diff --git a/.gitlab/ci/build.gitlab-ci.yml b/.gitlab/ci/build.gitlab-ci.yml
index e996def7984908ea751c5e033814e81b18a3d51b..b9fdd937b358b714fd83a36d8417ad2b417d0385 100644
--- a/.gitlab/ci/build.gitlab-ci.yml
+++ b/.gitlab/ci/build.gitlab-ci.yml
@@ -136,70 +136,70 @@ build:ubuntu_python:
paths:
- venv/
-build:windows_cpp:
- 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
- - '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
-
- - $env:CMAKE_PREFIX_PATH = '../install_cpp'
- - mkdir -p build_cpp
- - cd build_cpp
- - cmake -DCMAKE_INSTALL_PREFIX:PATH=../install_cpp -DCMAKE_BUILD_TYPE=Debug ..
- - cmake --build . -j2
- - cmake --install . --config Debug
-
- artifacts:
- expire_in: 1 week
- 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 (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
- - python -m pip install -r requirements.txt
- - python -m pip install .
- artifacts:
- expire_in: 1 week
- paths:
- - venv/
+# build:windows_cpp:
+# 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
+# - '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
+
+# - $env:CMAKE_PREFIX_PATH = '../install_cpp'
+# - mkdir -p build_cpp
+# - cd build_cpp
+# - cmake -DCMAKE_INSTALL_PREFIX:PATH=../install_cpp -DCMAKE_BUILD_TYPE=Debug ..
+# - cmake --build . -j2
+# - cmake --install . --config Debug
+
+# artifacts:
+# expire_in: 1 week
+# 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 (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
+# - python -m pip install -r requirements.txt
+# - python -m pip install .
+# artifacts:
+# expire_in: 1 week
+# paths:
+# - venv/
diff --git a/.gitlab/ci/test.gitlab-ci.yml b/.gitlab/ci/test.gitlab-ci.yml
index 05f567dd7430b0d3a801612ca5353a39288285d2..8f6b1e54109c4c2dcfa026fd477a93b6c0a1c641 100644
--- a/.gitlab/ci/test.gitlab-ci.yml
+++ b/.gitlab/ci/test.gitlab-ci.yml
@@ -27,23 +27,23 @@ test:ubuntu_python:
reports:
junit: ${CI_PROJECT_NAME}/xmlrunner-results.xml
-test:windows_cpp:
- stage: test
- needs: ["build:windows_cpp"]
- 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 python -Y
- # Update PATH
- - $env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User")
- script:
- - cd build_cpp
- - ctest --output-junit ctest-results.xml --output-on-failure
- artifacts:
- reports:
- junit: build_cpp/ctest-results.xml
+# test:windows_cpp:
+# stage: test
+# needs: ["build:windows_cpp"]
+# 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 python -Y
+# # Update PATH
+# - $env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User")
+# script:
+# - cd build_cpp
+# - ctest --output-junit ctest-results.xml --output-on-failure
+# artifacts:
+# reports:
+# junit: build_cpp/ctest-results.xml
diff --git a/CHANGELOG b/CHANGELOG
new file mode 100644
index 0000000000000000000000000000000000000000..82e90519cc6546e5fa2c2dfa76bc32893d7cad64
--- /dev/null
+++ b/CHANGELOG
@@ -0,0 +1,3 @@
+# Version 0.1.0 (January 23, 2024)
+
+Initial release
diff --git a/aidge_backend_cpu/unit_tests/test_tensor.py b/aidge_backend_cpu/unit_tests/test_tensor.py
index 438b6acd51791a52c9e308fb1aceaefb2a45fb29..37531b43cf7755dfb760e575450b70bfa9a6ff68 100644
--- a/aidge_backend_cpu/unit_tests/test_tensor.py
+++ b/aidge_backend_cpu/unit_tests/test_tensor.py
@@ -16,7 +16,7 @@ class test_tensor(unittest.TestCase):
self.assertTrue("cpu" in aidge_core.Tensor.get_available_backends())
def test_numpy_int_to_tensor(self):
- np_array = np.arange(9).reshape(1,1,3,3)
+ np_array = np.arange(9).reshape(1,1,3,3).astype(np.int32)
# Numpy -> Tensor
t = aidge_core.Tensor(np_array)
self.assertEqual(t.dtype(), aidge_core.DataType.Int32)
@@ -35,6 +35,16 @@ class test_tensor(unittest.TestCase):
for i,j in zip(t.dims(), nnarray.shape):
self.assertEqual(i,j)
+ def test_numpy_int64_to_tensor(self):
+ np_array = np.arange(9).reshape(1,1,3,3).astype(np.int64)
+ # Numpy -> Tensor
+ t = aidge_core.Tensor(np_array)
+ self.assertEqual(t.dtype(), aidge_core.DataType.Int64)
+ for i_t, i_n in zip(t, np_array.flatten()):
+ self.assertTrue(i_t == i_n)
+ for i,j in zip(t.dims(), np_array.shape):
+ self.assertEqual(i,j)
+
def test_numpy_float_to_tensor(self):
t = aidge_core.Tensor()
np_array = np.random.rand(1, 1, 3, 3).astype(np.float32)
@@ -49,7 +59,7 @@ class test_tensor(unittest.TestCase):
def test_get_set(self):
dims = [2,2,2]
- np_array = np.arange(8).reshape(dims)
+ np_array = np.arange(8).reshape(dims).astype(np.int32)
# Numpy -> Tensor
t = aidge_core.Tensor(np_array)
for i in range(8):
diff --git a/include/aidge/backend/cpu/data/TensorImpl.hpp b/include/aidge/backend/cpu/data/TensorImpl.hpp
index 012ff5af1c15e73fe76114a23ec62f9ef023bce2..c451b4a5beccacb7980c834d56b979c1b76cdd3f 100644
--- a/include/aidge/backend/cpu/data/TensorImpl.hpp
+++ b/include/aidge/backend/cpu/data/TensorImpl.hpp
@@ -1,29 +1,50 @@
+/********************************************************************************
+ * 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_DATA_TENSORIMPL_H_
#define AIDGE_CPU_DATA_TENSORIMPL_H_
#include "aidge/backend/TensorImpl.hpp"
#include "aidge/data/Tensor.hpp"
+#include "aidge/data/half.hpp"
#include "aidge/utils/Registrar.hpp"
#include "aidge/utils/Types.h"
+#include "aidge/utils/ErrorHandling.hpp"
+#include "aidge/utils/future_std/span.hpp"
namespace Aidge {
+
template <class T>
class TensorImpl_cpu : public TensorImpl {
- private:
+private:
const Tensor &mTensor; // Impl needs to access Tensor information, but is not
// supposed to change it!
- std::vector<T> mData;
+ /// Pointer to the data and its capacity
+ future_std::span<T> mData;
+ /// If this instance own the data, std::unique_ptr manages it
+ std::unique_ptr<T[]> mDataOwner;
- public:
+public:
static constexpr const char *Backend = "cpu";
TensorImpl_cpu(const Tensor &tensor) : TensorImpl(Backend), mTensor(tensor) {}
bool operator==(const TensorImpl &otherImpl) const override final {
+ const auto& typedOtherImpl = reinterpret_cast<const TensorImpl_cpu<T> &>(otherImpl);
+ AIDGE_INTERNAL_ASSERT(typedOtherImpl.size() >= mTensor.size());
+
std::size_t i = 0;
for (; i < mTensor.size() &&
- mData[i] == reinterpret_cast<const TensorImpl_cpu<T> &>(otherImpl).data()[i];
- ++i) {
+ *(mData.data()+i) == *static_cast<const T*>(typedOtherImpl.rawPtr(i));
+ ++i) {
}
return i == mTensor.size();
}
@@ -32,37 +53,130 @@ class TensorImpl_cpu : public TensorImpl {
return std::make_unique<TensorImpl_cpu<T>>(tensor);
}
- // native interface
- const std::vector<T> &data() const { return mData; }
+ inline std::size_t size() const noexcept override final { return mData.size(); }
+ inline std::size_t scalarSize() const noexcept override final { return sizeof(T); }
- std::size_t scalarSize() const override { return sizeof(T); }
+ void setDevice(DeviceIdx_t device) override final {
+ AIDGE_ASSERT(device == 0, "device cannot be != 0 for CPU backend");
+ }
- void copy(const void *src, NbElts_t length) override {
+ void copy(const void *src, NbElts_t length, NbElts_t offset = 0) override final {
+ AIDGE_ASSERT(length <= mData.size() || length <= mTensor.size(), "copy length is above capacity");
std::copy(static_cast<const T *>(src), static_cast<const T *>(src) + length,
- static_cast<T *>(rawPtr()));
+ static_cast<T *>(rawPtr()) + offset);
+ }
+
+ void copyCast(const void *src, NbElts_t length, const DataType srcDt) override final {
+ if (length == 0) {
+ return;
+ }
+
+ AIDGE_ASSERT(length <= mData.size() || length <= mTensor.size(), "copy length is above capacity");
+ switch (srcDt)
+ {
+ case DataType::Float64:
+ std::copy(static_cast<const double*>(src), static_cast<const double*>(src) + length,
+ static_cast<T *>(rawPtr()));
+ break;
+ case DataType::Float32:
+ std::copy(static_cast<const float*>(src), static_cast<const float*>(src) + length,
+ static_cast<T *>(rawPtr()));
+ break;
+ case DataType::Float16:
+ std::copy(static_cast<const half_float::half*>(src), static_cast<const half_float::half*>(src) + length,
+ static_cast<T *>(rawPtr()));
+ break;
+ case DataType::Int64:
+ std::copy(static_cast<const int64_t*>(src), static_cast<const int64_t*>(src) + length,
+ static_cast<T *>(rawPtr()));
+ break;
+ case DataType::UInt64:
+ std::copy(static_cast<const uint64_t*>(src), static_cast<const uint64_t*>(src) + length,
+ static_cast<T *>(rawPtr()));
+ break;
+ case DataType::Int32:
+ std::copy(static_cast<const int32_t*>(src), static_cast<const int32_t*>(src) + length,
+ static_cast<T *>(rawPtr()));
+ break;
+ case DataType::UInt32:
+ std::copy(static_cast<const uint32_t*>(src), static_cast<const uint32_t*>(src) + length,
+ static_cast<T *>(rawPtr()));
+ break;
+ case DataType::Int16:
+ std::copy(static_cast<const int16_t*>(src), static_cast<const int16_t*>(src) + length,
+ static_cast<T *>(rawPtr()));
+ break;
+ case DataType::UInt16:
+ std::copy(static_cast<const uint16_t*>(src), static_cast<const uint16_t*>(src) + length,
+ static_cast<T *>(rawPtr()));
+ break;
+ case DataType::Int8:
+ std::copy(static_cast<const int8_t*>(src), static_cast<const int8_t*>(src) + length,
+ static_cast<T *>(rawPtr()));
+ break;
+ case DataType::UInt8:
+ std::copy(static_cast<const uint8_t*>(src), static_cast<const uint8_t*>(src) + length,
+ static_cast<T *>(rawPtr()));
+ break;
+ default:
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Unsupported data type.");
+ break;
+ }
}
- void *rawPtr() override {
- lazyInit(mData);
- return mData.data();
+ void copyFromDevice(const void *src, NbElts_t length, const std::pair<std::string, DeviceIdx_t>& device) override final {
+ AIDGE_ASSERT(device.first == Backend, "backend must match");
+ AIDGE_ASSERT(device.second == 0, "device cannot be != 0 for CPU backend");
+ copy(src, length);
+ }
+
+ inline void copyFromHost(const void *src, NbElts_t length) override final {
+ copy(src, length);
+ }
+
+ void copyToHost(void *dst, NbElts_t length) const override final {
+ AIDGE_ASSERT(length <= mData.size() || length <= mTensor.size(), "copy length is above capacity");
+ const T* src = static_cast<const T*>(rawPtr());
+ std::copy(static_cast<const T *>(src), static_cast<const T *>(src) + length,
+ static_cast<T *>(dst));
+ }
+
+ void *rawPtr(NbElts_t offset = 0) override final {
+ lazyInit();
+ return (mData.data() + offset);
};
- void* getRaw(std::size_t idx){
- return static_cast<void*>(static_cast<T *>(rawPtr()) + idx);
- };
+ const void *rawPtr(NbElts_t offset = 0) const override final {
+ AIDGE_ASSERT(mData.size() >= mTensor.size(), "accessing uninitialized const rawPtr");
+ return (mData.data() + offset);
+ };
- virtual ~TensorImpl_cpu() = default;
+ void *hostPtr(NbElts_t offset = 0) override final {
+ lazyInit();
+ return (mData.data() + offset);
+ };
- void setRawPtr(void *ptr) override final {
- T *newPtr = static_cast<T *>(ptr);
- mData = std::vector<T>(newPtr, newPtr + mTensor.size());
+ const void *hostPtr(NbElts_t offset = 0) const override final {
+ AIDGE_ASSERT(mData.size() >= mTensor.size(), "accessing uninitialized const hostPtr");
+ return (mData.data() + offset);
};
- private:
- void lazyInit(std::vector<T> &data) {
- assert(mTensor.dataType() == NativeType<T>::type);
+ void setRawPtr(void *ptr, NbElts_t length) override final {
+ AIDGE_ASSERT(length >= mTensor.size(), "trying to set raw pointer of insufficient capacity");
+ mData = future_std::span<T>(static_cast<T *>(ptr), length);
+ mDataOwner.reset();
+ };
- if (data.size() != mTensor.size()) data.resize(mTensor.size());
+ virtual ~TensorImpl_cpu() = default;
+
+private:
+ void lazyInit() {
+ if (mData.size() < mTensor.size()) {
+ // Need more data, a re-allocation will occur
+ AIDGE_ASSERT(mData.empty() || mDataOwner != nullptr, "trying to enlarge non-owned data");
+ mDataOwner.reset(new T[mTensor.size()]);
+ mData = future_std::span<T>(mDataOwner.get(), mTensor.size());
+ }
}
};
@@ -71,8 +185,12 @@ static Registrar<Tensor> registrarTensorImpl_cpu_Float64(
{"cpu", DataType::Float64}, Aidge::TensorImpl_cpu<double>::create);
static Registrar<Tensor> registrarTensorImpl_cpu_Float32(
{"cpu", DataType::Float32}, Aidge::TensorImpl_cpu<float>::create);
+static Registrar<Tensor> registrarTensorImpl_cpu_Float16(
+ {"cpu", DataType::Float16}, Aidge::TensorImpl_cpu<half_float::half>::create);
static Registrar<Tensor> registrarTensorImpl_cpu_Int32(
{"cpu", DataType::Int32}, Aidge::TensorImpl_cpu<int>::create);
+static Registrar<Tensor> registrarTensorImpl_cpu_Int64(
+ {"cpu", DataType::Int64}, Aidge::TensorImpl_cpu<long>::create);
} // namespace
} // namespace Aidge
diff --git a/include/aidge/backend/cpu/operator/AddImpl.hpp b/include/aidge/backend/cpu/operator/AddImpl.hpp
index f5974f1509edd35ee1850ba707e6c23cd68f2b52..57669c628b4fa650f137c2b28c8c0a4584bf6c35 100644
--- a/include/aidge/backend/cpu/operator/AddImpl.hpp
+++ b/include/aidge/backend/cpu/operator/AddImpl.hpp
@@ -39,19 +39,7 @@ public:
return std::make_unique<AddImpl_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;
};
diff --git a/include/aidge/backend/cpu/operator/AvgPoolingImpl_forward_kernels.hpp b/include/aidge/backend/cpu/operator/AvgPoolingImpl_forward_kernels.hpp
index 5598cc9cdfd463b6e40e6801b74203b911a318e6..d6950e11e935a3f6d5548148d1c393a5340af224 100644
--- a/include/aidge/backend/cpu/operator/AvgPoolingImpl_forward_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/AvgPoolingImpl_forward_kernels.hpp
@@ -61,6 +61,7 @@ void AvgPoolingImpl2D_cpu_forward_kernel(const AvgPooling_Op<2>::Attrs &attrs,
for (std::size_t ch = 0; ch < dims[1]; ++ch) {
const std::size_t oIndex = (ch + batch*dims[1]) * oxSize * oySize;
const std::size_t iIndex = (ch + batch*dims[1]) * dims[2] * dims[3];
+ std::fill(output + oIndex, output+(oIndex+oxSize*oySize), 0);
for (std::size_t ox = 0; ox < oxSize; ++ox) {
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)));
diff --git a/include/aidge/backend/cpu/operator/ConvImpl_forward_kernels.hpp b/include/aidge/backend/cpu/operator/ConvImpl_forward_kernels.hpp
index cbd784698fcce5152c0bb42a192c327abb2b10dd..83607f280f53e5e477db7d8bbbbd1634dd9c584d 100644
--- a/include/aidge/backend/cpu/operator/ConvImpl_forward_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/ConvImpl_forward_kernels.hpp
@@ -14,6 +14,7 @@
#include "aidge/utils/Registrar.hpp"
+#include "aidge/data/half.hpp"
#include "aidge/backend/cpu/operator/ConvImpl.hpp"
#include "aidge/utils/Types.h"
#include "aidge/backend/cpu/data/GetCPUPtr.h"
@@ -151,6 +152,9 @@ namespace {
static Registrar<ConvImpl2DForward_cpu> registrarConvImpl2DForward_cpu_Float32(
{DataType::Float32, DataType::Float32, DataType::Float32, DataType::Float32},
Aidge::ConvImpl2D_cpu_forward_kernel<float, float, float, float>);
+static Registrar<ConvImpl2DForward_cpu> registrarConvImpl2DForward_cpu_Float16(
+ {DataType::Float16, DataType::Float16, DataType::Float16, DataType::Float16},
+ Aidge::ConvImpl2D_cpu_forward_kernel<half_float::half, half_float::half, half_float::half, half_float::half>);
static Registrar<ConvImpl2DForward_cpu> registrarConvImpl2DForward_cpu_Int32(
{DataType::Int32, DataType::Int32, DataType::Int32, DataType::Int32},
Aidge::ConvImpl2D_cpu_forward_kernel<int, int, int, int>);
diff --git a/include/aidge/backend/cpu/operator/SliceImpl.hpp b/include/aidge/backend/cpu/operator/SliceImpl.hpp
index 80e2f0fcef83a369561095f8e55a437f7acc9675..1cba5906064c51a4f0da2f1f3682b0828a080d43 100644
--- a/include/aidge/backend/cpu/operator/SliceImpl.hpp
+++ b/include/aidge/backend/cpu/operator/SliceImpl.hpp
@@ -13,7 +13,6 @@
#define AIDGE_CPU_OPERATOR_SLICEIMPL_H_
#include <memory>
-#include <tuple>
#include <vector>
#include "aidge/backend/OperatorImpl.hpp"
@@ -39,7 +38,6 @@ class SliceImplBackward_cpu
const void*,
void*)> {};
-
class SliceImpl_cpu : public OperatorImpl {
public:
SliceImpl_cpu(const Slice_Op& op) : OperatorImpl(op) {}
@@ -48,7 +46,6 @@ public:
return std::make_unique<SliceImpl_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,
@@ -58,14 +55,12 @@ public:
void updateConsummerProducer() override final;
void forward() override;
-
void backward() override;
};
-
namespace {
static Registrar<Slice_Op> registrarSliceImpl_cpu("cpu", Aidge::SliceImpl_cpu::create);
-} // namespace
+}
} // namespace Aidge
-#endif /* AIDGE_CPU_OPERATOR_LEAKYRELUIMPL_H_ */
\ No newline at end of file
+#endif /* AIDGE_CPU_OPERATOR_SLICEIMPL_H_ */
diff --git a/include/aidge/backend/cpu/operator/SliceImpl_forward_kernels.hpp b/include/aidge/backend/cpu/operator/SliceImpl_forward_kernels.hpp
index 7eb4b9dc2cb8dddc8b7fdaf4d63b8f1d39d879b0..9f08fab758a1d8c717ccb5f0a0357f94fd86e5e4 100644
--- a/include/aidge/backend/cpu/operator/SliceImpl_forward_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/SliceImpl_forward_kernels.hpp
@@ -12,57 +12,73 @@
#ifndef AIDGE_CPU_OPERATOR_SLICEIMPL_FORWARD_KERNEL_H_
#define AIDGE_CPU_OPERATOR_SLICEIMPL_FORWARD_KERNEL_H_
-#include "aidge/utils/Registrar.hpp"
-#include "aidge/operator/Slice.hpp"
-#include "aidge/backend/cpu/operator/SliceImpl.hpp"
-#include <vector>
#include <cstddef>
+#include <vector>
+#include "aidge/backend/cpu/operator/SliceImpl.hpp"
#include "aidge/data/Data.hpp"
+#include "aidge/operator/Slice.hpp"
+#include "aidge/utils/Registrar.hpp"
namespace Aidge {
template <class I>
void SliceImpl_cpu_forward_kernel(const typename Slice_Op::Attrs& attrs,
- const std::vector<std::size_t> inputDims,
- const void* input_,
- void* output_) {
+ const std::vector<std::size_t> inputDims,
+ const void* input_,
+ void* output_) {
+ std::vector<std::size_t> slicedDims = inputDims;
+
+ std::size_t beginning = 0;
+ DimSize_t nbAxes = std::get<2>(attrs).size();
+ for (std::size_t i = 0; i < nbAxes; ++i) {
+ // For each slice operation get the params and cast them to size_t
+ const std::int64_t axis_ = std::get<2>(attrs)[i];
+ const std::int64_t start_ = std::get<0>(attrs)[i];
+ const std::int64_t end_ = std::get<1>(attrs)[i];
+ const std::size_t axis = axis_ >= 0 ? axis_ : static_cast<std::size_t>(axis_ + static_cast<std::int32_t>(inputDims.size()));
+ const std::size_t start = start_ >= 0 ? start_ : start_ + inputDims[axis];
+ const std::size_t end = end_ >= 0 ? end_ : end_ + inputDims[axis];
+ std::size_t stride = 1;
+ for (std::size_t j = inputDims.size() - 1; j > axis; --j) stride *= inputDims[j];
+ beginning += start * stride;
+ const std::size_t sliceLength = end - start + 1;
+ slicedDims[axis] = sliceLength;
+ }
- const I* input = static_cast<const I*>(input_) + std::get<0>(attrs);
+ const I* input = static_cast<const I*>(input_) + beginning;
I* output = static_cast<I*>(output_);
- const std::vector<std::size_t> slicedDims = std::get<1>(attrs);
const std::size_t nbDims = slicedDims.size();
- // for inputDims = {4,5,5,3} & slicedDims = {3,2,2,1}, substractDims = {1,5,5,3}
+ // for inputDims = {4,5,5,3} & slicedDims = {3,2,2,1}, substractDims = {1,5,5,3}
std::vector<std::size_t> substractedDims = std::vector<std::size_t>(nbDims);
for (std::size_t i = 0; i < nbDims; ++i) {
substractedDims[i] = inputDims[i] - slicedDims[i];
}
- // for slicedDims = {3,2,2,1}, prodSlicedDims = {12,4,2,1}
+ // for slicedDims = {3,2,2,1}, prodSlicedDims = {12,4,2,1}
std::vector<std::size_t> prodSlicedDims = std::vector<std::size_t>(nbDims);
- std::vector<std::size_t> prodInputDims = std::vector<std::size_t>(nbDims+1);
- prodSlicedDims[nbDims - 1] = slicedDims[nbDims - 1];
- prodInputDims[nbDims - 1] = inputDims[nbDims - 1];
- prodInputDims[nbDims] = 1;
- for (std::size_t i = 2; i <= nbDims; ++i) {
- prodSlicedDims[nbDims - i] = prodSlicedDims[nbDims - i + 1]*slicedDims[nbDims - i];
- prodInputDims[nbDims - i] = prodInputDims[nbDims - i + 1]*inputDims[nbDims - i];
- }
+ std::vector<std::size_t> prodInputDims = std::vector<std::size_t>(nbDims + 1);
+ prodSlicedDims[nbDims - 1] = slicedDims[nbDims - 1];
+ prodInputDims[nbDims - 1] = inputDims[nbDims - 1];
+ prodInputDims[nbDims] = 1;
+ for (std::size_t i = 2; i <= nbDims; ++i) {
+ prodSlicedDims[nbDims - i] = prodSlicedDims[nbDims - i + 1] * slicedDims[nbDims - i];
+ prodInputDims[nbDims - i] = prodInputDims[nbDims - i + 1] * inputDims[nbDims - i];
+ }
- std::size_t j = 0;
- std::size_t i = 0;
- for (; j < prodSlicedDims[0];) {
- output[j] = input[i++];
+ std::size_t j = 0;
+ std::size_t i = 0;
+ for (; j < prodSlicedDims[0];) {
+ output[j] = input[i++];
++j;
- for (std::size_t idx = nbDims - 1; idx > 0; --idx) {
- i += j % prodSlicedDims[idx] == 0 ? substractedDims[idx]*prodInputDims[idx+1] : 0;
- }
- }
+ for (std::size_t idx = nbDims - 1; idx > 0; --idx) {
+ i += j % prodSlicedDims[idx] == 0 ? substractedDims[idx] * prodInputDims[idx + 1] : 0;
+ }
+ }
}
namespace {
-// DIM = 1
static Registrar<SliceImplForward_cpu> registrarSliceImplForward_cpu_Float32(
{DataType::Float32}, Aidge::SliceImpl_cpu_forward_kernel<float>);
static Registrar<SliceImplForward_cpu> registrarSliceImplForward_cpu_Int32(
diff --git a/src/operator/AddImpl.cpp b/src/operator/AddImpl.cpp
index 915109461cec4ce9e1db8a3de25526d7dc87f24d..7355ebcb3e8fb68bf74dbd1ce831bf471d285cb7 100644
--- a/src/operator/AddImpl.cpp
+++ b/src/operator/AddImpl.cpp
@@ -21,46 +21,11 @@
#include "aidge/backend/cpu/operator/AddImpl.hpp"
#include "aidge/backend/cpu/operator/AddImpl_forward_kernels.hpp"
-Aidge::NbElts_t Aidge::AddImpl_cpu::getNbRequiredData(const Aidge::IOIndex_t inputIdx) const {
- assert(mOp.getRawInput(inputIdx) && "requires valid input");
-
- // Requires the whole tensors
- const auto& inputDims = std::static_pointer_cast<Tensor>(mOp.getRawInput(inputIdx))->dims();
- return std::accumulate(inputDims.begin(), inputDims.end(), NbElts_t(1), std::multiplies<NbElts_t>());
-}
-
Aidge::NbElts_t Aidge::AddImpl_cpu::getNbRequiredProtected(const Aidge::IOIndex_t /*inputIdx*/) const {
- // for the direct convolution algorithm, convolutions can be in-place, if there is no padding!
+ // this implementation can be in-place
return 0;
}
-Aidge::NbElts_t Aidge::AddImpl_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.getRawOutput(0))->dims();
- return std::accumulate(outputDims.begin(), outputDims.end(), NbElts_t(1), std::multiplies<NbElts_t>());
-}
-
-Aidge::NbElts_t Aidge::AddImpl_cpu::getNbConsumedData(const Aidge::IOIndex_t inputIdx) const {
- assert(inputIdx < mNbConsumedData.size());
- return mNbConsumedData[inputIdx];
-}
-
-Aidge::NbElts_t Aidge::AddImpl_cpu::getNbProducedData(const Aidge::IOIndex_t outputIdx) const {
- assert(outputIdx < mNbProducedData.size());
- return mNbProducedData[outputIdx];
-}
-
-void Aidge::AddImpl_cpu::updateConsummerProducer() {
- for (IOIndex_t inputIdx = 0; static_cast<NbElts_t>(inputIdx) < mNbConsumedData.size(); ++inputIdx)
- mNbConsumedData[inputIdx]+= getNbRequiredData(inputIdx); // each input is consumed by the minimum amount for a forward pass
-
- mNbProducedData[0]+= getRequiredMemory(0, {});
-
-}
-
void Aidge::AddImpl_cpu::forward() {
assert(mOp.getRawInput(0) && "missing input in Add operator");
DataType datatypeFirstInput = std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dataType();
@@ -69,13 +34,31 @@ void Aidge::AddImpl_cpu::forward() {
assert(std::static_pointer_cast<Tensor>(mOp.getRawInput(i))->dataType() == datatypeFirstInput);
}
- auto kernelFunc = Registrar<AddImplForward_cpu>::create({
+ // Find the correct kernel type
+ const auto outputDataType = std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dataType();
+ const Registrar<AddImplForward_cpu>::registrar_key registrarKey = {
datatypeFirstInput,
- std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dataType()});
+ outputDataType};
+ Registrar<AddImplForward_cpu>::registrar_type kernelFunc;
+ if (Registrar<AddImplForward_cpu>::exists(registrarKey)) {
+ // One exists with the right inputs/output types
+ kernelFunc = Registrar<AddImplForward_cpu>::create(registrarKey);
+ }
+ else {
+ // Otherwise, fallback to the kernel with all types matching output type
+ kernelFunc = Registrar<AddImplForward_cpu>::create({
+ outputDataType, outputDataType});
+ }
+
+ // Convert input data (no overhead if not needed!)
+ // TODO: right now, if needed, memory will be allocated/deallocated at each
+ // call to forward(). We might put the following shared_ptr as members of
+ // this class to avoid that.
std::size_t nbDims = std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->nbDims();
std::vector<std::vector<std::size_t>> inputsDims;
std::vector<const void*> opInputs;
+ std::vector<std::shared_ptr<Tensor>> inputsFallback(mOp.nbInputs());
for (IOIndex_t i = 0; i < mOp.nbInputs(); ++i) {
std::vector<std::size_t> inputDims(nbDims, 1);
auto dims = std::static_pointer_cast<Tensor>(mOp.getRawInput(i))->dims();
@@ -85,7 +68,8 @@ void Aidge::AddImpl_cpu::forward() {
inputDims[idx] = dims[j];
}
inputsDims.push_back(inputDims);
- opInputs.push_back(getCPUPtr(mOp.getRawInput(i)));
+ const auto& input = std::static_pointer_cast<Tensor>(mOp.getRawInput(i))->refCastFrom(inputsFallback[i], *std::static_pointer_cast<Tensor>(mOp.getRawOutput(0)));
+ opInputs.push_back(input.getImpl()->rawPtr());
}
kernelFunc(opInputs,
@@ -93,4 +77,4 @@ void Aidge::AddImpl_cpu::forward() {
std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->size(),
std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dims(),
getCPUPtr(mOp.getRawOutput(0)));
-}
\ No newline at end of file
+}
diff --git a/src/operator/ConvImpl.cpp b/src/operator/ConvImpl.cpp
index d476f84717c0ed6f7bd45d68bd24b4d7ada6cbbd..b849142dd3abe0131fb0c6c448530a7669ce27dc 100644
--- a/src/operator/ConvImpl.cpp
+++ b/src/operator/ConvImpl.cpp
@@ -34,14 +34,35 @@ void Aidge::ConvImpl2D_cpu::forward() {
assert(mOp.getRawInput(2) && "missing input #2");
// Find the correct kernel type
- auto kernelFunc =
- Registrar<ConvImpl2DForward_cpu>::create({std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dataType(),
- std::static_pointer_cast<Tensor>(mOp.getRawInput(1))->dataType(),
- std::static_pointer_cast<Tensor>(mOp.getRawInput(2))->dataType(),
- std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dataType()});
+ const auto outputDataType = std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dataType();
+ const Registrar<ConvImpl2DForward_cpu>::registrar_key registrarKey = {
+ std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dataType(),
+ std::static_pointer_cast<Tensor>(mOp.getRawInput(1))->dataType(),
+ std::static_pointer_cast<Tensor>(mOp.getRawInput(2))->dataType(),
+ outputDataType};
+
+ Registrar<ConvImpl2DForward_cpu>::registrar_type kernelFunc;
+ if (Registrar<ConvImpl2DForward_cpu>::exists(registrarKey)) {
+ // One exists with the right inputs/output types
+ kernelFunc = Registrar<ConvImpl2DForward_cpu>::create(registrarKey);
+ }
+ else {
+ // Otherwise, fallback to the kernel with all types matching output type
+ kernelFunc = Registrar<ConvImpl2DForward_cpu>::create({
+ outputDataType, outputDataType, outputDataType, outputDataType});
+ }
+
+ // Convert input data (no overhead if not needed!)
+ // TODO: right now, if needed, memory will be allocated/deallocated at each
+ // call to forward(). We might put the following shared_ptr as members of
+ // this class to avoid that.
+ std::shared_ptr<Tensor> input0Fallback, input1Fallback, input2Fallback;
+ const auto& input0 = std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->refCastFrom(input0Fallback, *std::static_pointer_cast<Tensor>(mOp.getRawOutput(0)));
+ const auto& input1 = std::static_pointer_cast<Tensor>(mOp.getRawInput(1))->refCastFrom(input1Fallback, *std::static_pointer_cast<Tensor>(mOp.getRawOutput(0)));
+ const auto& input2 = std::static_pointer_cast<Tensor>(mOp.getRawInput(2))->refCastFrom(input2Fallback, *std::static_pointer_cast<Tensor>(mOp.getRawOutput(0)));
// Call kernel
kernelFunc(dynamic_cast<const Conv_Op<2>&>(mOp).getStaticAttributes(), std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->template dims<4>(),
- getCPUPtr(mOp.getRawInput(0)), getCPUPtr(mOp.getRawInput(1)),
- getCPUPtr(mOp.getRawInput(2)), getCPUPtr(mOp.getRawOutput(0)));
+ input0.getImpl()->rawPtr(), input1.getImpl()->rawPtr(), input2.getImpl()->rawPtr(),
+ getCPUPtr(mOp.getRawOutput(0)));
}
diff --git a/src/operator/FCImpl.cpp b/src/operator/FCImpl.cpp
index 14f59f6f7baff57602ad71c8c08023038963b5f0..bc4a7a7cab91049c623e9a9e95ee63367da00722 100644
--- a/src/operator/FCImpl.cpp
+++ b/src/operator/FCImpl.cpp
@@ -29,29 +29,37 @@ void Aidge::FCImpl_cpu::forward()
assert(std::static_pointer_cast<Tensor>(mOp.getRawInput(2)) && "missing input #2");
// Find the correct kernel type
- auto kernelFunc = Registrar<FCImplForward_cpu>::create(
- {std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dataType(),
- std::static_pointer_cast<Tensor>(mOp.getRawInput(1))->dataType(),
- std::static_pointer_cast<Tensor>(mOp.getRawInput(2))->dataType(),
- std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dataType()});
+ const auto outputDataType = std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dataType();
+ const Registrar<FCImplForward_cpu>::registrar_key registrarKey = {
+ std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dataType(),
+ std::static_pointer_cast<Tensor>(mOp.getRawInput(1))->dataType(),
+ std::static_pointer_cast<Tensor>(mOp.getRawInput(2))->dataType(),
+ outputDataType};
+
+ Registrar<FCImplForward_cpu>::registrar_type kernelFunc;
+ if (Registrar<FCImplForward_cpu>::exists(registrarKey)) {
+ // One exists with the right inputs/output types
+ kernelFunc = Registrar<FCImplForward_cpu>::create(registrarKey);
+ }
+ else {
+ // Otherwise, fallback to the kernel with all types matching output type
+ kernelFunc = Registrar<FCImplForward_cpu>::create({
+ outputDataType, outputDataType, outputDataType, outputDataType});
+ }
+
+ // Convert input data (no overhead if not needed!)
+ // TODO: right now, if needed, memory will be allocated/deallocated at each
+ // call to forward(). We might put the following shared_ptr as members of
+ // this class to avoid that.
+ std::shared_ptr<Tensor> input0Fallback, input1Fallback, input2Fallback;
+ const auto& input0 = std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->refCastFrom(input0Fallback, *std::static_pointer_cast<Tensor>(mOp.getRawOutput(0)));
+ const auto& input1 = std::static_pointer_cast<Tensor>(mOp.getRawInput(1))->refCastFrom(input1Fallback, *std::static_pointer_cast<Tensor>(mOp.getRawOutput(0)));
+ const auto& input2 = std::static_pointer_cast<Tensor>(mOp.getRawInput(2))->refCastFrom(input2Fallback, *std::static_pointer_cast<Tensor>(mOp.getRawOutput(0)));
// Call kernel
- // if (std::static_pointer_cast<Tensor>(mOp.getRawInput(0)->nbDims() == 4) {
- // kernelFunc(
- // mOp.getStaticAttributes(),
- // std::static_pointer_cast<Tensor>(std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->template dims<4>(),
- // getCPUPtr(mOp.getRawInput(0),
- // mOp.mInputs[1]->getImpl()->rawPtr(),
- // mOp.mInputs[2]->getImpl()->rawPtr(),
- // mOp.getOutput(0)->getImpl()->rawPtr());
- // }
- // else
- kernelFunc(
- dynamic_cast<const FC_Op&>(mOp).getStaticAttributes(),
- std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dims()[0],
- std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->sizeM1(),
- getCPUPtr(mOp.getRawInput(0)),
- getCPUPtr(mOp.getRawInput(1)),
- getCPUPtr(mOp.getRawInput(2)),
+ kernelFunc(dynamic_cast<const FC_Op&>(mOp).getStaticAttributes(),
+ input0.dims()[0],
+ input0.size() / input0.dims()[0],
+ input0.getImpl()->rawPtr(), input1.getImpl()->rawPtr(), input2.getImpl()->rawPtr(),
getCPUPtr(mOp.getRawOutput(0)));
}
diff --git a/src/operator/MatMulImpl.cpp b/src/operator/MatMulImpl.cpp
index 1abd75db070bbd3b197519318f5bf23c7b46ee5a..f02effb3172e2c0624c6c7532513a2b794ee3a89 100644
--- a/src/operator/MatMulImpl.cpp
+++ b/src/operator/MatMulImpl.cpp
@@ -47,7 +47,7 @@ void Aidge::MatMulImpl_cpu::forward()
kernelFunc(
dynamic_cast<const MatMul_Op&>(mOp).getStaticAttributes(),
std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dims()[0],
- std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->sizeM1(),
+ std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->size() / std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dims()[0],
getCPUPtr(mOp.getRawInput(0)),
getCPUPtr(mOp.getRawInput(1)),
getCPUPtr(mOp.getRawOutput(0)));
diff --git a/src/operator/SoftmaxImpl.cpp b/src/operator/SoftmaxImpl.cpp
index 428d32fc7a4c1a2b639d4f78601c78ab41376b47..c3086d8f9067996b9b0a8546b6deb3e281c777b4 100644
--- a/src/operator/SoftmaxImpl.cpp
+++ b/src/operator/SoftmaxImpl.cpp
@@ -38,7 +38,7 @@ void Aidge::SoftmaxImpl_cpu::forward() {
DimSize_t batchSize = std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dims()[0];
DimSize_t channelSize = std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dims()[1];
- DimSize_t featureSize = std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->sizeM1()/channelSize;
+ DimSize_t featureSize = (std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->size()/batchSize)/channelSize;
// Call kernel
kernelFunc(batchSize,
channelSize,
diff --git a/unit_tests/data/Test_TensorImpl.cpp b/unit_tests/data/Test_TensorImpl.cpp
index 6c75c4dc19ff1b646308858ad262441d43390122..b75c49077f190ed61486fea8eaa18152423a73ed 100644
--- a/unit_tests/data/Test_TensorImpl.cpp
+++ b/unit_tests/data/Test_TensorImpl.cpp
@@ -45,7 +45,7 @@ TEST_CASE("Tensor creation") {
REQUIRE(x.get<int>({0, 0, 1}) == 2);
REQUIRE(x.get<int>({0, 1, 1}) == 4);
REQUIRE(x.get<int>({1, 1, 0}) == 7);
- x.get<int>({1, 1, 1}) = 36;
+ x.set<int>({1, 1, 1}, 36);
REQUIRE(x.get<int>({1, 1, 1}) == 36);
}
diff --git a/unit_tests/operator/Test_MetaOperator.cpp b/unit_tests/operator/Test_MetaOperator.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..71646c92fa7f041d695a89858cf21ab0d0336f2c
--- /dev/null
+++ b/unit_tests/operator/Test_MetaOperator.cpp
@@ -0,0 +1,190 @@
+/********************************************************************************
+ * 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 <cmath>
+#include <cstdlib>
+#include <memory>
+
+#include "aidge/backend/cpu/operator/ConvImpl.hpp"
+#include "aidge/backend/cpu/operator/PadImpl.hpp"
+#include "aidge/data/Tensor.hpp"
+#include "aidge/operator/Conv.hpp"
+#include "aidge/operator/MetaOperator.hpp"
+#include "aidge/operator/MetaOperatorDefs.hpp"
+#include "aidge/operator/Pad.hpp"
+
+using namespace Aidge;
+
+TEST_CASE("[cpu/operator] MetaOperator/PaddedConv(forward)", "[MetaOperator][PaddedConv][CPU]") {
+ std::shared_ptr<Tensor> myWeights = std::make_shared<Tensor>(
+ Array4D<double, 4, 3, 3, 3>{{{{{6.20986394e-01, 1.19775136e-03, 7.22876095e-02},
+ {1.16492919e-01, 8.21634093e-02, 1.17413265e-01},
+ {2.23743494e-01, 3.99495413e-01, 5.55552411e-01}},
+ {{6.64970077e-01, 9.62199940e-01, 4.87531967e-01},
+ {6.12586558e-01, 8.09918671e-02, 8.40649383e-01},
+ {4.15264406e-01, 8.28247138e-01, 1.52301135e-01}},
+ {{1.76992844e-02, 7.78697112e-01, 8.14531592e-01},
+ {1.36960611e-01, 4.64806728e-01, 4.85150000e-01},
+ {4.34776520e-01, 9.51740977e-01, 9.05793799e-01}}},
+
+ {{{1.71925246e-02, 1.91082720e-01, 3.67982644e-01},
+ {1.56806559e-01, 6.22280998e-01, 3.15827594e-01},
+ {6.04359038e-01, 2.83095947e-01, 6.11168892e-01}},
+ {{2.76942832e-01, 1.89768419e-01, 8.07988176e-01},
+ {1.67925807e-01, 2.68356150e-01, 6.28875602e-01},
+ {1.69093357e-04, 9.64788636e-01, 7.29254981e-01}},
+ {{6.34030122e-01, 1.32087038e-01, 3.33857107e-01},
+ {7.63047502e-01, 5.12539506e-02, 9.77400493e-01},
+ {8.06151288e-01, 2.60237147e-01, 3.93729313e-01}}},
+
+ {{{5.84605240e-01, 4.74648725e-01, 8.54111741e-01},
+ {7.10897067e-02, 5.02579011e-01, 3.35236224e-01},
+ {9.08637408e-01, 8.02903830e-01, 2.83929907e-01}},
+ {{3.68206999e-01, 9.18579021e-02, 7.33168098e-01},
+ {1.59875539e-01, 9.13163381e-01, 3.59806060e-01},
+ {1.41295882e-01, 7.00312185e-01, 5.63728289e-01}},
+ {{9.39513546e-01, 1.91704891e-01, 1.11454944e-01},
+ {5.46298282e-01, 2.89698587e-01, 2.62612651e-01},
+ {1.18554992e-01, 4.32147376e-02, 7.53016994e-01}}},
+
+ {{{9.53179175e-01, 2.05041054e-02, 1.11318451e-01},
+ {8.67878485e-01, 2.93263422e-01, 8.03912714e-01},
+ {8.93620255e-01, 1.37831128e-01, 3.83640583e-01}},
+ {{3.96020188e-01, 6.24959320e-01, 1.90709175e-01},
+ {5.80538620e-01, 6.63031275e-01, 2.07247191e-01},
+ {5.65672171e-01, 5.57014317e-01, 9.26909496e-01}},
+ {{3.43901418e-01, 4.47741636e-01, 6.59249367e-01},
+ {7.34639028e-01, 2.84957200e-02, 9.70225217e-01},
+ {1.33578790e-02, 6.12054702e-01, 9.36685235e-02}}}}});
+ std::shared_ptr<Tensor> myBias = std::make_shared<Tensor>(
+ Array1D<double, 4>{{0.16884905, 0.27994487, 0.57227465, 0.06435205}});
+ std::shared_ptr<Tensor> myInput = std::make_shared<Tensor>(Array4D<double, 2, 3, 5, 5>{
+ // NCHW
+ {{{{0.43224481, 0.9047832, 0.18402257, 0.06162838, 0.52490127},
+ {0.27773404, 0.55402353, 0.9485062, 0.31197083, 0.80328607},
+ {0.85065842, 0.88226201, 0.54971951, 0.23360494, 0.53907884},
+ {0.33423098, 0.79564312, 0.80419414, 0.76839638, 0.87248221},
+ {0.77328729, 0.65749407, 0.47277589, 0.32889198, 0.93970518}},
+
+ {{0.66669145, 0.64193351, 0.45315988, 0.32794057, 0.38461822},
+ {0.72295814, 0.18395073, 0.85909664, 0.30010301, 0.56065865},
+ {0.34777938, 0.77869746, 0.33159421, 0.19540932, 0.77767906},
+ {0.5778391, 0.08218411, 0.27758371, 0.99017749, 0.61827997},
+ {0.10440745, 0.3197831, 0.89157608, 0.12216887, 0.950232}},
+
+ {{0.68073443, 0.2681118, 0.51848834, 0.62864493, 0.36717478},
+ {0.64106244, 0.43779425, 0.02771029, 0.78275231, 0.45693104},
+ {0.6487417, 0.01603838, 0.73869997, 0.96494221, 0.39588782},
+ {0.5975827, 0.90913292, 0.55036969, 0.4747373, 0.62460509},
+ {0.79675124, 0.02807549, 0.53227602, 0.88805927, 0.96646591}}},
+
+ {{{0.81851935, 0.21267665, 0.01580692, 0.54907998, 0.89010049},
+ {0.80165784, 0.55195592, 0.20740314, 0.22782844, 0.89205031},
+ {0.94217108, 0.58434542, 0.20738313, 0.79065873, 0.9371597},
+ {0.02254708, 0.95539178, 0.95165758, 0.53736666, 0.49100362},
+ {0.08018625, 0.69108027, 0.00329741, 0.74565761, 0.30899213}},
+
+ {{0.34868638, 0.12792604, 0.37382248, 0.0374756, 0.50653087},
+ {0.59614405, 0.64820746, 0.31470307, 0.62460364, 0.29253268},
+ {0.92864889, 0.51014224, 0.08921206, 0.11094072, 0.64691121},
+ {0.50586371, 0.6686477, 0.72511169, 0.41681783, 0.6325049},
+ {0.71594137, 0.73382767, 0.36589439, 0.03255165, 0.75006865}},
+
+ {{0.6294127, 0.85548534, 0.0902963, 0.28915773, 0.36564289},
+ {0.95873236, 0.6742374, 0.55679676, 0.6323497, 0.34072958},
+ {0.49694061, 0.79173045, 0.19738225, 0.14755281, 0.80818177},
+ {0.02332061, 0.74270703, 0.59415632, 0.08195934, 0.46295434},
+ {0.71426058, 0.85032931, 0.90750818, 0.28768431, 0.4401146}}}}});
+
+ std::shared_ptr<Tensor> myOutput = std::make_shared<Tensor>(
+ Array4D<double, 2, 4, 5, 5>{{{{{3.40294218, 3.74021220, 4.02050114, 4.07054710, 2.46286273},
+ {4.61770582, 6.70517588, 6.50356627, 6.29688787, 3.53332567},
+ {5.47480106, 5.92094421, 6.64605665, 7.95090199, 4.28721523},
+ {4.01485729, 6.06748962, 7.52447891, 7.37980652, 5.28401136},
+ {2.83065438, 3.62033439, 3.56222963, 5.56103945, 3.23335814}},
+
+ {{3.30230498, 4.92814112, 4.34710836, 3.96262765, 2.97987890},
+ {4.49693012, 6.68929291, 5.53603029, 5.68874264, 4.28756475},
+ {4.20528078, 6.82776880, 6.70569849, 7.12809610, 4.40845442},
+ {4.31169367, 6.73352146, 6.30962515, 7.45826864, 4.99164438},
+ {2.18136287, 4.28968000, 4.20080042, 4.89814138, 2.87394023}},
+
+ {{3.54787683, 4.35851812, 4.63881302, 4.23359537, 3.16992092},
+ {5.25099468, 7.54282856, 6.69849157, 5.64309788, 4.56919575},
+ {4.71914101, 7.52830601, 6.71450949, 7.81113863, 5.84658146},
+ {4.97893143, 7.39293909, 6.89905310, 8.14430809, 5.62998581},
+ {2.79735112, 4.80967140, 5.57630205, 5.38828325, 4.57078695}},
+
+ {{3.03048635, 5.04540300, 4.21824932, 4.87323284, 2.35113740},
+ {4.45167351, 6.47721338, 7.40922976, 6.70445728, 3.60700107},
+ {3.77927423, 6.82826376, 7.41777134, 7.57402420, 5.13131523},
+ {4.08747244, 7.07994175, 7.57206821, 8.51897335, 5.26987123},
+ {2.34426999, 4.60127831, 4.86486769, 6.01579571, 3.97803569}}},
+
+
+ {{{3.84700942, 4.25972605, 3.05269003, 3.78043652, 2.08771229},
+ {6.00459957, 6.05633259, 4.45951605, 4.54089880, 4.03066444},
+ {5.41579390, 7.29543972, 6.18680000, 5.58812714, 3.45964241},
+ {6.04531050, 7.70924091, 5.52207708, 5.02131319, 4.09403706},
+ {3.18092418, 4.45422697, 4.04294252, 3.86577177, 2.18776536}},
+
+ {{4.02600670, 4.27603531, 3.81011319, 4.03631020, 2.57254648},
+ {5.33471155, 5.72588634, 5.12079763, 5.11733150, 3.76836705},
+ {5.62947607, 5.92492962, 6.24170446, 6.44130468, 3.44276404},
+ {5.38414621, 6.02679539, 5.88985586, 5.90263271, 3.15044069},
+ {3.31261086, 4.44371319, 3.47660780, 4.15411520, 1.48961508}},
+
+ {{3.95879412, 4.17324543, 3.70114422, 3.27447152, 3.09713888},
+ {5.78258181, 6.57920837, 4.99913597, 6.20961237, 4.98552179},
+ {5.84685421, 7.19971228, 6.66386652, 6.68013430, 4.90963316},
+ {5.24417877, 7.06430531, 6.58512402, 6.02492285, 4.48986387},
+ {3.64294529, 5.00678444, 5.04760027, 4.72895622, 2.67990756}},
+
+ {{3.48610687, 4.12853813, 4.07563591, 3.51327014, 2.44217038},
+ {4.80529881, 7.33211374, 5.14774036, 4.77281189, 4.44612408},
+ {5.11703110, 7.55168772, 7.14374542, 6.43696356, 4.10621357},
+ {5.41270018, 6.85949135, 6.73503923, 5.74601364, 4.46150303},
+ {3.16612267, 4.38248920, 5.23248482, 4.21292210, 2.86031270}}}}});
+
+ std::shared_ptr<Node> myConv = Conv<2>(3, 4, {3, 3}, "myconv");
+ auto convOp = std::static_pointer_cast<OperatorTensor>(myConv->getOperator());
+
+ std::shared_ptr<Node> myPad =
+ Pad<2>({1, 1, 1, 1}, "myPad", PadBorderType::Constant, 0.0);
+ auto padOp = std::static_pointer_cast<OperatorTensor>(myPad->getOperator());
+
+ convOp->setInput(1, myWeights);
+ convOp->setInput(2, myBias);
+
+ myPad->addChild(myConv, 0, 0);
+ padOp->setInput(0, myInput);
+
+ padOp->setDataType(DataType::Float64);
+ padOp->setBackend("cpu");
+ padOp->computeOutputDims();
+ convOp->setDataType(DataType::Float64);
+ convOp->setBackend("cpu");
+ convOp->computeOutputDims();
+
+ myPad->forward();
+ myConv->forward();
+ convOp -> getOutput(0) -> print();
+
+ double* computedOutput = static_cast<double*>(convOp->getOutput(0)->getImpl()->rawPtr());
+ double* expectedOutput = static_cast<double*>(myOutput->getImpl()->rawPtr());
+ for (std::size_t i = 0; i < myOutput->size(); ++i) {
+ REQUIRE(std::abs(computedOutput[i] - expectedOutput[i]) < 1e-5);
+ }
+
+ std::shared_ptr<Node> myPaddedConv =
+ PaddedConv(3, 4, {3, 3}, "myPaddedConv", {1, 1}, {1, 1, 1, 1});
+}
\ No newline at end of file
diff --git a/unit_tests/operator/Test_SliceImpl.cpp b/unit_tests/operator/Test_SliceImpl.cpp
index 3e25c28f9caac61c64d38fa70879af79d20392bc..7a71f31e9850852cadd659c91683c30ddcbe9849 100644
--- a/unit_tests/operator/Test_SliceImpl.cpp
+++ b/unit_tests/operator/Test_SliceImpl.cpp
@@ -27,14 +27,14 @@ TEST_CASE("[cpu/operator] Slice(forward)", "[Slice][CPU]") {
{0, 1, 2,-3}
});
- std::shared_ptr<Node> mySlice = Slice(0, {4});
+ std::shared_ptr<Node> mySlice = Slice({0}, {3}, {0});
auto op = std::static_pointer_cast<OperatorTensor>(mySlice -> getOperator());
mySlice->getOperator()->associateInput(0,input0);
mySlice->getOperator()->setDataType(DataType::Int32);
mySlice->getOperator()->setBackend("cpu");
op->computeOutputDims();
mySlice->forward();
- // mySlice->getOperator()->output(0).print();
+
REQUIRE(*(op->getOutput(0)) == *expectedOutput);
REQUIRE(op->getOutput(0)->dims() == expectedOutput->dims());
REQUIRE(op->getOutput(0)->dataType() == expectedOutput->dataType());
@@ -54,7 +54,7 @@ TEST_CASE("[cpu/operator] Slice(forward)", "[Slice][CPU]") {
}
});
- std::shared_ptr<Node> mySlice = Slice(5, {2,3});
+ std::shared_ptr<Node> mySlice = Slice({0,5}, {1,7}, {0,1});
auto op = std::static_pointer_cast<OperatorTensor>(mySlice -> getOperator());
mySlice->getOperator()->associateInput(0,input0);
mySlice->getOperator()->setDataType(DataType::Int32);
@@ -88,7 +88,7 @@ TEST_CASE("[cpu/operator] Slice(forward)", "[Slice][CPU]") {
}
});
- std::shared_ptr<Node> mySlice = Slice(14, {1,1,3});
+ std::shared_ptr<Node> mySlice = Slice({0,1,4}, {0,1,6}, {0,1,2});
auto op = std::static_pointer_cast<OperatorTensor>(mySlice -> getOperator());
mySlice->getOperator()->associateInput(0,input0);
mySlice->getOperator()->setDataType(DataType::Int32);
@@ -151,7 +151,7 @@ TEST_CASE("[cpu/operator] Slice(forward)", "[Slice][CPU]") {
}
});
- std::shared_ptr<Node> mySlice = Slice(0, {2,2,2,10});
+ std::shared_ptr<Node> mySlice = Slice({0,0,0,0}, {1,1,1,9}, {0,1,2,3});
auto op = std::static_pointer_cast<OperatorTensor>(mySlice -> getOperator());
mySlice->getOperator()->associateInput(0,input0);
mySlice->getOperator()->setDataType(DataType::Int32);
diff --git a/unit_tests/operator/Test_SoftmaxImpl.cpp b/unit_tests/operator/Test_SoftmaxImpl.cpp
index 3d3c9fe4a0de0183e9069b814084aa80019adf0f..360b7440599030dbd93954e345f0d5986eb83b15 100644
--- a/unit_tests/operator/Test_SoftmaxImpl.cpp
+++ b/unit_tests/operator/Test_SoftmaxImpl.cpp
@@ -39,7 +39,7 @@ TEST_CASE("[cpu/operator] Softmax(forward)", "[Softmax][CPU]") {
}
});
- std::shared_ptr<Node> mySoftmax = Softmax();
+ std::shared_ptr<Node> mySoftmax = Softmax(1);
auto op = std::static_pointer_cast<OperatorTensor>(mySoftmax -> getOperator());
mySoftmax->getOperator()->associateInput(0,input);
mySoftmax->getOperator()->setDataType(DataType::Float32);
@@ -108,7 +108,7 @@ TEST_CASE("[cpu/operator] Softmax(forward)", "[Softmax][CPU]") {
}
});
- std::shared_ptr<Node> mySoftmax = Softmax();
+ std::shared_ptr<Node> mySoftmax = Softmax(1);
auto op = std::static_pointer_cast<OperatorTensor>(mySoftmax -> getOperator());
mySoftmax->getOperator()->associateInput(0,input);
mySoftmax->getOperator()->setDataType(DataType::Float32);
diff --git a/unit_tests/recipies/Test_ExplicitCastMove.cpp b/unit_tests/recipies/Test_ExplicitCastMove.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..7d169ba9ba949ead0bf96f80e53a47e1ca6c24d9
--- /dev/null
+++ b/unit_tests/recipies/Test_ExplicitCastMove.cpp
@@ -0,0 +1,46 @@
+/********************************************************************************
+ * 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 "aidge/recipies/Recipies.hpp"
+#include "aidge/operator/Conv.hpp"
+#include "aidge/operator/Producer.hpp"
+#include "aidge/graph/OpArgs.hpp"
+#include <cstddef>
+
+using namespace Aidge;
+
+TEST_CASE("[ExplicitCastMove] conv") {
+ auto conv1 = Conv(3, 32, {3, 3}, "conv1");
+ auto conv2 = Conv(32, 64, {3, 3}, "conv2");
+ auto conv3 = Conv(64, 10, {1, 1}, "conv3", {2, 2});
+
+ auto g1 = Sequential({
+ Producer({16, 3, 224, 224}, "dataProvider"),
+ conv1,
+ conv2,
+ conv3
+ });
+
+ g1->setBackend("cpu");
+ conv1->getOperator()->setDataType(DataType::Int32);
+ conv3->getOperator()->setDataType(DataType::Float64);
+
+ g1->save("explicitCastMove_before");
+ REQUIRE(g1->getNodes().size() == 10);
+
+ g1->forwardDims();
+ explicitCastMove(g1);
+
+ g1->save("explicitCastMove_after");
+ REQUIRE(g1->getNodes().size() == 13);
+}
diff --git a/unit_tests/recipies/Test_HorizontalTiling.cpp b/unit_tests/recipies/Test_HorizontalTiling.cpp
index b71a01d130a783caf5c643dfb0c3757b1c524e5e..268d94cc55821c41f9c3d4a8451b5730ecaf1bd0 100644
--- a/unit_tests/recipies/Test_HorizontalTiling.cpp
+++ b/unit_tests/recipies/Test_HorizontalTiling.cpp
@@ -183,26 +183,4 @@ TEST_CASE("[core/recipies] Tiling(transformation)", "[Tiling][Recipies]") {
}
}
}
-}
- // std::shared_ptr<GraphView> g = Sequential({
- // Conv(3, 16, {3,3}, "conv1"),
- // ReLU("relu1"),
- // Conv(16, 32, {1,1}, "conv2"),
- // Conv(32, 16, {1,1}, "conv3"),
- // Conv(16, 10, {3,3}, "conv4"),
- // ReLU("relu2")
- // });
-
- // for (auto& individualConv : g->match("Conv")) {
- // auto tiledConv = horizontalTiling(individualConv);
- // g->replace(individualConv, tiledConv);
- // }
- // }
-
- // SECTION("Create the GraphView with tiled layers") {
- // std::shared_ptr<GraphView> g;
- // g->addChild(horizontalTiling(Conv()))
- // }
-
-// }
-// } // namespace Aidge
\ No newline at end of file
+} // namespace Aidge
\ No newline at end of file
diff --git a/unit_tests/scheduler/Test_CastMove.cpp b/unit_tests/scheduler/Test_CastMove.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..a52b2b06901818f01117273d181d5d5388348f95
--- /dev/null
+++ b/unit_tests/scheduler/Test_CastMove.cpp
@@ -0,0 +1,246 @@
+/********************************************************************************
+ * 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 <memory>
+#include <string>
+
+#include "aidge/data/Tensor.hpp"
+#include "aidge/utils/TensorUtils.hpp"
+#include "aidge/graph/Node.hpp"
+#include "aidge/graph/GraphView.hpp"
+#include "aidge/graph/OpArgs.hpp"
+#include "aidge/scheduler/Scheduler.hpp"
+#include "aidge/recipies/Recipies.hpp"
+
+#include "aidge/backend/cpu.hpp"
+
+using namespace Aidge;
+
+TEST_CASE("[cpu/castmove] CastMove(forward)") {
+ std::shared_ptr<Tensor> inputTensor =
+ std::make_shared<Tensor>(Array4D<int, 2, 1, 5, 5>{{{{{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}}}}});
+
+ std::shared_ptr<Tensor> weight1 = std::make_shared<Tensor>(
+ Array4D<int, 3, 1, 3, 3>{{{{{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}}}}});
+
+ std::shared_ptr<Tensor> bias1 = std::make_shared<Tensor>(Array1D<int, 3>{{1, 2, 3}});
+
+ SECTION("Test implicit") {
+ std::shared_ptr<GraphView> g =
+ Sequential({
+ Conv(1, 3, {3, 3}, "conv1"),
+ Conv(3, 4, {1, 1}, "conv2"),
+ Conv(4, 3, {1, 1}, "conv3"),
+ FC(27, 5, false, "fc")});
+
+ g->getNode("conv1")->getOperator()->setInput(0, inputTensor);
+ g->getNode("conv1")->getOperator()->setInput(1, weight1);
+ g->getNode("conv1")->getOperator()->setInput(2, bias1);
+
+ std::shared_ptr<Tensor> weight2 =
+ std::make_shared<Tensor>(Array4D<int, 4, 3, 1, 1>{{{{{1}}, {{2}}, {{3}}},
+ {{{4}}, {{5}}, {{6}}},
+ {{{7}}, {{8}}, {{9}}},
+ {{{10}}, {{11}}, {{12}}}}});
+ std::shared_ptr<Tensor> bias2 = std::make_shared<Tensor>(Array1D<int, 4>{{1, 2, 3, 4}});
+ g->getNode("conv2")->getOperator()->setInput(1, weight2);
+ g->getNode("conv2")->getOperator()->setInput(2, bias2);
+ // *(g->getNode("conv2")->getOperator()->input(1, weight2);
+
+ std::shared_ptr<Tensor> weight3 = std::make_shared<Tensor>(
+ Array4D<int, 3, 4, 1, 1>{{{{{1}}, {{2}}, {{3}}, {{4}}},
+ {{{5}}, {{6}}, {{7}}, {{8}}},
+ {{{9}}, {{10}}, {{11}}, {{12}}}}});
+ std::shared_ptr<Tensor> bias3 = std::make_shared<Tensor>(Array1D<int, 3>{{1, 2, 3}});
+ g->getNode("conv3")->getOperator()->setInput(1, weight3);
+ g->getNode("conv3")->getOperator()->setInput(2, bias3);
+
+ std::shared_ptr<Tensor> weightfc = std::make_shared<Tensor>(
+ Array2D<int, 5, 27>{{{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
+ 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12},
+ {13, 14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
+ 12, 13, 14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+ {10, 11, 12, 13, 14, 15, 1, 2, 3, 4, 5, 6, 7, 8,
+ 9, 10, 11, 12, 13, 14, 15, 1, 2, 3, 4, 5, 6},
+ {7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2, 3, 4, 5,
+ 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2, 3},
+ {4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2,
+ 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}}});
+ std::shared_ptr<Tensor> biasfc = std::make_shared<Tensor>(Array1D<int, 5>{{1, 2, 3, 4, 5}});
+ g->getNode("fc")->getOperator()->setInput(1, weightfc);
+ g->getNode("fc")->getOperator()->setInput(2, biasfc);
+
+ // input->addChild(g);
+ g->setDataType(Aidge::DataType::Int32);
+ g->getNode("conv1")->getOperator()->setDataType(DataType::Float32);
+ g->getNode("conv3")->getOperator()->setDataType(DataType::Float64);
+
+ g->setBackend("cpu");
+ g->forwardDims();
+ SequentialScheduler scheduler(g);
+ REQUIRE_NOTHROW(scheduler.forward());
+ scheduler.saveSchedulingDiagram("schedulingSequential");
+
+ std::shared_ptr<Tensor> expectedOutput1 = std::make_shared<Tensor>(Array4D<int, 2, 3, 3, 3>{
+ {{{{367, 412, 457}, {592, 637, 682}, {817, 862, 907}},
+ {{854, 980, 1106}, {1484, 1610, 1736}, {2114, 2240, 2366}},
+ {{1341, 1548, 1755}, {2376, 2583, 2790}, {3411, 3618, 3825}}},
+ {{{1492, 1537, 1582}, {1717, 1762, 1807}, {1942, 1987, 2032}},
+ {{4004, 4130, 4256}, {4634, 4760, 4886}, {5264, 5390, 5516}},
+ {{6516, 6723, 6930}, {7551, 7758, 7965}, {8586, 8793, 9000}}}}});
+
+ std::shared_ptr<Tensor> expectedOutput2 = std::make_shared<Tensor>(Array4D<int, 2, 4, 3, 3>{
+ {{{{6099, 7017, 7935}, {10689, 11607, 12525}, {15279, 16197, 17115}},
+ {{13786, 15838, 17890}, {24046, 26098, 28150}, {34306, 36358, 38410}},
+ {{21473, 24659, 27845}, {37403, 40589, 43775}, {53333, 56519, 59705}},
+ {{29160, 33480, 37800}, {50760, 55080, 59400}, {72360, 76680, 81000}}},
+ {{{29049, 29967, 30885}, {33639, 34557, 35475}, {38229, 39147, 40065}},
+ {{65086, 67138, 69190}, {75346, 77398, 79450}, {85606, 87658, 89710}},
+ {{101123, 104309, 107495}, {117053, 120239, 123425}, {132983, 136169, 139355}},
+ {{137160, 141480, 145800}, {158760, 163080, 167400}, {180360, 184680, 189000}}}}});
+
+ std::shared_ptr<Tensor> expectedOutput3 = std::make_shared<Tensor>(Array4D<int, 2, 3, 3, 3>{
+ {{{{214731, 246591, 278451}, {374031, 405891, 437751}, {533331, 565191, 597051}},
+ {{496804, 570568, 644332}, {865624, 939388, 1013152}, {1234444, 1308208, 1381972}},
+ {{778877, 894545, 1010213}, {1357217, 1472885, 1588553}, {1935557, 2051225, 2166893}}},
+ {{{1011231, 1043091, 1074951}, {1170531, 1202391, 1234251}, {1329831, 1361691, 1393551}},
+ {{2340904, 2414668, 2488432}, {2709724, 2783488, 2857252}, {3078544, 3152308, 3226072}},
+ {{3670577, 3786245, 3901913}, {4248917, 4364585, 4480253}, {4827257, 4942925, 5058593}}}}});
+
+ Tensor expectedOutput4 = Array2D<int, 2, 5>{
+ {{205050376, 198925904, 181355097, 196978090, 238868348},
+ {598467376, 561797804, 560823897, 593043790, 698672948}}};
+ std::shared_ptr<Tensor> other1 = std::static_pointer_cast<OperatorTensor>(g->getNode("conv1")->getOperator())->getOutput(0);
+ REQUIRE(approxEq<float, int>(*other1, *expectedOutput1, 0.0, 1.0e-12));
+ std::shared_ptr<Tensor> other2 = std::static_pointer_cast<OperatorTensor>(g->getNode("conv2")->getOperator())->getOutput(0);
+ REQUIRE(approxEq<int>(*other2, *expectedOutput2, 0.0, 1.0e-12));
+ std::shared_ptr<Tensor> other3 = std::static_pointer_cast<OperatorTensor>(g->getNode("conv3")->getOperator())->getOutput(0);
+ REQUIRE(approxEq<double, int>(*other3, *expectedOutput3, 0.0, 1.0e-12));
+ std::shared_ptr<Tensor> other4 = std::static_pointer_cast<OperatorTensor>(g->getNode("fc")->getOperator())->getOutput(0);
+ REQUIRE(approxEq<int>(*other4, expectedOutput4, 0.0, 1.0e-12));
+ }
+
+ SECTION("Half") {
+ Tensor refTensor = Array2D<float, 3, 2>{{{0.0, 1.0},{2.1, 3.4},{5000.0, 1.0e5}}};
+ Tensor tensor(DataType::Float16);
+ tensor.copyCastFrom(refTensor);
+ REQUIRE(approxEq<float, half_float::half>(refTensor, tensor, 1.0e-3, 0.0));
+ }
+
+ SECTION("Test explicit") {
+ std::shared_ptr<GraphView> g =
+ Sequential({
+ Conv(1, 3, {3, 3}, "conv1"),
+ Conv(3, 4, {1, 1}, "conv2"),
+ Conv(4, 3, {1, 1}, "conv3"),
+ FC(27, 5, false, "fc")});
+
+ g->getNode("conv1")->getOperator()->setInput(0, inputTensor);
+ g->getNode("conv1")->getOperator()->setInput(1, weight1);
+ g->getNode("conv1")->getOperator()->setInput(2, bias1);
+
+ std::shared_ptr<Tensor> weight2 =
+ std::make_shared<Tensor>(Array4D<int, 4, 3, 1, 1>{{{{{1}}, {{2}}, {{3}}},
+ {{{4}}, {{5}}, {{6}}},
+ {{{7}}, {{8}}, {{9}}},
+ {{{10}}, {{11}}, {{12}}}}});
+ std::shared_ptr<Tensor> bias2 = std::make_shared<Tensor>(Array1D<int, 4>{{1, 2, 3, 4}});
+ g->getNode("conv2")->getOperator()->setInput(1, weight2);
+ g->getNode("conv2")->getOperator()->setInput(2, bias2);
+ // *(g->getNode("conv2")->getOperator()->input(1, weight2);
+
+ std::shared_ptr<Tensor> weight3 = std::make_shared<Tensor>(
+ Array4D<int, 3, 4, 1, 1>{{{{{1}}, {{2}}, {{3}}, {{4}}},
+ {{{5}}, {{6}}, {{7}}, {{8}}},
+ {{{9}}, {{10}}, {{11}}, {{12}}}}});
+ std::shared_ptr<Tensor> bias3 = std::make_shared<Tensor>(Array1D<int, 3>{{1, 2, 3}});
+ g->getNode("conv3")->getOperator()->setInput(1, weight3);
+ g->getNode("conv3")->getOperator()->setInput(2, bias3);
+
+ std::shared_ptr<Tensor> weightfc = std::make_shared<Tensor>(
+ Array2D<int, 5, 27>{{{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
+ 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12},
+ {13, 14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
+ 12, 13, 14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+ {10, 11, 12, 13, 14, 15, 1, 2, 3, 4, 5, 6, 7, 8,
+ 9, 10, 11, 12, 13, 14, 15, 1, 2, 3, 4, 5, 6},
+ {7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2, 3, 4, 5,
+ 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2, 3},
+ {4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2,
+ 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}}});
+ std::shared_ptr<Tensor> biasfc = std::make_shared<Tensor>(Array1D<int, 5>{{1, 2, 3, 4, 5}});
+ g->getNode("fc")->getOperator()->setInput(1, weightfc);
+ g->getNode("fc")->getOperator()->setInput(2, biasfc);
+
+ // input->addChild(g);
+ g->setDataType(Aidge::DataType::Int32);
+ g->getNode("conv1")->getOperator()->setDataType(DataType::Float32);
+ g->getNode("conv3")->getOperator()->setDataType(DataType::Float64);
+
+ explicitCastMove(g);
+ g->setBackend("cpu");
+ g->forwardDims();
+
+ SequentialScheduler scheduler(g);
+ REQUIRE_NOTHROW(scheduler.forward());
+ scheduler.saveSchedulingDiagram("schedulingSequential");
+
+ std::shared_ptr<Tensor> expectedOutput1 = std::make_shared<Tensor>(Array4D<int, 2, 3, 3, 3>{
+ {{{{367, 412, 457}, {592, 637, 682}, {817, 862, 907}},
+ {{854, 980, 1106}, {1484, 1610, 1736}, {2114, 2240, 2366}},
+ {{1341, 1548, 1755}, {2376, 2583, 2790}, {3411, 3618, 3825}}},
+ {{{1492, 1537, 1582}, {1717, 1762, 1807}, {1942, 1987, 2032}},
+ {{4004, 4130, 4256}, {4634, 4760, 4886}, {5264, 5390, 5516}},
+ {{6516, 6723, 6930}, {7551, 7758, 7965}, {8586, 8793, 9000}}}}});
+
+ std::shared_ptr<Tensor> expectedOutput2 = std::make_shared<Tensor>(Array4D<int, 2, 4, 3, 3>{
+ {{{{6099, 7017, 7935}, {10689, 11607, 12525}, {15279, 16197, 17115}},
+ {{13786, 15838, 17890}, {24046, 26098, 28150}, {34306, 36358, 38410}},
+ {{21473, 24659, 27845}, {37403, 40589, 43775}, {53333, 56519, 59705}},
+ {{29160, 33480, 37800}, {50760, 55080, 59400}, {72360, 76680, 81000}}},
+ {{{29049, 29967, 30885}, {33639, 34557, 35475}, {38229, 39147, 40065}},
+ {{65086, 67138, 69190}, {75346, 77398, 79450}, {85606, 87658, 89710}},
+ {{101123, 104309, 107495}, {117053, 120239, 123425}, {132983, 136169, 139355}},
+ {{137160, 141480, 145800}, {158760, 163080, 167400}, {180360, 184680, 189000}}}}});
+
+ std::shared_ptr<Tensor> expectedOutput3 = std::make_shared<Tensor>(Array4D<int, 2, 3, 3, 3>{
+ {{{{214731, 246591, 278451}, {374031, 405891, 437751}, {533331, 565191, 597051}},
+ {{496804, 570568, 644332}, {865624, 939388, 1013152}, {1234444, 1308208, 1381972}},
+ {{778877, 894545, 1010213}, {1357217, 1472885, 1588553}, {1935557, 2051225, 2166893}}},
+ {{{1011231, 1043091, 1074951}, {1170531, 1202391, 1234251}, {1329831, 1361691, 1393551}},
+ {{2340904, 2414668, 2488432}, {2709724, 2783488, 2857252}, {3078544, 3152308, 3226072}},
+ {{3670577, 3786245, 3901913}, {4248917, 4364585, 4480253}, {4827257, 4942925, 5058593}}}}});
+
+ Tensor expectedOutput4 = Array2D<int, 2, 5>{
+ {{205050376, 198925904, 181355097, 196978090, 238868348},
+ {598467376, 561797804, 560823897, 593043790, 698672948}}};
+ std::shared_ptr<Tensor> other1 = std::static_pointer_cast<OperatorTensor>(g->getNode("conv1")->getOperator())->getOutput(0);
+ REQUIRE(approxEq<float, int>(*other1, *expectedOutput1, 0.0, 1.0e-12));
+ std::shared_ptr<Tensor> other2 = std::static_pointer_cast<OperatorTensor>(g->getNode("conv2")->getOperator())->getOutput(0);
+ REQUIRE(approxEq<int>(*other2, *expectedOutput2, 0.0, 1.0e-12));
+ std::shared_ptr<Tensor> other3 = std::static_pointer_cast<OperatorTensor>(g->getNode("conv3")->getOperator())->getOutput(0);
+ REQUIRE(approxEq<double, int>(*other3, *expectedOutput3, 0.0, 1.0e-12));
+ std::shared_ptr<Tensor> other4 = std::static_pointer_cast<OperatorTensor>(g->getNode("fc")->getOperator())->getOutput(0);
+ REQUIRE(approxEq<int>(*other4, expectedOutput4, 0.0, 1.0e-12));
+ }
+}