diff options
Diffstat (limited to 'src/core/NEON/kernels/NENormalizationLayerKernel.cpp')
| -rw-r--r-- | src/core/NEON/kernels/NENormalizationLayerKernel.cpp | 150 |
1 files changed, 2 insertions, 148 deletions
diff --git a/src/core/NEON/kernels/NENormalizationLayerKernel.cpp b/src/core/NEON/kernels/NENormalizationLayerKernel.cpp index 776cb27d7a..253a93f196 100644 --- a/src/core/NEON/kernels/NENormalizationLayerKernel.cpp +++ b/src/core/NEON/kernels/NENormalizationLayerKernel.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -39,26 +39,17 @@ namespace Status validate_arguments(const ITensorInfo *input, const ITensorInfo *input_squared, const ITensorInfo *output, const NormalizationLayerInfo &norm_info) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, input_squared, output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, input_squared); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, input_squared); ARM_COMPUTE_RETURN_ERROR_ON_MSG(!(norm_info.norm_size() % 2), "Normalization size should be odd"); - if(is_data_type_fixed_point(input->data_type())) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, input_squared); - ARM_COMPUTE_RETURN_ERROR_ON_VALUE_NOT_REPRESENTABLE_IN_FIXED_POINT(norm_info.beta(), input); - ARM_COMPUTE_RETURN_ERROR_ON_VALUE_NOT_REPRESENTABLE_IN_FIXED_POINT(norm_info.kappa(), input); - ARM_COMPUTE_RETURN_ERROR_ON_VALUE_NOT_REPRESENTABLE_IN_FIXED_POINT(norm_info.scale_coeff(), input); - } - // Checks performed when output is configured if(output->total_size() != 0) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output); } return Status{}; @@ -162,44 +153,6 @@ void NENormalizationLayerKernel::configure(const ITensor *input, const ITensor * } break; } - case DataType::QS8: - { - switch(norm_info.type()) - { - case NormType::IN_MAP_1D: - _func = &NENormalizationLayerKernel::normalize_fixed_point<DataType::QS8, 0, false>; - break; - case NormType::IN_MAP_2D: - // Normalize over X and Y - _func = &NENormalizationLayerKernel::normalize_fixed_point<DataType::QS8, 0, true>; - break; - case NormType::CROSS_MAP: - _func = &NENormalizationLayerKernel::normalize_fixed_point<DataType::QS8, 2, false>; - break; - default: - break; - } - break; - } - case DataType::QS16: - { - switch(norm_info.type()) - { - case NormType::IN_MAP_1D: - _func = &NENormalizationLayerKernel::normalize_fixed_point<DataType::QS16, 0, false>; - break; - case NormType::IN_MAP_2D: - // Normalize over X and Y - _func = &NENormalizationLayerKernel::normalize_fixed_point<DataType::QS16, 0, true>; - break; - case NormType::CROSS_MAP: - _func = &NENormalizationLayerKernel::normalize_fixed_point<DataType::QS16, 2, false>; - break; - default: - break; - } - break; - } default: ARM_COMPUTE_ERROR("NOT SUPPORTED!"); } @@ -306,105 +259,6 @@ void NENormalizationLayerKernel::normalize_float(const Window &window) } } -template <DataType dt, unsigned int dim, bool do_2D_norm> -void NENormalizationLayerKernel::normalize_fixed_point(const Window &window) -{ - Iterator input(_input, window); - Iterator input_squared(_input_squared, window); - Iterator output(_output, window); - - const int dim_y = 1; - const int radius = _norm_info.norm_size() / 2; - const int total_size = _input->info()->dimension(dim) - 1; - const int input_squared_stride = _input_squared->info()->strides_in_bytes()[dim]; - // We account padding across X only and we iterate over rows - const int min_left = (dim == 2) ? 0 : -static_cast<int>(border_size().left); - const int max_right = (dim == 2) ? total_size : total_size + border_size().left; - const int min_top = 0; - const int max_bottom = _input->info()->dimension(dim_y) - 1; - - const int fixed_point_position = _input->info()->fixed_point_position(); - - if(dt == DataType::QS8) - { - const qint8x16_t coeff_vec = vdupq_n_qs8_f32(_norm_info.scale_coeff(), fixed_point_position); - const qint8x16_t beta_vec = vdupq_n_qs8_f32(_norm_info.beta(), fixed_point_position); - const qint8x16_t kappa_vec = vdupq_n_qs8_f32(_norm_info.kappa(), fixed_point_position); - - execute_window_loop(window, [&](const Coordinates & id) - { - // Get range to normalize - const int current_row = do_2D_norm ? id[dim_y] : 0; - const int current_slice = id[dim]; - const int first_row = do_2D_norm ? std::max(current_row - radius, min_top) : 0; - const int last_row = do_2D_norm ? std::min(current_row + radius, max_bottom) : 0; - const int first_slice = std::max(current_slice - radius, min_left); - const int last_slice = std::min(current_slice + radius, max_right); - - // Accumulate 2D In-Map values - qint8x16_t accu = vdupq_n_qs8(0); - for(int j = first_row; j <= last_row; ++j) - { - // Compute row displacement - const int row = (j - current_row) * _input_squared->info()->strides_in_bytes()[dim_y]; - const uint8_t *const input_squared_ptr = input_squared.ptr() + row - (current_slice * input_squared_stride); - for(int i = first_slice; i <= last_slice; ++i) - { - accu = vqaddq_qs8(accu, vld1q_qs8(reinterpret_cast<const qint8_t *>(input_squared_ptr + i * input_squared_stride))); - } - } - - // Normalize - const qint8x16_t accu_scale = vqmlaq_qs8(kappa_vec, coeff_vec, accu, fixed_point_position); - const qint8x16_t normalized = vqpowq_qs8(accu_scale, beta_vec, fixed_point_position); - const qint8x16_t normalized_pixel = vdivq_qs8(vld1q_qs8(reinterpret_cast<const qint8_t *>(input.ptr())), normalized, fixed_point_position); - vst1q_qs8(reinterpret_cast<qint8_t *>(output.ptr()), normalized_pixel); - }, - input, input_squared, output); - } - else if(dt == DataType::QS16) - { - const qint16x8_t coeff_vec = vdupq_n_qs16_f32(_norm_info.scale_coeff(), fixed_point_position); - const qint16x8_t beta_vec = vdupq_n_qs16_f32(_norm_info.beta(), fixed_point_position); - const qint16x8_t kappa_vec = vdupq_n_qs16_f32(_norm_info.kappa(), fixed_point_position); - - execute_window_loop(window, [&](const Coordinates & id) - { - // Get range to normalize - const int current_row = do_2D_norm ? id[dim_y] : 0; - const int current_slice = id[dim]; - const int first_row = do_2D_norm ? std::max(current_row - radius, min_top) : 0; - const int last_row = do_2D_norm ? std::min(current_row + radius, max_bottom) : 0; - const int first_slice = std::max(current_slice - radius, min_left); - const int last_slice = std::min(current_slice + radius, max_right); - - // Accumulate 2D In-Map values - qint16x8_t accu = vdupq_n_qs16(0); - for(int j = first_row; j <= last_row; ++j) - { - // Compute row displacement - const int row = (j - current_row) * _input_squared->info()->strides_in_bytes()[dim_y]; - const uint8_t *const input_squared_ptr = input_squared.ptr() + row - (current_slice * input_squared_stride); - for(int i = first_slice; i <= last_slice; ++i) - { - accu = vqaddq_qs16(accu, vld1q_qs16(reinterpret_cast<const qint16_t *>(input_squared_ptr + i * input_squared_stride))); - } - } - - // Normalize - const qint16x8_t accu_scale = vqmlaq_qs16(kappa_vec, coeff_vec, accu, fixed_point_position); - const qint16x8_t normalized = vqpowq_qs16(accu_scale, beta_vec, fixed_point_position); - const qint16x8_t normalized_pixel = vdivq_qs16(vld1q_qs16(reinterpret_cast<const qint16_t *>(input.ptr())), normalized, fixed_point_position); - vst1q_qs16(reinterpret_cast<qint16_t *>(output.ptr()), normalized_pixel); - }, - input, input_squared, output); - } - else - { - ARM_COMPUTE_ERROR("Not supported"); - } -} - Status NENormalizationLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *input_squared, const ITensorInfo *output, const NormalizationLayerInfo norm_info) { ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, input_squared, output, norm_info)); |