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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; \
\
::tflite::Dims<4> im2colDim; \
im2colDim.sizes[3] = (int)getSizeOfDimension(_outputShape, 0); \
im2colDim.sizes[2] = (int)getSizeOfDimension(_outputShape, 1); \
im2colDim.sizes[1] = (int)getSizeOfDimension(_outputShape, 2); \
im2colDim.sizes[0] = (int)inDepth * kernelHeight * kernelWidth; \
\
im2colDim.strides[0] = 1; \
for (int i = 1; i < 4; i++) \
{ \
im2colDim.strides[i] = im2colDim.strides[i - 1] * im2colDim.sizes[i - 1]; \
} \
Type *im2colData = nullptr; \
uint64_t im2colByteSize = sizeof(Type); \
std::unique_ptr<Type[]> im2colGuard; \
for (int i = 0; i < 4; i++) \
{ \
im2colByteSize *= im2colDim.sizes[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::optimized_ops::Conv(
reinterpret_cast<const float *>(_inputData), convertShapeToDims(_inputShape),
reinterpret_cast<const float *>(_kernelData), convertShapeToDims(_kernelShape),
reinterpret_cast<const float *>(_biasData), convertShapeToDims(_biasShape), _strideWidth,
_strideHeight, dilationWidthFactor, dilationHeightFactor, paddingWidth, paddingHeight,
output_activation_min, output_activation_max, reinterpret_cast<float *>(_outputData),
convertShapeToDims(_outputShape), im2colDataToPass, im2colDim);
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);
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(
_inputData, convertShapeToDims(_inputShape), inputOffset, _kernelData,
convertShapeToDims(_kernelShape), kernelOffset, reinterpret_cast<const int32_t *>(_biasData),
convertShapeToDims(_biasShape), _strideWidth, _strideHeight, paddingWidth, paddingHeight,
outputOffset, output_multiplier, output_shift, output_activation_min, output_activation_max,
_outputData, convertShapeToDims(_outputShape), im2colData, im2colDim, &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
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