Support slicing a shallow copy of a 3-d tensor (#83)

@@ -18,6 +18,7 @@ set(sources
   onnx-utils.cc
   parse-options.cc
   resample.cc
+  slice.cc
   symbol-table.cc
   text-utils.cc
   transpose.cc
@@ -121,6 +122,7 @@ endif()
 if(SHERPA_ONNX_ENABLE_TESTS)
   set(sherpa_onnx_test_srcs
     cat-test.cc
+    slice-test.cc
     transpose-test.cc
     unbind-test.cc
   )

@@ -57,9 +57,6 @@ Ort::Value GetEncoderOutFrame(OrtAllocator *allocator, Ort::Value *encoder_out,
 
   auto offset = num_frames * encoder_out_dim;
 
-  auto memory_info =
-      Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
-
   std::array<int64_t, 2> shape{batch_size, encoder_out_dim};
 
   Ort::Value ans =
@@ -90,9 +87,6 @@ Ort::Value Clone(OrtAllocator *allocator, const Ort::Value *v) {
   auto type_and_shape = v->GetTensorTypeAndShapeInfo();
   std::vector<int64_t> shape = type_and_shape.GetShape();
 
-  auto memory_info =
-      Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
-
   switch (type_and_shape.GetElementType()) {
     case ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32: {
       Ort::Value ans = Ort::Value::CreateTensor<int32_t>(

+// sherpa-onnx/csrc/slice-test.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/slice.h"
+
+#include <numeric>
+
+#include "gtest/gtest.h"
+#include "sherpa-onnx/csrc/onnx-utils.h"
+
+namespace sherpa_onnx {
+
+TEST(Slice, Slice3D) {
+  Ort::AllocatorWithDefaultOptions allocator;
+  std::array<int64_t, 3> shape{3, 5, 4};
+  Ort::Value v =
+      Ort::Value::CreateTensor<float>(allocator, shape.data(), shape.size());
+  float *p = v.GetTensorMutableData<float>();
+
+  std::iota(p, p + shape[0] * shape[1] * shape[2], 0);
+
+  auto v1 = Slice(&v, 0, 2, 5);
+  auto v2 = Slice(&v, 1, 2, 4);
+
+  Print3D(&v);
+  Print2D(&v1);
+  Print2D(&v2);
+
+  // TODO(fangjun): Check that the results are correct
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/csrc/slice.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/slice.h"
+
+#include <assert.h>
+
+#include <vector>
+
+namespace sherpa_onnx {
+
+template <typename T /*=float*/>
+Ort::Value Slice(const Ort::Value *v, int32_t dim0, int32_t dim1_start,
+                 int32_t dim1_end) {
+  std::vector<int64_t> shape = v->GetTensorTypeAndShapeInfo().GetShape();
+  assert(shape.size() == 3);
+
+  auto memory_info =
+      Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
+
+  std::array<int64_t, 2> ans_shape{dim1_end - dim1_start, shape[2]};
+  const T *src = v->GetTensorData<T>();
+  src += dim0 * shape[1] * shape[2] + dim1_start * shape[2];
+
+  return Ort::Value::CreateTensor(memory_info, const_cast<T *>(src),
+                                  ans_shape[0] * ans_shape[1], ans_shape.data(),
+                                  ans_shape.size());
+}
+
+template Ort::Value Slice<float>(const Ort::Value *v, int32_t dim0,
+                                 int32_t dim1_start, int32_t dim1_end);
+
+}  // namespace sherpa_onnx

+// 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 shallow copy by slicing v.
+ *
+ * It returns v[dim0, dim1_start:dim1_end]
+ *
+ * @param v A 3-D tensor. Its data type is T.
+ * @param dim0  Start 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 2-D tensor of shape (dim1_end-dim1_start, v.shape[2])
+ *
+ * @caution: The returned tensor is a shallow copy of `v`!
+ */
+template <typename T = float>
+Ort::Value Slice(const Ort::Value *v, int32_t dim0, int32_t dim1_start,
+                 int32_t dim1_end);
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_SLICE_H_