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Refactor: Imports #1681

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merged 8 commits into from
Apr 4, 2023
Merged

Refactor: Imports #1681

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8395534
remove from root
SkafteNicki Apr 3, 2023
1bdbf14
fix test imports
SkafteNicki Apr 3, 2023
dc48524
fix docstring imports
SkafteNicki Apr 3, 2023
d3d7d83
Merge branch 'master' into refactor/imports
SkafteNicki Apr 3, 2023
0d8dceb
Merge branch 'master' into refactor/imports
Borda Apr 3, 2023
e069873
revert init
Borda Apr 4, 2023
f78c894
Merge branch 'master' into refactor/imports
Borda Apr 4, 2023
5b85077
Merge branch 'master' into refactor/imports
mergify[bot] Apr 4, 2023
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144 changes: 23 additions & 121 deletions src/torchmetrics/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
import logging as __logging
import os

from torchmetrics.__about__ import * # noqa: F401, F403
from torchmetrics.__about__ import * # noqa: F403

_logger = __logging.getLogger("torchmetrics")
_logger.addHandler(__logging.StreamHandler())
Expand All @@ -13,13 +13,6 @@

from torchmetrics import functional # noqa: E402
from torchmetrics.aggregation import CatMetric, MaxMetric, MeanMetric, MinMetric, SumMetric # noqa: E402
from torchmetrics.audio import PermutationInvariantTraining # noqa: E402
from torchmetrics.audio import ( # noqa: E402
ScaleInvariantSignalDistortionRatio,
ScaleInvariantSignalNoiseRatio,
SignalDistortionRatio,
SignalNoiseRatio,
)
from torchmetrics.classification import ( # noqa: E402
AUROC,
ROC,
Expand All @@ -43,26 +36,10 @@
StatScores,
)
from torchmetrics.collections import MetricCollection # noqa: E402
from torchmetrics.detection import ModifiedPanopticQuality, PanopticQuality # noqa: E402
from torchmetrics.image import ( # noqa: E402
ErrorRelativeGlobalDimensionlessSynthesis,
MultiScaleStructuralSimilarityIndexMeasure,
PeakSignalNoiseRatio,
RelativeAverageSpectralError,
RootMeanSquaredErrorUsingSlidingWindow,
SpectralAngleMapper,
SpectralDistortionIndex,
StructuralSimilarityIndexMeasure,
TotalVariation,
UniversalImageQualityIndex,
)
from torchmetrics.metric import Metric # noqa: E402
from torchmetrics.nominal import CramersV # noqa: E402
from torchmetrics.nominal import PearsonsContingencyCoefficient # noqa: E402
from torchmetrics.nominal import TheilsU, TschuprowsT # noqa: E402
from torchmetrics.regression import ConcordanceCorrCoef # noqa: E402
from torchmetrics.regression import CosineSimilarity # noqa: E402
from torchmetrics.regression import ( # noqa: E402
ConcordanceCorrCoef,
CosineSimilarity,
ExplainedVariance,
KendallRankCorrCoef,
KLDivergence,
Expand All @@ -79,126 +56,51 @@
TweedieDevianceScore,
WeightedMeanAbsolutePercentageError,
)
from torchmetrics.retrieval import RetrievalFallOut # noqa: E402
from torchmetrics.retrieval import RetrievalHitRate # noqa: E402
from torchmetrics.retrieval import ( # noqa: E402
RetrievalMAP,
RetrievalMRR,
RetrievalNormalizedDCG,
RetrievalPrecision,
RetrievalPrecisionRecallCurve,
RetrievalRecall,
RetrievalRecallAtFixedPrecision,
RetrievalRPrecision,
)
from torchmetrics.text import ( # noqa: E402
BLEUScore,
CharErrorRate,
CHRFScore,
ExtendedEditDistance,
MatchErrorRate,
Perplexity,
SacreBLEUScore,
SQuAD,
TranslationEditRate,
WordErrorRate,
WordInfoLost,
WordInfoPreserved,
)
from torchmetrics.wrappers import BootStrapper # noqa: E402
from torchmetrics.wrappers import ClasswiseWrapper, MetricTracker, MinMaxMetric, MultioutputWrapper # noqa: E402

__all__ = [
"Metric",
"MetricCollection",
"functional",
"Accuracy",
"CatMetric",
"MaxMetric",
"MeanMetric",
"MinMetric",
"SumMetric",
"AUROC",
"ROC",
"Accuracy",
"AveragePrecision",
"BLEUScore",
"BootStrapper",
"CalibrationError",
"CatMetric",
"ClasswiseWrapper",
"CharErrorRate",
"CHRFScore",
"ConcordanceCorrCoef",
"CohenKappa",
"ConfusionMatrix",
"CosineSimilarity",
"CramersV",
"Dice",
"TweedieDevianceScore",
"ErrorRelativeGlobalDimensionlessSynthesis",
"ExactMatch",
"ExplainedVariance",
"ExtendedEditDistance",
"F1Score",
"FBetaScore",
"HammingDistance",
"HingeLoss",
"JaccardIndex",
"MatthewsCorrCoef",
"Precision",
"PrecisionRecallCurve",
"Recall",
"Specificity",
"StatScores",
"ConcordanceCorrCoef",
"CosineSimilarity",
"ExplainedVariance",
"KendallRankCorrCoef",
"KLDivergence",
"LogCoshError",
"MatchErrorRate",
"MatthewsCorrCoef",
"MaxMetric",
"MeanAbsoluteError",
"MeanAbsolutePercentageError",
"MeanMetric",
"MeanSquaredError",
"MeanSquaredLogError",
"Metric",
"MetricCollection",
"MetricTracker",
"MinMaxMetric",
"MinMetric",
"ModifiedPanopticQuality",
"MultioutputWrapper",
"MultiScaleStructuralSimilarityIndexMeasure",
"PanopticQuality",
"MinkowskiDistance",
"PearsonCorrCoef",
"PearsonsContingencyCoefficient",
"PermutationInvariantTraining",
"Perplexity",
"Precision",
"PrecisionRecallCurve",
"PeakSignalNoiseRatio",
"R2Score",
"Recall",
"RelativeAverageSpectralError",
"RetrievalFallOut",
"RetrievalHitRate",
"RetrievalMAP",
"RetrievalMRR",
"RetrievalNormalizedDCG",
"RetrievalPrecision",
"RetrievalRecall",
"RetrievalRPrecision",
"RetrievalPrecisionRecallCurve",
"RetrievalRecallAtFixedPrecision",
"ROC",
"RootMeanSquaredErrorUsingSlidingWindow",
"SacreBLEUScore",
"SignalDistortionRatio",
"ScaleInvariantSignalDistortionRatio",
"ScaleInvariantSignalNoiseRatio",
"SignalNoiseRatio",
"SpearmanCorrCoef",
"Specificity",
"SpectralAngleMapper",
"SpectralDistortionIndex",
"SQuAD",
"StructuralSimilarityIndexMeasure",
"StatScores",
"SumMetric",
"SymmetricMeanAbsolutePercentageError",
"TheilsU",
"TotalVariation",
"TranslationEditRate",
"TschuprowsT",
"UniversalImageQualityIndex",
"TweedieDevianceScore",
"WeightedMeanAbsolutePercentageError",
"WordErrorRate",
"WordInfoLost",
"WordInfoPreserved",
]
6 changes: 3 additions & 3 deletions src/torchmetrics/audio/sdr.py
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Expand Up @@ -183,7 +183,7 @@ class ScaleInvariantSignalDistortionRatio(Metric):

Example:
>>> from torch import tensor
>>> from torchmetrics import ScaleInvariantSignalDistortionRatio
>>> from torchmetrics.audio import ScaleInvariantSignalDistortionRatio
>>> target = tensor([3.0, -0.5, 2.0, 7.0])
>>> preds = tensor([2.5, 0.0, 2.0, 8.0])
>>> si_sdr = ScaleInvariantSignalDistortionRatio()
Expand Down Expand Up @@ -242,7 +242,7 @@ def plot(

>>> # Example plotting a single value
>>> import torch
>>> from torchmetrics.audio.sdr import ScaleInvariantSignalDistortionRatio
>>> from torchmetrics.audio import ScaleInvariantSignalDistortionRatio
>>> target = torch.randn(5)
>>> preds = torch.randn(5)
>>> metric = ScaleInvariantSignalDistortionRatio()
Expand All @@ -254,7 +254,7 @@ def plot(

>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.audio.sdr import ScaleInvariantSignalDistortionRatio
>>> from torchmetrics.audio import ScaleInvariantSignalDistortionRatio
>>> target = torch.randn(5)
>>> preds = torch.randn(5)
>>> metric = ScaleInvariantSignalDistortionRatio()
Expand Down
12 changes: 6 additions & 6 deletions src/torchmetrics/audio/snr.py
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Expand Up @@ -52,7 +52,7 @@ class SignalNoiseRatio(Metric):

Example:
>>> from torch import tensor
>>> from torchmetrics import SignalNoiseRatio
>>> from torchmetrics.audio import SignalNoiseRatio
>>> target = tensor([3.0, -0.5, 2.0, 7.0])
>>> preds = tensor([2.5, 0.0, 2.0, 8.0])
>>> snr = SignalNoiseRatio()
Expand Down Expand Up @@ -111,7 +111,7 @@ def plot(

>>> # Example plotting a single value
>>> import torch
>>> from torchmetrics.audio.snr import SignalNoiseRatio
>>> from torchmetrics.audio import SignalNoiseRatio
>>> metric = SignalNoiseRatio()
>>> metric.update(torch.rand(4), torch.rand(4))
>>> fig_, ax_ = metric.plot()
Expand All @@ -121,7 +121,7 @@ def plot(

>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.audio.snr import SignalNoiseRatio
>>> from torchmetrics.audio import SignalNoiseRatio
>>> metric = SignalNoiseRatio()
>>> values = [ ]
>>> for _ in range(10):
Expand Down Expand Up @@ -152,7 +152,7 @@ class ScaleInvariantSignalNoiseRatio(Metric):

Example:
>>> from torch import tensor
>>> from torchmetrics import ScaleInvariantSignalNoiseRatio
>>> from torchmetrics.audio import ScaleInvariantSignalNoiseRatio
>>> target = tensor([3.0, -0.5, 2.0, 7.0])
>>> preds = tensor([2.5, 0.0, 2.0, 8.0])
>>> si_snr = ScaleInvariantSignalNoiseRatio()
Expand Down Expand Up @@ -207,7 +207,7 @@ def plot(self, val: Union[Tensor, Sequence[Tensor], None] = None, ax: Optional[_

>>> # Example plotting a single value
>>> import torch
>>> from torchmetrics.audio.snr import ScaleInvariantSignalNoiseRatio
>>> from torchmetrics.audio import ScaleInvariantSignalNoiseRatio
>>> metric = ScaleInvariantSignalNoiseRatio()
>>> metric.update(torch.rand(4), torch.rand(4))
>>> fig_, ax_ = metric.plot()
Expand All @@ -217,7 +217,7 @@ def plot(self, val: Union[Tensor, Sequence[Tensor], None] = None, ax: Optional[_

>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.audio.snr import ScaleInvariantSignalNoiseRatio
>>> from torchmetrics.audio import ScaleInvariantSignalNoiseRatio
>>> metric = ScaleInvariantSignalNoiseRatio()
>>> values = [ ]
>>> for _ in range(10):
Expand Down
6 changes: 3 additions & 3 deletions src/torchmetrics/audio/stoi.py
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Expand Up @@ -64,7 +64,7 @@ class ShortTimeObjectiveIntelligibility(Metric):

Example:
>>> import torch
>>> from torchmetrics.audio.stoi import ShortTimeObjectiveIntelligibility
>>> from torchmetrics.audio import ShortTimeObjectiveIntelligibility
>>> g = torch.manual_seed(1)
>>> preds = torch.randn(8000)
>>> target = torch.randn(8000)
Expand Down Expand Up @@ -131,7 +131,7 @@ def plot(self, val: Union[Tensor, Sequence[Tensor], None] = None, ax: Optional[_

>>> # Example plotting a single value
>>> import torch
>>> from torchmetrics.audio.stoi import ShortTimeObjectiveIntelligibility
>>> from torchmetrics.audio import ShortTimeObjectiveIntelligibility
>>> g = torch.manual_seed(1)
>>> preds = torch.randn(8000)
>>> target = torch.randn(8000)
Expand All @@ -144,7 +144,7 @@ def plot(self, val: Union[Tensor, Sequence[Tensor], None] = None, ax: Optional[_

>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.audio.stoi import ShortTimeObjectiveIntelligibility
>>> from torchmetrics.audio import ShortTimeObjectiveIntelligibility
>>> metric = ShortTimeObjectiveIntelligibility(8000, False)
>>> g = torch.manual_seed(1)
>>> preds = torch.randn(8000)
Expand Down
2 changes: 1 addition & 1 deletion src/torchmetrics/detection/mean_ap.py
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Expand Up @@ -315,7 +315,7 @@ class MeanAveragePrecision(Metric):

Example:
>>> from torch import tensor
>>> from torchmetrics.detection.mean_ap import MeanAveragePrecision
>>> from torchmetrics.detection import MeanAveragePrecision
>>> preds = [
... dict(
... boxes=tensor([[258.0, 41.0, 606.0, 285.0]]),
Expand Down
6 changes: 3 additions & 3 deletions src/torchmetrics/detection/modified_panoptic_quality.py
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Expand Up @@ -67,7 +67,7 @@ class ModifiedPanopticQuality(Metric):

Example:
>>> from torch import tensor
>>> from torchmetrics import ModifiedPanopticQuality
>>> from torchmetrics.detection import ModifiedPanopticQuality
>>> preds = tensor([[[0, 0], [0, 1], [6, 0], [7, 0], [0, 2], [1, 0]]])
>>> target = tensor([[[0, 1], [0, 0], [6, 0], [7, 0], [6, 0], [255, 0]]])
>>> pq_modified = ModifiedPanopticQuality(things = {0, 1}, stuffs = {6, 7})
Expand Down Expand Up @@ -172,7 +172,7 @@ def plot(
:scale: 75

>>> from torch import tensor
>>> from torchmetrics import ModifiedPanopticQuality
>>> from torchmetrics.detection import ModifiedPanopticQuality
>>> preds = tensor([[[[6, 0], [0, 0], [6, 0], [6, 0]],
... [[0, 0], [0, 0], [6, 0], [0, 1]],
... [[0, 0], [0, 0], [6, 0], [0, 1]],
Expand All @@ -192,7 +192,7 @@ def plot(

>>> # Example plotting multiple values
>>> from torch import tensor
>>> from torchmetrics import ModifiedPanopticQuality
>>> from torchmetrics.detection import ModifiedPanopticQuality
>>> preds = tensor([[[[6, 0], [0, 0], [6, 0], [6, 0]],
... [[0, 0], [0, 0], [6, 0], [0, 1]],
... [[0, 0], [0, 0], [6, 0], [0, 1]],
Expand Down
6 changes: 3 additions & 3 deletions src/torchmetrics/detection/panoptic_quality.py
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Expand Up @@ -65,7 +65,7 @@ class PanopticQuality(Metric):

Example:
>>> from torch import tensor
>>> from torchmetrics import PanopticQuality
>>> from torchmetrics.detection import PanopticQuality
>>> preds = tensor([[[[6, 0], [0, 0], [6, 0], [6, 0]],
... [[0, 0], [0, 0], [6, 0], [0, 1]],
... [[0, 0], [0, 0], [6, 0], [0, 1]],
Expand Down Expand Up @@ -175,7 +175,7 @@ def plot(
:scale: 75

>>> from torch import tensor
>>> from torchmetrics import PanopticQuality
>>> from torchmetrics.detection import PanopticQuality
>>> preds = tensor([[[[6, 0], [0, 0], [6, 0], [6, 0]],
... [[0, 0], [0, 0], [6, 0], [0, 1]],
... [[0, 0], [0, 0], [6, 0], [0, 1]],
Expand All @@ -195,7 +195,7 @@ def plot(

>>> # Example plotting multiple values
>>> from torch import tensor
>>> from torchmetrics import PanopticQuality
>>> from torchmetrics.detection import PanopticQuality
>>> preds = tensor([[[[6, 0], [0, 0], [6, 0], [6, 0]],
... [[0, 0], [0, 0], [6, 0], [0, 1]],
... [[0, 0], [0, 0], [6, 0], [0, 1]],
Expand Down
6 changes: 3 additions & 3 deletions src/torchmetrics/image/d_lambda.py
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Expand Up @@ -55,7 +55,7 @@ class SpectralDistortionIndex(Metric):
Example:
>>> import torch
>>> _ = torch.manual_seed(42)
>>> from torchmetrics import SpectralDistortionIndex
>>> from torchmetrics.image import SpectralDistortionIndex
>>> preds = torch.rand([16, 3, 16, 16])
>>> target = torch.rand([16, 3, 16, 16])
>>> sdi = SpectralDistortionIndex()
Expand Down Expand Up @@ -127,7 +127,7 @@ def plot(
>>> # Example plotting a single value
>>> import torch
>>> _ = torch.manual_seed(42)
>>> from torchmetrics import SpectralDistortionIndex
>>> from torchmetrics.image import SpectralDistortionIndex
>>> preds = torch.rand([16, 3, 16, 16])
>>> target = torch.rand([16, 3, 16, 16])
>>> metric = SpectralDistortionIndex()
Expand All @@ -140,7 +140,7 @@ def plot(
>>> # Example plotting multiple values
>>> import torch
>>> _ = torch.manual_seed(42)
>>> from torchmetrics import SpectralDistortionIndex
>>> from torchmetrics.image import SpectralDistortionIndex
>>> preds = torch.rand([16, 3, 16, 16])
>>> target = torch.rand([16, 3, 16, 16])
>>> metric = SpectralDistortionIndex()
Expand Down
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