%global _empty_manifest_terminate_build 0
Name: python-torchmetrics
Version: 0.11.4
Release: 1
Summary: PyTorch native Metrics
License: Apache-2.0
URL: https://github.com/Lightning-AI/metrics
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/f3/ed/9f76e2d65d2e6d67a0ba097f34f4b618fb7466d731476d3d3440dfe2cb8e/torchmetrics-0.11.4.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-torch
Requires: python3-packaging
Requires: python3-typing-extensions
Requires: python3-pystoi
Requires: python3-pycocotools
Requires: python3-torchvision
Requires: python3-lpips
Requires: python3-torch-fidelity
Requires: python3-scipy
Requires: python3-transformers
Requires: python3-tqdm
Requires: python3-nltk
Requires: python3-regex
Requires: python3-pystoi
Requires: python3-pycocotools
Requires: python3-torchvision
Requires: python3-lpips
Requires: python3-torch-fidelity
Requires: python3-torchvision
Requires: python3-scipy
Requires: python3-transformers
Requires: python3-phmdoctest
Requires: python3-bert-score
Requires: python3-types-setuptools
Requires: python3-scipy
Requires: python3-fast-bss-eval
Requires: python3-mir-eval
Requires: python3-pytorch-msssim
Requires: python3-types-emoji
Requires: python3-types-tabulate
Requires: python3-types-protobuf
Requires: python3-types-requests
Requires: python3-pytest-timeout
Requires: python3-rouge-score
Requires: python3-fire
Requires: python3-cloudpickle
Requires: python3-kornia
Requires: python3-scikit-image
Requires: python3-huggingface-hub
Requires: python3-types-six
Requires: python3-requests
Requires: python3-pytest-cov
Requires: python3-coverage
Requires: python3-pytest-doctestplus
Requires: python3-dython
Requires: python3-sacrebleu
Requires: python3-pytest
Requires: python3-netcal
Requires: python3-psutil
Requires: python3-jiwer
Requires: python3-transformers
Requires: python3-scikit-learn
Requires: python3-mypy
Requires: python3-pandas
Requires: python3-pypesq
Requires: python3-torch-complex
Requires: python3-pytest-rerunfailures
Requires: python3-types-PyYAML
Requires: python3-tqdm
Requires: python3-nltk
Requires: python3-regex
%description
**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/metrics/blob/master/LICENSE)
[](https://github.com/Lightning-AI/metrics/actions/workflows/ci-tests-full.yml)
[](https://dev.azure.com/Lightning-AI/Metrics/_build/latest?definitionId=2&branchName=refs%2Ftags%2Fv0.11.4)
[](https://codecov.io/gh/Lightning-AI/metrics)
[](https://www.pytorchlightning.ai/community)
[](https://torchmetrics.readthedocs.io/en/latest/?badge=latest)
[](https://doi.org/10.5281/zenodo.5844769)
[](https://joss.theoj.org/papers/561d9bb59b400158bc8204e2639dca43)
[](https://results.pre-commit.ci/latest/github/Lightning-AI/metrics/master)
______________________________________________________________________
## 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/metrics.git@release/stable
```
Pip from archive
```bash
pip install https://github.com/Lightning-AI/metrics/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/metrics/archive/master.zip
```
______________________________________________________________________
## What is TorchMetrics
TorchMetrics is a collection of 90+ 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://pytorch-lightning.readthedocs.io/en/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://pytorchlightning.github.io/metrics/references/modules.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.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.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}")
# Reseting 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 implement the following methods:
```python
import torch
from torchmetrics import Metric
class MyAccuracy(Metric):
def __init__(self):
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):
# 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`](https://pytorch.org/docs/stable/nn.html), most metrics have both a [module-based](https://torchmetrics.readthedocs.io/en/latest/references/modules.html) and a [functional](https://torchmetrics.readthedocs.io/en/latest/references/functional.html) 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
We currently have implemented metrics within the following domains:
- Audio
- Classification
- Detection
- Information Retrieval
- Image
- Multimodal (Image-Text)
- Nominal
- Regression
- Text
In total TorchMetrics contains [90+ metrics](https://torchmetrics.readthedocs.io/en/v0.11.4all-metrics.html)!
## 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 [Slack](https://www.pytorchlightning.ai/community) to get help with becoming a contributor!
## Community
For help or questions, join our huge community on [Slack](https://www.pytorchlightning.ai/community)!
## 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/metrics/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.
%package -n python3-torchmetrics
Summary: PyTorch native Metrics
Provides: python-torchmetrics
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-torchmetrics
**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/metrics/blob/master/LICENSE)
[](https://github.com/Lightning-AI/metrics/actions/workflows/ci-tests-full.yml)
[](https://dev.azure.com/Lightning-AI/Metrics/_build/latest?definitionId=2&branchName=refs%2Ftags%2Fv0.11.4)
[](https://codecov.io/gh/Lightning-AI/metrics)
[](https://www.pytorchlightning.ai/community)
[](https://torchmetrics.readthedocs.io/en/latest/?badge=latest)
[](https://doi.org/10.5281/zenodo.5844769)
[](https://joss.theoj.org/papers/561d9bb59b400158bc8204e2639dca43)
[](https://results.pre-commit.ci/latest/github/Lightning-AI/metrics/master)
______________________________________________________________________
## 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/metrics.git@release/stable
```
Pip from archive
```bash
pip install https://github.com/Lightning-AI/metrics/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/metrics/archive/master.zip
```
______________________________________________________________________
## What is TorchMetrics
TorchMetrics is a collection of 90+ 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://pytorch-lightning.readthedocs.io/en/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://pytorchlightning.github.io/metrics/references/modules.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.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.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}")
# Reseting 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 implement the following methods:
```python
import torch
from torchmetrics import Metric
class MyAccuracy(Metric):
def __init__(self):
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):
# 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`](https://pytorch.org/docs/stable/nn.html), most metrics have both a [module-based](https://torchmetrics.readthedocs.io/en/latest/references/modules.html) and a [functional](https://torchmetrics.readthedocs.io/en/latest/references/functional.html) 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
We currently have implemented metrics within the following domains:
- Audio
- Classification
- Detection
- Information Retrieval
- Image
- Multimodal (Image-Text)
- Nominal
- Regression
- Text
In total TorchMetrics contains [90+ metrics](https://torchmetrics.readthedocs.io/en/v0.11.4all-metrics.html)!
## 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 [Slack](https://www.pytorchlightning.ai/community) to get help with becoming a contributor!
## Community
For help or questions, join our huge community on [Slack](https://www.pytorchlightning.ai/community)!
## 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/metrics/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.
%package help
Summary: Development documents and examples for torchmetrics
Provides: python3-torchmetrics-doc
%description help
**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/metrics/blob/master/LICENSE)
[](https://github.com/Lightning-AI/metrics/actions/workflows/ci-tests-full.yml)
[](https://dev.azure.com/Lightning-AI/Metrics/_build/latest?definitionId=2&branchName=refs%2Ftags%2Fv0.11.4)
[](https://codecov.io/gh/Lightning-AI/metrics)
[](https://www.pytorchlightning.ai/community)
[](https://torchmetrics.readthedocs.io/en/latest/?badge=latest)
[](https://doi.org/10.5281/zenodo.5844769)
[](https://joss.theoj.org/papers/561d9bb59b400158bc8204e2639dca43)
[](https://results.pre-commit.ci/latest/github/Lightning-AI/metrics/master)
______________________________________________________________________
## 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/metrics.git@release/stable
```
Pip from archive
```bash
pip install https://github.com/Lightning-AI/metrics/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/metrics/archive/master.zip
```
______________________________________________________________________
## What is TorchMetrics
TorchMetrics is a collection of 90+ 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://pytorch-lightning.readthedocs.io/en/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://pytorchlightning.github.io/metrics/references/modules.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.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.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}")
# Reseting 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 implement the following methods:
```python
import torch
from torchmetrics import Metric
class MyAccuracy(Metric):
def __init__(self):
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):
# 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`](https://pytorch.org/docs/stable/nn.html), most metrics have both a [module-based](https://torchmetrics.readthedocs.io/en/latest/references/modules.html) and a [functional](https://torchmetrics.readthedocs.io/en/latest/references/functional.html) 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
We currently have implemented metrics within the following domains:
- Audio
- Classification
- Detection
- Information Retrieval
- Image
- Multimodal (Image-Text)
- Nominal
- Regression
- Text
In total TorchMetrics contains [90+ metrics](https://torchmetrics.readthedocs.io/en/v0.11.4all-metrics.html)!
## 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 [Slack](https://www.pytorchlightning.ai/community) to get help with becoming a contributor!
## Community
For help or questions, join our huge community on [Slack](https://www.pytorchlightning.ai/community)!
## 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/metrics/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.
%prep
%autosetup -n torchmetrics-0.11.4
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-torchmetrics -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon Apr 10 2023 Python_Bot - 0.11.4-1
- Package Spec generated