Made the required changes to resolve the generated code errors

Author: Neel-Shah-29

tmva/sofie/inc/TMVA/ROperator_Reduce.hxx

@@ -110,8 +110,6 @@ public:
       else 
          out << " };\n";
       }
-      // no compute indices as function of strides
-      // as in the example I have sent you
 
       for (size_t k = 0; k < dim; k++) {
          size_t j;
@@ -125,7 +123,7 @@ public:
       // out << SP << "assert(idx[" << fAttrAxes << "] == 0);\n";  // we can avoid computing this for the reduction axis which by definition is always zero
 
       out << SP << "float sum = 0;\n";
-      out << SP << SP << " for (size_t k = 0; k < " << fShapeX[fAttrAxes] <<"; k++) { \n";
+      out << SP << " for (size_t k = 0; k < " << fShapeX[fAttrAxes] <<"; k++) { \n";
       out << SP << SP << "  idx_" << fAttrAxes << " = k;\n";
        // compute input index j 
       out << SP << "std::vector<size_t> inputStrides = {" ;
@@ -137,13 +135,13 @@ public:
          out << " };\n";
       }
       out << SP << SP << "size_t l = 0;\n";
-      for (size_t m = 0; m < dim; m++) {
-         size_t n;
-         for(n = 0; n < m; n++ )
+      
+         size_t n ;
+         for(n = 0; n < dim-1; n++ )
          out << SP << "l += idx_" << n << " * inputStrides[" << n << "];\n";
 
-         out << SP << "l +=  idx_" << m << ";\n";
-      }
+         out << SP << "l +=  idx_" << n << ";\n";
+      
       if(fReduceOpMode == ReduceMean){
          out << SP << SP << "sum += tensor_" << fNX << "[l];\n";
          out << SP << SP << "};\n"; 

tmva/sofie/test/TestCustomModelsFromONNX.cxx

@@ -36,6 +36,9 @@
 #include "ReduceMean_FromONNX.hxx"
 #include "input_models/references/ReduceMean.ref.hxx"
 
+#include "ReduceProd_FromONNX.hxx"
+#include "input_models/references/ReduceProd.ref.hxx"
+
 #include "LinearWithLeakyRelu_FromONNX.hxx"
 #include "input_models/references/LinearWithLeakyRelu.ref.hxx"
 
@@ -751,6 +754,28 @@ TEST(ONNX, AvgPool){
 
 }
 
+   TEST(ONNX, ReduceProd){
+   constexpr float TOLERANCE = DEFAULT_TOLERANCE;
+
+   // Preparing the standard  input
+   std::vector<float> input({
+      5, 2, 3,
+      5, 5, 4
+   });
+   
+   TMVA_SOFIE_ReduceProd::Session s("ReduceProd_FromONNX.dat");
+   std::vector<float> output = s.infer(input.data());
+   // Checking output size
+   EXPECT_EQ(output.size(), sizeof(ReduceProd_ExpectedOutput::output) / sizeof(float));
+   
+   float *correct = ReduceProd_ExpectedOutput::output;
+
+   // Checking every output value, one by one
+   for (size_t i = 0; i < output.size(); ++i) {
+      EXPECT_LE(std::abs(output[i] - correct[i]), TOLERANCE);
+   }
+
+}
 
 TEST(ONNX, RNNBatchwise)
 {

tmva/sofie/test/input_models/ReduceMean.onnx

@@ -1,5 +1,5 @@
 namespace Reduce_mean_ExpectedOutput{
 	float output[] = {
-        4
+        5.0, 3.5, 3.5
 	};
 } // namespace Reduce_mean_ExpectedOutput

tmva/sofie/test/input_models/ReduceProd.onnx

@@ -0,0 +1,5 @@
+namespace Reduce_mean_ExpectedOutput{
+	float output[] = {
+        25.0, 10.0, 12.0
+	};
+} // namespace Reduce_mean_ExpectedOutput