| /* |
| * Copyright (c) 2019 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" |
| |
| 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, bool has_biases> |
| 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) |
| { |
| 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, bool has_biases> |
| 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) |
| { |
| 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, bool has_biases, bool is_per_channel> |
| 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) |
| { |
| 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)); |
| } |
| |
| acc.at(i) = rounding_divide_by_exp2(saturating_doubling_high_mul(acc.at(i), output_multiplier.at(id.x() + i)), output_shift.at(id.x() + i)) + output_qoffset; |
| out_vals[i] = static_cast<T>(utility::clamp<int32_t, uint8_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, bool has_biases, bool is_per_channel> |
| 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) |
| { |
| 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) |
| { |
| const auto biases_val = *(reinterpret_cast<int32_t *>(biases_it.ptr() + m * sizeof(int32_t))); |
| |
| int32_t out_val = acc.at(m) + biases_val; |
| out_val = rounding_divide_by_exp2(saturating_doubling_high_mul(out_val, output_multiplier.at(id.x() + m)), |
| output_shift.at(id.x() + m)) |
| + output_qoffset; |
| *(reinterpret_cast<T *>(output_it.ptr() + m * sizeof(T))) = static_cast<T>(utility::clamp<int32_t, uint8_t>(out_val)); |
| } |
| else |
| { |
| int32_t out_val = rounding_divide_by_exp2(saturating_doubling_high_mul(acc.at(m), output_multiplier.at(id.x() + m)), |
| output_shift.at(id.x() + m)) |
| + output_qoffset; |
| *(reinterpret_cast<T *>(output_it.ptr() + m * sizeof(T))) = static_cast<T>(utility::clamp<int32_t, uint8_t>(out_val)); |
| } |
| } |
| }, |
| 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::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() |
| { |
| } |
| |
| 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; |
| |
| 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) |
| { |
| int out_mult = 0; |
| int out_shift = 0; |
| const float multiplier = input_scale * weights_scale.at(i) / output_scale; |
| ARM_COMPUTE_ERROR_ON(multiplier > 1.f); |
| arm_compute::quantization::calculate_quantized_multiplier_less_than_one(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 = (biases != nullptr) ? &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<uint8_t, uint8_t, 8, true, false> : |
| &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<uint8_t, uint8_t, 8, false, false>; |
| pad_vectors(_output_multiplier, _output_shift, 8); |
| break; |
| case DataType::QSYMM8_PER_CHANNEL: |
| _func = (biases != nullptr) ? &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<uint8_t, int8_t, 8, true, true> : |
| &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<uint8_t, int8_t, 8, false, true>; |
| pad_vectors(_output_multiplier, _output_shift, 8); |
| break; |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| _func = (biases != nullptr) ? &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<float16_t, float16_t, 4, true, false> : |
| &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<float16_t, float16_t, 4, false, false>; |
| pad_vectors(_output_multiplier, _output_shift, 4); |
| break; |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F32: |
| _func = (biases != nullptr) ? &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<float, float, 2, true, false> : |
| &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<float, float, 2, false, false>; |
| 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); |
| } |
| |
| template < typename T, typename TW, int S, bool has_biases, bool is_per_channel, 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) |
| { |
| 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, has_biases>(_input, _weights, _biases, _output, _conv_info, _dilation, window); |
| } |
| else |
| { |
| depthwise_loop_generic_fp<T, has_biases>(_input, _weights, _biases, _output, _conv_info, _dilation, _depth_multiplier, window); |
| } |
| } |
| |
| template <typename T, typename TW, int S, bool has_biases, bool is_per_channel, typename std::enable_if<std::is_same<T, uint8_t>::value, int>::type> |
| void NEDepthwiseConvolutionLayerNativeKernel::run_depthwise(const Window &window) |
| { |
| 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, has_biases, is_per_channel>(_input, _weights, _biases, _output, _conv_info, _dilation, _output_multiplier, _output_shift, window); |
| } |
| else |
| { |
| depthwise_loop_generic_quantized<T, TW, has_biases, is_per_channel>(_input, _weights, _biases, _output, _conv_info, _dilation, _depth_multiplier, _output_multiplier, _output_shift, window); |
| } |
| } |
| } // namespace arm_compute |