| /* |
| * Copyright (c) 2017-2018 ARM Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * Permission is hereby granted, free of charge, to any person obtaining a copy |
| * of this software and associated documentation files (the "Software"), to |
| * deal in the Software without restriction, including without limitation the |
| * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| * sell copies of the Software, and to permit persons to whom the Software is |
| * furnished to do so, subject to the following conditions: |
| * |
| * The above copyright notice and this permission notice shall be included in all |
| * copies or substantial portions of the Software. |
| * |
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| * SOFTWARE. |
| */ |
| #include "arm_compute/core/NEON/kernels/NEBatchNormalizationLayerKernel.h" |
| |
| #include "arm_compute/core/CPP/Validate.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/NEON/NEFixedPoint.h" |
| #include "arm_compute/core/NEON/NEMath.h" |
| #include "arm_compute/core/NEON/kernels/detail/NEActivationFunctionDetail.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/Window.h" |
| |
| #include <map> |
| |
| using namespace arm_compute; |
| |
| namespace |
| { |
| Status |
| validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *mean, const ITensorInfo *var, |
| const ITensorInfo *beta, const ITensorInfo *gamma, float epsilon, ActivationLayerInfo act_info) |
| { |
| ARM_COMPUTE_UNUSED(epsilon); |
| ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); |
| |
| if(act_info.enabled()) |
| { |
| ActivationLayerInfo::ActivationFunction act = act_info.activation(); |
| ARM_COMPUTE_RETURN_ERROR_ON(act != ActivationLayerInfo::ActivationLayerInfo::ActivationFunction::RELU |
| && act != ActivationLayerInfo::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU |
| && act != ActivationLayerInfo::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU); |
| ARM_COMPUTE_RETURN_ERROR_ON(act_info.b() > act_info.a()); |
| } |
| |
| if(nullptr != output) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| } |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, mean, var); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, var); |
| if(beta != nullptr) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, beta); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, beta); |
| } |
| if(gamma != nullptr) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, gamma); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, gamma); |
| } |
| ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL)) != mean->dimension(0)); |
| |
| return Status{}; |
| } |
| |
| std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output) |
| { |
| if(output != nullptr) |
| { |
| // Output tensor auto initialization if not yet initialized |
| auto_init_if_empty(*output, *input->clone()); |
| } |
| |
| unsigned int num_elems_processed_per_iteration = 16 / input->element_size(); |
| |
| Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); |
| AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); |
| AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); |
| bool window_changed = update_window_and_padding(win, input_access, output_access); |
| output_access.set_valid_region(win, input->valid_region()); |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| return std::make_pair(err, win); |
| } |
| } //namespace |
| |
| template <bool fused_activation, typename F> |
| void NEBatchNormalizationLayerKernel::batch_normalization_fp16_nchw(const Window &window) |
| { |
| ARM_COMPUTE_UNUSED(window); |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| Iterator input(_input, window); |
| Iterator output(_output, window); |
| |
| F activation_functor(_act_info); |
| |
| // Hold information about the current feature map we are iterating. |
| // Only compute denominator and NEON vectors once per feature map. |
| int slice = -1; |
| |
| const auto input_mean = reinterpret_cast<const float16_t *>(_mean->ptr_to_element(Coordinates(0, 0))); |
| const auto input_var = reinterpret_cast<const float16_t *>(_var->ptr_to_element(Coordinates(0, 0))); |
| const auto input_gamma = (_gamma != nullptr) ? reinterpret_cast<const float16_t *>(_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; |
| const auto input_beta = (_beta != nullptr) ? reinterpret_cast<const float16_t *>(_beta->ptr_to_element(Coordinates(0, 0))) : nullptr; |
| |
| float16x8_t mean_vec = vdupq_n_f16(0.0); |
| float16x8_t var_vec = vdupq_n_f16(0.0); |
| float16x8_t gamma_vec = vdupq_n_f16(1.0); |
| float16x8_t beta_vec = vdupq_n_f16(0.0); |
| float16x8_t denominator = vdupq_n_f16(0.0); |
| const float16x8_t epsilon_vec = vdupq_n_f16(_epsilon); |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| if(slice != id.z()) |
| { |
| // Conctruct vectors |
| mean_vec = vdupq_n_f16(*(input_mean + id.z())); |
| var_vec = vdupq_n_f16(*(input_var + id.z())); |
| if(input_gamma != nullptr) |
| { |
| gamma_vec = vdupq_n_f16(*(input_gamma + id.z())); |
| } |
| if(input_beta != nullptr) |
| { |
| beta_vec = vdupq_n_f16(*(input_beta + id.z())); |
| } |
| |
| // Calculate denominator |
| denominator = vinvsqrtq_f16(vaddq_f16(var_vec, epsilon_vec)); |
| slice = id.z(); |
| } |
| |
| // Calculate x bar and store results |
| const float16x8_t numerator = vsubq_f16(vld1q_f16(reinterpret_cast<const float16_t *>(input.ptr())), mean_vec); |
| const float16x8_t x_bar = vmulq_f16(numerator, denominator); |
| float16x8_t res = vaddq_f16(beta_vec, vmulq_f16(x_bar, gamma_vec)); |
| |
| // Perform fused activation |
| if(fused_activation) |
| { |
| activation_functor(res); |
| } |
| |
| vst1q_f16(reinterpret_cast<float16_t *>(output.ptr()), res); |
| }, |
| input, output); |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| } |
| |
| template <bool fused_activation, typename F> |
| void NEBatchNormalizationLayerKernel::batch_normalization_fp16_nhwc(const Window &window) |
| { |
| ARM_COMPUTE_UNUSED(window); |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| Iterator input(_input, window); |
| Iterator output(_output, window); |
| |
| F activation_functor(_act_info); |
| |
| const auto input_mean = reinterpret_cast<const float16_t *>(_mean->ptr_to_element(Coordinates(0, 0))); |
| const auto input_var = reinterpret_cast<const float16_t *>(_var->ptr_to_element(Coordinates(0, 0))); |
| const auto input_gamma = (_gamma != nullptr) ? reinterpret_cast<const float16_t *>(_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; |
| const auto input_beta = (_beta != nullptr) ? reinterpret_cast<const float16_t *>(_beta->ptr_to_element(Coordinates(0, 0))) : nullptr; |
| |
| const float16x8_t epsilon_vec = vdupq_n_f16(_epsilon); |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| // Conctruct vectors |
| const float16x8_t mean_vec = vld1q_f16(input_mean + id.x()); |
| const float16x8_t var_vec = vld1q_f16(input_var + id.x()); |
| const float16x8_t gamma_vec = (input_gamma != nullptr) ? vld1q_f16(input_gamma + id.x()) : vdupq_n_f16(1.0); |
| const float16x8_t beta_vec = (input_beta != nullptr) ? vld1q_f16(input_beta + id.x()) : vdupq_n_f16(0.0); |
| // Calculate denominator |
| const float16x8_t denominator = vinvsqrtq_f16(vaddq_f16(var_vec, epsilon_vec)); |
| |
| // Calculate x bar and store results |
| const float16x8_t numerator = vsubq_f16(vld1q_f16(reinterpret_cast<const float16_t *>(input.ptr())), mean_vec); |
| const float16x8_t x_bar = vmulq_f16(numerator, denominator); |
| float16x8_t res = vaddq_f16(beta_vec, vmulq_f16(x_bar, gamma_vec)); |
| |
| // Perform fused activation |
| if(fused_activation) |
| { |
| activation_functor(res); |
| } |
| |
| vst1q_f16(reinterpret_cast<float16_t *>(output.ptr()), res); |
| }, |
| input, output); |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| } |
| |
| template <bool fused_activation, typename F> |
| void NEBatchNormalizationLayerKernel::batch_normalization_fp32_nchw(const Window &window) |
| { |
| Iterator input(_input, window); |
| Iterator output(_output, window); |
| |
| F activation_functor(_act_info); |
| |
| // Hold information about the current feature map we are iterating. |
| // Only compute denominator and NEON vectors once per feature map. |
| int slice = -1; |
| |
| const auto input_mean = reinterpret_cast<const float *>(_mean->ptr_to_element(Coordinates(0, 0))); |
| const auto input_var = reinterpret_cast<const float *>(_var->ptr_to_element(Coordinates(0, 0))); |
| const auto input_gamma = (_gamma != nullptr) ? reinterpret_cast<const float *>(_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; |
| const auto input_beta = (_beta != nullptr) ? reinterpret_cast<const float *>(_beta->ptr_to_element(Coordinates(0, 0))) : nullptr; |
| |
| float32x4_t mean_vec = vdupq_n_f32(0.0); |
| float32x4_t var_vec = vdupq_n_f32(0.0); |
| float32x4_t gamma_vec = vdupq_n_f32(1.0); |
| float32x4_t beta_vec = vdupq_n_f32(0.0); |
| float32x4_t denominator = vdupq_n_f32(0.0); |
| const float32x4_t epsilon_vec = vdupq_n_f32(_epsilon); |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| if(slice != id.z()) |
| { |
| // Conctruct vectors |
| mean_vec = vdupq_n_f32(*(input_mean + id.z())); |
| var_vec = vdupq_n_f32(*(input_var + id.z())); |
| if(input_gamma != nullptr) |
| { |
| gamma_vec = vdupq_n_f32(*(input_gamma + id.z())); |
| } |
| if(input_beta != nullptr) |
| { |
| beta_vec = vdupq_n_f32(*(input_beta + id.z())); |
| } |
| |
| // Calculate denominator |
| denominator = vinvsqrtq_f32(vaddq_f32(var_vec, epsilon_vec)); |
| slice = id.z(); |
| } |
| |
| // Calculate x bar |
| const float32x4_t numerator = vsubq_f32(vld1q_f32(reinterpret_cast<const float *>(input.ptr())), mean_vec); |
| const float32x4_t x_bar = vmulq_f32(numerator, denominator); |
| float32x4_t res = vmlaq_f32(beta_vec, x_bar, gamma_vec); |
| |
| // Perform fused activation |
| if(fused_activation) |
| { |
| activation_functor(res); |
| } |
| |
| // Store results |
| vst1q_f32(reinterpret_cast<float *>(output.ptr()), res); |
| }, |
| input, output); |
| } |
| |
| template <bool fused_activation, typename F> |
| void NEBatchNormalizationLayerKernel::batch_normalization_fp32_nhwc(const Window &window) |
| { |
| Iterator input(_input, window); |
| Iterator output(_output, window); |
| |
| F activation_functor(_act_info); |
| |
| const auto input_mean = reinterpret_cast<const float *>(_mean->ptr_to_element(Coordinates(0, 0))); |
| const auto input_var = reinterpret_cast<const float *>(_var->ptr_to_element(Coordinates(0, 0))); |
| const auto input_gamma = (_gamma != nullptr) ? reinterpret_cast<const float *>(_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; |
| const auto input_beta = (_beta != nullptr) ? reinterpret_cast<const float *>(_beta->ptr_to_element(Coordinates(0, 0))) : nullptr; |
| |
| const float32x4_t epsilon_vec = vdupq_n_f32(_epsilon); |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| // Conctruct vectors |
| const float32x4_t mean_vec = vld1q_f32(input_mean + id.x()); |
| const float32x4_t var_vec = vld1q_f32(input_var + id.x()); |
| const float32x4_t gamma_vec = (input_gamma != nullptr) ? vld1q_f32(input_gamma + id.x()) : vdupq_n_f32(1.0); |
| const float32x4_t beta_vec = (input_beta != nullptr) ? vld1q_f32(input_beta + id.x()) : vdupq_n_f32(0.0); |
| // Calculate denominator |
| const float32x4_t denominator = vinvsqrtq_f32(vaddq_f32(var_vec, epsilon_vec)); |
| |
| // Calculate x bar |
| const float32x4_t numerator = vsubq_f32(vld1q_f32(reinterpret_cast<const float *>(input.ptr())), mean_vec); |
| const float32x4_t x_bar = vmulq_f32(numerator, denominator); |
| float32x4_t res = vmlaq_f32(beta_vec, x_bar, gamma_vec); |
| |
| // Perform fused activation |
| if(fused_activation) |
| { |
| activation_functor(res); |
| } |
| |
| // Store results |
| vst1q_f32(reinterpret_cast<float *>(output.ptr()), res); |
| }, |
| input, output); |
| } |
| |
| void NEBatchNormalizationLayerKernel::configure_non_fused() |
| { |
| const bool is_nhwc = _input->info()->data_layout() == DataLayout::NHWC; |
| switch(_input->info()->data_type()) |
| { |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| _func = (is_nhwc) ? &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nhwc<false, ::detail::dummy<float16_t, 8>> : |
| &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nchw<false, ::detail::dummy<float16_t, 8>>; |
| break; |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F32: |
| _func = (is_nhwc) ? &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nhwc<false, ::detail::dummy<float, 4>> : |
| &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nchw<false, ::detail::dummy<float, 4>>; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Element size not supported"); |
| break; |
| } |
| } |
| |
| void NEBatchNormalizationLayerKernel::configure_fused() |
| { |
| // NCHW Fused Batched Normalization with activation functions : FP32 |
| static std::map<ActivationLayerInfo::ActivationFunction, BatchNormFunctionPtr> bn_fused_map_f32_nchw = |
| { |
| { ActivationLayerInfo::ActivationFunction::RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nchw<true, ::detail::relu<float, 4>> }, |
| { ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nchw<true, ::detail::brelu<float, 4>> }, |
| { ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nchw<true, ::detail::lubrelu<float, 4>> } |
| }; |
| // NHWC Fused Batched Normalization with activation functions : FP32 |
| static std::map<ActivationLayerInfo::ActivationFunction, BatchNormFunctionPtr> bn_fused_map_f32_nhwc = |
| { |
| { ActivationLayerInfo::ActivationFunction::RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nhwc<true, ::detail::relu<float, 4>> }, |
| { ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nhwc<true, ::detail::brelu<float, 4>> }, |
| { ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nhwc<true, ::detail::lubrelu<float, 4>> } |
| }; |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| // NCHW Fused Batched Normalization with activation functions : FP16 |
| static std::map<ActivationLayerInfo::ActivationFunction, BatchNormFunctionPtr> bn_fused_map_f16_nchw = |
| { |
| { ActivationLayerInfo::ActivationFunction::RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nchw<true, ::detail::relu<float16_t, 8>> }, |
| { ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nchw<true, ::detail::brelu<float16_t, 8>> }, |
| { ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nchw<true, ::detail::lubrelu<float16_t, 8>> } |
| }; |
| // NHWC Fused Batched Normalization with activation functions : FP16 |
| static std::map<ActivationLayerInfo::ActivationFunction, BatchNormFunctionPtr> bn_fused_map_f16_nhwc = |
| { |
| { ActivationLayerInfo::ActivationFunction::RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nhwc<true, ::detail::relu<float16_t, 8>> }, |
| { ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nhwc<true, ::detail::brelu<float16_t, 8>> }, |
| { ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nhwc<true, ::detail::lubrelu<float16_t, 8>> } |
| }; |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| |
| switch(_input->info()->data_type()) |
| { |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| _func = (_input->info()->data_layout() == DataLayout::NHWC) ? bn_fused_map_f16_nhwc[_act_info.activation()] : bn_fused_map_f16_nchw[_act_info.activation()]; |
| break; |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F32: |
| _func = (_input->info()->data_layout() == DataLayout::NHWC) ? bn_fused_map_f32_nhwc[_act_info.activation()] : bn_fused_map_f32_nchw[_act_info.activation()]; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Element size not supported"); |
| break; |
| } |
| } |
| |
| NEBatchNormalizationLayerKernel::NEBatchNormalizationLayerKernel() |
| : _func(nullptr), _input(nullptr), _output(nullptr), _mean(nullptr), _var(nullptr), _gamma(nullptr), _beta(nullptr), _epsilon(), _act_info() |
| { |
| } |
| |
| void NEBatchNormalizationLayerKernel::configure(ITensor *input, ITensor *output, |
| const ITensor *mean, const ITensor *var, |
| const ITensor *beta, const ITensor *gamma, |
| float epsilon, ActivationLayerInfo act_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, mean, var); |
| |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (output != nullptr) ? output->info() : nullptr, |
| mean->info(), var->info(), |
| (beta != nullptr) ? beta->info() : nullptr, |
| (gamma != nullptr) ? gamma->info() : nullptr, |
| epsilon, act_info)); |
| |
| _input = input; |
| _output = input; |
| _mean = mean; |
| _var = var; |
| _gamma = gamma; |
| _beta = beta; |
| _epsilon = epsilon; |
| _act_info = act_info; |
| |
| const bool run_in_place = (output == nullptr) || (output == input); |
| if(!run_in_place) |
| { |
| _output = output; |
| } |
| |
| // Configure activation function to run |
| if(_act_info.enabled()) |
| { |
| configure_fused(); |
| } |
| else |
| { |
| configure_non_fused(); |
| } |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(input->info(), (run_in_place) ? nullptr : output->info()); |
| ARM_COMPUTE_ERROR_THROW_ON(win_config.first); |
| INEKernel::configure(win_config.second); |
| } |
| |
| Status NEBatchNormalizationLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, |
| const ITensorInfo *mean, const ITensorInfo *var, |
| const ITensorInfo *beta, const ITensorInfo *gamma, |
| float epsilon, ActivationLayerInfo act_info) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, mean, var, beta, gamma, epsilon, act_info)); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output ? output->clone().get() : nullptr).first); |
| |
| return Status{}; |
| } |
| |
| void NEBatchNormalizationLayerKernel::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); |
| ARM_COMPUTE_ERROR_ON(_func == nullptr); |
| |
| (this->*_func)(window); |
| } |