diff --git a/tools/onnx_subgraph/src/lib/graph.cpp b/tools/onnx_subgraph/src/lib/graph.cpp
index ecd095c0d5a..bef970990fb 100644
--- a/tools/onnx_subgraph/src/lib/graph.cpp
+++ b/tools/onnx_subgraph/src/lib/graph.cpp
@@ -16,8 +16,154 @@
 
 #include "graph.h"
 
+std::unordered_set<NodeTensor> getInitializer(const onnx::GraphProto &graph)
+{
+  std::unordered_set<NodeTensor> initializerNames;
+
+  for (const auto &initializer : graph.initializer())
+  {
+    NodeTensor nt;
+    nt.name = initializer.name();
+    std::vector<int64_t> shape;
+
+    for (const auto &dim : initializer.dims())
+    {
+      shape.push_back(dim);
+    }
+
+    nt.shape = shape;
+    initializerNames.insert(nt);
+  }
+
+  return initializerNames;
+}
+
+std::unordered_set<NodeTensor> getIOvalue(const onnx::GraphProto &graph)
+{
+  std::unordered_set<NodeTensor> IOvalue;
+
+  for (const auto &value_info : graph.value_info())
+  {
+    NodeTensor nt;
+    nt.name = value_info.name();
+    std::vector<int64_t> shape;
+
+    for (const auto &dim : value_info.type().tensor_type().shape().dim())
+    {
+      shape.push_back(dim.dim_value());
+    }
+
+    nt.shape = shape;
+    IOvalue.insert(nt);
+  }
+
+  for (auto value_info : graph.input())
+  {
+    NodeTensor nt;
+    nt.name = value_info.name();
+    std::vector<int64_t> shape;
+
+    for (const auto &dim : value_info.type().tensor_type().shape().dim())
+    {
+      shape.push_back(dim.dim_value());
+    }
+
+    nt.shape = shape;
+    IOvalue.insert(nt);
+  }
+
+  for (auto value_info : graph.output())
+  {
+    NodeTensor nt;
+    nt.name = value_info.name();
+    std::vector<int64_t> shape;
+
+    for (const auto &dim : value_info.type().tensor_type().shape().dim())
+    {
+      shape.push_back(dim.dim_value());
+    }
+
+    nt.shape = shape;
+    IOvalue.insert(nt);
+  }
+
+  return IOvalue;
+}
+
+std::unordered_set<NodeTensor>::const_iterator
+isInputFromInitializer(const std::string &name, const std::unordered_set<NodeTensor> &tensors)
+{
+  return std::find_if(tensors.begin(), tensors.end(),
+                      [&](const NodeTensor &tensor) { return tensor.name == name; });
+}
+
+void determineGraphInput(const onnx::GraphProto &g,
+                         const std::unordered_set<NodeTensor> &initializerNames,
+                         std::unordered_set<NodeTensor> &graphInputs)
+{
+  std::unordered_set<std::string> allnodeOutputs;
+
+  // Iterate over each node in the graph to collect all outputs
+  for (const auto &node : g.node())
+  {
+    // Get the output list of the current node
+    const auto &outputs = node.output();
+
+    // Insert each output into the set of all node outputs
+    for (const auto &output : outputs)
+    {
+      allnodeOutputs.insert(output);
+    }
+  }
+
+  // Iterate over each node in the graph to identify inputs not produced by any node
+  for (const auto &node : g.node())
+  {
+    // Get the input list of the current node
+    const auto &inputs = node.input();
+
+    // Check each input to determine if it is an external input to the graph
+    for (const auto &input : inputs)
+    {
+      // If the input is not found in the set of all node outputs, it is a graph input
+      if (std::find(allnodeOutputs.begin(), allnodeOutputs.end(), input) == allnodeOutputs.end())
+      {
+        auto iter = isInputFromInitializer(input, initializerNames);
+        NodeTensor nt;
+        nt.name = input;
+
+        if (iter != initializerNames.end())
+        {
+          graphInputs.insert(*iter);
+        }
+      }
+    }
+  }
+}
+
 onnx::GraphProto GetGraphFromOnnx(std::string &path)
 {
   onnx::ModelProto model;
+
+  std::ifstream input(path, std::ios::ate | std::ios::binary);
+  if (!input.is_open())
+  {
+    std::cout << "Error: Failed to open file: " << path << std::endl;
+    exit(-1);
+  }
+
+  // get current position in file
+  std::streamsize size = input.tellg();
+
+  // move to start of file
+  input.seekg(0, std::ios::beg);
+
+  // read raw data
+  std::vector<char> buffer(size);
+  input.read(buffer.data(), size);
+
+  // parse protobuf
+  model.ParseFromArray(buffer.data(), size);
+
   return model.graph();
 }