torchmetrics是由pytorch-lightning开源的指标计算库，内置了常见的80+指标计算逻辑，可以直接使用，也可以与pytorch-lightning结合一起使用。
安装 & 使用
pip install torchmetrics基础案例：
# import our library
import torchmetrics
# initialize metric
metric = torchmetrics.Accuracy()
# move the metric to device you want computations to take place
device = "cuda" if torch.cuda.is_available() else "cpu"
metric.to(device)
n_batches = 10
for i in range(n_batches):
# simulate a classification problem
preds = torch.randn(10, 5).softmax(dim=-1).to(device)
target = torch.randint(5, (10,)).to(device)
# metric on current batch
acc = metric(preds, target)
print(f"Accuracy on batch {i}: {acc}")
# metric on all batches using custom accumulation
acc = metric.compute()
print(f"Accuracy on all data: {acc}")Auido 任务指标
Perceptual Evaluation of Speech Quality (PESQ)
Permutation Invariant Training (PIT)
Scale-Invariant Signal-to-Distortion Ratio (SI-SDR)
Scale-Invariant Signal-to-Noise Ratio (SI-SNR)
Short-Time Objective Intelligibility (STOI)
Signal to Distortion Ratio (SDR)
Signal-to-Noise Ratio (SNR)
分类 任务指标
Accuracy
AUC
AUROC
Average Precision
Binned Average Precision
Binned Precision Recall Curve
Binned Recall At Fixed Precision
Calibration Error
Cohen Kappa
Confusion Matrix
Coverage Error
Dice Score
F1 Score
FBeta Score
Hamming Distance
Hinge Loss
Jaccard Index
KL Divergence
Label Ranking Average Precision
Label Ranking Loss
Matthews Corr. Coef.
Precision
Precision Recall
Precision Recall Curve
Recall
ROC
Specificity
Stat Scores
图像 任务指标
Error Relative Global Dim. Synthesis (ERGAS)
Frechet Inception Distance (FID)
Image Gradients
Inception Score
Kernel Inception Distance
Learned Perceptual Image Patch Similarity (LPIPS)
Multi-Scale SSIM
Peak Signal-to-Noise Ratio (PSNR)
Spectral Angle Mapper
Spectral Distortion Index
Structural Similarity Index Measure (SSIM)
Universal Image Quality Index
检测 任务指标
Mean-Average-Precision (mAP)
Pairwise 任务指标
Cosine Similarity
Euclidean Distance
Linear Similarity
Manhattan Distance
Regression 任务指标
Cosine Similarity
Explained Variance
Mean Absolute Error (MAE)
Mean Absolute Percentage Error (MAPE)
Mean Squared Error (MSE)
Mean Squared Log Error (MSLE)
Pearson Corr. Coef.
R2 Score
Spearman Corr. Coef.
Symmetric Mean Absolute Percentage Error (SMAPE)
Tweedie Deviance Score
Weighted MAPE
Retrieval 任务指标
Retrieval Fall-Out
Retrieval Hit Rate
Retrieval Mean Average Precision (MAP)
Retrieval Mean Reciprocal Rank (MRR)
Retrieval Normalized DCG
Retrieval Precision
Retrieval R-Precision
Retrieval Recall
Text 任务指标
BERT Score
BLEU Score
Char Error Rate
ChrF Score
Extended Edit Distance
Match Error Rate
ROUGE Score
Sacre BLEU Score
SQuAD
Translation Edit Rate (TER)
Word Error Rate
Word Info. Lost
Word Info. Preserved
转自：coggle
推荐阅读：
我的2022届互联网校招分享
我的2021总结
浅谈算法岗和开发岗的区别
互联网校招研发薪资汇总
2022届互联网求职现状，金9银10快变成铜9铁10！！
公众号：AI蜗牛车
保持谦逊、保持自律、保持进步
发送【蜗牛】获取一份《手把手AI项目》（AI蜗牛车著）
发送【1222】获取一份不错的leetcode刷题笔记
发送【AI四大名著】获取四本经典AI电子书