diff --git a/include/aidge/operator/MetaOperatorDefs.hpp b/include/aidge/operator/MetaOperatorDefs.hpp
index e270a78b6a0f6ed036d7048bed47204a2228417b..f80dd38f124a1730519ce6c1e0a4cefcfa8a359a 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);
}
////////////////////////////////////////////////////////////////////////////////
@@ -161,13 +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);
std::shared_ptr<MetaOperator_Op> LeakyOp();
-std::shared_ptr<Node> Leaky(const int nbTimeSteps, const float threshold, const float beta, const std::string& name = "");
+std::shared_ptr<Node> Leaky(const int nbTimeSteps,
+ const float threshold,
+ const float beta,
+ const std::string &name = "");
-} // namespace Aidge
+} // namespace Aidge
#endif /* AIDGE_CORE_OPERATOR_METAOPERATORDEFS_H_ */
diff --git a/src/operator/MetaOperatorDefs/Leaky.cpp b/src/operator/MetaOperatorDefs/Leaky.cpp
index 425d47a497fdc550b223b6e35cf173dbdd37d907..17296d1305db1a832e8e055916a95c7ea0a92f00 100644
--- a/src/operator/MetaOperatorDefs/Leaky.cpp
+++ b/src/operator/MetaOperatorDefs/Leaky.cpp
@@ -13,7 +13,10 @@ namespace Aidge {
constexpr auto memorizeOpDataOutputRecIndex = 1;
constexpr auto memorizeOpDataOutputIndex = 0;
-std::shared_ptr<Node> Leaky(const int nbTimeSteps, const float threshold, const float beta, const std::string &name) {
+std::shared_ptr<Node> Leaky(const int nbTimeSteps,
+ const float threshold,
+ const float beta,
+ const std::string &name) {
Log::warn("! Lots of parameters are hardcoded");
@@ -28,8 +31,7 @@ std::shared_ptr<Node> Leaky(const int nbTimeSteps, const float threshold, const
auto reset = Mul(!name.empty() ? name + "_reset" : "");
auto betaTensor = std::make_shared<Tensor>(beta);
- auto uthTensor =
- std::make_shared<Tensor>(static_cast<float>(threshold));
+ auto uthTensor = std::make_shared<Tensor>(static_cast<float>(threshold));
uniformFiller<float>(uthTensor, threshold, threshold);
auto decayRate = Producer(betaTensor, "leaky_beta", true);
@@ -59,7 +61,6 @@ std::shared_ptr<Node> Leaky(const int nbTimeSteps, const float threshold, const
// TODO: 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);
@@ -92,8 +93,9 @@ std::shared_ptr<Node> Leaky(const int nbTimeSteps, const float threshold, const
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 a stack operator after or node, and get correct results
+ // 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},