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Diffstat (limited to 'runtime/onert/backend/cpu/kernel/ConvolutionLayer.cc')
| -rw-r--r-- | runtime/onert/backend/cpu/kernel/ConvolutionLayer.cc | 159 |
1 files changed, 159 insertions, 0 deletions
diff --git a/runtime/onert/backend/cpu/kernel/ConvolutionLayer.cc b/runtime/onert/backend/cpu/kernel/ConvolutionLayer.cc new file mode 100644 index 000000000..398054527 --- /dev/null +++ b/runtime/onert/backend/cpu/kernel/ConvolutionLayer.cc @@ -0,0 +1,159 @@ +/* + * Copyright (c) 2018 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 <cker/operation/Conv.h> + +namespace onert +{ +namespace backend +{ +namespace cpu +{ +namespace kernel +{ +ConvolutionLayer::ConvolutionLayer() + : _input(nullptr), _kernel(nullptr), _bias(nullptr), _output(nullptr), + _paddingType(ir::PaddingType::EXPLICIT), _paddingLeft(0), _paddingTop(0), _paddingRight(0), + _paddingBottom(0), _strideWidth(0), _strideHeight(0), _activation(ir::Activation::NONE), + _conv_kernel(new nnfw::cker::Conv()), _prepare(false) +{ + // DO NOTHING +} + +ConvolutionLayer::~ConvolutionLayer() = default; + +void ConvolutionLayer::convFloat32() +{ + float output_activation_min, output_activation_max; + CalculateActivationRangeFloat(_activation, &output_activation_min, &output_activation_max); + + nnfw::cker::ConvParams op_params; + op_params.padding_type = getPaddingType(_paddingType); + op_params.padding_values.width = _paddingLeft; + op_params.padding_values.height = _paddingTop; + op_params.stride_width = _strideWidth; + op_params.stride_height = _strideHeight; + op_params.dilation_width_factor = 1; + op_params.dilation_height_factor = 1; + op_params.float_activation_min = output_activation_min; + op_params.float_activation_max = output_activation_max; + + nnfw::cker::Conv &kernel = *_conv_kernel; + if (!_prepare) + { + bool is_replaced_weights = false; + kernel.prepare(convertTensorToCkerShape(_kernel), + reinterpret_cast<const float *>(_kernel->buffer()), op_params.padding_type, + is_replaced_weights); + + if (is_replaced_weights) + { + // TODO Remove const_cast + const_cast<operand::Tensor *>(_kernel)->decrease_ref(); + } + _prepare = true; + } + kernel(op_params, convertTensorToCkerShape(_input), + reinterpret_cast<const float *>(_input->buffer()), convertTensorToCkerShape(_kernel), + reinterpret_cast<const float *>(_kernel->buffer()), convertTensorToCkerShape(_bias), + reinterpret_cast<const float *>(_bias->buffer()), convertTensorToCkerShape(_output), + reinterpret_cast<float *>(_output->buffer())); +} + +void ConvolutionLayer::convQuant8() +{ + int32_t output_activation_min = 0; + int32_t output_activation_max = 0; + CalculateActivationRangeUint8(_activation, _output, &output_activation_min, + &output_activation_max); + + double real_multiplier = 0.0; + int32_t output_multiplier = 0; + int32_t output_shift = 0; + GetQuantizedConvolutionMultiplier(_input, _kernel, _bias, _output, &real_multiplier); + QuantizeMultiplier(real_multiplier, &output_multiplier, &output_shift); + + nnfw::cker::ConvParams op_params; + op_params.stride_width = _strideWidth; + op_params.stride_height = _strideHeight; + op_params.dilation_width_factor = 1; + op_params.dilation_height_factor = 1; + op_params.padding_type = getPaddingType(_paddingType); + op_params.padding_values.width = _paddingLeft; + op_params.padding_values.height = _paddingTop; + op_params.input_offset = -_input->offset(); + op_params.weights_offset = -_kernel->offset(); + op_params.output_offset = _output->offset(); + op_params.output_multiplier = output_multiplier; + op_params.output_shift = output_shift; + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; + + nnfw::cker::Conv &kernel = *_conv_kernel; + if (!_prepare) + { + kernel.prepareQuant(convertTensorToCkerShape(_input), convertTensorToCkerShape(_kernel), + convertTensorToCkerShape(_output), _strideWidth, _strideHeight); + _prepare = true; + } + kernel(op_params, convertTensorToCkerShape(_input), + reinterpret_cast<const uint8_t *>(_input->buffer()), convertTensorToCkerShape(_kernel), + reinterpret_cast<const uint8_t *>(_kernel->buffer()), convertTensorToCkerShape(_bias), + reinterpret_cast<const int32_t *>(_bias->buffer()), convertTensorToCkerShape(_output), + reinterpret_cast<uint8_t *>(_output->buffer())); +} + +void ConvolutionLayer::configure(const operand::Tensor *input, const operand::Tensor *kernel, + const operand::Tensor *bias, const ir::PaddingType 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, + const ir::Activation activation, operand::Tensor *output) +{ + _input = input; + _kernel = kernel; + _bias = bias; + _paddingType = paddingType; + _paddingLeft = paddingLeft; + _paddingRight = paddingRight; + _paddingTop = paddingTop; + _paddingBottom = paddingBottom; + _strideWidth = strideWidth; + _strideHeight = strideHeight; + _activation = activation; + _output = output; +} + +void ConvolutionLayer::run() +{ + if (_input->data_type() == OperandType::FLOAT32) + { + convFloat32(); + } + else if (_input->data_type() == OperandType::QUANT8_ASYMM) + { + convQuant8(); + } +} + +#undef ANDROID_NN_CONV_PARAMETERS + +} // namespace kernel +} // namespace cpu +} // namespace backend +} // namespace onert |