def test_top_k(k: int, preds: torch.Tensor, target: torch.Tensor, reduce: str, expected: torch.Tensor):
""" A simple test to check that top_k works as expected """
class_metric = StatScores(top_k=k, reduce=reduce, num_classes=3)
class_metric.update(preds, target)
assert torch.equal(class_metric.compute(), expected.T)
assert torch.equal(stat_scores(preds, target, top_k=k, reduce=reduce, num_classes=3), expected.T)
def __init__(
self,
n_channel: int = 1,
n_class: int = 2,
learning_rate: float = 1e-4,
class_weight: List[float] = None,
backbone: Union[str, nn.Module] = "simple-cnn",
backbone_output_size: int = 0,
n_hidden: int = 512,
dropout: float = 0.2,
lr_scheduler: bool = False,
lr_scheduler_warmup_steps: int = 100,
lr_scheduler_total_steps: int = 0,
**kwargs,
):
super().__init__()
self.save_hyperparameters()
if isinstance(backbone, str):
self.backbone, backbone_output_size = get_backbone(
backbone,
channels=n_channel,
dropout=dropout,
**kwargs,
)
self.classifier = Classifier(backbone_output_size, n_class, n_hidden,
dropout)
if class_weight is not None:
class_weight = torch.tensor(class_weight, dtype=torch.float)
self.loss_fn = nn.CrossEntropyLoss(weight=class_weight)
self.train_accuracy = Accuracy()
self.val_accuracy = Accuracy()
self.test_metrics = MetricCollection([
Accuracy(),
F1(num_classes=self.hparams.n_class, average="macro"),
Recall(num_classes=self.hparams.n_class,
average="macro"), # balanced acc.
StatScores(
num_classes=self.hparams.n_class
if self.hparams.n_class > 2 else 1,
reduce="micro",
multiclass=self.hparams.n_class > 2,
),
])
def test_wrong_params(reduce, mdmc_reduce, num_classes, inputs, ignore_index):
"""Test a combination of parameters that are invalid and should raise an error.
This includes invalid ``reduce`` and ``mdmc_reduce`` parameter values, not setting
``num_classes`` when ``reduce='macro'`, not setting ``mdmc_reduce`` when inputs
are multi-dim multi-class``, setting ``ignore_index`` when inputs are binary, as well
as setting ``ignore_index`` to a value higher than the number of classes.
"""
with pytest.raises(ValueError):
stat_scores(
inputs.preds[0], inputs.target[0], reduce, mdmc_reduce, num_classes=num_classes, ignore_index=ignore_index
)
with pytest.raises(ValueError):
sts = StatScores(reduce=reduce, mdmc_reduce=mdmc_reduce, num_classes=num_classes, ignore_index=ignore_index)
sts(inputs.preds[0], inputs.target[0])
def test_wrong_threshold():
with pytest.raises(ValueError):
StatScores(threshold=1.5)