diff --git a/extra/onnx.py b/extra/onnx.py
index b3d1301c9a..ace358665a 100644
--- a/extra/onnx.py
+++ b/extra/onnx.py
@@ -38,7 +38,7 @@ def get_run_onnx(onnx_model: ModelProto):
       elif len(inp.int64_data) > 0:
         ret = Tensor(np.array(inp.int64_data, dtype=np.float32).reshape(inp.dims), requires_grad=False)
       elif len(inp.int32_data) > 0:
-        ret = Tensor(np.array(inp.int32_data, dtype=np.float32).reshape(inp.dims), requires_grad=False)
+        ret = Tensor(np.array(inp.int32_data, dtype=np.int32).reshape(inp.dims), requires_grad=False)
       else:
         ret = Tensor(np.frombuffer(inp.raw_data, dtype=tensor_dtype_to_np_dtype(inp.data_type)).reshape(inp.dims).astype(np.float32).copy(), requires_grad=False)
     else:
diff --git a/extra/onnx_ops.py b/extra/onnx_ops.py
index 87465dfade..666e260e7c 100644
--- a/extra/onnx_ops.py
+++ b/extra/onnx_ops.py
@@ -205,9 +205,10 @@ def Or(x:Tensor, y:Tensor): return Where((x==y), x, Tensor.ones(*x.shape)).cast(
 def Xor(x:Tensor, y:Tensor): return Where((x==y), Tensor.zeros(*x.shape), Tensor.ones(*x.shape)).cast(dtypes.bool)
 def Not(x:Tensor): return Where((x==1), Tensor.zeros(*x.shape), Tensor.ones(*x.shape)).cast(dtypes.bool)
 
-def ConstantOfShape(input, value=0.0):
+def ConstantOfShape(input, value:Tensor=None):
+  if value is None: value=Tensor([0.0])
   shape = [int(x) for x in safe_numpy(input)]
-  return Tensor.ones(*shape) * value
+  return Tensor.ones(*shape, dtype=value.dtype) * (value if input.shape !=(0,) else 1)
 
 # this is obviously wrong, but since we don't have types, it's better than nothing
 def Cast(input, to):