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unitorch.cli.scores¤

AccuracyScore¤

Tip

core/score/acc is the section for configuration of AccuracyScore.

Bases: Score

Source code in src/unitorch/cli/scores/__init__.py 
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def __init__(self, gate: Optional[float] = 0.5):
    super().__init__()
    self.gate = gate

gate instance-attribute ¤

gate = gate

from_config classmethod ¤

from_config(config, **kwargs)
Source code in src/unitorch/cli/scores/__init__.py 
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@classmethod
@config_defaults_init("core/score/acc")
def from_config(cls, config, **kwargs):
    pass

forward ¤

forward(
    outputs: Union[
        ClassificationOutputs,
        GenerationOutputs,
        SegmentationOutputs,
    ],
    targets: Union[
        ClassificationTargets,
        GenerationTargets,
        SegmentationTargets,
    ],
)
Source code in src/unitorch/cli/scores/__init__.py 
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def forward(
    self,
    outputs: Union[ClassificationOutputs, GenerationOutputs, SegmentationOutputs],
    targets: Union[ClassificationTargets, GenerationTargets, SegmentationTargets],
):
    if isinstance(outputs, GenerationOutputs):
        outputs = outputs.sequences
        outputs = outputs.view(-1, outputs.size(-1))

    if isinstance(targets, GenerationTargets):
        targets = targets.refs
        targets = targets.view(-1)

    if isinstance(outputs, SegmentationOutputs):
        outputs = outputs.outputs
        outputs = torch.cat([t.view(-1, t.size(-1)) for t in outputs])

    if isinstance(targets, SegmentationTargets):
        targets = targets.targets
        targets = torch.cat([t.view(-1) for t in targets])

    if isinstance(outputs, ClassificationOutputs):
        outputs = outputs.outputs

    if isinstance(targets, ClassificationTargets):
        targets = targets.targets

    if outputs.dim() == 2:
        outputs = (
            outputs.argmax(dim=-1)
            if outputs.size(-1) > 1
            else outputs[:, 0] > self.gate
        )

    if targets.dim() == 2 and targets.size(-1) == 1:
        targets = targets[:, 0]

    assert outputs.dim() == 1 and targets.dim() == 1

    return accuracy_score(targets, outputs)

RecallScore¤

Tip

core/score/rec is the section for configuration of RecallScore.

Bases: Score

Source code in src/unitorch/cli/scores/__init__.py 
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def __init__(self, gate: Optional[float] = 0.5):
    super().__init__()
    self.gate = gate

gate instance-attribute ¤

gate = gate

from_config classmethod ¤

from_config(config, **kwargs)
Source code in src/unitorch/cli/scores/__init__.py 
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@classmethod
@config_defaults_init("core/score/rec")
def from_config(cls, config, **kwargs):
    pass

forward ¤

forward(
    outputs: ClassificationOutputs,
    targets: ClassificationTargets,
)
Source code in src/unitorch/cli/scores/__init__.py 
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def forward(
    self,
    outputs: ClassificationOutputs,
    targets: ClassificationTargets,
):
    if isinstance(outputs, ClassificationOutputs):
        outputs = outputs.outputs
    if isinstance(targets, ClassificationTargets):
        targets = targets.targets

    if outputs.dim() == 2:
        outputs = (
            outputs.argmax(dim=-1)
            if outputs.size(-1) > 1
            else outputs[:, 0] > self.gate
        )

    if targets.dim() == 2 and targets.size(-1) == 1:
        targets = targets[:, 0]

    assert outputs.dim() == 1 and targets.dim() == 1

    return recall_score(targets, outputs, average="micro")

F1Score¤

Tip

core/score/f1 is the section for configuration of F1Score.

Bases: Score

Source code in src/unitorch/cli/scores/__init__.py 
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def __init__(self, gate: Optional[float] = 0.5):
    super().__init__()
    self.gate = gate

gate instance-attribute ¤

gate = gate

from_config classmethod ¤

from_config(config, **kwargs)
Source code in src/unitorch/cli/scores/__init__.py 
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@classmethod
@config_defaults_init("core/score/f1")
def from_config(cls, config, **kwargs):
    pass

forward ¤

forward(
    outputs: ClassificationOutputs,
    targets: ClassificationTargets,
)
Source code in src/unitorch/cli/scores/__init__.py 
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def forward(
    self,
    outputs: ClassificationOutputs,
    targets: ClassificationTargets,
):
    if isinstance(outputs, ClassificationOutputs):
        outputs = outputs.outputs
    if isinstance(targets, ClassificationTargets):
        targets = targets.targets

    if outputs.dim() == 2:
        outputs = (
            outputs.argmax(dim=-1)
            if outputs.size(-1) > 1
            else outputs[:, 0] > self.gate
        )

    if targets.dim() == 2 and targets.size(-1) == 1:
        targets = targets[:, 0]

    assert outputs.dim() == 1 and targets.dim() == 1

    return f1_score(targets, outputs, average="micro")

AUCScore¤

Tip

core/score/auc is the section for configuration of AUCScore.

Bases: Score

Source code in src/unitorch/cli/scores/__init__.py 
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def __init__(
    self,
):
    super().__init__()

from_config classmethod ¤

from_config(config, **kwargs)
Source code in src/unitorch/cli/scores/__init__.py 
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@classmethod
@config_defaults_init("core/score/auc")
def from_config(cls, config, **kwargs):
    pass

forward ¤

forward(
    outputs: ClassificationOutputs,
    targets: ClassificationTargets,
)
Source code in src/unitorch/cli/scores/__init__.py 
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def forward(
    self,
    outputs: ClassificationOutputs,
    targets: ClassificationTargets,
):
    if isinstance(outputs, ClassificationOutputs):
        outputs = outputs.outputs
    if isinstance(targets, ClassificationTargets):
        targets = targets.targets

    if outputs.dim() == 2:
        outputs = outputs[:, 1] if outputs.size(-1) > 1 else outputs[:, 0]

    if targets.dim() == 2 and targets.size(-1) == 1:
        targets = targets[:, 0]

    assert outputs.dim() == 1 and targets.dim() == 1

    return roc_auc_score(targets, outputs)

PRAUCScore¤

Tip

core/score/pr_auc is the section for configuration of PRAUCScore.

Bases: Score

Source code in src/unitorch/cli/scores/__init__.py 
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def __init__(
    self,
):
    super().__init__()

from_config classmethod ¤

from_config(config, **kwargs)
Source code in src/unitorch/cli/scores/__init__.py 
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@classmethod
@config_defaults_init("core/score/pr_auc")
def from_config(cls, config, **kwargs):
    pass

forward ¤

forward(
    outputs: ClassificationOutputs,
    targets: ClassificationTargets,
)
Source code in src/unitorch/cli/scores/__init__.py 
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def forward(
    self,
    outputs: ClassificationOutputs,
    targets: ClassificationTargets,
):
    if isinstance(outputs, ClassificationOutputs):
        outputs = outputs.outputs
    if isinstance(targets, ClassificationTargets):
        targets = targets.targets

    if outputs.dim() == 2:
        outputs = outputs[:, 1] if outputs.size(-1) > 1 else outputs[:, 0]

    if targets.dim() == 2 and targets.size(-1) == 1:
        targets = targets[:, 0]

    assert outputs.dim() == 1 and targets.dim() == 1
    precision, recall, _ = precision_recall_curve(targets, outputs)

    return auc(recall, precision)

NDCGScore¤

Tip

core/score/ndcg is the section for configuration of NDCGScore.

Bases: Score

Source code in src/unitorch/cli/scores/__init__.py 
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def __init__(
    self,
):
    super().__init__()

from_config classmethod ¤

from_config(config, **kwargs)
Source code in src/unitorch/cli/scores/__init__.py 
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@classmethod
@config_defaults_init("core/score/ndcg")
def from_config(cls, config, **kwargs):
    pass

forward ¤

forward(outputs: RankingOutputs, targets: RankingTargets)
Source code in src/unitorch/cli/scores/__init__.py 
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def forward(
    self,
    outputs: RankingOutputs,
    targets: RankingTargets,
):
    if isinstance(outputs, RankingOutputs):
        outputs = outputs.outputs
    if isinstance(targets, RankingTargets):
        masks = targets.masks
        targets = targets.targets

    outputs = outputs + (1 - masks) * (
        torch.min(outputs, -1, keepdim=True)[0] - 1e3
    )
    targets = targets * masks
    return ndcg_score(targets, outputs)

MatthewsCorrScore¤

Tip

core/score/mattcorr is the section for configuration of MattCorrScore.

Bases: Score

Source code in src/unitorch/cli/scores/__init__.py 
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def __init__(
    self,
):
    super().__init__()

from_config classmethod ¤

from_config(config, **kwargs)
Source code in src/unitorch/cli/scores/__init__.py 
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@classmethod
@config_defaults_init("core/score/mattcorr")
def from_config(cls, config, **kwargs):
    pass

forward ¤

forward(
    outputs: ClassificationOutputs,
    targets: ClassificationTargets,
)
Source code in src/unitorch/cli/scores/__init__.py 
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def forward(
    self,
    outputs: ClassificationOutputs,
    targets: ClassificationTargets,
):
    if isinstance(outputs, ClassificationOutputs):
        outputs = outputs.outputs
    if isinstance(targets, ClassificationTargets):
        targets = targets.targets

    if outputs.dim() == 2:
        outputs = (
            outputs.argmax(dim=-1)
            if outputs.size(-1) > 1
            else outputs[:, 0] > self.gate
        )

    if targets.dim() == 2 and targets.size(-1) == 1:
        targets = targets[:, 0]

    assert outputs.dim() == 1 and targets.dim() == 1

    return matthews_corrcoef(targets, outputs)

PearsonCorrScore¤

Tip

core/score/pearsonr_corr is the section for configuration of PearsonrCorrScore.

Bases: Score

Source code in src/unitorch/cli/scores/__init__.py 
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def __init__(
    self,
):
    super().__init__()

from_config classmethod ¤

from_config(config, **kwargs)
Source code in src/unitorch/cli/scores/__init__.py 
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@classmethod
@config_defaults_init("core/score/pearsonr_corr")
def from_config(cls, config, **kwargs):
    pass

forward ¤

forward(
    outputs: ClassificationOutputs,
    targets: ClassificationTargets,
)
Source code in src/unitorch/cli/scores/__init__.py 
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def forward(
    self,
    outputs: ClassificationOutputs,
    targets: ClassificationTargets,
):
    if isinstance(outputs, ClassificationOutputs):
        outputs = outputs.outputs
    if isinstance(targets, ClassificationTargets):
        targets = targets.targets

    outputs = outputs.view(-1)
    targets = targets.view(-1)

    assert outputs.numel() == targets.numel()

    return pearsonr(targets, outputs)

MAEScore¤

Tip

core/score/mae is the section for configuration of MAEScore.

Bases: Score

Source code in src/unitorch/cli/scores/__init__.py 
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def __init__(
    self,
):
    super().__init__()

from_config classmethod ¤

from_config(config, **kwargs)
Source code in src/unitorch/cli/scores/__init__.py 
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@classmethod
@config_defaults_init("core/score/mae")
def from_config(cls, config, **kwargs):
    pass

forward ¤

forward(
    outputs: Union[
        ClassificationOutputs, SegmentationOutputs
    ],
    targets: Union[
        ClassificationTargets, SegmentationTargets
    ],
)
Source code in src/unitorch/cli/scores/__init__.py 
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def forward(
    self,
    outputs: Union[ClassificationOutputs, SegmentationOutputs],
    targets: Union[ClassificationTargets, SegmentationTargets],
):
    if isinstance(outputs, ClassificationOutputs):
        outputs = outputs.outputs
    if isinstance(targets, ClassificationTargets):
        targets = targets.targets
    if isinstance(outputs, SegmentationOutputs):
        outputs = torch.cat([m.view(-1) for m in outputs.masks], dim=0)
    if isinstance(targets, SegmentationTargets):
        targets = torch.cat([m.view(-1) for m in targets.targets], dim=0)

    outputs = outputs.view(-1)
    targets = targets.view(-1)

    assert outputs.numel() == targets.numel()

    score = torch.mean(torch.abs(targets - outputs))

    return -float(score)

MSEScore¤

Tip

core/score/mse is the section for configuration of MSEScore.

Bases: Score

Source code in src/unitorch/cli/scores/__init__.py 
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def __init__(
    self,
):
    super().__init__()

from_config classmethod ¤

from_config(config, **kwargs)
Source code in src/unitorch/cli/scores/__init__.py 
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@classmethod
@config_defaults_init("core/score/mse")
def from_config(cls, config, **kwargs):
    pass

forward ¤

forward(
    outputs: Union[
        ClassificationOutputs, SegmentationOutputs
    ],
    targets: Union[
        ClassificationTargets, SegmentationTargets
    ],
)
Source code in src/unitorch/cli/scores/__init__.py 
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def forward(
    self,
    outputs: Union[ClassificationOutputs, SegmentationOutputs],
    targets: Union[ClassificationTargets, SegmentationTargets],
):
    if isinstance(outputs, ClassificationOutputs):
        outputs = outputs.outputs
    if isinstance(targets, ClassificationTargets):
        targets = targets.targets
    if isinstance(outputs, SegmentationOutputs):
        outputs = torch.cat([m.view(-1) for m in outputs.masks], dim=0)
    if isinstance(targets, SegmentationTargets):
        targets = torch.cat([m.view(-1) for m in targets.targets], dim=0)

    outputs = outputs.view(-1)
    targets = targets.view(-1)

    assert outputs.numel() == targets.numel()

    score = torch.sqrt(torch.mean(torch.pow(targets - outputs, 2)))

    return -float(score)

BLEUScore¤

Tip

core/score/bleu is the section for configuration of BLEUScore.

Bases: Score

Source code in src/unitorch/cli/scores/__init__.py 
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def __init__(
    self,
    ignore_tokens: Optional[List[int]] = [0, 1],
):
    super().__init__()
    self.ignore_tokens = ignore_tokens

ignore_tokens instance-attribute ¤

ignore_tokens = ignore_tokens

from_config classmethod ¤

from_config(config, **kwargs)
Source code in src/unitorch/cli/scores/__init__.py 
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@classmethod
@config_defaults_init("core/score/bleu")
def from_config(cls, config, **kwargs):
    pass

forward ¤

forward(
    outputs: GenerationOutputs, targets: GenerationTargets
)
Source code in src/unitorch/cli/scores/__init__.py 
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def forward(
    self,
    outputs: GenerationOutputs,
    targets: GenerationTargets,
):
    if isinstance(outputs, GenerationOutputs):
        outputs = outputs.sequences
    if isinstance(targets, GenerationTargets):
        targets = targets.refs
    return bleu_score(
        targets.long(),
        outputs.long(),
        ignore_tokens=self.ignore_tokens,
    )

ROUGE1Score¤

Tip

core/score/rouge1 is the section for configuration of ROUGE1Score.

Bases: Score

Source code in src/unitorch/cli/scores/__init__.py 
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def __init__(
    self,
    ignore_tokens: Optional[List[int]] = [0, 1],
):
    super().__init__()
    self.ignore_tokens = ignore_tokens

ignore_tokens instance-attribute ¤

ignore_tokens = ignore_tokens

from_config classmethod ¤

from_config(config, **kwargs)
Source code in src/unitorch/cli/scores/__init__.py 
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@classmethod
@config_defaults_init("core/score/rouge1")
def from_config(cls, config, **kwargs):
    pass

forward ¤

forward(
    outputs: GenerationOutputs, targets: GenerationTargets
)
Source code in src/unitorch/cli/scores/__init__.py 
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def forward(
    self,
    outputs: GenerationOutputs,
    targets: GenerationTargets,
):
    if isinstance(outputs, GenerationOutputs):
        outputs = outputs.sequences
    if isinstance(targets, GenerationTargets):
        targets = targets.refs

    return rouge1_score(
        targets.long(),
        outputs.long(),
        ignore_tokens=self.ignore_tokens,
    )["f1"]

ROUGE2Score¤

Tip

core/score/rouge2 is the section for configuration of ROUGE2Score.

Bases: Score

Source code in src/unitorch/cli/scores/__init__.py 
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def __init__(
    self,
    ignore_tokens: Optional[List[int]] = [0, 1],
):
    super().__init__()
    self.ignore_tokens = ignore_tokens

ignore_tokens instance-attribute ¤

ignore_tokens = ignore_tokens

from_config classmethod ¤

from_config(config, **kwargs)
Source code in src/unitorch/cli/scores/__init__.py 
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@classmethod
@config_defaults_init("core/score/rouge2")
def from_config(cls, config, **kwargs):
    pass

forward ¤

forward(
    outputs: GenerationOutputs, targets: GenerationTargets
)
Source code in src/unitorch/cli/scores/__init__.py 
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def forward(
    self,
    outputs: GenerationOutputs,
    targets: GenerationTargets,
):
    if isinstance(outputs, GenerationOutputs):
        outputs = outputs.sequences
    if isinstance(targets, GenerationTargets):
        targets = targets.refs

    return rouge2_score(
        targets.long(),
        outputs.long(),
        ignore_tokens=self.ignore_tokens,
    )["f1"]

ROUGELScore¤

Tip

core/score/rougel is the section for configuration of ROUGELScore.

Bases: Score

Source code in src/unitorch/cli/scores/__init__.py 
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def __init__(
    self,
    ignore_tokens: Optional[List[int]] = [0, 1],
):
    super().__init__()
    self.ignore_tokens = ignore_tokens

ignore_tokens instance-attribute ¤

ignore_tokens = ignore_tokens

from_config classmethod ¤

from_config(config, **kwargs)
Source code in src/unitorch/cli/scores/__init__.py 
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@classmethod
@config_defaults_init("core/score/rougel")
def from_config(cls, config, **kwargs):
    pass

forward ¤

forward(
    outputs: GenerationOutputs,
    targets: GenerationTargets = None,
)
Source code in src/unitorch/cli/scores/__init__.py 
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def forward(
    self,
    outputs: GenerationOutputs,
    targets: GenerationTargets = None,
):
    if isinstance(outputs, GenerationOutputs):
        outputs = outputs.sequences
    if isinstance(targets, GenerationTargets):
        targets = targets.refs

    return rougel_score(
        targets.long(),
        outputs.long(),
        ignore_tokens=self.ignore_tokens,
    )["f1"]

LossScore¤

Tip

core/score/loss is the section for configuration of LossScore.

Bases: Score

Source code in src/unitorch/cli/scores/__init__.py 
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def __init__(
    self,
):
    super().__init__()

from_config classmethod ¤

from_config(config, **kwargs)
Source code in src/unitorch/cli/scores/__init__.py 
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@classmethod
@config_defaults_init("core/score/loss")
def from_config(cls, config, **kwargs):
    pass

forward ¤

forward(outputs: LossOutputs, targets: ModelTargets)
Source code in src/unitorch/cli/scores/__init__.py 
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def forward(
    self,
    outputs: LossOutputs,
    targets: ModelTargets,
):
    if isinstance(outputs, LossOutputs):
        loss = outputs.loss

    return -float(torch.mean(loss))

mAPScore¤

Tip

core/score/mAP is the section for configuration of mAPScore.

Bases: Score

Source code in src/unitorch/cli/scores/__init__.py 
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def __init__(
    self,
):
    super().__init__()

from_config classmethod ¤

from_config(config, **kwargs)
Source code in src/unitorch/cli/scores/__init__.py 
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@classmethod
@config_defaults_init("core/score/mAP")
def from_config(cls, config, **kwargs):
    pass

forward ¤

forward(
    outputs: DetectionOutputs, targets: DetectionTargets
)
Source code in src/unitorch/cli/scores/__init__.py 
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def forward(
    self,
    outputs: DetectionOutputs,
    targets: DetectionTargets,
):
    if isinstance(outputs, DetectionOutputs):
        p_bboxes = outputs.bboxes
        p_scores = outputs.scores
        p_classes = outputs.classes
    if isinstance(targets, DetectionTargets):
        gt_bboxes = targets.bboxes
        gt_classes = targets.classes
    return map_score(
        predicted_bboxes=[t.numpy() for t in p_bboxes],
        predicted_scores=[t.numpy() for t in p_scores],
        predicted_classes=[t.numpy() for t in p_classes],
        ground_truth_bboxes=[t.numpy() for t in gt_bboxes],
        ground_truth_classes=[t.numpy() for t in gt_classes],
    )

mAP50Score¤

Tip

core/score/mAP50 is the section for configuration of mAP50Score.

Bases: Score

Source code in src/unitorch/cli/scores/__init__.py 
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def __init__(
    self,
):
    super().__init__()

from_config classmethod ¤

from_config(config, **kwargs)
Source code in src/unitorch/cli/scores/__init__.py 
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@classmethod
@config_defaults_init("core/score/mAP50")
def from_config(cls, config, **kwargs):
    pass

forward ¤

forward(
    outputs: DetectionOutputs, targets: DetectionTargets
)
Source code in src/unitorch/cli/scores/__init__.py 
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def forward(
    self,
    outputs: DetectionOutputs,
    targets: DetectionTargets,
):
    if isinstance(outputs, DetectionOutputs):
        p_bboxes = outputs.bboxes
        p_scores = outputs.scores
        p_classes = outputs.classes
    if isinstance(targets, DetectionTargets):
        gt_bboxes = targets.bboxes
        gt_classes = targets.classes
    return map50_score(
        predicted_bboxes=[t.numpy() for t in p_bboxes],
        predicted_scores=[t.numpy() for t in p_scores],
        predicted_classes=[t.numpy() for t in p_classes],
        ground_truth_bboxes=[t.numpy() for t in gt_bboxes],
        ground_truth_classes=[t.numpy() for t in gt_classes],
    )