Use deep copy in Clone() (#66)

@@ -171,9 +171,14 @@ class MainActivity : AppCompatActivity() {
     }
 
     private fun initModel() {
+        // Please change getModelConfig() to add new models
+        // See https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
+        // for a list of available models
+        val type = 0
+        println("Select model type ${type}")
         val config = OnlineRecognizerConfig(
             featConfig = getFeatureConfig(sampleRate = 16000.0f, featureDim = 80),
-            modelConfig = getModelConfig(type = 1)!!,
+            modelConfig = getModelConfig(type = type)!!,
             endpointConfig = getEndpointConfig(),
             enableEndpoint = true
         )

@@ -63,7 +63,7 @@ class ViewController: UIViewController {
         super.viewDidLoad()
         // Do any additional setup after loading the view.
         
-        resultLabel.text = "ASR with Next-gen Kaldi\n\nPress the Start button to run!"
+        resultLabel.text = "ASR with Next-gen Kaldi\n\nSee https://github.com/k2-fsa/sherpa-onnx\n\nPress the Start button to run!"
         recordBtn.setTitle("Start", for: .normal)
         initRecognizer()
         initRecorder()

@@ -37,7 +37,7 @@ template <typename T /*=float*/>
 Ort::Value Cat(OrtAllocator *allocator,
                const std::vector<const Ort::Value *> &values, int32_t dim) {
   if (values.size() == 1u) {
-    return Clone(values[0]);
+    return Clone(allocator, values[0]);
   }
 
   std::vector<int64_t> v0_shape =

@@ -100,8 +100,8 @@ void OnlineTransducerGreedySearchDecoder::Decode(
   for (int32_t t = 0; t != num_frames; ++t) {
     Ort::Value cur_encoder_out = GetFrame(&encoder_out, t);
     cur_encoder_out = Repeat(model_->Allocator(), &cur_encoder_out, batch_size);
-    Ort::Value logit =
-        model_->RunJoiner(std::move(cur_encoder_out), Clone(&decoder_out));
+    Ort::Value logit = model_->RunJoiner(
+        std::move(cur_encoder_out), Clone(model_->Allocator(), &decoder_out));
     const float *p_logit = logit.GetTensorData<float>();
 
     bool emitted = false;

@@ -53,7 +53,7 @@ void PrintModelMetadata(std::ostream &os, const Ort::ModelMetadata &meta_data) {
   }
 }
 
-Ort::Value Clone(const Ort::Value *v) {
+Ort::Value Clone(OrtAllocator *allocator, const Ort::Value *v) {
   auto type_and_shape = v->GetTensorTypeAndShapeInfo();
   std::vector<int64_t> shape = type_and_shape.GetShape();
 
@@ -61,21 +61,33 @@ Ort::Value Clone(const Ort::Value *v) {
       Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
 
   switch (type_and_shape.GetElementType()) {
-    case ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32:
-      return Ort::Value::CreateTensor(
-          memory_info,
-          const_cast<Ort::Value *>(v)->GetTensorMutableData<int32_t>(),
-          type_and_shape.GetElementCount(), shape.data(), shape.size());
-    case ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64:
-      return Ort::Value::CreateTensor(
-          memory_info,
-          const_cast<Ort::Value *>(v)->GetTensorMutableData<int64_t>(),
-          type_and_shape.GetElementCount(), shape.data(), shape.size());
-    case ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT:
-      return Ort::Value::CreateTensor(
-          memory_info,
-          const_cast<Ort::Value *>(v)->GetTensorMutableData<float>(),
-          type_and_shape.GetElementCount(), shape.data(), shape.size());
+    case ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32: {
+      Ort::Value ans = Ort::Value::CreateTensor<int32_t>(
+          allocator, shape.data(), shape.size());
+      const int32_t *start = v->GetTensorData<int32_t>();
+      const int32_t *end = start + type_and_shape.GetElementCount();
+      int32_t *dst = ans.GetTensorMutableData<int32_t>();
+      std::copy(start, end, dst);
+      return ans;
+    }
+    case ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64: {
+      Ort::Value ans = Ort::Value::CreateTensor<int64_t>(
+          allocator, shape.data(), shape.size());
+      const int64_t *start = v->GetTensorData<int64_t>();
+      const int64_t *end = start + type_and_shape.GetElementCount();
+      int64_t *dst = ans.GetTensorMutableData<int64_t>();
+      std::copy(start, end, dst);
+      return ans;
+    }
+    case ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT: {
+      Ort::Value ans = Ort::Value::CreateTensor<float>(allocator, shape.data(),
+                                                       shape.size());
+      const float *start = v->GetTensorData<float>();
+      const float *end = start + type_and_shape.GetElementCount();
+      float *dst = ans.GetTensorMutableData<float>();
+      std::copy(start, end, dst);
+      return ans;
+    }
     default:
       fprintf(stderr, "Unsupported type: %d\n",
               static_cast<int32_t>(type_and_shape.GetElementType()));

@@ -60,8 +60,8 @@ void GetOutputNames(Ort::Session *sess, std::vector<std::string> *output_names,
 void PrintModelMetadata(std::ostream &os,
                         const Ort::ModelMetadata &meta_data);  // NOLINT
 
-// Return a shallow copy of v
-Ort::Value Clone(const Ort::Value *v);
+// Return a deep copy of v
+Ort::Value Clone(OrtAllocator *allocator, const Ort::Value *v);
 
 // Print a 1-D tensor to stderr
 void Print1D(Ort::Value *v);

@@ -26,7 +26,7 @@ std::vector<Ort::Value> Unbind(OrtAllocator *allocator, const Ort::Value *value,
   int32_t n = static_cast<int32_t>(shape[dim]);
   if (n == 1) {
     std::vector<Ort::Value> ans;
-    ans.push_back(Clone(value));
+    ans.push_back(Clone(allocator, value));
     return ans;
   }