def test_full():
FULL_CALLBACKS = DEFAULT_CALLBACKS.copy()
FULL_CALLBACKS["_criterion"] = CriterionCallback
FULL_CALLBACKS["_optimizer"] = OptimizerCallback
FULL_CALLBACKS["_scheduler"] = SchedulerCallback
model = torch.nn.Linear(10, 10)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 10)
dataset = torch.utils.data.Dataset()
dataloader = torch.utils.data.DataLoader(dataset)
loaders = OrderedDict()
loaders["train"] = dataloader
exp = SupervisedExperiment(
model=model,
loaders=loaders,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
)
exp_callbacks = exp.get_callbacks("train")
exp_callbacks = OrderedDict(
sorted(exp_callbacks.items(), key=lambda t: t[0]))
FULL_CALLBACKS = OrderedDict(
sorted(FULL_CALLBACKS.items(), key=lambda t: t[0]))
assert exp_callbacks.keys() == FULL_CALLBACKS.keys()
cbs = zip(exp_callbacks.values(), FULL_CALLBACKS.values())
for callback, klass in cbs:
assert isinstance(callback, klass)
def test_defaults():
"""
Test on defaults for SupervisedExperiment class, which is child class of
BaseExperiment. That's why we check only default callbacks functionality
here
"""
model = torch.nn.Module()
dataset = torch.utils.data.Dataset()
dataloader = torch.utils.data.DataLoader(dataset)
loaders = OrderedDict()
loaders["train"] = dataloader
exp = SupervisedExperiment(model=model, loaders=loaders)
assert exp.get_callbacks("train").keys() == DEFAULT_CALLBACKS.keys()
cbs = zip(exp.get_callbacks("train").values(), DEFAULT_CALLBACKS.values())
for callback, klass in cbs:
assert isinstance(callback, klass)