diff --git a/onnx_export.py b/onnx_export.py
index 7b951b63..20264064 100644
--- a/onnx_export.py
+++ b/onnx_export.py
@@ -43,6 +43,8 @@ parser.add_argument('-b', '--batch-size', default=1, type=int,
                     metavar='N', help='mini-batch size (default: 1)')
 parser.add_argument('--img-size', default=None, type=int,
                     metavar='N', help='Input image dimension, uses model default if empty')
+parser.add_argument('--input-size', default=None, nargs=3, type=int, metavar='N',
+                    help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty')
 parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
                     help='Override mean pixel value of dataset')
 parser.add_argument('--std', type=float,  nargs='+', default=None, metavar='STD',
@@ -82,6 +84,14 @@ def main():
     if args.reparam:
         model = reparameterize_model(model)
 
+    if args.input_size is not None:
+        assert len(args.input_size) == 3, 'input-size should be N H W (channels, height, width)'
+        input_size = args.input_size
+    elif args.img_size is not None:
+        input_size = (3, args.img_size, args.img_size)
+    else:
+        input_size = None
+
     onnx_export(
         model,
         args.output,
@@ -93,7 +103,7 @@ def main():
         training=args.training,
         verbose=args.verbose,
         use_dynamo=args.dynamo,
-        input_size=(3, args.img_size, args.img_size),
+        input_size=input_size,
         batch_size=args.batch_size,
     )
 
diff --git a/timm/utils/onnx.py b/timm/utils/onnx.py
index 932bc8f9..5c4fd16f 100644
--- a/timm/utils/onnx.py
+++ b/timm/utils/onnx.py
@@ -43,7 +43,7 @@ def onnx_export(
 
     if example_input is None:
         if not input_size:
-            assert hasattr(model, 'default_cfg')
+            assert hasattr(model, 'default_cfg'), 'Cannot file model default config, input size must be provided'
             input_size = model.default_cfg.get('input_size')
         example_input = torch.randn((batch_size,) + input_size, requires_grad=training)
 
@@ -80,7 +80,7 @@ def onnx_export(
         export_output.save(output_file)
         torch_out = None
     else:
-        torch_out = torch.onnx._export(
+        torch_out = torch.onnx.export(
             model,
             example_input,
             output_file,