From 55a687d5e2cf3434e4c2564ac73959dcc7163304 Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Thu, 30 Jan 2020 12:00:23 +0000 Subject: COMPMID-2927: Add support for mixed precision in CLInstanceNormalizationLayer Signed-off-by: Georgios Pinitas Change-Id: I91482e2e4b723606aef76afef09a8277813e5d1b Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/2668 Comments-Addressed: Arm Jenkins Tested-by: Arm Jenkins Reviewed-by: Giorgio Arena --- .../kernels/CLInstanceNormalizationLayerKernel.h | 30 ++++++-------- arm_compute/core/KernelDescriptors.h | 25 +++++++++++ .../CL/functions/CLInstanceNormalizationLayer.h | 30 +++++++------- src/core/CL/cl_kernels/instance_normalization.cl | 48 +++++++++++----------- .../kernels/CLInstanceNormalizationLayerKernel.cpp | 33 +++++++-------- .../CL/functions/CLInstanceNormalizationLayer.cpp | 10 ++--- 6 files changed, 96 insertions(+), 80 deletions(-) diff --git a/arm_compute/core/CL/kernels/CLInstanceNormalizationLayerKernel.h b/arm_compute/core/CL/kernels/CLInstanceNormalizationLayerKernel.h index cf726d8cdd..9982cc2f1c 100644 --- a/arm_compute/core/CL/kernels/CLInstanceNormalizationLayerKernel.h +++ b/arm_compute/core/CL/kernels/CLInstanceNormalizationLayerKernel.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019 ARM Limited. + * Copyright (c) 2019-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -26,8 +26,11 @@ #include "arm_compute/core/CL/ICLKernel.h" +#include "arm_compute/core/KernelDescriptors.h" + namespace arm_compute { +// Forward declarations class ICLTensor; /** Interface for performing an instance normalization */ @@ -49,26 +52,22 @@ public: /** 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] gamma (Optional) The scale scalar value applied to the normalized tensor. Defaults to 1.0 - * @param[in] beta (Optional) The offset scalar value applied to the normalized tensor. Defaults to 0.0 - * @param[in] epsilon (Optional) Lower bound value for the normalization. Defaults to 1e-12 + * @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, float gamma = 1.0f, float beta = 0.0f, float epsilon = 1e-12f); + void configure(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 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. - * @param[in] gamma (Optional) The scale scalar value applied to the normalized tensor. Defaults to 1.0 - * @param[in] beta (Optional) The offset scalar value applied to the normalized tensor. Defaults to 0.0 - * @param[in] epsilon (Optional) Lower bound value for the normalization. Defaults to 1e-12 + * @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. + * @param[in] info Kernel meta-data descriptor * * @return a status */ - static Status validate(const ITensorInfo *input, const ITensorInfo *output, float gamma = 1.0f, float beta = 0.0f, float epsilon = 1e-12f); + static Status validate(const ITensorInfo *input, const ITensorInfo *output, const InstanceNormalizationLayerKernelInfo &info); // Inherited methods overridden: void run(const Window &window, cl::CommandQueue &queue) override; @@ -76,9 +75,6 @@ public: private: ICLTensor *_input; ICLTensor *_output; - float _gamma; - float _beta; - float _epsilon; bool _run_in_place; }; } // namespace arm_compute diff --git a/arm_compute/core/KernelDescriptors.h b/arm_compute/core/KernelDescriptors.h index d009ccc73d..4b04bebdef 100644 --- a/arm_compute/core/KernelDescriptors.h +++ b/arm_compute/core/KernelDescriptors.h @@ -92,5 +92,30 @@ struct DirectConvolutionLayerOutputStageKernelInfo int32_t result_offset_after_shift{ 0 }; /**< Result offset used for quantizing */ DataType output_data_type{ DataType::UNKNOWN }; /**< Output tensor data type to use if the output is not initialized */ }; + +struct InstanceNormalizationLayerKernelInfo +{ + /** Default constructor */ + InstanceNormalizationLayerKernelInfo() + : InstanceNormalizationLayerKernelInfo(1.f, 0.f, 1e-12, true) + { + } + /** Constructor + * + * @param[in] gamma The scale scalar value applied to the normalized tensor. + * @param[in] beta The offset scalar value applied to the normalized tensor + * @param[in] epsilon Lower bound value for the normalization. + * @param[in] use_mixed_precision Use mixed precision in case of FP16 execution. + */ + InstanceNormalizationLayerKernelInfo(float gamma, float beta, float epsilon, bool use_mixed_precision) + : gamma(gamma), beta(beta), epsilon(epsilon), use_mixed_precision(use_mixed_precision) + { + } + + float gamma; /**< The scale scalar value applied to the normalized tensor. Defaults to 1.0 */ + float beta; /**< The offset scalar value applied to the normalized tensor. Defaults to 0.0 */ + float epsilon; /**< Lower bound value for the normalization. Defaults to 1e-12 */ + bool use_mixed_precision; /**< Use mixed precision in case of FP16 execution. Defaults to true */ +}; } // namespace arm_compute #endif /* ARM_COMPUTE_CORE_KERNEL_DESCRIPTORS_H */ diff --git a/arm_compute/runtime/CL/functions/CLInstanceNormalizationLayer.h b/arm_compute/runtime/CL/functions/CLInstanceNormalizationLayer.h index ca0da513ad..ddd4b12eca 100644 --- a/arm_compute/runtime/CL/functions/CLInstanceNormalizationLayer.h +++ b/arm_compute/runtime/CL/functions/CLInstanceNormalizationLayer.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019 ARM Limited. + * Copyright (c) 2019-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -42,26 +42,28 @@ public: CLInstanceNormalizationLayer(); /** Set the input and output tensors. * - * @param[in, out] input Source tensor. In case of @p output tensor = nullptr this tensor will store the result of the normalization. - * Data types supported: F16/F32. Data layout supported: NHWC, NCHW - * @param[out] output Destination tensor. Data types and data layouts supported: same as @p input. - * @param[in] gamma (Optional) The scale scalar value applied to the normalized tensor. Defaults to 1.0 - * @param[in] beta (Optional) The offset scalar value applied to the normalized tensor. Defaults to 0.0 - * @param[in] epsilon (Optional) Lower bound value for the normalization. Defaults to 1e-12 + * @param[in, out] input Source tensor. In case of @p output tensor = nullptr this tensor will store the result of the normalization. + * Data types supported: F16/F32. Data layout supported: NHWC, NCHW + * @param[out] output Destination tensor. Data types and data layouts supported: same as @p input. + * @param[in] gamma (Optional) The scale scalar value applied to the normalized tensor. Defaults to 1.0 + * @param[in] beta (Optional) The offset scalar value applied to the normalized tensor. Defaults to 0.0 + * @param[in] epsilon (Optional) Lower bound value for the normalization. Defaults to 1e-12 + * @param[in] use_mixed_precision (Optional) Use mixed precision in case of FP16 execution */ - void configure(ICLTensor *input, ICLTensor *output, float gamma = 1.0f, float beta = 0.0f, float epsilon = 1e-12f); + void configure(ICLTensor *input, ICLTensor *output, float gamma = 1.0f, float beta = 0.0f, float epsilon = 1e-12f, bool use_mixed_precision = true); /** 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. - * @param[in] gamma (Optional) The scale scalar value applied to the normalized tensor. Defaults to 1.0 - * @param[in] beta (Optional) The offset scalar value applied to the normalized tensor. Defaults to 0.0 - * @param[in] epsilon (Optional) Lower bound value for the normalization. Defaults to 1e-12 + * @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. + * @param[in] gamma (Optional) The scale scalar value applied to the normalized tensor. Defaults to 1.0 + * @param[in] beta (Optional) The offset scalar value applied to the normalized tensor. Defaults to 0.0 + * @param[in] epsilon (Optional) Lower bound value for the normalization. Defaults to 1e-12 + * @param[in] use_mixed_precision (Optional) Use mixed precision in case of FP16 execution * * @return a status */ - static Status validate(const ITensorInfo *input, const ITensorInfo *output, float gamma = 1.0f, float beta = 0.0f, float epsilon = 1e-12f); + static Status validate(const ITensorInfo *input, const ITensorInfo *output, float gamma = 1.0f, float beta = 0.0f, float epsilon = 1e-12f, bool use_mixed_precision = true); }; } // namespace arm_compute #endif /* ARM_COMPUTE_CLINSTANCENORMALIZATIONLAYER_H */ diff --git a/src/core/CL/cl_kernels/instance_normalization.cl b/src/core/CL/cl_kernels/instance_normalization.cl index de7d57c69e..043012bc51 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 ARM Limited. + * Copyright (c) 2019-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -23,7 +23,7 @@ */ #include "helpers.h" -#if defined(VEC_SIZE) && defined(DATA_TYPE) && defined(GAMMA) && defined(BETA) && defined(EPSILON) && defined(DIM_X) && defined(DIM_Y) && defined(DIM_Z) +#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 @@ -63,8 +63,8 @@ __kernel void instance_normalization( Tensor4D out = CONVERT_TO_TENSOR4D_STRUCT_NO_STEP(output, 0); #endif /* IN_PLACE */ - float sum = 0.f; - float sum_sq = 0.f; + INTERNAL_DATA_TYPE sum = 0.f; + INTERNAL_DATA_TYPE sum_sq = 0.f; #if defined(NHWC) @@ -76,7 +76,7 @@ __kernel void instance_normalization( { for(int i_h = 0; i_h < DIM_Z; ++i_h) { - float data = (float) * ((__global DATA_TYPE *)tensor4D_offset(&in, ch, i_w, i_h, batch)); + 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; } @@ -87,9 +87,9 @@ __kernel void instance_normalization( const int batch = get_global_id(2) / DIM_Z; // Current batch const int elements_plane = DIM_X * DIM_Y; - VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) + VEC_DATA_TYPE(INTERNAL_DATA_TYPE, VEC_SIZE) part_sum = 0.f; - VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) + VEC_DATA_TYPE(INTERNAL_DATA_TYPE, VEC_SIZE) part_sum_sq = 0.f; // Calculate partial sum for(int y = 0; y < DIM_Y; ++y) @@ -98,15 +98,15 @@ __kernel void instance_normalization( for(; x <= (DIM_X - VEC_SIZE); x += VEC_SIZE) { // Load data - VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) - data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)tensor4D_offset(&in, x, y, ch, batch)); + 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)); part_sum += data; part_sum_sq += data * data; } // Left-overs loop for(; x < DIM_X; ++x) { - DATA_TYPE data = *((__global DATA_TYPE *)tensor4D_offset(&in, x, y, ch, batch)); + INTERNAL_DATA_TYPE data = (INTERNAL_DATA_TYPE)(*((__global DATA_TYPE *)tensor4D_offset(&in, x, y, ch, batch))); part_sum.s0 += data; part_sum_sq.s0 += data * data; } @@ -127,16 +127,14 @@ __kernel void instance_normalization( part_sum.s0 += part_sum.s1; part_sum_sq.s0 += part_sum_sq.s1; - sum = (float)part_sum.s0; - sum_sq = (float)part_sum_sq.s0; + sum = (INTERNAL_DATA_TYPE)part_sum.s0; + sum_sq = (INTERNAL_DATA_TYPE)part_sum_sq.s0; #endif // defined(NHWC) - const float mean_float = (sum / elements_plane); - const DATA_TYPE mean = (DATA_TYPE)mean_float; - const float var_float = (sum_sq / elements_plane) - (mean_float * mean_float); - const float multip_float = GAMMA / sqrt(var_float + EPSILON); - const DATA_TYPE multip = (DATA_TYPE)multip_float; + 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(NHWC) @@ -150,7 +148,7 @@ __kernel void instance_normalization( #else /* !IN_PLACE */ __global DATA_TYPE *output_address = (__global DATA_TYPE *)tensor4D_offset(&out, ch, i_w, i_h, batch); #endif /* IN_PLACE */ - *(output_address) = (*(input_address) - mean) * multip + (DATA_TYPE)BETA; + *(output_address) = (*(input_address) - mean) * multip + (INTERNAL_DATA_TYPE)BETA; } } @@ -167,13 +165,13 @@ __kernel void instance_normalization( __global DATA_TYPE *output_address = (__global DATA_TYPE *)tensor4D_offset(&out, x, y, ch, batch); #endif /* IN_PLACE */ - VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) - data = VLOAD(VEC_SIZE)(0, input_address); + VEC_DATA_TYPE(INTERNAL_DATA_TYPE, VEC_SIZE) + data = CONVERT(VLOAD(VEC_SIZE)(0, input_address), VEC_DATA_TYPE(INTERNAL_DATA_TYPE, VEC_SIZE)); - VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) - res = (data - mean) * multip + (DATA_TYPE)BETA; + VEC_DATA_TYPE(INTERNAL_DATA_TYPE, VEC_SIZE) + res = (data - mean) * multip + (INTERNAL_DATA_TYPE)BETA; VSTORE(VEC_SIZE) - (res, 0, output_address); + (CONVERT(res, VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)), 0, output_address); } // Left-overs loop for(; x < DIM_X; ++x) @@ -184,9 +182,9 @@ __kernel void instance_normalization( #else /* !IN_PLACE */ __global DATA_TYPE *output_address = (__global DATA_TYPE *)tensor4D_offset(&out, x, y, ch, batch); #endif /* IN_PLACE */ - *(output_address) = (*(input_address) - mean) * multip + (DATA_TYPE)BETA; + *(output_address) = (*(input_address) - mean) * multip + (INTERNAL_DATA_TYPE)BETA; } } #endif // defined(NHWC) } -#endif /* defined(VEC_SIZE) && defined(DATA_TYPE) && defined(GAMMA) && defined(BETA) && defined(EPSILON) && defined(DIM_X) && defined(DIM_Y) && defined(DIM_Z) */ +#endif /* 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) */ diff --git a/src/core/CL/kernels/CLInstanceNormalizationLayerKernel.cpp b/src/core/CL/kernels/CLInstanceNormalizationLayerKernel.cpp index 0f208573a1..5c2a3d993c 100644 --- a/src/core/CL/kernels/CLInstanceNormalizationLayerKernel.cpp +++ b/src/core/CL/kernels/CLInstanceNormalizationLayerKernel.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019 ARM Limited. + * Copyright (c) 2019-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -38,12 +38,9 @@ namespace arm_compute { namespace { -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, float gamma, float beta, float epsilon) +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const InstanceNormalizationLayerKernelInfo &info) { - ARM_COMPUTE_UNUSED(gamma); - ARM_COMPUTE_UNUSED(beta); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(epsilon == 0.f, "Epsilon must be different than 0"); - + ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.epsilon == 0.f, "Epsilon must be different than 0"); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(input, DataType::F16, DataType::F32); if(output != nullptr && output->total_size() != 0) @@ -74,33 +71,31 @@ std::tuple validate_and_configure_window(ITensorInfo *input, ITe } // namespace CLInstanceNormalizationLayerKernel::CLInstanceNormalizationLayerKernel() - : _input(nullptr), _output(nullptr), _gamma(1), _beta(0), _epsilon(1e-12), _run_in_place(false) + : _input(nullptr), _output(nullptr), _run_in_place(false) { } -void CLInstanceNormalizationLayerKernel::configure(ICLTensor *input, ICLTensor *output, float gamma, float beta, float epsilon) +void CLInstanceNormalizationLayerKernel::configure(ICLTensor *input, ICLTensor *output, const InstanceNormalizationLayerKernelInfo &info) { ARM_COMPUTE_ERROR_ON_NULLPTR(input); - _input = input; - _output = output == nullptr ? input : output; - _gamma = gamma; - _beta = beta; - _epsilon = epsilon; + _input = input; + _output = output == nullptr ? input : output; _run_in_place = (output == nullptr) || (output == input); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(_input->info(), _output->info(), gamma, beta, epsilon)); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(_input->info(), _output->info(), 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("-DINTERNAL_DATA_TYPE=" + (info.use_mixed_precision ? "float" : 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("-DGAMMA=" + float_to_string_with_full_precision(gamma)); - build_opts.add_option("-DBETA=" + float_to_string_with_full_precision(beta)); - build_opts.add_option("-DEPSILON=" + float_to_string_with_full_precision(epsilon)); + build_opts.add_option("-DGAMMA=" + float_to_string_with_full_precision(info.gamma)); + build_opts.add_option("-DBETA=" + float_to_string_with_full_precision(info.beta)); + build_opts.add_option("-DEPSILON=" + float_to_string_with_full_precision(info.epsilon)); build_opts.add_option_if(_run_in_place, "-DIN_PLACE"); build_opts.add_option_if(_input->info()->data_layout() == DataLayout::NHWC, "-DNHWC"); @@ -113,9 +108,9 @@ void CLInstanceNormalizationLayerKernel::configure(ICLTensor *input, ICLTensor * ICLKernel::configure_internal(std::get<1>(win_config)); } -Status CLInstanceNormalizationLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, float gamma, float beta, float epsilon) +Status CLInstanceNormalizationLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const InstanceNormalizationLayerKernelInfo &info) { - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, gamma, beta, epsilon)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, info)); ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), (output == nullptr ? input->clone().get() : output->clone().get())))); return Status{}; } diff --git a/src/runtime/CL/functions/CLInstanceNormalizationLayer.cpp b/src/runtime/CL/functions/CLInstanceNormalizationLayer.cpp index 2b0987fa2f..e639e74394 100644 --- a/src/runtime/CL/functions/CLInstanceNormalizationLayer.cpp +++ b/src/runtime/CL/functions/CLInstanceNormalizationLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019 ARM Limited. + * Copyright (c) 2019-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -32,15 +32,15 @@ CLInstanceNormalizationLayer::CLInstanceNormalizationLayer() { } -void CLInstanceNormalizationLayer::configure(ICLTensor *input, ICLTensor *output, float gamma, float beta, float epsilon) +void CLInstanceNormalizationLayer::configure(ICLTensor *input, ICLTensor *output, float gamma, float beta, float epsilon, bool use_mixed_precision) { auto k = arm_compute::support::cpp14::make_unique(); - k->configure(input, output, gamma, beta, epsilon); + k->configure(input, output, InstanceNormalizationLayerKernelInfo(gamma, beta, epsilon, use_mixed_precision)); _kernel = std::move(k); } -Status CLInstanceNormalizationLayer::validate(const ITensorInfo *input, const ITensorInfo *output, float gamma, float beta, float epsilon) +Status CLInstanceNormalizationLayer::validate(const ITensorInfo *input, const ITensorInfo *output, float gamma, float beta, float epsilon, bool use_mixed_precision) { - return CLInstanceNormalizationLayerKernel::validate(input, output, gamma, beta, epsilon); + return CLInstanceNormalizationLayerKernel::validate(input, output, InstanceNormalizationLayerKernelInfo(gamma, beta, epsilon, use_mixed_precision)); } } // namespace arm_compute \ No newline at end of file -- cgit v1.2.1