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
test_callbacks = OrderedDict(
[
("_timer", TimerCallback),
("_metrics", MetricManagerCallback),
("_validation", ValidationManagerCallback),
("_saver", CheckpointCallback),
("_console", ConsoleLogger),
("_tensorboard", TensorboardLogger),
("_exception", ExceptionCallback),
]
)
exp = SupervisedExperiment(model=model, loaders=loaders)
_test_callbacks(test_callbacks, exp)
def test_scheduler():
test_callbacks = OrderedDict(
[
("_timer", TimerCallback),
("_metrics", MetricManagerCallback),
("_validation", ValidationManagerCallback),
("_saver", CheckpointCallback),
("_console", ConsoleLogger),
("_tensorboard", TensorboardLogger),
("_exception", ExceptionCallback),
("_optimizer", OptimizerCallback),
("_scheduler", SchedulerCallback),
]
)
model = torch.nn.Linear(10, 10)
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,
optimizer=optimizer,
scheduler=scheduler,
)
_test_callbacks(test_callbacks, exp)
def test_infer_all():
test_callbacks = OrderedDict(
[
("_verbose", VerboseLogger),
("_check", CheckRunCallback),
("_exception", ExceptionCallback),
]
)
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,
verbose=True,
check_run=True,
)
_test_callbacks(test_callbacks, exp, "infer")
def test_criterion():
test_callbacks = OrderedDict(
[
("_timer", TimerCallback),
("_metrics", MetricManagerCallback),
("_validation", ValidationManagerCallback),
("_saver", CheckpointCallback),
("_console", ConsoleLogger),
("_tensorboard", TensorboardLogger),
("_exception", ExceptionCallback),
("_criterion", CriterionCallback),
]
)
model = torch.nn.Linear(10, 10)
criterion = torch.nn.CrossEntropyLoss()
optimizer = None
scheduler = None
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,
)
_test_callbacks(test_callbacks, exp)
def test_defaults_check():
test_callbacks = OrderedDict(
[
("_check", CheckRunCallback),
("_timer", TimerCallback),
("_metrics", MetricManagerCallback),
("_validation", ValidationManagerCallback),
("_saver", CheckpointCallback),
("_console", ConsoleLogger),
("_tensorboard", TensorboardLogger),
("_exception", ExceptionCallback),
]
)
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, check_run=True)
_test_callbacks(test_callbacks, exp)