Standardize code for MatMul.hpp and Test_TensorImpl.cpp

include/aidge/operator/MatMul.hpp

@@ -12,18 +12,14 @@
 #ifndef AIDGE_CORE_OPERATOR_MATMUL_H_
 #define AIDGE_CORE_OPERATOR_MATMUL_H_
 
-#include <array>
-#include <cmath>
-#include <numeric>
 #include <memory>
+#include <string>
 #include <vector>
 
 #include "aidge/utils/Types.h"
 #include "aidge/data/Tensor.hpp"
 #include "aidge/graph/Node.hpp"
 #include "aidge/operator/OperatorTensor.hpp"
-#include "aidge/operator/Producer.hpp"
-#include "aidge/utils/StaticAttributes.hpp"
 #include "aidge/utils/Registrar.hpp"
 
 namespace Aidge {
@@ -35,7 +31,7 @@ class MatMul_Op : public OperatorTensor,
 public:
     static const std::string Type;
 
-    MatMul_Op(): OperatorTensor(Type, 2, 0, 1) {}
+    MatMul_Op() : OperatorTensor(Type, 2, 0, 1) {}
 
     /**
      * @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).
@@ -50,23 +46,33 @@ public:
      * @brief Clone the operator using its copy-constructor.
      * @see Operator::MatMul_Op
      */
-    std::shared_ptr<Operator> clone() const override {
+    std::shared_ptr<Operator> clone() const override final {
         return std::make_shared<MatMul_Op>(*this);
     }
 
-
+    /**
+     * @brief Compute dimensions for the output Tensor following the same rules as
+     * numpy.matmul.
+     * @note - Both inputs are 2-D Tensors: classic matrix multiplication
+     * @note - Either input is N-D with N > 2: it is treated as a stack of matrices residing
+     * in the last two indexes and broadcast accordingly.
+     * @note - First input is 1-D: it is promoted to a matrix by prepending a 1 to its
+     * dimensions (D) -> (1,D). The prepended 1 is removed after computation.
+     * @note - Second input is 1-D: it is promoted to a matrix by appending a 1 to its
+     * dimensions (D) -> (D,1). The appended 1 is removed after computation.
+     */
     void computeOutputDims() override final;
 
 
-    void setBackend(const std::string& name, DeviceIdx_t device = 0) override {
+    void setBackend(const std::string& name, DeviceIdx_t device = 0) override final {
         mImpl = Registrar<MatMul_Op>::create(name)(*this);
         mOutputs[0]->setBackend(name, device);
     }
 
-    static const std::vector<std::string> getInputsName(){
+    static const std::vector<std::string> getInputsName() {
         return {"data_input1", "data_input2"};
     }
-    static const std::vector<std::string> getOutputsName(){
+    static const std::vector<std::string> getOutputsName() {
         return {"data_output"};
     }
 };

unit_tests/data/Test_TensorImpl.cpp

@@ -19,7 +19,7 @@
 
 using namespace Aidge;
 
-TEST_CASE("Tensor creation") {
+TEST_CASE("[core/data] Tensor creation") {
   SECTION("from const array") {
     Tensor x = Array3D<int, 2, 2, 2>{{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}};
 
@@ -59,7 +59,7 @@ TEST_CASE("Tensor creation") {
   }
 }
 
-TEST_CASE("Tensor methods") {
+TEST_CASE("[core/data] Tensor methods","[Tensor]") {
   Tensor x = Array3D<int, 2, 2, 2>{{
     {{1, 2},
      {3, 4}},
@@ -89,7 +89,7 @@ TEST_CASE("Tensor methods") {
     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());