def test_terminate_on_nan_and_inf(state_output, should_terminate):
torch.manual_seed(12)
def update_fn(engine, batch):
pass
trainer = Engine(update_fn)
trainer.state = State()
h = TerminateOnNan()
trainer.state.output = state_output
if isinstance(state_output, np.ndarray):
h._output_transform = lambda x: x.tolist()
h(trainer)
assert trainer.should_terminate == should_terminate
def test_terminate_on_nan_and_inf():
torch.manual_seed(12)
def update_fn(engine, batch):
pass
trainer = Engine(update_fn)
trainer.state = State()
h = TerminateOnNan()
trainer.state.output = 1.0
h(trainer)
assert not trainer.should_terminate
trainer.state.output = torch.tensor(123.45)
h(trainer)
assert not trainer.should_terminate
trainer.state.output = torch.asin(torch.randn(10, ))
h(trainer)
assert trainer.should_terminate
trainer.should_terminate = False
trainer.state.output = np.array([1.0, 2.0])
h._output_transform = lambda x: x.tolist()
h(trainer)
assert not trainer.should_terminate
h._output_transform = lambda x: x
trainer.state.output = torch.asin(torch.randn(4, 4))
h(trainer)
assert trainer.should_terminate
trainer.should_terminate = False
trainer.state.output = (10.0, 1.0 /
torch.randint(0, 2, size=(4, )).type(torch.float),
1.0)
h(trainer)
assert trainer.should_terminate
trainer.should_terminate = False
trainer.state.output = (1.0, torch.tensor(1.0), "abc")
h(trainer)
assert not trainer.should_terminate
trainer.state.output = 1.0 / torch.randint(0, 2, size=(4, 4)).type(
torch.float)
h(trainer)
assert trainer.should_terminate
trainer.should_terminate = False
trainer.state.output = (float("nan"), 10.0)
h(trainer)
assert trainer.should_terminate
trainer.should_terminate = False
trainer.state.output = float("inf")
h(trainer)
assert trainer.should_terminate
trainer.should_terminate = False
trainer.state.output = [float("nan"), 10.0]
h(trainer)
assert trainer.should_terminate
trainer.should_terminate = False
