[TensorV2] Add utility member functions to TensorV2 class @open sesame 03/07 10:42 #2489
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| * @param[in] d tensor dim | ||
| * @param[in] offset offset to be used from current | ||
| * @return Tensor object | ||
| * @throws std::invalid_argument if buf is null |
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Adding a note that the buffer is not owned by the mapped tensor would be better.
the user may be confused.
| std::default_delete<MemoryData>()); | ||
| offset = offset; | ||
| } | ||
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Could you explain a little bit more about this function's purpose? Just curious because I have not seen this in, let's say, TensorV1
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this is simply a helper function to set certain TensorBase variables in the Tensor class.
since Map() is a static function, the possible way to set TensorBase variables would be using a Tensor public function.
therefore, the SetTensorVar() function has been newly added.
This pull request adds several new utility member functions to the TensorV2 class, enabling users to perform various tasks with their tensors more easily and efficiently. These include saving and loading tensors, updating batches, getting argmax and max absolute values, and more. The implementation is based on the current Tensor class and aims to improve the overall usability and flexibility of the TensorV2 class. **Changes proposed in this PR:** - Added save() and read() methods to allow saving and loading of saved tensor data. - Added Map() method to create a new Tensor object from a buffer. - Added argmax() and max_abs() methods to retrieve the indices of max value by batch and the value of the maximum absolute element in a tensor. - Added updateBatch() to update tensor batch size. **Self-evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: Donghyeon Jeong <dhyeon.jeong@samsung.com>
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This pull request adds several new utility member functions to the TensorV2 class, enabling users to perform various tasks with their tensors more easily and efficiently.
These include saving and loading tensors, updating batches, getting argmax and max absolute values, and more.
The implementation is based on the current Tensor class and aims to improve the overall usability and flexibility of the TensorV2 class.
Changes proposed in this PR:
save()andread()methods to allow saving and loading of saved tensor data.Map()method to create a new Tensor object from a buffer.argmax()andmax_abs()methods to retrieve the indices of max value by batch and the value of the maximum absolute element in a tensor.updateBatch()to update tensor batch size.Self-evaluation: