diff options
Diffstat (limited to 'arm_compute')
5 files changed, 51 insertions, 41 deletions
diff --git a/arm_compute/core/CL/kernels/CLBatchNormalizationLayerKernel.h b/arm_compute/core/CL/kernels/CLBatchNormalizationLayerKernel.h index 6df7ae4fc7..add1dfbb8c 100644 --- a/arm_compute/core/CL/kernels/CLBatchNormalizationLayerKernel.h +++ b/arm_compute/core/CL/kernels/CLBatchNormalizationLayerKernel.h @@ -50,22 +50,25 @@ public: /** Set the input and output tensors. * - * @param[in] input Source tensor. 3 lower dimensions represent a single input with dimensions [width, height, FM]. Data types supported: QS8/QS16/F32. - * @param[out] output Destination tensor. Output will have the same number of dimensions as input. Data type supported: same as @p input - * The rest are optional and used for representing batches. - * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] var Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] gamma Gamma values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] beta Beta values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] epsilon Small value to avoid division with zero. + * @note If the output tensor is a nullptr, the batch normalization function will be performed in-place + * + * @param[in, out] input Source tensor. In case of @p output tensor = nullptr, this tensor will store the result. + * 3 lower dimensions represent a single input with dimensions [width, height, FM]. + * The rest are optional and used for representing batches. Data types supported: QS8/QS16/F16/F32. + * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input + * @param[in] var Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input + * @param[in] gamma Gamma values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input + * @param[in] beta Beta values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input + * @param[in] epsilon Small value to avoid division with zero. + * @param[out] output Destination tensor. Output will have the same number of dimensions as input. Data type supported: same as @p input */ - void configure(const ICLTensor *input, ICLTensor *output, const ICLTensor *mean, const ICLTensor *var, const ICLTensor *beta, const ICLTensor *gamma, float epsilon); + void configure(ICLTensor *input, ICLTensor *output, const ICLTensor *mean, const ICLTensor *var, const ICLTensor *beta, const ICLTensor *gamma, float epsilon); // Inherited methods overridden: void run(const Window &window, cl::CommandQueue &queue) override; private: - const ICLTensor *_input; + ICLTensor *_input; ICLTensor *_output; const ICLTensor *_mean; const ICLTensor *_var; diff --git a/arm_compute/core/NEON/kernels/NEBatchNormalizationLayerKernel.h b/arm_compute/core/NEON/kernels/NEBatchNormalizationLayerKernel.h index 29fcbd26a0..8ac70be727 100644 --- a/arm_compute/core/NEON/kernels/NEBatchNormalizationLayerKernel.h +++ b/arm_compute/core/NEON/kernels/NEBatchNormalizationLayerKernel.h @@ -49,24 +49,27 @@ public: ~NEBatchNormalizationLayerKernel() = default; /** Set the input and output tensors. * - * @param[in] input Source tensor. 3 lower dimensions represent a single input with dimensions [width, height, FM]. - * The rest are optional and used for representing batches. Data types supported: QS8/F32. - * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] var Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] gamma Gamma values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] beta Beta values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] epsilon Small value to avoid division with zero. - * @param[out] output Destination tensor. Output will have the same number of dimensions as input. Data type supported: same as @p input + * @note If the output tensor is a nullptr, the batch normalization function will be performed in-place + * + * @param[in, out] input Source tensor. In case of @p output tensor = nullptr, this tensor will store the result. + * 3 lower dimensions represent a single input with dimensions [width, height, FM]. + * The rest are optional and used for representing batches. Data types supported: QS8/QS16/F16/F32. + * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input + * @param[in] var Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input + * @param[in] gamma Gamma values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input + * @param[in] beta Beta values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input + * @param[in] epsilon Small value to avoid division with zero. + * @param[out] output Destination tensor. Output will have the same number of dimensions as input. Data type supported: same as @p input */ - void configure(const ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon); + void configure(ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon); // Inherited methods overridden: void run(const Window &window) override; private: - using BatchNormFunction = void(const ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon, const Window &window); + using BatchNormFunction = void(ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon, const Window &window); BatchNormFunction *_func; - const ITensor *_input; + ITensor *_input; ITensor *_output; const ITensor *_mean; const ITensor *_var; diff --git a/arm_compute/runtime/CL/functions/CLBatchNormalizationLayer.h b/arm_compute/runtime/CL/functions/CLBatchNormalizationLayer.h index 882786f1d6..ffb66bee60 100644 --- a/arm_compute/runtime/CL/functions/CLBatchNormalizationLayer.h +++ b/arm_compute/runtime/CL/functions/CLBatchNormalizationLayer.h @@ -46,16 +46,19 @@ public: CLBatchNormalizationLayer(); /** Set the input and output tensors. * - * @param[in] input Source tensor. 3 lower dimensions represent a single input with dimensions [width, height, FM]. - * The rest are optional and used for representing batches. Data types supported: QS8/QS16/F32. - * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] var Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] gamma Gamma values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] beta Beta values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] epsilon Small value to avoid division with zero. - * @param[out] output Destination tensor. Output will have the same number of dimensions as input. Data type supported: same as @p input + * @note If the output tensor is a nullptr, the batch normalization function will be performed in-place + * + * @param[in, out] input Source tensor. In case of @p output tensor = nullptr, this tensor will store the result. + * 3 lower dimensions represent a single input with dimensions [width, height, FM]. + * The rest are optional and used for representing batches. Data types supported: QS8/QS16/F16/F32. + * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input + * @param[in] var Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input + * @param[in] gamma Gamma values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input + * @param[in] beta Beta values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input + * @param[in] epsilon Small value to avoid division with zero. + * @param[out] output Destination tensor. Output will have the same number of dimensions as input. Data type supported: same as @p input */ - void configure(const ICLTensor *input, ICLTensor *output, const ICLTensor *mean, const ICLTensor *var, const ICLTensor *beta, const ICLTensor *gamma, float epsilon); + void configure(ICLTensor *input, ICLTensor *output, const ICLTensor *mean, const ICLTensor *var, const ICLTensor *beta, const ICLTensor *gamma, float epsilon); // Inherited methods overridden: void run() override; diff --git a/arm_compute/runtime/CL/functions/CLConvolutionLayer.h b/arm_compute/runtime/CL/functions/CLConvolutionLayer.h index aba88bd856..2057b6ff8a 100644 --- a/arm_compute/runtime/CL/functions/CLConvolutionLayer.h +++ b/arm_compute/runtime/CL/functions/CLConvolutionLayer.h @@ -34,8 +34,6 @@ #include "arm_compute/core/CL/kernels/CLIm2ColKernel.h" #include "arm_compute/core/CL/kernels/CLWeightsReshapeKernel.h" #include "arm_compute/core/Types.h" -#include "arm_compute/core/Types.h" -#include "arm_compute/runtime/CL/CLTensor.h" #include "arm_compute/runtime/CL/CLTensor.h" namespace arm_compute diff --git a/arm_compute/runtime/NEON/functions/NEBatchNormalizationLayer.h b/arm_compute/runtime/NEON/functions/NEBatchNormalizationLayer.h index b0b5c122cb..041b9e7290 100644 --- a/arm_compute/runtime/NEON/functions/NEBatchNormalizationLayer.h +++ b/arm_compute/runtime/NEON/functions/NEBatchNormalizationLayer.h @@ -45,16 +45,19 @@ public: NEBatchNormalizationLayer(); /** Set the input and output tensors. * - * @param[in] input Source tensor. 3 lower dimensions represent a single input with dimensions [width, height, FM]. - * The rest are optional and used for representing batches. Data types supported: QS8/F32. - * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] var Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] gamma Gamma values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] beta Beta values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] epsilon Small value to avoid division with zero. - * @param[out] output Destination tensor. Output will have the same number of dimensions as input. Data type supported: same as @p input + * @note If the output tensor is a nullptr, the batch normalization function will be performed in-place + * + * @param[in, out] input Source tensor. In case of @p output tensor = nullptr, this tensor will store the result. + * 3 lower dimensions represent a single input with dimensions [width, height, FM]. + * The rest are optional and used for representing batches. Data types supported: QS8/QS16/F16/F32. + * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input + * @param[in] var Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input + * @param[in] gamma Gamma values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input + * @param[in] beta Beta values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input + * @param[in] epsilon Small value to avoid division with zero. + * @param[out] output Destination tensor. Output will have the same number of dimensions as input. Data type supported: same as @p input */ - void configure(const ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon); + void configure(ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon); // Inherited methods overridden: void run() override; |