[mir_onnx] Replace DTYPE with DataType. (#7421)

Replace deprecated `DTYPE` with `DataType`.

Signed-off-by: Sergei Barannikov <s.barannikov@samsung.com>

diff --git a/compiler/mir-onnx-importer/ONNXHelpers.cpp b/compiler/mir-onnx-importer/ONNXHelpers.cpp
index 0b042a9..6059d3a 100644 (file)
--- a/compiler/mir-onnx-importer/ONNXHelpers.cpp
+++ b/compiler/mir-onnx-importer/ONNXHelpers.cpp
@@ -61,7 +61,7 @@ mir::TensorVariant fixGroupedKernel(int groups, const mir::TensorVariant &folded
     else
     {
       // fill element of output kernel with zero element
-      assert(folded_kernel.getDataType() == mir::DTYPE::FLOAT32 &&
+      assert(folded_kernel.getDataType() == mir::DataType::FLOAT32 &&
              "unsupported data type, add appropriate zero element creation");
       auto elem = reinterpret_cast<float *>(unfold_kernel.at(idx));
       *elem = 0.0f;
@@ -72,7 +72,7 @@ mir::TensorVariant fixGroupedKernel(int groups, const mir::TensorVariant &folded
 
 mir::TensorVariant createTensor(const onnx::TensorProto *tensor)
 {
-  mir::DTYPE type;
+  mir::DataType type;
   const void *src_data;
   mir::Shape shape(tensor->dims_size());
   for (int i = 0; i < tensor->dims_size(); ++i)
@@ -83,25 +83,25 @@ mir::TensorVariant createTensor(const onnx::TensorProto *tensor)
   if (tensor->float_data_size() != 0)
   {
     assert(tensor->data_type() == onnx::TensorProto::FLOAT);
-    type = mir::DTYPE::FLOAT32;
+    type = mir::DataType::FLOAT32;
     src_data = tensor->float_data().data();
   }
   else if (tensor->double_data_size() != 0)
   {
     assert(tensor->data_type() == onnx::TensorProto::DOUBLE);
-    type = mir::DTYPE::FLOAT64;
+    type = mir::DataType::FLOAT64;
     src_data = tensor->double_data().data();
   }
   else if (tensor->int32_data_size() != 0)
   {
     assert(tensor->data_type() == onnx::TensorProto::INT32);
-    type = mir::DTYPE::INT32;
+    type = mir::DataType::INT32;
     src_data = tensor->int32_data().data();
   }
   else if (tensor->int64_data_size() != 0)
   {
     assert(tensor->data_type() == onnx::TensorProto::INT64);
-    type = mir::DTYPE::INT64;
+    type = mir::DataType::INT64;
     src_data = tensor->int64_data().data();
   }
   else if (tensor->has_raw_data())
@@ -109,10 +109,10 @@ mir::TensorVariant createTensor(const onnx::TensorProto *tensor)
     switch (tensor->data_type())
     {
       case onnx::TensorProto::FLOAT:
-        type = mir::DTYPE::FLOAT32;
+        type = mir::DataType::FLOAT32;
         break;
       case onnx::TensorProto::INT64:
-        type = mir::DTYPE::INT64;
+        type = mir::DataType::INT64;
         break;
       default:
         throw std::runtime_error("Unsupported data type");

diff --git a/compiler/mir-onnx-importer/Op/Shape.cpp b/compiler/mir-onnx-importer/Op/Shape.cpp
index 9e563ef..1d4968c 100644 (file)
--- a/compiler/mir-onnx-importer/Op/Shape.cpp
+++ b/compiler/mir-onnx-importer/Op/Shape.cpp
@@ -37,7 +37,7 @@ void ShapeNodeConverter::convert(const onnx::NodeProto &onnx_node, ConverterCont
   {
     data[i] = input_shape.dim(i);
   }
-  mir::TensorVariant tensor(mir::DTYPE::FLOAT32, output_shape, data.data());
+  mir::TensorVariant tensor(mir::DataType::FLOAT32, output_shape, data.data());
   auto result = createOp<mir::ops::ConstantOp>(graph, tensor)->getOutput(0);
 
   context->setNodeOutputs(onnx_node, {result});