GitHub - Lightning-AI/torchmetrics: Machine learning metrics for distributed, scalable PyTorch applications. (original) (raw)

Installation

Simple installation from PyPI

Other installations

Install using conda

conda install -c conda-forge torchmetrics

Pip from source

with git

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

Pip from archive

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

Extra dependencies for specialized metrics:

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

Install latest developer version

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

What is TorchMetrics

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

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

Using TorchMetrics

Module metrics

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

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.

Example using DDP

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 Module metric

Implementing your own metric is as easy as subclassing an torch.nn.Module. Simply, subclass torchmetrics.Metricand 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_statefor 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))

Functional metrics

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 )

Covered domains and example metrics

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

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

Additional features

Plotting

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.

Contribute!

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!

Join our Discord to get help with becoming a contributor!

Community

For help or questions, join our huge community on Discord!

Citation

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.

If you want to cite this framework feel free to use GitHub's built-in citation option to generate a bibtex or APA-Style citation based on this file (but only if you loved it 😊).

License

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