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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Installation

Simple installation from PyPI

pip install torchmetrics

Other installations

Install using conda

conda install torchmetrics

Pip from source

# with git
pip install git+https://github.com/PytorchLightning/metrics.git@master

Pip from archive

pip install https://github.com/PyTorchLightning/metrics/archive/master.zip

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What is Torchmetrics

TorchMetrics is a collection of 25+ 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.

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!

• 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.Accuracy()

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)
    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

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.Accuracy()

# 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}")

    # Reseting internal state such that metric ready for new data
    metric.reset()

Implementing your own Module metric

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

class MyAccuracy(Metric):
    def __init__(self, dist_sync_on_step=False):
        # 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
	super().__init__(dist_sync_on_step=dist_sync_on_step)

        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):
        # update metric states
        preds, target = self._input_format(preds, target)
        assert preds.shape == target.shape

        self.correct += torch.sum(preds == target)
        self.total += target.numel()

    def compute(self):
        # compute final result
        return self.correct.float() / self.total

Functional metrics

Similar to torch.nn, most metrics have both a module-based and a 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.accuracy(preds, target)

Implemented metrics

• Accuracy
• AveragePrecision
• AUC
• AUROC
• F1
• Hamming Distance
• ROC
• ExplainedVariance
• MeanSquaredError
• R2Score
• bleu_score
• embedding_similarity

And many more!

Contribute!

The lightning + torchmetric 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 Slack to get help becoming a contributor!

Community

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

Citations

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. When/if a paper is written about this, we’ll be happy to cite these frameworks and the corresponding authors.

License

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