Rename onnx_inference.py to infer_onnx.py (#4)

- Rename onnx_inference.py to infer_onnx.py (c7d7a6f43f39e8f1b95ded6600786e774de9c4a1)


Co-authored-by: Xiaodong Wang <[email protected]>

onnx_inference.py → infer_onnx.py

@@ -53,9 +53,9 @@ def make_parser():
     parser = argparse.ArgumentParser("onnxruntime inference sample")
     parser.add_argument(
         "-m",
-        "--model",
+        "--onnx_model",
         type=str,
-        default="./yolov5s_qat.onnx",
+        default="./yolov5s.onnx",
         help="input your onnx model.",
     )
     parser.add_argument(
@@ -99,13 +99,13 @@ names = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', '
 
 if __name__ == '__main__':
     args = make_parser().parse_args()
-    onnx_path = args.model
+    onnx_path = args.onnx_model
     if args.ipu:
         providers = ["VitisAIExecutionProvider"]
         provider_options = [{"config_file": args.provider_config}]
-        onnx_model = onnxruntime.InferenceSession(onnx_path, providers=providers, provider_options=provider_options)
+        onnx_weight = onnxruntime.InferenceSession(onnx_path, providers=providers, provider_options=provider_options)
     else:
-        onnx_model = onnxruntime.InferenceSession(onnx_path)
+        onnx_weight = onnxruntime.InferenceSession(onnx_path)
     grid = np.load("./grid.npy", allow_pickle=True)
     anchor_grid = np.load("./anchor_grid.npy", allow_pickle=True)
     path = args.image_path 
@@ -114,8 +114,8 @@ if __name__ == '__main__':
 
     img0 = cv2.imread(path)
     img = pre_process(img0)
-    onnx_input = {onnx_model.get_inputs()[0].name: img.transpose(0, 2, 3, 1)}
-    onnx_output = onnx_model.run(None, onnx_input)
+    onnx_input = {onnx_weight.get_inputs()[0].name: img.transpose(0, 2, 3, 1)}
+    onnx_output = onnx_weight.run(None, onnx_input)
     onnx_output = [torch.tensor(item).permute(0, 3, 1, 2) for item in onnx_output]
     onnx_output = post_process(onnx_output)
     pred = non_max_suppression(
@@ -137,3 +137,4 @@ if __name__ == '__main__':
     # Stream results
     im0 = annotator.result()
     cv2.imwrite(new_path, im0)
+