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src/torchmetrics/__init__.py

@@ -2,7 +2,7 @@
 import logging as __logging
 import os
 
-from torchmetrics.__about__ import *  # noqa: F401, F403
+from torchmetrics.__about__ import *
 
 _logger = __logging.getLogger("torchmetrics")
 _logger.addHandler(__logging.StreamHandler())
@@ -11,16 +11,16 @@
 _PACKAGE_ROOT = os.path.dirname(__file__)
 _PROJECT_ROOT = os.path.dirname(_PACKAGE_ROOT)
 
-from torchmetrics import functional  # noqa: E402
-from torchmetrics.aggregation import CatMetric, MaxMetric, MeanMetric, MinMetric, SumMetric  # noqa: E402
-from torchmetrics.audio import (  # noqa: E402
+from torchmetrics import functional
+from torchmetrics.aggregation import CatMetric, MaxMetric, MeanMetric, MinMetric, SumMetric
+from torchmetrics.audio import (
     PermutationInvariantTraining,
     ScaleInvariantSignalDistortionRatio,
     ScaleInvariantSignalNoiseRatio,
     SignalDistortionRatio,
     SignalNoiseRatio,
 )
-from torchmetrics.classification import (  # noqa: E402
+from torchmetrics.classification import (
     AUROC,
     ROC,
     Accuracy,
@@ -42,8 +42,8 @@
     Specificity,
     StatScores,
 )
-from torchmetrics.collections import MetricCollection  # noqa: E402
-from torchmetrics.image import (  # noqa: E402
+from torchmetrics.collections import MetricCollection
+from torchmetrics.image import (
     ErrorRelativeGlobalDimensionlessSynthesis,
     MultiScaleStructuralSimilarityIndexMeasure,
     PeakSignalNoiseRatio,
@@ -53,9 +53,9 @@
     TotalVariation,
     UniversalImageQualityIndex,
 )
-from torchmetrics.metric import Metric  # noqa: E402
-from torchmetrics.nominal import CramersV, PearsonsContingencyCoefficient, TheilsU, TschuprowsT  # noqa: E402
-from torchmetrics.regression import (  # noqa: E402
+from torchmetrics.metric import Metric
+from torchmetrics.nominal import CramersV, PearsonsContingencyCoefficient, TheilsU, TschuprowsT
+from torchmetrics.regression import (
     ConcordanceCorrCoef,
     CosineSimilarity,
     ExplainedVariance,
@@ -73,7 +73,7 @@
     TweedieDevianceScore,
     WeightedMeanAbsolutePercentageError,
 )
-from torchmetrics.retrieval import (  # noqa: E402
+from torchmetrics.retrieval import (
     RetrievalFallOut,
     RetrievalHitRate,
     RetrievalMAP,
@@ -85,7 +85,7 @@
     RetrievalRecallAtFixedPrecision,
     RetrievalRPrecision,
 )
-from torchmetrics.text import (  # noqa: E402
+from torchmetrics.text import (
     BLEUScore,
     CharErrorRate,
     CHRFScore,
@@ -99,7 +99,7 @@
     WordInfoLost,
     WordInfoPreserved,
 )
-from torchmetrics.wrappers import (  # noqa: E402
+from torchmetrics.wrappers import (
     BootStrapper,
     ClasswiseWrapper,
     MetricTracker,

src/torchmetrics/audio/__init__.py

@@ -11,13 +11,13 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from torchmetrics.audio.pit import PermutationInvariantTraining  # noqa: F401
-from torchmetrics.audio.sdr import ScaleInvariantSignalDistortionRatio, SignalDistortionRatio  # noqa: F401
-from torchmetrics.audio.snr import ScaleInvariantSignalNoiseRatio, SignalNoiseRatio  # noqa: F401
+from torchmetrics.audio.pit import PermutationInvariantTraining
+from torchmetrics.audio.sdr import ScaleInvariantSignalDistortionRatio, SignalDistortionRatio
+from torchmetrics.audio.snr import ScaleInvariantSignalNoiseRatio, SignalNoiseRatio
 from torchmetrics.utilities.imports import _PESQ_AVAILABLE, _PYSTOI_AVAILABLE
 
 if _PESQ_AVAILABLE:
-    from torchmetrics.audio.pesq import PerceptualEvaluationSpeechQuality  # noqa: F401
+    from torchmetrics.audio.pesq import PerceptualEvaluationSpeechQuality
 
 if _PYSTOI_AVAILABLE:
-    from torchmetrics.audio.stoi import ShortTimeObjectiveIntelligibility  # noqa: F401
+    from torchmetrics.audio.stoi import ShortTimeObjectiveIntelligibility

src/torchmetrics/detection/__init__.py

@@ -14,4 +14,4 @@
 from torchmetrics.utilities.imports import _TORCHVISION_GREATER_EQUAL_0_8
 
 if _TORCHVISION_GREATER_EQUAL_0_8:
-    from torchmetrics.detection.mean_ap import MeanAveragePrecision  # noqa: F401
+    from torchmetrics.detection.mean_ap import MeanAveragePrecision

src/torchmetrics/detection/mean_ap.py

@@ -861,7 +861,7 @@ def __calculate_recall_precision_scores(
         det_matches = torch.cat([e["dtMatches"][:, :max_det] for e in img_eval_cls_bbox], axis=1)[:, inds]
         det_ignore = torch.cat([e["dtIgnore"][:, :max_det] for e in img_eval_cls_bbox], axis=1)[:, inds]
         gt_ignore = torch.cat([e["gtIgnore"] for e in img_eval_cls_bbox])
-        npig = torch.count_nonzero(gt_ignore == False)  # noqa: E712
+        npig = torch.count_nonzero(gt_ignore == False)
         if npig == 0:
             return recall, precision, scores
         tps = torch.logical_and(det_matches, torch.logical_not(det_ignore))

src/torchmetrics/functional/__init__.py

@@ -95,8 +95,8 @@
 from torchmetrics.utilities.imports import _TRANSFORMERS_AVAILABLE
 
 if _TRANSFORMERS_AVAILABLE:
-    from torchmetrics.functional.text.bert import bert_score  # noqa: F401
-    from torchmetrics.functional.text.infolm import infolm  # noqa: F401
+    from torchmetrics.functional.text.bert import bert_score
+    from torchmetrics.functional.text.infolm import infolm
 
 __all__ = [
     "accuracy",

src/torchmetrics/functional/audio/__init__.py

@@ -11,16 +11,16 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from torchmetrics.functional.audio.pit import permutation_invariant_training, pit_permutate  # noqa: F401
-from torchmetrics.functional.audio.sdr import (  # noqa: F401
+from torchmetrics.functional.audio.pit import permutation_invariant_training, pit_permutate
+from torchmetrics.functional.audio.sdr import (
     scale_invariant_signal_distortion_ratio,
     signal_distortion_ratio,
 )
-from torchmetrics.functional.audio.snr import scale_invariant_signal_noise_ratio, signal_noise_ratio  # noqa: F401
+from torchmetrics.functional.audio.snr import scale_invariant_signal_noise_ratio, signal_noise_ratio
 from torchmetrics.utilities.imports import _PESQ_AVAILABLE, _PYSTOI_AVAILABLE
 
 if _PESQ_AVAILABLE:
-    from torchmetrics.functional.audio.pesq import perceptual_evaluation_speech_quality  # noqa: F401
+    from torchmetrics.functional.audio.pesq import perceptual_evaluation_speech_quality
 
 if _PYSTOI_AVAILABLE:
-    from torchmetrics.functional.audio.stoi import short_time_objective_intelligibility  # noqa: F401
+    from torchmetrics.functional.audio.stoi import short_time_objective_intelligibility

src/torchmetrics/functional/classification/__init__.py

@@ -11,47 +11,47 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from torchmetrics.functional.classification.accuracy import (  # noqa: F401
+from torchmetrics.functional.classification.accuracy import (
     accuracy,
     binary_accuracy,
     multiclass_accuracy,
     multilabel_accuracy,
 )
-from torchmetrics.functional.classification.auroc import (  # noqa: F401
+from torchmetrics.functional.classification.auroc import (
     auroc,
     binary_auroc,
     multiclass_auroc,
     multilabel_auroc,
 )
-from torchmetrics.functional.classification.average_precision import (  # noqa: F401
+from torchmetrics.functional.classification.average_precision import (
     average_precision,
     binary_average_precision,
     multiclass_average_precision,
     multilabel_average_precision,
 )
-from torchmetrics.functional.classification.calibration_error import (  # noqa: F401
+from torchmetrics.functional.classification.calibration_error import (
     binary_calibration_error,
     calibration_error,
     multiclass_calibration_error,
 )
-from torchmetrics.functional.classification.cohen_kappa import (  # noqa: F401
+from torchmetrics.functional.classification.cohen_kappa import (
     binary_cohen_kappa,
     cohen_kappa,
     multiclass_cohen_kappa,
 )
-from torchmetrics.functional.classification.confusion_matrix import (  # noqa: F401
+from torchmetrics.functional.classification.confusion_matrix import (
     binary_confusion_matrix,
     confusion_matrix,
     multiclass_confusion_matrix,
     multilabel_confusion_matrix,
 )
-from torchmetrics.functional.classification.dice import dice  # noqa: F401
-from torchmetrics.functional.classification.exact_match import (  # noqa: F401
+from torchmetrics.functional.classification.dice import dice
+from torchmetrics.functional.classification.exact_match import (
     exact_match,
     multiclass_exact_match,
     multilabel_exact_match,
 )
-from torchmetrics.functional.classification.f_beta import (  # noqa: F401
+from torchmetrics.functional.classification.f_beta import (
     binary_f1_score,
     binary_fbeta_score,
     f1_score,
@@ -61,30 +61,30 @@
     multilabel_f1_score,
     multilabel_fbeta_score,
 )
-from torchmetrics.functional.classification.hamming import (  # noqa: F401
+from torchmetrics.functional.classification.hamming import (
     binary_hamming_distance,
     hamming_distance,
     multiclass_hamming_distance,
     multilabel_hamming_distance,
 )
-from torchmetrics.functional.classification.hinge import (  # noqa: F401
+from torchmetrics.functional.classification.hinge import (
     binary_hinge_loss,
     hinge_loss,
     multiclass_hinge_loss,
 )
-from torchmetrics.functional.classification.jaccard import (  # noqa: F401
+from torchmetrics.functional.classification.jaccard import (
     binary_jaccard_index,
     jaccard_index,
     multiclass_jaccard_index,
     multilabel_jaccard_index,
 )
-from torchmetrics.functional.classification.matthews_corrcoef import (  # noqa: F401
+from torchmetrics.functional.classification.matthews_corrcoef import (
     binary_matthews_corrcoef,
     matthews_corrcoef,
     multiclass_matthews_corrcoef,
     multilabel_matthews_corrcoef,
 )
-from torchmetrics.functional.classification.precision_recall import (  # noqa: F401
+from torchmetrics.functional.classification.precision_recall import (
     binary_precision,
     binary_recall,
     multiclass_precision,
@@ -94,30 +94,30 @@
     precision,
     recall,
 )
-from torchmetrics.functional.classification.precision_recall_curve import (  # noqa: F401
+from torchmetrics.functional.classification.precision_recall_curve import (
     binary_precision_recall_curve,
     multiclass_precision_recall_curve,
     multilabel_precision_recall_curve,
     precision_recall_curve,
 )
-from torchmetrics.functional.classification.ranking import (  # noqa: F401
+from torchmetrics.functional.classification.ranking import (
     multilabel_coverage_error,
     multilabel_ranking_average_precision,
     multilabel_ranking_loss,
 )
-from torchmetrics.functional.classification.recall_at_fixed_precision import (  # noqa: F401
+from torchmetrics.functional.classification.recall_at_fixed_precision import (
     binary_recall_at_fixed_precision,
     multiclass_recall_at_fixed_precision,
     multilabel_recall_at_fixed_precision,
 )
-from torchmetrics.functional.classification.roc import binary_roc, multiclass_roc, multilabel_roc, roc  # noqa: F401
-from torchmetrics.functional.classification.specificity import (  # noqa: F401
+from torchmetrics.functional.classification.roc import binary_roc, multiclass_roc, multilabel_roc, roc
+from torchmetrics.functional.classification.specificity import (
     binary_specificity,
     multiclass_specificity,
     multilabel_specificity,
     specificity,
 )
-from torchmetrics.functional.classification.stat_scores import (  # noqa: F401
+from torchmetrics.functional.classification.stat_scores import (
     binary_stat_scores,
     multiclass_stat_scores,
     multilabel_stat_scores,

src/torchmetrics/functional/image/__init__.py

@@ -11,14 +11,14 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from torchmetrics.functional.image.d_lambda import spectral_distortion_index  # noqa: F401
-from torchmetrics.functional.image.ergas import error_relative_global_dimensionless_synthesis  # noqa: F401
-from torchmetrics.functional.image.gradients import image_gradients  # noqa: F401
-from torchmetrics.functional.image.psnr import peak_signal_noise_ratio  # noqa: F401
-from torchmetrics.functional.image.sam import spectral_angle_mapper  # noqa: F401
-from torchmetrics.functional.image.ssim import (  # noqa: F401
+from torchmetrics.functional.image.d_lambda import spectral_distortion_index
+from torchmetrics.functional.image.ergas import error_relative_global_dimensionless_synthesis
+from torchmetrics.functional.image.gradients import image_gradients
+from torchmetrics.functional.image.psnr import peak_signal_noise_ratio
+from torchmetrics.functional.image.sam import spectral_angle_mapper
+from torchmetrics.functional.image.ssim import (
     multiscale_structural_similarity_index_measure,
     structural_similarity_index_measure,
 )
-from torchmetrics.functional.image.tv import total_variation  # noqa: F401
-from torchmetrics.functional.image.uqi import universal_image_quality_index  # noqa: F401
+from torchmetrics.functional.image.tv import total_variation
+from torchmetrics.functional.image.uqi import universal_image_quality_index

src/torchmetrics/functional/multimodal/__init__.py

@@ -14,4 +14,4 @@
 from torchmetrics.utilities.imports import _TRANSFORMERS_AVAILABLE
 
 if _TRANSFORMERS_AVAILABLE:
-    from torchmetrics.functional.multimodal.clip_score import clip_score  # noqa: F401
+    from torchmetrics.functional.multimodal.clip_score import clip_score

src/torchmetrics/functional/nominal/__init__.py

@@ -11,10 +11,10 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from torchmetrics.functional.nominal.cramers import cramers_v, cramers_v_matrix  # noqa: F401
-from torchmetrics.functional.nominal.pearson import (  # noqa: F401
+from torchmetrics.functional.nominal.cramers import cramers_v, cramers_v_matrix
+from torchmetrics.functional.nominal.pearson import (
     pearsons_contingency_coefficient,
     pearsons_contingency_coefficient_matrix,
 )
-from torchmetrics.functional.nominal.theils_u import theils_u, theils_u_matrix  # noqa: F401
-from torchmetrics.functional.nominal.tschuprows import tschuprows_t, tschuprows_t_matrix  # noqa: F401
+from torchmetrics.functional.nominal.theils_u import theils_u, theils_u_matrix
+from torchmetrics.functional.nominal.tschuprows import tschuprows_t, tschuprows_t_matrix

src/torchmetrics/functional/pairwise/__init__.py

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from torchmetrics.functional.pairwise.cosine import pairwise_cosine_similarity  # noqa: F401
-from torchmetrics.functional.pairwise.euclidean import pairwise_euclidean_distance  # noqa: F401
-from torchmetrics.functional.pairwise.linear import pairwise_linear_similarity  # noqa: F401
-from torchmetrics.functional.pairwise.manhattan import pairwise_manhattan_distance  # noqa: F401
+from torchmetrics.functional.pairwise.cosine import pairwise_cosine_similarity
+from torchmetrics.functional.pairwise.euclidean import pairwise_euclidean_distance
+from torchmetrics.functional.pairwise.linear import pairwise_linear_similarity
+from torchmetrics.functional.pairwise.manhattan import pairwise_manhattan_distance

src/torchmetrics/functional/regression/__init__.py

@@ -11,18 +11,18 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from torchmetrics.functional.regression.concordance import concordance_corrcoef  # noqa: F401
-from torchmetrics.functional.regression.cosine_similarity import cosine_similarity  # noqa: F401
-from torchmetrics.functional.regression.explained_variance import explained_variance  # noqa: F401
-from torchmetrics.functional.regression.kendall import kendall_rank_corrcoef  # noqa: F401
-from torchmetrics.functional.regression.kl_divergence import kl_divergence  # noqa: F401
-from torchmetrics.functional.regression.log_cosh import log_cosh_error  # noqa: F401
-from torchmetrics.functional.regression.log_mse import mean_squared_log_error  # noqa: F401
-from torchmetrics.functional.regression.mae import mean_absolute_error  # noqa: F401
-from torchmetrics.functional.regression.mape import mean_absolute_percentage_error  # noqa: F401
-from torchmetrics.functional.regression.mse import mean_squared_error  # noqa: F401
-from torchmetrics.functional.regression.pearson import pearson_corrcoef  # noqa: F401
-from torchmetrics.functional.regression.r2 import r2_score  # noqa: F401
-from torchmetrics.functional.regression.spearman import spearman_corrcoef  # noqa: F401
-from torchmetrics.functional.regression.tweedie_deviance import tweedie_deviance_score  # noqa: F401
-from torchmetrics.functional.regression.wmape import weighted_mean_absolute_percentage_error  # noqa: F401
+from torchmetrics.functional.regression.concordance import concordance_corrcoef
+from torchmetrics.functional.regression.cosine_similarity import cosine_similarity
+from torchmetrics.functional.regression.explained_variance import explained_variance
+from torchmetrics.functional.regression.kendall import kendall_rank_corrcoef
+from torchmetrics.functional.regression.kl_divergence import kl_divergence
+from torchmetrics.functional.regression.log_cosh import log_cosh_error
+from torchmetrics.functional.regression.log_mse import mean_squared_log_error
+from torchmetrics.functional.regression.mae import mean_absolute_error
+from torchmetrics.functional.regression.mape import mean_absolute_percentage_error
+from torchmetrics.functional.regression.mse import mean_squared_error
+from torchmetrics.functional.regression.pearson import pearson_corrcoef
+from torchmetrics.functional.regression.r2 import r2_score
+from torchmetrics.functional.regression.spearman import spearman_corrcoef
+from torchmetrics.functional.regression.tweedie_deviance import tweedie_deviance_score
+from torchmetrics.functional.regression.wmape import weighted_mean_absolute_percentage_error

src/torchmetrics/functional/retrieval/__init__.py

@@ -12,12 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from torchmetrics.functional.retrieval.average_precision import retrieval_average_precision  # noqa: F401
-from torchmetrics.functional.retrieval.fall_out import retrieval_fall_out  # noqa: F401
-from torchmetrics.functional.retrieval.hit_rate import retrieval_hit_rate  # noqa: F401
-from torchmetrics.functional.retrieval.ndcg import retrieval_normalized_dcg  # noqa: F401
-from torchmetrics.functional.retrieval.precision import retrieval_precision  # noqa: F401
-from torchmetrics.functional.retrieval.precision_recall_curve import retrieval_precision_recall_curve  # noqa: F401
-from torchmetrics.functional.retrieval.r_precision import retrieval_r_precision  # noqa: F401
-from torchmetrics.functional.retrieval.recall import retrieval_recall  # noqa: F401
-from torchmetrics.functional.retrieval.reciprocal_rank import retrieval_reciprocal_rank  # noqa: F401
+from torchmetrics.functional.retrieval.average_precision import retrieval_average_precision
+from torchmetrics.functional.retrieval.fall_out import retrieval_fall_out
+from torchmetrics.functional.retrieval.hit_rate import retrieval_hit_rate
+from torchmetrics.functional.retrieval.ndcg import retrieval_normalized_dcg
+from torchmetrics.functional.retrieval.precision import retrieval_precision
+from torchmetrics.functional.retrieval.precision_recall_curve import retrieval_precision_recall_curve
+from torchmetrics.functional.retrieval.r_precision import retrieval_r_precision
+from torchmetrics.functional.retrieval.recall import retrieval_recall
+from torchmetrics.functional.retrieval.reciprocal_rank import retrieval_reciprocal_rank