From 9c83962af9e7bd99b64a5d48dc8b518515e72333 Mon Sep 17 00:00:00 2001
From: Jerome Hue <jerome.hue@cea.fr>
Date: Tue, 4 Mar 2025 10:59:06 +0100
Subject: [PATCH] Improve Heavisise Backward Test
Compare the result of surrogate gradient to the real Atan operator.
---
unit_tests/operator/Test_HeavisideImpl.cpp | 36 +++++++++++++++++++++-
1 file changed, 35 insertions(+), 1 deletion(-)
diff --git a/unit_tests/operator/Test_HeavisideImpl.cpp b/unit_tests/operator/Test_HeavisideImpl.cpp
index 2743027c..9241f0d3 100644
--- a/unit_tests/operator/Test_HeavisideImpl.cpp
+++ b/unit_tests/operator/Test_HeavisideImpl.cpp
@@ -11,7 +11,10 @@
#include "aidge/backend/cpu/operator/HeavisideImpl_kernels.hpp"
+#include <aidge/operator/Atan.hpp>
#include <aidge/operator/Memorize.hpp>
+#include <aidge/operator/Mul.hpp>
+#include <aidge/operator/Producer.hpp>
#include <aidge/utils/Types.h>
#include <memory>
#include <cstdlib>
@@ -100,7 +103,6 @@ TEST_CASE("[cpu/operator] Heaviside(forward)", "[Heaviside][CPU]") {
}
}
-// TODO: Make this work for random intput.
TEST_CASE("[cpu/operator] Heaviside(backward)", "[Heaviside][CPU]") {
std::random_device rd;
@@ -115,6 +117,7 @@ TEST_CASE("[cpu/operator] Heaviside(backward)", "[Heaviside][CPU]") {
op->setDataType(DataType::Float32);
op->setBackend("cpu");
+
auto inputTensor = std::make_shared<Tensor>(std::vector<std::size_t>{tensorSize});
inputTensor->setDataType(DataType::Float32);
@@ -125,6 +128,29 @@ TEST_CASE("[cpu/operator] Heaviside(backward)", "[Heaviside][CPU]") {
inputData[i] = valueDist(gen);
}
+ // Compare it to the real Atan implementation
+ auto mul = Mul();
+ auto pi = std::make_shared<Tensor>(Array1D<float,1>{M_PI});
+ auto producer = Producer(pi);
+ auto atan = Atan();
+ auto mulOp = std::static_pointer_cast<OperatorTensor>(mul->getOperator());
+ auto piOp = std::static_pointer_cast<OperatorTensor>(producer->getOperator());
+ auto atanOp = std::static_pointer_cast<OperatorTensor>(atan->getOperator());
+ mulOp->setBackend("cpu");
+ piOp->setBackend("cpu");
+ atanOp->setBackend("cpu");
+ mulOp->setDataType(DataType::Float32);
+ piOp->setDataType(DataType::Float32);
+ atanOp->setDataType(DataType::Float32);
+
+
+ producer->addChild(mul,0,0);
+ mulOp->setInput(IOIndex_t(1), inputTensor);
+ mulOp->forward();
+ auto outmul = mulOp->getOutput(0);
+ atanOp->setInput(0, inputTensor);
+ atanOp->forward();
+
auto gradTensor = std::make_shared<Tensor>(std::vector<std::size_t>{tensorSize});
gradTensor->setDataType(DataType::Float32);
gradTensor->setBackend("cpu");
@@ -142,6 +168,10 @@ TEST_CASE("[cpu/operator] Heaviside(backward)", "[Heaviside][CPU]") {
// Backward pass
op->backward();
+
+ atanOp->setOutput(0, outmul);
+ atanOp->getOutput(0)->setGrad(gradTensor);
+ atanOp->backward();
// Compute expected gradient manually
auto expectedGrad = std::make_shared<Tensor>(std::vector<std::size_t>{tensorSize});
@@ -155,5 +185,9 @@ TEST_CASE("[cpu/operator] Heaviside(backward)", "[Heaviside][CPU]") {
// Compare actual gradient with expected gradient
REQUIRE(approxEq<float>(*(op->getInput(0)->grad()), *expectedGrad));
+
+ // Compare Atan(pi*input) to expected Gradient
+ REQUIRE(approxEq<float>(*(atanOp->getInput(0)->grad()), *expectedGrad));
}
+
}
--
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