::SIGNED);
// Inherited methods overridden:
* @param[out] stddev (Optional) Output standard deviation of pixel values.
* @param[out] global_sum_squared (Optional if stddev is not set, required if stddev is set) Keeps global sum of squared pixel values (Buffer size: 1 cl_ulong).
*/
- void configure(CLCompileContext &compile_context, const ICLImage *input, float *mean, cl::Buffer *global_sum, float *stddev = nullptr, cl::Buffer *global_sum_squared = nullptr);
+ void configure(const CLCompileContext &compile_context, const ICLImage *input, float *mean, cl::Buffer *global_sum, float *stddev = nullptr, cl::Buffer *global_sum_squared = nullptr);
/** Static function to check if given info will lead to a valid configuration of @ref CLMeanStdDevKernel.
*
* @param[in] input Input image info. Data types supported: U8.
* @param[out] output (Optional) Destination tensor. It can be nullptr in case of in-place computation. Data type supported: same as @p input
* @param[in] epsilon (Optional) Small float to avoid division by zero in case of zero standard deviation. Defaults to 1e-8.
*/
- void configure(CLCompileContext &compile_context, ICLTensor *input, ICLTensor *output = nullptr, float epsilon = 1e-8f);
+ void configure(const CLCompileContext &compile_context, ICLTensor *input, ICLTensor *output = nullptr, float epsilon = 1e-8f);
/** Static function to check if given info will lead to a valid configuration of @ref CLMeanStdDevNormalizationKernel
*
* @param[in] input Source tensor info with 2 dimensions. In case of @p output tensor info = nullptr,
* @param[out] output The output tensor. Data types supported: U8.
* @param[in] border_undefined True if the border mode is undefined. False if it's replicate or constant.
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, bool border_undefined);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, bool border_undefined);
// Inherited methods overridden:
BorderSize border_size() const override;
* @param[in] constant_value The value used to fill the planes of the tensor
* @param[in] window Window to be used in case setting only part of a tensor. Default is nullptr.
*/
- void configure(CLCompileContext &compile_context, ICLTensor *tensor, const PixelValue &constant_value, Window *window = nullptr);
+ void configure(const CLCompileContext &compile_context, ICLTensor *tensor, const PixelValue &constant_value, Window *window = nullptr);
/** Static function to check if given info will lead to a valid configuration of @ref CLMemsetKernel
*
* @param[in] tensor Source tensor info. Data types supported: All.
* @param[out] output Output tensor with shape [2, batches, ...] which stores the minimum and maximum values for each 3D input tensor.
* The dimensions over the second must match the batched dimensions of the input tensor. Data types supported: F32.
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output);
/** Static function to check if given info will lead to a valid configuration of @ref CLMinMaxLayerKernel
*
* @param[in] input Input tensor info. Data types supported: F32.
* @param[in] input Input Image. Data types supported: U8/S16/F32.
* @param[out] min_max Buffer of 2 elements to store the min value at position 0 and the max value at position 1. Data type supported: S32 if input type is U8/S16, F32 if input type is F32.
*/
- void configure(CLCompileContext &compile_context, const ICLImage *input, cl::Buffer *min_max);
+ void configure(const CLCompileContext &compile_context, const ICLImage *input, cl::Buffer *min_max);
// Inherited methods overridden:
void run(const Window &window, cl::CommandQueue &queue) override;
* @param[out] min_loc (Optional) Array of Coordinates2D used to store minimum value locations.
* @param[out] max_loc (Optional) Array of Coordinates2D used to store maximum value locations.
*/
- void configure(CLCompileContext &compile_context, const ICLImage *input, cl::Buffer *min_max, cl::Buffer *min_max_count,
+ void configure(const CLCompileContext &compile_context, const ICLImage *input, cl::Buffer *min_max, cl::Buffer *min_max_count,
ICLCoordinates2DArray *min_loc = nullptr, ICLCoordinates2DArray *max_loc = nullptr);
// Inherited methods overridden:
* @param[in] mask The given mask. Will be used only if pattern is specified to PATTERN_OTHER
* @param[in] border_undefined True if the border mode is undefined. False if it's replicate or constant.
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, NonLinearFilterFunction function,
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, NonLinearFilterFunction function,
unsigned int mask_size, MatrixPattern pattern, const uint8_t *mask,
bool border_undefined);
* @param[out] output Destination tensor. Data types supported: U8, F32. (Must be the same as the input tensor)
* @param[in] border_undefined True if the border mode is undefined. False if it's replicate or constant.
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, bool border_undefined);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, bool border_undefined);
// Inherited methods overridden:
BorderSize border_size() const override;
* Data layouts supported: same as @p input.
* @param[in] norm_info Normalization layer information like the normalization type, normalization size and other parameters.
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, NormalizationLayerInfo norm_info);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, NormalizationLayerInfo norm_info);
/** Static function to check if given info will lead to a valid configuration of @ref CLNormalizationLayerKernel
*
* @param[in] input Source tensor. 3 lower dims represent a single input with dimensions [width, height, IFM],
* @param[in] std Standard deviation values tensor. 1 dimension with size equal to the number of input channels.
* Data types supported: same as @p input
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const ICLTensor *mean, const ICLTensor *std);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const ICLTensor *mean, const ICLTensor *std);
/** Static function to check if given info will lead to a valid configuration of @ref CLNormalizePlanarYUVLayerKernel
*
* @param[in] input Source tensor info. 3 lower dimensions represent a single input with dimensions [width, height, channels].
* @param[in] mode (Optional) Controls whether the padding should be filled with @p constant_value using CONSTANT,
* or reflect the input, either including the border values (SYMMETRIC) or not (REFLECT).
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const PaddingList &padding, PixelValue constant_value = PixelValue(),
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const PaddingList &padding, PixelValue constant_value = PixelValue(),
PaddingMode mode = PaddingMode::CONSTANT);
/** Static function to check if given info will lead to a valid configuration of @ref CLPadLayerKernel
*
* @param[in] output The output tensor. Data types supported: Same as @p input
* @param[in] perm Permutation vector
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const PermutationVector &perm);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const PermutationVector &perm);
/** Static function to check if given info will lead to a valid configuration of @ref CLPermuteKernel
*
* @note Arbitrary permutation vectors are supported with rank not greater than 4
* @param[in] rounding_policy Rounding policy. Supported rounding modes: to zero, to nearest even.
* @param[in] act_info (Optional) Activation layer information in case of a fused activation.
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float scale,
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float scale,
ConvertPolicy overflow_policy, RoundingPolicy rounding_policy, const ActivationLayerInfo &act_info = ActivationLayerInfo());
/** Static function to check if given info will lead to a valid configuration of @ref CLPixelWiseMultiplicationKernel
*
* @param[out] output The output tensor, Data types supported: same as @p input1. Number of channels supported: same as @p input1.
* @param[in] act_info (Optional) Activation layer information in case of a fused activation.
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, const ActivationLayerInfo &act_info = ActivationLayerInfo());
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, const ActivationLayerInfo &act_info = ActivationLayerInfo());
/** Static function to check if given info will lead to a valid configuration of @ref CLComplexPixelWiseMultiplicationKernel
*
* @param[in] input1 An input tensor info. Data types supported: F32. Number of channels supported: 2.
* @param[in] pool_info Contains pooling operation information described in @ref PoolingLayerInfo.
* @param[out] indices (optional) The indices of the maximal values. Data type supported: U32.
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const PoolingLayerInfo &pool_info, ICLTensor *indices = nullptr);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const PoolingLayerInfo &pool_info, ICLTensor *indices = nullptr);
/** Static function to check if given info will lead to a valid configuration of @ref CLPoolingLayerKernel
*
* @param[in] input Source tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
* @param[in] max Maximum prior box values
* @param[in] aspect_ratios Aspect ratio values
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, const PriorBoxLayerInfo &info, cl::Buffer *min, cl::Buffer *max,
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, const PriorBoxLayerInfo &info, cl::Buffer *min, cl::Buffer *max,
cl::Buffer *aspect_ratios);
/** Static function to check if given info will lead to a valid configuration of @ref CLPriorBoxLayerKernel
*
* @param[in] weight Weight tensor. Data types supported: Same as @p input.
* @param[in] bias Bias tensor. Data types supported: S32.
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const ICLTensor *weight, const ICLTensor *bias);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const ICLTensor *weight, const ICLTensor *bias);
/** Static function to check if given info will lead to a valid configuration of @ref CLQLSTMLayerNormalizationKernel
*
* @param[in] input Source tensor info with 2 dimensions. Data types supported: QSYMM16.
*
* @note Output auto initialization is not supported by this kernel
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output);
/** Static function to check if given info will lead to a valid configuration of @ref CLQuantizationLayerKernel
*
* @param[in] input Input tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F32/F16.
* @note The z dimensions of @p output tensor and @p input tensor must be the same.
* @note The fourth dimension of @p output tensor must be the same as the number of elements in @p rois array.
*/
- void configure(CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *rois, ICLTensor *output, const ROIPoolingLayerInfo &pool_info);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *rois, ICLTensor *output, const ROIPoolingLayerInfo &pool_info);
/** Static function to check if given info will lead to a valid configuration of @ref CLROIAlignLayerKernel
*
* @param[in] input Source tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
configure(CLKernelLibrary::get().get_compile_context(), input0, input1, output, lhs_info, rhs_info, gemm_info);
}
-void CLGEMMLowpMatrixMultiplyNativeKernel::configure(CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, const GEMMLHSMatrixInfo &lhs_info,
+void CLGEMMLowpMatrixMultiplyNativeKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, const GEMMLHSMatrixInfo &lhs_info,
const GEMMRHSMatrixInfo &rhs_info,
const GEMMReshapeInfo &gemm_info)
{
configure(CLKernelLibrary::get().get_compile_context(), input0, input1, output, lhs_info, rhs_info, gemm_info);
}
-void CLGEMMLowpMatrixMultiplyReshapedKernel::configure(CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, const GEMMLHSMatrixInfo &lhs_info,
+void CLGEMMLowpMatrixMultiplyReshapedKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, const GEMMLHSMatrixInfo &lhs_info,
const GEMMRHSMatrixInfo &rhs_info,
const GEMMReshapeInfo &gemm_info)
{
configure(CLKernelLibrary::get().get_compile_context(), input0, input1, output, gemm_info, vector_sum_col, vector_sum_row, bias, output_multipliers, output_shifts);
}
-void CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel::configure(CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, const GEMMKernelInfo &gemm_info,
+void CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, const GEMMKernelInfo &gemm_info,
const ICLTensor *vector_sum_col, const ICLTensor *vector_sum_row, const ICLTensor *bias,
const ICLTensor *output_multipliers, const ICLTensor *output_shifts)
{
configure(CLKernelLibrary::get().get_compile_context(), mm_result, vector_sum_col, vector_sum_row, bias, k, a_offset, b_offset);
}
-void CLGEMMLowpOffsetContributionKernel::configure(CLCompileContext &compile_context, ICLTensor *mm_result, const ICLTensor *vector_sum_col, const ICLTensor *vector_sum_row, const ICLTensor *bias,
+void CLGEMMLowpOffsetContributionKernel::configure(const CLCompileContext &compile_context, ICLTensor *mm_result, const ICLTensor *vector_sum_col, const ICLTensor *vector_sum_row, const ICLTensor *bias,
int32_t k, int32_t a_offset,
int32_t b_offset)
{
configure(CLKernelLibrary::get().get_compile_context(), mm_result, vector_sum_col, vector_sum_row, bias, output, k, a_offset, b_offset, output_stage, output_multipliers, output_shifts);
}
-void CLGEMMLowpOffsetContributionOutputStageKernel::configure(CLCompileContext &compile_context, const ICLTensor *mm_result, const ICLTensor *vector_sum_col, const ICLTensor *vector_sum_row,
+void CLGEMMLowpOffsetContributionOutputStageKernel::configure(const CLCompileContext &compile_context, const ICLTensor *mm_result, const ICLTensor *vector_sum_col, const ICLTensor *vector_sum_row,
const ICLTensor *bias, ICLTensor *output,
int32_t k, int32_t a_offset, int32_t b_offset, const GEMMLowpOutputStageInfo &output_stage,
const ICLTensor *output_multipliers, const ICLTensor *output_shifts)
configure(CLKernelLibrary::get().get_compile_context(), input, bias, output, info);
}
-void CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output,
+void CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output,
const GEMMLowpOutputStageInfo *info)
{
// Perform validate step
configure(CLKernelLibrary::get().get_compile_context(), input, bias, output, output_stage);
}
-void CLGEMMLowpQuantizeDownInt32ScaleKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const GEMMLowpOutputStageInfo *output_stage)
+void CLGEMMLowpQuantizeDownInt32ScaleKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const GEMMLowpOutputStageInfo *output_stage)
{
// Perform validate step
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
configure(CLKernelLibrary::get().get_compile_context(), input, bias, output, result_fixedpoint_multiplier, result_shift, min, max);
}
-void CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output,
+void CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output,
int result_fixedpoint_multiplier, int result_shift,
int min, int max)
{
configure(CLKernelLibrary::get().get_compile_context(), input, bias, output, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max);
}
-void CLGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output,
+void CLGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output,
int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift,
int min, int max)
{
configure(CLKernelLibrary::get().get_compile_context(), input, bias, output, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max);
}
-void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output,
+void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output,
int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift,
int min, int max)
{
configure(CLKernelLibrary::get().get_compile_context(), mtx_a, vector_sum_row, info);
}
-void CLGEMMLowpMatrixAReductionKernel::configure(CLCompileContext &compile_context, const ICLTensor *mtx_a, ICLTensor *vector_sum_row, const GEMMLowpReductionKernelInfo &info)
+void CLGEMMLowpMatrixAReductionKernel::configure(const CLCompileContext &compile_context, const ICLTensor *mtx_a, ICLTensor *vector_sum_row, const GEMMLowpReductionKernelInfo &info)
{
// Perform validate step
ARM_COMPUTE_ERROR_ON_NULLPTR(mtx_a, vector_sum_row);
configure(CLKernelLibrary::get().get_compile_context(), mtx_b, vector_sum_col, info);
}
-void CLGEMMLowpMatrixBReductionKernel::configure(CLCompileContext &compile_context, const ICLTensor *mtx_b, ICLTensor *vector_sum_col, const GEMMLowpReductionKernelInfo &info)
+void CLGEMMLowpMatrixBReductionKernel::configure(const CLCompileContext &compile_context, const ICLTensor *mtx_b, ICLTensor *vector_sum_col, const GEMMLowpReductionKernelInfo &info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(mtx_b, vector_sum_col);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_matrix_b_reduction(mtx_b->info(), vector_sum_col->info()));
configure(CLKernelLibrary::get().get_compile_context(), accum, biases);
}
-void CLGEMMMatrixAccumulateBiasesKernel::configure(CLCompileContext &compile_context, ICLTensor *accum, const ICLTensor *biases)
+void CLGEMMMatrixAccumulateBiasesKernel::configure(const CLCompileContext &compile_context, ICLTensor *accum, const ICLTensor *biases)
{
// Perform validate step
ARM_COMPUTE_ERROR_ON_NULLPTR(accum, biases);
configure(CLKernelLibrary::get().get_compile_context(), input0, input1, input2, output, alpha, beta, is_interleaved_transposed, reshape_info, fp_mixed_precision, activation_info);
}
-void CLGEMMMatrixMultiplyKernel::configure(CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta,
+void CLGEMMMatrixMultiplyKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta,
bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, bool fp_mixed_precision, const ActivationLayerInfo &activation_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);
configure(CLKernelLibrary::get().get_compile_context(), input0, input1, input2, output, alpha, beta, lhs_info, rhs_info, gemm_info);
}
-void CLGEMMMatrixMultiplyNativeKernel::configure(CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha,
+void CLGEMMMatrixMultiplyNativeKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha,
float beta,
const GEMMLHSMatrixInfo &lhs_info,
const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info)
configure(CLKernelLibrary::get().get_compile_context(), input0, input1, input2, output, alpha, beta, lhs_info, rhs_info, gemm_info);
}
-void CLGEMMMatrixMultiplyReshapedKernel::configure(CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha,
+void CLGEMMMatrixMultiplyReshapedKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha,
float beta,
const GEMMLHSMatrixInfo &lhs_info,
const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info)
configure(CLKernelLibrary::get().get_compile_context(), input0, input1, input2, output, alpha, beta, lhs_info, rhs_info, gemm_info);
}
-void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::configure(CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha,
+void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha,
float beta,
const GEMMLHSMatrixInfo &lhs_info,
const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info)
configure(CLKernelLibrary::get().get_compile_context(), input0, input1, output);
}
-void CLGEMMMatrixVectorMultiplyKernel::configure(CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output)
+void CLGEMMMatrixVectorMultiplyKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info()));
configure(CLKernelLibrary::get().get_compile_context(), input, output, lhs_info, reinterpret_input_as_3d);
}
-void CLGEMMReshapeLHSMatrixKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d)
+void CLGEMMReshapeLHSMatrixKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
configure(CLKernelLibrary::get().get_compile_context(), input, output, rhs_info);
}
-void CLGEMMReshapeRHSMatrixKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const GEMMRHSMatrixInfo &rhs_info)
+void CLGEMMReshapeRHSMatrixKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const GEMMRHSMatrixInfo &rhs_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
configure(CLKernelLibrary::get().get_compile_context(), input, indices, output, axis);
}
-void CLGatherKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *indices, ICLTensor *output, int axis)
+void CLGatherKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *indices, ICLTensor *output, int axis)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, indices);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), indices->info(), output->info(), axis));
configure(CLKernelLibrary::get().get_compile_context(), input, output, border_undefined);
}
-void CLGaussian3x3Kernel::configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, bool border_undefined)
+void CLGaussian3x3Kernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, bool border_undefined)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8);
configure(CLKernelLibrary::get().get_compile_context(), input, output, border_undefined);
}
-void CLGaussian5x5HorKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, bool border_undefined)
+void CLGaussian5x5HorKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, bool border_undefined)
{
const std::array<int16_t, 5> matrix = { 1, 4, 6, 4, 1 };
configure(CLKernelLibrary::get().get_compile_context(), input, output, border_undefined);
}
-void CLGaussian5x5VertKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, bool border_undefined)
+void CLGaussian5x5VertKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, bool border_undefined)
{
const uint32_t scale = 256;
const std::array<int16_t, 5> matrix = { 1, 4, 6, 4, 1 };
configure(CLKernelLibrary::get().get_compile_context(), input, output);
}
-void CLGaussianPyramidHorKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output)
+void CLGaussianPyramidHorKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U16);
configure(CLKernelLibrary::get().get_compile_context(), input, output);
}
-void CLGaussianPyramidVertKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output)
+void CLGaussianPyramidVertKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U16);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8);
configure(CLKernelLibrary::get().get_compile_context(), anchors, all_anchors, info);
}
-void CLComputeAllAnchorsKernel::configure(CLCompileContext &compile_context, const ICLTensor *anchors, ICLTensor *all_anchors, const ComputeAnchorsInfo &info)
+void CLComputeAllAnchorsKernel::configure(const CLCompileContext &compile_context, const ICLTensor *anchors, ICLTensor *all_anchors, const ComputeAnchorsInfo &info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(anchors, all_anchors);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(anchors->info(), all_anchors->info(), info));
configure(CLKernelLibrary::get().get_compile_context(), input_magnitude, input_phase, output, hog_info);
}
-void CLHOGOrientationBinningKernel::configure(CLCompileContext &compile_context, const ICLTensor *input_magnitude, const ICLTensor *input_phase, ICLTensor *output, const HOGInfo *hog_info)
+void CLHOGOrientationBinningKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input_magnitude, const ICLTensor *input_phase, ICLTensor *output, const HOGInfo *hog_info)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input_magnitude, 1, DataType::S16);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input_phase, 1, DataType::U8);
configure(CLKernelLibrary::get().get_compile_context(), input, output, hog_info);
}
-void CLHOGBlockNormalizationKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const HOGInfo *hog_info)
+void CLHOGBlockNormalizationKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const HOGInfo *hog_info)
{
ARM_COMPUTE_ERROR_ON(hog_info == nullptr);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, hog_info->num_bins(), DataType::F32);
configure(CLKernelLibrary::get().get_compile_context(), input, hog, detection_windows, num_detection_windows, detection_window_stride, threshold, idx_class);
}
-void CLHOGDetectorKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, const ICLHOG *hog, ICLDetectionWindowArray *detection_windows, cl::Buffer *num_detection_windows,
+void CLHOGDetectorKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLHOG *hog, ICLDetectionWindowArray *detection_windows, cl::Buffer *num_detection_windows,
const Size2D &detection_window_stride,
float threshold, uint16_t idx_class)
{
configure(CLKernelLibrary::get().get_compile_context(), input1, input2, output, block_size, norm_factor, strength_thresh, sensitivity, border_undefined);
}
-void CLHarrisScoreKernel::configure(CLCompileContext &compile_context, const ICLImage *input1, const ICLImage *input2, ICLImage *output,
+void CLHarrisScoreKernel::configure(const CLCompileContext &compile_context, const ICLImage *input1, const ICLImage *input2, ICLImage *output,
int32_t block_size, float norm_factor, float strength_thresh, float sensitivity,
bool border_undefined)
{
configure(CLKernelLibrary::get().get_compile_context(), input, height_offset, output);
}
-void CLHeightConcatenateLayerKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, unsigned int height_offset, ICLTensor *output)
+void CLHeightConcatenateLayerKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, unsigned int height_offset, ICLTensor *output)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), height_offset, output->info()));
configure(CLKernelLibrary::get().get_compile_context(), input, output);
}
-void CLHistogramKernel::configure(CLCompileContext &compile_context, const ICLImage *input, ICLDistribution1D *output)
+void CLHistogramKernel::configure(const CLCompileContext &compile_context, const ICLImage *input, ICLDistribution1D *output)
{
ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(input);
ARM_COMPUTE_ERROR_ON(nullptr == output);
configure(CLKernelLibrary::get().get_compile_context(), input, output);
}
-void CLHistogramBorderKernel::configure(CLCompileContext &compile_context, const ICLImage *input, ICLDistribution1D *output)
+void CLHistogramBorderKernel::configure(const CLCompileContext &compile_context, const ICLImage *input, ICLDistribution1D *output)
{
ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(input);
ARM_COMPUTE_ERROR_ON(nullptr == output);
configure(CLKernelLibrary::get().get_compile_context(), input, output, kernel_dims, conv_info, has_bias, dilation, num_groups);
}
-void CLIm2ColKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias,
+void CLIm2ColKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias,
const Size2D &dilation,
unsigned int num_groups)
{
configure(CLKernelLibrary::get().get_compile_context(), input, output, info);
}
-void CLInstanceNormalizationLayerKernel::configure(CLCompileContext &compile_context, ICLTensor *input, ICLTensor *output, const InstanceNormalizationLayerKernelInfo &info)
+void CLInstanceNormalizationLayerKernel::configure(const CLCompileContext &compile_context, ICLTensor *input, ICLTensor *output, const InstanceNormalizationLayerKernelInfo &info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input);
configure(CLKernelLibrary::get().get_compile_context(), input, output);
}
-void CLIntegralImageHorKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output)
+void CLIntegralImageHorKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U32);
configure(CLKernelLibrary::get().get_compile_context(), in_out);
}
-void CLIntegralImageVertKernel::configure(CLCompileContext &compile_context, ICLTensor *in_out)
+void CLIntegralImageVertKernel::configure(const CLCompileContext &compile_context, ICLTensor *in_out)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(in_out, 1, DataType::U32);
configure(CLKernelLibrary::get().get_compile_context(), input, sum, output, axis, epsilon);
}
-void CLL2NormalizeLayerKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *sum, ICLTensor *output, int axis, float epsilon)
+void CLL2NormalizeLayerKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *sum, ICLTensor *output, int axis, float epsilon)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, sum, output);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), sum->info(), output->info(), axis, epsilon));
configure(CLKernelLibrary::get().get_compile_context(), old_points, new_points_estimates, old_points_internal, new_points_internal, use_initial_estimate, level, num_levels, pyramid_scale);
}
-void CLLKTrackerInitKernel::configure(CLCompileContext &compile_context, const ICLKeyPointArray *old_points, const ICLKeyPointArray *new_points_estimates,
+void CLLKTrackerInitKernel::configure(const CLCompileContext &compile_context, const ICLKeyPointArray *old_points, const ICLKeyPointArray *new_points_estimates,
ICLLKInternalKeypointArray *old_points_internal, ICLLKInternalKeypointArray *new_points_internal,
bool use_initial_estimate, size_t level, size_t num_levels, float pyramid_scale)
configure(CLKernelLibrary::get().get_compile_context(), new_points_internal, new_points);
}
-void CLLKTrackerFinalizeKernel::configure(CLCompileContext &compile_context, ICLLKInternalKeypointArray *new_points_internal, ICLKeyPointArray *new_points)
+void CLLKTrackerFinalizeKernel::configure(const CLCompileContext &compile_context, ICLLKInternalKeypointArray *new_points_internal, ICLKeyPointArray *new_points)
{
ARM_COMPUTE_ERROR_ON(new_points_internal == nullptr);
configure(CLKernelLibrary::get().get_compile_context(), old_input, old_scharr_gx, old_scharr_gy, old_points_internal, new_points_internal, coeff_table, old_ival, window_dimension, level);
}
-void CLLKTrackerStage0Kernel::configure(CLCompileContext &compile_context, const ICLTensor *old_input, const ICLTensor *old_scharr_gx, const ICLTensor *old_scharr_gy,
+void CLLKTrackerStage0Kernel::configure(const CLCompileContext &compile_context, const ICLTensor *old_input, const ICLTensor *old_scharr_gx, const ICLTensor *old_scharr_gy,
ICLLKInternalKeypointArray *old_points_internal, ICLLKInternalKeypointArray *new_points_internal,
ICLCoefficientTableArray *coeff_table, ICLOldValArray *old_ival,
size_t window_dimension, size_t level)
configure(CLKernelLibrary::get().get_compile_context(), new_input, new_points_internal, coeff_table, old_ival, termination, epsilon, num_iterations, window_dimension, level);
}
-void CLLKTrackerStage1Kernel::configure(CLCompileContext &compile_context, const ICLTensor *new_input, ICLLKInternalKeypointArray *new_points_internal, ICLCoefficientTableArray *coeff_table,
+void CLLKTrackerStage1Kernel::configure(const CLCompileContext &compile_context, const ICLTensor *new_input, ICLLKInternalKeypointArray *new_points_internal, ICLCoefficientTableArray *coeff_table,
ICLOldValArray *old_ival,
Termination termination, float epsilon, size_t num_iterations, size_t window_dimension, size_t level)
configure(CLKernelLibrary::get().get_compile_context(), input0, input1, output);
}
-void CLLocallyConnectedMatrixMultiplyKernel::configure(CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output)
+void CLLocallyConnectedMatrixMultiplyKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info()));
configure(CLKernelLibrary::get().get_compile_context(), gx, gy, magnitude, phase, mag_type, phase_type);
}
-void CLMagnitudePhaseKernel::configure(CLCompileContext &compile_context, const ICLTensor *gx, const ICLTensor *gy, ICLTensor *magnitude, ICLTensor *phase,
+void CLMagnitudePhaseKernel::configure(const CLCompileContext &compile_context, const ICLTensor *gx, const ICLTensor *gy, ICLTensor *magnitude, ICLTensor *phase,
MagnitudeType mag_type, PhaseType phase_type)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(gx, 1, DataType::S16, DataType::S32);
configure(CLKernelLibrary::get().get_compile_context(), input, mean, global_sum, stddev, global_sum_squared);
}
-void CLMeanStdDevKernel::configure(CLCompileContext &compile_context, const ICLImage *input, float *mean, cl::Buffer *global_sum, float *stddev, cl::Buffer *global_sum_squared)
+void CLMeanStdDevKernel::configure(const CLCompileContext &compile_context, const ICLImage *input, float *mean, cl::Buffer *global_sum, float *stddev, cl::Buffer *global_sum_squared)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, mean, global_sum);
ARM_COMPUTE_ERROR_ON(stddev && nullptr == global_sum_squared);
configure(CLKernelLibrary::get().get_compile_context(), input, output, epsilon);
}
-void CLMeanStdDevNormalizationKernel::configure(CLCompileContext &compile_context, ICLTensor *input, ICLTensor *output, float epsilon)
+void CLMeanStdDevNormalizationKernel::configure(const CLCompileContext &compile_context, ICLTensor *input, ICLTensor *output, float epsilon)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input);
configure(CLKernelLibrary::get().get_compile_context(), input, output, border_undefined);
}
-void CLMedian3x3Kernel::configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, bool border_undefined)
+void CLMedian3x3Kernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, bool border_undefined)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8);
configure(CLKernelLibrary::get().get_compile_context(), tensor, constant_value, window);
}
-void CLMemsetKernel::configure(CLCompileContext &compile_context, ICLTensor *tensor,
+void CLMemsetKernel::configure(const CLCompileContext &compile_context, ICLTensor *tensor,
const PixelValue &constant_value,
Window *window)
{
configure(CLKernelLibrary::get().get_compile_context(), input, output);
}
-void CLMinMaxLayerKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output)
+void CLMinMaxLayerKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info()));
configure(CLKernelLibrary::get().get_compile_context(), input, min_max);
}
-void CLMinMaxKernel::configure(CLCompileContext &compile_context, const ICLImage *input, cl::Buffer *min_max)
+void CLMinMaxKernel::configure(const CLCompileContext &compile_context, const ICLImage *input, cl::Buffer *min_max)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S16, DataType::F32);
ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(input);
configure(CLKernelLibrary::get().get_compile_context(), input, min_max, min_max_count, min_loc, max_loc);
}
-void CLMinMaxLocationKernel::configure(CLCompileContext &compile_context, const ICLImage *input, cl::Buffer *min_max, cl::Buffer *min_max_count, ICLCoordinates2DArray *min_loc,
+void CLMinMaxLocationKernel::configure(const CLCompileContext &compile_context, const ICLImage *input, cl::Buffer *min_max, cl::Buffer *min_max_count, ICLCoordinates2DArray *min_loc,
ICLCoordinates2DArray *max_loc)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S16, DataType::F32);
configure(CLKernelLibrary::get().get_compile_context(), input, output, function, mask_size, pattern, mask, border_undefined);
}
-void CLNonLinearFilterKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, NonLinearFilterFunction function,
+void CLNonLinearFilterKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, NonLinearFilterFunction function,
unsigned int mask_size, MatrixPattern pattern, const uint8_t *mask,
bool border_undefined)
{
configure(CLKernelLibrary::get().get_compile_context(), input, output, border_undefined);
}
-void CLNonMaximaSuppression3x3Kernel::configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, bool border_undefined)
+void CLNonMaximaSuppression3x3Kernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, bool border_undefined)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::F32);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::F32);
configure(CLKernelLibrary::get().get_compile_context(), input, output, norm_info);
}
-void CLNormalizationLayerKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, NormalizationLayerInfo norm_info)
+void CLNormalizationLayerKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, NormalizationLayerInfo norm_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
configure(CLKernelLibrary::get().get_compile_context(), input, output, mean, std);
}
-void CLNormalizePlanarYUVLayerKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const ICLTensor *mean, const ICLTensor *std)
+void CLNormalizePlanarYUVLayerKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const ICLTensor *mean, const ICLTensor *std)
{
// Perform validation step
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, mean, std);
configure(CLKernelLibrary::get().get_compile_context(), input, output, padding, constant_value, mode);
}
-void CLPadLayerKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const PaddingList &padding, PixelValue constant_value, PaddingMode mode)
+void CLPadLayerKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const PaddingList &padding, PixelValue constant_value, PaddingMode mode)
{
// Perform validation step
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
configure(CLKernelLibrary::get().get_compile_context(), input, output, perm);
}
-void CLPermuteKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const PermutationVector &perm)
+void CLPermuteKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const PermutationVector &perm)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), perm));
configure(CLKernelLibrary::get().get_compile_context(), input1, input2, output, scale, overflow_policy, rounding_policy, act_info);
}
-void CLPixelWiseMultiplicationKernel::configure(CLCompileContext &compile_context, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float scale,
+void CLPixelWiseMultiplicationKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float scale,
ConvertPolicy overflow_policy, RoundingPolicy rounding_policy, const ActivationLayerInfo &act_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
configure(CLKernelLibrary::get().get_compile_context(), input1, input2, output, act_info);
}
-void CLComplexPixelWiseMultiplicationKernel::configure(CLCompileContext &compile_context, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, const ActivationLayerInfo &act_info)
+void CLComplexPixelWiseMultiplicationKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, const ActivationLayerInfo &act_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_complex(input1->info(), input2->info(), output->info(), act_info));
configure(CLKernelLibrary::get().get_compile_context(), input, output, pool_info, indices);
}
-void CLPoolingLayerKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const PoolingLayerInfo &pool_info, ICLTensor *indices)
+void CLPoolingLayerKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const PoolingLayerInfo &pool_info, ICLTensor *indices)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
configure(CLKernelLibrary::get().get_compile_context(), input1, input2, output, info, min, max, aspect_ratios);
}
-void CLPriorBoxLayerKernel::configure(CLCompileContext &compile_context, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, const PriorBoxLayerInfo &info, cl::Buffer *min,
+void CLPriorBoxLayerKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, const PriorBoxLayerInfo &info, cl::Buffer *min,
cl::Buffer *max, cl::Buffer *aspect_ratios)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
{
}
-void CLQLSTMLayerNormalizationKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const ICLTensor *weight, const ICLTensor *bias)
+void CLQLSTMLayerNormalizationKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const ICLTensor *weight, const ICLTensor *bias)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weight, bias, output);
configure(CLKernelLibrary::get().get_compile_context(), input, output);
}
-void CLQuantizationLayerKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output)
+void CLQuantizationLayerKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info()));
configure(CLKernelLibrary::get().get_compile_context(), input, rois, output, pool_info);
}
-void CLROIAlignLayerKernel::configure(CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *rois, ICLTensor *output, const ROIPoolingLayerInfo &pool_info)
+void CLROIAlignLayerKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *rois, ICLTensor *output, const ROIPoolingLayerInfo &pool_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, rois);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), rois->info(), output->info(), pool_info));
projects / platform / upstream / armcl.git / commitdiff