Fix Accuracy metric

Context

The Equal operator was mistakenly called And and since this was fixed in aidge_core!311 (merged) this MR is intended to fix the operator's name in Accuracy metric

Modified files

• Accuracy.cpp and Test_Accuracy.cpp, replace And operator with Equal

src/metrics/Accuracy.cpp

@@ -19,7 +19,7 @@
 #include "aidge/operator/OperatorTensor.hpp"
 #include "aidge/operator/ArgMax.hpp"
 #include "aidge/operator/ReduceSum.hpp"
-#include "aidge/operator/And.hpp"
+#include "aidge/operator/Equal.hpp"
 #include "aidge/recipes/GraphViewHelper.hpp"
 #include "aidge/scheduler/Scheduler.hpp"
 #include "aidge/scheduler/SequentialScheduler.hpp"
@@ -34,7 +34,7 @@ Aidge::Tensor Aidge::metrics::Accuracy(std::shared_ptr<Tensor>& prediction,
     The graph used is the following:
 
     pred->ArgMax
-                  ->And->ReduceSum
+                  ->Equal->ReduceSum
     label->ArgMax
     */
 
@@ -60,7 +60,7 @@ Aidge::Tensor Aidge::metrics::Accuracy(std::shared_ptr<Tensor>& prediction,
     const std::shared_ptr<Node> argmax_perd_node = ArgMax(axis);
     const std::shared_ptr<Node> argmax_target_node = ArgMax(axis);
 
-    const std::shared_ptr<Node> and_node = And();
+    const std::shared_ptr<Node> equal_node = Equal();
     const std::shared_ptr<Node> rs_node = ReduceSum();
 
     const std::shared_ptr<Node> pred_node = Producer(prediction, "pred");
@@ -68,14 +68,14 @@ Aidge::Tensor Aidge::metrics::Accuracy(std::shared_ptr<Tensor>& prediction,
     const std::shared_ptr<Node> label_node = Producer(target, "label");
     label_node->addChild(argmax_target_node);
 
-    argmax_perd_node->addChild(and_node,0,0);
-    argmax_target_node->addChild(and_node,0,1);
+    argmax_perd_node->addChild(equal_node,0,0);
+    argmax_target_node->addChild(equal_node,0,1);
 
-    // and_node->addChild(rs_node,0,0);
+    // equal_node->addChild(rs_node,0,0);
 
     // Create the graph
     std::shared_ptr<GraphView> gv_local =
-        Sequential({ and_node, rs_node});
+        Sequential({ equal_node, rs_node});
 
     gv_local->add({pred_node,argmax_perd_node, label_node,argmax_target_node});
     gv_local->compile(prediction->getImpl()->backend(), prediction->dataType());