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Diffstat (limited to 'src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp')
| -rw-r--r-- | src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp | 317 |
1 files changed, 0 insertions, 317 deletions
diff --git a/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp b/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp deleted file mode 100644 index 0be9d8f92b..0000000000 --- a/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp +++ /dev/null @@ -1,317 +0,0 @@ -/* - * Copyright (c) 2017-2020 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/NEDepthwiseConvolutionLayer3x3Kernel.h" -#include "arm_compute/core/NEON/kernels/detail/NEDirectConvolutionDetail.h" - -#include "arm_compute/core/AccessWindowStatic.h" -#include "arm_compute/core/CPP/Validate.h" -#include "arm_compute/core/Coordinates.h" -#include "arm_compute/core/Error.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/ITensor.h" -#include "arm_compute/core/NEON/INEKernel.h" -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/TensorShape.h" -#include "arm_compute/core/Types.h" -#include "arm_compute/core/Utils.h" -#include "arm_compute/core/Validate.h" -#include "arm_compute/core/Window.h" -#include "arm_compute/core/utils/misc/ShapeCalculator.h" - -namespace arm_compute -{ -namespace -{ -template <typename T1, typename T2, unsigned int stridex> -class convolver_3x3 -{ -public: - static void convolve(const Window &window, unsigned int num_elems_written_per_iteration, - const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation) - { - const int input_offset = -input->info()->quantization_info().uniform().offset; - const int weights_offset = -weights->info()->quantization_info().uniform().offset; - - const int input_stride_x = input->info()->strides_in_bytes().x(); - const int input_stride_y = input->info()->strides_in_bytes().y(); - const int input_stride_z = input->info()->strides_in_bytes().z(); - const int input_stride_w = input->info()->strides_in_bytes()[3]; - const int output_stride_y = output->info()->strides_in_bytes().y(); - const int kernel_stride_y = weights->info()->strides_in_bytes().y(); - const int kernel_stride_z = weights->info()->strides_in_bytes().z(); - const int output_w = output->info()->dimension(0); - const int output_h = output->info()->dimension(1); - const int delta_input = detail::get_input_num_elems_processed(num_elems_written_per_iteration, stridex); - const unsigned int conv_stride_y = std::get<1>(conv_info.stride()); - const unsigned int conv_pad_x = conv_info.pad_left(); - const unsigned int conv_pad_y = conv_info.pad_top(); - - // setup output window for the iterator - Window window_out = window; - window_out.set(Window::DimX, Window::Dimension(0, output->info()->dimension(Window::DimX), output->info()->dimension(Window::DimX))); - window_out.set(Window::DimY, Window::Dimension(0, output->info()->dimension(Window::DimY), output->info()->dimension(Window::DimY))); - - // setup input window for the iterator - Window window_in = window; - // Iteration of input is taken care of in execute_window_loop - window_in.set(Window::DimX, Window::Dimension(0, 0, 0)); - window_in.set(Window::DimY, Window::Dimension(0, 0, 0)); - window_in.set(Window::DimZ, Window::Dimension(0, 0, 0)); - - Window window_k = calculate_max_window(*weights->info(), Steps(1u)); - - Iterator in(input, window_in); - Iterator out(output, window_out); - Iterator w(weights, window_k); - - const uint8_t *weights_ptr = w.ptr(); - - execute_window_loop(window_out, [&](const Coordinates & id) - { - int ih = 0; - int oh = 0; - - const uint8_t *input_ptr = in.ptr() - conv_pad_x * input_stride_x - conv_pad_y * input_stride_y + (id.z() / depth_multiplier) * input_stride_z + input_stride_w * id[3]; - const uint8_t *ptr_weights_base = weights_ptr + id.z() * kernel_stride_z; - - const auto ptr_weights_r0 = reinterpret_cast<const T1 *>(ptr_weights_base); - const auto ptr_weights_r1 = reinterpret_cast<const T1 *>(ptr_weights_base + kernel_stride_y); - const auto ptr_weights_r2 = reinterpret_cast<const T1 *>(ptr_weights_base + kernel_stride_y * 2); - const auto vw_r0 = detail::load_matrix_row(ptr_weights_r0, weights_offset); - const auto vw_r1 = detail::load_matrix_row(ptr_weights_r1, weights_offset); - const auto vw_r2 = detail::load_matrix_row(ptr_weights_r2, weights_offset); - - for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y) - { - auto in_top = reinterpret_cast<const T1 *>(input_ptr + (ih + 0) * input_stride_y); - auto in_mid = reinterpret_cast<const T1 *>(input_ptr + (ih + dilation.y()) * input_stride_y); - auto in_low = reinterpret_cast<const T1 *>(input_ptr + (ih + 2 * dilation.y()) * input_stride_y); // uint8/int8 - auto p_out = reinterpret_cast<T2 *>(out.ptr() + oh * output_stride_y); // int32 - - for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration, - in_top += delta_input, in_mid += delta_input, in_low += delta_input, - p_out += num_elems_written_per_iteration) - { - if(dilation == Size2D(1U, 1U)) - { - detail::convolve_3x3<false>(in_top, in_mid, in_low, p_out, vw_r0, vw_r1, vw_r2, stridex, input_offset); - } - else - { - auto vres = detail::convolve_3x3_dilation(in_top, in_mid, in_low, vw_r0, vw_r1, vw_r2, dilation.x(), stridex, input_offset); - detail::store_results<stridex>(p_out, vres); - } - } - } - }, - out); - } -}; - -template <typename T1, typename T2> -inline void convolve_3x3(const Window &window, unsigned int num_elems_written_per_iteration, - const ITensor *input, const ITensor *weights, ITensor *output, - const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation) -{ - const unsigned int conv_stride_x = std::get<0>(conv_info.stride()); - switch(conv_stride_x) - { - case 1: - convolver_3x3<T1, T2, 1>::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info, depth_multiplier, dilation); - break; - case 2: - convolver_3x3<T1, T2, 2>::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info, depth_multiplier, dilation); - break; - case 3: - convolver_3x3<T1, T2, 3>::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info, depth_multiplier, dilation); - break; - default: - ARM_COMPUTE_ERROR("Not implemented"); - } -} - -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation) -{ - ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input); - 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_MISMATCHING_DATA_TYPES(input, weights); - - const DataLayout data_layout = input->data_layout(); - const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); - const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); - - ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(width_idx) != 3 || weights->dimension(height_idx) != 3); - ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride().first < 1 || conv_info.stride().first > 3); - - 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); - - if(is_data_type_quantized_asymmetric(input->data_type())) - { - ARM_COMPUTE_RETURN_ERROR_ON(output->data_type() != DataType::S32); - } - else - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); - } - } - - return Status{}; -} - -std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, - const Size2D &dilation) -{ - Window win; - bool window_changed = false; - - // Get convolved dimensions - const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation); - const DataType output_dt = is_data_type_quantized_asymmetric(input->data_type()) ? DataType::S32 : input->data_type(); - - // 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_data_type(output_dt).set_quantization_info(output->quantization_info())); - - // Configure kernel window (generic) - const unsigned int conv_stride_x = conv_info.stride().first; - const unsigned int conv_stride_y = conv_info.stride().second; - const unsigned int conv_pad_top = conv_info.pad_top(); - const unsigned int conv_pad_left = conv_info.pad_left(); - - unsigned int num_elems_written_per_iteration = 16 >> conv_stride_x; - unsigned int num_elems_read_per_iteration = 0; - - switch(input->data_type()) - { - case DataType::QASYMM8: - case DataType::QASYMM8_SIGNED: - num_elems_read_per_iteration = 16 + 15 * (dilation.x() - 1); - break; -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - case DataType::F16: - num_elems_written_per_iteration = 32 >> conv_stride_x; - num_elems_read_per_iteration = 24 + 23 * (dilation.x() - 1); - break; -#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - case DataType::F32: - num_elems_read_per_iteration = 12 + 11 * (dilation.x() - 1); - break; - default: - ARM_COMPUTE_ERROR("Data type not supported."); - } - - // Configure kernel window - win = calculate_max_window(*output, Steps(num_elems_written_per_iteration)); - - AccessWindowRectangle input_access(input, -conv_pad_left, -conv_pad_top, num_elems_read_per_iteration, 3 + 2 * (dilation.y() - 1), conv_stride_x, conv_stride_y); - AccessWindowStatic weights_access(weights, 0, 0, 3, 3); - AccessWindowHorizontal output_access(output, 0, num_elems_written_per_iteration); - - window_changed = update_window_and_padding(win, input_access, weights_access, output_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 - -NEDepthwiseConvolutionLayer3x3Kernel::NEDepthwiseConvolutionLayer3x3Kernel() - : _border_size(0), _input(), _output(), _weights(), _conv_info(), _num_elems_written_per_iteration(0), _depth_multiplier(1), _dilation() -{ -} - -BorderSize NEDepthwiseConvolutionLayer3x3Kernel::border_size() const -{ - return _border_size; -} - -void NEDepthwiseConvolutionLayer3x3Kernel::configure(const ITensor *input, const ITensor *weights, 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(), output->info(), conv_info, depth_multiplier, dilation)); - - _input = input; - _output = output; - _weights = weights; - _conv_info = conv_info; - _depth_multiplier = depth_multiplier; - switch(input->info()->data_type()) - { - case DataType::QASYMM8: - case DataType::QASYMM8_SIGNED: - case DataType::F32: - _num_elems_written_per_iteration = 16 >> _conv_info.stride().first; - break; - case DataType::F16: - _num_elems_written_per_iteration = 32 >> _conv_info.stride().first; - break; - default: - ARM_COMPUTE_ERROR("Data type not supported."); - } - _border_size = BorderSize(_conv_info.pad_top(), _conv_info.pad_right(), _conv_info.pad_bottom(), _conv_info.pad_left()); - _dilation = dilation; - auto win_config = validate_and_configure_window(_input->info(), _weights->info(), _output->info(), _conv_info, _depth_multiplier, dilation); - ARM_COMPUTE_ERROR_THROW_ON(win_config.first); - INEKernel::configure(win_config.second); -} - -Status NEDepthwiseConvolutionLayer3x3Kernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, - const Size2D &dilation) -{ - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, output, conv_info, depth_multiplier, dilation)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), output->clone().get(), conv_info, depth_multiplier, dilation).first); - return Status{}; -} - -void NEDepthwiseConvolutionLayer3x3Kernel::run(const Window &window, const ThreadInfo &info) -{ - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_UNUSED(info); - - ARM_COMPUTE_UNUSED(info); - - switch(_input->info()->data_type()) - { -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - case DataType::F16: - convolve_3x3<float16_t, float16_t>(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier, _dilation); - break; -#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - case DataType::F32: - convolve_3x3<float, float>(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier, _dilation); - break; - case DataType::QASYMM8: - convolve_3x3<uint8_t, int32_t>(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier, _dilation); - break; - case DataType::QASYMM8_SIGNED: - convolve_3x3<int8_t, int32_t>(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier, _dilation); - break; - default: - ARM_COMPUTE_ERROR("Not implemented"); - } -} -} // namespace arm_compute |