Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/PytorchLightning/metrics
Machine learning metrics for distributed, scalable PyTorch applications.
https://github.com/PytorchLightning/metrics
analyses data-science deep-learning machine-learning metrics python pytorch
Last synced: 4 days ago
JSON representation
Machine learning metrics for distributed, scalable PyTorch applications.
- Host: GitHub
- URL: https://github.com/PytorchLightning/metrics
- Owner: Lightning-AI
- License: apache-2.0
- Created: 2020-12-22T20:02:42.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2024-10-28T23:30:34.000Z (about 2 months ago)
- Last Synced: 2024-10-29T09:17:11.804Z (about 2 months ago)
- Topics: analyses, data-science, deep-learning, machine-learning, metrics, python, pytorch
- Language: Python
- Homepage: https://lightning.ai/docs/torchmetrics/
- Size: 11.6 MB
- Stars: 2,126
- Watchers: 32
- Forks: 404
- Open Issues: 93
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
- Citation: CITATION.cff
- Codeowners: .github/CODEOWNERS
Awesome Lists containing this project
- StarryDivineSky - PytorchLightning/metrics
README
**Machine learning metrics for distributed, scalable PyTorch applications.**
______________________________________________________________________
What is Torchmetrics •
Implementing a metric •
Built-in metrics •
Docs •
Community •
License
______________________________________________________________________
[](https://pypi.org/project/torchmetrics/)
[](https://badge.fury.io/py/torchmetrics)
[
](https://pepy.tech/project/torchmetrics)
[](https://anaconda.org/conda-forge/torchmetrics)
[](https://github.com/Lightning-AI/torchmetrics/blob/master/LICENSE)
[](https://github.com/Lightning-AI/torchmetrics/actions/workflows/ci-tests.yml)
[](https://dev.azure.com/Lightning-AI/Metrics/_build/latest?definitionId=54&branchName=master)
[](https://codecov.io/gh/Lightning-AI/torchmetrics)
[](https://results.pre-commit.ci/latest/github/Lightning-AI/torchmetrics/master)
[](https://torchmetrics.readthedocs.io/en/latest/?badge=latest)
[](https://discord.gg/VptPCZkGNa)
[](https://doi.org/10.5281/zenodo.5844769)
[](https://joss.theoj.org/papers/561d9bb59b400158bc8204e2639dca43)
______________________________________________________________________
## Installation
Simple installation from PyPI
```bash
pip install torchmetrics
```
Other installations
Install using conda
```bash
conda install -c conda-forge torchmetrics
```
Pip from source
```bash
# with git
pip install git+https://github.com/Lightning-AI/torchmetrics.git@release/stable
```
Pip from archive
```bash
pip install https://github.com/Lightning-AI/torchmetrics/archive/refs/heads/release/stable.zip
```
Extra dependencies for specialized metrics:
```bash
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
```bash
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:
- 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](https://lightning.ai/docs/pytorch/stable/) 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](https://lightning.ai/docs/torchmetrics/stable/references/metric.html) 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:
```python
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
```python
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`](https://pytorch.org/docs/stable/generated/torch.nn.Module.html). Simply, subclass `torchmetrics.Metric`
and just implement the `update` and `compute` methods:
```python
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))
```
### Functional metrics
Similar to [`torch.nn`](https://pytorch.org/docs/stable/nn.html), most metrics have both a [module-based](https://lightning.ai/docs/torchmetrics/stable/references/metric.html) and functional version.
The functional versions are simple python functions that as input take [torch.tensors](https://pytorch.org/docs/stable/tensors.html) and return the corresponding metric as a [torch.tensor](https://pytorch.org/docs/stable/tensors.html).
```python
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](https://lightning.ai/docs/torchmetrics/stable/all-metrics.html), which
covers the following domains:
- Audio
- Classification
- Detection
- Information Retrieval
- Image
- Multimodal (Image-Text)
- 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.
### 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!
```python
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](_samples/plotting.py).
## 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](https://discord.com/invite/tfXFetEZxv) to get help with becoming a contributor!
## Community
For help or questions, join our huge community on [Discord](https://discord.com/invite/tfXFetEZxv)!
## 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](https://github.com/Lightning-AI/torchmetrics/blob/master/CITATION.cff) (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.