diff --git a/include/aidge/graphRegex/GraphRegex.hpp b/include/aidge/graphRegex/GraphRegex.hpp
index b62a42fcfeb258e5c659eaeb6681190482f37aa4..573447cf934b196e8b0c32d7a58e1977f5aa5f9a 100644
--- a/include/aidge/graphRegex/GraphRegex.hpp
+++ b/include/aidge/graphRegex/GraphRegex.hpp
@@ -12,13 +12,12 @@
namespace Aidge{
/**
- * type for recipes function use in query and resolve
-*/
+ * @brief type for recipes function use in query and resolve
+ */
using RecipesFunctionType = std::function<void(std::shared_ptr<MatchSolution>)>;
/**
- * @brief class which is the hight level interface for graph matching, used to simplify match definition
- *
+ * @brief high level interface for graph matching, used to simplify match definition
*/
class GraphRegex{
diff --git a/include/aidge/operator/OperatorTensor.hpp b/include/aidge/operator/OperatorTensor.hpp
index 1097454fce62f645eb83c491498031738847e96c..c8cdd93810e18bd3cdd0a2d080e54aae2d787c66 100644
--- a/include/aidge/operator/OperatorTensor.hpp
+++ b/include/aidge/operator/OperatorTensor.hpp
@@ -40,6 +40,14 @@ protected:
public:
OperatorTensor() = delete;
+ /**
+ * @brief Operator tensor constructor. This function is not meant to be called directly but by a derived class constructor
+ * every operator class derive from this class.
+ *
+ * @param[in] type : type of operator (i.e. "Add", "AveragePool",...)
+ * @param[in] inputsCategory : describes the type of each input.
+ * @param[in] nbOut : Number of tensors this operator will output
+ */
OperatorTensor(const std::string& type, const std::vector<InputCategory>& inputsCategory,
const IOIndex_t nbOut);
@@ -79,6 +87,15 @@ public:
* For each dataInput Tensor of the Operator, the first index and dimensions of the feature area.
*/
virtual std::vector<std::pair<std::vector<Aidge::DimSize_t>, std::vector<DimSize_t>>> computeReceptiveField(const std::vector<DimSize_t>& firstEltDims, const std::vector<DimSize_t>& outputDims, const IOIndex_t outputIdx = 0) const;
+
+ /**
+ * @brief Will compute the dimensions of operator's output tensor given the input sizes
+ * If the output dimensions cannot be computed because it depends on some undefined inputs then forwardDims will return false and enter in TOKEN mode for subsequent tensors.
+ * - TOKEN mode means that forwarddims will only ensure that all inputs and outputs of the graph the node is within are connected.
+ * @param[in] allowDataDependency if set to true, this means that this operator output dimensions depends on the dimensions of optionnal parameter tensors.
+ * @return true if dims have been properly forwarded. false otherwise. If set to false, then forwardDims will enter in token mode.
+ *
+ */
virtual bool forwardDims(bool allowDataDependency = false);
virtual bool dimsForwarded() const;
///////////////////////////////////////////////////
diff --git a/include/aidge/operator/Producer.hpp b/include/aidge/operator/Producer.hpp
index 257a6965be4c08735f23ae575ffe104bb706593a..c52e779cbfec04f9ae6796c3bb6f21407c0cb0fb 100644
--- a/include/aidge/operator/Producer.hpp
+++ b/include/aidge/operator/Producer.hpp
@@ -103,7 +103,7 @@ public:
void forward() override final;
void backward() override final {
- // fmt::print("Basic Producer backward() function.\n");
+ Log::debug("Basic Producer backward() function.");
}
void setOutput(const IOIndex_t outputIdx, const std::shared_ptr<Data>& data) const override;
diff --git a/python_binding/utils/pybind_Log.cpp b/python_binding/utils/pybind_Log.cpp
index f70a4bfab54ee14194ea04f96efa33a6b8e04201..ca8d1f33086fb5093c76826e5a2f53df873badf5 100644
--- a/python_binding/utils/pybind_Log.cpp
+++ b/python_binding/utils/pybind_Log.cpp
@@ -78,6 +78,13 @@ void init_Log(py::module& m){
.def_static("set_console_level", &Log::setConsoleLevel, py::arg("level"),
R"mydelimiter(
Set the minimum log level displayed in the console.
+ Available `Level`s in ascending order :
+ - Level.Debug
+ - Level.Info
+ - Level.Notice
+ - Level.Warn
+ - Level.Error
+ - Level.Fatal
:param level: Log level.
:type level: Level
@@ -93,6 +100,13 @@ void init_Log(py::module& m){
.def_static("set_file_level", &Log::setFileLevel, py::arg("level"),
R"mydelimiter(
Set the minimum log level saved in the log file.
+ Available `Level`s in ascending order :
+ - Level.Debug
+ - Level.Info
+ - Level.Notice
+ - Level.Warn
+ - Level.Error
+ - Level.Fatal
:param level: Log level.
:type level: Level
diff --git a/src/graph/Node.cpp b/src/graph/Node.cpp
index 1035deb366a9c5df6ff08cd87ebd65a11c2b6e78..382052535cc6b5cd8089f720b8fa9f8d3a0ebce1 100644
--- a/src/graph/Node.cpp
+++ b/src/graph/Node.cpp
@@ -211,7 +211,7 @@ void Aidge::Node::setInputId(const IOIndex_t inId, const IOIndex_t newNodeoutId)
"Input index ({}) is out of bound ({}) for node {} (of type {})",
inId, nbInputs(), name(), type());
if (mIdOutParents[inId] != gk_IODefaultIndex) {
- Log::notice("Notice: filling a Tensor already attributed");
+ Log::notice("Filling a Tensor already attributed.");
auto originalParent = input(inId);
// remove original parent reference to child
// find the output ID for original Parent
@@ -279,7 +279,7 @@ void Aidge::Node::addChild(std::shared_ptr<GraphView> otherView, const IOIndex_t
void Aidge::Node::addParent(const std::shared_ptr<Node> other_node, const IOIndex_t inId) {
if (getParent(inId) != nullptr) {
- Log::notice("Notice: you are replacing an existing parent for node {} (of type {})", name(), type());
+ Log::notice("You are replacing an existing parent for node {} (of type {}).", name(), type());
}
AIDGE_ASSERT(inId != gk_IODefaultIndex && inId < nbInputs(),
"Input index ({}) is out of bound ({}) for node {} (of type {})",
diff --git a/src/scheduler/Scheduler.cpp b/src/scheduler/Scheduler.cpp
index 9c2109bf6fdfb1a1fbc57afc3fd09e08a1dfc2db..851f1895c3862ed3deedc73f2ee70f6835b4a8a3 100644
--- a/src/scheduler/Scheduler.cpp
+++ b/src/scheduler/Scheduler.cpp
@@ -33,6 +33,7 @@
#include "aidge/operator/MetaOperator.hpp"
#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/operator/Producer.hpp"
+#include "aidge/utils/Log.hpp"
#include "aidge/utils/Types.h"
@@ -665,7 +666,7 @@ Aidge::Elts_t Aidge::Scheduler::getNbAvailableData(const std::shared_ptr<Node>&
// => This means data was fed manually to the input, without a Producer
// In this case, we assume a single-use data (unlike a Producer, which
// keep producing the data each time it is needed).
- fmt::print("No producer node attached to input#{} for node {} ({})\n", inputIdx, node->name(), node->type());
+ Log::warn("No producer node attached to input#{} for node {} ({})\n", inputIdx, node->name(), node->type());
return Elts_t::DataElts(std::static_pointer_cast<Tensor>(node->getOperator()->getRawInput(inputIdx))->size());
}