[tools/onnx-subgraph] Add readme.md for environment and existing code

tools/onnx_subgraph/README.md

@@ -0,0 +1,54 @@
+# onnx_autosubgraph
+onnx-subgraph tool provides  model auto partitionioning of onnx model to several sub models by
+operator, performance and model size limitations,with the order and input / output names of
+sub models
+
+# How to build the onnx-subgraph
+## OS environment dependence
+     1. ubuntu >=20.04
+     2. GCC >= 9.4.0
+     3. cmake >= 3.10
+     4. python >= 3.8
+     5. apt-get install libprotobuf-dev protobuf-compiler libjsoncpp-dev
+
+## Python packages dependence
+    onnx                         1.16.0
+    onnxruntime                  1.18.1
+    onnxsim                      0.4.36
+    torch                        2.3.1
+    scikit-image
+    scikit-learn
+    pandas
+    tqdm
+
+## building the onnx-subgraph
+    1. cd onnx-subgraph
+    2. mkdir build & cd build
+    3. cmake .. & make
+    4. we can get following output at ./build
+          └── scripts
+              ├── extract_onnx.py
+              └── test_model_download.sh
+              └── subgraphs_ios.txt
+
+# How to use the onnx-subgraph
+## Pre-steps
+### Download the test AI models
+    1. bash scripts/test_model_download.sh, then "resnet-test.onnx" will be got in ./build
+    2. you can change to any other onnx files as your needs, or edit the download link in 
+	   "scripts/test_model_download.sh"
+
+## Parse the onnx model
+    note: subgraphs_ios.txt will be generated in future code, suppose we already have it as 
+    the example file now
+
+## Split the onnx model to subgraphs
+    1. python scripts/extract_onnx.py \
+              -s ./scripts/subgraphs_ios.txt \
+              -m ./resnet-test.onnx
+       after extraction done, the subgraphs will be saved at './subgraphs'
+       subgraphs
+       ├── CPUsubgraph0.onnx
+       └── CPUsubgraph1.onnx
+       ├── NPUsubgraph0.onnx
+       └── NPUsubgraph1.onnx

tools/onnx_subgraph/extract_onnx.py

@@ -36,8 +36,13 @@ def split_onnx_ios(instrfile, input_path, out_folder='subgraphs/'):
         model.graph.value_info.append(output)
     onnx.save(model, input_path)
 
-    f1 = open(instrfile, "r")
-    lines = f1.readlines()
+    try:
+        with open(instrfile, "r") as f1:
+            lines = f1.readlines()
+    except Exception as e:
+        print(e)
+        raise
+
     cpu_count = 0
     npu_count = 0
     count = 0
@@ -61,8 +66,6 @@ def split_onnx_ios(instrfile, input_path, out_folder='subgraphs/'):
             print("succeed", count)
             count = count + 1
 
-    f1.close()
-
 
 if __name__ == "__main__":
     arg_parser = argparse.ArgumentParser()