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Diffstat (limited to 'runtime/neurun/backend/srcn/kernel/ConvolutionLayer.cc')
| -rw-r--r-- | runtime/neurun/backend/srcn/kernel/ConvolutionLayer.cc | 233 |
1 files changed, 0 insertions, 233 deletions
diff --git a/runtime/neurun/backend/srcn/kernel/ConvolutionLayer.cc b/runtime/neurun/backend/srcn/kernel/ConvolutionLayer.cc deleted file mode 100644 index 4e70f6319..000000000 --- a/runtime/neurun/backend/srcn/kernel/ConvolutionLayer.cc +++ /dev/null @@ -1,233 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ConvolutionLayer.h" - -#include "OperationUtils.h" -#include <ncnn/srcn/srcn_conv.h> - -namespace neurun -{ -namespace backend -{ -namespace srcn -{ -namespace kernel -{ - -ConvolutionLayer::ConvolutionLayer() - : _inputData(), _kernelData(), _biasData(), _outputData(), _inputDescr(), _kernelDescr(), - _biasDescr(), _outputDescr(), _paddingType(0), _paddingLeft(0), _paddingTop(0), - _paddingRight(0), _paddingBottom(0), _strideWidth(0), _strideHeight(0), - _inputType(OperandType::FLOAT32), _layout(nnfw::srcn::col_major), _winograd_weights(nullptr), - _sparse_weights(nullptr) -{ - // DO NOTHING -} - -ConvolutionLayer::~ConvolutionLayer() -{ - // TODO Move managing constant _winograd_data and sparse - nnfw::srcn::winograd_release(_winograd_weights); - size_t depth_index = _layout == nnfw::srcn::col_major ? 3 : 1; - nnfw::srcn::sparse_release(_outputDescr.dimensions[depth_index], _sparse_weights); -} - -void ConvolutionLayer::convFloat32() -{ - nnfw::srcn::convMat_t in_mat, out_mat, kernel_mat; - nnfw::srcn::convParams_t in_param; - - assert(_layout == nnfw::srcn::col_major || _layout == nnfw::srcn::row_major); - size_t height_index = _layout == nnfw::srcn::col_major ? 1 : 2; - size_t width_index = _layout == nnfw::srcn::col_major ? 2 : 3; - size_t depth_index = _layout == nnfw::srcn::col_major ? 3 : 1; - size_t kernel_input_depth_index = _layout == nnfw::srcn::col_major ? 3 : 1; - size_t kernel_output_depth_index = 0; - - const int batches = MatchingDim(_inputDescr, 0, _outputDescr, 0); - const int input_height = _inputDescr.dimensions[height_index]; - const int input_width = _inputDescr.dimensions[width_index]; - const int input_depth = - MatchingDim(_inputDescr, depth_index, _kernelDescr, kernel_input_depth_index); - in_mat.c = input_depth; - in_mat.w = input_width; - in_mat.h = input_height; - in_mat.n = batches; - in_mat.data = _inputData.f; - - const int output_height = _outputDescr.dimensions[height_index]; - const int output_width = _outputDescr.dimensions[width_index]; - const int output_depth = - MatchingDim(_kernelDescr, kernel_output_depth_index, _outputDescr, depth_index); - out_mat.c = output_depth; - out_mat.w = output_width; - out_mat.h = output_height; - out_mat.n = batches; - out_mat.data = _outputData.f; - - const int outch = _kernelDescr.dimensions[kernel_output_depth_index]; - const int inch = _kernelDescr.dimensions[kernel_input_depth_index]; - const int ker_height = _kernelDescr.dimensions[height_index]; - const int ker_width = _kernelDescr.dimensions[width_index]; - kernel_mat.c = input_depth; - kernel_mat.w = ker_width; - kernel_mat.h = ker_height; - kernel_mat.n = output_depth; - kernel_mat.data = _kernelData.f; - - in_param.kernel_w = ker_width; - in_param.kernel_h = ker_height; - in_param.stride_w = _strideWidth; - in_param.stride_h = _strideHeight; - in_param.padding = _paddingType; - in_param.pad_w = _paddingLeft; - in_param.pad_h = _paddingTop; - in_param.dilation_w = 1; - in_param.dilation_h = 1; - - nnfw::srcn::winogradParams_t winograd_param; - winograd_param.kernel_w = ker_width; - winograd_param.kernel_h = ker_height; - winograd_param.stride_w = _strideWidth; - winograd_param.stride_h = _strideHeight; - winograd_param.dilation_w = 1; - winograd_param.dilation_h = 1; - winograd_param.batch = batches; - winograd_param.w = ker_width; - winograd_param.h = ker_height; - winograd_param.inch = inch; - winograd_param.outch = outch; - winograd_param.num_threads = 4; - - winograd_param.conv_type = _layout; - winograd_param.weight_data = _kernelData.f; - - // Without winograd - if (nnfw::srcn::check_winograd(winograd_param)) - { - _winograd_weights = nnfw::srcn::trans_weight2winograd(winograd_param, nullptr); - } - _sparse_weights = nnfw::srcn::trans_weight2sparse(kernel_mat); - - nnfw::srcn::srcn_convolution2D(in_mat, kernel_mat, out_mat, in_param, _winograd_weights, 4, - _layout); - - // Add biases - if (_biasData.f == nullptr) - { - return; - } - // TODO Optimize - uint32_t strides[4] = { - _outputDescr.dimensions[1] * _outputDescr.dimensions[2] * _outputDescr.dimensions[3], - _outputDescr.dimensions[2] * _outputDescr.dimensions[3], _outputDescr.dimensions[3], 1}; - if (_layout == nnfw::srcn::convType_t::col_major) - { - for (uint32_t c = 0; c < _outputDescr.dimensions[3]; ++c) - { - if (_biasData.f[c] != 0) - { - for (uint32_t b = 0; b < _outputDescr.dimensions[0]; ++b) - { - for (uint32_t h = 0; h < _outputDescr.dimensions[1]; ++h) - { - for (uint32_t w = 0; w < _outputDescr.dimensions[2]; ++w) - { - _outputData.f[b * strides[0] + h * strides[1] + w * strides[2] + c * strides[3]] += - _biasData.f[c]; - } - } - } - } - } - } - else if (_layout == nnfw::srcn::convType_t::row_major) - { - for (uint32_t c = 0; c < _outputDescr.dimensions[1]; ++c) - { - if (_biasData.f[c] != 0) - { - for (uint32_t b = 0; b < _outputDescr.dimensions[0]; ++b) - { - for (uint32_t h = 0; h < _outputDescr.dimensions[2]; ++h) - { - for (uint32_t w = 0; w < _outputDescr.dimensions[3]; ++w) - { - _outputData.f[b * strides[0] + c * strides[1] + h * strides[2] + w * strides[3]] += - _biasData.f[c]; - } - } - } - } - } - } - else - { - throw std::runtime_error("Wrong Layout"); - } -} - -void ConvolutionLayer::configure(uint8_t *inputData, const TensorDescriptor inputDescr, - uint8_t *kernelData, const TensorDescriptor kernelDescr, - uint8_t *biasData, const TensorDescriptor biasDescr, - const uint32_t paddingType, const uint32_t paddingLeft, - const uint32_t paddingRight, const uint32_t paddingTop, - const uint32_t paddingBottom, const uint32_t strideWidth, - const uint32_t strideHeight, uint8_t *outputData, - const TensorDescriptor outputDescr, ir::Layout layout) -{ - assert(inputDescr.dimensions.size() == 4); - assert(kernelDescr.dimensions.size() == 4); - assert(biasDescr.dimensions.size() == 1); - assert(outputDescr.dimensions.size() == 4); - assert(inputDescr.type == kernelDescr.type && inputDescr.type == outputDescr.type); - // TODO Add assertions validating height and width with padding - _layout = convertLayout(layout); - _inputData.u8 = inputData; - _inputDescr = inputDescr; - _inputType = inputDescr.type; - _kernelData.u8 = kernelData; - _kernelDescr = kernelDescr; - _biasData.u8 = biasData; - _biasDescr = biasDescr; - _paddingType = paddingType; - _paddingLeft = paddingLeft; - _paddingRight = paddingRight; - _paddingTop = paddingTop; - _paddingBottom = paddingBottom; - _strideWidth = strideWidth; - _strideHeight = strideHeight; - _outputData.u8 = outputData; - _outputDescr = outputDescr; -} - -void ConvolutionLayer::run() -{ - if (_inputType == OperandType::FLOAT32) - { - convFloat32(); - } - else if (_inputType == OperandType::QUANT8_ASYMM) - { - throw std::runtime_error("NYI"); - } -} - -} // namespace kernel -} // namespace srcn -} // namespace backend -} // namespace neurun |