From 7d0adc602b3a3ff66184632fd388b25384a9bc99 Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Fri, 4 Sep 2020 15:25:24 +0100 Subject: COMPMID-3151: Remove NEDepthwiseConvolutionLayer3x3Kernel Prefer NEDepthwiseConvolutionLayerNativeKernel as it has a native format of NHWC avoiding extra transformation to the NCHW domain. Signed-off-by: Georgios Pinitas Change-Id: If5d8de11691b8ef7f4c3816941f87417d0c8646b Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3930 Tested-by: Arm Jenkins Reviewed-by: Michele Di Giorgio Comments-Addressed: Arm Jenkins --- .../NEDepthwiseConvolutionLayer3x3Kernel.cpp | 317 --------------------- .../NEON/functions/NEDepthwiseConvolutionLayer.cpp | 230 +++------------ 2 files changed, 32 insertions(+), 515 deletions(-) delete mode 100644 src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp (limited to 'src') diff --git a/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp b/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp deleted file mode 100644 index 134ebb0e41..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 -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(ptr_weights_base); - const auto ptr_weights_r1 = reinterpret_cast(ptr_weights_base + kernel_stride_y); - const auto ptr_weights_r2 = reinterpret_cast(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(input_ptr + (ih + 0) * input_stride_y); - auto in_mid = reinterpret_cast(input_ptr + (ih + dilation.y()) * input_stride_y); - auto in_low = reinterpret_cast(input_ptr + (ih + 2 * dilation.y()) * input_stride_y); // uint8/int8 - auto p_out = reinterpret_cast(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(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(p_out, vres); - } - } - } - }, - out); - } -}; - -template -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::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info, depth_multiplier, dilation); - break; - case 2: - convolver_3x3::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info, depth_multiplier, dilation); - break; - case 3: - convolver_3x3::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 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(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(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier, _dilation); - break; - case DataType::QASYMM8: - convolve_3x3(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier, _dilation); - break; - case DataType::QASYMM8_SIGNED: - convolve_3x3(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier, _dilation); - break; - default: - ARM_COMPUTE_ERROR("Not implemented"); - } -} -} // namespace arm_compute diff --git a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp index cfdf2038b9..915a2830bf 100644 --- a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp @@ -58,24 +58,7 @@ Status validate_arguments_optimized(const ITensorInfo *input, const ITensorInfo ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(channel_idx)); } - const bool is_quantized = (!is_data_type_quantized_per_channel(weights->data_type())) && is_data_type_quantized_asymmetric(input->data_type()); - - if(!NEDepthwiseConvolutionAssemblyDispatch::is_optimized_supported(input, weights, conv_info, depth_multiplier, dilation)) - { - TensorInfo accumulator = TensorInfo(output->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32)); - ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseConvolutionLayer3x3Kernel::validate(input, weights, is_quantized ? &accumulator : output, conv_info, depth_multiplier, dilation)); - - if(is_quantized) - { - DirectConvolutionLayerOutputStageKernelInfo direct_conv_info; - direct_conv_info.output_data_type = input->data_type(); - ARM_COMPUTE_RETURN_ON_ERROR(NEDirectConvolutionLayerOutputStageKernel::validate(&accumulator, biases, output, direct_conv_info)); - } - } - else - { - ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseConvolutionAssemblyDispatch::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation)); - } + ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseConvolutionAssemblyDispatch::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation)); //Validate Activation Layer if(act_info.enabled()) @@ -87,117 +70,34 @@ Status validate_arguments_optimized(const ITensorInfo *input, const ITensorInfo } // namespace NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerOptimizedInternal::NEDepthwiseConvolutionLayerOptimizedInternal(std::shared_ptr memory_manager) - : _memory_group(memory_manager), _dwc_kernel(), _dwc_optimized_func(memory_manager), _output_stage_kernel(), _border_handler(), _permute_input(), _permute_weights(), _permute_output(), - _activationlayer_function(), _accumulator(), _permuted_input(), _permuted_weights(), _permuted_output(), _original_weights(nullptr), _has_bias(false), _is_quantized(false), _is_optimized(false), - _is_nchw(true), _permute(false), _is_activationlayer_enabled(false), _is_prepared(false) + : _memory_group(memory_manager), _dwc_optimized_func(memory_manager), _output_stage_kernel(), _border_handler(), _permute_input(), _permute_weights(), _permute_output(), _activationlayer_function(), + _accumulator(), _permuted_input(), _permuted_weights(), _permuted_output(), _original_weights(nullptr), _has_bias(false), _is_quantized(false), _is_nchw(true), _permute(false), + _is_activationlayer_enabled(false), _is_prepared(false) { } -void NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerOptimizedInternal::configure_generic(ITensor *input, - const ITensor *weights, - const ITensor *biases, - ITensor *output, - const PadStrideInfo &conv_info, - unsigned int depth_multiplier, - const ActivationLayerInfo &act_info, - const Size2D &dilation) +void NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerOptimizedInternal::configure(ITensor *input, + const ITensor *weights, + const ITensor *biases, + ITensor *output, const PadStrideInfo &conv_info, + unsigned int depth_multiplier, + const ActivationLayerInfo &act_info, + const Size2D &dilation) { - ARM_COMPUTE_UNUSED(act_info); - - PixelValue zero_value(0.f); - - // Initialize the intermediate accumulator tensor in case of quantized input - if(_is_quantized) - { - TensorShape accum_shape = output->info()->tensor_shape(); - DataLayout accum_layout = output->info()->data_layout(); - if(!_is_nchw) - { - permute(accum_shape, PermutationVector(1U, 2U, 0U)); - accum_layout = DataLayout::NCHW; - } - - _memory_group.manage(&_accumulator); - _accumulator.allocator()->init(TensorInfo(accum_shape, 1, DataType::S32, output->info()->quantization_info())); - _accumulator.info()->set_data_layout(accum_layout); - zero_value = PixelValue(static_cast(input->info()->quantization_info().uniform().offset)); - } - - if(!_is_nchw) - { - _memory_group.manage(&_permuted_input); - _memory_group.manage(&_permuted_output); - - // Configure the function to transform the input tensor from NHWC -> NCHW - _permute_input.configure(input, &_permuted_input, PermutationVector(1U, 2U, 0U)); - _permuted_input.info()->set_data_layout(DataLayout::NCHW); - - // Configure the function to transform the weights tensor from HWI -> IHW - _permute_weights.configure(weights, &_permuted_weights, PermutationVector(1U, 2U, 0U)); - _permuted_weights.info()->set_data_layout(DataLayout::NCHW); - _permuted_output.info()->set_quantization_info(output->info()->quantization_info()); - - // Configure depthwise - _dwc_kernel.configure(&_permuted_input, &_permuted_weights, (_is_quantized) ? &_accumulator : &_permuted_output, conv_info, depth_multiplier, dilation); - - // Configure border handler - _border_handler.configure(&_permuted_input, _dwc_kernel.border_size(), BorderMode::CONSTANT, zero_value); - - // Allocate tensors - _permuted_input.allocator()->allocate(); - } - else - { - // Configure depthwise convolution kernel - _dwc_kernel.configure(input, weights, (_is_quantized) ? &_accumulator : output, conv_info, depth_multiplier, dilation); - - // Configure border handler - _border_handler.configure(input, _dwc_kernel.border_size(), BorderMode::CONSTANT, zero_value); - } - - // Configure biases accumulation - if(_is_quantized) - { - const UniformQuantizationInfo iq_info = input->info()->quantization_info().uniform(); - const UniformQuantizationInfo wq_info = weights->info()->quantization_info().uniform(); - const UniformQuantizationInfo oq_info = (output->info()->total_size() == 0) ? iq_info : output->info()->quantization_info().uniform(); - - float multiplier = (iq_info.scale * wq_info.scale) / oq_info.scale; - int32_t output_multiplier; - int32_t output_shift; - quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift); - - DirectConvolutionLayerOutputStageKernelInfo direct_conv_info; - direct_conv_info.result_fixedpoint_multiplier = output_multiplier; - direct_conv_info.result_shift = output_shift; - direct_conv_info.result_offset_after_shift = oq_info.offset; - direct_conv_info.output_data_type = input->info()->data_type(); - _output_stage_kernel.configure(&_accumulator, biases, _is_nchw ? output : &_permuted_output, direct_conv_info); - _accumulator.allocator()->allocate(); - } - else if(_has_bias) - { - _output_stage_kernel.configure(_is_nchw ? output : &_permuted_output, biases); - } + ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); + // Perform validation step + ARM_COMPUTE_ERROR_THROW_ON(NEDepthwiseConvolutionLayerOptimizedInternal::validate(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), + output->info(), conv_info, depth_multiplier, act_info, dilation)); - // Permute output - if(!_is_nchw) - { - // Configure the function to transform the convoluted output to NHWC - _permute_output.configure(&_permuted_output, output, PermutationVector(2U, 0U, 1U)); - _permuted_output.allocator()->allocate(); - } -} + _original_weights = weights; + _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); + _has_bias = biases != nullptr; + _is_nchw = input->info()->data_layout() == DataLayout::NCHW; + _permute = _is_nchw; + _is_prepared = false; + _is_activationlayer_enabled = act_info.enabled(); -void NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerOptimizedInternal::configure_optimized(const ITensor *input, - const ITensor *weights, - const ITensor *biases, - ITensor *output, - const PadStrideInfo &conv_info, - unsigned int depth_multiplier, - const ActivationLayerInfo &act_info, - const Size2D &dilation) -{ + // Configure pipeline ActivationLayerInfo act_info_to_use = ActivationLayerInfo(); const bool is_relu = arm_compute::utils::info_helpers::is_relu(act_info); const bool is_relu6 = arm_compute::utils::info_helpers::is_relu6(act_info); @@ -238,43 +138,6 @@ void NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerOptimizedInternal:: { _dwc_optimized_func.configure(input, weights, biases, output, conv_info, depth_multiplier, act_info_to_use, dilation); } -} - -void NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerOptimizedInternal::configure(ITensor *input, - const ITensor *weights, - const ITensor *biases, - ITensor *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier, - const ActivationLayerInfo &act_info, - const Size2D &dilation) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); - // Perform validation step - ARM_COMPUTE_ERROR_THROW_ON(NEDepthwiseConvolutionLayerOptimizedInternal::validate(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), - output->info(), conv_info, depth_multiplier, act_info, dilation)); - - _original_weights = weights; - _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); - _has_bias = biases != nullptr; - _is_optimized = NEDepthwiseConvolutionAssemblyDispatch::is_optimized_supported(input->info(), - weights->info(), - conv_info, - depth_multiplier, - dilation); - _is_nchw = input->info()->data_layout() == DataLayout::NCHW; - _permute = _is_optimized == _is_nchw; - _is_prepared = false; - _is_activationlayer_enabled = act_info.enabled(); - - // Configure appropriate pipeline - if(_is_optimized) - { - configure_optimized(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation); - } - else - { - configure_generic(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation); - } // Configure activation if(_is_activationlayer_enabled) @@ -295,29 +158,18 @@ Status NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerOptimizedInternal return validate_arguments_optimized(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation); } -void NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerOptimizedInternal::run_generic() +void NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerOptimizedInternal::run() { - // Fill border - NEScheduler::get().schedule(&_border_handler, Window::DimX); - - // Execute depthwise convolution - NEScheduler::get().schedule(&_dwc_kernel, Window::DimX); + prepare(); - // Add biases - if(_has_bias || _is_quantized) - { - NEScheduler::get().schedule(&_output_stage_kernel, Window::DimX); - } + MemoryGroupResourceScope scope_mg(_memory_group); - // Permute output - if(!_is_nchw) + // Permute input + if(_permute) { - _permute_output.run(); + _permute_input.run(); } -} -void NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerOptimizedInternal::run_optimized() -{ // Run assembly function _dwc_optimized_func.run(); @@ -326,21 +178,6 @@ void NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerOptimizedInternal:: { _permute_output.run(); } -} - -void NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerOptimizedInternal::run() -{ - prepare(); - - MemoryGroupResourceScope scope_mg(_memory_group); - - // Permute input - if(_permute) - { - _permute_input.run(); - } - - _is_optimized ? run_optimized() : run_generic(); // Run activation if(_is_activationlayer_enabled) @@ -362,13 +199,10 @@ void NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerOptimizedInternal:: } // Prepare optimized function - if(_is_optimized) + _dwc_optimized_func.prepare(); + if(!_permuted_weights.is_used()) { - _dwc_optimized_func.prepare(); - if(!_permuted_weights.is_used()) - { - _permuted_weights.allocator()->free(); - } + _permuted_weights.allocator()->free(); } _is_prepared = true; -- cgit v1.2.1