Add PackPaddedSequence (#85)

sherpa-onnx/csrc/packed-sequence-test.cc

@@ -0,0 +1,52 @@
+// sherpa-onnx/csrc/packed-sequence-test.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/packed-sequence.h"
+
+#include <numeric>
+
+#include "gtest/gtest.h"
+#include "sherpa-onnx/csrc/onnx-utils.h"
+
+namespace sherpa_onnx {
+
+TEST(PackedSequence, Case1) {
+  Ort::AllocatorWithDefaultOptions allocator;
+  std::array<int64_t, 3> shape{5, 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);
+
+  Ort::Value length =
+      Ort::Value::CreateTensor<int64_t>(allocator, shape.data(), 1);
+  int64_t *p_length = length.GetTensorMutableData<int64_t>();
+  p_length[0] = 1;
+  p_length[1] = 2;
+  p_length[2] = 3;
+  p_length[3] = 5;
+  p_length[4] = 2;
+
+  auto packed_seq = PackPaddedSequence(allocator, &v, &length);
+  fprintf(stderr, "sorted indexes: ");
+  for (auto i : packed_seq.sorted_indexes) {
+    fprintf(stderr, "%d ", static_cast<int32_t>(i));
+  }
+  fprintf(stderr, "\n");
+  // output index:   0 1 2 3 4
+  // sorted indexes: 3 2 1 4 0
+  // length:         5 3 2 2 1
+  Print3D(&v);
+  Print2D(&packed_seq.data);
+  fprintf(stderr, "batch sizes per time step: ");
+  for (auto i : packed_seq.batch_sizes) {
+    fprintf(stderr, "%d ", static_cast<int32_t>(i));
+  }
+  fprintf(stderr, "\n");
+
+  // TODO(fangjun): Check that the return value is correct
+}
+
+}  // namespace sherpa_onnx