From a28eb541a80b45c7c4baf92c480f9d707b58fdd9 Mon Sep 17 00:00:00 2001
From: hrouis <houssemeddine.rouis92@gmail.com>
Date: Fri, 9 Aug 2024 14:51:18 +0200
Subject: [PATCH] add noop_with_empty_axes attr
---
.../ReduceMeanImpl_forward_kernels.hpp | 5 ++-
.../ReduceSumImpl_forward_kernels.hpp | 5 ++-
unit_tests/operator/Test_ReduceMeanImpl.cpp | 32 +++++++++++++++++--
unit_tests/operator/Test_ReduceSumImpl.cpp | 30 ++++++++++++++++-
4 files changed, 67 insertions(+), 5 deletions(-)
diff --git a/include/aidge/backend/cpu/operator/ReduceMeanImpl_forward_kernels.hpp b/include/aidge/backend/cpu/operator/ReduceMeanImpl_forward_kernels.hpp
index bba355e1..fb14893f 100644
--- a/include/aidge/backend/cpu/operator/ReduceMeanImpl_forward_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/ReduceMeanImpl_forward_kernels.hpp
@@ -38,7 +38,10 @@ void ReduceMeanImpl_cpu_forward_kernel(const std::vector<std::int32_t>& axes,
const std::size_t nb_dims = inputDims.size();
const std::size_t totalElements = std::accumulate(inputDims.cbegin(), inputDims.cend(), 1, std::multiplies<std::size_t>());
- if (axes.size() == 1) {
+ if (axes.empty()){
+ std::copy_n(input,totalElements, output);
+ }
+ else if (axes.size() == 1) {
const std::size_t stride_pre = std::accumulate(inputDims.cbegin(), inputDims.cbegin() + axes[0], 1, std::multiplies<std::size_t>());
const std::size_t stride_post = std::accumulate(inputDims.crbegin(), inputDims.crbegin() + nb_dims -1 - axes[0], 1, std::multiplies<std::size_t>());
diff --git a/include/aidge/backend/cpu/operator/ReduceSumImpl_forward_kernels.hpp b/include/aidge/backend/cpu/operator/ReduceSumImpl_forward_kernels.hpp
index 9e6c056a..f215065a 100644
--- a/include/aidge/backend/cpu/operator/ReduceSumImpl_forward_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/ReduceSumImpl_forward_kernels.hpp
@@ -38,7 +38,10 @@ void ReduceSumImpl_cpu_forward_kernel(const std::vector<std::int32_t>& axes,
const std::size_t nb_dims = inputDims.size();
const std::size_t totalElements = std::accumulate(inputDims.cbegin(), inputDims.cend(), 1, std::multiplies<std::size_t>());
- if (axes.size() == 1) {
+ if (axes.empty()){
+ std::copy_n(input,totalElements, output);
+ }
+ else if (axes.size() == 1) {
const std::size_t stride_pre = std::accumulate(inputDims.cbegin(), inputDims.cbegin() + axes[0], 1, std::multiplies<std::size_t>());
const std::size_t stride_post = std::accumulate(inputDims.crbegin(), inputDims.crbegin() + nb_dims -1 - axes[0], 1, std::multiplies<std::size_t>());
diff --git a/unit_tests/operator/Test_ReduceMeanImpl.cpp b/unit_tests/operator/Test_ReduceMeanImpl.cpp
index 02696227..58ed69e7 100644
--- a/unit_tests/operator/Test_ReduceMeanImpl.cpp
+++ b/unit_tests/operator/Test_ReduceMeanImpl.cpp
@@ -157,7 +157,7 @@ TEST_CASE("[cpu/operator] ReduceMean(forward)", "[ReduceMean][CPU]") {
{18.25}
});
- std::shared_ptr<Node> myReduceMean = ReduceMean({0, 1, 2}, 0);
+ std::shared_ptr<Node> myReduceMean = ReduceMean({}, 0);
auto op = std::static_pointer_cast<OperatorTensor>(myReduceMean -> getOperator());
op->associateInput(0,myInput);
op->setDataType(DataType::Float32);
@@ -179,7 +179,7 @@ TEST_CASE("[cpu/operator] ReduceMean(forward)", "[ReduceMean][CPU]") {
{0.1293547f}
});
- std::shared_ptr<Node> myReduceMean = ReduceMean({0, 1}, 0);
+ std::shared_ptr<Node> myReduceMean = ReduceMean({}, 0);
auto op = std::static_pointer_cast<OperatorTensor>(myReduceMean -> getOperator());
op->associateInput(0,myInput);
op->setDataType(DataType::Float32);
@@ -189,5 +189,33 @@ TEST_CASE("[cpu/operator] ReduceMean(forward)", "[ReduceMean][CPU]") {
// approxEq<float>(*(op->getOutput(0)), *myOutput);
REQUIRE(approxEq<float>(*(op->getOutput(0)), *myOutput));
}
+ SECTION("noop_with_empty_axes") {
+ std::shared_ptr<Tensor> myInput = std::make_shared<Tensor>(Array3D<float,3,2,2> {
+ {
+ {
+ { 5.0, 1.0 },
+ { 20.0, 2.0 }
+ },
+ {
+ { 30.0, 1.0 },
+ { 40.0, 2.0 }
+ },
+ {
+ { 55.0, 1.0 },
+ { 60.0, 2.0 }
+ }
+ }
+ });
+
+ std::shared_ptr<Node> myReduceSum = ReduceSum({}, 0, 1);
+ auto op = std::static_pointer_cast<OperatorTensor>(myReduceSum -> getOperator());
+ op->associateInput(0,myInput);
+ op->setDataType(DataType::Float32);
+ op->setBackend("cpu");
+ myReduceSum->forward();
+ op->getOutput(0)->print();
+
+ REQUIRE(*(op->getOutput(0)) == *myInput);
+ }
}
}
\ No newline at end of file
diff --git a/unit_tests/operator/Test_ReduceSumImpl.cpp b/unit_tests/operator/Test_ReduceSumImpl.cpp
index d9fe4a22..b93fc8fd 100644
--- a/unit_tests/operator/Test_ReduceSumImpl.cpp
+++ b/unit_tests/operator/Test_ReduceSumImpl.cpp
@@ -157,7 +157,7 @@ TEST_CASE("[cpu/operator] ReduceSum(forward)", "[ReduceSum][CPU]") {
{219.0}
});
- std::shared_ptr<Node> myReduceSum = ReduceSum({0, 1, 2}, 0);
+ std::shared_ptr<Node> myReduceSum = ReduceSum({}, 0);
auto op = std::static_pointer_cast<OperatorTensor>(myReduceSum -> getOperator());
op->associateInput(0,myInput);
op->setDataType(DataType::Float32);
@@ -189,5 +189,33 @@ TEST_CASE("[cpu/operator] ReduceSum(forward)", "[ReduceSum][CPU]") {
// approxEq<float>(*(op->getOutput(0)), *myOutput);
REQUIRE(approxEq<float>(*(op->getOutput(0)), *myOutput));
}
+ SECTION("noop_with_empty_axes") {
+ std::shared_ptr<Tensor> myInput = std::make_shared<Tensor>(Array3D<float,3,2,2> {
+ {
+ {
+ { 5.0, 1.0 },
+ { 20.0, 2.0 }
+ },
+ {
+ { 30.0, 1.0 },
+ { 40.0, 2.0 }
+ },
+ {
+ { 55.0, 1.0 },
+ { 60.0, 2.0 }
+ }
+ }
+ });
+
+ std::shared_ptr<Node> myReduceSum = ReduceSum({}, 0, 1);
+ auto op = std::static_pointer_cast<OperatorTensor>(myReduceSum -> getOperator());
+ op->associateInput(0,myInput);
+ op->setDataType(DataType::Float32);
+ op->setBackend("cpu");
+ myReduceSum->forward();
+ op->getOutput(0)->print();
+
+ REQUIRE(*(op->getOutput(0)) == *myInput);
+ }
}
}
\ No newline at end of file
--
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