Add script to convert models based on onnx model conversion example (#1413)

Allows for a model to be converted to the same opset but turn on infer_shapes through
onnx. This allows us to get an idea of what should be valid for the output of nodes
in a network.

Usecase:

python3 tools/convert_onnx_version.py --model <model_name> --opset=<same_as_model> --infer_shapes --output <new_model_name>

tools/convert_onnx_version.py

+#####################################################################################
+# The MIT License (MIT)
+#
+# Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+#####################################################################################
+import argparse
+import onnx
+from onnx import version_converter
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description=
+        'MIGraphX Onnx Model Convertion. Use to convert the opset of the input model to MIGraphX\'s'
+    )
+    req_args = parser.add_argument_group(title='required arguments')
+    req_args.add_argument('--model',
+                          type=str,
+                          required=True,
+                          help='path to onnx file')
+    req_args.add_argument('--output',
+                          type=str,
+                          required=True,
+                          help='path to output onnx file')
+    req_args.add_argument('--opset',
+                          type=int,
+                          required=True,
+                          help='The output opset')
+    req_args.add_argument('--infer_shapes',
+                          action='store_true',
+                          help='Infer shapes for output model')
+    parser.add_argument('--verbose',
+                        action='store_true',
+                        help='show verbose information (for debugging)')
+    args = parser.parse_args()
+
+    return args
+
+
+def main():
+    args = parse_args()
+
+    model_path = args.model
+    out_model_path = args.output
+    target_opset = args.opset
+    verbose = args.verbose
+    infer_shapes = args.infer_shapes
+
+    original_model = onnx.load(model_path)
+    if verbose:
+        print(f"The model before conversion:\n{original_model}")
+
+    # A full list of supported adapters can be found here:
+    # https://github.com/onnx/onnx/blob/main/onnx/version_converter.py#L21
+    # Apply the version conversion on the original model
+    converted_model = version_converter.convert_version(
+        original_model, target_opset)
+
+    if infer_shapes:
+        converted_model = onnx.shape_inference.infer_shapes(converted_model)
+
+    if verbose:
+        print(f"The model after conversion:\n{converted_model}")
+
+    # Save the ONNX model
+    onnx.save(converted_model, out_model_path)
+
+
+if __name__ == '__main__':
+    main()