The torchmetrics is a Metrics API created for easy metric development and usage in PyTorch and PyTorch Lightning. It is rigorously tested for all edge cases and includes a growing list of common metric implementations.

The metrics API provides update(), compute(), reset() functions to the user. The metric base class inherits torch.nn.Module which allows us to call metric(...) directly. The forward() method of the base Metric class serves the dual purpose of calling update() on its input and simultaneously returning the value of the metric over the provided input.

These metrics work with DDP in PyTorch and PyTorch Lightning by default. When .compute() is called in distributed mode, the internal state of each metric is synced and reduced across each process, so that the logic present in .compute() is applied to state information from all processes.

This metrics API is independent of PyTorch Lightning. Metrics can directly be used in PyTorch as shown in the example:

from torchmetrics.classification import Accuracy

train_accuracy = Accuracy()
valid_accuracy = Accuracy()

for epoch in range(epochs):
    for x, y in train_data:
        y_hat = model(x)

        # training step accuracy
        batch_acc = train_accuracy(y_hat, y)
        print(f"Accuracy of batch{i} is {batch_acc}")

    for x, y in valid_data:
        y_hat = model(x)
        valid_accuracy.update(y_hat, y)

    # total accuracy over all training batches
    total_train_accuracy = train_accuracy.compute()

    # total accuracy over all validation batches
    total_valid_accuracy = valid_accuracy.compute()

    print(f"Training acc for epoch {epoch}: {total_train_accuracy}")
    print(f"Validation acc for epoch {epoch}: {total_valid_accuracy}")

    # Reset metric states after each epoch


Metrics contain internal states that keep track of the data seen so far. Do not mix metric states across training, validation and testing. It is highly recommended to re-initialize the metric per mode as shown in the examples above.


Metric states are not added to the models state_dict by default. To change this, after initializing the metric, the method .persistent(mode) can be used to enable (mode=True) or disable (mode=False) this behaviour.


Due to specialized logic around metric states, we in general do not recommend that metrics are initialized inside other metrics (nested metrics), as this can lead to weird behaviour. Instead consider subclassing a metric or use torchmetrics.MetricCollection.

Metrics and devices

Metrics are simple subclasses of Module and their metric states behave similar to buffers and parameters of modules. This means that metrics states should be moved to the same device as the input of the metric:

from torchmetrics import Accuracy

target = torch.tensor([1, 1, 0, 0], device=torch.device("cuda", 0))
preds = torch.tensor([0, 1, 0, 0], device=torch.device("cuda", 0))

# Metric states are always initialized on cpu, and needs to be moved to
# the correct device
confmat = Accuracy(num_classes=2).to(torch.device("cuda", 0))
out = confmat(preds, target)
print(out.device) # cuda:0

However, when properly defined inside a Module or LightningModule the metric will be be automatically move to the same device as the the module when using .to(device). Being properly defined means that the metric is correctly identified as a child module of the model (check .children() attribute of the model). Therefore, metrics cannot be placed in native python list and dict, as they will not be correctly identified as child modules. Instead of list use ModuleList and instead of dict use ModuleDict. Furthermore, when working with multiple metrics the native MetricCollection module can also be used to wrap multiple metrics.

from torchmetrics import Accuracy, MetricCollection

class MyModule(torch.nn.Module):
    def __init__(self):
        # valid ways metrics will be identified as child modules
        self.metric1 = Accuracy()
        self.metric2 = nn.ModuleList(Accuracy())
        self.metric3 = nn.ModuleDict({'accuracy': Accuracy()})
        self.metric4 = MetricCollection([Accuracy()]) # torchmetrics build-in collection class

    def forward(self, batch):
        data, target = batch
        preds = self(data)
        val1 = self.metric1(preds, target)
        val2 = self.metric2[0](preds, target)
        val3 = self.metric3['accuracy'](preds, target)
        val4 = self.metric4(preds, target)

You can always check which device the metric is located on using the .device property.

Metrics in Dataparallel (DP) mode

When using metrics in Dataparallel (DP) mode, one should be aware DP will both create and clean-up replicas of Metric objects during a single forward pass. This has the consequence, that the metric state of the replicas will as default be destroyed before we can sync them. It is therefore recommended, when using metrics in DP mode, to initialize them with dist_sync_on_step=True such that metric states are synchonized between the main process and the replicas before they are destroyed.

Addtionally, if metrics are used together with a LightningModule the metric update/logging should be done in the <mode>_step_end method (where <mode> is either training, validation or test), else it will lead to wrong accumulation. In practice do the following:

def training_step(self, batch, batch_idx):
    data, target = batch
    preds = self(data)
    return {'loss': loss, 'preds': preds, 'target': target}

def training_step_end(self, outputs):
    #update and log
    self.metric(outputs['preds'], outputs['target'])
    self.log('metric', self.metric)

Metrics in Distributed Data Parallel (DDP) mode

When using metrics in Distributed Data Parallel (DDP) mode, one should be aware that DDP will add additional samples to your dataset if the size of your dataset is not equally divisible by batch_size * num_processors. The added samples will always be replicates of datapoints already in your dataset. This is done to secure an equal load for all processes. However, this has the consequence that the calculated metric value will be sligtly bias towards those replicated samples, leading to a wrong result.

During training and/or validation this may not be important, however it is highly recommended when evaluating the test dataset to only run on a single gpu or use a join context in conjunction with DDP to prevent this behaviour.

Metrics and 16-bit precision

Most metrics in our collection can be used with 16-bit precision (torch.half) tensors. However, we have found the following limitations:

  • In general pytorch had better support for 16-bit precision much earlier on GPU than CPU. Therefore, we recommend that anyone that want to use metrics with half precision on CPU, upgrade to atleast pytorch v1.6 where support for operations such as addition, subtraction, multiplication ect. was added.

  • Some metrics does not work at all in half precision on CPU. We have explicitly stated this in their docstring, but they are also listed below:

You can always check the precision/dtype of the metric by checking the .dtype property.

Metric Arithmetics

Metrics support most of python built-in operators for arithmetic, logic and bitwise operations.

For example for a metric that should return the sum of two different metrics, implementing a new metric is an overhead that is not necessary. It can now be done with:

first_metric = MyFirstMetric()
second_metric = MySecondMetric()

new_metric = first_metric + second_metric

new_metric.update(*args, **kwargs) now calls update of first_metric and second_metric. It forwards all positional arguments but forwards only the keyword arguments that are available in respective metric’s update declaration. Similarly new_metric.compute() now calls compute of first_metric and second_metric and adds the results up. It is important to note that all implemented operations always returns a new metric object. This means that the line first_metric == second_metric will not return a bool indicating if first_metric and second_metric is the same metric, but will return a new metric that checks if the first_metric.compute() == second_metric.compute().

This pattern is implemented for the following operators (with a being metrics and b being metrics, tensors, integer or floats):

  • Addition (a + b)

  • Bitwise AND (a & b)

  • Equality (a == b)

  • Floordivision (a // b)

  • Greater Equal (a >= b)

  • Greater (a > b)

  • Less Equal (a <= b)

  • Less (a < b)

  • Matrix Multiplication (a @ b)

  • Modulo (a % b)

  • Multiplication (a * b)

  • Inequality (a != b)

  • Bitwise OR (a | b)

  • Power (a ** b)

  • Subtraction (a - b)

  • True Division (a / b)

  • Bitwise XOR (a ^ b)

  • Absolute Value (abs(a))

  • Inversion (~a)

  • Negative Value (neg(a))

  • Positive Value (pos(a))

  • Indexing (a[0])


Some of these operations are only fully supported from Pytorch v1.4 and onwards, explicitly we found: add, mul, rmatmul, rsub, rmod


In many cases it is beneficial to evaluate the model output by multiple metrics. In this case the MetricCollection class may come in handy. It accepts a sequence of metrics and wraps these into a single callable metric class, with the same interface as any other metric.


from torchmetrics import MetricCollection, Accuracy, Precision, Recall
target = torch.tensor([0, 2, 0, 2, 0, 1, 0, 2])
preds = torch.tensor([2, 1, 2, 0, 1, 2, 2, 2])
metric_collection = MetricCollection([
    Precision(num_classes=3, average='macro'),
    Recall(num_classes=3, average='macro')
print(metric_collection(preds, target))
{'Accuracy': tensor(0.1250),
 'Precision': tensor(0.0667),
 'Recall': tensor(0.1111)}

Similarly it can also reduce the amount of code required to log multiple metrics inside your LightningModule

from torchmetrics import Accuracy, MetricCollection, Precision, Recall

class MyModule(LightningModule):
    def __init__(self):
        metrics = MetricCollection([Accuracy(), Precision(), Recall()])
        self.train_metrics = metrics.clone(prefix='train_')
        self.valid_metrics = metrics.clone(prefix='val_')

    def training_step(self, batch, batch_idx):
        logits = self(x)
        # ...
        output = self.train_metrics(logits, y)
        # use log_dict instead of log
        # metrics are logged with keys: train_Accuracy, train_Precision and train_Recall

    def validation_step(self, batch, batch_idx):
        logits = self(x)
        # ...
        output = self.valid_metrics(logits, y)
        # use log_dict instead of log
        # metrics are logged with keys: val_Accuracy, val_Precision and val_Recall


MetricCollection as default assumes that all the metrics in the collection have the same call signature. If this is not the case, input that should be given to different metrics can given as keyword arguments to the collection.

An additional advantage of using the MetricCollection object is that it will automatically try to reduce the computations needed by finding groups of metrics that share the same underlying metric state. If such a group of metrics is found only one of them is actually updated and the updated state will be broadcasted to the rest of the metrics within the group. In the example above, this will lead to a 2x-3x lower computational cost compared to disabling this feature. However, this speedup comes with a fixed cost upfront, where the state-groups have to be determined after the first update. This overhead can be significantly higher then gains speed-up for very a low number of steps (approx. up to 100) but still leads to an overall speedup for everything beyond that. In case the groups are known beforehand, these can also be set manually to avoid this extra cost of the dynamic search. See the compute_groups argument in the class docs below for more information on this topic.

class torchmetrics.MetricCollection(metrics, *additional_metrics, prefix=None, postfix=None, compute_groups=True)[source]

MetricCollection class can be used to chain metrics that have the same call pattern into one single class.

  • metrics (Union[Metric, Sequence[Metric], Dict[str, Metric]]) –

    One of the following

    • list or tuple (sequence): if metrics are passed in as a list or tuple, will use the metrics class name as key for output dict. Therefore, two metrics of the same class cannot be chained this way.

    • arguments: similar to passing in as a list, metrics passed in as arguments will use their metric class name as key for the output dict.

    • dict: if metrics are passed in as a dict, will use each key in the dict as key for output dict. Use this format if you want to chain together multiple of the same metric with different parameters. Note that the keys in the output dict will be sorted alphabetically.

  • prefix (Optional[str]) – a string to append in front of the keys of the output dict

  • postfix (Optional[str]) – a string to append after the keys of the output dict

  • compute_groups (Union[bool, List[List[str]]]) – By default the MetricCollection will try to reduce the computations needed for the metrics in the collection by checking if they belong to the same compute group. All metrics in a compute group share the same metric state and are therefore only different in their compute step e.g. accuracy, precision and recall can all be computed from the true positives/negatives and false positives/negatives. By default, this argument is True which enables this feature. Set this argument to False for disabling this behaviour. Can also be set to a list of list of metrics for setting the compute groups yourself.

  • ValueError – If one of the elements of metrics is not an instance of pl.metrics.Metric.

  • ValueError – If two elements in metrics have the same name.

  • ValueError – If metrics is not a list, tuple or a dict.

  • ValueError – If metrics is dict and additional_metrics are passed in.

  • ValueError – If prefix is set and it is not a string.

  • ValueError – If postfix is set and it is not a string.

Example (input as list):
>>> import torch
>>> from pprint import pprint
>>> from torchmetrics import MetricCollection, Accuracy, Precision, Recall, MeanSquaredError
>>> target = torch.tensor([0, 2, 0, 2, 0, 1, 0, 2])
>>> preds = torch.tensor([2, 1, 2, 0, 1, 2, 2, 2])
>>> metrics = MetricCollection([Accuracy(),
...                             Precision(num_classes=3, average='macro'),
...                             Recall(num_classes=3, average='macro')])
>>> metrics(preds, target)
{'Accuracy': tensor(0.1250), 'Precision': tensor(0.0667), 'Recall': tensor(0.1111)}
Example (input as arguments):
>>> metrics = MetricCollection(Accuracy(), Precision(num_classes=3, average='macro'),
...                            Recall(num_classes=3, average='macro'))
>>> metrics(preds, target)
{'Accuracy': tensor(0.1250), 'Precision': tensor(0.0667), 'Recall': tensor(0.1111)}
Example (input as dict):
>>> metrics = MetricCollection({'micro_recall': Recall(num_classes=3, average='micro'),
...                             'macro_recall': Recall(num_classes=3, average='macro')})
>>> same_metric = metrics.clone()
>>> pprint(metrics(preds, target))
{'macro_recall': tensor(0.1111), 'micro_recall': tensor(0.1250)}
>>> pprint(same_metric(preds, target))
{'macro_recall': tensor(0.1111), 'micro_recall': tensor(0.1250)}
Example (specification of compute groups):
>>> metrics = MetricCollection(
...     Accuracy(),
...     Precision(num_classes=3, average='macro'),
...     MeanSquaredError(),
...     compute_groups=[['Accuracy', 'Precision'], ['MeanSquaredError']]
... )
>>> pprint(metrics(preds, target))
{'Accuracy': tensor(0.1250), 'MeanSquaredError': tensor(2.3750), 'Precision': tensor(0.0667)}

Initializes internal Module state, shared by both nn.Module and ScriptModule.

add_metrics(metrics, *additional_metrics)[source]

Add new metrics to Metric Collection.

Return type


clone(prefix=None, postfix=None)[source]

Make a copy of the metric collection :type _sphinx_paramlinks_torchmetrics.MetricCollection.clone.prefix: Optional[str] :param _sphinx_paramlinks_torchmetrics.MetricCollection.clone.prefix: a string to append in front of the metric keys :type _sphinx_paramlinks_torchmetrics.MetricCollection.clone.postfix: Optional[str] :param _sphinx_paramlinks_torchmetrics.MetricCollection.clone.postfix: a string to append after the keys of the output dict

Return type



Compute the result for each metric in the collection.

Return type

Dict[str, Any]

forward(*args, **kwargs)[source]

Iteratively call forward for each metric.

Positional arguments (args) will be passed to every metric in the collection, while keyword arguments (kwargs) will be filtered based on the signature of the individual metric.

Return type

Dict[str, Any]


Return an iterable of the ModuleDict key/value pairs. :type _sphinx_paramlinks_torchmetrics.MetricCollection.items.keep_base: bool :param _sphinx_paramlinks_torchmetrics.MetricCollection.items.keep_base: Whether to add prefix/postfix on the items collection.

Return type

Iterable[Tuple[str, Module]]


Return an iterable of the ModuleDict key. :type _sphinx_paramlinks_torchmetrics.MetricCollection.keys.keep_base: bool :param _sphinx_paramlinks_torchmetrics.MetricCollection.keys.keep_base: Whether to add prefix/postfix on the items collection.

Return type



Method for post-init to change if metric states should be saved to its state_dict.

Return type



Iteratively call reset for each metric.

Return type


update(*args, **kwargs)[source]

Iteratively call update for each metric.

Positional arguments (args) will be passed to every metric in the collection, while keyword arguments (kwargs) will be filtered based on the signature of the individual metric.

Return type


property compute_groups: Dict[int, List[str]][source]

Return a dict with the current compute groups in the collection.

Return type

Dict[int, List[str]]

Module vs Functional Metrics

The functional metrics follow the simple paradigm input in, output out. This means they don’t provide any advanced mechanisms for syncing across DDP nodes or aggregation over batches. They simply compute the metric value based on the given inputs.

Also, the integration within other parts of PyTorch Lightning will never be as tight as with the Module-based interface. If you look for just computing the values, the functional metrics are the way to go. However, if you are looking for the best integration and user experience, please consider also using the Module interface.

Metrics and differentiability

Metrics support backpropagation, if all computations involved in the metric calculation are differentiable. All modular metric classes have the property is_differentiable that determines if a metric is differentiable or not.

However, note that the cached state is detached from the computational graph and cannot be back-propagated. Not doing this would mean storing the computational graph for each update call, which can lead to out-of-memory errors. In practise this means that:

MyMetric.is_differentiable  # returns True if metric is differentiable
metric = MyMetric()
val = metric(pred, target)  # this value can be back-propagated
val = metric.compute()  # this value cannot be back-propagated

A functional metric is differentiable if its corresponding modular metric is differentiable.

Metrics and hyperparameter optimization

If you want to directly optimize a metric it needs to support backpropagation (see section above). However, if you are just interested in using a metric for hyperparameter tuning and are not sure if the metric should be maximized or minimized, all modular metric classes have the higher_is_better property that can be used to determine this:

# returns True because accuracy is optimal when it is maximized

# returns False because the mean squared error is optimal when it is minimized

Advanced metric settings

The following is a list of additional arguments that can be given to any metric class (in the **kwargs argument) that will alter how metric states are stored and synced.

If you are running metrics on GPU and are encountering that you are running out of GPU VRAM then the following argument can help:

  • compute_on_cpu will automatically move the metric states to cpu after calling update, making sure that GPU memory is not filling up. The consequence will be that the compute method will be called on CPU instead of GPU. Only applies to metric states that are lists.

If you are running in a distributed environment, TorchMetrics will automatically take care of the distributed synchronization for you. However, the following three keyword arguments can be given to any metric class for further control over the distributed aggregation:

  • dist_sync_on_step: This argument is bool that indicates if the metric should syncronize between different devices every time forward is called. Setting this to True is in general not recommended as syncronization is an expensive operation to do after each batch.

  • process_group: By default we syncronize across the world i.e. all proceses being computed on. You can provide an torch._C._distributed_c10d.ProcessGroup in this argument to specify exactly what devices should be syncronized over.

  • dist_sync_fn: By default we use torch.distributed.all_gather() to perform the synchronization between devices. Provide another callable function for this argument to perform custom distributed synchronization.

Read the Docs v: v0.8.2
On Read the Docs
Project Home

Free document hosting provided by Read the Docs.