def test_main(self):
named_ops = [(str(idx), self.Operator()) for idx in range(3)]
op_container = ops.OperatorContainer(named_ops)
actuals = op_container.named_children()
desireds = named_ops
assert_named_modules_identical(actuals, desireds)
def op_container(self):
return ops.OperatorContainer(
(
("regularization", self.RegularizationOperator()),
("comparison", self.ComparisonOperator()),
)
)
def get_container():
return ops.OperatorContainer(
(
("regularization", RegularizationTestOperator()),
("comparison", ComparisonTestOperator()),
)
)
def test_call(self):
input = torch.tensor(0.0)
named_ops = [(str(idx), self.Operator(idx + 1.0)) for idx in range(3)]
op_container = ops.OperatorContainer(named_ops)
actual = op_container(input)
desired = pystiche.LossDict([(name, input + op.bias) for name, op in named_ops])
ptu.assert_allclose(actual, desired)
def test_OperatorContainer(self):
class TestOperator(ops.Operator):
def process_input_image(self, image):
pass
named_ops = [(str(idx), TestOperator()) for idx in range(3)]
op_container = ops.OperatorContainer(named_ops)
actuals = op_container.named_children()
desireds = named_ops
self.assertNamedChildrenEqual(actuals, desireds)
def test_OperatorContainer_getitem(self):
class TestOperator(ops.Operator):
def __init__(self, bias):
super().__init__()
self.bias = bias
def process_input_image(self, image):
return image + self.bias
named_ops = [(str(idx), TestOperator(idx + 1.0)) for idx in range(3)]
op_container = ops.OperatorContainer(named_ops)
for name, _ in named_ops:
actual = op_container[name]
desired = getattr(op_container, name)
self.assertIs(actual, desired)
def test_OperatorContainer_call(self):
class TestOperator(ops.Operator):
def __init__(self, bias):
super().__init__()
self.bias = bias
def process_input_image(self, image):
return image + self.bias
input = torch.tensor(0.0)
named_ops = [(str(idx), TestOperator(idx + 1.0)) for idx in range(3)]
op_container = ops.OperatorContainer(named_ops)
actual = op_container(input)
desired = pystiche.LossDict([(name, input + op.bias) for name, op in named_ops])
self.assertTensorDictAlmostEqual(actual, desired)
