diff --git a/hawp/export.py b/hawp/export.py
index 10bce3c..361d68f 100644
--- a/hawp/export.py
+++ b/hawp/export.py
@@ -1,3 +1,5 @@
+import argparse
+
 import numpy as np
 import onnx
 import onnxruntime as ort
@@ -7,44 +9,69 @@
 from .predicting import WireframeParser
 
 
-INPUT_FILE = "/home/ckanesan/Data/semantic-keypoints/826840/Image_000001.jpg"
-OUTPUT_FILE = "hawp.onnx"
+def cli():
+    parser = argparse.ArgumentParser(
+        prog="python -m hawp.export",
+        usage="%(prog)s [options] image",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument("image", help="input image")
+    parser.add_argument(
+        "-o", "--output", default="hawp.onnx", nargs="?", help="Path at which to write the exported model"
+    )
+
+    return parser.parse_args()
+
 
 def to_numpy(tensor):
+    if isinstance(tensor, int):
+        return tensor
     return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
 
-def check_model():
-    model = onnx.load(OUTPUT_FILE)
+
+def check_model(model_file):
+    model = onnx.load(model_file)
     onnx.checker.check_model(model)
 
-    inferred_model = onnx.shape_inference.infer_shapes(model)
-    onnx.save(inferred_model, "hawp_inf.onnx")
 
-def verify_model(input, output):
-    ort_session = ort.InferenceSession(OUTPUT_FILE, providers=["CUDAExecutionProvider"])
-    ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(input)}
+def verify_model(model_file, input, output):
+    ort_session = ort.InferenceSession(model_file, providers=["CUDAExecutionProvider"])
+    ort_inputs = {"image": to_numpy(input)}
     ort_outs = ort_session.run(None, ort_inputs)
 
     # compare ONNX Runtime and PyTorch results
-    np.testing.assert_allclose(to_numpy(output), ort_outs[0], rtol=1e-03, atol=1e-05)
+    for obs, exp in zip(ort_outs, output):
+        np.testing.assert_allclose(to_numpy(exp), obs, rtol=1e-03, atol=1e-05)
 
 
-def export(cfg):
+def export(input_file, output_file):
     wireframe_parser = WireframeParser(export_mode=True)
-    for _ in wireframe_parser.images([INPUT_FILE]):
+    for _ in wireframe_parser.images([input_file]):
         pass
 
     model = wireframe_parser.model
     [input] = wireframe_parser.inputs
     [output] = wireframe_parser.outputs
-    torch.onnx.export(model, input, OUTPUT_FILE, opset_version=11, )
+    output_names = [
+        "vertices",
+        "v_confidences",
+        "edges",
+        "edge_weights",
+        "frame_width",
+        "frame_height",
+    ]
+    torch.onnx.export(
+        model, input, output_file, opset_version=11, input_names=["image"], output_names=output_names
+    )
 
     return input, output
 
 
 if __name__ == "__main__":
     cfg.freeze()
-    input, output = export(cfg)
-    check_model()
-    #verify_model(input, output)
+    args = cli()
 
+    input, output = export(args.image, args.output)
+    check_model(args.output)
+    img, _ = input
+    verify_model(args.output, img, output)