add pad_sequence (#84)

sherpa-onnx/csrc/pad-sequence.cc

@@ -0,0 +1,53 @@
+// sherpa-onnx/csrc/pad-sequence.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/pad-sequence.h"
+
+#include <assert.h>
+
+#include <algorithm>
+#include <vector>
+
+namespace sherpa_onnx {
+
+Ort::Value PadSequence(OrtAllocator *allocator,
+                       const std::vector<const Ort::Value *> &values,
+                       float padding_value) {
+  int32_t batch_size = static_cast<int32_t>(values.size());
+
+  std::vector<int64_t> shape0 =
+      values[0]->GetTensorTypeAndShapeInfo().GetShape();
+  assert(shape0.size() == 2);
+
+  auto feature_dim = shape0[1];
+  auto max_T = shape0[0];
+
+  for (int32_t i = 1; i != batch_size; ++i) {
+    auto shape = values[i]->GetTensorTypeAndShapeInfo().GetShape();
+
+    assert(shape.size() == 2);
+    assert(shape[1] == feature_dim);
+
+    max_T = std::max(max_T, shape[0]);
+  }
+  std::array<int64_t, 3> ans_shape{batch_size, max_T, feature_dim};
+
+  Ort::Value ans = Ort::Value::CreateTensor<float>(allocator, ans_shape.data(),
+                                                   ans_shape.size());
+  float *dst = ans.GetTensorMutableData<float>();
+  std::fill(dst, dst + batch_size * max_T * feature_dim, padding_value);
+
+  for (const auto *v : values) {
+    const float *src = v->GetTensorData<float>();
+    auto shape = v->GetTensorTypeAndShapeInfo().GetShape();
+    std::copy(src, src + shape[0] * shape[1], dst);
+    dst += max_T * feature_dim;
+  }
+
+  return ans;
+
+  // TODO(fangjun): Check that the returned value is correct.
+}
+
+}  // namespace sherpa_onnx