def _try_cast_integer_to_float(g, *args):
floating_scalar_types = ['Half', 'Float', 'Double']
old_type = None
# Cast the input tensor to Float if its scalarType is known and is not floating number.
# If casting is performed, return the old scalarType, otherwise return None.
if args[0].type().kind() == "DimensionedTensorType" or args[0].type().kind() == "CompleteTensorType":
old_type = args[0].type().scalarType()
if old_type not in floating_scalar_types:
args = tuple(_cast_Float(g, arg, False) for arg in args)
else:
return (None,) + args
else:
warnings.warn("Only floating datatype is supported for these operators: "
"{Greater, Less, MatMul, PRelu, Gemm, Flatten}. This might cause "
"the onnx model to be incorrect, if inputs have integer datatypes.")
return (old_type,) + args
def _try_cast_integer_to_float(g, *args):
floating_scalar_types = ["Half", "Float", "Double"]
old_type = None
# Cast the input tensor to Float if its scalarType is known and is not floating number.
# If casting is performed, return the old scalarType, otherwise return None.
arg0_type = args[0].type().scalarType()
if arg0_type is not None:
old_type = arg0_type
if old_type not in floating_scalar_types:
# TODO(justinchuby): Remove the type ignore hint once _cast_Float is
# properly defined.
# NOTE: _cast_Float is generated programmatically so we need to make the
# type checker happy with ignore[attr-defined].
args = tuple(opset9._cast_Float(g, arg, False) for arg in args) # type: ignore[attr-defined]
else:
return (None,) + args
else:
warnings.warn(
"Only floating datatype is supported for these operators: "
"{Greater, Less, MatMul, PRelu, Gemm, Flatten}. This might cause "
"the onnx model to be incorrect, if inputs have integer datatypes."
)
return (old_type,) + args
def nonzero(g, input):
return t(g, g.op('NonZero', _cast_Float(g, input, False)))
