slice.h
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// sherpa-onnx/csrc/slice.h
//
// Copyright (c) 2023 Xiaomi Corporation
#ifndef SHERPA_ONNX_CSRC_SLICE_H_
#define SHERPA_ONNX_CSRC_SLICE_H_
#include "onnxruntime_cxx_api.h" // NOLINT
namespace sherpa_onnx {
/** Get a deep copy by slicing a 3-D tensor v.
*
* It returns v[dim0_start:dim0_end, dim1_start:dim1_end, :]
*
* @param allocator
* @param v A 2-D tensor. Its data type is T.
* @param dim0_start Start index of the first dimension..
* @param dim0_end End index of the first dimension..
* @param dim1_start Start index of the second dimension.
* @param dim1_end End index of the second dimension.
*
* @return Return a 3-D tensor of shape
* (dim0_end-dim0_start, dim1_end-dim1_start, v.shape[2])
*/
template <typename T = float>
Ort::Value Slice(OrtAllocator *allocator, const Ort::Value *v,
int32_t dim0_start, int32_t dim0_end, int32_t dim1_start,
int32_t dim1_end);
/** Get a deep copy by slicing a 2-D tensor v.
*
* It returns v[dim0_start:dim0_end, :]
*
* @param allocator
* @param v A 2-D tensor. Its data type is T.
* @param dim0_start Start index of the first dimension..
* @param dim0_end End index of the first dimension..
*
* @return Return a 2-D tensor of shape
* (dim0_end-dim0_start, v.shape[1])
*/
template <typename T = float>
Ort::Value Slice(OrtAllocator *allocator, const Ort::Value *v,
int32_t dim0_start, int32_t dim0_end);
} // namespace sherpa_onnx
#endif // SHERPA_ONNX_CSRC_SLICE_H_