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Commit 86852f74 authored by Maxence Naud's avatar Maxence Naud
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Merge branch 'dev' into 'main' 

v0.2.2

See merge request !28
parents 75a59b1e aed8f560
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Tags v0.2.2
1 merge request!28v0.2.2
Pipeline #61688 passed with warnings
Stage: static_analysis
Stage: build
Stage: test
Stage: coverage
Stage: release
Stage: deploy
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CHANGELOG
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View file @ 86852f74
# Version 0.2.2 (December 12, 2024)
# Version 0.1.1 (May 14, 2024)
* Fix loss function to return Tensor with gradient
......
CMakeLists.txt
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− 1
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......@@ -5,7 +5,7 @@ file(STRINGS "${CMAKE_SOURCE_DIR}/version.txt" version)
project(aidge_learning
VERSION ${version}
DESCRIPTION "Functions and alogrithms to train models in the AIDGE framework"
DESCRIPTION "Functions and alogrithms to train models in the AIDGE framework"
LANGUAGES CXX)
message(STATUS "Project name: ${CMAKE_PROJECT_NAME}")
......
include/aidge/learning/optimizer/Adam.hpp
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......@@ -35,12 +35,12 @@ class Adam: public Optimizer, public StaticAttributes<AdamAttr, float, float, fl
private:
std::vector<Tensor> mMomentum1;
std::vector<Tensor> mMomentum2;
Tensor mLR{std::vector<std::size_t>({1})};
Tensor mBeta1{std::vector<std::size_t>({1})};
Tensor mReversedBeta1{std::vector<std::size_t>({1})};
Tensor mBeta2{std::vector<std::size_t>({1})};
Tensor mReversedBeta2{std::vector<std::size_t>({1})};
Tensor mEpsilon{std::vector<std::size_t>({1})};
Tensor mLR{1.0f};
Tensor mBeta1;
Tensor mReversedBeta1;
Tensor mBeta2;
Tensor mReversedBeta2;
Tensor mEpsilon;
public:
using Attributes_ = StaticAttributes<AdamAttr, float, float, float>;
......@@ -51,19 +51,17 @@ public:
: Optimizer(),
Attributes_(attr<AdamAttr::Beta1>(beta1),
attr<AdamAttr::Beta2>(beta2),
attr<AdamAttr::Epsilon>(epsilon))
attr<AdamAttr::Epsilon>(epsilon)),
mBeta1(beta1),
mReversedBeta1(1.0f - beta1),
mBeta2(beta2),
mReversedBeta2(1.0f - beta2),
mEpsilon(epsilon)
{
mBeta1 = Tensor(Array1D<float, 1>{{beta1}});
mReversedBeta1 = Tensor(Array1D<float, 1>{{1.0f - beta1}});
mBeta2 = Tensor(Array1D<float, 1>{{beta2}});
mReversedBeta2 = Tensor(Array1D<float, 1>{{1.0f - beta2}});
mEpsilon = Tensor(Array1D<float, 1>{{epsilon}});
}
void update() override final {
mLR = Tensor(Array1D<float, 1>{{learningRate()}});
mLR = Tensor(learningRate());
mLR.setBackend(mParameters[0]->getImpl()->backend());
if (mParameters[0]->getImpl()->backend() != mBeta1.getImpl()->backend()) {
......@@ -73,11 +71,11 @@ public:
mReversedBeta2.setBackend(mParameters[0]->getImpl()->backend());
}
Tensor alpha = Tensor(Array1D<float, 1>{{ static_cast<float>(learningRate() * std::sqrt(1.0f - std::pow(this->getAttr<AdamAttr::Beta2>(), mLRScheduler.step() + 1))
/ (1.0f - std::pow(this->getAttr<AdamAttr::Beta1>(), mLRScheduler.step() + 1))) }});
Tensor alpha = Tensor(learningRate() * std::sqrt(1.0f - std::pow(this->getAttr<AdamAttr::Beta2>(), static_cast<float>(mLRScheduler.step() + 1)))
/ (1.0f - std::pow(this->getAttr<AdamAttr::Beta1>(), static_cast<float>(mLRScheduler.step() + 1))));
alpha.setBackend(mParameters[0]->getImpl()->backend());
Tensor epsilon = Tensor(Array1D<float, 1>{{ static_cast<float>(this->getAttr<AdamAttr::Epsilon>() * std::sqrt(1.0f - std::pow(this->getAttr<AdamAttr::Beta2>(), mLRScheduler.step() + 1))) }});
Tensor epsilon = Tensor(this->getAttr<AdamAttr::Epsilon>() * std::sqrt(1.0f - std::pow(this->getAttr<AdamAttr::Beta2>(), static_cast<float>(mLRScheduler.step() + 1))));
epsilon.setBackend(mParameters[0]->getImpl()->backend());
if (mLRScheduler.step() == 0) {
......@@ -90,13 +88,13 @@ public:
mMomentum2[i].zeros();
}
}
for (std::size_t i = 0; i < mParameters.size(); ++i) {
mMomentum1[i] = mBeta1 * mMomentum1[i] + mReversedBeta1 * (*mParameters[i]->grad());
mMomentum2[i] = mBeta2 * mMomentum2[i] + mReversedBeta2 * (*mParameters[i]->grad()) * (*mParameters[i]->grad());
*mParameters[i] = *mParameters[i] - alpha * mMomentum1[i] / (mMomentum2[i].sqrt() + epsilon);
*mParameters[i] -= alpha * mMomentum1[i] / (mMomentum2[i].sqrt() + epsilon);
}
mLRScheduler.update();
}
......
include/aidge/learning/optimizer/SGD.hpp
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......@@ -47,23 +47,23 @@ public:
Attributes_(attr<SGDAttr::Momentum>(momentum),
attr<SGDAttr::Dampening>(dampening))
{
mMomentum = Tensor(Array1D<float, 1>{{momentum}});
mReversedDampening = Tensor(Array1D<float, 1>{{1.0f - dampening}});
mMomentum = Tensor(momentum);
mReversedDampening = Tensor(1.0f - dampening);
}
void update() override final {
mLR = Tensor(Array1D<float, 1>{{learningRate()}});
mLR = Tensor(learningRate());
mLR.setBackend(mParameters[0]->getImpl()->backend());
if (mLRScheduler.step() == 0) {
for (std::size_t i = 0; i < mParameters.size(); ++i) {
mGradientInertia[i] = mParameters[i]->grad()->clone();
*mParameters[i] = *mParameters[i] - mLR*mGradientInertia[i];
*mParameters[i] -= mLR*mGradientInertia[i];
}
} else {
for (std::size_t i = 0; i < mParameters.size(); ++i) {
mGradientInertia[i] = mMomentum*mGradientInertia[i] + mReversedDampening*(*mParameters[i]->grad());
*mParameters[i] = *mParameters[i] - mLR*mGradientInertia[i];
*mParameters[i] -= mLR*mGradientInertia[i];
}
}
mLRScheduler.update();
......
src/loss/classification/BCE.cpp
+ 3
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......@@ -60,8 +60,8 @@ Aidge::Tensor Aidge::loss::BCE(std::shared_ptr<Tensor>& prediction,
const std::shared_ptr<Node> target_node = Producer(target, "label");
// Define nodes: add1 = prediction + eps1, add2 = target + eps1
const std::shared_ptr<Node> add1_node = Add(2, "add1");
const std::shared_ptr<Node> add2_node = Add(2, "add2");
const std::shared_ptr<Node> add1_node = Add("add1");
const std::shared_ptr<Node> add2_node = Add("add2");
prediction_node->addChild(add1_node, 0, 0);
Producer(std::make_shared<Tensor>(Array1D<float, 1>{{eps1}}))
->addChild(add1_node, 0, 1);
......@@ -107,7 +107,7 @@ Aidge::Tensor Aidge::loss::BCE(std::shared_ptr<Tensor>& prediction,
sub1_node->addChild(div2_node, 0, 1);
// Define node: add3 = (target + eps1) / (prediction + eps1) - (1 - target + eps2)/(1 - prediction + eps2)
const std::shared_ptr<Node> add3_node = Add(2, "add3");
const std::shared_ptr<Node> add3_node = Add("add3");
div1_node->addChild(add3_node, 0, 0);
div2_node->addChild(add3_node, 0, 1);
......
unit_tests/CMakeLists.txt
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......@@ -4,7 +4,7 @@ include(FetchContent)
FetchContent_Declare(
Catch2
GIT_REPOSITORY https://github.com/catchorg/Catch2.git
GIT_TAG v3.0.1 # or a later release
GIT_TAG v3.7.1 # or a later release
)
FetchContent_MakeAvailable(Catch2)
......@@ -45,11 +45,11 @@ if(aidge_backend_cuda_FOUND)
# Enable CUDA language support and separable compilation for the target
enable_language(CUDA)
set_target_properties(${tests_exe} PROPERTIES CUDA_SEPARABLE_COMPILATION ON)
target_include_directories(${tests_exe} PRIVATE ${CUDAToolkit_INCLUDE_DIRS})
# Link manually specified CUDA libraries if the targets are not available
target_link_libraries(${tests_exe}
PUBLIC
PUBLIC
_aidge_backend_cuda
CUDA::cudart
CUDA::cublas
......
unit_tests/optimizer/Test_Adam.cpp
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......@@ -130,8 +130,8 @@ TEST_CASE("[learning/Adam] update", "[Optimizer][Adam]") {
for (std::size_t step = 0; step < 10; ++step) {
// truth
float lr2 = lr * std::sqrt(1.0f - std::pow(beta2, step + 1)) / (1.0f - std::pow(beta1, step + 1));
float epsilon2 = epsilon * std::sqrt(1.0f - std::pow(beta2, step + 1));
float lr2 = lr * std::sqrt(1.0f - std::pow(beta2, static_cast<float>(step + 1))) / (1.0f - std::pow(beta1, static_cast<float>(step + 1)));
float epsilon2 = epsilon * std::sqrt(1.0f - std::pow(beta2, static_cast<float>(step + 1)));
for (std::size_t t = 0; t < nb_tensors; ++t) {
for (std::size_t i = 0; i < size_tensors[t]; ++i) {
val_momentum1_tensors[t][i] = beta1 * val_momentum1_tensors[t][i] + (1.0f - beta1) * val_grad_tensors[t][i];
......
unit_tests/optimizer/Test_SGD.cpp
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......@@ -9,13 +9,15 @@
*
********************************************************************************/
#include <catch2/catch_test_macros.hpp>
#include <cstddef> // std::size_t
#include <memory>
#include <random> // std::random_device, std::mt19937, std::uniform_int_distribution
#include <set>
#include <vector>
#include <catch2/catch_test_macros.hpp>
#include <fmt/core.h>
#include "aidge/data/Tensor.hpp"
#include "aidge/backend/cpu/data/TensorImpl.hpp"
#include "aidge/learning/learningRate/LRScheduler.hpp"
......@@ -81,9 +83,7 @@ TEST_CASE("[learning/SGD] update", "[Optimizer][SGD]") {
tensors[i] = std::make_shared<Tensor>(dims);
tensors[i]->setBackend("cpu");
tensors[i]->getImpl()->setRawPtr(val_tensors[i].get(), size_tensors[i]);
optim_tensors[i] = std::make_shared<Tensor>(dims);
optim_tensors[i]->setBackend("cpu");
optim_tensors[i]->getImpl()->copy(val_tensors[i].get(), size_tensors[i]);
optim_tensors[i] = std::make_shared<Tensor>(tensors[i]->clone());
// optim_tensors[i]->initGrad();
grad_tensors[i] = std::make_shared<Tensor>(dims);
......
version.txt
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0.2.1
0.2.2
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