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Basic Supervised Learning

Merged Basic Supervised Learning
learning into dev
Merged Maxence Naud requested to merge learning into dev
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include/aidge/backend/cpu/operator/ReduceMeanImpl_forward_kernels.hpp
+ 44
− 34
@@ -12,10 +12,12 @@
#ifndef AIDGE_CPU_OPERATOR_REDUCEMEANIMPL_FORWARD_KERNEL_H_
#define AIDGE_CPU_OPERATOR_REDUCEMEANIMPL_FORWARD_KERNEL_H_
#include <cstddef>
#include <algorithm> // std::copy, std::for_each
#include <numeric> //std::accumulate
#include <algorithm> // std::for_each
#include <cstddef> // std::size_t
#include <cstdint> // std::int32_t
#include <functional> //std::multiplies
#include <numeric> //std::accumulate
#include <vector>
#include "aidge/backend/cpu/operator/ReduceMeanImpl.hpp"
#include "aidge/data/Data.hpp"
@@ -23,8 +25,8 @@
#include "aidge/utils/Registrar.hpp"
namespace Aidge {
template <class I, class O, DimSize_t DIM>
void ReduceMeanImpl_cpu_forward_kernel(const typename ReduceMean_Op<DIM>::Attrs& attrs,
template <class I, class O>
void ReduceMeanImpl_cpu_forward_kernel(const typename ReduceMean_Op::Attrs& attrs,
const std::vector<DimSize_t>& inputDims,
const void* input_,
void* output_) {
@@ -32,14 +34,15 @@ void ReduceMeanImpl_cpu_forward_kernel(const typename ReduceMean_Op<DIM>::Attrs&
const I* input = static_cast<const I*>(input_);
O* output = static_cast<O*>(output_);
const std::vector<std::int32_t>& axes = std::get<0>(attrs);
const std::size_t nb_dims = inputDims.size();
const std::size_t totalElements = std::accumulate(inputDims.cbegin(), inputDims.cend(), 1, std::multiplies<std::size_t>());
if (DIM == 1) {
const std::size_t stride_pre = std::accumulate(inputDims.cbegin(), inputDims.cbegin() + std::get<0>(attrs)[0], 1, std::multiplies<std::size_t>());
const std::size_t stride_post = std::accumulate(inputDims.crbegin(), inputDims.crbegin() + nb_dims -1 - std::get<0>(attrs)[0], 1, std::multiplies<std::size_t>());
if (axes.size() == 1) {
const std::size_t stride_pre = std::accumulate(inputDims.cbegin(), inputDims.cbegin() + axes[0], 1, std::multiplies<std::size_t>());
const std::size_t stride_post = std::accumulate(inputDims.crbegin(), inputDims.crbegin() + nb_dims -1 - axes[0], 1, std::multiplies<std::size_t>());
const std::size_t dim_i = inputDims[std::get<0>(attrs)[0]];
const std::size_t dim_i = inputDims[axes[0]];
for (std::size_t pre = 0; pre < stride_pre; ++pre) {
for (std::size_t post = 0; post < stride_post; ++post) {
const std::size_t idx_i = pre * dim_i * stride_post + post;
@@ -68,7 +71,7 @@ void ReduceMeanImpl_cpu_forward_kernel(const typename ReduceMean_Op<DIM>::Attrs&
const I* inputAccumulation = input;
I* outputAccumulation = nullptr;
for (const auto& axisInt : std::get<0>(attrs)) {
for (const auto& axisInt : axes) {
const std::size_t a = static_cast<std::size_t>(axisInt);
outputElements /= inputDims[a];
outputAccumulation = new I[outputElements];
@@ -93,7 +96,7 @@ void ReduceMeanImpl_cpu_forward_kernel(const typename ReduceMean_Op<DIM>::Attrs&
// Copy elements from inputAccumulation to output while dividing by divisor
I divisor = totalElements / outputElements;
std::transform(inputAccumulation, inputAccumulation + outputElements, output,
[divisor](int element) { return element / divisor; });
[divisor](I element) { return element / divisor; });
if (outputAccumulation) {
delete[] outputAccumulation;
}
@@ -103,29 +106,36 @@ void ReduceMeanImpl_cpu_forward_kernel(const typename ReduceMean_Op<DIM>::Attrs&
}
namespace {
// DIM = 1
static Registrar<ReduceMeanImpl1DForward_cpu> registrarReduceMeanImplForward_1D_cpu_Float32(
{DataType::Float32, DataType::Float32}, Aidge::ReduceMeanImpl_cpu_forward_kernel<float, float,1>);
static Registrar<ReduceMeanImpl1DForward_cpu> registrarReduceMeanImplForward_1D_cpu_Int32(
{DataType::Int32, DataType::Int32}, Aidge::ReduceMeanImpl_cpu_forward_kernel<int, int,1>);
static Registrar<ReduceMeanImpl1DForward_cpu> registrarReduceMeanImplForward_1D_cpu_Float64(
{DataType::Float64, DataType::Float64}, Aidge::ReduceMeanImpl_cpu_forward_kernel<double, double,1>);
// DIM = 2
static Registrar<ReduceMeanImpl2DForward_cpu> registrarReduceMeanImplForward_2D_cpu_Float32(
{DataType::Float32, DataType::Float32}, Aidge::ReduceMeanImpl_cpu_forward_kernel<float, float,2>);
static Registrar<ReduceMeanImpl2DForward_cpu> registrarReduceMeanImplForward_2D_cpu_Int32(
{DataType::Int32, DataType::Int32}, Aidge::ReduceMeanImpl_cpu_forward_kernel<int, int,2>);
static Registrar<ReduceMeanImpl2DForward_cpu> registrarReduceMeanImplForward_2D_cpu_Float64(
{DataType::Float64, DataType::Float64}, Aidge::ReduceMeanImpl_cpu_forward_kernel<double, double,2>);
// DIM = 3
static Registrar<ReduceMeanImpl3DForward_cpu> registrarReduceMeanImplForward_3D_cpu_Float32(
{DataType::Float32, DataType::Float32}, Aidge::ReduceMeanImpl_cpu_forward_kernel<float, float,3>);
static Registrar<ReduceMeanImpl3DForward_cpu> registrarReduceMeanImplForward_3D_cpu_Int32(
{DataType::Int32, DataType::Int32}, Aidge::ReduceMeanImpl_cpu_forward_kernel<int, int,3>);
static Registrar<ReduceMeanImpl3DForward_cpu> registrarReduceMeanImplForward_3D_cpu_Float64(
{DataType::Float64, DataType::Float64}, Aidge::ReduceMeanImpl_cpu_forward_kernel<double, double,3>);
static Registrar<ReduceMeanImplForward_cpu> registrarReduceMeanImplForward_cpu_Float32(
{DataType::Float32, DataType::Float32}, Aidge::ReduceMeanImpl_cpu_forward_kernel<float, float>);
static Registrar<ReduceMeanImplForward_cpu> registrarReduceMeanImplForward_cpu_Int32(
{DataType::Int32, DataType::Int32}, Aidge::ReduceMeanImpl_cpu_forward_kernel<int, int>);
static Registrar<ReduceMeanImplForward_cpu> registrarReduceMeanImplForward_cpu_Float64(
{DataType::Float64, DataType::Float64}, Aidge::ReduceMeanImpl_cpu_forward_kernel<double, double>);
// // DIM = 1
// static Registrar<ReduceMeanImpl1DForward_cpu> registrarReduceMeanImplForward_1D_cpu_Float32(
// {DataType::Float32, DataType::Float32}, Aidge::ReduceMeanImpl_cpu_forward_kernel<float, float,1>);
// static Registrar<ReduceMeanImpl1DForward_cpu> registrarReduceMeanImplForward_1D_cpu_Int32(
// {DataType::Int32, DataType::Int32}, Aidge::ReduceMeanImpl_cpu_forward_kernel<int, int,1>);
// static Registrar<ReduceMeanImpl1DForward_cpu> registrarReduceMeanImplForward_1D_cpu_Float64(
// {DataType::Float64, DataType::Float64}, Aidge::ReduceMeanImpl_cpu_forward_kernel<double, double,1>);
// // DIM = 2
// static Registrar<ReduceMeanImpl2DForward_cpu> registrarReduceMeanImplForward_2D_cpu_Float32(
// {DataType::Float32, DataType::Float32}, Aidge::ReduceMeanImpl_cpu_forward_kernel<float, float,2>);
// static Registrar<ReduceMeanImpl2DForward_cpu> registrarReduceMeanImplForward_2D_cpu_Int32(
// {DataType::Int32, DataType::Int32}, Aidge::ReduceMeanImpl_cpu_forward_kernel<int, int,2>);
// static Registrar<ReduceMeanImpl2DForward_cpu> registrarReduceMeanImplForward_2D_cpu_Float64(
// {DataType::Float64, DataType::Float64}, Aidge::ReduceMeanImpl_cpu_forward_kernel<double, double,2>);
// // DIM = 3
// static Registrar<ReduceMeanImpl3DForward_cpu> registrarReduceMeanImplForward_3D_cpu_Float32(
// {DataType::Float32, DataType::Float32}, Aidge::ReduceMeanImpl_cpu_forward_kernel<float, float,3>);
// static Registrar<ReduceMeanImpl3DForward_cpu> registrarReduceMeanImplForward_3D_cpu_Int32(
// {DataType::Int32, DataType::Int32}, Aidge::ReduceMeanImpl_cpu_forward_kernel<int, int,3>);
// static Registrar<ReduceMeanImpl3DForward_cpu> registrarReduceMeanImplForward_3D_cpu_Float64(
// {DataType::Float64, DataType::Float64}, Aidge::ReduceMeanImpl_cpu_forward_kernel<double, double,3>);
} // namespace
} // namespace Aidge

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