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15 changes: 13 additions & 2 deletions tests/test_backend.py
Original file line number Diff line number Diff line change
Expand Up @@ -3488,6 +3488,17 @@ def func(x):
return tf.identity(picks, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})

@check_opset_min_version(9, "IsNaN")
def test_where_ismulinf(self):
x_val1 = np.array([np.inf], dtype=np.float32)
x_val2 = np.array([0], dtype=np.float32)
true_result = np.array([np.inf], dtype=np.float32)
def func(x1, x2):
mul = tf.multiply(x1, x2)
picks = tf.where(x1 < mul, true_result, x2)
return tf.identity(picks, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val1, _INPUT1: x_val2})

@check_opset_min_version(9, "Where for strings needs opset 9")
@skip_tfjs("Technically tf where doesn't support strings and tfjs doesn't like it")
def test_where_string(self):
Expand Down Expand Up @@ -5542,7 +5553,7 @@ def func(x):
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val0}, rtol=1e-6, atol=1e-4)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, rtol=1e-6, atol=1e-4)

x_val = np.random.random(size=[4, 3]).astype(np.float32) * 2048. - 1024
x_val = np.random.random(size=[4, 3]).astype(np.float32) * 2048. - 1024.
x_val[0, 0] = -1024
x_val[0, 1] = -1023
x_val[0, 2] = 1024
Expand Down Expand Up @@ -5579,7 +5590,7 @@ def func(x):
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val0}, rtol=1e-6, atol=1e-4)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, rtol=1e-6, atol=1e-4)

x_val = np.random.random(size=[4, 3]).astype(np.float32) * 2048. - 1024
x_val = np.random.random(size=[4, 3]).astype(np.float32) * 2048. - 1024.
x_val[0, 0] = -1024
x_val[0, 1] = -1023
x_val[0, 2] = 1024
Expand Down
9 changes: 8 additions & 1 deletion tf2onnx/onnx_opset/controlflow.py
Original file line number Diff line number Diff line change
Expand Up @@ -204,8 +204,15 @@ def version_9(cls, ctx, node, **kwargs):
if eq_node.input[0] == eq_node.input[1]:
handles_nan = True
for inp in node.inputs[1:]:
if inp.is_const() and np.any(np.isnan(inp.get_tensor_value(as_list=False))):
if handles_nan:
break
if inp.is_const() and (np.any(np.isnan(inp.get_tensor_value(as_list=False))) or \
np.any(np.isinf(inp.get_tensor_value(as_list=False)))):
handles_nan = True
if inp.type == "Mul":
inp0 = inp.inputs[0].is_const() and np.any(np.isinf(inp.inputs[0].get_tensor_value(as_list=False)))
inp1 = inp.inputs[1].is_const() and np.any(np.isinf(inp.inputs[1].get_tensor_value(as_list=False)))
handles_nan = inp0 or inp1

if ctx.get_dtype(node.output[0]) != TensorProto.STRING and not handles_nan:
# Due to bad ORT implementation, Mul/Add ops are faster than Where op
Expand Down