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https://github.com/pytorch/torcheval

A library that contains a rich collection of performant PyTorch model metrics, a simple interface to create new metrics, a toolkit to facilitate metric computation in distributed training and tools for PyTorch model evaluations.
https://github.com/pytorch/torcheval

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A library that contains a rich collection of performant PyTorch model metrics, a simple interface to create new metrics, a toolkit to facilitate metric computation in distributed training and tools for PyTorch model evaluations.

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# TorchEval





build status

pypi version

pypi nightly version

bsd license



docs




**This library is currently in Alpha and currently does not have a stable release. The API may change and may not be backward compatible. If you have suggestions for improvements, please open a GitHub issue. We'd love to hear your feedback.**



A library that contains a rich collection of performant PyTorch model metrics, a simple interface to create new metrics, a toolkit to facilitate metric computation in distributed training and tools for PyTorch model evaluations.



## Installing TorchEval

Requires Python >= 3.8 and PyTorch >= 1.11



From pip:



```bash

pip install torcheval

```



For nighly build version

```bash

pip install --pre torcheval-nightly

```



From source:



```bash

git clone https://github.com/pytorch/torcheval

cd torcheval

pip install -r requirements.txt

python setup.py install

```



## Quick Start



Take a look at the [quickstart notebook](https://github.com/pytorch/torcheval/blob/main/examples/Introducing_TorchEval.ipynb), or fork it on [Colab](https://colab.research.google.com/github/pytorch/torcheval/blob/main/examples/Introducing_TorchEval.ipynb).



There are more examples in the [examples](https://github.com/pytorch/torcheval/blob/main/examples) directory:



```bash

cd torcheval

python examples/simple_example.py

```



## Documentation



Documentation can be found at at [pytorch.org/torcheval](https://pytorch.org/torcheval)



## Using TorchEval



TorchEval can be run on CPU, GPU, and in a multi-process or multi-GPU setting. Metrics are provided in two interfaces, functional and class based. The functional interfaces can be found in `torcheval.metrics.functional` and are useful when your program runs in a single process setting. To use multi-process or multi-gpu configurations, the class-based interfaces, found in `torcheval.metrics` provide a much simpler experience. The class based interfaces also allow you to defer some of the computation of the metric by calling `update()` multiple times before `compute()`. This can be advantageous even in a single process setting due to saved computation overhead.



### Single Process

For use in a single process program, the simplest use case utilizes a functional metric. We simply import the metric function and feed in our outputs and targets. The example below shows a minimal PyTorch training loop that evaluates the multiclass accuracy of every fourth batch of data.



#### Functional Version (immediate computation of metric)

```python

import torch

from torcheval.metrics.functional import multiclass_accuracy



NUM_BATCHES = 16

BATCH_SIZE = 8

INPUT_SIZE = 10

NUM_CLASSES = 6

eval_frequency = 4



model = torch.nn.Sequential(torch.nn.Linear(INPUT_SIZE, NUM_CLASSES), torch.nn.ReLU())

optim = torch.optim.Adagrad(model.parameters(), lr=0.001)

loss_fn = torch.nn.CrossEntropyLoss()



metric_history = []

for batch in range(NUM_BATCHES):

    input = torch.rand(size=(BATCH_SIZE, INPUT_SIZE))

    target = torch.randint(size=(BATCH_SIZE,), high=NUM_CLASSES)

    outputs = model(input)



    loss = loss_fn(outputs, target)

    optim.zero_grad()

    loss.backward()

    optim.step()



    # metric only computed every 4 batches,

    # data from previous three batches is lost

    if (batch + 1) % eval_frequency == 0:

        metric_history.append(multiclass_accuracy(outputs, target))

```

### Single Process with Deferred Computation



#### Class Version (enables deferred computation of metric)

```python

import torch

from torcheval.metrics import MulticlassAccuracy



NUM_BATCHES = 16

BATCH_SIZE = 8

INPUT_SIZE = 10

NUM_CLASSES = 6

eval_frequency = 4



model = torch.nn.Sequential(torch.nn.Linear(INPUT_SIZE, NUM_CLASSES), torch.nn.ReLU())

optim = torch.optim.Adagrad(model.parameters(), lr=0.001)

loss_fn = torch.nn.CrossEntropyLoss()

metric = MulticlassAccuracy()



metric_history = []

for batch in range(NUM_BATCHES):

    input = torch.rand(size=(BATCH_SIZE, INPUT_SIZE))

    target = torch.randint(size=(BATCH_SIZE,), high=NUM_CLASSES)

    outputs = model(input)



    loss = loss_fn(outputs, target)

    optim.zero_grad()

    loss.backward()

    optim.step()



    # metric only computed every 4 batches,

    # data from previous three batches is included

    metric.update(input, target)

    if (batch + 1) % eval_frequency == 0:

        metric_history.append(metric.compute())

        # remove old data so that the next call

        # to compute is only based off next 4 batches

        metric.reset()

```



### Multi-Process or Multi-GPU

For usage on multiple devices a minimal example is given below. In the normal `torch.distributed` paradigm, each device is allocated its own process gets a unique numerical ID called a "global rank", counting up from 0.



#### Class Version (enables deferred computation and multi-processing)

```python

import torch

from torcheval.metrics.toolkit import sync_and_compute

from torcheval.metrics import MulticlassAccuracy



# Using torch.distributed

local_rank = int(os.environ["LOCAL_RANK"]) #rank on local machine, i.e. unique ID within a machine

global_rank = int(os.environ["RANK"]) #rank in global pool, i.e. unique ID within the entire process group

world_size  = int(os.environ["WORLD_SIZE"]) #total number of processes or "ranks" in the entire process group



device = torch.device(

    f"cuda:{local_rank}"

    if torch.cuda.is_available() and torch.cuda.device_count() >= world_size

    else "cpu"

)



metric = MulticlassAccuracy(device=device)

num_epochs, num_batches = 4, 8



for epoch in range(num_epochs):

    for i in range(num_batches):

        input = torch.randint(high=5, size=(10,), device=device)

        target = torch.randint(high=5, size=(10,), device=device)



        # Add data to metric locally

        metric.update(input, target)



        # metric.compute() will returns metric value from

        # all seen data on the local process since last reset()

        local_compute_result = metric.compute()



        # sync_and_compute(metric) syncs metric data across all ranks and computes the metric value

        global_compute_result = sync_and_compute(metric)

        if global_rank == 0:

            print(global_compute_result)



    # metric.reset() clears the data on each process so that subsequent

    # calls to compute() only act on new data

    metric.reset()

```

See the [example directory](https://github.com/pytorch/torcheval/tree/main/examples) for more examples.



## Contributing

We welcome PRs! See the [CONTRIBUTING](CONTRIBUTING.md) file.



## License

TorchEval is BSD licensed, as found in the [LICENSE](LICENSE) file.