diff options
Diffstat (limited to 'src/core/NEON/kernels/NEDepthwiseConvolutionLayerNativeKernel.cpp')
| -rw-r--r-- | src/core/NEON/kernels/NEDepthwiseConvolutionLayerNativeKernel.cpp | 648 |
1 files changed, 0 insertions, 648 deletions
diff --git a/src/core/NEON/kernels/NEDepthwiseConvolutionLayerNativeKernel.cpp b/src/core/NEON/kernels/NEDepthwiseConvolutionLayerNativeKernel.cpp deleted file mode 100644 index ef196ab904..0000000000 --- a/src/core/NEON/kernels/NEDepthwiseConvolutionLayerNativeKernel.cpp +++ /dev/null @@ -1,648 +0,0 @@ -/* - * Copyright (c) 2019-2020 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/NEDepthwiseConvolutionLayerNativeKernel.h" - -#include "arm_compute/core/AccessWindowStatic.h" -#include "arm_compute/core/CPP/Validate.h" -#include "arm_compute/core/NEON/wrapper/traits.h" -#include "arm_compute/core/NEON/wrapper/wrapper.h" -#include "arm_compute/core/utils/misc/ShapeCalculator.h" -#include "arm_compute/core/utils/quantization/AsymmHelpers.h" -#include "src/core/NEON/kernels/convolution/depthwise/impl_qa8_qa8.hpp" -#include "support/ToolchainSupport.h" - -namespace arm_compute -{ -namespace -{ -void pad_vectors(std::vector<int> &mult, std::vector<int> &shift, int vec_size) -{ - ARM_COMPUTE_ERROR_ON(mult.size() != shift.size()); - while(mult.size() % vec_size != 0) - { - mult.push_back(0); - shift.push_back(0); - } -} - -template <typename T, int S> -void depthwise_loop_multiplier1_fp(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, - const Size2D &dilation, const Window &window, bool has_biases) -{ - using VectorType = typename wrapper::traits::neon_vector<T, S>::type; - using TagType = typename wrapper::traits::neon_vector<T, S>::tag_type; - - const size_t input_stride_y = input->info()->strides_in_bytes().y(); - const size_t input_stride_z = input->info()->strides_in_bytes().z(); - const size_t input_max_offset = input->info()->strides_in_bytes().z() * input->info()->dimension(2) - (input->info()->padding().bottom + input->info()->padding().top) * - input->info()->strides_in_bytes().y(); - const size_t weights_width = weights->info()->dimension(1); - const size_t weights_height = weights->info()->dimension(2); - const size_t weights_stride_y = weights->info()->strides_in_bytes().y(); - const size_t weights_stride_z = weights->info()->strides_in_bytes().z(); - const size_t conv_stride_x = conv_info.stride().first; - const size_t conv_stride_y = conv_info.stride().second; - const size_t conv_pad_left = conv_info.pad_left(); - const size_t conv_pad_top = conv_info.pad_top(); - - Window win_input = window; - win_input.set(Window::DimY, Window::Dimension(0, 0, 0)); - win_input.set(Window::DimZ, Window::Dimension(0, 0, 0)); - - Window win_weights = win_input; - win_weights.set(3, Window::Dimension(0, 0, 0)); - - Iterator input_it(input, win_input); - Iterator weights_it(weights, win_weights); - Iterator output_it(output, window); - Iterator biases_it{}; - - if(has_biases) - { - biases_it = Iterator(biases, win_weights); - } - - execute_window_loop(window, [&](const Coordinates & id) - { - VectorType acc = wrapper::vdup_n(static_cast<T>(0), TagType{}); - - const int input_y = id.y() * conv_stride_x - conv_pad_left; - const int input_z = id.z() * conv_stride_y - conv_pad_top; - int input_offset = input_y * input_stride_y + input_z * input_stride_z; - - auto weights_ptr = weights_it.ptr(); - for(size_t h = 0; h < weights_height; ++h) - { - int offs = input_offset; - for(size_t w = 0; w < weights_width; ++w) - { - const auto input_vals = wrapper::vload(reinterpret_cast<T *>(input_it.ptr() + std::min(static_cast<size_t>(offs), input_max_offset))); - const auto weights_vals = wrapper::vload(reinterpret_cast<T *>(weights_ptr + w * weights_stride_y)); - - acc = wrapper::vmla(acc, weights_vals, input_vals); - offs += dilation.x() * input_stride_y; - } - - weights_ptr += weights_stride_z; - input_offset += dilation.y() * input_stride_z; - } - - if(has_biases) - { - const auto biases_vals = wrapper::vload(reinterpret_cast<T *>(biases_it.ptr())); - acc = wrapper::vadd(acc, biases_vals); - } - - wrapper::vstore(reinterpret_cast<T *>(output_it.ptr()), acc); - }, - input_it, weights_it, biases_it, output_it); -} - -template <typename T> -void depthwise_loop_generic_fp(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, - const Size2D &dilation, unsigned int depth_multiplier, const Window &window, bool has_biases) -{ - const size_t input_stride_y = input->info()->strides_in_bytes().y(); - const size_t input_stride_z = input->info()->strides_in_bytes().z(); - const size_t input_max_offset = input->info()->strides_in_bytes().z() * input->info()->dimension(2) - (input->info()->padding().bottom + input->info()->padding().top) * - input->info()->strides_in_bytes().y(); - const size_t weights_width = weights->info()->dimension(1); - const size_t weights_height = weights->info()->dimension(2); - const size_t weights_stride_y = weights->info()->strides_in_bytes().y(); - const size_t weights_stride_z = weights->info()->strides_in_bytes().z(); - const size_t conv_stride_x = conv_info.stride().first; - const size_t conv_stride_y = conv_info.stride().second; - const size_t conv_pad_left = conv_info.pad_left(); - const size_t conv_pad_top = conv_info.pad_top(); - - Window win_input = window; - win_input.set(Window::DimY, Window::Dimension(0, 0, 0)); - win_input.set(Window::DimZ, Window::Dimension(0, 0, 0)); - - Window win_weights = win_input; - win_weights.set(3, Window::Dimension(0, 0, 0)); - - win_input.set_dimension_step(Window::DimX, 1); - - Iterator input_it(input, win_input); - Iterator weights_it(weights, win_weights); - Iterator output_it(output, window); - Iterator biases_it{}; - - if(has_biases) - { - biases_it = Iterator(biases, win_weights); - } - - execute_window_loop(window, [&](const Coordinates & id) - { - std::vector<T> acc(depth_multiplier, static_cast<T>(0)); - - const int input_y = id.y() * conv_stride_x - conv_pad_left; - const int input_z = id.z() * conv_stride_y - conv_pad_top; - int input_offset = input_y * input_stride_y + input_z * input_stride_z; - - auto weights_ptr = weights_it.ptr(); - for(size_t h = 0; h < weights_height; ++h) - { - int offs = input_offset; - for(size_t w = 0; w < weights_width; ++w) - { - const auto input_val = *(reinterpret_cast<T *>(input_it.ptr() + std::min(static_cast<size_t>(offs), input_max_offset))); - - for(size_t m = 0; m < depth_multiplier; ++m) - { - const auto weights_val = *(reinterpret_cast<T *>(weights_ptr + m * sizeof(T) + w * weights_stride_y)); - acc.at(m) = support::cpp11::fma(weights_val, input_val, acc.at(m)); - } - - offs += dilation.x() * input_stride_y; - } - - weights_ptr += weights_stride_z; - input_offset += dilation.y() * input_stride_z; - } - - if(has_biases) - { - for(size_t m = 0; m < depth_multiplier; ++m) - { - const auto biases_val = *(reinterpret_cast<T *>(biases_it.ptr() + m * sizeof(T))); - *(reinterpret_cast<T *>(output_it.ptr() + m * sizeof(T))) = acc.at(m) + biases_val; - } - } - else - { - for(size_t m = 0; m < depth_multiplier; ++m) - { - *(reinterpret_cast<T *>(output_it.ptr() + m * sizeof(T))) = acc.at(m); - } - } - }, - input_it, weights_it, biases_it, output_it); -} - -template <typename T, typename TW, int S> -void depthwise_loop_multiplier1_quantized(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, - const Size2D &dilation, std::vector<int> output_multiplier, std::vector<int> output_shift, const Window &window, bool has_biases) -{ - using VectorType = typename wrapper::traits::neon_vector<T, S>::type; - using TagType = typename wrapper::traits::neon_vector<T, S>::tag_type; - - const size_t input_stride_y = input->info()->strides_in_bytes().y(); - const size_t input_stride_z = input->info()->strides_in_bytes().z(); - const size_t input_max_offset = input->info()->strides_in_bytes().z() * input->info()->dimension(2) - (input->info()->padding().bottom + input->info()->padding().top) * - input->info()->strides_in_bytes().y(); - const size_t weights_width = weights->info()->dimension(1); - const size_t weights_height = weights->info()->dimension(2); - const size_t weights_stride_y = weights->info()->strides_in_bytes().y(); - const size_t weights_stride_z = weights->info()->strides_in_bytes().z(); - const size_t conv_stride_x = conv_info.stride().first; - const size_t conv_stride_y = conv_info.stride().second; - const size_t conv_pad_left = conv_info.pad_left(); - const size_t conv_pad_top = conv_info.pad_top(); - - const int32_t input_qoffset = input->info()->quantization_info().uniform().offset; - const int32_t weights_qoffset = weights->info()->quantization_info().uniform().offset; - const int32_t output_qoffset = output->info()->quantization_info().uniform().offset; - const int32_t k_offset = weights_width * weights_height * input_qoffset * weights_qoffset; - - Window win_input = window; - win_input.set(Window::DimY, Window::Dimension(0, 0, 0)); - win_input.set(Window::DimZ, Window::Dimension(0, 0, 0)); - - Window win_weights = win_input; - win_weights.set(3, Window::Dimension(0, 0, 0)); - - Iterator input_it(input, win_input); - Iterator weights_it(weights, win_weights); - Iterator output_it(output, window); - Iterator biases_it{}; - - if(has_biases) - { - biases_it = Iterator(biases, win_weights); - } - - execute_window_loop(window, [&](const Coordinates & id) - { - std::vector<int32_t> acc(S, 0); - std::vector<int32_t> in_sum(S, 0); - std::vector<int32_t> we_sum(S, 0); - - const int input_y = id.y() * conv_stride_x - conv_pad_left; - const int input_z = id.z() * conv_stride_y - conv_pad_top; - int input_offset = input_y * input_stride_y + input_z * input_stride_z; - - auto weights_ptr = weights_it.ptr(); - for(size_t h = 0; h < weights_height; ++h) - { - int offs = input_offset; - for(size_t w = 0; w < weights_width; ++w) - { - const auto input_vals = wrapper::vload(reinterpret_cast<T *>(input_it.ptr() + std::min(static_cast<size_t>(offs), input_max_offset))); - const auto weights_vals = wrapper::vload(reinterpret_cast<TW *>(weights_ptr + w * weights_stride_y)); - - for(int i = 0; i < S; ++i) - { - acc.at(i) += input_vals[i] * weights_vals[i]; - in_sum.at(i) += input_vals[i]; - we_sum.at(i) += weights_vals[i]; - } - - offs += dilation.x() * input_stride_y; - } - - weights_ptr += weights_stride_z; - input_offset += dilation.y() * input_stride_z; - } - - VectorType out_vals = wrapper::vdup_n(static_cast<T>(0), TagType{}); - for(int i = 0; i < S; ++i) - { - acc.at(i) -= in_sum.at(i) * weights_qoffset; - acc.at(i) -= we_sum.at(i) * input_qoffset; - acc.at(i) += k_offset; - - if(has_biases) - { - acc.at(i) += *reinterpret_cast<int32_t *>(biases_it.ptr() + i * sizeof(int32_t)); - } - - const int out_mul = output_multiplier.at(id.x() + i); - const int out_shift = output_shift.at(id.x() + i); - if(out_shift < 0) - { - acc.at(i) = saturating_doubling_high_mul(acc.at(i) * (1 << (-out_shift)), out_mul) + output_qoffset; - } - else - { - acc.at(i) = rounding_divide_by_exp2(saturating_doubling_high_mul(acc.at(i), out_mul), out_shift) + output_qoffset; - } - out_vals[i] = static_cast<T>(utility::clamp<int32_t, T>(acc.at(i))); - } - - wrapper::vstore(reinterpret_cast<T *>(output_it.ptr()), out_vals); - }, - input_it, weights_it, biases_it, output_it); -} - -template <typename T, typename TW> -void depthwise_loop_generic_quantized(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, - const Size2D &dilation, unsigned int depth_multiplier, std::vector<int> output_multiplier, std::vector<int> output_shift, const Window &window, bool has_biases) -{ - const size_t input_stride_y = input->info()->strides_in_bytes().y(); - const size_t input_stride_z = input->info()->strides_in_bytes().z(); - const size_t input_max_offset = input->info()->strides_in_bytes().z() * input->info()->dimension(2) - (input->info()->padding().bottom + input->info()->padding().top) * - input->info()->strides_in_bytes().y(); - const size_t weights_width = weights->info()->dimension(1); - const size_t weights_height = weights->info()->dimension(2); - const size_t weights_stride_y = weights->info()->strides_in_bytes().y(); - const size_t weights_stride_z = weights->info()->strides_in_bytes().z(); - const size_t conv_stride_x = conv_info.stride().first; - const size_t conv_stride_y = conv_info.stride().second; - const size_t conv_pad_left = conv_info.pad_left(); - const size_t conv_pad_top = conv_info.pad_top(); - - const int32_t input_qoffset = input->info()->quantization_info().uniform().offset; - const int32_t weights_qoffset = weights->info()->quantization_info().uniform().offset; - const int32_t output_qoffset = output->info()->quantization_info().uniform().offset; - const int32_t k_offset = weights_width * weights_height * input_qoffset * weights_qoffset; - - Window win_input = window; - win_input.set(Window::DimY, Window::Dimension(0, 0, 0)); - win_input.set(Window::DimZ, Window::Dimension(0, 0, 0)); - - Window win_weights = win_input; - win_weights.set(3, Window::Dimension(0, 0, 0)); - - win_input.set_dimension_step(Window::DimX, 1); - - Iterator input_it(input, win_input); - Iterator weights_it(weights, win_weights); - Iterator output_it(output, window); - Iterator biases_it{}; - - if(has_biases) - { - biases_it = Iterator(biases, win_weights); - } - - execute_window_loop(window, [&](const Coordinates & id) - { - std::vector<int32_t> acc(depth_multiplier, 0); - std::vector<int32_t> we_sum(depth_multiplier, 0); - int32_t in_sum = 0; - - const int input_y = id.y() * conv_stride_x - conv_pad_left; - const int input_z = id.z() * conv_stride_y - conv_pad_top; - int input_offset = input_y * input_stride_y + input_z * input_stride_z; - - auto weights_ptr = weights_it.ptr(); - for(size_t h = 0; h < weights_height; ++h) - { - int offs = input_offset; - for(size_t w = 0; w < weights_width; ++w) - { - const auto input_val = *(reinterpret_cast<T *>(input_it.ptr() + std::min(static_cast<size_t>(offs), input_max_offset))); - - for(size_t m = 0; m < depth_multiplier; ++m) - { - const auto weights_val = *(reinterpret_cast<TW *>(weights_ptr + m * sizeof(T) + w * weights_stride_y)); - acc.at(m) += input_val * weights_val; - - we_sum.at(m) += weights_val; - } - - offs += dilation.x() * input_stride_y; - in_sum += input_val; - } - - weights_ptr += weights_stride_z; - input_offset += dilation.y() * input_stride_z; - } - - for(size_t m = 0; m < depth_multiplier; ++m) - { - acc.at(m) -= in_sum * weights_qoffset; - acc.at(m) -= we_sum.at(m) * input_qoffset; - acc.at(m) += k_offset; - - if(has_biases) - { - acc.at(m) += *(reinterpret_cast<int32_t *>(biases_it.ptr() + m * sizeof(int32_t))); - } - - const int out_mul = output_multiplier.at(id.x() + m); - const int out_shift = output_shift.at(id.x() + m); - if(out_shift < 0) - { - acc.at(m) = saturating_doubling_high_mul(acc.at(m) * (1 << (-out_shift)), out_mul) + output_qoffset; - } - else - { - acc.at(m) = rounding_divide_by_exp2(saturating_doubling_high_mul(acc.at(m), out_mul), out_shift) + output_qoffset; - } - *(reinterpret_cast<T *>(output_it.ptr() + m * sizeof(T))) = static_cast<T>(utility::clamp<int32_t, T>(acc.at(m))); - } - }, - input_it, weights_it, biases_it, output_it); -} - -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, - const Size2D &dilation) -{ - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); - ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input); - ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON(depth_multiplier == 0); - ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(1) + (weights->dimension(1) - 1) * (dilation.x() - 1) > input->dimension(1) + conv_info.pad_left() + conv_info.pad_right()); - ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(2) + (weights->dimension(2) - 1) * (dilation.y() - 1) > input->dimension(2) + conv_info.pad_top() + conv_info.pad_bottom()); - ARM_COMPUTE_RETURN_ERROR_ON((input->dimension(0) * depth_multiplier) != weights->dimension(0)); - ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1)); - ARM_COMPUTE_RETURN_ERROR_ON((conv_info.stride().first < 1) || (conv_info.stride().second < 1)); - - if(is_data_type_quantized_per_channel(weights->data_type())) - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QSYMM8_PER_CHANNEL); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); - ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) != weights->quantization_info().scale().size()); - } - else - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output); - } - - if(biases != nullptr) - { - ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); - ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(0)); - - if(is_data_type_quantized_asymmetric(input->data_type())) - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); - } - else - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); - } - } - - if(output->total_size() != 0) - { - const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); - } - - return Status{}; -} - -std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *biases, - ITensorInfo *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier, const Size2D &dilation) -{ - // Get convolved dimensions - const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation); - - // Output auto inizialitation if not yet initialized - auto_init_if_empty(*output, input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape).set_quantization_info(output->quantization_info())); - - // Configure kernel window (generic) - const unsigned int num_elems_read_per_iteration = (depth_multiplier == 1) ? 8 / element_size_from_data_type(input->data_type()) : 1; - const unsigned int num_elems_written_per_iteration = num_elems_read_per_iteration * depth_multiplier; - - // Configure kernel window - Window win = calculate_max_window(*output, Steps(num_elems_written_per_iteration)); - - AccessWindowStatic input_access(input, 0, -conv_info.pad_left(), ceil_to_multiple(num_elems_read_per_iteration, input->dimension(0)), - input->dimension(1) + std::max(std::max(conv_info.pad_right(), conv_info.pad_bottom()), conv_info.pad_top())); - AccessWindowHorizontal weights_access(weights, 0, num_elems_written_per_iteration); - AccessWindowHorizontal output_access(output, 0, num_elems_written_per_iteration); - - bool window_changed = update_window_and_padding(win, input_access, weights_access, output_access); - - if(biases != nullptr) - { - AccessWindowHorizontal biases_access(biases, 0, num_elems_written_per_iteration); - window_changed |= update_window_and_padding(win, biases_access); - } - - output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); - - Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; - return std::make_pair(err, win); -} -} // namespace - -NEDepthwiseConvolutionLayerNativeKernel::NEDepthwiseConvolutionLayerNativeKernel() - : _func(), _border_size(0), _input(), _weights(), _biases(), _output(), _conv_info(), _depth_multiplier(1), _dilation(), _output_multiplier(), _output_shift(), _has_biases() -{ -} - -BorderSize NEDepthwiseConvolutionLayerNativeKernel::border_size() const -{ - return _border_size; -} - -void NEDepthwiseConvolutionLayerNativeKernel::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, - const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), conv_info, depth_multiplier, dilation)); - - _input = input; - _weights = weights; - _biases = biases; - _output = output; - _conv_info = conv_info; - _depth_multiplier = depth_multiplier; - _border_size = BorderSize(_conv_info.pad_left(), 0, std::max(std::max(conv_info.pad_right(), conv_info.pad_bottom()), conv_info.pad_top()), 0); - _dilation = dilation; - _has_biases = (biases != nullptr); - - if(is_data_type_quantized(_input->info()->data_type())) - { - const auto input_scale = input->info()->quantization_info().uniform().scale; - const auto output_scale = output->info()->quantization_info().uniform().scale; - - auto weights_scale = weights->info()->quantization_info().scale(); - if(!is_data_type_quantized_per_channel(_weights->info()->data_type())) - { - for(size_t i = 1; i < _weights->info()->dimension(0); ++i) - { - weights_scale.push_back(weights_scale.front()); - } - } - - for(size_t i = 0; i < weights_scale.size(); ++i) - { - int32_t out_mult = 0; - int32_t out_shift = 0; - const float multiplier = input_scale * weights_scale.at(i) / output_scale; - arm_compute::quantization::calculate_quantized_multiplier(multiplier, &out_mult, &out_shift); - - _output_multiplier.push_back(out_mult); - _output_shift.push_back(out_shift); - } - } - - switch(_weights->info()->data_type()) - { - case DataType::QASYMM8: - _func = &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<uint8_t, uint8_t, 8>; - pad_vectors(_output_multiplier, _output_shift, 8); - break; - case DataType::QASYMM8_SIGNED: - _func = &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<int8_t, int8_t, 8>; - pad_vectors(_output_multiplier, _output_shift, 8); - break; - case DataType::QSYMM8_PER_CHANNEL: - if(_input->info()->data_type() == DataType::QASYMM8) - { - _func = &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<uint8_t, int8_t, 8>; - } - else - { - _func = &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<int8_t, int8_t, 8>; - } - pad_vectors(_output_multiplier, _output_shift, 8); - break; -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - case DataType::F16: - _func = &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<float16_t, float16_t, 4>; - pad_vectors(_output_multiplier, _output_shift, 4); - break; -#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - case DataType::F32: - _func = &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<float, float, 2>; - pad_vectors(_output_multiplier, _output_shift, 2); - break; - default: - ARM_COMPUTE_ERROR("Data type not supported"); - break; - } - - auto win_config = validate_and_configure_window(_input->info(), _weights->info(), (biases != nullptr) ? biases->info() : nullptr, _output->info(), _conv_info, _depth_multiplier, dilation); - ARM_COMPUTE_ERROR_THROW_ON(win_config.first); - INEKernel::configure(win_config.second); -} - -Status NEDepthwiseConvolutionLayerNativeKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier, - const Size2D &dilation) -{ - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info, depth_multiplier, dilation)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), (biases != nullptr) ? biases->clone().get() : nullptr, output->clone().get(), conv_info, - depth_multiplier, dilation) - .first); - return Status{}; -} - -void NEDepthwiseConvolutionLayerNativeKernel::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); - - (this->*_func)(window, _has_biases); -} - -template < typename T, typename TW, int S, typename std::enable_if < std::is_same<T, float>::value -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - || std::is_same<T, float16_t>::value -#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - , - int >::type > -void NEDepthwiseConvolutionLayerNativeKernel::run_depthwise(const Window &window, bool has_biases) -{ - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); - - if(_depth_multiplier == 1) - { - depthwise_loop_multiplier1_fp<T, S>(_input, _weights, _biases, _output, _conv_info, _dilation, window, has_biases); - } - else - { - depthwise_loop_generic_fp<T>(_input, _weights, _biases, _output, _conv_info, _dilation, _depth_multiplier, window, has_biases); - } -} - -template <typename T, typename TW, int S, typename> -void NEDepthwiseConvolutionLayerNativeKernel::run_depthwise(const Window &window, bool has_biases) -{ - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); - - if(_depth_multiplier == 1) - { - depthwise_loop_multiplier1_quantized<T, TW, S>(_input, _weights, _biases, _output, _conv_info, _dilation, _output_multiplier, _output_shift, window, has_biases); - } - else - { - depthwise_loop_generic_quantized<T, TW>(_input, _weights, _biases, _output, _conv_info, _dilation, _depth_multiplier, _output_multiplier, _output_shift, window, has_biases); - } -} -} // namespace arm_compute |