diff --git a/.gitlab/ci/build.gitlab-ci.yml b/.gitlab/ci/build.gitlab-ci.yml
index 68fcb6b4bc0dac08c4f0029ec1f2d3404226c1c2..365b6ddf4d009476122188adf1770243022ee663 100644
--- a/.gitlab/ci/build.gitlab-ci.yml
+++ b/.gitlab/ci/build.gitlab-ci.yml
@@ -23,6 +23,98 @@ build:ubuntu_cpp:
- build_cpp/
- install_cpp/
+build:ubuntu_cpp_g++10:
+ stage: build
+ needs: []
+ tags:
+ - docker
+
+ script:
+ # Download dependencies
+ # aidge_core
+ - 'curl --location --output build_artifacts.zip "https://gitlab.eclipse.org/api/v4/projects/5139/jobs/artifacts/main/download?job=build:ubuntu_cpp"'
+ - unzip -o build_artifacts.zip -d .
+ - rm -rf build_cpp
+
+ # Build current module
+ - export CMAKE_PREFIX_PATH=../install_cpp
+ - apt install -y g++-10
+ - mkdir -p build_cpp
+ - mkdir -p install_cpp
+ - cd build_cpp
+ - export CXX=/usr/bin/g++-10
+ - cmake -DCMAKE_INSTALL_PREFIX:PATH=../install_cpp -DCMAKE_BUILD_TYPE=Debug -DWERROR=ON -DCOVERAGE=ON ..
+ - make -j4 all install
+
+build:ubuntu_cpp_g++12:
+ stage: build
+ needs: []
+ tags:
+ - docker
+
+ script:
+ # Download dependencies
+ # aidge_core
+ - 'curl --location --output build_artifacts.zip "https://gitlab.eclipse.org/api/v4/projects/5139/jobs/artifacts/main/download?job=build:ubuntu_cpp"'
+ - unzip -o build_artifacts.zip -d .
+ - rm -rf build_cpp
+
+ # Build current module
+ - export CMAKE_PREFIX_PATH=../install_cpp
+ - apt install -y g++-12
+ - mkdir -p build_cpp
+ - mkdir -p install_cpp
+ - cd build_cpp
+ - export CXX=/usr/bin/g++-12
+ - cmake -DCMAKE_INSTALL_PREFIX:PATH=../install_cpp -DCMAKE_BUILD_TYPE=Debug -DWERROR=ON -DCOVERAGE=ON ..
+ - make -j4 all install
+
+build:ubuntu_cpp_clang12:
+ stage: build
+ needs: []
+ tags:
+ - docker
+
+ script:
+ # Download dependencies
+ # aidge_core
+ - 'curl --location --output build_artifacts.zip "https://gitlab.eclipse.org/api/v4/projects/5139/jobs/artifacts/main/download?job=build:ubuntu_cpp"'
+ - unzip -o build_artifacts.zip -d .
+ - rm -rf build_cpp
+
+ # Build current module
+ - export CMAKE_PREFIX_PATH=../install_cpp
+ - apt install -y clang-12
+ - mkdir -p build_cpp
+ - mkdir -p install_cpp
+ - cd build_cpp
+ - export CXX=/usr/bin/clang++-12
+ - cmake -DCMAKE_INSTALL_PREFIX:PATH=../install_cpp -DCMAKE_BUILD_TYPE=Debug -DWERROR=ON -DCOVERAGE=ON ..
+ - make -j4 all install
+
+build:ubuntu_cpp_clang15:
+ stage: build
+ needs: []
+ tags:
+ - docker
+
+ script:
+ # Download dependencies
+ # aidge_core
+ - 'curl --location --output build_artifacts.zip "https://gitlab.eclipse.org/api/v4/projects/5139/jobs/artifacts/main/download?job=build:ubuntu_cpp"'
+ - unzip -o build_artifacts.zip -d .
+ - rm -rf build_cpp
+
+ # Build current module
+ - export CMAKE_PREFIX_PATH=../install_cpp
+ - apt install -y clang-15
+ - mkdir -p build_cpp
+ - mkdir -p install_cpp
+ - cd build_cpp
+ - export CXX=/usr/bin/clang++-15
+ - cmake -DCMAKE_INSTALL_PREFIX:PATH=../install_cpp -DCMAKE_BUILD_TYPE=Debug -DWERROR=ON -DCOVERAGE=ON ..
+ - make -j4 all install
+
build:ubuntu_python:
stage: build
needs: []
@@ -84,3 +176,42 @@ build:windows_cpp:
paths:
- build_cpp/
- install_cpp/
+
+build:windows_python:
+ stage: build
+ needs: []
+ tags:
+ - windows
+
+ image: buildtools
+ before_script:
+ # Install Chocolatey
+ - Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1'))
+ # Install dependencies
+ - choco install cmake.install --installargs '"ADD_CMAKE_TO_PATH=System"' -Y
+ - choco install git -Y
+ - choco install python -Y
+ # Update PATH
+ - $env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User")
+ script:
+ # Download dependencies
+ # aidge_core (CPP)
+ - 'curl "https://gitlab.eclipse.org/api/v4/projects/5139/jobs/artifacts/main/download?job=build:windows_cpp" -o build_artifacts.zip'
+ - Expand-Archive -Path .\build_artifacts.zip -DestinationPath . -Force
+ - Remove-Item .\build_cpp\ -Recurse
+ # aidge_core (Python)
+ - 'curl "https://gitlab.eclipse.org/api/v4/projects/5139/jobs/artifacts/main/download?job=build:windows_python" -o build_artifacts.zip'
+ - Expand-Archive -Path .\build_artifacts.zip -DestinationPath . -Force
+
+ - python -m pip install virtualenv
+ - virtualenv venv
+ - venv\Scripts\Activate.ps1
+ # Numpy dependancy for unit test
+ - python -m pip install numpy
+ - $env:AIDGE_INSTALL = "$pwd" + "install"
+ - $env:CMAKE_PREFIX_PATH = "../install_cpp"
+ - python -m pip install .
+ artifacts:
+ expire_in: 1 week
+ paths:
+ - venv/
diff --git a/include/aidge/backend/cpu.hpp b/include/aidge/backend/cpu.hpp
index e3fa457c5cf557328a7fbf2049b9e577c44391f9..4d334e2a492962ab40efaf9bf1fe64682f225db2 100644
--- a/include/aidge/backend/cpu.hpp
+++ b/include/aidge/backend/cpu.hpp
@@ -13,7 +13,6 @@
#define AIDGE_CPU_IMPORTS_H_
#include "aidge/backend/cpu/data/TensorImpl.hpp"
-
#include "aidge/backend/cpu/operator/AddImpl.hpp"
#include "aidge/backend/cpu/operator/AvgPoolingImpl.hpp"
#include "aidge/backend/cpu/operator/MaxPoolingImpl.hpp"
@@ -23,10 +22,10 @@
#include "aidge/backend/cpu/operator/FCImpl.hpp"
#include "aidge/backend/cpu/operator/LeakyReLUImpl.hpp"
#include "aidge/backend/cpu/operator/MatMulImpl.hpp"
+#include "aidge/backend/cpu/operator/PadImpl.hpp"
#include "aidge/backend/cpu/operator/ProducerImpl.hpp"
#include "aidge/backend/cpu/operator/ReLUImpl.hpp"
-#include "aidge/backend/cpu/operator/ScalingImpl.hpp"
-#include "aidge/backend/cpu/operator/SliceImpl.hpp"
#include "aidge/backend/cpu/operator/SoftmaxImpl.hpp"
+#include "aidge/backend/cpu/operator/ScalingImpl.hpp"
#endif /* AIDGE_CPU_IMPORTS_H_ */
\ No newline at end of file
diff --git a/include/aidge/backend/cpu/operator/AddImpl.hpp b/include/aidge/backend/cpu/operator/AddImpl.hpp
index 6e1cd03a3af81ee85f4f9e0e212af7c02089734e..99c83e2c6f91d50475ff7896dbe286286f7c8e2d 100644
--- a/include/aidge/backend/cpu/operator/AddImpl.hpp
+++ b/include/aidge/backend/cpu/operator/AddImpl.hpp
@@ -74,12 +74,12 @@ class AddImpl_cpu : public OperatorImpl {
return std::accumulate(inputDims.begin(), inputDims.end(), NbElts_t(1), std::multiplies<NbElts_t>());
}
- NbElts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final {
+ NbElts_t getNbRequiredProtected(const IOIndex_t /*inputIdx*/) const override final {
// for the direct convolution algorithm, convolutions can be in-place, if there is no padding!
return 0;
}
- NbElts_t getRequiredMemory(const IOIndex_t outputIdx, const std::vector<DimSize_t>& inputsSize) const override final {
+ NbElts_t getRequiredMemory(const IOIndex_t outputIdx, const std::vector<DimSize_t>& /*inputsSize*/) const override final {
// Requires the whole tensors, regardless of available data on inputs
assert(outputIdx == 0 && "operator has only one output");
(void) outputIdx;
@@ -99,11 +99,11 @@ class AddImpl_cpu : public OperatorImpl {
}
void updateConsummerProducer() override final;
- void forward() {
+ void forward() override {
// nothing
}
- void backward() { printf("Not implemented yet.\n"); }
+ void backward() override { printf("Not implemented yet.\n"); }
};
template <>
@@ -133,9 +133,9 @@ class AddImpl_cpu<1> : public OperatorImpl {
NbElts_t getNbProducedData(const IOIndex_t /*outputIdx*/) const override final;
void updateConsummerProducer() override final;
- void forward();
+ void forward() override;
- void backward();
+ void backward() override;
};
template <>
@@ -165,9 +165,9 @@ class AddImpl_cpu<2> : public OperatorImpl {
NbElts_t getNbProducedData(const IOIndex_t /*outputIdx*/) const override final;
void updateConsummerProducer() override final;
- void forward();
+ void forward() override;
- void backward();
+ void backward() override;
};
template <>
@@ -196,9 +196,9 @@ class AddImpl_cpu<3> : public OperatorImpl {
NbElts_t getNbProducedData(const IOIndex_t outputIdx) const override final;
void updateConsummerProducer() override final;
- void forward();
+ void forward() override;
- void backward();
+ void backward() override;
};
namespace {
diff --git a/include/aidge/backend/cpu/operator/AvgPoolingImpl.hpp b/include/aidge/backend/cpu/operator/AvgPoolingImpl.hpp
index cfbcadfe6b719369618955a14c4cde5733ef6773..635c246c95d5625f9cb703e34d4d1fbbda833998 100644
--- a/include/aidge/backend/cpu/operator/AvgPoolingImpl.hpp
+++ b/include/aidge/backend/cpu/operator/AvgPoolingImpl.hpp
@@ -56,9 +56,9 @@ class AvgPoolingImpl2D_cpu : public OperatorImpl {
NbElts_t getNbProducedData(const IOIndex_t outputIdx) const override final;
void updateConsummerProducer() override final;
- void forward();
+ void forward() override;
- void backward();
+ void backward() override;
};
namespace {
diff --git a/include/aidge/backend/cpu/operator/AvgPoolingImpl_forward_kernels.hpp b/include/aidge/backend/cpu/operator/AvgPoolingImpl_forward_kernels.hpp
index 60b4923bdc18674da52be9bd07d9947fb9790f0d..ea46a540ad04b6227d6ec01c965e2eb99806d5e1 100644
--- a/include/aidge/backend/cpu/operator/AvgPoolingImpl_forward_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/AvgPoolingImpl_forward_kernels.hpp
@@ -43,11 +43,11 @@ void AvgPoolingImpl2D_cpu_forward_kernel(const AvgPooling_Op<2>::Attrs &attrs,
// output H size
const std::size_t oxSize =
- static_cast<std::size_t>(std::floor(static_cast<float>(dims[2] + std::get<2>(attrs)[0] + std::get<2>(attrs)[2] - std::get<1>(attrs)[0] + std::get<0>(attrs)[0]) /
+ static_cast<std::size_t>(std::floor(static_cast<float>(dims[2] - std::get<1>(attrs)[0] + std::get<0>(attrs)[0]) /
static_cast<float>(std::get<0>(attrs)[0])));
// output W size
const std::size_t oySize =
- static_cast<std::size_t>(std::floor(static_cast<float>(dims[3] + std::get<2>(attrs)[1] + std::get<2>(attrs)[3] - std::get<1>(attrs)[1] + std::get<0>(attrs)[1])/
+ static_cast<std::size_t>(std::floor(static_cast<float>(dims[3] - std::get<1>(attrs)[1] + std::get<0>(attrs)[1]) /
static_cast<float>(std::get<0>(attrs)[1])));
// TODO: kernel computation
@@ -61,11 +61,11 @@ void AvgPoolingImpl2D_cpu_forward_kernel(const AvgPooling_Op<2>::Attrs &attrs,
const std::size_t oIndex = (ch + batch*dims[1]) * oxSize * oySize;
const std::size_t iIndex = (ch + batch*dims[1]) * dims[2] * dims[3];
for (std::size_t ox = 0; ox < oxSize; ++ox) {
- const signedsize difx = static_cast<signedsize>(std::get<2>(attrs)[0] - ox * std::get<0>(attrs)[0]);
+ const signedsize difx = static_cast<signedsize>(- ox * std::get<0>(attrs)[0]);
const std::size_t sxMin = static_cast<std::size_t>(std::max(difx, signedsize(0)));
const std::size_t sxMax = (static_cast<signedsize>(dims[2]) + difx) < 0 ? 0 : ((dims[2] + difx) > std::get<1>(attrs)[0] ? std::get<1>(attrs)[0] : dims[2] + difx);
for (std::size_t oy = 0; oy < oySize; ++oy) {
- const signedsize dify = static_cast<signedsize>(std::get<2>(attrs)[1] - oy * std::get<0>(attrs)[1]);
+ const signedsize dify = static_cast<signedsize>(- oy * std::get<0>(attrs)[1]);
const std::size_t syMin = static_cast<std::size_t>(std::max(dify, signedsize(0)));
const std::size_t syMax = (static_cast<signedsize>(dims[3]) + dify) < 0 ? 0 : ((dims[3] + dify) > std::get<1>(attrs)[1] ? std::get<1>(attrs)[1] : dims[3] + dify);
const std::size_t oIndexFull = oIndex + ox*oySize + oy;
diff --git a/include/aidge/backend/cpu/operator/BatchNormImpl.hpp b/include/aidge/backend/cpu/operator/BatchNormImpl.hpp
index 30557f6cbba05829b3cc9e17364ae4d933a568cf..f46113543d7c4a9d55dc7710fec74cca7fb3814d 100644
--- a/include/aidge/backend/cpu/operator/BatchNormImpl.hpp
+++ b/include/aidge/backend/cpu/operator/BatchNormImpl.hpp
@@ -71,9 +71,9 @@ class BatchNormImpl2D_cpu : public OperatorImpl {
NbElts_t getNbProducedData(const IOIndex_t outputIdx) const override final;
void updateConsummerProducer() override final;
- void forward();
+ void forward() override;
- void backward();
+ void backward() override;
};
namespace {
diff --git a/include/aidge/backend/cpu/operator/ConvDepthWiseImpl.hpp b/include/aidge/backend/cpu/operator/ConvDepthWiseImpl.hpp
index 2826b635590c5d19f34c8e4beee20fc8dba2183b..a5a144f50d6fbe1eb6c63827cad6a654777b60ae 100644
--- a/include/aidge/backend/cpu/operator/ConvDepthWiseImpl.hpp
+++ b/include/aidge/backend/cpu/operator/ConvDepthWiseImpl.hpp
@@ -58,9 +58,9 @@ class ConvDepthWiseImpl2D_cpu : public OperatorImpl {
NbElts_t getNbProducedData(const IOIndex_t outputIdx) const override final;
void updateConsummerProducer() override final;
- void forward();
+ void forward() override;
- void backward();
+ void backward() override;
};
namespace {
diff --git a/include/aidge/backend/cpu/operator/ConvDepthWiseImpl_forward_kernels.hpp b/include/aidge/backend/cpu/operator/ConvDepthWiseImpl_forward_kernels.hpp
index 669bdbc898528b0f96a59dd3c6f8e438ae1291e4..5aa29ac55740d46bba873bb9d85a04cd004cc3bd 100644
--- a/include/aidge/backend/cpu/operator/ConvDepthWiseImpl_forward_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/ConvDepthWiseImpl_forward_kernels.hpp
@@ -9,7 +9,7 @@
*
********************************************************************************/
-#ifndef AIDGE_CPU_OPERATOR_CONVDEPTHWISEIMP_FORWARD_KERNEL_H_
+#ifndef AIDGE_CPU_OPERATOR_CONVDEPTHWISEIMPL_FORWARD_KERNEL_H_
#define AIDGE_CPU_OPERATOR_CONVDEPTHWISEIMPL_FORWARD_KERNEL_H_
#include "aidge/utils/Registrar.hpp"
@@ -46,11 +46,11 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const ConvDepthWise_Op<2>::Attrs &at
// output H size
const std::size_t oxSize =
- static_cast<std::size_t>(std::floor(static_cast<float>(dims[2] + std::get<4>(attrs)[0] + std::get<4>(attrs)[2] - std::get<3>(attrs)[0] + std::get<0>(attrs)[0]) /
+ static_cast<std::size_t>(std::floor(static_cast<float>(dims[2] - std::get<3>(attrs)[0] + std::get<0>(attrs)[0]) /
static_cast<float>(std::get<0>(attrs)[0])));
// output W size
const std::size_t oySize =
- static_cast<std::size_t>(std::floor(static_cast<float>(dims[3] + std::get<4>(attrs)[1] + std::get<4>(attrs)[3] - std::get<3>(attrs)[1] + std::get<0>(attrs)[1]) /
+ static_cast<std::size_t>(std::floor(static_cast<float>(dims[3] - std::get<3>(attrs)[1] + std::get<0>(attrs)[1]) /
static_cast<float>(std::get<0>(attrs)[1])));
// TODO: kernel computation
@@ -67,16 +67,16 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const ConvDepthWise_Op<2>::Attrs &at
const std::size_t iIndex = (ch + batch*dims[1]) * dims[2] * dims[3];
const std::size_t wIndex = ch * std::get<3>(attrs)[0] * std::get<3>(attrs)[1];
for (std::size_t ox = 0; ox < oxSize; ++ox) {
- const signedsize difx = static_cast<signedsize>(std::get<4>(attrs)[0] - ox * std::get<0>(attrs)[0]);
+ const signedsize difx = static_cast<signedsize>(- ox * std::get<0>(attrs)[0]);
const std::size_t sxMin = static_cast<std::size_t>(std::max(difx, signedsize(0)));
const std::size_t sxMax = (static_cast<signedsize>(dims[2]) + difx) < 0 ? 0 : ((dims[2] + difx) > std::get<3>(attrs)[0] ? std::get<3>(attrs)[0] : dims[2] + difx);
for (std::size_t oy = 0; oy < oySize; ++oy) {
- const signedsize dify = static_cast<signedsize>(std::get<4>(attrs)[1] - oy * std::get<0>(attrs)[1]);
+ const signedsize dify = static_cast<signedsize>(- oy * std::get<0>(attrs)[1]);
const std::size_t syMin = static_cast<std::size_t>(std::max(dify, signedsize(0)));
const std::size_t syMax = (static_cast<signedsize>(dims[3]) + dify) < 0 ? 0 : ((dims[3] + dify) > std::get<3>(attrs)[1] ? std::get<3>(attrs)[1] : dims[3] + dify);
const std::size_t oIndexFull = oIndex + ox*oySize + oy;
- const signedsize ix = static_cast<signedsize>(ox * std::get<0>(attrs)[0]) - std::get<4>(attrs)[0];
- const signedsize iy = static_cast<signedsize>(oy * std::get<0>(attrs)[1]) - std::get<4>(attrs)[1];
+ const signedsize ix = static_cast<signedsize>(ox * std::get<0>(attrs)[0]);
+ const signedsize iy = static_cast<signedsize>(oy * std::get<0>(attrs)[1]);
if (sxMin == 0 && syMin == 0 && sxMax == 3 && syMax == 3) {
output[oIndexFull] += (weights[wIndex + 0*std::get<3>(attrs)[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+0)*dims[3] + static_cast<std::size_t>(iy+0)] +
diff --git a/include/aidge/backend/cpu/operator/ConvImpl.hpp b/include/aidge/backend/cpu/operator/ConvImpl.hpp
index b9411fe0f1ac079d9857cc8f2178fc98fadc3a77..fba1fd6aad72189ea9e18b5da0d1fc2613d0cc69 100644
--- a/include/aidge/backend/cpu/operator/ConvImpl.hpp
+++ b/include/aidge/backend/cpu/operator/ConvImpl.hpp
@@ -58,9 +58,9 @@ class ConvImpl2D_cpu : public OperatorImpl {
NbElts_t getNbProducedData(const IOIndex_t outputIdx) const override final;
void updateConsummerProducer() override final;
- void forward();
+ void forward() override;
- void backward();
+ void backward() override;
};
namespace {
diff --git a/include/aidge/backend/cpu/operator/ConvImpl_forward_kernels.hpp b/include/aidge/backend/cpu/operator/ConvImpl_forward_kernels.hpp
index 9d4d6dfdfcc114e47e478089c4d5a42c2bee0f28..03e2c35170432181c7a9b3934d61f0bd18471876 100644
--- a/include/aidge/backend/cpu/operator/ConvImpl_forward_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/ConvImpl_forward_kernels.hpp
@@ -88,11 +88,11 @@ void ConvImpl2D_cpu_forward_kernel(const Conv_Op<2>::Attrs &attrs, const std::ar
// output H size
const std::size_t oxSize =
- static_cast<std::size_t>(std::floor(static_cast<float>(dims[2] + std::get<5>(attrs)[0] + std::get<5>(attrs)[2] - std::get<4>(attrs)[0] + std::get<0>(attrs)[0]) /
+ static_cast<std::size_t>(std::floor(static_cast<float>(dims[2] - std::get<4>(attrs)[0] + std::get<0>(attrs)[0]) /
static_cast<float>(std::get<0>(attrs)[0])));
// output W size
const std::size_t oySize =
- static_cast<std::size_t>(std::floor(static_cast<float>(dims[3] + std::get<5>(attrs)[1] + std::get<5>(attrs)[3] - std::get<4>(attrs)[1] + std::get<0>(attrs)[1]) /
+ static_cast<std::size_t>(std::floor(static_cast<float>(dims[3] - std::get<4>(attrs)[1] + std::get<0>(attrs)[1]) /
static_cast<float>(std::get<0>(attrs)[1])));
// TODO: kernel computation
@@ -110,16 +110,16 @@ void ConvImpl2D_cpu_forward_kernel(const Conv_Op<2>::Attrs &attrs, const std::ar
const std::size_t iIndex = (inCh + batch*dims[1]) * dims[2] * dims[3];
const std::size_t wIndex = (inCh + outCh*dims[1]) * std::get<4>(attrs)[0] * std::get<4>(attrs)[1];
for (std::size_t ox = 0; ox < oxSize; ++ox) {
- const signedsize difx = static_cast<signedsize>(std::get<5>(attrs)[0] - ox * std::get<0>(attrs)[0]);
+ const signedsize difx = static_cast<signedsize>(- ox * std::get<0>(attrs)[0]);
const std::size_t sxMin = static_cast<std::size_t>(std::max(difx, signedsize(0)));
const std::size_t sxMax = (static_cast<signedsize>(dims[2]) + difx) < 0 ? 0 : ((dims[2] + difx) > std::get<4>(attrs)[0] ? std::get<4>(attrs)[0] : dims[2] + difx);
for (std::size_t oy = 0; oy < oySize; ++oy) {
- const signedsize dify = static_cast<signedsize>(std::get<5>(attrs)[1] - oy * std::get<0>(attrs)[1]);
+ const signedsize dify = static_cast<signedsize>(- oy * std::get<0>(attrs)[1]);
const std::size_t syMin = static_cast<std::size_t>(std::max(dify, signedsize(0)));
const std::size_t syMax = (static_cast<signedsize>(dims[3]) + dify) < 0 ? 0 : ((dims[3] + dify) > std::get<4>(attrs)[1] ? std::get<4>(attrs)[1] : dims[3] + dify);
const std::size_t oIndexFull = oIndex + ox*oySize + oy;
- const signedsize ix = static_cast<signedsize>(ox * std::get<0>(attrs)[0]) - std::get<5>(attrs)[0];
- const signedsize iy = static_cast<signedsize>(oy * std::get<0>(attrs)[1]) - std::get<5>(attrs)[1];
+ const signedsize ix = static_cast<signedsize>(ox * std::get<0>(attrs)[0]);
+ const signedsize iy = static_cast<signedsize>(oy * std::get<0>(attrs)[1]);
if (sxMin == 0 && syMin == 0 && sxMax == 3 && syMax == 3) {
output[oIndexFull] += (weights[wIndex + 0*std::get<4>(attrs)[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+0)*dims[3] + static_cast<std::size_t>(iy+0)] +
diff --git a/include/aidge/backend/cpu/operator/FCImpl.hpp b/include/aidge/backend/cpu/operator/FCImpl.hpp
index 1dfa40439dbba9cdd4fe3436fea30f771678c1ff..875456d185bf28ec06c2491bd5c57ce7e6236e49 100644
--- a/include/aidge/backend/cpu/operator/FCImpl.hpp
+++ b/include/aidge/backend/cpu/operator/FCImpl.hpp
@@ -51,9 +51,9 @@ class FCImpl_cpu : public OperatorImpl {
NbElts_t getNbConsumedData(const IOIndex_t inputIdx) const override final;
NbElts_t getNbProducedData(const IOIndex_t outputIdx) const override final;
void updateConsummerProducer() override final;
- void forward();
+ void forward() override;
- void backward();
+ void backward() override;
};
namespace {
diff --git a/include/aidge/backend/cpu/operator/LeakyReLUImpl.hpp b/include/aidge/backend/cpu/operator/LeakyReLUImpl.hpp
index 386ef999fddbda184edee88723d213f53ff62ded..dc895c2758bda2aa766227eb628e990316a5bd52 100644
--- a/include/aidge/backend/cpu/operator/LeakyReLUImpl.hpp
+++ b/include/aidge/backend/cpu/operator/LeakyReLUImpl.hpp
@@ -50,9 +50,9 @@ class LeakyReLUImpl_cpu : public OperatorImpl {
NbElts_t getNbConsumedData(const IOIndex_t inputIdx) const override final;
NbElts_t getNbProducedData(const IOIndex_t outputIdx) const override final;
void updateConsummerProducer() override final;
- void forward();
+ void forward() override;
- void backward();
+ void backward() override;
};
namespace {
diff --git a/include/aidge/backend/cpu/operator/MatMulImpl.hpp b/include/aidge/backend/cpu/operator/MatMulImpl.hpp
index bf8e31efd253ee8855f3473ef0b4a60c59a04b5f..504406c7a7f2741abfa68d934c20e15bfcce59dd 100644
--- a/include/aidge/backend/cpu/operator/MatMulImpl.hpp
+++ b/include/aidge/backend/cpu/operator/MatMulImpl.hpp
@@ -64,8 +64,8 @@ public:
void updateConsummerProducer() override final;
- void forward();
- void backward();
+ void forward() override;
+ void backward() override;
};
namespace {
diff --git a/include/aidge/backend/cpu/operator/MaxPoolingImpl.hpp b/include/aidge/backend/cpu/operator/MaxPoolingImpl.hpp
index ef7835c742235f96e36423b9f5388efc4649199f..ca4480653b8315ab3d986eac1c460e2d7c17b844 100644
--- a/include/aidge/backend/cpu/operator/MaxPoolingImpl.hpp
+++ b/include/aidge/backend/cpu/operator/MaxPoolingImpl.hpp
@@ -56,9 +56,9 @@ class MaxPoolingImpl2D_cpu : public OperatorImpl {
NbElts_t getNbProducedData(const IOIndex_t outputIdx) const override final;
void updateConsummerProducer() override final;
- void forward();
+ void forward() override;
- void backward();
+ void backward() override;
};
namespace {
diff --git a/include/aidge/backend/cpu/operator/MaxPoolingImpl_forward_kernels.hpp b/include/aidge/backend/cpu/operator/MaxPoolingImpl_forward_kernels.hpp
index eae73f8d8fb7546bb234e7ad7dce38b59378b4ee..caa99e8678a72c7fd3c77fe8b7579ea739ac64c7 100644
--- a/include/aidge/backend/cpu/operator/MaxPoolingImpl_forward_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/MaxPoolingImpl_forward_kernels.hpp
@@ -42,15 +42,14 @@ void MaxPoolingImpl2D_cpu_forward_kernel(const MaxPooling_Op<2>::Attrs &attrs,
std::array<DimSize_t, 2> strideDims = std::get<0>(attrs);
std::array<DimSize_t, 2> kernelDims = std::get<1>(attrs);
- std::array<DimSize_t, 4> paddingDims = std::get<2>(attrs);
// output H size
const std::size_t oxSize =
- static_cast<std::size_t>(std::floor(static_cast<float>(dims[2] + paddingDims[0] + paddingDims[2] - kernelDims[0] + strideDims[0]) /
+ static_cast<std::size_t>(std::floor(static_cast<float>(dims[2] - kernelDims[0] + strideDims[0]) /
static_cast<float>(strideDims[0])));
// output W size
const std::size_t oySize =
- static_cast<std::size_t>(std::floor(static_cast<float>(dims[3] + paddingDims[1] + paddingDims[3] - kernelDims[1] + strideDims[1]) /
+ static_cast<std::size_t>(std::floor(static_cast<float>(dims[3] - kernelDims[1] + strideDims[1]) /
static_cast<float>(strideDims[1])));
// TODO: kernel computation
@@ -64,11 +63,11 @@ void MaxPoolingImpl2D_cpu_forward_kernel(const MaxPooling_Op<2>::Attrs &attrs,
const std::size_t oIndex = (ch + batch*dims[1]) * oxSize * oySize;
const std::size_t iIndex = (ch + batch*dims[1]) * dims[2] * dims[3];
for (std::size_t ox = 0; ox < oxSize; ++ox) {
- const signedsize difx = static_cast<signedsize>(paddingDims[0] - ox * strideDims[0]);
+ const signedsize difx = static_cast<signedsize>(- ox * strideDims[0]);
const std::size_t sxMin = static_cast<std::size_t>(std::max(difx, signedsize(0)));
const std::size_t sxMax = (static_cast<signedsize>(dims[2]) + difx) < 0 ? 0 : ((dims[2] + difx) > kernelDims[0] ? kernelDims[0] : dims[2] + difx);
for (std::size_t oy = 0; oy < oySize; ++oy) {
- const signedsize dify = static_cast<signedsize>(paddingDims[1] - oy * strideDims[1]);
+ const signedsize dify = static_cast<signedsize>(- oy * strideDims[1]);
const std::size_t syMin = static_cast<std::size_t>(std::max(dify, signedsize(0)));
const std::size_t syMax = (static_cast<signedsize>(dims[3]) + dify) < 0 ? 0 : ((dims[3] + dify) > kernelDims[1] ? kernelDims[1] : dims[3] + dify);
const std::size_t oIndexFull = oIndex + ox*oySize + oy;
diff --git a/include/aidge/backend/cpu/operator/PadImpl.hpp b/include/aidge/backend/cpu/operator/PadImpl.hpp
new file mode 100644
index 0000000000000000000000000000000000000000..1a3202a7c71a6669e191fefa7e7b29849fc017f8
--- /dev/null
+++ b/include/aidge/backend/cpu/operator/PadImpl.hpp
@@ -0,0 +1,72 @@
+/********************************************************************************
+ * Copyright (c) 2023 CEA-List
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Eclipse Public License 2.0 which is available at
+ * http://www.eclipse.org/legal/epl-2.0.
+ *
+ * SPDX-License-Identifier: EPL-2.0
+ *
+ ********************************************************************************/
+
+#ifndef AIDGE_CPU_OPERATOR_PADIMPL_H_
+#define AIDGE_CPU_OPERATOR_PADIMPL_H_
+
+#include <array>
+#include <memory>
+#include <tuple>
+#include <vector>
+
+#include "aidge/backend/OperatorImpl.hpp"
+#include "aidge/operator/Pad.hpp"
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/utils/Types.h"
+
+namespace Aidge {
+// class Pad_Op;
+
+// compute kernel registry for forward and backward
+class PadImpl2DForward_cpu
+ : public Registrable<PadImpl2DForward_cpu,
+ std::tuple<DataType, DataType>,
+ void(const Pad_Op<2>::Attrs &, const std::array<DimSize_t, 4> &, const void *,
+ void *)> {};
+class PadImpl2DBackward_cpu
+ : public Registrable<PadImpl2DBackward_cpu,
+ std::tuple<DataType, DataType>,
+ void(const Pad_Op<2>::Attrs &, const std::array<DimSize_t, 4> &, const void *,
+ void *)> {};
+
+class PadImpl2D_cpu : public OperatorImpl {
+ private:
+ const Pad_Op<2> &mOp;
+ std::array<NbElts_t, 1> mNbConsumedData = {0};
+ std::array<NbElts_t, 1> mNbProducedData = {0};
+
+ public:
+ PadImpl2D_cpu(const Pad_Op<2> &op) : mOp(op) {}
+
+ static std::unique_ptr<PadImpl2D_cpu> create(const Pad_Op<2> &op) {
+ return std::make_unique<PadImpl2D_cpu>(op);
+ }
+
+ public:
+ NbElts_t getNbRequiredData(const IOIndex_t inputIdx) const override final;
+ NbElts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final;
+ NbElts_t getRequiredMemory(const IOIndex_t /*outputIdx*/, const std::vector<DimSize_t> &/*inputsSize*/) const override final;
+ NbElts_t getNbConsumedData(const IOIndex_t inputIdx) const override final;
+ NbElts_t getNbProducedData(const IOIndex_t outputIdx) const override final;
+ void updateConsummerProducer() override final;
+
+ void forward() override;
+
+ void backward() override;
+};
+
+namespace {
+// add cpu backend to Pad_Op<2> implementation registry
+static Registrar<Pad_Op<2>> registrarPadImpl2D_cpu("cpu", Aidge::PadImpl2D_cpu::create);
+} // namespace
+} // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_PADIMPL_H_ */
diff --git a/include/aidge/backend/cpu/operator/PadImpl_forward_kernels.hpp b/include/aidge/backend/cpu/operator/PadImpl_forward_kernels.hpp
new file mode 100644
index 0000000000000000000000000000000000000000..1babc3ecf2ff2fa8db2fd5ffbd34d89b8d7d88b6
--- /dev/null
+++ b/include/aidge/backend/cpu/operator/PadImpl_forward_kernels.hpp
@@ -0,0 +1,110 @@
+/********************************************************************************
+ * Copyright (c) 2023 CEA-List
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Eclipse Public License 2.0 which is available at
+ * http://www.eclipse.org/legal/epl-2.0.
+ *
+ * SPDX-License-Identifier: EPL-2.0
+ *
+ ********************************************************************************/
+
+#ifndef AIDGE_CPU_OPERATOR_PADIMPL_FORWARD_KERNEL_H_
+#define AIDGE_CPU_OPERATOR_PADIMPL_FORWARD_KERNEL_H_
+
+#include "aidge/utils/Registrar.hpp"
+
+#include "aidge/backend/cpu/operator/PadImpl.hpp"
+#include "aidge/utils/Types.h"
+#include <cmath>
+#include <array>
+#include <algorithm>
+
+namespace Aidge {
+/**
+ * @brief Forward kernel for 2D Padding on CPU backend.
+ * @tparam I Input data type.
+ * @tparam O Output data type.
+ * @param attrs tuple of Parameters from the Operator
+ * @param dims Array of input dimensions.
+ * @param input_ const input Tensor.
+ * @param output_ Output Tensor.
+ */
+template <class I, class O>
+void PadImpl2D_cpu_forward_kernel(const Pad_Op<2>::Attrs &attrs, const std::array<DimSize_t, 4> &dims,
+ const void *input_, void *output_)
+{
+ const I *input = static_cast<const I *>(input_);
+ O *output = static_cast<O *>(output_);
+
+ const std::size_t oySize = dims[2] + std::get<0>(attrs)[0][0] + std::get<0>(attrs)[0][1];
+ const std::size_t oxSize = dims[3] + std::get<0>(attrs)[1][0] + std::get<0>(attrs)[1][1];
+
+ for (std::size_t batch = 0; batch < dims[0]; ++batch) {
+ for (std::size_t ch = 0; ch < dims[1]; ++ch) {
+ const std::size_t iIndex = (ch + batch*dims[1]) * dims[2] * dims[3];
+ const std::size_t oIndex = (ch + batch*dims[1]) * oxSize * oySize;
+
+ for (unsigned int oy = 0; oy < oySize; ++oy) {
+ for (unsigned int ox = 0; ox < oxSize; ++ox) {
+ const std::size_t oIndexFull = oIndex + ox*oySize + oy;
+
+ O outputValue = std::get<2>(attrs);
+
+ if (std::get<1>(attrs) == PadBorderType::Constant) {
+ int ix = static_cast<int>(ox) - static_cast<int>(std::get<0>(attrs)[1][1]);
+ int iy = static_cast<int>(oy) - static_cast<int>(std::get<0>(attrs)[0][1]);
+
+ if (ix >= 0 && ix < static_cast<int>(dims[3]) && iy >= 0 && iy < static_cast<int>(dims[2])) {
+ outputValue = input[iIndex + static_cast<std::size_t>(ix)*dims[2] + static_cast<std::size_t>(iy)];
+ }
+ }
+ else if (std::get<1>(attrs) == PadBorderType::Replicate) {
+ int ix = std::max(0, std::min(static_cast<int>(dims[3]) - 1, static_cast<int>(ox) - static_cast<int>(std::get<0>(attrs)[1][1])));
+ int iy = std::max(0, std::min(static_cast<int>(dims[2]) - 1, static_cast<int>(oy) - static_cast<int>(std::get<0>(attrs)[0][1])));
+
+ outputValue = input[iIndex + static_cast<std::size_t>(ix)*dims[2] + static_cast<std::size_t>(iy)];
+ }
+ else if (std::get<1>(attrs) == PadBorderType::Reflect) {
+ int ix = static_cast<int>(ox) - static_cast<int>(std::get<0>(attrs)[1][1]);
+ int iy = static_cast<int>(oy) - static_cast<int>(std::get<0>(attrs)[0][1]);
+
+ if (ix < 0)
+ ix = 0 - ix;
+ if (iy < 0)
+ iy = 0 - iy;
+ if (ix >= static_cast<int>(dims[3]))
+ ix = static_cast<int>(dims[3]) - ix;
+ if (iy >= static_cast<int>(dims[2]))
+ iy = static_cast<int>(dims[2]) - iy;
+
+ outputValue = input[iIndex + static_cast<std::size_t>(ix)*dims[2] + static_cast<std::size_t>(iy)];
+ }
+ else if (std::get<1>(attrs) == PadBorderType::Wrap) {
+ int ix = (static_cast<int>(dims[3]) + static_cast<int>(ox) - static_cast<int>(std::get<0>(attrs)[1][1])) % static_cast<int>(dims[3]);
+ int iy = (static_cast<int>(dims[2]) + static_cast<int>(oy) - static_cast<int>(std::get<0>(attrs)[0][1])) % static_cast<int>(dims[2]);
+
+ outputValue = input[iIndex + static_cast<std::size_t>(ix)*dims[2] + static_cast<std::size_t>(iy)];
+ }
+
+ output[oIndexFull] = outputValue;
+ }
+ }
+ }
+ }
+}
+
+namespace {
+static Registrar<PadImpl2DForward_cpu> registrarPadImpl2DForward_cpu_Float32(
+ {DataType::Float32, DataType::Float32},
+ Aidge::PadImpl2D_cpu_forward_kernel<float, float>);
+static Registrar<PadImpl2DForward_cpu> registrarPadImpl2DForward_cpu_Int32(
+ {DataType::Int32, DataType::Int32},
+ Aidge::PadImpl2D_cpu_forward_kernel<int, int>);
+static Registrar<PadImpl2DForward_cpu> registrarPadImpl2DForward_cpu_Float64(
+ {DataType::Float64, DataType::Float64},
+ Aidge::PadImpl2D_cpu_forward_kernel<double, double>);
+} // namespace
+} // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_PADIMPL_FORWARD_KERNEL_H_ */
diff --git a/include/aidge/backend/cpu/operator/ProducerImpl.hpp b/include/aidge/backend/cpu/operator/ProducerImpl.hpp
index 032172dbf0995fc62ce631aa5eba1cabf2374ad3..f23dfc2656ae5c91c8879f24474efc2eb0b04231 100644
--- a/include/aidge/backend/cpu/operator/ProducerImpl.hpp
+++ b/include/aidge/backend/cpu/operator/ProducerImpl.hpp
@@ -39,9 +39,9 @@ class ProducerImpl_cpu : public OperatorImpl {
NbElts_t getNbProducedData(const IOIndex_t outputIdx) const override final;
void updateConsummerProducer() override final;
- void forward();
+ void forward() override;
- void backward();
+ void backward() override;
};
namespace {
diff --git a/include/aidge/backend/cpu/operator/ReLUImpl.hpp b/include/aidge/backend/cpu/operator/ReLUImpl.hpp
index 537bdeeaf89b388a82e819330649c2ae3445c590..dd785d1d4231eff562fce8a814934aefd715c23f 100644
--- a/include/aidge/backend/cpu/operator/ReLUImpl.hpp
+++ b/include/aidge/backend/cpu/operator/ReLUImpl.hpp
@@ -50,9 +50,9 @@ class ReLUImpl_cpu : public OperatorImpl {
NbElts_t getNbConsumedData(const IOIndex_t inputIdx) const override final;
NbElts_t getNbProducedData(const IOIndex_t outputIdx) const override final;
void updateConsummerProducer() override final;
- void forward();
+ void forward() override;
- void backward();
+ void backward() override;
};
namespace {
diff --git a/include/aidge/backend/cpu/operator/ScalingImpl.hpp b/include/aidge/backend/cpu/operator/ScalingImpl.hpp
index 37549349b9f5ffbf443d976135db05b4cec209b7..58ca58510e7e89faac95ef5df637aaf3ac8ed98d 100644
--- a/include/aidge/backend/cpu/operator/ScalingImpl.hpp
+++ b/include/aidge/backend/cpu/operator/ScalingImpl.hpp
@@ -54,9 +54,9 @@ class ScalingImpl_cpu : public OperatorImpl {
void updateConsummerProducer() override final;
- void forward();
+ void forward() override;
- void backward();
+ void backward() override;
};
namespace {
diff --git a/include/aidge/backend/cpu/operator/SoftmaxImpl.hpp b/include/aidge/backend/cpu/operator/SoftmaxImpl.hpp
index 08567ab98e55233f1f578e82cb39ac5681f0a839..e2b30a59d9a15627e8d71e7c0b7f031dcf79964d 100644
--- a/include/aidge/backend/cpu/operator/SoftmaxImpl.hpp
+++ b/include/aidge/backend/cpu/operator/SoftmaxImpl.hpp
@@ -50,9 +50,9 @@ class SoftmaxImpl_cpu : public OperatorImpl {
NbElts_t getNbConsumedData(const IOIndex_t inputIdx) const override final;
NbElts_t getNbProducedData(const IOIndex_t outputIdx) const override final;
void updateConsummerProducer() override final;
- void forward();
+ void forward() override;
- void backward();
+ void backward() override;
};
namespace {
diff --git a/setup.py b/setup.py
index 16305afdfdfa5de2e328460d9e96c77eb96a9d98..b88329e54feab78e39bd79be0a129030098e216a 100644
--- a/setup.py
+++ b/setup.py
@@ -70,7 +70,8 @@ class CMakeBuild(build_ext):
self.spawn(['cmake', str(cwd), param_py, '-DTEST=OFF', f'-DCMAKE_INSTALL_PREFIX:PATH={install_path}'])
if not self.dry_run:
- self.spawn(['make', 'all', 'install', '-j', max_jobs])
+ self.spawn(['cmake', '--build', '.', '--config', 'Debug', '-j', max_jobs])
+ self.spawn(['cmake', '--install', '.', '--config', 'Debug'])
os.chdir(str(cwd))
aidge_package = build_lib / (get_project_name())
@@ -81,7 +82,7 @@ class CMakeBuild(build_ext):
# Copy all shared object files from build_temp/lib to aidge_package
for root, _, files in os.walk(build_temp.absolute()):
for file in files:
- if file.endswith('.so') and (root != str(aidge_package.absolute())):
+ if (file.endswith('.so') or file.endswith('.pyd')) and (root != str(aidge_package.absolute())):
currentFile=os.path.join(root, file)
shutil.copy(currentFile, str(aidge_package.absolute()))
@@ -100,7 +101,6 @@ if __name__ == '__main__':
long_description_content_type="text/markdown",
long_description="\n".join(DOCLINES[2:]),
classifiers=[c for c in CLASSIFIERS.split('\n') if c],
- platforms=["Linux"],
packages=find_packages(where="."),
include_package_data=True,
ext_modules=[CMakeExtension(get_project_name())],
diff --git a/src/operator/PadImpl.cpp b/src/operator/PadImpl.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..a7d2fb4e3fd7d10a59bc019c992cfad263954c17
--- /dev/null
+++ b/src/operator/PadImpl.cpp
@@ -0,0 +1,92 @@
+/********************************************************************************
+ * Copyright (c) 2023 CEA-List
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Eclipse Public License 2.0 which is available at
+ * http://www.eclipse.org/legal/epl-2.0.
+ *
+ * SPDX-License-Identifier: EPL-2.0
+ *
+ ********************************************************************************/
+
+#include <cassert>
+#include <chrono> // std::chrono::milliseconds
+#include <numeric> // std::accumulate
+#include <thread> // std::this_thread::sleep_for
+#include <vector>
+
+#include "aidge/utils/Types.h"
+#include "aidge/operator/Conv.hpp"
+
+#include "aidge/backend/cpu/operator/PadImpl.hpp"
+#include "aidge/backend/cpu/operator/PadImpl_forward_kernels.hpp"
+
+Aidge::NbElts_t Aidge::PadImpl2D_cpu::getNbRequiredData(const Aidge::IOIndex_t inputIdx) const {
+ assert(inputIdx == 0 && "operator has only one input");
+ (void) inputIdx;
+
+ // Requires the whole tensors
+ const auto &inputDims = std::static_pointer_cast<Tensor>(mOp.getInput(0))->dims();
+
+ return std::accumulate(inputDims.begin(), inputDims.end(), Aidge::NbElts_t(1), std::multiplies<NbElts_t>());
+}
+
+Aidge::NbElts_t Aidge::PadImpl2D_cpu::getNbRequiredProtected(IOIndex_t inputIdx) const {
+ assert(inputIdx == 0 && "operator has only one input");
+ (void) inputIdx;
+
+ // Padding cannot be in-place!
+ // We must ensure that we do not override data that has not been consummed yet.
+ const auto &inputDims = std::static_pointer_cast<Tensor>(mOp.getInput(0))->dims();
+ const size_t inputSize = std::accumulate(inputDims.begin(), inputDims.end(), Aidge::NbElts_t(1), std::multiplies<NbElts_t>());
+ const auto &outputDims = std::static_pointer_cast<Tensor>(mOp.getOutput(0))->dims();
+ const size_t outputSize = std::accumulate(outputDims.begin(), inputDims.end(), Aidge::NbElts_t(1), std::multiplies<NbElts_t>());
+
+ return (outputSize - inputSize);
+}
+
+Aidge::NbElts_t Aidge::PadImpl2D_cpu::getRequiredMemory(const Aidge::IOIndex_t outputIdx,
+ const std::vector<Aidge::DimSize_t> &/*inputsSize*/) const {
+ // Requires the whole tensors, regardless of available data on inputs
+ assert(outputIdx == 0 && "operator has only one output");
+ (void) outputIdx;
+
+ const auto &outputDims = std::static_pointer_cast<Tensor>(mOp.getOutput(0))->dims();
+ return std::accumulate(outputDims.begin(), outputDims.end(), NbElts_t(1), std::multiplies<NbElts_t>());
+}
+
+Aidge::NbElts_t Aidge::PadImpl2D_cpu::getNbConsumedData(Aidge::IOIndex_t inputIdx) const {
+ assert(static_cast<std::size_t>(inputIdx) < mNbConsumedData.size());
+ return mNbConsumedData[static_cast<std::size_t>(inputIdx)];
+}
+
+Aidge::NbElts_t Aidge::PadImpl2D_cpu::getNbProducedData(Aidge::IOIndex_t outputIdx) const {
+ assert((outputIdx == 0) && (static_cast<std::size_t>(outputIdx) < mNbProducedData.size()));
+ return mNbProducedData[static_cast<std::size_t>(outputIdx)];
+}
+
+void Aidge::PadImpl2D_cpu::updateConsummerProducer(){
+ // Update producer-consumer data
+ for (std::size_t inputIdx = 0; inputIdx < mNbConsumedData.size(); ++inputIdx)
+ mNbConsumedData[inputIdx] += getNbRequiredData(static_cast<IOIndex_t>(inputIdx)); // each input is consumed by the minimum
+ // amount for a forward pass
+
+ mNbProducedData[0] += getRequiredMemory(0, {});
+}
+
+void Aidge::PadImpl2D_cpu::forward() {
+ // FIXME: uncomment the following code once memory handling will work
+ assert(mOp.getInput(0) && "missing input #0");
+
+ // Find the correct kernel type
+ auto kernelFunc =
+ Registrar<PadImpl2DForward_cpu>::create({mOp.getInput(0)->dataType(), mOp.getOutput(0)->dataType()});
+
+ // Call kernel
+ kernelFunc(mOp.getStaticAttributes(), std::static_pointer_cast<Tensor>(mOp.getInput(0))->dims<4>(),
+ mOp.getInput(0)->getImpl()->rawPtr(), mOp.getOutput(0)->getImpl()->rawPtr());
+
+
+}
+
+void Aidge::PadImpl2D_cpu::backward() { printf("Not implemented yet.\n"); }
diff --git a/unit_tests/operator/Test_ConvImpl.cpp b/unit_tests/operator/Test_ConvImpl.cpp
index 23ff1aaebcfb79a4d4b1abc4f1a77f1c6de63b21..891f0e94b02d07d41751728e83fa9b42e4b89be8 100644
--- a/unit_tests/operator/Test_ConvImpl.cpp
+++ b/unit_tests/operator/Test_ConvImpl.cpp
@@ -156,165 +156,8 @@ TEST_CASE("[cpu/operator] Conv(forward)") {
// myConv->getOperator()->getOutput(0)->print();
REQUIRE(*(myConv->getOperator()->getOutput(0)) == *myOutput);
}
- SECTION("test Padding") {
- std::shared_ptr<Node> myConv = Conv(3,4,{3,3}, "myconv", {1,1}, {1,1,1,1});
- myConv->getOperator()->setDatatype(DataType::Int32);
- myConv->getOperator()->setBackend("cpu");
- std::shared_ptr<Tensor> myWeights = std::make_shared<Tensor>(Array4D<int,4,3,3,3> {
- {
- {
- {{ 0, 1, 2},
- { 3, 4, 5},
- { 6, 7, 8}},
- {{ 9, 10, 11},
- { 12, 13, 14},
- { 15, 16, 17}},
- {{ 18, 19, 20},
- { 21, 22, 23},
- { 24, 25, 26}}
- },
- {
- {{ 27, 28, 29},
- { 30, 31, 32},
- { 33, 34, 35}},
- {{ 36, 37, 38},
- { 39, 40, 41},
- { 42, 43, 44}},
- {{ 45, 46, 47},
- { 48, 49, 50},
- { 51, 52, 53}}
- },
- {
- {{ 54, 55, 56},
- { 57, 58, 59},
- { 60, 61, 62}},
- {{ 63, 64, 65},
- { 66, 67, 68},
- { 69, 70, 71}},
- {{ 72, 73, 74},
- { 75, 76, 77},
- { 78, 79, 80}}
- },
- {
- {{ 81, 82, 83},
- { 84, 85, 86},
- { 87, 88, 89}},
- {{ 90, 91, 92},
- { 93, 94, 95},
- { 96, 97, 98}},
- {{ 99, 100, 101},
- {102, 103, 104},
- {105, 106, 107}}
- }
- }
- });
- std::shared_ptr<Tensor> myBias = std::make_shared<Tensor>(Array1D<int,4> {{7,0,9,0}});
- std::shared_ptr<Tensor> myInput = std::make_shared<Tensor>(Array4D<int,2,3,5,5> { //NCHW
- {
- {
- {{ 0, 1, 2, 3, 4},
- { 5, 6, 7, 8, 9},
- { 10, 11, 12, 13, 14},
- { 15, 16, 17, 18, 19},
- { 20, 21, 22, 23, 24}},
-
- {{ 25, 26, 27, 28, 29},
- { 30, 31, 32, 33, 34},
- { 35, 36, 37, 38, 39},
- { 40, 41, 42, 43, 44},
- { 45, 46, 47, 48, 49}},
-
- {{ 50, 51, 52, 53, 54},
- { 55, 56, 57, 58, 59},
- { 60, 61, 62, 63, 64},
- { 65, 66, 67, 68, 69},
- { 70, 71, 72, 73, 74}}
- },
- {
- {{ 75, 76, 77, 78, 79},
- { 80, 81, 82, 83, 84},
- { 85, 86, 87, 88, 89},
- { 90, 91, 92, 93, 94},
- { 95, 96, 97, 98, 99}},
-
- {{100, 101, 102, 103, 104},
- {105, 106, 107, 108, 109},
- {110, 111, 112, 113, 114},
- {115, 116, 117, 118, 119},
- {120, 121, 122, 123, 124}},
-
- {{125, 126, 127, 128, 129},
- {130, 131, 132, 133, 134},
- {135, 136, 137, 138, 139},
- {140, 141, 142, 143, 144},
- {145, 146, 147, 148, 149}}
- }
- }
- });
- std::shared_ptr<Tensor> myOutput = std::make_shared<Tensor>(Array4D<int,2,4,5,5> {
- {
- {
- {{ 6895, 10225, 10486, 10747, 7063},
- { 10303, 15226, 15577, 15928, 10429},
- { 11518, 16981, 17332, 17683, 11554},
- { 12733, 18736, 19087, 19438, 12679},
- { 8047, 11791, 11998, 12205, 7927}},
-
- {{ 15960, 24069, 24816, 25563, 17100},
- { 25119, 37818, 38898, 39978, 26703},
- { 28764, 43218, 44298, 45378, 30258},
- { 32409, 48618, 49698, 50778, 33813},
- { 21972, 32925, 33618, 34311, 22824}},
-
- {{ 25041, 37929, 39162, 40395, 27153},
- { 39951, 60426, 62235, 64044, 42993},
- { 46026, 69471, 71280, 73089, 48978},
- { 52101, 78516, 80325, 82134, 54963},
- { 35913, 54075, 55254, 56433, 37737}},
-
- {{ 34104, 51771, 53490, 55209, 37188},
- { 54765, 83016, 85554, 88092, 59265},
- { 63270, 95706, 98244, 100782, 67680},
- { 71775, 108396, 110934, 113472, 76095},
- { 49836, 75207, 76872, 78537, 52632}}
- },
- {
- {{ 20395, 29800, 30061, 30322, 19663},
- { 28528, 41551, 41902, 42253, 27304},
- { 29743, 43306, 43657, 44008, 28429},
- { 30958, 45061, 45412, 45763, 29554},
- { 18847, 27316, 27523, 27730, 17827}},
-
- {{ 53760, 80094, 80841, 81588, 54000},
- { 79794, 118818, 119898, 120978, 80028},
- { 83439, 124218, 125298, 126378, 83583},
- { 87084, 129618, 130698, 131778, 87138},
- { 57072, 84900, 85593, 86286, 57024}},
-
- {{ 87141, 130404, 131637, 132870, 88353},
- {131076, 196101, 197910, 199719, 132768},
- {137151, 205146, 206955, 208764, 138753},
- {143226, 214191, 216000, 217809, 144738},
- { 95313, 142500, 143679, 144858, 96237}},
-
- {{120504, 180696, 182415, 184134, 122688},
- {182340, 273366, 275904, 278442, 185490},
- {190845, 286056, 288594, 291132, 193905},
- {199350, 298746, 301284, 303822, 202320},
- {133536, 200082, 201747, 203412, 135432}}
- }
- }
- });
- myConv->getOperator()->associateInput(0,myInput);
- myConv->getOperator()->associateInput(1,myWeights);
- myConv->getOperator()->associateInput(2,myBias);
- myConv->getOperator()->computeOutputDims();
- myConv->forward();
-
- REQUIRE(*(myConv->getOperator()->getOutput(0)) == *myOutput);
- }
SECTION("Point-wise") {
- std::shared_ptr<Node> myConv = Conv(3,4,{1,1}, "myconv", {1,1}, {0,0,0,0});
+ std::shared_ptr<Node> myConv = Conv(3,4,{1,1}, "myconv", {1,1});
myConv->getOperator()->setDatatype(DataType::Float32);
myConv->getOperator()->setBackend("cpu");
myConv->getOperator()->input(0) = Array4D<float,2,3,3,3> {
diff --git a/unit_tests/operator/Test_PadImpl.cpp b/unit_tests/operator/Test_PadImpl.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..f40b62faa8207d770b895258c786580f82ae7f21
--- /dev/null
+++ b/unit_tests/operator/Test_PadImpl.cpp
@@ -0,0 +1,569 @@
+/********************************************************************************
+ * Copyright (c) 2023 CEA-List
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Eclipse Public License 2.0 which is available at
+ * http://www.eclipse.org/legal/epl-2.0.
+ *
+ * SPDX-License-Identifier: EPL-2.0
+ *
+ ********************************************************************************/
+
+#include <catch2/catch_test_macros.hpp>
+#include <cstdlib>
+#include <memory>
+
+#include "aidge/data/Tensor.hpp"
+#include "aidge/operator/Pad.hpp"
+
+#include "aidge/backend/cpu.hpp"
+
+using namespace Aidge;
+
+TEST_CASE("[cpu/operator] Pad(forward)") {
+ SECTION("Symmetric Pad") {
+ const int pv = 0; // pad value
+
+ std::shared_ptr<Node> myPad = Pad({{1, 1}, {1, 1}}, "mypad", PadBorderType::Constant, static_cast<double>(pv));
+ myPad->getOperator()->setDatatype(DataType::Int32);
+ myPad->getOperator()->setBackend("cpu");
+ std::shared_ptr<Tensor> myInput = std::make_shared<Tensor>(Array4D<int,2,3,5,5> { //NCHW
+ {
+ {
+ {{ 0, 1, 2, 3, 4},
+ { 5, 6, 7, 8, 9},
+ { 10, 11, 12, 13, 14},
+ { 15, 16, 17, 18, 19},
+ { 20, 21, 22, 23, 24}},
+
+ {{ 25, 26, 27, 28, 29},
+ { 30, 31, 32, 33, 34},
+ { 35, 36, 37, 38, 39},
+ { 40, 41, 42, 43, 44},
+ { 45, 46, 47, 48, 49}},
+
+ {{ 50, 51, 52, 53, 54},
+ { 55, 56, 57, 58, 59},
+ { 60, 61, 62, 63, 64},
+ { 65, 66, 67, 68, 69},
+ { 70, 71, 72, 73, 74}}
+ },
+ {
+ {{ 75, 76, 77, 78, 79},
+ { 80, 81, 82, 83, 84},
+ { 85, 86, 87, 88, 89},
+ { 90, 91, 92, 93, 94},
+ { 95, 96, 97, 98, 99}},
+
+ {{100, 101, 102, 103, 104},
+ {105, 106, 107, 108, 109},
+ {110, 111, 112, 113, 114},
+ {115, 116, 117, 118, 119},
+ {120, 121, 122, 123, 124}},
+
+ {{125, 126, 127, 128, 129},
+ {130, 131, 132, 133, 134},
+ {135, 136, 137, 138, 139},
+ {140, 141, 142, 143, 144},
+ {145, 146, 147, 148, 149}}
+ }
+ }
+ });
+ std::shared_ptr<Tensor> myOutput = std::make_shared<Tensor>(Array4D<int,2,3,7,7> { //NCHW
+ {
+ {
+ {{ pv, pv, pv, pv, pv, pv, pv},
+ { pv, 0, 1, 2, 3, 4, pv},
+ { pv, 5, 6, 7, 8, 9, pv},
+ { pv, 10, 11, 12, 13, 14, pv},
+ { pv, 15, 16, 17, 18, 19, pv},
+ { pv, 20, 21, 22, 23, 24, pv},
+ { pv, pv, pv, pv, pv, pv, pv}},
+
+ {{ pv, pv, pv, pv, pv, pv, pv},
+ { pv, 25, 26, 27, 28, 29, pv},
+ { pv, 30, 31, 32, 33, 34, pv},
+ { pv, 35, 36, 37, 38, 39, pv},
+ { pv, 40, 41, 42, 43, 44, pv},
+ { pv, 45, 46, 47, 48, 49, pv},
+ { pv, pv, pv, pv, pv, pv, pv}},
+
+ {{ pv, pv, pv, pv, pv, pv, pv},
+ { pv, 50, 51, 52, 53, 54, pv},
+ { pv, 55, 56, 57, 58, 59, pv},
+ { pv, 60, 61, 62, 63, 64, pv},
+ { pv, 65, 66, 67, 68, 69, pv},
+ { pv, 70, 71, 72, 73, 74, pv},
+ { pv, pv, pv, pv, pv, pv, pv}}
+ },
+ {
+ {{ pv, pv, pv, pv, pv, pv, pv},
+ { pv, 75, 76, 77, 78, 79, pv},
+ { pv, 80, 81, 82, 83, 84, pv},
+ { pv, 85, 86, 87, 88, 89, pv},
+ { pv, 90, 91, 92, 93, 94, pv},
+ { pv, 95, 96, 97, 98, 99, pv},
+ { pv, pv, pv, pv, pv, pv, pv}},
+
+ {{ pv, pv, pv, pv, pv, pv, pv},
+ {pv, 100, 101, 102, 103, 104, pv},
+ {pv, 105, 106, 107, 108, 109, pv},
+ {pv, 110, 111, 112, 113, 114, pv},
+ {pv, 115, 116, 117, 118, 119, pv},
+ {pv, 120, 121, 122, 123, 124, pv},
+ { pv, pv, pv, pv, pv, pv, pv}},
+
+ {{ pv, pv, pv, pv, pv, pv, pv},
+ {pv, 125, 126, 127, 128, 129, pv},
+ {pv, 130, 131, 132, 133, 134, pv},
+ {pv, 135, 136, 137, 138, 139, pv},
+ {pv, 140, 141, 142, 143, 144, pv},
+ {pv, 145, 146, 147, 148, 149, pv},
+ { pv, pv, pv, pv, pv, pv, pv}}
+ }
+ }
+ });
+
+ myPad->getOperator()->associateInput(0,myInput);
+ myPad->getOperator()->computeOutputDims();
+ myPad->forward();
+ // myPad->getOperator()->getOutput(0)->print();
+ REQUIRE(*(myPad->getOperator()->getOutput(0)) == *myOutput);
+ }
+
+ SECTION("Asymmetric Pad") {
+ const int pv = 0; // pad value
+
+ std::shared_ptr<Node> myPad = Pad({{1, 0}, {0, 1}}, "mypad", PadBorderType::Constant, static_cast<double>(pv));
+ myPad->getOperator()->setDatatype(DataType::Int32);
+ myPad->getOperator()->setBackend("cpu");
+ std::shared_ptr<Tensor> myInput = std::make_shared<Tensor>(Array4D<int,2,3,5,5> { //NCHW
+ {
+ {
+ {{ 0, 1, 2, 3, 4},
+ { 5, 6, 7, 8, 9},
+ { 10, 11, 12, 13, 14},
+ { 15, 16, 17, 18, 19},
+ { 20, 21, 22, 23, 24}},
+
+ {{ 25, 26, 27, 28, 29},
+ { 30, 31, 32, 33, 34},
+ { 35, 36, 37, 38, 39},
+ { 40, 41, 42, 43, 44},
+ { 45, 46, 47, 48, 49}},
+
+ {{ 50, 51, 52, 53, 54},
+ { 55, 56, 57, 58, 59},
+ { 60, 61, 62, 63, 64},
+ { 65, 66, 67, 68, 69},
+ { 70, 71, 72, 73, 74}}
+ },
+ {
+ {{ 75, 76, 77, 78, 79},
+ { 80, 81, 82, 83, 84},
+ { 85, 86, 87, 88, 89},
+ { 90, 91, 92, 93, 94},
+ { 95, 96, 97, 98, 99}},
+
+ {{100, 101, 102, 103, 104},
+ {105, 106, 107, 108, 109},
+ {110, 111, 112, 113, 114},
+ {115, 116, 117, 118, 119},
+ {120, 121, 122, 123, 124}},
+
+ {{125, 126, 127, 128, 129},
+ {130, 131, 132, 133, 134},
+ {135, 136, 137, 138, 139},
+ {140, 141, 142, 143, 144},
+ {145, 146, 147, 148, 149}}
+ }
+ }
+ });
+ std::shared_ptr<Tensor> myOutput = std::make_shared<Tensor>(Array4D<int,2,3,6,6> { //NCHW
+ {
+ {
+ {{ pv, pv, pv, pv, pv, pv},
+ { 0, 1, 2, 3, 4, pv},
+ { 5, 6, 7, 8, 9, pv},
+ { 10, 11, 12, 13, 14, pv},
+ { 15, 16, 17, 18, 19, pv},
+ { 20, 21, 22, 23, 24, pv}},
+
+ {{ pv, pv, pv, pv, pv, pv},
+ { 25, 26, 27, 28, 29, pv},
+ { 30, 31, 32, 33, 34, pv},
+ { 35, 36, 37, 38, 39, pv},
+ { 40, 41, 42, 43, 44, pv},
+ { 45, 46, 47, 48, 49, pv}},
+
+ {{ pv, pv, pv, pv, pv, pv},
+ { 50, 51, 52, 53, 54, pv},
+ { 55, 56, 57, 58, 59, pv},
+ { 60, 61, 62, 63, 64, pv},
+ { 65, 66, 67, 68, 69, pv},
+ { 70, 71, 72, 73, 74, pv}}
+ },
+ {
+ {{ pv, pv, pv, pv, pv, pv},
+ { 75, 76, 77, 78, 79, pv},
+ { 80, 81, 82, 83, 84, pv},
+ { 85, 86, 87, 88, 89, pv},
+ { 90, 91, 92, 93, 94, pv},
+ { 95, 96, 97, 98, 99, pv}},
+
+ {{ pv, pv, pv, pv, pv, pv},
+ { 100, 101, 102, 103, 104, pv},
+ { 105, 106, 107, 108, 109, pv},
+ { 110, 111, 112, 113, 114, pv},
+ { 115, 116, 117, 118, 119, pv},
+ { 120, 121, 122, 123, 124, pv}},
+
+ {{ pv, pv, pv, pv, pv, pv},
+ { 125, 126, 127, 128, 129, pv},
+ { 130, 131, 132, 133, 134, pv},
+ { 135, 136, 137, 138, 139, pv},
+ { 140, 141, 142, 143, 144, pv},
+ { 145, 146, 147, 148, 149, pv}}
+ }
+ }
+ });
+
+ myPad->getOperator()->associateInput(0,myInput);
+ myPad->getOperator()->computeOutputDims();
+ myPad->forward();
+ // myPad->getOperator()->getOutput(0)->print();
+ REQUIRE(*(myPad->getOperator()->getOutput(0)) == *myOutput);
+ }
+
+ SECTION("Pad Replicate") {
+ std::shared_ptr<Node> myPad = Pad({{1, 1}, {1, 1}}, "mypad", PadBorderType::Replicate);
+ myPad->getOperator()->setDatatype(DataType::Int32);
+ myPad->getOperator()->setBackend("cpu");
+ std::shared_ptr<Tensor> myInput = std::make_shared<Tensor>(Array4D<int,2,3,5,5> { //NCHW
+ {
+ {
+ {{ 0, 1, 2, 3, 4},
+ { 5, 6, 7, 8, 9},
+ { 10, 11, 12, 13, 14},
+ { 15, 16, 17, 18, 19},
+ { 20, 21, 22, 23, 24}},
+
+ {{ 25, 26, 27, 28, 29},
+ { 30, 31, 32, 33, 34},
+ { 35, 36, 37, 38, 39},
+ { 40, 41, 42, 43, 44},
+ { 45, 46, 47, 48, 49}},
+
+ {{ 50, 51, 52, 53, 54},
+ { 55, 56, 57, 58, 59},
+ { 60, 61, 62, 63, 64},
+ { 65, 66, 67, 68, 69},
+ { 70, 71, 72, 73, 74}}
+ },
+ {
+ {{ 75, 76, 77, 78, 79},
+ { 80, 81, 82, 83, 84},
+ { 85, 86, 87, 88, 89},
+ { 90, 91, 92, 93, 94},
+ { 95, 96, 97, 98, 99}},
+
+ {{100, 101, 102, 103, 104},
+ {105, 106, 107, 108, 109},
+ {110, 111, 112, 113, 114},
+ {115, 116, 117, 118, 119},
+ {120, 121, 122, 123, 124}},
+
+ {{125, 126, 127, 128, 129},
+ {130, 131, 132, 133, 134},
+ {135, 136, 137, 138, 139},
+ {140, 141, 142, 143, 144},
+ {145, 146, 147, 148, 149}}
+ }
+ }
+ });
+ std::shared_ptr<Tensor> myOutput = std::make_shared<Tensor>(Array4D<int,2,3,7,7> { //NCHW
+ {
+ {
+ {{ 0, 0, 1, 2, 3, 4, 4},
+ { 0, 0, 1, 2, 3, 4, 4},
+ { 5, 5, 6, 7, 8, 9, 9},
+ { 10, 10, 11, 12, 13, 14, 14},
+ { 15, 15, 16, 17, 18, 19, 19},
+ { 20, 20, 21, 22, 23, 24, 24},
+ { 20, 20, 21, 22, 23, 24, 24}},
+
+ {{ 25, 25, 26, 27, 28, 29, 29},
+ { 25, 25, 26, 27, 28, 29, 29},
+ { 30, 30, 31, 32, 33, 34, 34},
+ { 35, 35, 36, 37, 38, 39, 39},
+ { 40, 40, 41, 42, 43, 44, 44},
+ { 45, 45, 46, 47, 48, 49, 49},
+ { 45, 45, 46, 47, 48, 49, 49}},
+
+ {{ 50, 50, 51, 52, 53, 54, 54},
+ { 50, 50, 51, 52, 53, 54, 54},
+ { 55, 55, 56, 57, 58, 59, 59},
+ { 60, 60, 61, 62, 63, 64, 64},
+ { 65, 65, 66, 67, 68, 69, 69},
+ { 70, 70, 71, 72, 73, 74, 74},
+ { 70, 70, 71, 72, 73, 74, 74}}
+ },
+ {
+ {{ 75, 75, 76, 77, 78, 79, 79},
+ { 75, 75, 76, 77, 78, 79, 79},
+ { 80, 80, 81, 82, 83, 84, 84},
+ { 85, 85, 86, 87, 88, 89, 89},
+ { 90, 90, 91, 92, 93, 94, 94},
+ { 95, 95, 96, 97, 98, 99, 99},
+ { 95, 95, 96, 97, 98, 99, 99}},
+
+ {{100, 100, 101, 102, 103, 104, 104},
+ {100, 100, 101, 102, 103, 104, 104},
+ {105, 105, 106, 107, 108, 109, 109},
+ {110, 110, 111, 112, 113, 114, 114},
+ {115, 115, 116, 117, 118, 119, 119},
+ {120, 120, 121, 122, 123, 124, 124},
+ {120, 120, 121, 122, 123, 124, 124}},
+
+ {{125, 125, 126, 127, 128, 129, 129},
+ {125, 125, 126, 127, 128, 129, 129},
+ {130, 130, 131, 132, 133, 134, 134},
+ {135, 135, 136, 137, 138, 139, 139},
+ {140, 140, 141, 142, 143, 144, 144},
+ {145, 145, 146, 147, 148, 149, 149},
+ {145, 145, 146, 147, 148, 149, 149}}
+ }
+ }
+ });
+
+ myPad->getOperator()->associateInput(0,myInput);
+ myPad->getOperator()->computeOutputDims();
+ myPad->forward();
+ // myPad->getOperator()->getOutput(0)->print();
+ REQUIRE(*(myPad->getOperator()->getOutput(0)) == *myOutput);
+ }
+
+ SECTION("Pad Reflect") {
+ std::shared_ptr<Node> myPad = Pad({{1, 1}, {1, 1}}, "mypad", PadBorderType::Reflect);
+ myPad->getOperator()->setDatatype(DataType::Int32);
+ myPad->getOperator()->setBackend("cpu");
+ std::shared_ptr<Tensor> myInput = std::make_shared<Tensor>(Array4D<int,2,3,5,5> { //NCHW
+ {
+ {
+ {{ 0, 1, 2, 3, 4},
+ { 5, 6, 7, 8, 9},
+ { 10, 11, 12, 13, 14},
+ { 15, 16, 17, 18, 19},
+ { 20, 21, 22, 23, 24}},
+
+ {{ 25, 26, 27, 28, 29},
+ { 30, 31, 32, 33, 34},
+ { 35, 36, 37, 38, 39},
+ { 40, 41, 42, 43, 44},
+ { 45, 46, 47, 48, 49}},
+
+ {{ 50, 51, 52, 53, 54},
+ { 55, 56, 57, 58, 59},
+ { 60, 61, 62, 63, 64},
+ { 65, 66, 67, 68, 69},
+ { 70, 71, 72, 73, 74}}
+ },
+ {
+ {{ 75, 76, 77, 78, 79},
+ { 80, 81, 82, 83, 84},
+ { 85, 86, 87, 88, 89},
+ { 90, 91, 92, 93, 94},
+ { 95, 96, 97, 98, 99}},
+
+ {{100, 101, 102, 103, 104},
+ {105, 106, 107, 108, 109},
+ {110, 111, 112, 113, 114},
+ {115, 116, 117, 118, 119},
+ {120, 121, 122, 123, 124}},
+
+ {{125, 126, 127, 128, 129},
+ {130, 131, 132, 133, 134},
+ {135, 136, 137, 138, 139},
+ {140, 141, 142, 143, 144},
+ {145, 146, 147, 148, 149}}
+ }
+ }
+ });
+ std::shared_ptr<Tensor> myOutput = std::make_shared<Tensor>(Array4D<int,2,3,7,7> { //NCHW
+ {
+ {
+ {
+ { 6, 5, 6, 7, 8, 9, 5},
+ { 1, 0, 1, 2, 3, 4, 0},
+ { 6, 5, 6, 7, 8, 9, 5},
+ { 11, 10, 11, 12, 13, 14, 10},
+ { 16, 15, 16, 17, 18, 19, 15},
+ { 21, 20, 21, 22, 23, 24, 20},
+ { 1, 0, 1, 2, 3, 4, 0}
+ },
+ {
+ { 31, 30, 31, 32, 33, 34, 30},
+ { 26, 25, 26, 27, 28, 29, 25},
+ { 31, 30, 31, 32, 33, 34, 30},
+ { 36, 35, 36, 37, 38, 39, 35},
+ { 41, 40, 41, 42, 43, 44, 40},
+ { 46, 45, 46, 47, 48, 49, 45},
+ { 26, 25, 26, 27, 28, 29, 25}
+ },
+ {
+ { 56, 55, 56, 57, 58, 59, 55},
+ { 51, 50, 51, 52, 53, 54, 50},
+ { 56, 55, 56, 57, 58, 59, 55},
+ { 61, 60, 61, 62, 63, 64, 60},
+ { 66, 65, 66, 67, 68, 69, 65},
+ { 71, 70, 71, 72, 73, 74, 70},
+ { 51, 50, 51, 52, 53, 54, 50}
+ }
+ },
+ {
+ {
+ { 81, 80, 81, 82, 83, 84, 80},
+ { 76, 75, 76, 77, 78, 79, 75},
+ { 81, 80, 81, 82, 83, 84, 80},
+ { 86, 85, 86, 87, 88, 89, 85},
+ { 91, 90, 91, 92, 93, 94, 90},
+ { 96, 95, 96, 97, 98, 99, 95},
+ { 76, 75, 76, 77, 78, 79, 75}
+ },
+ {
+ { 106, 105, 106, 107, 108, 109, 105},
+ { 101, 100, 101, 102, 103, 104, 100},
+ { 106, 105, 106, 107, 108, 109, 105},
+ { 111, 110, 111, 112, 113, 114, 110},
+ { 116, 115, 116, 117, 118, 119, 115},
+ { 121, 120, 121, 122, 123, 124, 120},
+ { 101, 100, 101, 102, 103, 104, 100}
+ },
+ {
+ { 131, 130, 131, 132, 133, 134, 130},
+ { 126, 125, 126, 127, 128, 129, 125},
+ { 131, 130, 131, 132, 133, 134, 130},
+ { 136, 135, 136, 137, 138, 139, 135},
+ { 141, 140, 141, 142, 143, 144, 140},
+ { 146, 145, 146, 147, 148, 149, 145},
+ { 126, 125, 126, 127, 128, 129, 125}
+ }
+ }
+ }
+ });
+
+ myPad->getOperator()->associateInput(0,myInput);
+ myPad->getOperator()->computeOutputDims();
+ myPad->forward();
+ myPad->getOperator()->getOutput(0)->print();
+ REQUIRE(*(myPad->getOperator()->getOutput(0)) == *myOutput);
+ }
+
+ SECTION("Pad Wrap") {
+ std::shared_ptr<Node> myPad = Pad({{1, 1}, {1, 1}}, "mypad", PadBorderType::Wrap);
+ myPad->getOperator()->setDatatype(DataType::Int32);
+ myPad->getOperator()->setBackend("cpu");
+ std::shared_ptr<Tensor> myInput = std::make_shared<Tensor>(Array4D<int,2,3,5,5> { //NCHW
+ {
+ {
+ {{ 0, 1, 2, 3, 4},
+ { 5, 6, 7, 8, 9},
+ { 10, 11, 12, 13, 14},
+ { 15, 16, 17, 18, 19},
+ { 20, 21, 22, 23, 24}},
+
+ {{ 25, 26, 27, 28, 29},
+ { 30, 31, 32, 33, 34},
+ { 35, 36, 37, 38, 39},
+ { 40, 41, 42, 43, 44},
+ { 45, 46, 47, 48, 49}},
+
+ {{ 50, 51, 52, 53, 54},
+ { 55, 56, 57, 58, 59},
+ { 60, 61, 62, 63, 64},
+ { 65, 66, 67, 68, 69},
+ { 70, 71, 72, 73, 74}}
+ },
+ {
+ {{ 75, 76, 77, 78, 79},
+ { 80, 81, 82, 83, 84},
+ { 85, 86, 87, 88, 89},
+ { 90, 91, 92, 93, 94},
+ { 95, 96, 97, 98, 99}},
+
+ {{100, 101, 102, 103, 104},
+ {105, 106, 107, 108, 109},
+ {110, 111, 112, 113, 114},
+ {115, 116, 117, 118, 119},
+ {120, 121, 122, 123, 124}},
+
+ {{125, 126, 127, 128, 129},
+ {130, 131, 132, 133, 134},
+ {135, 136, 137, 138, 139},
+ {140, 141, 142, 143, 144},
+ {145, 146, 147, 148, 149}}
+ }
+ }
+ });
+ std::shared_ptr<Tensor> myOutput = std::make_shared<Tensor>(Array4D<int,2,3,7,7> { //NCHW
+ {
+ {
+ {{ 24, 20, 21, 22, 23, 24, 20},
+ { 4, 0, 1, 2, 3, 4, 0},
+ { 9, 5, 6, 7, 8, 9, 5},
+ { 14, 10, 11, 12, 13, 14, 10},
+ { 19, 15, 16, 17, 18, 19, 15},
+ { 24, 20, 21, 22, 23, 24, 20},
+ { 4, 0, 1, 2, 3, 4, 0}},
+
+ {{ 49, 45, 46, 47, 48, 49, 45},
+ { 29, 25, 26, 27, 28, 29, 25},
+ { 34, 30, 31, 32, 33, 34, 30},
+ { 39, 35, 36, 37, 38, 39, 35},
+ { 44, 40, 41, 42, 43, 44, 40},
+ { 49, 45, 46, 47, 48, 49, 45},
+ { 29, 25, 26, 27, 28, 29, 25}},
+
+ {{ 74, 70, 71, 72, 73, 74, 70},
+ { 54, 50, 51, 52, 53, 54, 50},
+ { 59, 55, 56, 57, 58, 59, 55},
+ { 64, 60, 61, 62, 63, 64, 60},
+ { 69, 65, 66, 67, 68, 69, 65},
+ { 74, 70, 71, 72, 73, 74, 70},
+ { 54, 50, 51, 52, 53, 54, 50}}
+ },
+ {
+ {{ 99, 95, 96, 97, 98, 99, 95},
+ { 79, 75, 76, 77, 78, 79, 75},
+ { 84, 80, 81, 82, 83, 84, 80},
+ { 89, 85, 86, 87, 88, 89, 85},
+ { 94, 90, 91, 92, 93, 94, 90},
+ { 99, 95, 96, 97, 98, 99, 95},
+ { 79, 75, 76, 77, 78, 79, 75}},
+
+ {{124, 120, 121, 122, 123, 124, 120},
+ {104, 100, 101, 102, 103, 104, 100},
+ {109, 105, 106, 107, 108, 109, 105},
+ {114, 110, 111, 112, 113, 114, 110},
+ {119, 115, 116, 117, 118, 119, 115},
+ {124, 120, 121, 122, 123, 124, 120},
+ {104, 100, 101, 102, 103, 104, 100}},
+
+ {{149, 145, 146, 147, 148, 149, 145},
+ {129, 125, 126, 127, 128, 129, 125},
+ {134, 130, 131, 132, 133, 134, 130},
+ {139, 135, 136, 137, 138, 139, 135},
+ {144, 140, 141, 142, 143, 144, 140},
+ {149, 145, 146, 147, 148, 149, 145},
+ {129, 125, 126, 127, 128, 129, 125}}
+ }
+ }
+ });
+
+ myPad->getOperator()->associateInput(0,myInput);
+ myPad->getOperator()->computeOutputDims();
+ myPad->forward();
+ // myPad->getOperator()->getOutput(0)->print();
+ REQUIRE(*(myPad->getOperator()->getOutput(0)) == *myOutput);
+ }
+}
\ No newline at end of file
diff --git a/unit_tests/operator/Test_PaddedConv.cpp b/unit_tests/operator/Test_PaddedConv.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..e41be85ab00faae1af7239c43b74a34f558a663c
--- /dev/null
+++ b/unit_tests/operator/Test_PaddedConv.cpp
@@ -0,0 +1,319 @@
+/********************************************************************************
+ * Copyright (c) 2023 CEA-List
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Eclipse Public License 2.0 which is available at
+ * http://www.eclipse.org/legal/epl-2.0.
+ *
+ * SPDX-License-Identifier: EPL-2.0
+ *
+ ********************************************************************************/
+
+#include <catch2/catch_test_macros.hpp>
+#include <cstdlib>
+#include <memory>
+
+#include "aidge/data/Tensor.hpp"
+#include "aidge/operator/MetaOperator.hpp"
+#include "aidge/operator/MetaOperatorDefs.hpp"
+
+#include "aidge/backend/cpu.hpp"
+
+using namespace Aidge;
+
+TEST_CASE("[cpu/operator] PaddedConv(forward)") {
+ SECTION("Classic Conv") {
+ std::shared_ptr<Node> myConv = PaddedConv(3,4,{3,3}, "myconv");
+ myConv->getOperator()->setDatatype(DataType::Int32);
+ myConv->getOperator()->setBackend("cpu");
+ std::shared_ptr<Tensor> myWeights = std::make_shared<Tensor>(Array4D<int,4,3,3,3> {
+ {
+ {
+ {{ 0, 1, 2},
+ { 3, 4, 5},
+ { 6, 7, 8}},
+ {{ 9, 10, 11},
+ { 12, 13, 14},
+ { 15, 16, 17}},
+ {{ 18, 19, 20},
+ { 21, 22, 23},
+ { 24, 25, 26}}
+ },
+ {
+ {{ 27, 28, 29},
+ { 30, 31, 32},
+ { 33, 34, 35}},
+ {{ 36, 37, 38},
+ { 39, 40, 41},
+ { 42, 43, 44}},
+ {{ 45, 46, 47},
+ { 48, 49, 50},
+ { 51, 52, 53}}
+ },
+ {
+ {{ 54, 55, 56},
+ { 57, 58, 59},
+ { 60, 61, 62}},
+ {{ 63, 64, 65},
+ { 66, 67, 68},
+ { 69, 70, 71}},
+ {{ 72, 73, 74},
+ { 75, 76, 77},
+ { 78, 79, 80}}
+ },
+ {
+ {{ 81, 82, 83},
+ { 84, 85, 86},
+ { 87, 88, 89}},
+ {{ 90, 91, 92},
+ { 93, 94, 95},
+ { 96, 97, 98}},
+ {{ 99, 100, 101},
+ {102, 103, 104},
+ {105, 106, 107}}
+ }
+ }
+ });
+ std::shared_ptr<Tensor> myBias = std::make_shared<Tensor>(Array1D<int,4> {{7,0,9,0}});
+ std::shared_ptr<Tensor> myInput = std::make_shared<Tensor>(Array4D<int,2,3,5,5> { //NCHW
+ {
+ {
+ {{ 0, 1, 2, 3, 4},
+ { 5, 6, 7, 8, 9},
+ { 10, 11, 12, 13, 14},
+ { 15, 16, 17, 18, 19},
+ { 20, 21, 22, 23, 24}},
+
+ {{ 25, 26, 27, 28, 29},
+ { 30, 31, 32, 33, 34},
+ { 35, 36, 37, 38, 39},
+ { 40, 41, 42, 43, 44},
+ { 45, 46, 47, 48, 49}},
+
+ {{ 50, 51, 52, 53, 54},
+ { 55, 56, 57, 58, 59},
+ { 60, 61, 62, 63, 64},
+ { 65, 66, 67, 68, 69},
+ { 70, 71, 72, 73, 74}}
+ },
+ {
+ {{ 75, 76, 77, 78, 79},
+ { 80, 81, 82, 83, 84},
+ { 85, 86, 87, 88, 89},
+ { 90, 91, 92, 93, 94},
+ { 95, 96, 97, 98, 99}},
+
+ {{100, 101, 102, 103, 104},
+ {105, 106, 107, 108, 109},
+ {110, 111, 112, 113, 114},
+ {115, 116, 117, 118, 119},
+ {120, 121, 122, 123, 124}},
+
+ {{125, 126, 127, 128, 129},
+ {130, 131, 132, 133, 134},
+ {135, 136, 137, 138, 139},
+ {140, 141, 142, 143, 144},
+ {145, 146, 147, 148, 149}}
+ }
+ }
+ });
+ std::shared_ptr<Tensor> myOutput = std::make_shared<Tensor>(Array4D<int,2,4,3,3> {
+ {
+ {
+ {{ 15226, 15577, 15928},
+ { 16981, 17332, 17683},
+ { 18736, 19087, 19438}},
+ {{ 37818, 38898, 39978},
+ { 43218, 44298, 45378},
+ { 48618, 49698, 50778}},
+ {{ 60426, 62235, 64044},
+ { 69471, 71280, 73089},
+ { 78516, 80325, 82134}},
+ {{ 83016, 85554, 88092},
+ { 95706, 98244, 100782},
+ {108396, 110934, 113472}}
+ },
+ {
+ {{ 41551, 41902, 42253},
+ { 43306, 43657, 44008},
+ { 45061, 45412, 45763}},
+ {{118818, 119898, 120978},
+ {124218, 125298, 126378},
+ {129618, 130698, 131778}},
+ {{196101, 197910, 199719},
+ {205146, 206955, 208764},
+ {214191, 216000, 217809}},
+ {{273366, 275904, 278442},
+ {286056, 288594, 291132},
+ {298746, 301284, 303822}}
+ }
+ }
+ });
+
+ myConv->getOperator()->associateInput(0,myInput);
+ myConv->getOperator()->associateInput(1,myWeights);
+ myConv->getOperator()->associateInput(2,myBias);
+ myConv->getOperator()->computeOutputDims();
+ myConv->forward();
+
+ REQUIRE(*(myConv->getOperator()->getOutput(0)) == *myOutput);
+ }
+ SECTION("test Padding") {
+ std::shared_ptr<Node> myConv = PaddedConv(3,4,{3,3}, "myconv", {1,1}, {1,1,1,1});
+ myConv->getOperator()->setDatatype(DataType::Int32);
+ myConv->getOperator()->setBackend("cpu");
+ std::shared_ptr<Tensor> myWeights = std::make_shared<Tensor>(Array4D<int,4,3,3,3> {
+ {
+ {
+ {{ 0, 1, 2},
+ { 3, 4, 5},
+ { 6, 7, 8}},
+ {{ 9, 10, 11},
+ { 12, 13, 14},
+ { 15, 16, 17}},
+ {{ 18, 19, 20},
+ { 21, 22, 23},
+ { 24, 25, 26}}
+ },
+ {
+ {{ 27, 28, 29},
+ { 30, 31, 32},
+ { 33, 34, 35}},
+ {{ 36, 37, 38},
+ { 39, 40, 41},
+ { 42, 43, 44}},
+ {{ 45, 46, 47},
+ { 48, 49, 50},
+ { 51, 52, 53}}
+ },
+ {
+ {{ 54, 55, 56},
+ { 57, 58, 59},
+ { 60, 61, 62}},
+ {{ 63, 64, 65},
+ { 66, 67, 68},
+ { 69, 70, 71}},
+ {{ 72, 73, 74},
+ { 75, 76, 77},
+ { 78, 79, 80}}
+ },
+ {
+ {{ 81, 82, 83},
+ { 84, 85, 86},
+ { 87, 88, 89}},
+ {{ 90, 91, 92},
+ { 93, 94, 95},
+ { 96, 97, 98}},
+ {{ 99, 100, 101},
+ {102, 103, 104},
+ {105, 106, 107}}
+ }
+ }
+ });
+ std::shared_ptr<Tensor> myBias = std::make_shared<Tensor>(Array1D<int,4> {{7,0,9,0}});
+ std::shared_ptr<Tensor> myInput = std::make_shared<Tensor>(Array4D<int,2,3,5,5> { //NCHW
+ {
+ {
+ {{ 0, 1, 2, 3, 4},
+ { 5, 6, 7, 8, 9},
+ { 10, 11, 12, 13, 14},
+ { 15, 16, 17, 18, 19},
+ { 20, 21, 22, 23, 24}},
+
+ {{ 25, 26, 27, 28, 29},
+ { 30, 31, 32, 33, 34},
+ { 35, 36, 37, 38, 39},
+ { 40, 41, 42, 43, 44},
+ { 45, 46, 47, 48, 49}},
+
+ {{ 50, 51, 52, 53, 54},
+ { 55, 56, 57, 58, 59},
+ { 60, 61, 62, 63, 64},
+ { 65, 66, 67, 68, 69},
+ { 70, 71, 72, 73, 74}}
+ },
+ {
+ {{ 75, 76, 77, 78, 79},
+ { 80, 81, 82, 83, 84},
+ { 85, 86, 87, 88, 89},
+ { 90, 91, 92, 93, 94},
+ { 95, 96, 97, 98, 99}},
+
+ {{100, 101, 102, 103, 104},
+ {105, 106, 107, 108, 109},
+ {110, 111, 112, 113, 114},
+ {115, 116, 117, 118, 119},
+ {120, 121, 122, 123, 124}},
+
+ {{125, 126, 127, 128, 129},
+ {130, 131, 132, 133, 134},
+ {135, 136, 137, 138, 139},
+ {140, 141, 142, 143, 144},
+ {145, 146, 147, 148, 149}}
+ }
+ }
+ });
+ std::shared_ptr<Tensor> myOutput = std::make_shared<Tensor>(Array4D<int,2,4,5,5> {
+ {
+ {
+ {{ 6895, 10225, 10486, 10747, 7063},
+ { 10303, 15226, 15577, 15928, 10429},
+ { 11518, 16981, 17332, 17683, 11554},
+ { 12733, 18736, 19087, 19438, 12679},
+ { 8047, 11791, 11998, 12205, 7927}},
+
+ {{ 15960, 24069, 24816, 25563, 17100},
+ { 25119, 37818, 38898, 39978, 26703},
+ { 28764, 43218, 44298, 45378, 30258},
+ { 32409, 48618, 49698, 50778, 33813},
+ { 21972, 32925, 33618, 34311, 22824}},
+
+ {{ 25041, 37929, 39162, 40395, 27153},
+ { 39951, 60426, 62235, 64044, 42993},
+ { 46026, 69471, 71280, 73089, 48978},
+ { 52101, 78516, 80325, 82134, 54963},
+ { 35913, 54075, 55254, 56433, 37737}},
+
+ {{ 34104, 51771, 53490, 55209, 37188},
+ { 54765, 83016, 85554, 88092, 59265},
+ { 63270, 95706, 98244, 100782, 67680},
+ { 71775, 108396, 110934, 113472, 76095},
+ { 49836, 75207, 76872, 78537, 52632}}
+ },
+ {
+ {{ 20395, 29800, 30061, 30322, 19663},
+ { 28528, 41551, 41902, 42253, 27304},
+ { 29743, 43306, 43657, 44008, 28429},
+ { 30958, 45061, 45412, 45763, 29554},
+ { 18847, 27316, 27523, 27730, 17827}},
+
+ {{ 53760, 80094, 80841, 81588, 54000},
+ { 79794, 118818, 119898, 120978, 80028},
+ { 83439, 124218, 125298, 126378, 83583},
+ { 87084, 129618, 130698, 131778, 87138},
+ { 57072, 84900, 85593, 86286, 57024}},
+
+ {{ 87141, 130404, 131637, 132870, 88353},
+ {131076, 196101, 197910, 199719, 132768},
+ {137151, 205146, 206955, 208764, 138753},
+ {143226, 214191, 216000, 217809, 144738},
+ { 95313, 142500, 143679, 144858, 96237}},
+
+ {{120504, 180696, 182415, 184134, 122688},
+ {182340, 273366, 275904, 278442, 185490},
+ {190845, 286056, 288594, 291132, 193905},
+ {199350, 298746, 301284, 303822, 202320},
+ {133536, 200082, 201747, 203412, 135432}}
+ }
+ }
+ });
+
+ myConv->getOperator()->associateInput(0,myInput);
+ myConv->getOperator()->associateInput(1,myWeights);
+ myConv->getOperator()->associateInput(2,myBias);
+ myConv->getOperator()->computeOutputDims();
+ myConv->forward();
+
+ REQUIRE(*(myConv->getOperator()->getOutput(0)) == *myOutput);
+ }
+}