add support for operations of tensor with different dims

include/aidge/backend/cpu/operator/DivImpl_forward_kernels.hpp

@@ -39,6 +39,13 @@ void DivImpl_cpu_forward_kernel(std::size_t input1Length,
             output[i] = input_1[i] / input_2[0];
         }
     }
+    else // input_2 is 1d and of size the number of channels of input_1
+    {
+        for (std::size_t i = 0; i < input1Length; ++i) {
+            std::size_t channelIdx = i % input2Length;
+            output[i] = input_1[i] / input_2[channelIdx];
+        }
+    }
 }
 
 namespace {

include/aidge/backend/cpu/operator/PowImpl_forward_kernels.hpp

@@ -41,6 +41,13 @@ void PowImpl_cpu_forward_kernel(std::size_t input1Length,
             output[i] = std::pow(input_1[i], input_2[0]);
         }
     }
+    else // input_2 is 1d and of size the number of channels of input_1
+    {
+        for (std::size_t i = 0; i < input1Length; ++i) {
+            std::size_t channelIdx = i % input2Length;
+            output[i] = std::pow(input_1[i], input_2[channelIdx]);
+        }
+    }
 }
 
 namespace {

src/operator/DivImpl.cpp

@@ -30,10 +30,11 @@ void Aidge::DivImpl_cpu::forward() {
     assert(mOp.getInput(0) && "missing input #0");
     assert(mOp.getInput(1) && "missing input #1");
 
-    // TODO add support for when input1 is a 1d tensor of size the channels of input0
     assert(((mOp.getInput(1)->size() == 1) || 
-            (mOp.getInput(1)->size() == mOp.getInput(0)->size())) &&
-           "input #1 must either be a tensor of size 1 or the same size of input #0");
+            (mOp.getInput(1)->size() == mOp.getInput(0)->size()) ||
+            (mOp.getInput(1)->nbDims() == 1 && mOp.getInput(1)->size() == mOp.getInput(0)->dims()[mOp.getInput(0)->nbDims()-1])
+           ) &&
+           "input #1 must either be a tensor of size 1, the number of channels of input # or the same size of input #0");
 
     // Find the correct kernel type
     auto kernelFunc = Registrar<DivImplForward_cpu>::create({

src/operator/PowImpl.cpp

@@ -29,11 +29,12 @@ Aidge::NbElts_t Aidge::PowImpl_cpu::getNbRequiredProtected(const Aidge::IOIndex_
 void Aidge::PowImpl_cpu::forward() {
     assert(mOp.getInput(0) && "missing input #0");
     assert(mOp.getInput(1) && "missing input #1");
-    
-    // TODO add support for when input1 is a 1d tensor of size the channels of input0
+
     assert(((mOp.getInput(1)->size() == 1) || 
-            (mOp.getInput(1)->size() == mOp.getInput(0)->size())) &&
-           "input #1 must either be a tensor of size 1 or the same size of input #0");
+            (mOp.getInput(1)->size() == mOp.getInput(0)->size()) ||
+            (mOp.getInput(1)->nbDims() == 1 && mOp.getInput(1)->size() == mOp.getInput(0)->dims()[mOp.getInput(0)->nbDims()-1])
+           ) &&
+           "input #1 must either be a tensor of size 1, the number of channels of input # or the same size of input #0");
 
     // Find the correct kernel type
     auto kernelFunc = Registrar<PowImplForward_cpu>::create({

unit_tests/operator/Test_DivImpl.cpp

@@ -88,6 +88,44 @@ TEST_CASE("[cpu/operator] Div(forward)") {
 
     }
 
+    SECTION("3D Tensor by 1D Tensor") {
+        std::shared_ptr<Tensor> input_1 = std::make_shared<Tensor>(Array3D<float,2,2,3> {
+            {
+                {{0.24180168, 0.44319558, 0.06437260},
+                 {0.21270001, 0.34570599, 0.44151264}},
+                {{0.62294692, 0.98043168, 0.18628585},
+                 {0.33591706, 0.03432965, 0.32130069}}
+            }
+        });
+        std::shared_ptr<Tensor> input_2 =  std::make_shared<Tensor>(Array1D<float,3>{
+            {0.63475525, 0.58620811, 0.69340748}
+        });
+        std::shared_ptr<Tensor> expectedOutput = std::make_shared<Tensor>(Array3D<float,2,2,3> {
+            {
+                {{0.38093686, 0.75603795, 0.09283517},
+                 {0.33508980, 0.58973253, 0.63672900}},
+
+                {{0.98139703, 1.67249763, 0.26865280},
+                 {0.52920723, 0.05856223, 0.46336490}}
+            }
+        });
+
+        std::shared_ptr<Node> myDiv = Div();
+        myDiv->getOperator()->setDatatype(DataType::Float32);
+        myDiv->getOperator()->setBackend("cpu");
+        myDiv->getOperator()->associateInput(0, input_1);
+        myDiv->getOperator()->associateInput(1, input_2);
+        myDiv->getOperator()->computeOutputDims();
+        myDiv->forward();
+
+        float* resPtr = static_cast<float*>(myDiv->getOperator()->getOutput(0)->getImpl()->rawPtr());
+        float* expectedPtr = static_cast<float*>(expectedOutput->getImpl()->rawPtr());
+        for (std::size_t i = 0; i< 4; ++i) {
+            REQUIRE(std::abs(resPtr[i]-expectedPtr[i]) < 0.00001);
+        }
+
+    }
+
     SECTION("4D Tensor") {
         std::shared_ptr<Tensor> input_1 = std::make_shared<Tensor>(Array4D<float,2,3,3,3> {
             {

unit_tests/operator/Test_PowImpl.cpp

@@ -52,6 +52,45 @@ TEST_CASE("[cpu/operator] Pow(forward)") {
 
     }
 
+    SECTION("3D Tensor by 1D Tensor") {
+        std::shared_ptr<Tensor> input_1 = std::make_shared<Tensor>(Array3D<float,2,2,3> {
+            {
+                {{0.87519985, 0.10536593, 0.20268351},
+                 {0.75532353, 0.95977652, 0.03897029}},
+
+                {{0.67554104, 0.35499334, 0.27741563},
+                 {0.94270861, 0.48397779, 0.35532343}}
+            }
+        });
+        std::shared_ptr<Tensor> input_2 =  std::make_shared<Tensor>(Array1D<float,3>{
+            {0.39333701, 0.08719915, 0.16713941}
+        });
+        std::shared_ptr<Tensor> expectedOutput = std::make_shared<Tensor>(Array3D<float,2,2,3> {
+            {
+                {{0.94891787, 0.82182676, 0.76584703},
+                 {0.89549923, 0.99642646, 0.58137459}},
+
+                {{0.85702944, 0.91364944, 0.80709606},
+                 {0.97706109, 0.93867886, 0.84118503}}
+            }
+        });
+
+        std::shared_ptr<Node> myPow = Pow();
+        myPow->getOperator()->setDatatype(DataType::Float32);
+        myPow->getOperator()->setBackend("cpu");
+        myPow->getOperator()->associateInput(0, input_1);
+        myPow->getOperator()->associateInput(1, input_2);
+        myPow->getOperator()->computeOutputDims();
+        myPow->forward();
+
+        float* resPtr = static_cast<float*>(myPow->getOperator()->getOutput(0)->getImpl()->rawPtr());
+        float* expectedPtr = static_cast<float*>(expectedOutput->getImpl()->rawPtr());
+        for (std::size_t i = 0; i< 4; ++i) {
+            REQUIRE(std::abs(resPtr[i]-expectedPtr[i]) < 0.00001);
+        }
+
+    }
+
     SECTION("2D Tensors") {
         std::shared_ptr<Tensor> input_1 = std::make_shared<Tensor>(Array2D<float,2,2> {
             {