Merge dev into dataloader

aidge_core/unit_tests/test_operator_binding.py

@@ -125,6 +125,23 @@ class test_operator_binding(unittest.TestCase):
         generic_op.forward() # Increment idx
         self.assertEqual(customImpl.idx, 1)
 
+    def test_magic_meth(self):
+        myVar = 2
+        myBool = True
+        # Test dynamic attribute set
+        gop = aidge_core.GenericOperator("test", 1, 0, 1, "FictiveName", myVar=myVar).get_operator()
+        gop.myBool = myBool
+        # Test variable set by kwargs
+        self.assertEqual(gop.myVar, myVar)
+        # Test set attr
+        self.assertEqual(gop.myBool, myBool)
+
+        # Test static attribute set !
+        prod = aidge_core.Producer([1]).get_operator()
+        self.assertEqual(prod.Constant, False)
+        prod.Constant = True # By default Constant is False
+        self.assertEqual(prod.Constant, True)
+
 
 
 if __name__ == '__main__':

aidge_core/unit_tests/test_tensor.py

@@ -10,16 +10,16 @@ SPDX-License-Identifier: EPL-2.0
 
 import unittest
 import aidge_core
-
 from functools import reduce
+
 import numpy as np
 
+
 class test_tensor(unittest.TestCase):
-    """
+    """Test tensor binding
     """
     def setUp(self):
         pass
-
     def tearDown(self):
         pass
 
@@ -35,10 +35,60 @@ class test_tensor(unittest.TestCase):
             idx = t.get_idx(coord)
             self.assertEqual(idx, i)
 
-if __name__ == '__main__':
-    unittest.main()
+    def test_getavailable_backends(self):
+        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).astype(np.int32)
+        # Numpy -> Tensor
+        t = aidge_core.Tensor(np_array)
+        self.assertEqual(t.dtype(), aidge_core.DataType.Int32)
+        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_tensor_int_to_numpy(self):
+        np_array = np.arange(9).reshape(1,1,3,3)
+        # Numpy -> Tensor
+        t = aidge_core.Tensor(np_array)
+        # Tensor -> Numpy
+        nnarray = np.array(t)
+        for i_nn, i_n in zip(nnarray.flatten(), np_array.flatten()):
+            self.assertTrue(i_nn == i_n)
+        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)
+        # Numpy -> Tensor
+        t = aidge_core.Tensor(np_array)
+        self.assertEqual(t.dtype(), aidge_core.DataType.Float32)
+        for i_t, i_n in zip(t, np_array.flatten()):
+            self.assertTrue(i_t == i_n) # TODO : May need to change this to a difference
+        for i,j in zip(t.dims(), np_array.shape):
+            self.assertEqual(i,j)
 
+    def test_get_set(self):
+        dims = [2,2,2]
 
+        np_array = np.arange(8).reshape(dims).astype(np.int32)
+        # Numpy -> Tensor
+        t = aidge_core.Tensor(np_array)
+        for i in range(8):
+            self.assertEqual(t[i], i)
+            t[i] = 5
+            self.assertEqual(t[i], 5)
 
+if __name__ == '__main__':
+    unittest.main()

include/aidge/aidge.hpp

@@ -15,6 +15,10 @@
 #include "aidge/backend/OperatorImpl.hpp"
 #include "aidge/backend/TensorImpl.hpp"
 #include "aidge/backend/StimulusImpl.hpp"
+
+#include "aidge/backend/cpu/data/TensorImpl.hpp"
+#include "aidge/backend/cpu/data/GetCPUPtr.h"
+
 #include "aidge/data/Data.hpp"
 #include "aidge/data/Tensor.hpp"
 #include "aidge/data/Database.hpp"

include/aidge/backend/cpu/data/GetCPUPtr.h

+/********************************************************************************
+ * 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_GETCPUPTR_H_
+#define AIDGE_CPU_DATA_GETCPUPTR_H_
+
+#include "aidge/data/Tensor.hpp"
+
+namespace Aidge {
+inline void *getCPUPtr(std::shared_ptr<Aidge::Data> const &data) {
+  const auto tensor = std::static_pointer_cast<Tensor>(data);
+  return tensor->getImpl()->hostPtr(tensor->getImplOffset());
+}
+} // namespace Aidge
+
+#endif // AIDGE_CPU_DATA_GETCPUPTR_H_
\ No newline at end of file

include/aidge/backend/cpu/data/TensorImpl.hpp

+/********************************************************************************
+ * 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:
+    /// 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:
+    static constexpr const char *Backend = "cpu";
+
+    TensorImpl_cpu(DeviceIdx_t device, NbElts_t length) : TensorImpl(Backend, device, length) {}
+
+    bool operator==(const TensorImpl &otherImpl) const override final {
+        const auto& typedOtherImpl = reinterpret_cast<const TensorImpl_cpu<T> &>(otherImpl);
+        AIDGE_INTERNAL_ASSERT(typedOtherImpl.size() >= mNbElts);
+
+        std::size_t i = 0;
+        for (; i < mNbElts &&
+               *(mData.data()+i) == *static_cast<const T*>(typedOtherImpl.rawPtr(i));
+               ++i) {
+        }
+        return i == mNbElts;
+    }
+
+    static std::shared_ptr<TensorImpl_cpu> create(DeviceIdx_t device, NbElts_t length) {
+        return std::make_shared<TensorImpl_cpu<T>>(device, length);
+    }
+
+    inline std::size_t scalarSize() const noexcept override final { return sizeof(T); }
+
+    void copy(const void *src, NbElts_t length, NbElts_t offset = 0) override final {
+        const T* srcT = static_cast<const T *>(src);
+        T* dstT = static_cast<T *>(rawPtr(offset));
+
+        AIDGE_ASSERT(length <= mData.size() || length <= mNbElts, "copy length is above capacity");
+        AIDGE_ASSERT(dstT < srcT || dstT >= srcT + length, "overlapping copy is not supported");
+        std::copy(srcT, srcT + length, dstT);
+    }
+
+    void copyCast(const void *src, const DataType srcDt, NbElts_t length, NbElts_t offset = 0) override final {
+        if (length == 0) {
+            return;
+        }
+
+        T* dstT = static_cast<T *>(rawPtr(offset));
+        AIDGE_ASSERT(length <= mData.size() || length <= mNbElts, "copy length is above capacity");
+        switch (srcDt)
+        {
+            case DataType::Float64:
+                std::copy(static_cast<const double*>(src), static_cast<const double*>(src) + length,
+                        dstT);
+                break;
+            case DataType::Float32:
+                std::copy(static_cast<const float*>(src), static_cast<const float*>(src) + length,
+                        dstT);
+                break;
+            case DataType::Float16:
+                std::copy(static_cast<const half_float::half*>(src), static_cast<const half_float::half*>(src) + length,
+                        dstT);
+                break;
+            case DataType::Int64:
+                std::copy(static_cast<const int64_t*>(src), static_cast<const int64_t*>(src) + length,
+                        dstT);
+                break;
+            case DataType::UInt64:
+                std::copy(static_cast<const uint64_t*>(src), static_cast<const uint64_t*>(src) + length,
+                        dstT);
+                break;
+            case DataType::Int32:
+                std::copy(static_cast<const int32_t*>(src), static_cast<const int32_t*>(src) + length,
+                        dstT);
+                break;
+            case DataType::UInt32:
+                std::copy(static_cast<const uint32_t*>(src), static_cast<const uint32_t*>(src) + length,
+                        dstT);
+                break;
+            case DataType::Int16:
+                std::copy(static_cast<const int16_t*>(src), static_cast<const int16_t*>(src) + length,
+                        dstT);
+                break;
+            case DataType::UInt16:
+                std::copy(static_cast<const uint16_t*>(src), static_cast<const uint16_t*>(src) + length,
+                        dstT);
+                break;
+            case DataType::Int8:
+                std::copy(static_cast<const int8_t*>(src), static_cast<const int8_t*>(src) + length,
+                        dstT);
+                break;
+            case DataType::UInt8:
+                std::copy(static_cast<const uint8_t*>(src), static_cast<const uint8_t*>(src) + length,
+                        dstT);
+                break;
+            default:
+                AIDGE_THROW_OR_ABORT(std::runtime_error, "Unsupported data type.");
+                break;
+        }
+    }
+
+    void copyFromDevice(const void *src, const std::pair<std::string, DeviceIdx_t>& device, NbElts_t length, NbElts_t offset = 0) 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, offset);
+    }
+
+    inline void copyFromHost(const void *src, NbElts_t length, NbElts_t offset = 0) override final {
+        copy(src, length, offset);
+    }
+
+    void copyToHost(void *dst, NbElts_t length, NbElts_t offset = 0) const override final {
+        const T* src = static_cast<const T*>(rawPtr(offset));
+        AIDGE_ASSERT(length <= mData.size() || length <= mNbElts, "copy length is above capacity");
+        std::copy(src, src + length, static_cast<T *>(dst));
+    }
+
+    void *rawPtr(NbElts_t offset = 0) override final {
+        lazyInit();
+        return (mData.data() + offset);
+    };
+
+    const void *rawPtr(NbElts_t offset = 0) const override final {
+        AIDGE_ASSERT(mData.size() >= mNbElts, "accessing uninitialized const rawPtr");
+        return (mData.data() + offset);
+    };
+
+    void *hostPtr(NbElts_t offset = 0) override final {
+        lazyInit();
+        return (mData.data() + offset);
+    };
+
+    const void *hostPtr(NbElts_t offset = 0) const override final {
+        AIDGE_ASSERT(mData.size() >= mNbElts, "accessing uninitialized const hostPtr");
+        return (mData.data() + offset);
+    };
+
+    void setRawPtr(void *ptr, NbElts_t length) override final {
+        AIDGE_ASSERT(length >= mNbElts, "trying to set raw pointer of insufficient capacity");
+        mData = future_std::span<T>(static_cast<T *>(ptr), length);
+        mDataOwner.reset();
+    };
+
+    virtual ~TensorImpl_cpu() = default;
+
+private:
+    void lazyInit() {
+        if (mData.size() < mNbElts) {
+            // Need more data, a re-allocation will occur
+            AIDGE_ASSERT(mData.empty() || mDataOwner != nullptr, "trying to enlarge non-owned data");
+            mDataOwner.reset(new T[mNbElts]);
+            mData = future_std::span<T>(mDataOwner.get(), mNbElts);
+        }
+    }
+};
+
+namespace {
+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
+
+#endif /* AIDGE_CPU_DATA_TENSORIMPL_H_ */

include/aidge/operator/ReduceMean.hpp

@@ -12,8 +12,10 @@
 #ifndef AIDGE_CORE_OPERATOR_REDUCEMEAN_H_
 #define AIDGE_CORE_OPERATOR_REDUCEMEAN_H_
 
+#include <algorithm>  // std::for_each
 #include <array>
 #include <cmath>
+#include <cstdint>    // std::int32_t
 #include <numeric>
 #include <vector>
 
@@ -31,18 +33,18 @@ enum class ReduceMeanAttr { Axes, KeepDims };
 template <DimIdx_t DIM>
 class ReduceMean_Op : public OperatorTensor,
                 public Registrable<ReduceMean_Op<DIM>, std::string, std::unique_ptr<OperatorImpl>(const ReduceMean_Op<DIM> &)>,
-                public StaticAttributes<ReduceMeanAttr, std::array<int, DIM>, DimSize_t> {
+                public StaticAttributes<ReduceMeanAttr, std::array<std::int32_t, DIM>, DimSize_t> {
 
    public:
     static const std::string Type;
 
     ReduceMean_Op() = delete;
 
-    using Attributes_ = StaticAttributes<ReduceMeanAttr, std::array<int, DIM>, DimSize_t>;
+    using Attributes_ = StaticAttributes<ReduceMeanAttr, std::array<std::int32_t, DIM>, DimSize_t>;
     template <ReduceMeanAttr e>
     using attr = typename Attributes_::template attr<e>;
 
-    constexpr ReduceMean_Op(const std::array<int, DIM> &axes, DimSize_t keep_dims)
+    constexpr ReduceMean_Op(const std::array<std::int32_t, DIM> &axes, DimSize_t keep_dims)
         : OperatorTensor(Type, 1, 0, 1),
           Attributes_(attr<ReduceMeanAttr::Axes>(axes),
                       attr<ReduceMeanAttr::KeepDims>(keep_dims)) {}
@@ -67,29 +69,28 @@ class ReduceMean_Op : public OperatorTensor,
     }
 
     void computeOutputDims() override final {
+        if (!getInput(0)) {
+            AIDGE_THROW_OR_ABORT(std::runtime_error, "Every input should be associated with a Tensor");
+        }
         if (!getInput(0)->empty()) {
-            std::vector<DimSize_t> outDims;
-            for(std::size_t d=0; d<getInput(0)->dims().size(); ++d)
-            {
-                bool reducedDim =  false;
-                for(std::size_t i=0; i<DIM; ++i)
-                {
-                    int axis_ = this->template getAttr<ReduceMeanAttr::Axes>()[i];
-                    std::size_t axis= axis_>=0? axis_: axis_ + getInput(0)->nbDims();
-                    if(axis == d)
-                    {
-                        reducedDim = true;
-                        break;
-                    }
-                }
-                if(reducedDim)
-                {
-                    if(this->template getAttr<ReduceMeanAttr::KeepDims>())
-                        outDims.push_back(1);
-                }
-                else
-                    outDims.push_back(getInput(0)->dims()[d]);
+            // make Axes attribute positive
+            std::array<std::int32_t, DIM>& axes = this->template getAttr<ReduceMeanAttr::Axes>();
+            std::for_each(axes.begin(), axes.end(), [&] (std::int32_t& val) {
+                if (val < 0)
+                    val+=static_cast<std::int32_t>(getInput(0)->nbDims());
+            });
+            std::sort(axes.begin(), axes.end());
+
+            // build output dimensions
+            std::vector<DimSize_t> outDims = getInput(0)->dims();
+            if (this->template getAttr<ReduceMeanAttr::KeepDims>()) {
+                std::for_each(axes.begin(), axes.end(), [&outDims] (const std::int32_t& val) { outDims[val] = 1; });
+            }
+            else {
+                for (auto it = axes.crbegin(); it != axes.crend(); ++it)
+                    outDims.erase(outDims.begin() + static_cast<std::size_t>(*it));
             }
+
             if(outDims.size()>0)
                 mOutputs[0]->resize(outDims);
             else
@@ -111,7 +112,7 @@ class ReduceMean_Op : public OperatorTensor,
 };
 
 template <std::array<DimSize_t, 1>::size_type DIM>
-inline std::shared_ptr<Node> ReduceMean(const std::array<int, DIM> &axes,
+inline std::shared_ptr<Node> ReduceMean(const std::array<std::int32_t, DIM> &axes,
                                         DimSize_t keep_dims=1,
                                         const std::string& name = "") {
     // FIXME: properly handle default w&b initialization in every cases
@@ -123,7 +124,7 @@ inline std::shared_ptr<Node> ReduceMean(const std::array<int, DIM> &axes,
 // helper with C-style array instead of std::array for kernel_dims to allow automatic template DIM deduction
 template <DimSize_t DIM>
 inline std::shared_ptr<Node> ReduceMean(
-    int const (&axes)[DIM],
+    std::int32_t const (&axes)[DIM],
     DimSize_t keep_dims = 1,
     const std::string& name = "") {
     static_assert(DIM<=MaxDim,"Too many kernel dimensions required by ReduceMean, not supported");

include/aidge/utils/Attributes.hpp

@@ -69,6 +69,11 @@ public:
     *  be agnostic from its return type.
     */
     virtual py::object getAttrPy(const std::string& name) const = 0;
+    /* Bindable set function, does not recquire any templating.
+    *  This is thanks to py::object which allow the function to
+    *  be agnostic from ``value`` type.
+    */
+    virtual void setAttrPy(const std::string& name, py::object&& value) = 0;
 #endif
     virtual ~Attributes() {}
 };

include/aidge/utils/DynamicAttributes.hpp

@@ -135,7 +135,7 @@ public:
         assert(res.second && "attribute already exists");
     }
 
-    void setAttrPy(const std::string& name, py::object&& value)
+    void setAttrPy(const std::string& name, py::object&& value) override final
     {
         auto resPy = mAttrsPy.emplace(std::make_pair(name, value));
         if (!resPy.second)
@@ -204,7 +204,7 @@ private:
     // Stores C++ attributes (copy) and Python-only attributes
     // Code should be compiled with -fvisibility=hidden
     // See https://pybind11.readthedocs.io/en/stable/faq.html:
-    // “‘SomeClass’ declared with greater visibility than the type of its 
+    // “‘SomeClass’ declared with greater visibility than the type of its
     // field ‘SomeClass::member’ [-Wattributes]”
     // This map will only be populated if Python interpreter is running
     std::map<std::string, py::object> mAttrsPy;

include/aidge/utils/StaticAttributes.hpp

@@ -202,6 +202,22 @@ public:
     }
 
     #ifdef PYBIND
+    /**
+     * @brief Return a set of attributes defined.
+     * This method is used to automatically retrieve attributes in the documentation.
+     * This method is a duplicate of ``getAttrsName`` but static.
+     *
+     * @return std::set<std::string>
+     */
+    static std::set<std::string> staticGetAttrsName() {
+        std::set<std::string> attrsName;
+        for (std::size_t i = 0; i < size(EnumStrings<ATTRS_ENUM>::data); ++i) {
+            attrsName.insert(EnumStrings<ATTRS_ENUM>::data[i]);
+        }
+        return attrsName;
+    }
+
+
     py::object getAttrPy(const std::string& name) const override {
         for (std::size_t i = 0; i < size(EnumStrings<ATTRS_ENUM>::data); ++i) {
             if (name == EnumStrings<ATTRS_ENUM>::data[i]) {
@@ -212,7 +228,22 @@ public:
         }
 
         AIDGE_THROW_OR_ABORT(py::value_error, "attribute \"%s\" not found", name.c_str());
-    };
+    }
+
+
+    void setAttrPy(const std::string& name, py::object&& value) override final{
+        for (std::size_t i = 0; i < size(EnumStrings<ATTRS_ENUM>::data); ++i) {
+            if (name == EnumStrings<ATTRS_ENUM>::data[i]) {
+                // Cannot update attribute using reference has it would require templating
+                // Use a dirty
+                auto tmpAttr = py::cast(mAttrs);
+                py::detail::accessor_policies::tuple_item::set(tmpAttr, static_cast<py::size_t>(i), value);
+                mAttrs = py::cast<std::tuple<T...>>(tmpAttr);
+                return;
+            }
+        }
+        AIDGE_THROW_OR_ABORT(py::value_error, "attribute \"%s\" not found", name.c_str());
+    }
     #endif
 
 private:

python_binding/data/pybind_Tensor.cpp

@@ -31,24 +31,26 @@ void addCtor(py::class_<Tensor,
                         Registrable<Tensor,
                                     std::tuple<std::string, DataType>,
                                     std::shared_ptr<TensorImpl>(DeviceIdx_t device, std::vector<DimSize_t> dims)>>& mTensor){
-    mTensor.def(py::init([]( py::array_t<T, py::array::c_style | py::array::forcecast> b) {
+    mTensor.def(py::init([](
+        py::array_t<T, py::array::c_style | py::array::forcecast> b,
+        std::string backend = "cpu") {
         /* Request a buffer descriptor from Python */
         py::buffer_info info = b.request();
         Tensor* newTensor = new Tensor();
         newTensor->setDataType(NativeType<T>::type);
         const std::vector<DimSize_t> dims(info.shape.begin(), info.shape.end());
         newTensor->resize(dims);
-        // TODO : Find a better way to choose backend
+
         std::set<std::string> availableBackends = Tensor::getAvailableBackends();
-        if (availableBackends.find("cpu") != availableBackends.end()){
-            newTensor->setBackend("cpu");
+        if (availableBackends.find(backend) != availableBackends.end()){
+            newTensor->setBackend(backend);
             newTensor->getImpl()->copyFromHost(static_cast<T*>(info.ptr), newTensor->size());
         }else{
-            printf("Warning : Could not use aidge_cpu backend, verify you have `import aidge_cpu`\n");
+            AIDGE_THROW_OR_ABORT(py::value_error, "Could not find backend %s, verify you have `import aidge_backend_%s`.\n", backend.c_str(), backend.c_str());
         }
 
         return newTensor;
-    }))
+    }), py::arg("array"), py::arg("backend")="cpu")
     .def("__setitem__", (void (Tensor::*)(std::size_t, T)) &Tensor::set)
     .def("__setitem__", (void (Tensor::*)(std::vector<std::size_t>, T)) &Tensor::set)
     ;

python_binding/operator/pybind_AvgPooling.cpp

@@ -26,7 +26,7 @@ namespace py = pybind11;
 namespace Aidge {
 
 template <DimIdx_t DIM> void declare_AvgPoolingOp(py::module &m) {
-  py::class_<AvgPooling_Op<DIM>, std::shared_ptr<AvgPooling_Op<DIM>>, OperatorTensor, Attributes>(
+  py::class_<AvgPooling_Op<DIM>, std::shared_ptr<AvgPooling_Op<DIM>>, Attributes, OperatorTensor>(
     m, ("AvgPoolingOp" + std::to_string(DIM) + "D").c_str(),
     py::multiple_inheritance())
   .def(py::init<const std::array<DimSize_t, DIM> &,
@@ -34,7 +34,8 @@ template <DimIdx_t DIM> void declare_AvgPoolingOp(py::module &m) {
         py::arg("kernel_dims"),
         py::arg("stride_dims"))
   .def("get_inputs_name", &AvgPooling_Op<DIM>::getInputsName)
-  .def("get_outputs_name", &AvgPooling_Op<DIM>::getOutputsName);
+  .def("get_outputs_name", &AvgPooling_Op<DIM>::getOutputsName)
+  .def("attributes_name", &AvgPooling_Op<DIM>::staticGetAttrsName);
 
   m.def(("AvgPooling" + std::to_string(DIM) + "D").c_str(), [](const std::vector<DimSize_t>& kernel_dims,
                                                                   const std::string& name,

python_binding/operator/pybind_BatchNorm.cpp

@@ -21,9 +21,10 @@ namespace Aidge {
 
 template <DimSize_t DIM>
 void declare_BatchNormOp(py::module& m) {
-    py::class_<BatchNorm_Op<DIM>, std::shared_ptr<BatchNorm_Op<DIM>>, OperatorTensor, Attributes>(m, ("BatchNormOp" + std::to_string(DIM) + "D").c_str(), py::multiple_inheritance())
+    py::class_<BatchNorm_Op<DIM>, std::shared_ptr<BatchNorm_Op<DIM>>, Attributes, OperatorTensor>(m, ("BatchNormOp" + std::to_string(DIM) + "D").c_str(), py::multiple_inheritance())
     .def("get_inputs_name", &BatchNorm_Op<DIM>::getInputsName)
-    .def("get_outputs_name", &BatchNorm_Op<DIM>::getOutputsName);
+    .def("get_outputs_name", &BatchNorm_Op<DIM>::getOutputsName)
+    .def("attributes_name", &BatchNorm_Op<DIM>::staticGetAttrsName);
 
     m.def(("BatchNorm" + std::to_string(DIM) + "D").c_str(), &BatchNorm<DIM>, py::arg("nbFeatures"), py::arg("epsilon") = 1.0e-5F, py::arg("momentum") = 0.1F, py::arg("name") = "");
 }

python_binding/operator/pybind_Concat.cpp

@@ -19,9 +19,10 @@ namespace py = pybind11;
 namespace Aidge {
 
 void init_Concat(py::module& m) {
-    py::class_<Concat_Op, std::shared_ptr<Concat_Op>, OperatorTensor, Attributes>(m, "ConcatOp", py::multiple_inheritance())
+    py::class_<Concat_Op, std::shared_ptr<Concat_Op>, Attributes, OperatorTensor>(m, "ConcatOp", py::multiple_inheritance())
     .def("get_inputs_name", &Concat_Op::getInputsName)
-    .def("get_outputs_name", &Concat_Op::getOutputsName);
+    .def("get_outputs_name", &Concat_Op::getOutputsName)
+    .def("attributes_name", &Concat_Op::staticGetAttrsName);
 
     m.def("Concat", &Concat, py::arg("nbIn"), py::arg("axis"), py::arg("name") = "");
 }

python_binding/operator/pybind_Conv.cpp

@@ -24,7 +24,7 @@ namespace py = pybind11;
 namespace Aidge {
 
 template <DimIdx_t DIM> void declare_ConvOp(py::module &m) {
-  py::class_<Conv_Op<DIM>, std::shared_ptr<Conv_Op<DIM>>, OperatorTensor, Attributes>(
+  py::class_<Conv_Op<DIM>, std::shared_ptr<Conv_Op<DIM>>, Attributes, OperatorTensor>(
     m, ("ConvOp" + std::to_string(DIM) + "D").c_str(),
     py::multiple_inheritance())
   .def(py::init<DimSize_t,
@@ -39,6 +39,7 @@ template <DimIdx_t DIM> void declare_ConvOp(py::module &m) {
         py::arg("dilation_dims"))
     .def("get_inputs_name", &Conv_Op<DIM>::getInputsName)
     .def("get_outputs_name", &Conv_Op<DIM>::getOutputsName)
+    .def("attributes_name", &Conv_Op<DIM>::staticGetAttrsName)
     ;
 
   m.def(("Conv" + std::to_string(DIM) + "D").c_str(), [](DimSize_t in_channels,

python_binding/operator/pybind_ConvDepthWise.cpp

@@ -26,7 +26,7 @@ namespace py = pybind11;
 namespace Aidge {
 
 template <DimIdx_t DIM> void declare_ConvDepthWiseOp(py::module &m) {
-  py::class_<ConvDepthWise_Op<DIM>, std::shared_ptr<ConvDepthWise_Op<DIM>>, OperatorTensor, Attributes>(
+  py::class_<ConvDepthWise_Op<DIM>, std::shared_ptr<ConvDepthWise_Op<DIM>>, Attributes, OperatorTensor>(
     m, ("ConvDepthWiseOp" + std::to_string(DIM) + "D").c_str(),
     py::multiple_inheritance())
   .def(py::init<const DimSize_t,
@@ -38,7 +38,8 @@ template <DimIdx_t DIM> void declare_ConvDepthWiseOp(py::module &m) {
         py::arg("stride_dims"),
         py::arg("dilation_dims"))
   .def("get_inputs_name", &ConvDepthWise_Op<DIM>::getInputsName)
-  .def("get_outputs_name", &ConvDepthWise_Op<DIM>::getOutputsName);
+  .def("get_outputs_name", &ConvDepthWise_Op<DIM>::getOutputsName)
+  .def("attributes_name", &ConvDepthWise_Op<DIM>::staticGetAttrsName);
 
   m.def(("ConvDepthWise" + std::to_string(DIM) + "D").c_str(), [](const DimSize_t nb_channels,
                                                                   const std::vector<DimSize_t>& kernel_dims,

python_binding/operator/pybind_FC.cpp

@@ -20,9 +20,10 @@ namespace py = pybind11;
 namespace Aidge {
 
 void declare_FC(py::module &m) {
-  py::class_<FC_Op, std::shared_ptr<FC_Op>, OperatorTensor, Attributes>(m, "FCOp", py::multiple_inheritance())
+  py::class_<FC_Op, std::shared_ptr<FC_Op>, Attributes, OperatorTensor>(m, "FCOp", py::multiple_inheritance())
   .def("get_inputs_name", &FC_Op::getInputsName)
-  .def("get_outputs_name", &FC_Op::getOutputsName);
+  .def("get_outputs_name", &FC_Op::getOutputsName)
+  .def("attributes_name", &FC_Op::staticGetAttrsName);
 
   m.def("FC", &FC, py::arg("in_channels"), py::arg("out_channels"), py::arg("nobias") = false, py::arg("name") = "");
 }

python_binding/operator/pybind_Gather.cpp

@@ -19,9 +19,10 @@ namespace py = pybind11;
 namespace Aidge {
 
 void init_Gather(py::module& m) {
-    py::class_<Gather_Op, std::shared_ptr<Gather_Op>, OperatorTensor, Attributes>(m, "GatherOp", py::multiple_inheritance())
+    py::class_<Gather_Op, std::shared_ptr<Gather_Op>, Attributes, OperatorTensor>(m, "GatherOp", py::multiple_inheritance())
     .def("get_inputs_name", &Gather_Op::getInputsName)
-    .def("get_outputs_name", &Gather_Op::getOutputsName);
+    .def("get_outputs_name", &Gather_Op::getOutputsName)
+    .def("attributes_name", &Gather_Op::staticGetAttrsName);
 
     m.def("Gather", &Gather, py::arg("indices"), py::arg("gathered_shape"), py::arg("axis"), py::arg("name") = "");
 }

python_binding/operator/pybind_GenericOperator.cpp

@@ -21,13 +21,36 @@ namespace py = pybind11;
 namespace Aidge {
 
 void init_GenericOperator(py::module& m) {
-    py::class_<GenericOperator_Op, std::shared_ptr<GenericOperator_Op>, OperatorTensor, DynamicAttributes>(m, "GenericOperatorOp",
+    py::class_<GenericOperator_Op, std::shared_ptr<GenericOperator_Op>, DynamicAttributes, OperatorTensor>(m, "GenericOperatorOp",
                                                                                   py::multiple_inheritance())
     .def_readonly_static("identity", &GenericOperator_Op::Identity)
     .def("compute_output_dims", &GenericOperator_Op::computeOutputDims)
     .def("set_compute_output_dims", &GenericOperator_Op::setComputeOutputDims, py::arg("computation_function"));
 
-    m.def("GenericOperator", &GenericOperator, py::arg("type"), py::arg("nb_data"), py::arg("nb_param"), py::arg("nb_out"),
-          py::arg("name") = "");
+    // &GenericOperator
+    m.def("GenericOperator",
+        []( const std::string& type,
+            IOIndex_t nbData,
+            IOIndex_t nbParam,
+            IOIndex_t nbOut,
+            const std::string& name,
+            const py::kwargs kwargs){
+            std::shared_ptr<Node> genericNode = GenericOperator(
+                type,
+                nbData,
+                nbParam,
+                nbOut,
+                name
+            );
+            if (kwargs){
+                std::shared_ptr<GenericOperator_Op> gop = std::static_pointer_cast<GenericOperator_Op>(genericNode->getOperator());
+                for (auto item : kwargs) {
+                    std::string key = py::cast<std::string>(item.first);
+                    py::object value = py::reinterpret_borrow<py::object>(item.second);
+                    gop->setAttrPy(key, std::move(value));
+                }
+            }
+            return genericNode;
+        }, py::arg("type"), py::arg("nb_data"), py::arg("nb_param"), py::arg("nb_out"), py::arg("name") = "");
 }
 }  // namespace Aidge

python_binding/operator/pybind_LeakyReLU.cpp

@@ -18,9 +18,10 @@ namespace py = pybind11;
 namespace Aidge {
 
 void init_LeakyReLU(py::module& m) {
-    py::class_<LeakyReLU_Op, std::shared_ptr<LeakyReLU_Op>, OperatorTensor, Attributes>(m, "LeakyReLUOp", py::multiple_inheritance())
+    py::class_<LeakyReLU_Op, std::shared_ptr<LeakyReLU_Op>, Attributes, OperatorTensor>(m, "LeakyReLUOp", py::multiple_inheritance())
     .def("get_inputs_name", &LeakyReLU_Op::getInputsName)
-    .def("get_outputs_name", &LeakyReLU_Op::getOutputsName);
+    .def("get_outputs_name", &LeakyReLU_Op::getOutputsName)
+    .def("attributes_name", &LeakyReLU_Op::staticGetAttrsName);
 
     m.def("LeakyReLU", &LeakyReLU, py::arg("negative_slope") = 0.0f, py::arg("name") = "");
 }

python_binding/operator/pybind_Matmul.cpp

@@ -20,9 +20,10 @@ namespace py = pybind11;
 namespace Aidge {
 
 void declare_MatMul(py::module &m) {
-  py::class_<MatMul_Op, std::shared_ptr<MatMul_Op>, OperatorTensor, Attributes>(m, "MatMulOp", py::multiple_inheritance())
+  py::class_<MatMul_Op, std::shared_ptr<MatMul_Op>, Attributes, OperatorTensor>(m, "MatMulOp", py::multiple_inheritance())
   .def("get_inputs_name", &MatMul_Op::getInputsName)
-  .def("get_outputs_name", &MatMul_Op::getOutputsName);
+  .def("get_outputs_name", &MatMul_Op::getOutputsName)
+  .def("attributes_name", &MatMul_Op::staticGetAttrsName);
 
   m.def("MatMul", &MatMul, py::arg("in_channels"), py::arg("out_channels"), py::arg("name") = "");
 }