diff --git a/include/aidge/operator/MetaOperatorDefs.hpp b/include/aidge/operator/MetaOperatorDefs.hpp
index 481a7795e24acd006acfc66a0fccde1b8da747e7..c9de36b1f032f986f6bd4461198ecff77d64d1f7 100644
--- a/include/aidge/operator/MetaOperatorDefs.hpp
+++ b/include/aidge/operator/MetaOperatorDefs.hpp
@@ -19,11 +19,11 @@
#include "aidge/graph/GraphView.hpp"
#include "aidge/graph/Node.hpp"
#include "aidge/graph/OpArgs.hpp" // Sequential
-#include "aidge/operator/MetaOperator.hpp"
#include "aidge/operator/AvgPooling.hpp"
-#include "aidge/operator/MaxPooling.hpp"
#include "aidge/operator/Conv.hpp"
#include "aidge/operator/ConvDepthWise.hpp"
+#include "aidge/operator/MaxPooling.hpp"
+#include "aidge/operator/MetaOperator.hpp"
#include "aidge/operator/Pad.hpp"
#include "aidge/operator/Sigmoid.hpp"
#include "aidge/utils/ArrayHelpers.hpp"
@@ -31,128 +31,174 @@
namespace Aidge {
-
template <std::array<DimSize_t, 1>::size_type DIM>
-extern std::shared_ptr<Node> PaddedConv(DimSize_t in_channels,
- DimSize_t out_channels,
- const std::array<DimSize_t, DIM> &kernel_dims,
- const std::string& name = "",
- const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1),
- const std::array<DimSize_t, 2*DIM> &padding_dims = create_array<DimSize_t,2*DIM>(0),
- const std::array<DimSize_t, DIM> &dilation_dims = create_array<DimSize_t,DIM>(1),
- bool no_bias = false);
+extern std::shared_ptr<Node>
+PaddedConv(DimSize_t in_channels,
+ DimSize_t out_channels,
+ const std::array<DimSize_t, DIM> &kernel_dims,
+ const std::string &name = "",
+ const std::array<DimSize_t, DIM> &stride_dims =
+ create_array<DimSize_t, DIM>(1),
+ const std::array<DimSize_t, 2 * DIM> &padding_dims =
+ create_array<DimSize_t, 2 * DIM>(0),
+ const std::array<DimSize_t, DIM> &dilation_dims =
+ create_array<DimSize_t, DIM>(1),
+ bool no_bias = false);
template <std::array<DimSize_t, 1>::size_type DIM>
-extern std::shared_ptr<MetaOperator_Op> PaddedConv_Op(
- const std::array<DimSize_t, DIM> &kernel_dims,
- const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1),
- const std::array<DimSize_t, 2*DIM> &padding_dims = create_array<DimSize_t,2*DIM>(0),
- const std::array<DimSize_t, DIM> &dilation_dims = create_array<DimSize_t,DIM>(1));
-
-// helper with C-style array instead of std::array for kernel_dims to allow automatic template DIM deduction
+extern std::shared_ptr<MetaOperator_Op>
+PaddedConv_Op(const std::array<DimSize_t, DIM> &kernel_dims,
+ const std::array<DimSize_t, DIM> &stride_dims =
+ create_array<DimSize_t, DIM>(1),
+ const std::array<DimSize_t, 2 * DIM> &padding_dims =
+ create_array<DimSize_t, 2 * DIM>(0),
+ const std::array<DimSize_t, DIM> &dilation_dims =
+ create_array<DimSize_t, DIM>(1));
+
+// helper with C-style array instead of std::array for kernel_dims to allow
+// automatic template DIM deduction
template <DimSize_t DIM>
-extern std::shared_ptr<Node> PaddedConv(
- DimSize_t in_channels,
- DimSize_t out_channels,
- DimSize_t const (&kernel_dims)[DIM],
- const std::string& name = "",
- const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1),
- const std::array<DimSize_t, 2*DIM> &padding_dims = create_array<DimSize_t,2*DIM>(0),
- const std::array<DimSize_t, DIM> &dilation_dims = create_array<DimSize_t,DIM>(1),
- bool no_bias = false);
+extern std::shared_ptr<Node>
+PaddedConv(DimSize_t in_channels,
+ DimSize_t out_channels,
+ DimSize_t const (&kernel_dims)[DIM],
+ const std::string &name = "",
+ const std::array<DimSize_t, DIM> &stride_dims =
+ create_array<DimSize_t, DIM>(1),
+ const std::array<DimSize_t, 2 * DIM> &padding_dims =
+ create_array<DimSize_t, 2 * DIM>(0),
+ const std::array<DimSize_t, DIM> &dilation_dims =
+ create_array<DimSize_t, DIM>(1),
+ bool no_bias = false);
////////////////////////////////////////////////////////////////////////////////
template <std::array<DimSize_t, 1>::size_type DIM>
-std::shared_ptr<Node> PaddedConvDepthWise(const DimSize_t nb_channels,
- const std::array<DimSize_t, DIM> &kernel_dims,
- const std::string& name = "",
- const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1),
- const std::array<DimSize_t, 2*DIM> &padding_dims = create_array<DimSize_t,2*DIM>(0),
- const std::array<DimSize_t, DIM> &dilation_dims = create_array<DimSize_t,DIM>(1),
- bool no_bias = false);
+std::shared_ptr<Node>
+PaddedConvDepthWise(const DimSize_t nb_channels,
+ const std::array<DimSize_t, DIM> &kernel_dims,
+ const std::string &name = "",
+ const std::array<DimSize_t, DIM> &stride_dims =
+ create_array<DimSize_t, DIM>(1),
+ const std::array<DimSize_t, 2 * DIM> &padding_dims =
+ create_array<DimSize_t, 2 * DIM>(0),
+ const std::array<DimSize_t, DIM> &dilation_dims =
+ create_array<DimSize_t, DIM>(1),
+ bool no_bias = false);
template <std::array<DimSize_t, 1>::size_type DIM>
-std::shared_ptr<MetaOperator_Op> PaddedConvDepthWise_Op(
- const std::array<DimSize_t, DIM> &kernel_dims,
- const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1),
- const std::array<DimSize_t, 2*DIM> &padding_dims = create_array<DimSize_t,2*DIM>(0),
- const std::array<DimSize_t, DIM> &dilation_dims = create_array<DimSize_t,DIM>(1));
-
-// helper with C-style array instead of std::array for kernel_dims to allow automatic template DIM deduction
+std::shared_ptr<MetaOperator_Op>
+PaddedConvDepthWise_Op(const std::array<DimSize_t, DIM> &kernel_dims,
+ const std::array<DimSize_t, DIM> &stride_dims =
+ create_array<DimSize_t, DIM>(1),
+ const std::array<DimSize_t, 2 * DIM> &padding_dims =
+ create_array<DimSize_t, 2 * DIM>(0),
+ const std::array<DimSize_t, DIM> &dilation_dims =
+ create_array<DimSize_t, DIM>(1));
+
+// helper with C-style array instead of std::array for kernel_dims to allow
+// automatic template DIM deduction
template <DimSize_t DIM>
-inline std::shared_ptr<Node> PaddedConvDepthWise(
- const DimSize_t nb_channels,
- DimSize_t const (&kernel_dims)[DIM],
- const std::string& name = "",
- const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1),
- const std::array<DimSize_t, 2*DIM> &padding_dims = create_array<DimSize_t,2*DIM>(0),
- const std::array<DimSize_t, DIM> &dilation_dims = create_array<DimSize_t,DIM>(1),
- bool no_bias = false);
+inline std::shared_ptr<Node>
+PaddedConvDepthWise(const DimSize_t nb_channels,
+ DimSize_t const (&kernel_dims)[DIM],
+ const std::string &name = "",
+ const std::array<DimSize_t, DIM> &stride_dims =
+ create_array<DimSize_t, DIM>(1),
+ const std::array<DimSize_t, 2 * DIM> &padding_dims =
+ create_array<DimSize_t, 2 * DIM>(0),
+ const std::array<DimSize_t, DIM> &dilation_dims =
+ create_array<DimSize_t, DIM>(1),
+ bool no_bias = false);
////////////////////////////////////////////////////////////////////////////////
-
template <std::array<DimSize_t, 1>::size_type DIM>
-extern std::shared_ptr<Node> PaddedAvgPooling(const std::array<DimSize_t, DIM> &kernel_dims,
- const std::string& name = "",
- const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1),
- const std::array<DimSize_t, 2*DIM> &padding_dims = create_array<DimSize_t,2*DIM>(0));
-
+extern std::shared_ptr<Node>
+PaddedAvgPooling(const std::array<DimSize_t, DIM> &kernel_dims,
+ const std::string &name = "",
+ const std::array<DimSize_t, DIM> &stride_dims =
+ create_array<DimSize_t, DIM>(1),
+ const std::array<DimSize_t, 2 * DIM> &padding_dims =
+ create_array<DimSize_t, 2 * DIM>(0));
template <std::array<DimSize_t, 1>::size_type DIM>
-extern std::shared_ptr<MetaOperator_Op> PaddedAvgPooling_Op(const std::array<DimSize_t, DIM> &kernel_dims,
- const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1),
- const std::array<DimSize_t, 2*DIM> &padding_dims = create_array<DimSize_t,2*DIM>(0));
-
-// helper with C-style array instead of std::array for kernel_dims to allow automatic template DIM deduction
+extern std::shared_ptr<MetaOperator_Op>
+PaddedAvgPooling_Op(const std::array<DimSize_t, DIM> &kernel_dims,
+ const std::array<DimSize_t, DIM> &stride_dims =
+ create_array<DimSize_t, DIM>(1),
+ const std::array<DimSize_t, 2 * DIM> &padding_dims =
+ create_array<DimSize_t, 2 * DIM>(0));
+
+// helper with C-style array instead of std::array for kernel_dims to allow
+// automatic template DIM deduction
template <DimSize_t DIM>
-extern std::shared_ptr<Node> PaddedAvgPooling(DimSize_t const (&kernel_dims)[DIM],
- const std::string& name = "",
- const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1),
- const std::array<DimSize_t, 2*DIM> &padding_dims = create_array<DimSize_t,2*DIM>(0));
+extern std::shared_ptr<Node>
+PaddedAvgPooling(DimSize_t const (&kernel_dims)[DIM],
+ const std::string &name = "",
+ const std::array<DimSize_t, DIM> &stride_dims =
+ create_array<DimSize_t, DIM>(1),
+ const std::array<DimSize_t, 2 * DIM> &padding_dims =
+ create_array<DimSize_t, 2 * DIM>(0));
////////////////////////////////////////////////////////////////////////////////
template <std::array<DimSize_t, 1>::size_type DIM>
-inline std::shared_ptr<Node> PaddedMaxPooling(const std::array<DimSize_t, DIM> &kernel_dims,
- const std::string& name = "",
- const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1),
- const std::array<DimSize_t, 2*DIM> &padding_dims = create_array<DimSize_t,2*DIM>(0),
- bool ceil_mode = false)
-{
- auto graph = Sequential({
- Pad<DIM>(padding_dims, (!name.empty()) ? name + "_pad" : ""),
- MaxPooling(kernel_dims, (!name.empty()) ? name + "_maxpooling" : "", stride_dims, ceil_mode)
- });
-
- return MetaOperator(("PaddedMaxPooling" + std::to_string(DIM) + "D").c_str(), graph, {}, name);
+inline std::shared_ptr<Node>
+PaddedMaxPooling(const std::array<DimSize_t, DIM> &kernel_dims,
+ const std::string &name = "",
+ const std::array<DimSize_t, DIM> &stride_dims =
+ create_array<DimSize_t, DIM>(1),
+ const std::array<DimSize_t, 2 * DIM> &padding_dims =
+ create_array<DimSize_t, 2 * DIM>(0),
+ bool ceil_mode = false) {
+ auto graph = Sequential(
+ {Pad<DIM>(padding_dims, (!name.empty()) ? name + "_pad" : ""),
+ MaxPooling(kernel_dims,
+ (!name.empty()) ? name + "_maxpooling" : "",
+ stride_dims,
+ ceil_mode)});
+
+ return MetaOperator(
+ ("PaddedMaxPooling" + std::to_string(DIM) + "D").c_str(),
+ graph,
+ {},
+ name);
}
template <std::array<DimSize_t, 1>::size_type DIM>
-inline std::shared_ptr<MetaOperator_Op> PaddedMaxPooling_Op(const std::array<DimSize_t, DIM> &kernel_dims,
- const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1),
- const std::array<DimSize_t, 2*DIM> &padding_dims = create_array<DimSize_t,2*DIM>(0),
- bool ceil_mode = false)
-{
- auto graph = Sequential({
- Pad<DIM>(padding_dims, ""),
- MaxPooling(kernel_dims, "", stride_dims, ceil_mode)
- });
-
- return std::make_shared<MetaOperator_Op>(("PaddedMaxPooling" + std::to_string(DIM) + "D").c_str(), graph);
+inline std::shared_ptr<MetaOperator_Op>
+PaddedMaxPooling_Op(const std::array<DimSize_t, DIM> &kernel_dims,
+ const std::array<DimSize_t, DIM> &stride_dims =
+ create_array<DimSize_t, DIM>(1),
+ const std::array<DimSize_t, 2 * DIM> &padding_dims =
+ create_array<DimSize_t, 2 * DIM>(0),
+ bool ceil_mode = false) {
+ auto graph =
+ Sequential({Pad<DIM>(padding_dims, ""),
+ MaxPooling(kernel_dims, "", stride_dims, ceil_mode)});
+
+ return std::make_shared<MetaOperator_Op>(
+ ("PaddedMaxPooling" + std::to_string(DIM) + "D").c_str(),
+ graph);
}
-// helper with C-style array instead of std::array for kernel_dims to allow automatic template DIM deduction
+// helper with C-style array instead of std::array for kernel_dims to allow
+// automatic template DIM deduction
template <DimSize_t DIM>
-inline std::shared_ptr<Node> PaddedMaxPooling(
- DimSize_t const (&kernel_dims)[DIM],
- const std::string& name = "",
- const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1),
- const std::array<DimSize_t, 2*DIM> &padding_dims = create_array<DimSize_t,2*DIM>(0),
- bool ceil_mode= false)
-{
- return PaddedMaxPooling(to_array(kernel_dims), name, stride_dims, padding_dims, ceil_mode);
+inline std::shared_ptr<Node>
+PaddedMaxPooling(DimSize_t const (&kernel_dims)[DIM],
+ const std::string &name = "",
+ const std::array<DimSize_t, DIM> &stride_dims =
+ create_array<DimSize_t, DIM>(1),
+ const std::array<DimSize_t, 2 * DIM> &padding_dims =
+ create_array<DimSize_t, 2 * DIM>(0),
+ bool ceil_mode = false) {
+ return PaddedMaxPooling(to_array(kernel_dims),
+ name,
+ stride_dims,
+ padding_dims,
+ ceil_mode);
}
////////////////////////////////////////////////////////////////////////////////
@@ -161,10 +207,16 @@ std::shared_ptr<Node> LSTM(DimSize_t in_channels,
DimSize_t hidden_channels,
DimSize_t seq_length,
bool noBias = false,
- const std::string& name = "");
+ const std::string &name = "");
std::shared_ptr<MetaOperator_Op> LSTM_Op(DimSize_t seq_length);
-} // namespace Aidge
+std::shared_ptr<MetaOperator_Op> LeakyOp();
+std::shared_ptr<Node> Leaky(const int nbTimeSteps,
+ const float beta,
+ const float threshold = 1.0,
+ const std::string &name = "");
+
+} // namespace Aidge
#endif /* AIDGE_CORE_OPERATOR_METAOPERATORDEFS_H_ */
diff --git a/python_binding/operator/pybind_MetaOperatorDefs.cpp b/python_binding/operator/pybind_MetaOperatorDefs.cpp
index 5f173068af0f1140830d458979ec924c38ade078..b2811fbaab2b6cd33dc2b105f0044cd8a5edbbc7 100644
--- a/python_binding/operator/pybind_MetaOperatorDefs.cpp
+++ b/python_binding/operator/pybind_MetaOperatorDefs.cpp
@@ -179,6 +179,14 @@ void declare_LSTMOp(py::module &m) {
py::arg("seq_length"));
}
+void declare_LeakyOp(py::module &m) {
+ m.def("Leaky", &Leaky,
+ py::arg("nb_timesteps"),
+ py::arg("beta"),
+ py::arg("threshold") = 1.0,
+ py::arg("name") = "");
+}
+
void init_MetaOperatorDefs(py::module &m) {
declare_PaddedConvOp<1>(m);
declare_PaddedConvOp<2>(m);
@@ -193,6 +201,7 @@ void init_MetaOperatorDefs(py::module &m) {
declare_PaddedMaxPoolingOp<2>(m);
// declare_PaddedMaxPoolingOp<3>(m);
declare_LSTMOp(m);
+ declare_LeakyOp(m);
py::class_<MetaOperator_Op, std::shared_ptr<MetaOperator_Op>, OperatorTensor>(m, "MetaOperatorOp", py::multiple_inheritance())
.def(py::init<const char *, const std::shared_ptr<GraphView>&, const std::vector<InputCategory>&>(),
diff --git a/src/operator/MetaOperatorDefs/LSTM.cpp b/src/operator/MetaOperatorDefs/LSTM.cpp
index 2ed548805010a6cc87950c4d1f7b89edbea4f75c..22c0469b34b52670a910f63604d02f3f8bf6eab7 100644
--- a/src/operator/MetaOperatorDefs/LSTM.cpp
+++ b/src/operator/MetaOperatorDefs/LSTM.cpp
@@ -11,7 +11,6 @@
#include "aidge/operator/MetaOperatorDefs.hpp"
-#include <array>
#include <memory>
#include <string>
@@ -20,7 +19,6 @@
#include "aidge/operator/Mul.hpp"
#include "aidge/operator/FC.hpp"
#include "aidge/operator/Identity.hpp"
-#include "aidge/operator/Concat.hpp"
#include "aidge/operator/Tanh.hpp"
namespace Aidge {
diff --git a/src/operator/MetaOperatorDefs/Leaky.cpp b/src/operator/MetaOperatorDefs/Leaky.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..33b2535329e9522b30120102204f2e34f755b9c5
--- /dev/null
+++ b/src/operator/MetaOperatorDefs/Leaky.cpp
@@ -0,0 +1,113 @@
+#include "aidge/filler/Filler.hpp"
+#include "aidge/operator/Add.hpp"
+#include "aidge/operator/Heaviside.hpp"
+#include "aidge/operator/Identity.hpp"
+#include "aidge/operator/Memorize.hpp"
+#include "aidge/operator/MetaOperatorDefs.hpp"
+#include "aidge/operator/Mul.hpp"
+#include "aidge/operator/Producer.hpp"
+#include "aidge/operator/Sub.hpp"
+
+namespace Aidge {
+
+constexpr auto memorizeOpDataOutputRecIndex = 1;
+constexpr auto memorizeOpDataOutputIndex = 0;
+
+std::shared_ptr<Node> Leaky(const int nbTimeSteps,
+ const float beta,
+ const float threshold,
+ const std::string &name) {
+
+ auto microGraph = std::make_shared<GraphView>();
+
+ auto inputNode = Identity((!name.empty()) ? name + "_input" : "");
+ auto addNode = Add(!name.empty() ? name + "_add" : "");
+ auto mulNode = Mul(!name.empty() ? name + "_mul" : "");
+ auto subNode = Sub(!name.empty() ? name + "_sub" : "");
+ auto hsNode = Heaviside(0, !name.empty() ? name + "_hs" : "");
+ auto subNode2 = Sub(!name.empty() ? name + "_threshold" : "");
+ auto reset = Mul(!name.empty() ? name + "_reset" : "");
+
+ auto betaTensor = std::make_shared<Tensor>(beta);
+ auto uthTensor = std::make_shared<Tensor>(static_cast<float>(threshold));
+ uniformFiller<float>(uthTensor, threshold, threshold);
+
+ auto decayRate = Producer(betaTensor, "leaky_beta", true);
+ auto uth = Producer(uthTensor, "leaky_uth", true);
+
+ auto potentialMem =
+ Memorize(nbTimeSteps, (!name.empty()) ? name + "_potential" : "");
+ auto spikeMem =
+ Memorize(nbTimeSteps, (!name.empty()) ? name + "_spike" : "");
+
+ // U[t] = Input[T] + beta * U[T-1] - S[T-1] * U_th
+ // with S[T] = | 1, if U[T] - U_th > 0
+ // | 0 otherwise
+
+ // beta * U[T-1]
+ decayRate->addChild(/*otherNode=*/mulNode, /*outId=*/0, /*otherInId=*/1);
+ potentialMem->addChild(mulNode, 1, 0);
+
+ // Input[T] + beta * U[T-1]
+ mulNode->addChild(/*otherNode=*/addNode, /*outId=*/0, /*otherInId=*/1);
+ inputNode->addChild(/*otherNode=*/addNode, /*outId=*/0, /*otherInId=*/0);
+
+ // S[T-1] * U_th
+ spikeMem->addChild(reset,
+ /*outId=*/memorizeOpDataOutputRecIndex,
+ /*otherInId=*/0);
+
+ // TODO(#219) Handle hard/soft reset
+ uth->addChild(reset, 0, 1);
+
+ // Input[T] + beta * U[T-1] - S[T-1] * U_th
+ addNode->addChild(subNode, 0, 0);
+ reset->addChild(subNode, 0, 1);
+
+ // U[t] = (Input[T] + beta * U[T-1]) - S[T-1]
+ subNode->addChild(potentialMem, 0, 0);
+
+ // U[T] - U_th
+ subNode->addChild(subNode2, 0, 0);
+ uth->addChild(subNode2, 0, 1);
+
+ // with S[T] = | 1, if U[T] - U_th > 0
+ subNode2->addChild(hsNode, 0, 0);
+ hsNode->addChild(spikeMem, 0, 0);
+
+ microGraph->add(inputNode);
+ microGraph->add({addNode,
+ mulNode,
+ potentialMem,
+ decayRate,
+ uth,
+ spikeMem,
+ hsNode,
+ subNode,
+ subNode2,
+ reset},
+ false);
+
+ microGraph->setOrderedInputs(
+ {{inputNode, 0}, {potentialMem, 1}, {spikeMem, 1}});
+
+ // NOTE: Outputs are NOT the memory nodes (as it is done in LSTM), to avoid
+ // producing data during init. This way, we can plug an operator after
+ // our node, and get correct results.
+ microGraph->setOrderedOutputs({//{potentialMem, memorizeOpDataOutputIndex},
+ //{spikeMem, memorizeOpDataOutputIndex}
+ {subNode, 0},
+ {hsNode, 0}});
+
+ auto metaOp = MetaOperator(/*type*/ "Leaky",
+ /*graph*/ microGraph,
+ /*forcedInputsCategory=*/{},
+ /*name*/ "leaky");
+
+ return metaOp;
+}
+
+std::shared_ptr<MetaOperator_Op> LeakyOp() {
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Not implemented yet");
+}
+} // namespace Aidge
diff --git a/unit_tests/operator/Test_MetaOperator.cpp b/unit_tests/operator/Test_MetaOperator.cpp
index 6711e1524fe0595b4effd68397f8cb684df590a9..042b04f01bdc1430c8b9f1b9df6951f12b821ed1 100644
--- a/unit_tests/operator/Test_MetaOperator.cpp
+++ b/unit_tests/operator/Test_MetaOperator.cpp
@@ -23,6 +23,7 @@
#include "aidge/graph/GraphView.hpp"
#include "aidge/graph/Testing.hpp"
#include "aidge/recipes/Recipes.hpp"
+#include "aidge/utils/ErrorHandling.hpp"
using namespace Aidge;
@@ -145,4 +146,17 @@ TEST_CASE("[core/operators] MetaOperator", "[Operator][MetaOperator]") {
REQUIRE(g->getNodes().size() == 33);
}
+
+ SECTION("Leaky") {
+ auto myLeaky = Leaky(10, 1.0, 0.9);
+ auto op = std::static_pointer_cast<OperatorTensor>(myLeaky->getOperator());
+
+ auto inputs = myLeaky->inputs();
+
+ // Two memorize nodes + real data input
+ REQUIRE(myLeaky->nbInputs() == 3);
+ // Outputs for spike and memory + 2 Memorize node
+ REQUIRE(myLeaky->nbOutputs() == 4);
+ REQUIRE(true);
+ }
}