1 files changed, 49 insertions, 37 deletions

diff --git a/runtimes/neurun/test/graph/operation/SetIO.cc b/runtimes/neurun/test/graph/operation/SetIO.cc
index a475bdcc9..88e111e97 100644
--- a/runtimes/neurun/test/graph/operation/SetIO.cc
+++ b/runtimes/neurun/test/graph/operation/SetIO.cc
@@ -17,72 +17,84 @@
 #include <gtest/gtest.h>
 
 #include "graph/Graph.h"
-#include "cpp14/memory.h"
+#include "model/Model.h"
+#include "model/Index.h"
+#include "model/OperandIndexSequence.h"
 #include "model/operation/Conv2DNode.h"
 #include "model/operation/ConcatNode.h"
-#include "model/operand/Index.h"
-#include "model/operand/IndexSet.h"
+
+#include <cpp14/memory.h>
 
 #include <stdexcept>
 
-using Index = neurun::model::operand::IO::Index;
-using IndexSet = neurun::model::operand::IndexSet;
-using GraphNodeInitParam = neurun::model::operation::Node::InitParam;
+using Index = neurun::model::IOIndex;
+using IndexSet = neurun::model::OperandIndexSequence;
 
 TEST(graph_operation_setIO, operation_setIO_conv)
 {
-  neurun::graph::Graph graph;
+  neurun::model::Model model;
 
-  neurun::model::operand::Shape shape{1u};
-  neurun::model::operand::TypeInfo type{ANEURALNETWORKS_TENSOR_INT32, 0, 0};
-  shape.dim(0) = 3;
+  neurun::model::Shape shape{3};
+  neurun::model::TypeInfo type{neurun::model::DataType::INT32};
 
   // Add Conv
-  std::vector<uint32_t> params;
-  for (int i = 0; i < 7; ++i)
-  {
-    params.emplace_back(graph.addOperand(shape, type).asInt());
-  }
-  uint32_t outoperand = graph.addOperand(shape, type).asInt();
-
   using GraphNode = neurun::model::operation::Conv2DNode;
 
-  auto conv =
-      nnfw::cpp14::make_unique<GraphNode>(GraphNodeInitParam{7, params.data(), 1, &outoperand});
-  ASSERT_EQ(conv->getInputs().at(Index{0}).asInt(), params[0]);
+  auto input_operand = model.operands.emplace(shape, type);
+  auto kernel_operand = model.operands.emplace(shape, type);
+  auto bias_operand = model.operands.emplace(shape, type);
+  IndexSet inputs{input_operand, kernel_operand, bias_operand};
+
+  GraphNode::Param conv_params;
+  conv_params.padding.type = neurun::model::PaddingType::SAME;
+  conv_params.stride.horizontal = 1;
+  conv_params.stride.vertical = 1;
+  conv_params.activation = neurun::model::Activation::NONE;
+
+  auto output_operand = model.operands.emplace(shape, type).value();
+  IndexSet outputs{output_operand};
+
+  auto conv = nnfw::cpp14::make_unique<GraphNode>(inputs, outputs, conv_params);
+
+  ASSERT_NE(conv, nullptr);
+  ASSERT_EQ(conv->getInputs().at(Index{0}).value(), inputs.at(0).value());
   conv->setInputs({8, 9, 10});
-  ASSERT_NE(conv->getInputs().at(Index{0}).asInt(), params[0]);
-  ASSERT_EQ(conv->getInputs().at(Index{0}).asInt(), 8);
+  ASSERT_NE(conv->getInputs().at(Index{0}).value(), inputs.at(0).value());
+  ASSERT_EQ(conv->getInputs().at(Index{0}).value(), 8);
 }
 
 TEST(graph_operation_setIO, operation_setIO_concat)
 {
-  neurun::graph::Graph graph;
+  neurun::model::Model model;
+
+  neurun::model::Shape shape{3};
+
+  neurun::model::TypeInfo type{neurun::model::DataType::INT32};
 
-  neurun::model::operand::Shape shape{1u};
-  neurun::model::operand::TypeInfo type{ANEURALNETWORKS_TENSOR_INT32, 0, 0};
-  shape.dim(0) = 3;
+  using GraphNode = neurun::model::operation::ConcatNode;
 
   // Add Concat
-  std::vector<uint32_t> params;
-  for (int i = 0; i < 7; ++i)
+  IndexSet inputs;
+  for (int i = 0; i < 6; ++i)
   {
-    params.emplace_back(graph.addOperand(shape, type).asInt());
+    inputs.append(model.operands.emplace(shape, type));
   }
-  uint32_t outoperand = graph.addOperand(shape, type).asInt();
 
-  using GraphNode = neurun::model::operation::ConcatNode;
+  GraphNode::Param concat_params{0};
+
+  auto output_operand = model.operands.emplace(shape, type).value();
+  IndexSet outputs{output_operand};
 
-  auto concat =
-      nnfw::cpp14::make_unique<GraphNode>(GraphNodeInitParam{7, params.data(), 1, &outoperand});
+  auto concat = nnfw::cpp14::make_unique<GraphNode>(inputs, outputs, concat_params);
 
+  ASSERT_NE(concat, nullptr);
   ASSERT_EQ(concat->getInputs().size(), 6);
-  ASSERT_EQ(concat->getInputs().at(Index{0}).asInt(), params[0]);
+  ASSERT_EQ(concat->getInputs().at(Index{0}).value(), inputs.at(0).value());
 
   concat->setInputs({80, 6, 9, 11});
   ASSERT_EQ(concat->getInputs().size(), 4);
-  ASSERT_NE(concat->getInputs().at(Index{0}).asInt(), params[0]);
-  ASSERT_EQ(concat->getInputs().at(Index{0}).asInt(), 80);
-  ASSERT_EQ(concat->getInputs().at(Index{2}).asInt(), 9);
+  ASSERT_NE(concat->getInputs().at(Index{0}).value(), inputs.at(0).value());
+  ASSERT_EQ(concat->getInputs().at(Index{0}).value(), 80);
+  ASSERT_EQ(concat->getInputs().at(Index{2}).value(), 9);
   ASSERT_THROW(concat->getInputs().at(Index{5}), std::out_of_range);
 }