From fe7ae817755577be29f4c07aa27d8ef9e821da45 Mon Sep 17 00:00:00 2001 From: Pablo Marquez Tello Date: Wed, 3 Mar 2021 12:12:35 +0000 Subject: CLInstanceNormalizationLayer NHWC optimisation * Make changes to split the workload into two kernels. One kernel precomputes mean and variance and the second kernel just loads these precomputed values. * The new approach runs %30 faster than the original code for NHWC workloads like 32x192x256. * Resolves MLCE-337 Change-Id: I8356fcefa2d131ab4dcb32268ce7142421d073e4 Signed-off-by: Pablo Marquez Tello Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5355 Tested-by: Arm Jenkins Comments-Addressed: Arm Jenkins Reviewed-by: Manuel Bottini Reviewed-by: Michele Di Giorgio --- src/core/CL/CLKernelLibrary.cpp | 1 + src/core/CL/cl_kernels/instance_normalization.cl | 155 ++++++++++++++------- .../kernels/CLInstanceNormalizationLayerKernel.cpp | 96 ++++++++++++- .../kernels/CLInstanceNormalizationLayerKernel.h | 57 ++++++-- 4 files changed, 244 insertions(+), 65 deletions(-) (limited to 'src/core') diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp index eef204fde9..002a14400f 100644 --- a/src/core/CL/CLKernelLibrary.cpp +++ b/src/core/CL/CLKernelLibrary.cpp @@ -356,6 +356,7 @@ const std::map CLKernelLibrary::_kernel_program_map = { "im2col9x9_nhwc", "im2col.cl" }, { "im2col_generic_nhwc", "im2col.cl" }, { "instance_normalization", "instance_normalization.cl" }, + { "compute_mean_var", "instance_normalization.cl" }, { "l2_normalize_x", "l2_normalize.cl" }, { "l2_normalize_y", "l2_normalize.cl" }, { "l2_normalize_z", "l2_normalize.cl" }, diff --git a/src/core/CL/cl_kernels/instance_normalization.cl b/src/core/CL/cl_kernels/instance_normalization.cl index 480d9cd20c..d2507d94dd 100644 --- a/src/core/CL/cl_kernels/instance_normalization.cl +++ b/src/core/CL/cl_kernels/instance_normalization.cl @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019-2020 Arm Limited. + * Copyright (c) 2019-2021 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -23,14 +23,11 @@ */ #include "helpers.h" -#if defined(VEC_SIZE) && defined(DATA_TYPE) && defined(INTERNAL_DATA_TYPE) && defined(GAMMA) && defined(BETA) && defined(EPSILON) && defined(DIM_X) && defined(DIM_Y) && defined(DIM_Z) -/** This function normalizes the input 2D tensor across the first dimension with respect to mean and standard deviation of the same dimension. +#if defined(VEC_SIZE) && defined(DATA_TYPE) && defined(DIM_X) && defined(DIM_Y) && defined(DIM_Z) +/** This function computes the mean and variance of each plane of the input tensor and provides it as output. * * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 * @attention Data type should be passed using the -DDATA_TYPE=data_type compile flag, e.g. -DDATA_TYPE=float - * @attention The scale scalar value applied to the normalized tensor should be passed using the -DGAMMA=value compile flag, e.g. -DGAMMA=1.3 - * @attention The offset scalar value applied to the normalized tensor should be passed using the -DBETA=value compile flag, e.g. -DBETA=2.4 - * @attention Normalization epsilon parameter should be given as a preprocessor argument with -DEPSILON=value. e.g. -DEPSILON=0.001f * @attention Dimensions X, Y, and Z should be given as a preprocessor argument with -DDIM_X=value, -DDIM_Y=value, -DDIM_Z=value. e.g. -DDIM_X=6, -DDIM_Y=2, -DDIM_Z=7 * * @param[in] input_ptr Pointer to the first source tensor. Supported data types: F16/F32 @@ -40,6 +37,8 @@ * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] input_stride_z Stride of the first source tensor in Z dimension (in bytes) * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] input_step_w input_stride_w * number of elements along W processed per workitem(in bytes) * @param[in] input_offset_first_element_in_bytes The offset of the first element in the first source tensor * @param[out] output_ptr (Optional) Pointer to the destination tensor. Supported data types: same as @p input_ptr * @param[in] output_stride_x (Optional) Stride of the destination tensor in X dimension (in bytes) @@ -50,46 +49,40 @@ * @param[in] output_step_z (Optional) output_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination tensor */ -__kernel void instance_normalization( - TENSOR4D_DECLARATION(input) -#ifndef IN_PLACE - , - TENSOR4D_DECLARATION(output) -#endif /* IN_PLACE */ -) +__kernel void compute_mean_var( + TENSOR4D_DECLARATION(input), + TENSOR3D_DECLARATION(output)) { - Tensor4D in = CONVERT_TO_TENSOR4D_STRUCT_NO_STEP(input, 0); -#ifndef IN_PLACE - Tensor4D out = CONVERT_TO_TENSOR4D_STRUCT_NO_STEP(output, 0); -#endif /* IN_PLACE */ - - INTERNAL_DATA_TYPE sum = 0.f; - INTERNAL_DATA_TYPE sum_sq = 0.f; + Tensor4D in = CONVERT_TO_TENSOR4D_STRUCT_NO_STEP(input, 0); + Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(output); #if defined(NHWC) - const int ch = get_global_id(0); // Current channel - const int batch = get_global_id(2); // Current batch + const int batch = get_global_id(1); // Current batch const int elements_plane = DIM_Y * DIM_Z; - - for(int i_w = 0; i_w < DIM_Y; ++i_w) + float part_sum = 0.f; + float part_sum_sq = 0.f; + const int in_offset = input_offset_first_element_in_bytes + batch * input_stride_w + ch * sizeof(DATA_TYPE); + for(int i = 0; i < (DIM_Y * DIM_Z); ++i) { - for(int i_h = 0; i_h < DIM_Z; ++i_h) - { - INTERNAL_DATA_TYPE data = (INTERNAL_DATA_TYPE) * ((__global DATA_TYPE *)tensor4D_offset(&in, ch, i_w, i_h, batch)); - sum += data; - sum_sq += data * data; - } + const float data = *((__global DATA_TYPE *)(input_ptr + in_offset + i * input_stride_y)); + part_sum += data; + part_sum_sq += data * data; } - + float mean = (part_sum / elements_plane); + float var = (part_sum_sq / elements_plane) - (mean * mean); + __global DATA_TYPE *output_address0 = (__global DATA_TYPE *)tensor3D_offset(&out, ch, 0, batch); + *output_address0 = mean; + __global DATA_TYPE *output_address1 = (__global DATA_TYPE *)tensor3D_offset(&out, ch, 1, batch); + *output_address1 = var; #else // !defined(NHWC) const int ch = get_global_id(2) % DIM_Z; // Current channel const int batch = get_global_id(2) / DIM_Z; // Current batch const int elements_plane = DIM_X * DIM_Y; - VEC_DATA_TYPE(INTERNAL_DATA_TYPE, VEC_SIZE) + VEC_DATA_TYPE(float, VEC_SIZE) part_sum = 0.f; - VEC_DATA_TYPE(INTERNAL_DATA_TYPE, VEC_SIZE) + VEC_DATA_TYPE(float, VEC_SIZE) part_sum_sq = 0.f; // Calculate partial sum for(int y = 0; y < DIM_Y; ++y) @@ -98,15 +91,15 @@ __kernel void instance_normalization( for(; x <= (DIM_X - VEC_SIZE); x += VEC_SIZE) { // Load data - VEC_DATA_TYPE(INTERNAL_DATA_TYPE, VEC_SIZE) - data = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)tensor4D_offset(&in, x, y, ch, batch)), VEC_DATA_TYPE(INTERNAL_DATA_TYPE, VEC_SIZE)); + VEC_DATA_TYPE(float, VEC_SIZE) + data = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)tensor4D_offset(&in, x, y, ch, batch)), VEC_DATA_TYPE(float, VEC_SIZE)); part_sum += data; part_sum_sq += data * data; } // Left-overs loop for(; x < DIM_X; ++x) { - INTERNAL_DATA_TYPE data = (INTERNAL_DATA_TYPE)(*((__global DATA_TYPE *)tensor4D_offset(&in, x, y, ch, batch))); + float data = (float)(*((__global DATA_TYPE *)tensor4D_offset(&in, x, y, ch, batch))); part_sum.s0 += data; part_sum_sq.s0 += data * data; } @@ -127,29 +120,93 @@ __kernel void instance_normalization( part_sum.s0 += part_sum.s1; part_sum_sq.s0 += part_sum_sq.s1; - sum = (INTERNAL_DATA_TYPE)part_sum.s0; - sum_sq = (INTERNAL_DATA_TYPE)part_sum_sq.s0; + float sum = (float)part_sum.s0; + float sum_sq = (float)part_sum_sq.s0; + + const float mean = (sum / elements_plane); + const float var = (sum_sq / elements_plane) - (mean * mean); + + __global DATA_TYPE *output_address0 = (__global DATA_TYPE *)tensor3D_offset(&out, ch, 0, batch); + *output_address0 = mean; + __global DATA_TYPE *output_address1 = (__global DATA_TYPE *)tensor3D_offset(&out, ch, 1, batch); + *output_address1 = var; #endif // defined(NHWC) +} +#endif /* defined(VEC_SIZE) && defined(DATA_TYPE) && defined(DIM_X) && defined(DIM_Y) && defined(DIM_Z) */ - const INTERNAL_DATA_TYPE mean = (sum / elements_plane); - const INTERNAL_DATA_TYPE var = (sum_sq / elements_plane) - (mean * mean); - const INTERNAL_DATA_TYPE multip = GAMMA / sqrt(var + EPSILON); +#if defined(VEC_SIZE) && defined(DATA_TYPE) && defined(INTERNAL_DATA_TYPE) && defined(GAMMA) && defined(BETA) && defined(EPSILON) && defined(DIM_X) && defined(DIM_Y) && defined(DIM_Z) +/** This function normalizes the input 2D tensor across the first dimension with respect to mean and standard deviation of the same dimension. + * + * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 + * @attention Data type should be passed using the -DDATA_TYPE=data_type compile flag, e.g. -DDATA_TYPE=float + * @attention The scale scalar value applied to the normalized tensor should be passed using the -DGAMMA=value compile flag, e.g. -DGAMMA=1.3 + * @attention The offset scalar value applied to the normalized tensor should be passed using the -DBETA=value compile flag, e.g. -DBETA=2.4 + * @attention Normalization epsilon parameter should be given as a preprocessor argument with -DEPSILON=value. e.g. -DEPSILON=0.001f + * @attention Dimensions X, Y, and Z should be given as a preprocessor argument with -DDIM_X=value, -DDIM_Y=value, -DDIM_Z=value. e.g. -DDIM_X=6, -DDIM_Y=2, -DDIM_Z=7 + * + * @param[in] input_ptr Pointer to the first source tensor. Supported data types: F16/F32 + * @param[in] input_stride_x Stride of the first source tensor in X dimension (in bytes) + * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input_stride_y Stride of the first source tensor in Y dimension (in bytes) + * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input_stride_z Stride of the first source tensor in Z dimension (in bytes) + * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input_offset_first_element_in_bytes The offset of the first element in the first source tensor + * @param[out] output_ptr (Optional) Pointer to the destination tensor. Supported data types: same as @p input_ptr + * @param[in] output_stride_x (Optional) Stride of the destination tensor in X dimension (in bytes) + * @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y (Optional) Stride of the destination tensor in Y dimension (in bytes) + * @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z (Optional) Stride of the destination tensor in Z dimension (in bytes) + * @param[in] output_step_z (Optional) output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination tensor + */ +__kernel void instance_normalization( + TENSOR4D_DECLARATION(input), + TENSOR3D_DECLARATION(mean_var) +#ifndef IN_PLACE + , + TENSOR4D_DECLARATION(output) +#endif /* IN_PLACE */ +) +{ + Tensor4D in = CONVERT_TO_TENSOR4D_STRUCT_NO_STEP(input, 0); + Tensor3D mean_var = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(mean_var); +#ifndef IN_PLACE + Tensor4D out = CONVERT_TO_TENSOR4D_STRUCT_NO_STEP(output, 0); +#endif /* IN_PLACE */ #if defined(NHWC) + const int ch = get_global_id(0); // Current channel + const int batch = get_global_id(2); // Current batch +#else /* defined(NHWC) */ + const int ch = get_global_id(2) % DIM_Z; // Current channel + const int batch = get_global_id(2) / DIM_Z; // Current batch +#endif /* defined(NHWC) */ - for(int i_w = 0; i_w < DIM_Y; ++i_w) + const __global DATA_TYPE *mean_ptr = (__global DATA_TYPE *)tensor3D_offset(&mean_var, ch, 0, batch); + const __global DATA_TYPE *var_ptr = (__global DATA_TYPE *)tensor3D_offset(&mean_var, ch, 1, batch); + const INTERNAL_DATA_TYPE mean = (INTERNAL_DATA_TYPE) * mean_ptr; + const INTERNAL_DATA_TYPE var = (INTERNAL_DATA_TYPE) * var_ptr; + const INTERNAL_DATA_TYPE multip = GAMMA / sqrt(var + EPSILON); + const INTERNAL_DATA_TYPE beta = (INTERNAL_DATA_TYPE)BETA; + +#if defined(NHWC) + const int in_offset = input_offset_first_element_in_bytes + batch * input_stride_w + ch * sizeof(DATA_TYPE); +#ifndef IN_PLACE + const int out_offset = output_offset_first_element_in_bytes + batch * input_stride_w + ch * sizeof(DATA_TYPE); +#endif /* IN_PLACE */ + + for(int i = 0; i < (DIM_Y * DIM_Z); ++i) { - for(int i_h = 0; i_h < DIM_Z; ++i_h) - { - __global DATA_TYPE *input_address = (__global DATA_TYPE *)tensor4D_offset(&in, ch, i_w, i_h, batch); + __global DATA_TYPE *input_address = (__global DATA_TYPE *)(input_ptr + in_offset + i * input_stride_y); #ifdef IN_PLACE - __global DATA_TYPE *output_address = input_address; + __global DATA_TYPE *output_address = input_address; #else /* !IN_PLACE */ - __global DATA_TYPE *output_address = (__global DATA_TYPE *)tensor4D_offset(&out, ch, i_w, i_h, batch); + __global DATA_TYPE *output_address = (__global DATA_TYPE *)(output_ptr + out_offset + i * output_stride_y); #endif /* IN_PLACE */ - *(output_address) = (*(input_address) - mean) * multip + (INTERNAL_DATA_TYPE)BETA; - } + *(output_address) = (*(input_address) - mean) * multip + beta; } #else // !defined(NHWC) diff --git a/src/core/CL/kernels/CLInstanceNormalizationLayerKernel.cpp b/src/core/CL/kernels/CLInstanceNormalizationLayerKernel.cpp index 50c4e24c33..80a42cc3f5 100644 --- a/src/core/CL/kernels/CLInstanceNormalizationLayerKernel.cpp +++ b/src/core/CL/kernels/CLInstanceNormalizationLayerKernel.cpp @@ -32,7 +32,6 @@ #include "src/core/CL/CLValidate.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" - #include "support/StringSupport.h" namespace arm_compute @@ -54,25 +53,108 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, c return Status{}; } + +Status validate_arguments_meanvar(const ITensorInfo *input, const ITensorInfo *output) +{ + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(input, DataType::F16, DataType::F32); + + if(output != nullptr && output->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_channels() != output->num_channels(), "Input and output have different number of channels"); + } + + return Status{}; +} } // namespace -CLInstanceNormalizationLayerKernel::CLInstanceNormalizationLayerKernel() - : _input(nullptr), _output(nullptr), _run_in_place(false) +CLComputeMeanVariance::CLComputeMeanVariance() + : _input(nullptr), _output(nullptr) +{ +} + +void CLComputeMeanVariance::configure(const CLCompileContext &compile_context, ICLTensor *input, ICLTensor *output) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input); + auto padding_info = get_padding_info({ input, output }); + + _input = input; + _output = output == nullptr ? input : output; + + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_meanvar(_input->info(), _output->info())); + const unsigned int num_elems_processed_per_iteration = 16 / input->info()->element_size(); + + CLBuildOptions build_opts; + build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); + build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration)); + build_opts.add_option("-DDIM_X=" + support::cpp11::to_string(input->info()->dimension(0))); + build_opts.add_option("-DDIM_Y=" + support::cpp11::to_string(input->info()->dimension(1))); + build_opts.add_option("-DDIM_Z=" + support::cpp11::to_string(input->info()->dimension(2))); + build_opts.add_option_if(_input->info()->data_layout() == DataLayout::NHWC, "-DNHWC"); + // Create kernel + _kernel = create_kernel(compile_context, "compute_mean_var", build_opts.options()); + + // We handle the planes manually + Window win = calculate_max_window(*(input->info()), Steps(1)); + const auto data_layout = input->info()->data_layout(); + const unsigned int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); + const unsigned int batches_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES); + const unsigned int input_channel = input->info()->dimension(channel_idx); + const unsigned int input_batches = input->info()->dimension(batches_idx); + const TensorShape out_shape(input_channel, 2u, input_batches); + + // Output auto initialization if not yet initialized + auto_init_if_empty(*output->info(), out_shape, 1, input->info()->data_type()); + + ICLKernel::configure_internal(win); + ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info)); +} + +Status CLComputeMeanVariance::validate(const ITensorInfo *input, const ITensorInfo *output) { + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_meanvar(input, output)); + return Status{}; } -void CLInstanceNormalizationLayerKernel::configure(ICLTensor *input, ICLTensor *output, const InstanceNormalizationLayerKernelInfo &info) +void CLComputeMeanVariance::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); + + Window collapsed_window = window.collapse(window, Window::DimZ); + + // We will process the planes together + if(_input->info()->data_layout() == DataLayout::NCHW) + { + collapsed_window.set(Window::DimX, Window::Dimension(0, 1, 1)); + collapsed_window.set(Window::DimY, Window::Dimension(0, 1, 1)); + } + else + { + collapsed_window.set(Window::DimZ, Window::Dimension(0, 1, 1)); + collapsed_window.set(Window::DimY, Window::Dimension(0, _input->info()->dimension(3), 1)); + } + unsigned int idx = 0; + add_4D_tensor_argument(idx, _input, collapsed_window); + add_3D_tensor_argument(idx, _output, collapsed_window); + + enqueue(queue, *this, collapsed_window, lws_hint()); +} + +CLInstanceNormalizationLayerKernel::CLInstanceNormalizationLayerKernel() + : _input(nullptr), _output(nullptr), _mean(nullptr), _run_in_place(false) { - configure(CLKernelLibrary::get().get_compile_context(), input, output, info); } -void CLInstanceNormalizationLayerKernel::configure(const CLCompileContext &compile_context, ICLTensor *input, ICLTensor *output, const InstanceNormalizationLayerKernelInfo &info) +void CLInstanceNormalizationLayerKernel::configure(const CLCompileContext &compile_context, ICLTensor *input, ICLTensor *mean_var, ICLTensor *output, const InstanceNormalizationLayerKernelInfo &info) { ARM_COMPUTE_ERROR_ON_NULLPTR(input); auto padding_info = get_padding_info({ input, output }); _input = input; _output = output == nullptr ? input : output; + _mean = mean_var; _run_in_place = (output == nullptr) || (output == input); ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(_input->info(), _output->info(), info)); @@ -132,6 +214,8 @@ void CLInstanceNormalizationLayerKernel::run(const Window &window, cl::CommandQu unsigned int idx = 0; add_4D_tensor_argument(idx, _input, collapsed_window); + add_3D_tensor_argument(idx, _mean, collapsed_window); + if(!_run_in_place) { add_4D_tensor_argument(idx, _output, collapsed_window); diff --git a/src/core/CL/kernels/CLInstanceNormalizationLayerKernel.h b/src/core/CL/kernels/CLInstanceNormalizationLayerKernel.h index d4444f0b20..33a3ff97c3 100644 --- a/src/core/CL/kernels/CLInstanceNormalizationLayerKernel.h +++ b/src/core/CL/kernels/CLInstanceNormalizationLayerKernel.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019-2020 Arm Limited. + * Copyright (c) 2019-2021 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -50,23 +50,16 @@ public: /** Default destructor */ ~CLInstanceNormalizationLayerKernel() = default; - /** Set the input and output tensors. - * - * @param[in, out] input Source tensor. Data types supported: F16/F32. Data layout supported: NCHW, NHWC - * In case of @p output tensor = nullptr this tensor will store the result of the normalization. - * @param[out] output Destination tensor. Data types and data layouts supported: same as @p input. - * @param[in] info Kernel meta-data descriptor - */ - void configure(ICLTensor *input, ICLTensor *output, const InstanceNormalizationLayerKernelInfo &info); /** Set the input and output tensors. * * @param[in] compile_context The compile context to be used. * @param[in, out] input Source tensor. Data types supported: F16/F32. Data layout supported: NCHW, NHWC * In case of @p output tensor = nullptr this tensor will store the result of the normalization. + * @param[in] mean_var Tensor containing the precomputed mean and variance values. Data types supported: F32. * @param[out] output Destination tensor. Data types and data layouts supported: same as @p input. * @param[in] info Kernel meta-data descriptor */ - void configure(const CLCompileContext &compile_context, ICLTensor *input, ICLTensor *output, const InstanceNormalizationLayerKernelInfo &info); + void configure(const CLCompileContext &compile_context, ICLTensor *input, ICLTensor *mean_var, ICLTensor *output, const InstanceNormalizationLayerKernelInfo &info); /** Static function to check if given info will lead to a valid configuration of @ref CLInstanceNormalizationLayer. * @@ -84,7 +77,51 @@ public: private: ICLTensor *_input; ICLTensor *_output; + ICLTensor *_mean; bool _run_in_place; }; + +/** Interface for compute Mean and Variance per channel */ +class CLComputeMeanVariance : public ICLKernel +{ +public: + /** Constructor */ + CLComputeMeanVariance(); + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLComputeMeanVariance(const CLComputeMeanVariance &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLComputeMeanVariance &operator=(const CLComputeMeanVariance &) = delete; + /** Default Move Constructor. */ + CLComputeMeanVariance(CLComputeMeanVariance &&) = default; + /** Default move assignment operator */ + CLComputeMeanVariance &operator=(CLComputeMeanVariance &&) = default; + /** Default destructor */ + ~CLComputeMeanVariance() = default; + + /** Set the input and output tensors. + * + * @param[in] compile_context The compile context to be used. + * @param[in, out] input Source tensor. Data types supported: F16/F32. Data layout supported: NCHW, NHWC + * In case of @p output tensor = nullptr this tensor will store the result of the normalization. + * @param[out] output Destination tensor. Data types and data layouts supported: same as @p input. + */ + void configure(const CLCompileContext &compile_context, ICLTensor *input, ICLTensor *output); + + /** Static function to check if given info will lead to a valid configuration of @ref CLInstanceNormalizationLayer. + * + * @param[in] input Source tensor info. Data types supported: F16/F32. Data layout supported: NHWC, NCHW + * @param[in] output Destination tensor info. Data types and data layouts supported: same as @p input. + * + * @return a status + */ + static Status validate(const ITensorInfo *input, const ITensorInfo *output); + + // Inherited methods overridden: + void run(const Window &window, cl::CommandQueue &queue) override; + +private: + ICLTensor *_input; + ICLTensor *_output; +}; } // namespace arm_compute #endif /*ARM_COMPUTE_CLINSTANCENORMALIZATIONLAYERKERNEL_H */ -- cgit v1.2.1