[WIP][NF] Start Tensor changes

- [Add] ``zeros()`` member function to set implpementation elements to 0
- [unit_tests][NF] Add many more cases in Test_TensorImpl.cpp
- [include] update includes in Tensor.hpp

include/aidge/backend/TensorImpl.hpp

@@ -67,7 +67,7 @@ private:
 class TensorImpl {
 public:
     TensorImpl() = delete;
-    TensorImpl(const char *backend, DeviceIdx_t device, std::vector<DimSize_t> dims) : mBackend(backend), mDevice(device) 
+    TensorImpl(const char *backend, DeviceIdx_t device, std::vector<DimSize_t> dims) : mBackend(backend), mDevice(device)
     {
         resize(dims);
     };
@@ -148,7 +148,7 @@ public:
     };
 
     /**
-     * Set the size, in number of elements, that must be stored.
+     * @brief Set the size, in number of elements, that must be stored.
     */
     virtual void resize(std::vector<DimSize_t> dims) {
         size_t product = 1;
@@ -159,14 +159,20 @@ public:
     }
 
     /**
-     * Return the number of elements stored.
+     * @brief Return the number of elements stored.
     */
     inline std::size_t size() const noexcept { return mNbElts; }
 
     /**
-     * Return the size (in bytes) of one element (scalar).
+     * @brief Return the size (in bytes) of one element (scalar).
     */
     virtual std::size_t scalarSize() const noexcept = 0;
+
+    /**
+     * @brief Set every element of the implementation to zero.
+     */
+    virtual void zeros() = 0;
+
     constexpr const char *backend() const { return mBackend; }
     virtual ~TensorImpl() = default;
     virtual bool operator==(const TensorImpl &othImpl) const = 0;

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

@@ -53,6 +53,15 @@ public:
 
     inline std::size_t scalarSize() const noexcept override final { return sizeof(T); }
 
+    void zeros() override final {
+        if (mData.empty()) {
+            lazyInit();
+        }
+        for (std::size_t i = 0; i < mData.size(); ++i) {
+            *(mData.data() + i) = T(0);
+        }
+    }
+
     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));

include/aidge/data/Tensor.hpp

@@ -92,7 +92,7 @@ class Tensor : public Data,
             newTensor.makeContiguous();
         }
         else {
-            std::shared_ptr<TensorImpl> newImpl = Registrar<Tensor>::create({mImpl->backend(), mDataType})(mImpl->device().second, mDims);          
+            std::shared_ptr<TensorImpl> newImpl = Registrar<Tensor>::create({mImpl->backend(), mDataType})(mImpl->device().second, mDims);
             newImpl->copy(mImpl->rawPtr(mImplOffset), mSize);
             newTensor.setImpl(newImpl);
         }
@@ -454,6 +454,15 @@ class Tensor : public Data,
      */
     bool empty() const { return mDims.empty(); }
 
+    /**
+     * @brief Set each element of the tensor to zero.
+     */
+    void zeros() const {
+        if (mImpl) {
+            mImpl->zeros();
+        }
+    }
+
     template <typename expectedType>
     const expectedType& get(std::size_t idx) const {
         AIDGE_ASSERT(NativeType<expectedType>::type == mDataType, "wrong data type");
@@ -589,7 +598,7 @@ class Tensor : public Data,
      * @param flatIdx 1D contiguous index of the value considering a flatten, contiguous, tensor.
      * @return std::vector<DimSize_t>
      */
-    std::vector<std::size_t> getCoord(std::size_t flatIdx) const {
+    std::vector<std::size_t> getCoord(const std::size_t flatIdx) const {
         std::vector<std::size_t> coordIdx = std::vector<std::size_t>(mDims.size());
         std::size_t idx = flatIdx;
         for (std::size_t i = mDims.size() - 1; i > 0; --i){
@@ -635,10 +644,11 @@ class Tensor : public Data,
     }
 
     /**
-     * Returns a sub-tensor with one or more dimension less.
-     * For instance, t.extract({1}) on a CHW tensor will return the HW tensor
+     * @brief Returns a sub-tensor with equal or lower number of dimensions.
+     *
+     * For instance, ``t.extract({1})`` on a CHW tensor will return the HW tensor
      * of channel #1.
-     * Likewise, t.extract({0, 1}) on a NCHW tensor will return the HW tensor
+     * Likewise, ``t.extract({0, 1})`` on a NCHW tensor will return the HW tensor
      * of batch #0 and channel #1.
      * No memory copy is performed, the returned tensor does not own the memory.
      * If the number of coordinates matches the number of dimensions, an empty
@@ -652,7 +662,7 @@ class Tensor : public Data,
     Tensor extract(const std::vector<std::size_t>& coordIdx) const;
 
     /**
-     * Returns a sub-tensor at some coordinate and with some dimension.
+     * @brief Returns a sub-tensor at some coordinate and with some dimension.
      *
      * @param coordIdx First coordinates of the sub-tensor to extract
      * @param dims Dimensions of the sub-tensor to extract

include/aidge/utils/ArrayHelpers.hpp

@@ -103,11 +103,32 @@ constexpr std::array<T, N + 1> append(T t, std::array<T, N> a) {
 // Generic helper for initializing a Tensor
 template <typename T, std::size_t SIZE_0>
 struct Array1D {
+    Array1D(std::initializer_list<T> list) {
+        auto it = list.begin();
+        for (std::size_t i = 0; i < SIZE_0; ++i, ++it) {
+            data[i] = *it;
+        }
+    }
+    Array1D(const T (&dataArray)[SIZE_0]) {
+        std::copy_n(&dataArray[0], SIZE_0, &data[0]);
+    }
     T data[SIZE_0];
 };
 
 template <typename T, std::size_t SIZE_0, std::size_t SIZE_1>
 struct Array2D {
+    Array2D(std::initializer_list<std::initializer_list<T>> list) {
+        auto it1 = list.begin();
+        for (std::size_t i = 0; i < SIZE_0; ++i, ++it1) {
+            auto it2 = it1->begin();
+            for (std::size_t j = 0; j < SIZE_1; ++j, ++it2) {
+                data[i][j] = *it2;
+            }
+        }
+    }
+    Array2D(const T (&dataArray)[SIZE_0][SIZE_1]) {
+        std::copy_n(&dataArray[0][0], SIZE_0 * SIZE_1, &data[0][0]);
+    }
     T data[SIZE_0][SIZE_1];
 };
 

src/data/Tensor.cpp

@@ -10,8 +10,13 @@
  ********************************************************************************/
 
 #include "aidge/data/Tensor.hpp"
-#include "aidge/utils/Types.h"
+
+#include <cstddef>
+#include <vector>
+
 #include "aidge/utils/ErrorHandling.hpp"
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/utils/Types.h"
 
 Aidge::Tensor Aidge::Tensor::extract(const std::vector<std::size_t>& coordIdx) const {
     AIDGE_ASSERT(isContiguous(), "Tensor must be contiguous");
@@ -46,13 +51,13 @@ void Aidge::Tensor::makeContiguous() {
         // Create a new storage that will be contiguous
         std::shared_ptr<TensorImpl> newImpl = Registrar<Tensor>::create({mImpl->backend(), mDataType})(mImpl->device().second, mDims);
         // Copy elements from old to new storage
-        size_t idx = 0;
+        std::size_t idx = 0;
         while (idx < mSize) {
-            const size_t storageIdx = getStorageIdx(getCoord(idx));
+            const std::size_t storageIdx = getStorageIdx(getCoord(idx));
 
             // Determine the size of the contiguous chunk
-            size_t copySize = 1;
-            while (idx + copySize < mSize && 
+            std::size_t copySize = 1;
+            while (idx + copySize < mSize &&
                 getStorageIdx(getCoord(idx + copySize)) == storageIdx + copySize)
             {
                 ++copySize;

unit_tests/data/Test_TensorImpl.cpp

@@ -10,6 +10,10 @@
  ********************************************************************************/
 
 #include <array>
+#include <cstddef>
+#include <cstdint>  //std::uint16_t
+#include <random>
+#include <vector>
 
 #include <catch2/catch_test_macros.hpp>
 
@@ -19,82 +23,180 @@
 
 using namespace Aidge;
 
-TEST_CASE("Tensor creation") {
-  SECTION("from const array") {
-    Tensor x = Array3D<int, 2, 2, 2>{{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}};
-
-    Tensor xCopy = Array3D<int, 2, 2, 2>{{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}};
+TEST_CASE("[backend/cpu/data] Tensor", "[Tensor]") {
+    Tensor x;
+
+    SECTION("TensorUtils, constructor from const arrays") {
+        // construction from different types and sizes
+        REQUIRE_NOTHROW(x = Array1D<int, 2>{{1, 2}});
+        x.print();
+        REQUIRE_NOTHROW(x = Array2D<int, 2, 2>{{{1, 2}, {3, 4}}});
+        x.print();
+        REQUIRE_NOTHROW(x = Array3D<std::uint8_t, 2, 2, 2>{{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}});
+        REQUIRE_NOTHROW(x = Array3D<int, 2, 2, 2>{{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}});
+        REQUIRE_NOTHROW(x = Array3D<float, 2, 2, 2>{{{{1.0f, 2.0f}, {3.0f, 4.0f}}, {{5.0f, 6.0f}, {7.0f, 8.0f}}}});
+        REQUIRE_NOTHROW(x = Array3D<double, 2, 2, 2>{{{{1., 2.}, {3., 4.}}, {{5., 6.}, {7., 8.}}}});
+
+        REQUIRE_NOTHROW(x = Array4D<int, 2, 2, 2, 2>{{{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}},
+                                                    {{{9,10}, {11,12}}, {{13,14},{15,16}}}}});
+    }
+    x = Array3D<int, 2, 2, 2>{{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}};
 
-    Tensor xFloat =
-        Array3D<float, 2, 2, 2>{{{{1., 2.}, {3., 4.}}, {{5., 6.}, {7., 8.}}}};
 
     SECTION("Tensor features") {
-      REQUIRE(x.nbDims() == 3);
-      REQUIRE(x.dims()[0] == 2);
-      REQUIRE(x.dims()[1] == 2);
-      REQUIRE(x.dims()[2] == 2);
-      REQUIRE(x.size() == 8);
+        x = Array3D<int, 2, 2, 2>{{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}};
+        REQUIRE(x.nbDims() == 3);
+        REQUIRE(x.dims()[0] == 2);
+        REQUIRE(x.dims()[1] == 2);
+        REQUIRE(x.dims()[2] == 2);
+        REQUIRE(x.size() == 8);
     }
 
     SECTION("Access to array") {
-      REQUIRE(static_cast<int *>(x.getImpl()->rawPtr())[0] == 1);
-      REQUIRE(static_cast<int *>(x.getImpl()->rawPtr())[7] == 8);
+        x = Array3D<int, 2, 2, 2>{{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}};
+        REQUIRE(static_cast<int *>(x.getImpl()->rawPtr())[0] == 1);
+        REQUIRE(static_cast<int *>(x.getImpl()->rawPtr())[7] == 8);
     }
 
     SECTION("get function") {
-      REQUIRE(x.get<int>({0, 0, 0}) == 1);
-      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.set<int>({1, 1, 1}, 36);
-      REQUIRE(x.get<int>({1, 1, 1}) == 36);
+        x = Array3D<int, 2, 2, 2>{{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}};
+        REQUIRE(x.get<int>({0, 0, 0}) == 1);
+        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.set<int>({1, 1, 1}, 36);
+        REQUIRE(x.get<int>(7) == 36);
+        x.set<int>(7, 40);
+        REQUIRE(x.get<int>({1, 1, 1}) == 40);
     }
 
-    SECTION("Pretty printing for debug") { REQUIRE_NOTHROW(x.print()); }
+    SECTION("Pretty printing for debug") {
+        x = Array3D<int, 2, 2, 2>{{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}};
+        REQUIRE_NOTHROW(x.print());
+    }
 
     SECTION("Tensor (in)equality") {
-      REQUIRE(x == xCopy);
-      REQUIRE_FALSE(x == xFloat);
+        Tensor xCopy = Array3D<int, 2, 2, 2>{{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}};
+        Tensor xFloat = Array3D<float, 2, 2, 2>{{{{1.0f, 2.0f}, {3.0f, 4.0f}}, {{5.0f, 6.0f}, {7.0f, 8.0f}}}};
+
+        REQUIRE(x == xCopy);
+        REQUIRE_FALSE(x == xFloat);
     }
-  }
-}
 
-TEST_CASE("Tensor methods") {
-  Tensor x = Array3D<int, 2, 2, 2>{{
-    {{1, 2},
-     {3, 4}},
-    {{5, 6},
-     {7, 8}}
-  }};
-
-  Tensor xCopy = Array3D<int, 2, 2, 2>{{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}};
-
-  Tensor xFloat =
-      Array3D<float, 2, 2, 2>{{{{1., 2.}, {3., 4.}}, {{5., 6.}, {7., 8.}}}};
-
-  SECTION("Tensor sharing") {
-    Tensor xCopyCtor(x);
-    REQUIRE(xCopyCtor.getImpl() == x.getImpl());
-
-    Tensor xEqOp = x;
-    REQUIRE(xEqOp.getImpl() == x.getImpl());
-
-    Tensor xCloned = x.clone();
-    REQUIRE(xCloned.getImpl() != x.getImpl());
-    REQUIRE(xCloned == x);
-  }
-
-  SECTION("Tensor extract") {
-    Tensor y = x.extract({0, 1});
-    REQUIRE(y.getImpl() == x.getImpl());
-    REQUIRE(approxEq<int>(y, Array1D<int, 2>{{3, 4}}));
-    REQUIRE(y.isContiguous());
-    
-    Tensor y2 = x.extract({0, 1, 1}, {2, 1, 1});
-    REQUIRE(y2.getImpl() == x.getImpl());
-    REQUIRE(!y2.isContiguous());
-    Tensor y3 = y2.clone();
-    REQUIRE(y3.isContiguous());
-    REQUIRE(approxEq<int>(y3, Array3D<int, 2, 1, 1>{{{{4}}, {{8}}}}));
-  }
+    constexpr std::uint16_t NBTRIALS = 10;
+
+    // Create a random number generator
+    std::random_device rd;
+    std::mt19937 gen(rd());
+    std::uniform_int_distribution<std::size_t> dist(1, 10);
+    std::uniform_int_distribution<std::size_t> nbDims(1, 5);
+
+    x.setDataType(DataType::Int32);
+    x.setBackend("cpu");
+
+    SECTION("Tensor sharing") {
+        for (std::uint16_t trial = 0; trial < NBTRIALS; ++trial) {
+            // create Tensor
+            const std::size_t nb_dims = nbDims(gen) + 1;
+            std::vector<std::size_t> dims(nb_dims);
+            for (std::size_t i = 0; i < nb_dims; ++i) {
+                dims[i] = dist(gen);
+            }
+            x.resize(dims);
+
+            // copy constructor
+            Tensor xCopyCtor(x);
+            REQUIRE(xCopyCtor.getImpl() == x.getImpl());
+
+            // copy assignment operator
+            Tensor xCopyAssignmentOp = x;
+            REQUIRE(xCopyAssignmentOp.getImpl() == x.getImpl());
+
+            Tensor xCloned = x.clone();
+            REQUIRE(xCloned.getImpl() != x.getImpl());
+            REQUIRE(xCloned == x);
+        }
+    }
+    SECTION("zeros()") {
+        for (std::uint16_t trial = 0; trial < NBTRIALS; ++trial) {
+            // create Tensor
+            constexpr std::size_t nb_dims = 3;
+            const std::size_t dim0 = nbDims(gen);
+            const std::size_t dim1 = nbDims(gen);
+            const std::size_t dim2 = nbDims(gen);
+            const std::vector<std::size_t> dims = {dim0, dim1, dim2};
+            int array0[dim0][dim1][dim2];
+            for (std::size_t i = 0; i < dim0; ++i) {
+                for (std::size_t j = 0; j < dim1; ++j) {
+                    for (std::size_t k = 0; k < dim2; ++k) {
+                        array0[i][j][k] = dist(gen);
+                    }
+                }
+            }
+            x.resize(dims);
+
+            x.zeros();
+            for (std::size_t i = 0; i < dim0; ++i) {
+                for (std::size_t j = 0; j < dim1; ++j) {
+                    const std::size_t idx = (i * dim1 + j) * dim2;
+                    for (std::size_t k = 0; k < dim2; ++k) {
+                        int val = *static_cast<int*>(x.getImpl()->hostPtr(idx + k));
+                        if (val != 0) {
+                            throw std::runtime_error("Value should be 0");
+                        }
+                        // REQUIRE(*static_cast<int*>(x.getImpl()->hostPtr(idx + k)) == 0);
+                    }
+                }
+            }
+        }
+    }
+
+    SECTION("Tensor extract") {
+        x = Array3D<int, 2, 2, 2>{{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}};
+        Tensor y;
+        Tensor y0;
+        Tensor y1;
+        Tensor y2;
+        Tensor y3;
+
+        for (std::uint16_t trial = 0; trial < NBTRIALS; ++trial) {
+            // create Tensor
+            const std::size_t nb_dims = 3;
+            const std::size_t dim0 = nbDims(gen) + 1; // dim0 >= 2
+            const std::size_t dim1 = nbDims(gen) + 1;
+            const std::size_t dim2 = nbDims(gen) + 1;
+            std::vector<std::size_t> dims = {dim0, dim1, dim2};
+            int array0[dim0][dim1][dim2];
+            for (std::size_t i = 0; i < dim0; ++i) {
+                for (std::size_t j = 0; j < dim1; ++j) {
+                    for (std::size_t k = 0; k < dim2; ++k) {
+                        array0[i][j][k] = dist(gen);
+                    }
+                }
+            }
+            x.resize(dims);
+
+            REQUIRE(x.isContiguous());
+            // extract Tensor slice from one set of coordinates
+            REQUIRE_NOTHROW(y0 = x.extract({}));
+            REQUIRE_NOTHROW(y1 = x.extract({nbDims(gen)}));
+            REQUIRE_NOTHROW(y2 = x.extract({nbDims(gen), nbDims(gen)}));
+            REQUIRE_NOTHROW(y3 = x.extract({nbDims(gen), nbDims(gen), nbDims(gen)}));
+            REQUIRE_THROWS(y = x.extract({0, dim0 + 1, 0}));
+
+            REQUIRE_NOTHROW(y = x.extract({0, 1}));
+            REQUIRE(y.getImpl() == x.getImpl()); // shared implem
+            REQUIRE(!y.isContiguous());
+
+            Tensor yClone = y.clone(); // when copying data, they are contiguous in memory
+            REQUIRE(yClone.isContiguous());
+            // int yTruth[2][1][1] =
+            REQUIRE(approxEq<int>(yClone, Array3D<int, 2, 1, 1>{{{{4}}, {{8}}}}));
+
+            y = x.extract({0, 1});
+            REQUIRE(y.getImpl() == x.getImpl());
+            REQUIRE(approxEq<int>(y, Array1D<int, 2>{{3, 4}}));
+            REQUIRE(y.isContiguous());
+        }
+    }
 }