Vit operators
- [Add] OperatorTensors with Kernel implementaiton and unit-tests
- Erf
- Concat
- Gather
- ReduceMean
- Reshape
- Slice
- Transpose
- [Softmax kernel] update to support doing the operation on the attribute axis and not on a fixed one.
Merge request reports
Activity
6 * http://www.eclipse.org/legal/epl-2.0. 7 * 8 * SPDX-License-Identifier: EPL-2.0 9 * 10 ********************************************************************************/ 11 12 #ifndef AIDGE_CPU_OPERATOR_SLICEIMPL_FORWARD_KERNEL_H_ 13 #define AIDGE_CPU_OPERATOR_SLICEIMPL_FORWARD_KERNEL_H_ 14 15 #include "aidge/utils/Registrar.hpp" 16 17 #include "aidge/backend/cpu/operator/SliceImpl.hpp" 18 19 namespace Aidge { 20 template <class I, class O> 21 void SliceImpl_cpu_forward_kernel(const std::vector<DimSize_t> inputDims, -
@cmoineau this is what I was suggesting in our last exchange, what do you think of this kind of kernels, they are more generic (no need to add template for each dimension of input) and I think a bit faster (avoid nested loops), however they are not that intuitive (more time to code and understand). Feel free to tag people who might have an input on the matter.
Edited by Houssem ROUIS 
Hi, @pineapple did an implementation of the slice operator : https://gitlab.eclipse.org/eclipse/aidge/aidge_backend_cpu/-/blob/5a9d94c5263b5f36631e68ddc4746c35f7015d17/include/aidge/backend/cpu/operator/SliceImpl_forward_kernels.hpp so we will be able to compare both apporaches
I think we can open the discussion with @pineapple and @olivierbichler
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