Machine learning metrics for distributed, scalable PyTorch applications.

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What is Torchmetrics • Implementing a metric • Built-in metrics • Docs • Community • License

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Simple installation from PyPI

pip install torchmetrics

conda install -c conda-forge torchmetrics

uv add torchmetrics

# with git pip install git+https://github.com/Lightning-AI/torchmetrics.git@release/stable

pip install https://github.com/Lightning-AI/torchmetrics/archive/refs/heads/release/stable.zip

pip install torchmetrics[audio] pip install torchmetrics[image] pip install torchmetrics[text] pip install torchmetrics[all] # install all of the above

pip install https://github.com/Lightning-AI/torchmetrics/archive/master.zip

TorchMetrics is a collection of 100+ PyTorch metrics implementations and an easy-to-use API to create custom metrics. It offers:

• A standardized interface to increase reproducibility
• Reduces boilerplate
• Automatic accumulation over batches
• Metrics optimized for distributed-training
• Automatic synchronization between multiple devices

You can use TorchMetrics with any PyTorch model or with PyTorch Lightning to enjoy additional features such as:

• Module metrics are automatically placed on the correct device.
• Native support for logging metrics in Lightning to reduce even more boilerplate.

The module-based metrics contain internal metric states (similar to the parameters of the PyTorch module) that automate accumulation and synchronization across devices!

• Automatic accumulation over multiple batches
• Automatic synchronization between multiple devices
• Metric arithmetic

This can be run on CPU, single GPU or multi-GPUs!

For the single GPU/CPU case:

import torch # import our library import torchmetrics # initialize metric metric = torchmetrics.classification.Accuracy(task="multiclass", num_classes=5) # 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}")

Module metric usage remains the same when using multiple GPUs or multiple nodes.

import os import torch import torch.distributed as dist import torch.multiprocessing as mp from torch import nn from torch.nn.parallel import DistributedDataParallel as DDP import torchmetrics def metric_ddp(rank, world_size): os.environ["MASTER_ADDR"] = "localhost" os.environ["MASTER_PORT"] = "12355" # create default process group dist.init_process_group("gloo", rank=rank, world_size=world_size) # initialize model metric = torchmetrics.classification.Accuracy(task="multiclass", num_classes=5) # define a model and append your metric to it # this allows metric states to be placed on correct accelerators when # .to(device) is called on the model model = nn.Linear(10, 10) model.metric = metric model = model.to(rank) # initialize DDP model = DDP(model, device_ids=[rank]) n_epochs = 5 # this shows iteration over multiple training epochs for n in range(n_epochs): # this will be replaced by a DataLoader with a DistributedSampler n_batches = 10 for i in range(n_batches): # simulate a classification problem preds = torch.randn(10, 5).softmax(dim=-1) target = torch.randint(5, (10,)) # metric on current batch acc = metric(preds, target) if rank == 0: # print only for rank 0 print(f"Accuracy on batch {i}: {acc}") # metric on all batches and all accelerators using custom accumulation # accuracy is same across both accelerators acc = metric.compute() print(f"Accuracy on all data: {acc}, accelerator rank: {rank}") # Resetting internal state such that metric ready for new data metric.reset() # cleanup dist.destroy_process_group() if __name__ == "__main__": world_size = 2 # number of gpus to parallelize over mp.spawn(metric_ddp, args=(world_size,), nprocs=world_size, join=True)

Implementing your own metric is as easy as subclassing an torch.nn.Module. Simply, subclass torchmetrics.Metric and just implement the update and compute methods:

import torch from torchmetrics import Metric class MyAccuracy(Metric): def __init__(self): # remember to call super super().__init__() # call `self.add_state`for every internal state that is needed for the metrics computations # dist_reduce_fx indicates the function that should be used to reduce # state from multiple processes self.add_state("correct", default=torch.tensor(0), dist_reduce_fx="sum") self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum") def update(self, preds: torch.Tensor, target: torch.Tensor) -> None: # extract predicted class index for computing accuracy preds = preds.argmax(dim=-1) assert preds.shape == target.shape # update metric states self.correct += torch.sum(preds == target) self.total += target.numel() def compute(self) -> torch.Tensor: # compute final result return self.correct.float() / self.total my_metric = MyAccuracy() preds = torch.randn(10, 5).softmax(dim=-1) target = torch.randint(5, (10,)) print(my_metric(preds, target))

Similar to torch.nn, most metrics have both a module-based and functional version. The functional versions are simple python functions that as input take torch.tensors and return the corresponding metric as a torch.tensor.

import torch # import our library import torchmetrics # simulate a classification problem preds = torch.randn(10, 5).softmax(dim=-1) target = torch.randint(5, (10,)) acc = torchmetrics.functional.classification.multiclass_accuracy( preds, target, num_classes=5 )

In total TorchMetrics contains 100+ metrics, which covers the following domains:

• Audio
• Classification
• Detection
• Information Retrieval
• Image
• Multimodal (Image-Text-3D Talking Heads)
• Nominal
• Regression
• Segmentation
• Text

Each domain may require some additional dependencies which can be installed with pip install torchmetrics[audio], pip install torchmetrics['image'] etc.

Visualization of metrics can be important to help understand what is going on with your machine learning algorithms. Torchmetrics have built-in plotting support (install dependencies with pip install torchmetrics[visual]) for nearly all modular metrics through the .plot method. Simply call the method to get a simple visualization of any metric!

import torch from torchmetrics.classification import MulticlassAccuracy, MulticlassConfusionMatrix num_classes = 3 # this will generate two distributions that comes more similar as iterations increase w = torch.randn(num_classes) target = lambda it: torch.multinomial((it * w).softmax(dim=-1), 100, replacement=True) preds = lambda it: torch.multinomial((it * w).softmax(dim=-1), 100, replacement=True) acc = MulticlassAccuracy(num_classes=num_classes, average="micro") acc_per_class = MulticlassAccuracy(num_classes=num_classes, average=None) confmat = MulticlassConfusionMatrix(num_classes=num_classes) # plot single value for i in range(5): acc_per_class.update(preds(i), target(i)) confmat.update(preds(i), target(i)) fig1, ax1 = acc_per_class.plot() fig2, ax2 = confmat.plot() # plot multiple values values = [] for i in range(10): values.append(acc(preds(i), target(i))) fig3, ax3 = acc.plot(values)

For examples of plotting different metrics try running this example file.

The lightning + TorchMetrics team is hard at work adding even more metrics. But we're looking for incredible contributors like you to submit new metrics and improve existing ones!

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We’re excited to continue the strong legacy of open source software and have been inspired over the years by Caffe, Theano, Keras, PyTorch, torchbearer, ignite, sklearn and fast.ai.

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Please observe the Apache 2.0 license that is listed in this repository. In addition, the Lightning framework is Patent Pending.