Merge branch 'dev' into topk

cmake/PybindModuleCreation.cmake

@@ -6,7 +6,7 @@ function(generate_python_binding name target_to_bind)
     FetchContent_Declare(
     PyBind11
     GIT_REPOSITORY https://github.com/pybind/pybind11.git
-    GIT_TAG        v2.10.4 # or a later release
+    GIT_TAG        v2.13.6 # or a later release
     )
     FetchContent_MakeAvailable(PyBind11)
 

include/aidge/utils/TensorUtils.hpp

@@ -14,7 +14,7 @@
 #include "aidge/data/Tensor.hpp"
 #include <cmath> // std::abs
 
-#if FMT_VERSION < 100000  // If fmt version is less than 10.0.0
+#if FMT_VERSION < 110000  // If fmt version is less than 11.0.0
     #include <fmt/core.h>
 #else
     #include <fmt/base.h>

src/operator/Clip.cpp

@@ -52,50 +52,36 @@ bool Clip_Op::dimsForwarded() const {
 
 bool Clip_Op::forwardDims(bool allowDataDependency)
 {
-    if (getInput(1) )
+    if (getInput(1))
     {
-        if( this->min() != std::numeric_limits<float>::lowest())
-        {
-            Log::notice("{} : ignoring non-empty min attribute because input#1 "
-                  "take precedence",
-                  type());
-        }
-        if (!allowDataDependency) {
-        Log::warn("{} : unable to forwardDims() because output dims are data "
-                    "dependent on input#1",
-                    type());
-        return false;
-        }
         std::shared_ptr<Tensor> fallback;
-        const auto& minV = mInputs[1]->refCastFrom(fallback, NativeType_v<float>, "cpu");
-        this->min() = *(static_cast<float*>(minV.getImpl()->hostPtr()));
+        const auto& minTensor = mInputs[1]->refCastFrom(fallback, DataType::Float32, "cpu");
+        float minValue = *(static_cast<float*>(minTensor.getImpl()->hostPtr()));
+
+        if(this->min() != std::numeric_limits<float>::lowest() && this->min() != minValue)
+            Log::notice("{} : ignoring non-empty min attribute because input#1 take precedence", type());
+
+        this->min() = minValue;
     }
     if (getInput(2))
     {
-       if( this->max() != std::numeric_limits<float>::max())
-        {
-            Log::notice("{} : ignoring non-empty max attribute because input#2 "
-                  "take precedence",
-                  type());
-        }
-        if (!allowDataDependency) {
-        Log::warn("{} : unable to forwardDims() because output dims are data "
-                    "dependent on input#2",
-                    type());
-        return false;
-        }
         std::shared_ptr<Tensor> fallback;
-        const auto& maxV = mInputs[2]->refCastFrom(fallback, NativeType_v<float>, "cpu");
-        this->max() = *(static_cast<float*>(maxV.getImpl()->hostPtr()));
+        const auto& maxTensor = mInputs[2]->refCastFrom(fallback, DataType::Float32, "cpu");
+        float maxValue = *(static_cast<float*>(maxTensor.getImpl()->hostPtr()));
+
+       if(this->max() != std::numeric_limits<float>::max() && this->max() != maxValue)
+            Log::notice("{} : ignoring non-empty max attribute because input#2 take precedence", type());
+
+        this->max() = maxValue;
     }
-    if (!inputsAssociated(false)) {
+
+    if (!inputsAssociated(false)) 
         return false;
-    }
-    else if ((getInput(1) && !getInput(1)->empty())  || (getInput(2) && !getInput(2)->empty()))
-    {
-        AIDGE_THROW_OR_ABORT(std::runtime_error,"Expected Input#1 and Input#2 to be scalar (Tensors of empty shapes)");
-    }
-    mOutputs[0] -> resize(getInput(0)->dims());
+    else if ((getInput(1) && !getInput(1)->empty()) || (getInput(2) && !getInput(2)->empty()))
+        AIDGE_THROW_OR_ABORT(std::runtime_error, "Expected Input#1 and Input#2 to be scalar (Tensors of empty shapes)");
+
+    mOutputs[0]->resize(getInput(0)->dims());
+
     return true;
 }
 void Clip_Op::setBackend(const std::string& name, DeviceIdx_t device) {