feat_operator_convtranspose

Context

issue : aidge#235 (closed)

• ADDED :
  • feat : [ADD] PaddedConvTranspose operator
  • feat : [ADD] convtranspose operator
  • feat : added getDataType() function to OperatorTensor
  • feat : approxEqual, better error checking & error messages - replaced tensor->size by tensor->dims comparison - replaced fmt::print by log::error - replaced assert with AIDGE_ASSERT
• FIXES :
  • fix : Expand shape input was set as Param instead of Data
  • fix : missing header in Types.h
• CHORES :
  • chore : [DOC] added pybind documentation to graphview.compile
• Conv
  • feat : [Conv] added check to ensure dilation & stride attributes values are strictly positive
  • fix : [Conv] added check to ensure that dilation & stride values were all >= 1
    • better warning message for conv operator
  • chore : [Conv] updated the method for attribute accession to ease code reading
  • fix : [Conv] added check to ensure that dilation & stride values were all >= 1 & better warning message for conv operator constructor
  • chore : [Conv] updated the method for attribute accession to ease code reading

include/aidge/operator/Conv.hpp

@@ -12,6 +12,7 @@
 #ifndef AIDGE_CORE_OPERATOR_CONV_H_
 #define AIDGE_CORE_OPERATOR_CONV_H_
 
+#include <algorithm>
 #include <array>
 #include <cmath>    // std::floor
 #include <cstddef>  // std::size_t
@@ -237,10 +238,16 @@ std::shared_ptr<Node> Conv(DimSize_t inChannels,
                            bool noBias = false);
 
 /**
- * @brief Helper function for Conv with C-style arrays.
+ * @brief Perform a convolution on the input Tensor.
  *
- * This helper function allows automatic template deduction of the number of dimensions (DIM)
- * based on the kernel dimensions provided.
+ * @tparam DIM Number of dimensions for the feature map.
+ * @param inChannels Number of input channels.
+ * @param outChannels Number of output channels.
+ * @param kernelDims Dimensions of the kernel. Must be the same number of dimensions as the feature map.
+ * @param name Name of the operator.
+ * @param strideDims Dimensions of the stride attribute. Must be the same number of dimensions as the feature map.
+ * @param dilationDims Dimensions of the dilation attribute. Must be the same number of dimensions as the feature map.
+ * @return std::shared_ptr<Node> A Node containing the operator.
  */
 template <DimSize_t DIM>
 inline std::shared_ptr<Node> Conv(
@@ -251,8 +258,22 @@ inline std::shared_ptr<Node> Conv(
     const std::array<DimSize_t, DIM> &strideDims = create_array<DimSize_t,DIM>(1),
     const std::array<DimSize_t, DIM> &dilationDims = create_array<DimSize_t,DIM>(1),
     bool noBias = false) {
-    static_assert(DIM<=MaxDim,"Too many kernel dimensions required by Conv, not supported");
-    return Conv(inChannels, outChannels, to_array(kernelDims), name, strideDims, dilationDims, noBias);
+    AIDGE_ASSERT(DIM<=MaxDim,"{}: Too many kernel dimensions required, maximum allowed : {} ", Conv_Op<DIM>::Type, MaxDim);
+    AIDGE_ASSERT(!std::any_of(dilationDims.cbegin(),
+                              dilationDims.cend(),
+                              [](DimSize_t val) { return val == 0; }),
+                 "Conv : at least of of the dilation dimension is 0, expecting "
+                 "strictly positive values. Got {}",
+                 Conv_Op<DIM>::Type,
+                 dilationDims);
+    AIDGE_ASSERT(!std::any_of(strideDims.cbegin(),
+                              strideDims.cend(),
+                              [](DimSize_t val) { return val == 0; }),
+                 "{}: at least one of the stride dimension is 0, expecting "
+                 "strictly positive values. Got {}.",
+                 Conv_Op<DIM>::Type,
+                 strideDims);
+    return Conv<DIM>(inChannels, outChannels, to_array(kernelDims), name, strideDims, dilationDims, noBias);
 }
 
 }  // namespace Aidge