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__init__.py
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__init__.py
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# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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
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
from torchmetrics.functional.classification.accuracy import accuracy
from torchmetrics.functional.classification.auroc import auroc
from torchmetrics.functional.classification.average_precision import average_precision
from torchmetrics.functional.classification.calibration_error import calibration_error
from torchmetrics.functional.classification.cohen_kappa import cohen_kappa
from torchmetrics.functional.classification.confusion_matrix import confusion_matrix
from torchmetrics.functional.classification.dice import dice, dice_score
from torchmetrics.functional.classification.f_beta import f1_score, fbeta_score
from torchmetrics.functional.classification.hamming import hamming_distance
from torchmetrics.functional.classification.hinge import hinge_loss
from torchmetrics.functional.classification.jaccard import jaccard_index
from torchmetrics.functional.classification.matthews_corrcoef import matthews_corrcoef
from torchmetrics.functional.classification.precision_recall import precision, precision_recall, recall
from torchmetrics.functional.classification.precision_recall_curve import precision_recall_curve
from torchmetrics.functional.classification.roc import roc
from torchmetrics.functional.classification.specificity import specificity
from torchmetrics.functional.classification.stat_scores import stat_scores
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
from torchmetrics.functional.image.uqi import universal_image_quality_index
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
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.symmetric_mape import symmetric_mean_absolute_percentage_error
from torchmetrics.functional.regression.tweedie_deviance import tweedie_deviance_score
from torchmetrics.functional.regression.wmape import weighted_mean_absolute_percentage_error
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
from torchmetrics.functional.text.bleu import bleu_score
from torchmetrics.functional.text.cer import char_error_rate
from torchmetrics.functional.text.chrf import chrf_score
from torchmetrics.functional.text.eed import extended_edit_distance
from torchmetrics.functional.text.mer import match_error_rate
from torchmetrics.functional.text.perplexity import perplexity
from torchmetrics.functional.text.rouge import rouge_score
from torchmetrics.functional.text.sacre_bleu import sacre_bleu_score
from torchmetrics.functional.text.squad import squad
from torchmetrics.functional.text.ter import translation_edit_rate
from torchmetrics.functional.text.wer import word_error_rate
from torchmetrics.functional.text.wil import word_information_lost
from torchmetrics.functional.text.wip import word_information_preserved
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
__all__ = [
"accuracy",
"auroc",
"average_precision",
"bleu_score",
"calibration_error",
"char_error_rate",
"chrf_score",
"concordance_corrcoef",
"cohen_kappa",
"confusion_matrix",
"cosine_similarity",
"tweedie_deviance_score",
"dice_score",
"dice",
"error_relative_global_dimensionless_synthesis",
"explained_variance",
"extended_edit_distance",
"f1_score",
"fbeta_score",
"hamming_distance",
"hinge_loss",
"image_gradients",
"jaccard_index",
"kendall_rank_corrcoef",
"kl_divergence",
"log_cosh_error",
"match_error_rate",
"matthews_corrcoef",
"mean_absolute_error",
"mean_absolute_percentage_error",
"mean_squared_error",
"mean_squared_log_error",
"multiscale_structural_similarity_index_measure",
"pairwise_cosine_similarity",
"pairwise_euclidean_distance",
"pairwise_linear_similarity",
"pairwise_manhattan_distance",
"pearson_corrcoef",
"permutation_invariant_training",
"perplexity",
"pit_permutate",
"precision",
"precision_recall",
"precision_recall_curve",
"peak_signal_noise_ratio",
"r2_score",
"recall",
"retrieval_average_precision",
"retrieval_fall_out",
"retrieval_hit_rate",
"retrieval_normalized_dcg",
"retrieval_precision",
"retrieval_r_precision",
"retrieval_recall",
"retrieval_reciprocal_rank",
"retrieval_precision_recall_curve",
"roc",
"rouge_score",
"sacre_bleu_score",
"signal_distortion_ratio",
"scale_invariant_signal_distortion_ratio",
"scale_invariant_signal_noise_ratio",
"signal_noise_ratio",
"spearman_corrcoef",
"specificity",
"spectral_distortion_index",
"squad",
"structural_similarity_index_measure",
"stat_scores",
"symmetric_mean_absolute_percentage_error",
"total_variation",
"translation_edit_rate",
"universal_image_quality_index",
"spectral_angle_mapper",
"weighted_mean_absolute_percentage_error",
"word_error_rate",
"word_information_lost",
"word_information_preserved",
]