Fix/PowBackwardKernel

Context

Fixes Pow Backward Kernel

Fixes #22 (closed)

Modified files

• PowImpl_backward_kernels.hpp, PowImpl.hpp, PowImpl.cpp and Test_PowImpl.cpp, add Pow Backward kernel and unit tests;

TODO

• Fix Pow Backward kernel

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

@@ -31,14 +31,10 @@ void PowImpl_cpu_forward_kernel(const std::vector<std::size_t>& input1Dims,
     const I2* input_2 = static_cast<const I2*>(input2_);
     O* output = static_cast<O*>(output_);
 
-    size_t totalElements = 1;
-    for (size_t dimSize : outputDims) {
-        totalElements *= dimSize;
-    }
-
+    std::size_t totalElements = std::accumulate(outputDims.cbegin(), outputDims.cend(), std::size_t(1), std::multiplies<std::size_t>());
 	for (std::size_t oIndex = 0; oIndex < totalElements; ++oIndex) 
 	{
-		std::vector<size_t> indexes = getMultiDimIndices(outputDims, oIndex);
+		std::vector<std::size_t> indexes = getMultiDimIndices(outputDims, oIndex);
 
 		std::size_t idx1 = getFlattenedIndex(input1Dims, indexes);
 		std::size_t idx2 = getFlattenedIndex(input2Dims, indexes);
@@ -63,24 +59,24 @@ void PowImpl_cpu_backward_kernel(const std::vector<std::size_t>& input0Dims,
     const O* gradOut = static_cast<const O*>(gradOutput_);
 
     // Fill input grads with zeros
-	auto input0Elements = std::accumulate(input0Dims.cbegin(), input0Dims.cend(), std::size_t(1), std::multiplies<std::size_t>());
+	std::size_t input0Elements = std::accumulate(input0Dims.cbegin(), input0Dims.cend(), std::size_t(1), std::multiplies<std::size_t>());
 	std::fill(grad0, grad0 + input0Elements, I1(0));
-	auto input1Elements = std::accumulate(input1Dims.cbegin(), input1Dims.cend(), std::size_t(1), std::multiplies<std::size_t>());
-	std::fill(grad1, grad1 + input1Elements, I1(0));
+	std::size_t input1Elements = std::accumulate(input1Dims.cbegin(), input1Dims.cend(), std::size_t(1), std::multiplies<std::size_t>());
+	std::fill(grad1, grad1 + input1Elements, I2(0));
 
-	auto totalElements = std::accumulate(outputDims.cbegin(), outputDims.cend(), std::size_t(1), std::multiplies<std::size_t>());
-    for (size_t i = 0; i < totalElements; ++i)
+	std::size_t totalElements = std::accumulate(outputDims.cbegin(), outputDims.cend(), std::size_t(1), std::multiplies<std::size_t>());
+    for (size_t oIndex = 0; oIndex < totalElements; ++oIndex)
     {
         // Compute indexes in inputs 0 and 1 to support broadcasting
-        std::vector<std::size_t> indexes = getMultiDimIndices(outputDims, i);
+        std::vector<std::size_t> indexes = getMultiDimIndices(outputDims, oIndex);
         std::size_t idx0 = getFlattenedIndex(input0Dims, indexes);
         std::size_t idx1 = getFlattenedIndex(input1Dims, indexes);
 
         // grad0 = grad_output * (input1 * pow(input0, (input1 -1)))
-        grad0[idx0] += gradOut[i]*input1[idx1]* std::pow(input0[idx0], input1[idx1]-1);
+        grad0[idx0] += gradOut[oIndex]*input1[idx1]* std::pow(input0[idx0], input1[idx1]-1);
 
         // grad1 = grad_output * (output * ln(input0))
-        grad1[idx1] += gradOut[i] * std::pow(input0[idx0], input1[idx1]) * std::log(input0[idx0]);
+        grad1[idx1] += gradOut[oIndex] * std::pow(input0[idx0], input1[idx1]) * std::log(input0[idx0]);
     }
 }