From 980a9168b81d778f4902973b4920b54c103907e0 Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Wed, 3 Jun 2020 20:16:46 +0100 Subject: COMPMID-3177: Remove padding from NEBatchNormalizationLayer Signed-off-by: Georgios Pinitas Change-Id: I9be23e6ef1f552eb159e39fda16c82fa20124094 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3307 Tested-by: Arm Jenkins Reviewed-by: Gian Marco Iodice Comments-Addressed: Arm Jenkins --- .../kernels/NEBatchNormalizationLayerKernel.cpp | 372 +++++++++------------ 1 file changed, 166 insertions(+), 206 deletions(-) (limited to 'src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp') diff --git a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp index 6bd30ee845..3d84ce8449 100644 --- a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp +++ b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2019 ARM Limited. + * Copyright (c) 2017-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -33,10 +33,12 @@ #include "arm_compute/core/Validate.h" #include "arm_compute/core/Window.h" -#include +#include "arm_compute/core/NEON/wrapper/wrapper.h" -using namespace arm_compute; +#include +namespace arm_compute +{ namespace { Status @@ -82,56 +84,41 @@ validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const IT std::pair validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, ITensorInfo *mean, ITensorInfo *var, ITensorInfo *gamma, ITensorInfo *beta) { - if(output != nullptr) - { - // Output tensor auto initialization if not yet initialized - auto_init_if_empty(*output, *input->clone()); - } + ARM_COMPUTE_UNUSED(mean, var, gamma, beta); - 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); - bool window_changed = update_window_and_padding(win, input_access); + // Configure kernel window + Window win = calculate_max_window(*input, Steps()); if(output != nullptr) { - AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); - window_changed |= update_window_and_padding(win, output_access); - output_access.set_valid_region(win, input->valid_region()); - } - - // Mean, var, gamma and beta get parallelized for the NHWC case as they follow the channel dimension, which is along the first axis - if(input->data_layout() == DataLayout::NHWC) - { - AccessWindowHorizontal mean_access(mean, 0, num_elems_processed_per_iteration); - AccessWindowHorizontal var_access(var, 0, num_elems_processed_per_iteration); - window_changed |= update_window_and_padding(win, mean_access, var_access); + // Output auto initialization if not yet initialized + auto_init_if_empty(*output, *input->clone()); - if(gamma != nullptr) - { - AccessWindowHorizontal gamma_access(gamma, 0, num_elems_processed_per_iteration); - window_changed |= update_window_and_padding(win, gamma_access); - } - if(beta != nullptr) - { - AccessWindowHorizontal beta_access(beta, 0, num_elems_processed_per_iteration); - window_changed |= update_window_and_padding(win, beta_access); - } + // NEBatchNormalizationLayerKernel doesn't need padding so update_window_and_padding() can be skipped + Coordinates coord; + coord.set_num_dimensions(output->num_dimensions()); + output->set_valid_region(ValidRegion(coord, output->tensor_shape())); } - Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; - return std::make_pair(err, win); + return std::make_pair(Status{}, win); } } //namespace -template -void NEBatchNormalizationLayerKernel::batch_normalization_fp16_nchw(const Window &window) +template +void NEBatchNormalizationLayerKernel::batch_normalization_nchw(const Window &window) { - ARM_COMPUTE_UNUSED(window); -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - Iterator input(_input, window); - Iterator output(_output, window); + /** NEON vector tag type. */ + using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t; + + const int window_step_x = 16 / sizeof(T); + const auto window_start_x = static_cast(window.x().start()); + const auto window_end_x = static_cast(window.x().end()); + + Window win_to_use = window; + win_to_use.set(Window::DimX, Window::Dimension(0, 1, 1)); + + Iterator input(_input, win_to_use); + Iterator output(_output, win_to_use); F activation_functor(_act_info); @@ -139,196 +126,168 @@ void NEBatchNormalizationLayerKernel::batch_normalization_fp16_nchw(const Window // Only compute denominator and NEON vectors once per feature map. int slice = -1; - const auto input_mean = reinterpret_cast(_mean->ptr_to_element(Coordinates(0, 0))); - const auto input_var = reinterpret_cast(_var->ptr_to_element(Coordinates(0, 0))); - const auto input_gamma = (_gamma != nullptr) ? reinterpret_cast(_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; - const auto input_beta = (_beta != nullptr) ? reinterpret_cast(_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) + const auto input_mean = reinterpret_cast(_mean->ptr_to_element(Coordinates(0, 0))); + const auto input_var = reinterpret_cast(_var->ptr_to_element(Coordinates(0, 0))); + const auto input_gamma = (_gamma != nullptr) ? reinterpret_cast(_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; + const auto input_beta = (_beta != nullptr) ? reinterpret_cast(_beta->ptr_to_element(Coordinates(0, 0))) : nullptr; + + T mean = static_cast(0); + T var = static_cast(0); + T gamma = static_cast(1); + T beta = static_cast(0); + T denominator = static_cast(0); + + auto mean_vec = wrapper::vdup_n(mean, ExactTagType{}); + auto var_vec = wrapper::vdup_n(var, ExactTagType{}); + auto gamma_vec = wrapper::vdup_n(gamma, ExactTagType{}); + auto beta_vec = wrapper::vdup_n(beta, ExactTagType{}); + auto denominator_vec = wrapper::vdup_n(denominator, ExactTagType{}); + const auto epsilon_vec = wrapper::vdup_n(static_cast(_epsilon), ExactTagType{}); + execute_window_loop(win_to_use, [&](const Coordinates & id) { + const auto input_ptr = reinterpret_cast(input.ptr()); + const auto output_ptr = reinterpret_cast(output.ptr()); + if(slice != id.z()) { - // Conctruct vectors - mean_vec = vdupq_n_f16(*(input_mean + id.z())); - var_vec = vdupq_n_f16(*(input_var + id.z())); + mean = input_mean[id.z()]; + var = input_var[id.z()]; + mean_vec = wrapper::vdup_n(mean, ExactTagType{}); + var_vec = wrapper::vdup_n(var, ExactTagType{}); if(input_gamma != nullptr) { - gamma_vec = vdupq_n_f16(*(input_gamma + id.z())); + gamma = input_gamma[id.z()]; + gamma_vec = wrapper::vdup_n(gamma, ExactTagType{}); } if(input_beta != nullptr) { - beta_vec = vdupq_n_f16(*(input_beta + id.z())); + beta = input_beta[id.z()]; + beta_vec = wrapper::vdup_n(beta, ExactTagType{}); } // Calculate denominator - denominator = vinvsqrtq_f16(vaddq_f16(var_vec, epsilon_vec)); - slice = id.z(); + denominator_vec = wrapper::vinvsqrt(wrapper::vadd(var_vec, epsilon_vec)); + denominator = wrapper::vgetlane(denominator_vec, 0); + slice = id.z(); } - // Calculate x bar and store results - const float16x8_t numerator = vsubq_f16(vld1q_f16(reinterpret_cast(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) + // Perform core calculations using vector operations + int x = window_start_x; + for(; x <= (window_end_x - window_step_x); x += window_step_x) { - activation_functor(res); - } + // Calculate x bar + const auto numerator = wrapper::vsub(wrapper::vloadq(input_ptr + x), mean_vec); + const auto x_bar = wrapper::vmul(numerator, denominator_vec); + auto res = wrapper::vmla(beta_vec, x_bar, gamma_vec); - vst1q_f16(reinterpret_cast(output.ptr()), res); - }, - input, output); -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ -} + // Perform fused activation + if(fused_activation) + { + activation_functor(res); + } -template -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); + // Store results + wrapper::vstore(output_ptr + x, res); + } - F activation_functor(_act_info); + // Compute left-over elements + for(; x < window_end_x; ++x) + { + const T numerator = input_ptr[x] - mean; + const T x_bar = numerator * denominator; + T res = beta + x_bar * gamma; - const auto input_mean = reinterpret_cast(_mean->ptr_to_element(Coordinates(0, 0))); - const auto input_var = reinterpret_cast(_var->ptr_to_element(Coordinates(0, 0))); - const auto input_gamma = (_gamma != nullptr) ? reinterpret_cast(_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; - const auto input_beta = (_beta != nullptr) ? reinterpret_cast(_beta->ptr_to_element(Coordinates(0, 0))) : nullptr; + // Perform fused activation + if(fused_activation) + { + activation_functor(res); + } - 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(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); + // Store results + *(output_ptr + x) = res; } - - vst1q_f16(reinterpret_cast(output.ptr()), res); }, input, output); -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ } -template -void NEBatchNormalizationLayerKernel::batch_normalization_fp32_nchw(const Window &window) +template +void NEBatchNormalizationLayerKernel::batch_normalization_nhwc(const Window &window) { - Iterator input(_input, window); - Iterator output(_output, window); + /** NEON vector tag type. */ + using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t; + + const int window_step_x = 16 / sizeof(T); + const auto window_start_x = static_cast(window.x().start()); + const auto window_end_x = static_cast(window.x().end()); + + Window win_collapsed = window.collapse_if_possible(window, Window::DimZ); + win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1)); + + Iterator input(_input, win_collapsed); + Iterator output(_output, win_collapsed); 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(_mean->ptr_to_element(Coordinates(0, 0))); + const auto input_var = reinterpret_cast(_var->ptr_to_element(Coordinates(0, 0))); + const auto input_gamma = (_gamma != nullptr) ? reinterpret_cast(_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; + const auto input_beta = (_beta != nullptr) ? reinterpret_cast(_beta->ptr_to_element(Coordinates(0, 0))) : nullptr; - const auto input_mean = reinterpret_cast(_mean->ptr_to_element(Coordinates(0, 0))); - const auto input_var = reinterpret_cast(_var->ptr_to_element(Coordinates(0, 0))); - const auto input_gamma = (_gamma != nullptr) ? reinterpret_cast(_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; - const auto input_beta = (_beta != nullptr) ? reinterpret_cast(_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) + const auto epsilon_vec = wrapper::vdup_n(static_cast(_epsilon), ExactTagType{}); + execute_window_loop(win_collapsed, [&](const Coordinates &) { - if(slice != id.z()) + const auto input_ptr = reinterpret_cast(input.ptr()); + const auto output_ptr = reinterpret_cast(output.ptr()); + + // Perform core calculations using vector operations + int x = window_start_x; + for(; x <= (window_end_x - window_step_x); x += window_step_x) { // 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())); - } + const auto mean_vec = wrapper::vloadq(input_mean + x); + const auto var_vec = wrapper::vloadq(input_var + x); + const auto gamma_vec = (input_gamma != nullptr) ? wrapper::vloadq(input_gamma + x) : wrapper::vdup_n(static_cast(1.f), ExactTagType{}); + const auto beta_vec = (input_beta != nullptr) ? wrapper::vloadq(input_beta + x) : wrapper::vdup_n(static_cast(0.f), ExactTagType{}); // Calculate denominator - denominator = vinvsqrtq_f32(vaddq_f32(var_vec, epsilon_vec)); - slice = id.z(); - } + const auto denominator = wrapper::vinvsqrt(wrapper::vadd(var_vec, epsilon_vec)); - // Calculate x bar - const float32x4_t numerator = vsubq_f32(vld1q_f32(reinterpret_cast(input.ptr())), mean_vec); - const float32x4_t x_bar = vmulq_f32(numerator, denominator); - float32x4_t res = vmlaq_f32(beta_vec, x_bar, gamma_vec); + // Calculate x bar + const auto numerator = wrapper::vsub(wrapper::vloadq(input_ptr + x), mean_vec); + const auto x_bar = wrapper::vmul(numerator, denominator); + auto res = wrapper::vmla(beta_vec, x_bar, gamma_vec); - // Perform fused activation - if(fused_activation) - { - activation_functor(res); - } + // Perform fused activation + if(fused_activation) + { + activation_functor(res); + } - // Store results - vst1q_f32(reinterpret_cast(output.ptr()), res); - }, - input, output); -} + // Store results + wrapper::vstore(output_ptr + x, res); + } -template -void NEBatchNormalizationLayerKernel::batch_normalization_fp32_nhwc(const Window &window) -{ - Iterator input(_input, window); - Iterator output(_output, window); + // Compute left-over elements + for(; x < window_end_x; ++x) + { + // Conctruct vectors + const T gamma = (input_gamma != nullptr) ? input_gamma[x] : 1.f; + const T beta = (input_beta != nullptr) ? input_beta[x] : 0.f; - F activation_functor(_act_info); + const T denominator = sqrt(input_var[x] + _epsilon); + const T numerator = input_ptr[x] - input_mean[x]; + const T x_bar = numerator / denominator; + T res = beta + x_bar * gamma; - const auto input_mean = reinterpret_cast(_mean->ptr_to_element(Coordinates(0, 0))); - const auto input_var = reinterpret_cast(_var->ptr_to_element(Coordinates(0, 0))); - const auto input_gamma = (_gamma != nullptr) ? reinterpret_cast(_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; - const auto input_beta = (_beta != nullptr) ? reinterpret_cast(_beta->ptr_to_element(Coordinates(0, 0))) : nullptr; + // Perform fused activation + if(fused_activation) + { + activation_functor(res); + } - 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(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 + *reinterpret_cast(output_ptr + x) = res; } - - // Store results - vst1q_f32(reinterpret_cast(output.ptr()), res); }, input, output); } @@ -340,13 +299,13 @@ void NEBatchNormalizationLayerKernel::configure_non_fused() { #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F16: - _func = (is_nhwc) ? &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nhwc> : - &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nchw>; + _func = (is_nhwc) ? &NEBatchNormalizationLayerKernel::batch_normalization_nhwc> : + &NEBatchNormalizationLayerKernel::batch_normalization_nchw>; break; #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F32: - _func = (is_nhwc) ? &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nhwc> : - &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nchw>; + _func = (is_nhwc) ? &NEBatchNormalizationLayerKernel::batch_normalization_nhwc> : + &NEBatchNormalizationLayerKernel::batch_normalization_nchw>; break; default: ARM_COMPUTE_ERROR("Element size not supported"); @@ -359,31 +318,31 @@ void NEBatchNormalizationLayerKernel::configure_fused() // NCHW Fused Batched Normalization with activation functions : FP32 static std::map bn_fused_map_f32_nchw = { - { ActivationLayerInfo::ActivationFunction::RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nchw> }, - { ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nchw> }, - { ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nchw> } + { ActivationLayerInfo::ActivationFunction::RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nchw> }, + { ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nchw> }, + { ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nchw> } }; // NHWC Fused Batched Normalization with activation functions : FP32 static std::map bn_fused_map_f32_nhwc = { - { ActivationLayerInfo::ActivationFunction::RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nhwc> }, - { ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nhwc> }, - { ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nhwc> } + { ActivationLayerInfo::ActivationFunction::RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nhwc> }, + { ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nhwc> }, + { ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nhwc> } }; #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC // NCHW Fused Batched Normalization with activation functions : FP16 static std::map bn_fused_map_f16_nchw = { - { ActivationLayerInfo::ActivationFunction::RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nchw> }, - { ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nchw> }, - { ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nchw> } + { ActivationLayerInfo::ActivationFunction::RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nchw> }, + { ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nchw> }, + { ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nchw> } }; // NHWC Fused Batched Normalization with activation functions : FP16 static std::map bn_fused_map_f16_nhwc = { - { ActivationLayerInfo::ActivationFunction::RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nhwc> }, - { ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nhwc> }, - { ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nhwc> } + { ActivationLayerInfo::ActivationFunction::RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nhwc> }, + { ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nhwc> }, + { ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nhwc> } }; #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC @@ -475,3 +434,4 @@ void NEBatchNormalizationLayerKernel::run(const Window &window, const ThreadInfo (this->*_func)(window); } +} // namespace arm_compute -- cgit v1.2.1