Updated new operators

include/aidge/operator/Ln.hpp

@@ -30,7 +30,7 @@ class Ln_Op : public OperatorTensor,
 public:
     static const std::string Type;
 
-    Ln_Op() : OperatorTensor(Type, 1, 0, 1) {}
+    Ln_Op() : OperatorTensor(Type, {InputCategory::Data}, 1) {}
 
     /**
      * @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).

include/aidge/operator/Resize.hpp

@@ -31,7 +31,7 @@ public:
     static const std::string Type;
 
     Resize_Op()
-        : OperatorTensor(Type, 4, 0, 1){}
+        : OperatorTensor(Type, {InputCategory::Data, InputCategory::OptionalData, InputCategory::OptionalData, InputCategory::OptionalData}, 1){}
         
     /**
      * @brief Copy-constructor. Copy the operator attributes and its output tensor(s),

include/aidge/operator/ShiftGELU.hpp

@@ -32,7 +32,7 @@ class ShiftGELU_Op : public OperatorTensor,
 public:
     static const std::string Type;
 
-    ShiftGELU_Op() : OperatorTensor(Type, 1, 0, 1) {}
+    ShiftGELU_Op() : OperatorTensor(Type, {InputCategory::Data}, 1) {}
 
     /**
      * @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).

include/aidge/operator/ShiftMax.hpp

@@ -32,7 +32,7 @@ class ShiftMax_Op : public OperatorTensor,
 public:
     static const std::string Type;
 
-    ShiftMax_Op() : OperatorTensor(Type, 1, 0, 1) {}
+    ShiftMax_Op() : OperatorTensor(Type, {InputCategory::Data}, 1) {}
 
     /**
      * @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).

src/operator/Resize.cpp

@@ -40,77 +40,65 @@ bool Aidge::Resize_Op::dimsForwarded() const {
 }
 
 bool Aidge::Resize_Op::forwardDims(bool allowDataDependency) {
-    
-    // check inputs have been associated
-    if (!getInput(0)) {
-        AIDGE_THROW_OR_ABORT(std::runtime_error, "{}: input #0 should be associated with a Tensor", type());
-    }
-
-    AIDGE_ASSERT(getInput(0)->nbDims() ==  4,\
+    if (inputsAssociated()) {
+        AIDGE_ASSERT(getInput(0)->nbDims() == 4,
             "input tensor must have dimensions = 4 (batch, channel, height, width).");
 
-    if (getInput(0)->empty()) {
-        return false;
-    }
-
-    bool input1ROIPresent           = getInput(1) && !getInput(1)->empty();
-    bool input2ScalesPresent        = getInput(2) && !getInput(2)->empty();
-    bool input3SizesPresent         = getInput(3) && !getInput(3)->empty();
-
-    AIDGE_ASSERT(input2ScalesPresent != input3SizesPresent, "Only one of scales and  sizes can be specified.")
-
-    if (input1ROIPresent) {
-
-        AIDGE_THROW_OR_ABORT(std::runtime_error, "Input #1 (ROI) is given and it is not supported.");
+        const bool input1ROIPresent           = getInput(1) && !getInput(1)->empty();
+        const bool input2ScalesPresent        = getInput(2) && !getInput(2)->empty();
+        const bool input3SizesPresent         = getInput(3) && !getInput(3)->empty();
 
-    } else if (input2ScalesPresent)  {
+        AIDGE_ASSERT(input2ScalesPresent != input3SizesPresent, "Only one of scales and  sizes can be specified.")
 
-        if (!allowDataDependency) {
-            Log::warn("Resize_Op: cannot execute forwardDims() as the output dimensions depend on the input #2");
-            return false;
+        if (input1ROIPresent) {
+            AIDGE_THROW_OR_ABORT(std::runtime_error, "Input #1 (ROI) is given and it is not supported.");
         }
+        else if (input2ScalesPresent)  {
+            if (!allowDataDependency) {
+                Log::warn("Resize_Op: cannot execute forwardDims() as the output dimensions depend on the input #2");
+                return false;
+            }
 
-        AIDGE_ASSERT(getInput(0)->nbDims() ==  getInput(2)->size(),\
-             "input #0 and input #2 (Scales) must have the same dimensions.");
-    
-        std::vector<DimSize_t>      outDims = getInput(0)->dims();
-        const std::vector<DimSize_t> inDims = getInput(0)->dims();
+            AIDGE_ASSERT(getInput(0)->nbDims() ==  getInput(2)->size(),
+                "input #0 and input #2 (Scales) must have the same dimensions.");
 
-        std::shared_ptr<Tensor> fallback;
-        const auto& scales = getInput(2)->refCastFrom(fallback, NativeType<int64_t>::type, "cpu");
+            std::vector<DimSize_t>      outDims = getInput(0)->dims();
+            const std::vector<DimSize_t> inDims = getInput(0)->dims();
 
-        for (std::size_t dim=0; dim < getInput(2)->size(); ++dim) {
-            outDims[dim] = inDims[dim]*static_cast<int64_t*>(scales.getImpl()->hostPtr())[dim];
-        }
-
-        mOutputs[0]->resize(outDims);
+            std::shared_ptr<Tensor> fallback;
+            const auto& scales = getInput(2)->refCastFrom(fallback, NativeType<int64_t>::type, "cpu");
 
-        return true;
+            for (std::size_t dim=0; dim < getInput(2)->size(); ++dim) {
+                outDims[dim] = inDims[dim]*static_cast<int64_t*>(scales.getImpl()->hostPtr())[dim];
+            }
 
-    } else if (input3SizesPresent) {
-
-        if (!allowDataDependency) {
-            Log::warn("Resize_Op: cannot execute forwardDims() as the output dimensions depend on the input #3");
-            return false;
+            mOutputs[0]->resize(outDims);
+            return true;
         }
-
-        AIDGE_ASSERT(getInput(0)->nbDims() ==  getInput(3)->size(),\
-             "input #0 and input #3 (Sizes) must have the same dimensions.");
-        
-        std::vector<DimSize_t> outDims = getInput(0)->dims();
-
-        std::shared_ptr<Tensor> fallback;
-        const auto& sizes = getInput(3)->refCastFrom(fallback, NativeType<int64_t>::type, "cpu");
-
-        for (std::size_t dim=0; dim < getInput(3)->size(); ++dim) {            
-            outDims[dim] = static_cast<int64_t*>(sizes.getImpl()->hostPtr())[dim];
+        else if (input3SizesPresent) {
+            if (!allowDataDependency) {
+                Log::warn("Resize_Op: cannot execute forwardDims() as the output dimensions depend on the input #3");
+                return false;
+            }
+
+            AIDGE_ASSERT(getInput(0)->nbDims() ==  getInput(3)->size(),
+                "input #0 and input #3 (Sizes) must have the same dimensions.");
+
+            std::vector<DimSize_t> outDims = getInput(0)->dims();
+
+            std::shared_ptr<Tensor> fallback;
+            const auto& sizes = getInput(3)->refCastFrom(fallback, NativeType<int64_t>::type, "cpu");
+
+            for (std::size_t dim=0; dim < getInput(3)->size(); ++dim) {            
+                outDims[dim] = static_cast<int64_t*>(sizes.getImpl()->hostPtr())[dim];
+            }
+            
+            mOutputs[0]->resize(outDims);
+            return true;
+        }
+        else {
+            AIDGE_THROW_OR_ABORT(std::runtime_error, "Error: Either Input #2 or Input #3 must be present.");
         }
-        
-        mOutputs[0]->resize(outDims);
-        
-        return true;
-    } else {
-        AIDGE_THROW_OR_ABORT(std::runtime_error, "Error: Either Input #2 or Input #3 must be present.");
     }
 
     return false;