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-rw-r--r--runtimes/neurun/src/kernel/cpu/ConvolutionLayer.cc228
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diff --git a/runtimes/neurun/src/kernel/cpu/ConvolutionLayer.cc b/runtimes/neurun/src/kernel/cpu/ConvolutionLayer.cc
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--- a/runtimes/neurun/src/kernel/cpu/ConvolutionLayer.cc
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-/*
- * 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 "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h"
-#include "kernel/cpu/OperationUtils.h"
-
-#include <mutex>
-
-namespace neurun
-{
-namespace kernel
-{
-namespace cpu
-{
-
-// If possible we will use this static buffer for the tensor.
-static constexpr int kStaticBufferSize = 1605632;
-static char static_scratch_buffer[kStaticBufferSize];
-static std::mutex executionMutex;
-
-#define ANDROID_NN_CONV_PARAMETERS(Type) \
- uint32_t height = getSizeOfDimension(_inputShape, 1); \
- uint32_t width = getSizeOfDimension(_inputShape, 2); \
- uint32_t kernelHeight = getSizeOfDimension(_kernelShape, 1); \
- uint32_t kernelWidth = getSizeOfDimension(_kernelShape, 2); \
- uint32_t outHeight = getSizeOfDimension(_outputShape, 1); \
- uint32_t outWidth = getSizeOfDimension(_outputShape, 2); \
- uint32_t inDepth = getSizeOfDimension(_inputShape, 3); \
- \
- uint32_t paddingHeight = (uint32_t)_paddingTop; \
- uint32_t paddingWidth = (uint32_t)_paddingLeft; \
- \
- Shape im2colShape; \
- im2colShape.dimensions.resize(4); \
- im2colShape.dimensions[0] = getSizeOfDimension(_outputShape, 0); \
- im2colShape.dimensions[1] = getSizeOfDimension(_outputShape, 1); \
- im2colShape.dimensions[2] = getSizeOfDimension(_outputShape, 2); \
- im2colShape.dimensions[3] = inDepth * kernelHeight * kernelWidth; \
- \
- Type *im2colData = nullptr; \
- uint64_t im2colByteSize = sizeof(Type); \
- std::unique_ptr<Type[]> im2colGuard; \
- for (int i = 0; i < 4; i++) \
- { \
- im2colByteSize *= im2colShape.dimensions[i]; \
- } \
- /* http://b/77982879, tflite::optimized_ops::Conv uses int for offsets */ \
- if (im2colByteSize >= 0x7fffffff) \
- { \
- std::cout << "Conv size is too large, not enough memory" << std::endl; \
- return false; \
- } \
- if (im2colByteSize <= kStaticBufferSize) \
- { \
- im2colData = reinterpret_cast<Type *>(static_scratch_buffer); \
- } \
- else \
- { \
- im2colData = new (std::nothrow) Type[im2colByteSize / sizeof(Type)]; \
- if (im2colData == nullptr) \
- { \
- std::cout << "Conv size is too large, not enough memory" << std::endl; \
- return false; \
- } \
- im2colGuard.reset(im2colData); \
- }
-
-ConvolutionLayer::ConvolutionLayer()
- : _inputData(nullptr), _kernelData(nullptr), _outputData(nullptr), _biasData(nullptr),
- _inputShape(), _kernelShape(), _outputShape(), _biasShape(), _paddingLeft(0), _paddingTop(0),
- _paddingRight(0), _paddingBottom(0), _strideWidth(0), _strideHeight(0),
- _activation(ANEURALNETWORKS_FUSED_NONE), _inputType(OperandType::SCALAR_FLOAT32)
-{
- // DO NOTHING
-}
-
-bool ConvolutionLayer::convFloat32()
-{
- ANDROID_NN_CONV_PARAMETERS(float)
-
- const ::tflite::Dims<4> &kernel_dim = convertShapeToDims(_kernelShape);
- const int kernel_width = ArraySize(kernel_dim, 1);
- const int kernel_height = ArraySize(kernel_dim, 2);
- const bool need_im2col =
- _strideWidth != 1 || _strideHeight != 1 || kernel_width != 1 || kernel_height != 1;
-
- float *im2colDataToPass = nullptr;
- if (need_im2col)
- {
- im2colDataToPass = im2colData;
- }
-
- float output_activation_min, output_activation_max;
- CalculateActivationRangeFloat(_activation, &output_activation_min, &output_activation_max);
- int32_t dilationWidthFactor = 1, dilationHeightFactor = 1;
-
- ::tflite::ConvParams op_params;
- op_params.padding_type = ::tflite::PaddingType::kSame;
- op_params.padding_values.width = paddingWidth;
- op_params.padding_values.height = paddingHeight;
- op_params.stride_width = _strideWidth;
- op_params.stride_height = _strideHeight;
- op_params.dilation_width_factor = dilationWidthFactor;
- op_params.dilation_height_factor = dilationHeightFactor;
- op_params.float_activation_min = output_activation_min;
- op_params.float_activation_max = output_activation_max;
-
- ::tflite::optimized_ops::Conv(
- op_params, convertShapeToTFLiteShape(_inputShape),
- reinterpret_cast<const float *>(_inputData), convertShapeToTFLiteShape(_kernelShape),
- reinterpret_cast<const float *>(_kernelData), convertShapeToTFLiteShape(_biasShape),
- reinterpret_cast<const float *>(_biasData), convertShapeToTFLiteShape(_outputShape),
- reinterpret_cast<float *>(_outputData), convertShapeToTFLiteShape(im2colShape),
- im2colDataToPass);
- return true;
-}
-
-bool ConvolutionLayer::convQuant8()
-{
- ANDROID_NN_CONV_PARAMETERS(uint8_t)
-
- int32_t inputOffset = -_inputShape.offset;
- int32_t kernelOffset = -_kernelShape.offset;
- int32_t outputOffset = _outputShape.offset;
- float real_multiplier = 0.0;
- int32_t output_multiplier = 0;
- int32_t output_shift = 0;
- int32_t output_activation_min = 0;
- int32_t output_activation_max = 0;
- if (!GetQuantizedConvolutionMultipler(_inputShape, _kernelShape, _biasShape, _outputShape,
- &real_multiplier) ||
- !QuantizeMultiplierSmallerThanOne(real_multiplier, &output_multiplier, &output_shift))
- {
- return false;
- }
- CalculateActivationRangeUint8(_activation, _outputShape, &output_activation_min,
- &output_activation_max);
- int32_t dilationWidthFactor = 1, dilationHeightFactor = 1;
-
- ::tflite::ConvParams op_params;
- op_params.padding_type = ::tflite::PaddingType::kSame;
- op_params.padding_values.width = paddingWidth;
- op_params.padding_values.height = paddingHeight;
- op_params.stride_width = _strideWidth;
- op_params.stride_height = _strideHeight;
- op_params.dilation_width_factor = dilationWidthFactor;
- op_params.dilation_height_factor = dilationHeightFactor;
- op_params.input_offset = inputOffset;
- op_params.weights_offset = kernelOffset;
- op_params.output_offset = outputOffset;
- 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;
-
- static gemmlowp::GemmContext gemm_context;
- // Prevent concurrent executions that may access the scratch buffer and
- // gemm_context.
- std::unique_lock<std::mutex> lock(executionMutex);
- // Alow gemmlowp automatically decide how many threads to use.
- gemm_context.set_max_num_threads(0);
- ::tflite::optimized_ops::Conv(
- op_params, convertShapeToTFLiteShape(_inputShape), _inputData,
- convertShapeToTFLiteShape(_kernelShape), _kernelData, convertShapeToTFLiteShape(_biasShape),
- reinterpret_cast<const int32_t *>(_biasData), convertShapeToTFLiteShape(_outputShape),
- _outputData, convertShapeToTFLiteShape(im2colShape), im2colData, &gemm_context);
- return true;
-}
-
-void ConvolutionLayer::configure(uint8_t *inputData, const Shape inputShape, uint8_t *kernelData,
- const Shape kernelShape, uint8_t *biasData, const Shape biasShape,
- 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 FuseCode activation, uint8_t *outputData,
- const Shape outputShape)
-{
- _inputData = inputData;
- _inputShape = inputShape;
- _inputType = inputShape.type;
- _kernelData = kernelData;
- _kernelShape = kernelShape;
- _biasData = biasData;
- _biasShape = biasShape;
- _paddingLeft = paddingLeft;
- _paddingRight = paddingRight;
- _paddingTop = paddingTop;
- _paddingBottom = paddingBottom;
- _strideWidth = strideWidth;
- _strideHeight = strideHeight;
- _activation = activation;
- _outputData = outputData;
- _outputShape = outputShape;
-}
-
-void ConvolutionLayer::run()
-{
- if (_inputType == OperandType::TENSOR_FLOAT32)
- {
- convFloat32();
- }
- else if (_inputType == OperandType::TENSOR_QUANT8_ASYMM)
- {
- throw std::runtime_error{"ConvolutionLayer : Not tested for TENSOR_QUANT8_ASYMM"};
- // convQuant8();
- }
-}
-
-#undef ANDROID_NN_CONV_PARAMETERS
-
-} // namespace cpu
-} // namespace kernel
-} // namespace neurun
generated by cgit v1.2.3 (git 2.25.1) at 2024-05-16 08:34:05 +0000