Table of Contents
Shortcuts

TorchMetrics documentation

TorchMetrics is a collection of Machine learning metrics for distributed, scalable PyTorch models and an easy-to-use API to create custom metrics. It offers the following benefits:

  • Optimized for distributed-training

  • A standardized interface to increase reproducibility

  • Reduces Boilerplate

  • Distributed-training compatible

  • Rigorously tested

  • Automatic accumulation over batches

  • Automatic synchronization between multiple devices

You can use TorchMetrics in any PyTorch model, or with in PyTorch Lightning to enjoy additional features:

  • This means that your data will always be placed on the same device as your metrics.

  • Native support for logging metrics in Lightning to reduce even more boilerplate.

Using TorchMetrics

Module metrics

import torch
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.

Functional metrics

import torch
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)

Implementing a metric

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

More reading

Quick Start

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 reproducability

  • Reduces Boilerplate

  • Distrubuted-training compatible

  • Rigorously tested

  • Automatic accumulation over batches

  • Automatic synchronization between multiple devices

You can use TorchMetrics in any PyTorch model, or with in PyTorch Lightning to enjoy additional features:

  • This means that your data will always be placed on the same device as your metrics.

  • Native support for logging metrics in Lightning to reduce even more boilerplate.

Install

You can install TorchMetrics using pip or conda:

pip install torchmetrics

Using TorchMetrics

Functional metrics

Similar to torch.nn, most metrics have both a class-based and a functional version. The functional versions implement the basic operations required for computing each metric. They are simple python functions that as input take torch.tensors and return the corresponding metric as a torch.tensor. The code-snippet below shows a simple example for calculating the accuracy using the functional interface:

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)

Module metrics

Nearly all functional metrics have a corresponding class-based metric that calls it a functional counterpart underneath. The class-based metrics are characterized by having one or more internal metrics states (similar to the parameters of the PyTorch module) that allow them to offer additional functionalities:

  • Accumulation of multiple batches

  • Automatic synchronization between multiple devices

  • Metric arithmetic

The code below shows how to use the class-based interface:

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}")

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

Implementing your own metric

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

  1. Implement __init__ where you call self.add_state for every internal state that is needed for the metrics computations

  2. Implement update method, where all logic that is necessary for updating metric states go

  3. Implement compute method, where the final metric computations happens

For practical examples and more info about implementing a metric, please see this page.

Overview

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(compute_on_step=False)

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)

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

Note

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.

Note

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.

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)

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.

Metrics in Distributed Data Parallel (DDP) mode

When using metrics in Distributed Data Parallel (DPP) 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:

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])

Note

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

MetricCollection

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 theses into a single callable metric class, with the same interface as any other metric.

Example:

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([
    Accuracy(),
    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
        self.log_dict(output)

    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
        self.log_dict(output)

Note

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.

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

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

Parameters

  • 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

Raises

  • 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
>>> 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)}
>>> metrics.persistent()

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

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

MetricCollection

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]

items(keep_base=False)[source]

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]]

keys(keep_base=False)[source]

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.

persistent(mode=True)[source]

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

Return type

None

reset()[source]

Iteratively call reset for each metric

Return type

None

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.

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 metrics have a property that determines if a metric is differentible or not.

@property
def is_differentiable(self) -> bool:
    return True/False

However, note that the cached state is detached from the computational graph and cannot be backpropagated. 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:

metric = MyMetric()
val = metric(pred, target) # this value can be backpropagated
val = metric.compute() # this value cannot be backpropagated

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

Implementing a Metric

To implement your own custom metric, subclass the base Metric class and implement the following methods:

  • __init__(): Each state variable should be called using self.add_state(...).

  • update(): Any code needed to update the state given any inputs to the metric.

  • compute(): Computes a final value from the state of the metric.

All you need to do is call add_state correctly to implement a custom metric with DDP. reset() is called on metric state variables added using add_state().

To see how metric states are synchronized across distributed processes, refer to add_state() docs from the base Metric class.

Example implementation:

from torchmetrics import Metric

class MyAccuracy(Metric):
    def __init__(self, dist_sync_on_step=False):
        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):
        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):
        return self.correct.float() / self.total

Internal implementation details

This section briefly describes how metrics work internally. We encourage looking at the source code for more info. Internally, Lightning wraps the user defined update() and compute() method. We do this to automatically synchronize and reduce metric states across multiple devices. More precisely, calling update() does the following internally:

  1. Clears computed cache.

  2. Calls user-defined update().

Similarly, calling compute() does the following internally:

  1. Syncs metric states between processes.

  2. Reduce gathered metric states.

  3. Calls the user defined compute() method on the gathered metric states.

  4. Cache computed result.

From a user’s standpoint this has one important side-effect: computed results are cached. This means that no matter how many times compute is called after one and another, it will continue to return the same result. The cache is first emptied on the next call to update.

forward serves the dual purpose of both returning the metric on the current data and updating the internal metric state for accumulating over multiple batches. The forward() method achieves this by combining calls to update and compute in the following way (assuming metric is initialized with compute_on_step=True):

  1. Calls update() to update the global metric state (for accumulation over multiple batches)

  2. Caches the global state.

  3. Calls reset() to clear global metric state.

  4. Calls update() to update local metric state.

  5. Calls compute() to calculate metric for current batch.

  6. Restores the global state.

This procedure has the consequence of calling the user defined update twice during a single forward call (one to update global statistics and one for getting the batch statistics).

class torchmetrics.Metric(compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None)[source]

Base class for all metrics present in the Metrics API.

Implements add_state(), forward(), reset() and a few other things to handle distributed synchronization and per-step metric computation.

Override update() and compute() functions to implement your own metric. Use add_state() to register metric state variables which keep track of state on each call of update() and are synchronized across processes when compute() is called.

Note

Metric state variables can either be torch.Tensors or an empty list which can we used to store torch.Tensors`.

Note

Different metrics only override update() and not forward(). A call to update() is valid, but it won’t return the metric value at the current step. A call to forward() automatically calls update() and also returns the metric value at the current step.

Parameters

  • compute_on_step (bool) – Forward only calls update() and returns None if this is set to False. default: True

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step.

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

  • dist_sync_fn (Optional[Callable]) – Callback that performs the allgather operation on the metric state. When None, DDP will be used to perform the allgather.

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

add_state(name, default, dist_reduce_fx=None, persistent=False)[source]

Adds metric state variable. Only used by subclasses.

Parameters

  • name (str) – The name of the state variable. The variable will then be accessible at self.name.

  • default – Default value of the state; can either be a torch.Tensor or an empty list. The state will be reset to this value when self.reset() is called.

  • dist_reduce_fx (Optional) – Function to reduce state across multiple processes in distributed mode. If value is "sum", "mean", or "cat", we will use torch.sum, torch.mean, and torch.cat respectively, each with argument dim=0. Note that the "cat" reduction only makes sense if the state is a list, and not a tensor. The user can also pass a custom function in this parameter.

  • persistent (Optional) – whether the state will be saved as part of the modules state_dict. Default is False.

Note

Setting dist_reduce_fx to None will return the metric state synchronized across different processes. However, there won’t be any reduction function applied to the synchronized metric state.

The metric states would be synced as follows

  • If the metric state is torch.Tensor, the synced value will be a stacked torch.Tensor across the process dimension if the metric state was a torch.Tensor. The original torch.Tensor metric state retains dimension and hence the synchronized output will be of shape (num_process, ...).

  • If the metric state is a list, the synced value will be a list containing the combined elements from all processes.

Note

When passing a custom function to dist_reduce_fx, expect the synchronized metric state to follow the format discussed in the above note.

Raises

  • ValueError – If default is not a tensor or an empty list.

  • ValueError – If dist_reduce_fx is not callable or one of "mean", "sum", "cat", None.

clone()[source]

Make a copy of the metric

abstract compute()[source]

Override this method to compute the final metric value from state variables synchronized across the distributed backend.

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

Automatically calls update(). Returns the metric value over inputs if compute_on_step is True.

persistent(mode=False)[source]

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

reset()[source]

This method automatically resets the metric state variables to their default value.

state_dict(destination=None, prefix='', keep_vars=False)[source]

Returns a dictionary containing a whole state of the module.

Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names.

Returns

a dictionary containing a whole state of the module

Return type

dict

Example:

>>> module.state_dict().keys()
['bias', 'weight']
abstract update()[source]

Override this method to update the state variables of your metric class.

Return type

None

Contributing your metric to Torchmetrics

Wanting to contribute the metric you have implemented? Great, we are always open to adding more metrics to torchmetrics as long as they serve a general purpose. However, to keep all our metrics consistent we request that the implementation and tests gets formatted in the following way:

  1. Start by reading our contribution guidelines.

  2. First implement the functional backend. This takes cares of all the logic that goes into the metric. The code should be put into a single file placed under torchmetrics/functional/"domain"/"new_metric".py where domain is the type of metric (classification, regression, nlp etc) and new_metric is the name of the metric. In this file, there should be the following three functions:

  1. _new_metric_update(...): everything that has to do with type/shape checking and all logic required before distributed syncing need to go here.

  2. _new_metric_compute(...): all remaining logic.

  3. new_metric(...): essentially wraps the _update and _compute private functions into one public function that makes up the functional interface for the metric.

Note

The functional accuracy metric is a great example of this division of logic.

  1. In a corresponding file placed in torchmetrics/"domain"/"new_metric".py create the module interface:

  1. Create a new module metric by subclassing torchmetrics.Metric.

  2. In the __init__ of the module call self.add_state for as many metric states are needed for the metric to proper accumulate metric statistics.

  3. The module interface should essentially call the private _new_metric_update(...) in its update method and similarly the _new_metric_compute(...) function in its compute. No logic should really be implemented in the module interface. We do this to not have duplicate code to maintain.

Note

The module Accuracy metric that corresponds to the above functional example showcases these steps.

  1. Remember to add binding to the different relevant __init__ files.

  2. Testing is key to keeping torchmetrics trustworthy. This is why we have a very rigid testing protocol. This means that we in most cases require the metric to be tested against some other common framework (sklearn, scipy etc).

  1. Create a testing file in tests/"domain"/test_"new_metric".py. Only one file is needed as it is intended to test both the functional and module interface.

  2. In that file, start by defining a number of test inputs that your metric should be evaluated on.

  3. Create a testclass class NewMetric(MetricTester) that inherits from tests.helpers.testers.MetricTester. This testclass should essentially implement the test_"new_metric"_class and test_"new_metric"_fn methods that respectively tests the module interface and the functional interface.

  4. The testclass should be parameterized (using @pytest.mark.parametrize) by the different test inputs defined initially. Additionally, the test_"new_metric"_class method should also be parameterized with an ddp parameter such that it gets tested in a distributed setting. If your metric has additional parameters, then make sure to also parameterize these such that different combinations of inputs and parameters gets tested.

  5. (optional) If your metric raises any exception, please add tests that showcase this.

Note

The test file for accuracy metric shows how to implement such tests.

If you only can figure out part of the steps, do not fear to send a PR. We will much rather receive working metrics that are not formatted exactly like our codebase, than not receiving any. Formatting can always be applied. We will gladly guide and/or help implement the remaining :]

TorchMetrics in PyTorch Lightning

TorchMetrics was originaly created as part of PyTorch Lightning, a powerful deep learning research framework designed for scaling models without boilerplate.

While TorchMetrics was built to be used with native PyTorch, using TorchMetrics with Lightning offers additional benefits:

  • Module metrics are automatically placed on the correct device when properly defined inside a LightningModule. This means that your data will always be placed on the same device as your metrics.

  • Native support for logging metrics in Lightning using self.log inside your LightningModule.

  • The .reset() method of the metric will automatically be called at the end of an epoch.

The example below shows how to use a metric in your LightningModule:

class MyModel(LightningModule):

    def __init__(self):
        ...
        self.accuracy = torchmetrics.Accuracy()

    def training_step(self, batch, batch_idx):
        x, y = batch
        preds = self(x)
        ...
        # log step metric
        self.log('train_acc_step', self.accuracy(preds, y))
        ...

    def training_epoch_end(self, outs):
        # log epoch metric
        self.log('train_acc_epoch', self.accuracy.compute())

Logging TorchMetrics

Metric objects can also be directly logged in Lightning using the LightningModule self.log method. Lightning will log the metric based on on_step and on_epoch flags present in self.log(...). If on_epoch is True, the logger automatically logs the end of epoch metric value by calling .compute().

Note

sync_dist, sync_dist_op, sync_dist_group, reduce_fx and tbptt_reduce_fx flags from self.log(...) don’t affect the metric logging in any manner. The metric class contains its own distributed synchronization logic.

This however is only true for metrics that inherit the base class Metric, and thus the functional metric API provides no support for in-built distributed synchronization or reduction functions.

class MyModule(LightningModule):

    def __init__(self):
        ...
        self.train_acc = torchmetrics.Accuracy()
        self.valid_acc = torchmetrics.Accuracy()

    def training_step(self, batch, batch_idx):
        x, y = batch
        preds = self(x)
        ...
        self.train_acc(preds, y)
        self.log('train_acc', self.train_acc, on_step=True, on_epoch=False)

    def validation_step(self, batch, batch_idx):
        logits = self(x)
        ...
        self.valid_acc(logits, y)
        self.log('valid_acc', self.valid_acc, on_step=True, on_epoch=True)

Note

If using metrics in data parallel mode (dp), the metric update/logging should be done in the <mode>_step_end method (where <mode> is either training, validation or test). This is due to metric states else being destroyed after each forward pass, leading to wrong accumulation. In practice do the following:

class MyModule(LightningModule):

    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)

For more details see Lightning Docs

Module metrics

Base class

The base Metric class is an abstract base class that are used as the building block for all other Module metrics.

class torchmetrics.Metric(compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None)[source]

Base class for all metrics present in the Metrics API.

Implements add_state(), forward(), reset() and a few other things to handle distributed synchronization and per-step metric computation.

Override update() and compute() functions to implement your own metric. Use add_state() to register metric state variables which keep track of state on each call of update() and are synchronized across processes when compute() is called.

Note

Metric state variables can either be torch.Tensors or an empty list which can we used to store torch.Tensors`.

Note

Different metrics only override update() and not forward(). A call to update() is valid, but it won’t return the metric value at the current step. A call to forward() automatically calls update() and also returns the metric value at the current step.

Parameters

  • compute_on_step (bool) – Forward only calls update() and returns None if this is set to False. default: True

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step.

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

  • dist_sync_fn (Optional[Callable]) – Callback that performs the allgather operation on the metric state. When None, DDP will be used to perform the allgather.

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

add_state(name, default, dist_reduce_fx=None, persistent=False)[source]

Adds metric state variable. Only used by subclasses.

Parameters

  • name (str) – The name of the state variable. The variable will then be accessible at self.name.

  • default – Default value of the state; can either be a torch.Tensor or an empty list. The state will be reset to this value when self.reset() is called.

  • dist_reduce_fx (Optional) – Function to reduce state across multiple processes in distributed mode. If value is "sum", "mean", or "cat", we will use torch.sum, torch.mean, and torch.cat respectively, each with argument dim=0. Note that the "cat" reduction only makes sense if the state is a list, and not a tensor. The user can also pass a custom function in this parameter.

  • persistent (Optional) – whether the state will be saved as part of the modules state_dict. Default is False.

Note

Setting dist_reduce_fx to None will return the metric state synchronized across different processes. However, there won’t be any reduction function applied to the synchronized metric state.

The metric states would be synced as follows

  • If the metric state is torch.Tensor, the synced value will be a stacked torch.Tensor across the process dimension if the metric state was a torch.Tensor. The original torch.Tensor metric state retains dimension and hence the synchronized output will be of shape (num_process, ...).

  • If the metric state is a list, the synced value will be a list containing the combined elements from all processes.

Note

When passing a custom function to dist_reduce_fx, expect the synchronized metric state to follow the format discussed in the above note.

Raises

  • ValueError – If default is not a tensor or an empty list.

  • ValueError – If dist_reduce_fx is not callable or one of "mean", "sum", "cat", None.

clone()[source]

Make a copy of the metric

abstract compute()[source]

Override this method to compute the final metric value from state variables synchronized across the distributed backend.

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

Automatically calls update(). Returns the metric value over inputs if compute_on_step is True.

persistent(mode=False)[source]

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

reset()[source]

This method automatically resets the metric state variables to their default value.

state_dict(destination=None, prefix='', keep_vars=False)[source]

Returns a dictionary containing a whole state of the module.

Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names.

Returns

a dictionary containing a whole state of the module

Return type

dict

Example:

>>> module.state_dict().keys()
['bias', 'weight']
abstract update()[source]

Override this method to update the state variables of your metric class.

Return type

None

We also have an AverageMeter class that is helpful for defining ad-hoc metrics, when creating your own metric type might be too burdensome.

class torchmetrics.AverageMeter(compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None)[source]

Computes the average of a stream of values.

Forward accepts

  • value (float tensor): (...)

  • weight (float tensor): (...)

Parameters

  • compute_on_step (bool) – Forward only calls update() and returns None if this is set to False. default: True

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step.

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

  • dist_sync_fn (Optional[Callable]) – Callback that performs the allgather operation on the metric state. When None, DDP will be used to perform the allgather.

Example::
>>> from torchmetrics import AverageMeter
>>> avg = AverageMeter()
>>> avg.update(3)
>>> avg.update(1)
>>> avg.compute()
tensor(2.)

>>> avg = AverageMeter()
>>> values = torch.tensor([1., 2., 3.])
>>> avg(values)
tensor(2.)

>>> avg = AverageMeter()
>>> values = torch.tensor([1., 2.])
>>> weights = torch.tensor([3., 1.])
>>> avg(values, weights)
tensor(1.2500)

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

compute()[source]

Override this method to compute the final metric value from state variables synchronized across the distributed backend.

Return type

Tensor

update(value, weight=1.0)[source]

Updates the average with.

Parameters

  • value (Union[Tensor, float]) – A tensor of observations (can also be a scalar value)

  • weight (Union[Tensor, float]) – The weight of each observation (automatically broadcasted to fit value)

Return type

None

Classification Metrics

Input types

For the purposes of classification metrics, inputs (predictions and targets) are split into these categories (N stands for the batch size and C for number of classes):

*dtype binary means integers that are either 0 or 1

Type

preds shape

preds dtype

target shape

target dtype

Binary

(N,)

float

(N,)

binary*

Multi-class

(N,)

int

(N,)

int

Multi-class with probabilities

(N, C)

float

(N,)

int

Multi-label

(N, …)

float

(N, …)

binary*

Multi-dimensional multi-class

(N, …)

int

(N, …)

int

Multi-dimensional multi-class with probabilities

(N, C, …)

float

(N, …)

int

Note

All dimensions of size 1 (except N) are “squeezed out” at the beginning, so that, for example, a tensor of shape (N, 1) is treated as (N, ).

When predictions or targets are integers, it is assumed that class labels start at 0, i.e. the possible class labels are 0, 1, 2, 3, etc. Below are some examples of different input types

# Binary inputs
binary_preds  = torch.tensor([0.6, 0.1, 0.9])
binary_target = torch.tensor([1, 0, 2])

# Multi-class inputs
mc_preds  = torch.tensor([0, 2, 1])
mc_target = torch.tensor([0, 1, 2])

# Multi-class inputs with probabilities
mc_preds_probs  = torch.tensor([[0.8, 0.2, 0], [0.1, 0.2, 0.7], [0.3, 0.6, 0.1]])
mc_target_probs = torch.tensor([0, 1, 2])

# Multi-label inputs
ml_preds  = torch.tensor([[0.2, 0.8, 0.9], [0.5, 0.6, 0.1], [0.3, 0.1, 0.1]])
ml_target = torch.tensor([[0, 1, 1], [1, 0, 0], [0, 0, 0]])
Using the multiclass parameter

In some cases, you might have inputs which appear to be (multi-dimensional) multi-class but are actually binary/multi-label - for example, if both predictions and targets are integer (binary) tensors. Or it could be the other way around, you want to treat binary/multi-label inputs as 2-class (multi-dimensional) multi-class inputs.

For these cases, the metrics where this distinction would make a difference, expose the multiclass argument. Let’s see how this is used on the example of StatScores metric.

First, let’s consider the case with label predictions with 2 classes, which we want to treat as binary.

from torchmetrics.functional import stat_scores

# These inputs are supposed to be binary, but appear as multi-class
preds  = torch.tensor([0, 1, 0])
target = torch.tensor([1, 1, 0])

As you can see below, by default the inputs are treated as multi-class. We can set multiclass=False to treat the inputs as binary - which is the same as converting the predictions to float beforehand.

>>> stat_scores(preds, target, reduce='macro', num_classes=2)
tensor([[1, 1, 1, 0, 1],
        [1, 0, 1, 1, 2]])
>>> stat_scores(preds, target, reduce='macro', num_classes=1, multiclass=False)
tensor([[1, 0, 1, 1, 2]])
>>> stat_scores(preds.float(), target, reduce='macro', num_classes=1)
tensor([[1, 0, 1, 1, 2]])

Next, consider the opposite example: inputs are binary (as predictions are probabilities), but we would like to treat them as 2-class multi-class, to obtain the metric for both classes.

preds  = torch.tensor([0.2, 0.7, 0.3])
target = torch.tensor([1, 1, 0])

In this case we can set multiclass=True, to treat the inputs as multi-class.

>>> stat_scores(preds, target, reduce='macro', num_classes=1)
tensor([[1, 0, 1, 1, 2]])
>>> stat_scores(preds, target, reduce='macro', num_classes=2, multiclass=True)
tensor([[1, 1, 1, 0, 1],
        [1, 0, 1, 1, 2]])

Accuracy

class torchmetrics.Accuracy(threshold=0.5, num_classes=None, average='micro', mdmc_average='global', ignore_index=None, top_k=None, multiclass=None, subset_accuracy=False, compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None)[source]

Computes Accuracy:

\text{Accuracy} = \frac{1}{N}\sum_i^N 1(y_i = \hat{y}_i)

Where y is a tensor of target values, and \hat{y} is a tensor of predictions.

For multi-class and multi-dimensional multi-class data with probability predictions, the parameter top_k generalizes this metric to a Top-K accuracy metric: for each sample the top-K highest probability items are considered to find the correct label.

For multi-label and multi-dimensional multi-class inputs, this metric computes the “global” accuracy by default, which counts all labels or sub-samples separately. This can be changed to subset accuracy (which requires all labels or sub-samples in the sample to be correctly predicted) by setting subset_accuracy=True.

Accepts all input types listed in Input types.

Parameters

  • num_classes (Optional[int]) – Number of classes. Necessary for 'macro', 'weighted' and None average methods.

  • threshold (float) – Threshold probability value for transforming probability predictions to binary (0,1) predictions, in the case of binary or multi-label inputs.

  • average (str) –

    Defines the reduction that is applied. Should be one of the following:

    • 'micro' [default]: Calculate the metric globally, across all samples and classes.

    • 'macro': Calculate the metric for each class separately, and average the metrics across classes (with equal weights for each class).

    • 'weighted': Calculate the metric for each class separately, and average the metrics across classes, weighting each class by its support (tp + fn).

    • 'none' or None: Calculate the metric for each class separately, and return the metric for every class.

    • 'samples': Calculate the metric for each sample, and average the metrics across samples (with equal weights for each sample).

    Note

    What is considered a sample in the multi-dimensional multi-class case depends on the value of mdmc_average.

  • mdmc_average (Optional[str]) –

    Defines how averaging is done for multi-dimensional multi-class inputs (on top of the average parameter). Should be one of the following:

    • None [default]: Should be left unchanged if your data is not multi-dimensional multi-class.

    • 'samplewise': In this case, the statistics are computed separately for each sample on the N axis, and then averaged over samples. The computation for each sample is done by treating the flattened extra axes ... (see Input types) as the N dimension within the sample, and computing the metric for the sample based on that.

    • 'global': In this case the N and ... dimensions of the inputs (see Input types) are flattened into a new N_X sample axis, i.e. the inputs are treated as if they were (N_X, C). From here on the average parameter applies as usual.

  • ignore_index (Optional[int]) – Integer specifying a target class to ignore. If given, this class index does not contribute to the returned score, regardless of reduction method. If an index is ignored, and average=None or 'none', the score for the ignored class will be returned as nan.

  • top_k (Optional[int]) –

    Number of highest probability predictions considered to find the correct label, relevant only for (multi-dimensional) multi-class inputs with probability predictions. The default value (None) will be interpreted as 1 for these inputs.

    Should be left at default (None) for all other types of inputs.

  • multiclass (Optional[bool]) – Used only in certain special cases, where you want to treat inputs as a different type than what they appear to be. See the parameter’s documentation section for a more detailed explanation and examples.

  • subset_accuracy (bool) –

    Whether to compute subset accuracy for multi-label and multi-dimensional multi-class inputs (has no effect for other input types).

    • For multi-label inputs, if the parameter is set to True, then all labels for each sample must be correctly predicted for the sample to count as correct. If it is set to False, then all labels are counted separately - this is equivalent to flattening inputs beforehand (i.e. preds = preds.flatten() and same for target).

    • For multi-dimensional multi-class inputs, if the parameter is set to True, then all sub-sample (on the extra axis) must be correct for the sample to be counted as correct. If it is set to False, then all sub-samples are counter separately - this is equivalent, in the case of label predictions, to flattening the inputs beforehand (i.e. preds = preds.flatten() and same for target). Note that the top_k parameter still applies in both cases, if set.

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False.

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

  • dist_sync_fn (Optional[Callable]) – Callback that performs the allgather operation on the metric state. When None, DDP will be used to perform the allgather

Raises

  • ValueError – If threshold is not between 0 and 1.

  • ValueError – If top_k is not an integer larger than 0.

  • ValueError – If average is none of "micro", "macro", "weighted", "samples", "none", None.

  • ValueError – If two different input modes are provided, eg. using mult-label with multi-class.

  • ValueError – If top_k parameter is set for multi-label inputs.

Example

>>> import torch
>>> from torchmetrics import Accuracy
>>> target = torch.tensor([0, 1, 2, 3])
>>> preds = torch.tensor([0, 2, 1, 3])
>>> accuracy = Accuracy()
>>> accuracy(preds, target)
tensor(0.5000)

>>> target = torch.tensor([0, 1, 2])
>>> preds = torch.tensor([[0.1, 0.9, 0], [0.3, 0.1, 0.6], [0.2, 0.5, 0.3]])
>>> accuracy = Accuracy(top_k=2)
>>> accuracy(preds, target)
tensor(0.6667)

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

compute()[source]

Computes accuracy based on inputs passed in to update previously.

Return type

Tensor

update(preds, target)[source]

Update state with predictions and targets. See Input types for more information on input types.

Parameters

  • preds (Tensor) – Predictions from model (probabilities, or labels)

  • target (Tensor) – Ground truth labels

AveragePrecision

class torchmetrics.AveragePrecision(num_classes=None, pos_label=None, compute_on_step=True, dist_sync_on_step=False, process_group=None)[source]

Computes the average precision score, which summarises the precision recall curve into one number. Works for both binary and multiclass problems. In the case of multiclass, the values will be calculated based on a one-vs-the-rest approach.

Forward accepts

  • preds (float tensor): (N, ...) (binary) or (N, C, ...) (multiclass) tensor with probabilities, where C is the number of classes.

  • target (long tensor): (N, ...) with integer labels

Parameters

  • num_classes (Optional[int]) – integer with number of classes. Not nessesary to provide for binary problems.

  • pos_label (Optional[int]) – integer determining the positive class. Default is None which for binary problem is translate to 1. For multiclass problems this argument should not be set as we iteratively change it in the range [0,num_classes-1]

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False. default: True

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step. default: False

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

Example (binary case):
>>> from torchmetrics import AveragePrecision
>>> pred = torch.tensor([0, 1, 2, 3])
>>> target = torch.tensor([0, 1, 1, 1])
>>> average_precision = AveragePrecision(pos_label=1)
>>> average_precision(pred, target)
tensor(1.)
Example (multiclass case):
>>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
...                      [0.05, 0.75, 0.05, 0.05, 0.05],
...                      [0.05, 0.05, 0.75, 0.05, 0.05],
...                      [0.05, 0.05, 0.05, 0.75, 0.05]])
>>> target = torch.tensor([0, 1, 3, 2])
>>> average_precision = AveragePrecision(num_classes=5)
>>> average_precision(pred, target)
[tensor(1.), tensor(1.), tensor(0.2500), tensor(0.2500), tensor(nan)]

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

compute()[source]

Compute the average precision score

Return type

Union[Tensor, List[Tensor]]

Returns

tensor with average precision. If multiclass will return list of such tensors, one for each class

update(preds, target)[source]

Update state with predictions and targets.

Parameters

  • preds (Tensor) – Predictions from model

  • target (Tensor) – Ground truth values

AUC

class torchmetrics.AUC(reorder=False, compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None)[source]

Computes Area Under the Curve (AUC) using the trapezoidal rule

Forward accepts two input tensors that should be 1D and have the same number of elements

Parameters

  • reorder (bool) – AUC expects its first input to be sorted. If this is not the case, setting this argument to True will use a stable sorting algorithm to sort the input in descending order

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False.

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step.

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

  • dist_sync_fn (Optional[Callable]) – Callback that performs the allgather operation on the metric state. When None, DDP will be used to perform the allgather.

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

compute()[source]

Computes AUC based on inputs passed in to update previously.

Return type

Tensor

update(x, y)[source]

Update state with predictions and targets.

Parameters

  • x (Tensor) – Predictions from model (probabilities, or labels)

  • y (Tensor) – Ground truth labels

property is_differentiable[source]

AUC metrics is considered as non differentiable so it should have false value for is_differentiable property

AUROC

class torchmetrics.AUROC(num_classes=None, pos_label=None, average='macro', max_fpr=None, compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None)[source]

Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC). Works for both binary, multilabel and multiclass problems. In the case of multiclass, the values will be calculated based on a one-vs-the-rest approach.

Forward accepts

  • preds (float tensor): (N, ...) (binary) or (N, C, ...) (multiclass) tensor with probabilities, where C is the number of classes.

  • target (long tensor): (N, ...) or (N, C, ...) with integer labels

For non-binary input, if the preds and target tensor have the same size the input will be interpretated as multilabel and if preds have one dimension more than the target tensor the input will be interpretated as multiclass.

Parameters

  • num_classes (Optional[int]) – integer with number of classes. Not nessesary to provide for binary problems.

  • pos_label (Optional[int]) – integer determining the positive class. Default is None which for binary problem is translate to 1. For multiclass problems this argument should not be set as we iteratively change it in the range [0,num_classes-1]

  • average (Optional[str]) –

    • 'micro' computes metric globally. Only works for multilabel problems

    • 'macro' computes metric for each class and uniformly averages them

    • 'weighted' computes metric for each class and does a weighted-average, where each class is weighted by their support (accounts for class imbalance)

    • None computes and returns the metric per class

  • max_fpr (Optional[float]) – If not None, calculates standardized partial AUC over the range [0, max_fpr]. Should be a float between 0 and 1.

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False. default: True

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step.

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

  • dist_sync_fn (Optional[Callable]) – Callback that performs the allgather operation on the metric state. When None, DDP will be used to perform the allgather

Raises

  • ValueError – If average is none of None, "macro" or "weighted".

  • ValueError – If max_fpr is not a float in the range (0, 1].

  • RuntimeError – If PyTorch version is below 1.6 since max_fpr requires torch.bucketize which is not available below 1.6.

  • ValueError – If the mode of data (binary, multi-label, multi-class) changes between batches.

Example (binary case):
>>> from torchmetrics import AUROC
>>> preds = torch.tensor([0.13, 0.26, 0.08, 0.19, 0.34])
>>> target = torch.tensor([0, 0, 1, 1, 1])
>>> auroc = AUROC(pos_label=1)
>>> auroc(preds, target)
tensor(0.5000)
Example (multiclass case):
>>> preds = torch.tensor([[0.90, 0.05, 0.05],
...                       [0.05, 0.90, 0.05],
...                       [0.05, 0.05, 0.90],
...                       [0.85, 0.05, 0.10],
...                       [0.10, 0.10, 0.80]])
>>> target = torch.tensor([0, 1, 1, 2, 2])
>>> auroc = AUROC(num_classes=3)
>>> auroc(preds, target)
tensor(0.7778)

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

compute()[source]

Computes AUROC based on inputs passed in to update previously.

Return type

Tensor

update(preds, target)[source]

Update state with predictions and targets.

Parameters

  • preds (Tensor) – Predictions from model (probabilities, or labels)

  • target (Tensor) – Ground truth labels

property is_differentiable[source]

AUROC metrics is considered as non differentiable so it should have false value for is_differentiable property

BinnedAveragePrecision

class torchmetrics.BinnedAveragePrecision(num_classes, num_thresholds=100, compute_on_step=True, dist_sync_on_step=False, process_group=None)[source]

Computes the average precision score, which summarises the precision recall curve into one number. Works for both binary and multiclass problems. In the case of multiclass, the values will be calculated based on a one-vs-the-rest approach.

Computation is performed in constant-memory by computing precision and recall for num_thresholds buckets/thresholds (evenly distributed between 0 and 1).

Forward accepts

  • preds (float tensor): (N, ...) (binary) or (N, C, ...) (multiclass) tensor with probabilities, where C is the number of classes.

  • target (long tensor): (N, ...) with integer labels

Parameters

  • num_classes (int) – integer with number of classes. Not nessesary to provide for binary problems.

  • num_thresholds (int) – number of bins used for computation. More bins will lead to more detailed curve and accurate estimates, but will be slower and consume more memory. Default 100

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False. default: True

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

Example (binary case):
>>> from torchmetrics import BinnedAveragePrecision
>>> pred = torch.tensor([0, 1, 2, 3])
>>> target = torch.tensor([0, 1, 1, 1])
>>> average_precision = BinnedAveragePrecision(num_classes=1, num_thresholds=10)
>>> average_precision(pred, target)
tensor(1.0000)
Example (multiclass case):
>>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
...                      [0.05, 0.75, 0.05, 0.05, 0.05],
...                      [0.05, 0.05, 0.75, 0.05, 0.05],
...                      [0.05, 0.05, 0.05, 0.75, 0.05]])
>>> target = torch.tensor([0, 1, 3, 2])
>>> average_precision = BinnedAveragePrecision(num_classes=5, num_thresholds=10)
>>> average_precision(pred, target)
[tensor(1.0000), tensor(1.0000), tensor(0.2500), tensor(0.2500), tensor(-0.)]

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

compute()[source]

Returns float tensor of size n_classes

Return type

Union[List[Tensor], Tensor]

BinnedPrecisionRecallCurve

class torchmetrics.BinnedPrecisionRecallCurve(num_classes, num_thresholds=100, compute_on_step=True, dist_sync_on_step=False, process_group=None)[source]

Computes precision-recall pairs for different thresholds. Works for both binary and multiclass problems. In the case of multiclass, the values will be calculated based on a one-vs-the-rest approach.

Computation is performed in constant-memory by computing precision and recall for num_thresholds buckets/thresholds (evenly distributed between 0 and 1).

Forward accepts

  • preds (float tensor): (N, ...) (binary) or (N, C, ...) (multiclass) tensor with probabilities, where C is the number of classes.

  • target (long tensor): (N, ...) or (N, C, ...) with integer labels

Parameters

  • num_classes (int) – integer with number of classes. For binary, set to 1.

  • num_thresholds (int) – number of bins used for computation. More bins will lead to more detailed curve and accurate estimates, but will be slower and consume more memory. Default 100

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False. default: True

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step. default: False

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

Example (binary case):
>>> from torchmetrics import BinnedPrecisionRecallCurve
>>> pred = torch.tensor([0, 0.1, 0.8, 0.4])
>>> target = torch.tensor([0, 1, 1, 0])
>>> pr_curve = BinnedPrecisionRecallCurve(num_classes=1, num_thresholds=5)
>>> precision, recall, thresholds = pr_curve(pred, target)
>>> precision
tensor([0.5000, 0.5000, 1.0000, 1.0000, 1.0000, 1.0000])
>>> recall
tensor([1.0000, 0.5000, 0.5000, 0.5000, 0.0000, 0.0000])
>>> thresholds
tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000])
Example (multiclass case):
>>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
...                      [0.05, 0.75, 0.05, 0.05, 0.05],
...                      [0.05, 0.05, 0.75, 0.05, 0.05],
...                      [0.05, 0.05, 0.05, 0.75, 0.05]])
>>> target = torch.tensor([0, 1, 3, 2])
>>> pr_curve = BinnedPrecisionRecallCurve(num_classes=5, num_thresholds=3)
>>> precision, recall, thresholds = pr_curve(pred, target)
>>> precision   
[tensor([0.2500, 1.0000, 1.0000, 1.0000]),
tensor([0.2500, 1.0000, 1.0000, 1.0000]),
tensor([2.5000e-01, 1.0000e-06, 1.0000e+00, 1.0000e+00]),
tensor([2.5000e-01, 1.0000e-06, 1.0000e+00, 1.0000e+00]),
tensor([2.5000e-07, 1.0000e+00, 1.0000e+00, 1.0000e+00])]
>>> recall   
[tensor([1.0000, 1.0000, 0.0000, 0.0000]),
tensor([1.0000, 1.0000, 0.0000, 0.0000]),
tensor([1.0000, 0.0000, 0.0000, 0.0000]),
tensor([1.0000, 0.0000, 0.0000, 0.0000]),
tensor([0., 0., 0., 0.])]
>>> thresholds   
[tensor([0.0000, 0.5000, 1.0000]),
tensor([0.0000, 0.5000, 1.0000]),
tensor([0.0000, 0.5000, 1.0000]),
tensor([0.0000, 0.5000, 1.0000]),
tensor([0.0000, 0.5000, 1.0000])]

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

compute()[source]

Returns float tensor of size n_classes

Return type

Tuple[Tensor, Tensor, Tensor]

update(preds, targets)[source]
Args

preds: (n_samples, n_classes) tensor targets: (n_samples, n_classes) tensor

Return type

None

BinnedRecallAtFixedPrecision

class torchmetrics.BinnedRecallAtFixedPrecision(num_classes, min_precision, num_thresholds=100, compute_on_step=True, dist_sync_on_step=False, process_group=None)[source]

Computes the higest possible recall value given the minimum precision thresholds provided.

Computation is performed in constant-memory by computing precision and recall for num_thresholds buckets/thresholds (evenly distributed between 0 and 1).

Forward accepts

  • preds (float tensor): (N, ...) (binary) or (N, C, ...) (multiclass) tensor with probabilities, where C is the number of classes.

  • target (long tensor): (N, ...) with integer labels

Parameters

  • num_classes (int) – integer with number of classes. Provide 1 for for binary problems.

  • min_precision (float) – float value specifying minimum precision threshold.

  • num_thresholds (int) – number of bins used for computation. More bins will lead to more detailed curve and accurate estimates, but will be slower and consume more memory. Default 100

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False. default: True

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

Example (binary case):
>>> from torchmetrics import BinnedRecallAtFixedPrecision
>>> pred = torch.tensor([0, 0.2, 0.5, 0.8])
>>> target = torch.tensor([0, 1, 1, 0])
>>> average_precision = BinnedRecallAtFixedPrecision(num_classes=1, num_thresholds=10, min_precision=0.5)
>>> average_precision(pred, target)
(tensor(1.0000), tensor(0.1111))
Example (multiclass case):
>>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
...                      [0.05, 0.75, 0.05, 0.05, 0.05],
...                      [0.05, 0.05, 0.75, 0.05, 0.05],
...                      [0.05, 0.05, 0.05, 0.75, 0.05]])
>>> target = torch.tensor([0, 1, 3, 2])
>>> average_precision = BinnedRecallAtFixedPrecision(num_classes=5, num_thresholds=10, min_precision=0.5)
>>> average_precision(pred, target)   
(tensor([1.0000, 1.0000, 0.0000, 0.0000, 0.0000]),
tensor([6.6667e-01, 6.6667e-01, 1.0000e+06, 1.0000e+06, 1.0000e+06]))

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

compute()[source]

Returns float tensor of size n_classes

Return type

Tuple[Tensor, Tensor]

CohenKappa

class torchmetrics.CohenKappa(num_classes, weights=None, threshold=0.5, compute_on_step=True, dist_sync_on_step=False, process_group=None)[source]

Calculates Cohen’s kappa score that measures inter-annotator agreement. It is defined as

\kappa = (p_o - p_e) / (1 - p_e)

where p_o is the empirical probability of agreement and p_e is the expected agreement when both annotators assign labels randomly. Note that p_e is estimated using a per-annotator empirical prior over the class labels.

Works with binary, multiclass, and multilabel data. Accepts probabilities from a model output or integer class values in prediction. Works with multi-dimensional preds and target.

Forward accepts

  • preds (float or long tensor): (N, ...) or (N, C, ...) where C is the number of classes

  • target (long tensor): (N, ...)

If preds and target are the same shape and preds is a float tensor, we use the self.threshold argument to convert into integer labels. This is the case for binary and multi-label probabilities.

If preds has an extra dimension as in the case of multi-class scores we perform an argmax on dim=1.

Parameters

  • num_classes (int) – Number of classes in the dataset.

  • weights (Optional[str]) – Weighting type to calculate the score. Choose from - None or 'none': no weighting - 'linear': linear weighting - 'quadratic': quadratic weighting

  • threshold (float) – Threshold value for binary or multi-label probabilites. default: 0.5

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False. default: True

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step. default: False

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

Example

>>> from torchmetrics import CohenKappa
>>> target = torch.tensor([1, 1, 0, 0])
>>> preds = torch.tensor([0, 1, 0, 0])
>>> cohenkappa = CohenKappa(num_classes=2)
>>> cohenkappa(preds, target)
tensor(0.5000)

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

compute()[source]

Computes cohen kappa score

Return type

Tensor

update(preds, target)[source]

Update state with predictions and targets.

Parameters

  • preds (Tensor) – Predictions from model

  • target (Tensor) – Ground truth values

property is_differentiable[source]

cohen kappa is not differentiable since the implementation is based on calculating the confusion matrix which in general is not differentiable

ConfusionMatrix

class torchmetrics.ConfusionMatrix(num_classes, normalize=None, threshold=0.5, multilabel=False, compute_on_step=True, dist_sync_on_step=False, process_group=None)[source]

Computes the confusion matrix. Works with binary, multiclass, and multilabel data. Accepts probabilities from a model output or integer class values in prediction. Works with multi-dimensional preds and target, but it should be noted that additional dimensions will be flattened.

Forward accepts

  • preds (float or long tensor): (N, ...) or (N, C, ...) where C is the number of classes

  • target (long tensor): (N, ...)

If preds and target are the same shape and preds is a float tensor, we use the self.threshold argument to convert into integer labels. This is the case for binary and multi-label probabilities.

If preds has an extra dimension as in the case of multi-class scores we perform an argmax on dim=1.

If working with multilabel data, setting the is_multilabel argument to True will make sure that a confusion matrix gets calculated per label.

Parameters

  • num_classes (int) – Number of classes in the dataset.

  • normalize (Optional[str]) –

    Normalization mode for confusion matrix. Choose from

    • None or 'none': no normalization (default)

    • 'true': normalization over the targets (most commonly used)

    • 'pred': normalization over the predictions

    • 'all': normalization over the whole matrix

  • threshold (float) – Threshold value for binary or multi-label probabilites. default: 0.5

  • multilabel (bool) – determines if data is multilabel or not.

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False. default: True

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step. default: False

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

Example (binary data):
>>> from torchmetrics import ConfusionMatrix
>>> target = torch.tensor([1, 1, 0, 0])
>>> preds = torch.tensor([0, 1, 0, 0])
>>> confmat = ConfusionMatrix(num_classes=2)
>>> confmat(preds, target)
tensor([[2., 0.],
        [1., 1.]])
Example (multiclass data):
>>> target = torch.tensor([2, 1, 0, 0])
>>> preds = torch.tensor([2, 1, 0, 1])
>>> confmat = ConfusionMatrix(num_classes=3)
>>> confmat(preds, target)
tensor([[1., 1., 0.],
        [0., 1., 0.],
        [0., 0., 1.]])
Example (multilabel data):
>>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]])
>>> confmat = ConfusionMatrix(num_classes=3, multilabel=True)
>>> confmat(preds, target)  
tensor([[[1., 0.], [0., 1.]],
        [[1., 0.], [1., 0.]],
        [[0., 1.], [0., 1.]]])

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

compute()[source]

Computes confusion matrix.

Return type

Tensor

Returns

If multilabel=False this will be a [n_classes, n_classes] tensor and if multilabel=True this will be a [n_classes, 2, 2] tensor

update(preds, target)[source]

Update state with predictions and targets.

Parameters

  • preds (Tensor) – Predictions from model

  • target (Tensor) – Ground truth values

F1

class torchmetrics.F1(num_classes=None, threshold=0.5, average='micro', mdmc_average=None, ignore_index=None, top_k=None, multiclass=None, compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None, multilabel=None)[source]

Computes F1 metric. F1 metrics correspond to a harmonic mean of the precision and recall scores.

Works with binary, multiclass, and multilabel data. Accepts logits from a model output or integer class values in prediction. Works with multi-dimensional preds and target.

Forward accepts

  • preds (float or long tensor): (N, ...) or (N, C, ...) where C is the number of classes

  • target (long tensor): (N, ...)

If preds and target are the same shape and preds is a float tensor, we use the self.threshold argument. This is the case for binary and multi-label logits.

If preds has an extra dimension as in the case of multi-class scores we perform an argmax on dim=1.

Parameters

  • num_classes (Optional[int]) – Number of classes. Necessary for 'macro', 'weighted' and None average methods.

  • threshold (float) – Threshold probability value for transforming probability predictions to binary (0,1) predictions, in the case of binary or multi-label inputs.

  • average (str) –

    Defines the reduction that is applied. Should be one of the following:

    • 'micro' [default]: Calculate the metric globally, across all samples and classes.

    • 'macro': Calculate the metric for each class separately, and average the metrics across classes (with equal weights for each class).

    • 'weighted': Calculate the metric for each class separately, and average the metrics across classes, weighting each class by its support (tp + fn).

    • 'none' or None: Calculate the metric for each class separately, and return the metric for every class.

    • 'samples': Calculate the metric for each sample, and average the metrics across samples (with equal weights for each sample).

    Note

    What is considered a sample in the multi-dimensional multi-class case depends on the value of mdmc_average.

  • mdmc_average (Optional[str]) –

    Defines how averaging is done for multi-dimensional multi-class inputs (on top of the average parameter). Should be one of the following:

    • None [default]: Should be left unchanged if your data is not multi-dimensional multi-class.

    • 'samplewise': In this case, the statistics are computed separately for each sample on the N axis, and then averaged over samples. The computation for each sample is done by treating the flattened extra axes ... (see Input types) as the N dimension within the sample, and computing the metric for the sample based on that.

    • 'global': In this case the N and ... dimensions of the inputs (see Input types) are flattened into a new N_X sample axis, i.e. the inputs are treated as if they were (N_X, C). From here on the average parameter applies as usual.

  • ignore_index (Optional[int]) – Integer specifying a target class to ignore. If given, this class index does not contribute to the returned score, regardless of reduction method. If an index is ignored, and average=None or 'none', the score for the ignored class will be returned as nan.

  • top_k (Optional[int]) –

    Number of highest probability entries for each sample to convert to 1s - relevant only for inputs with probability predictions. If this parameter is set for multi-label inputs, it will take precedence over threshold. For (multi-dim) multi-class inputs, this parameter defaults to 1.

    Should be left unset (None) for inputs with label predictions.

  • multiclass (Optional[bool]) – Used only in certain special cases, where you want to treat inputs as a different type than what they appear to be. See the parameter’s documentation section for a more detailed explanation and examples.

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False.

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

  • dist_sync_fn (Optional[Callable]) – Callback that performs the allgather operation on the metric state. When None, DDP will be used to perform the allgather.

  • multilabel (Optional[bool]) –

    Deprecated since version 0.3: Argument will not have any effect and will be removed in v0.4, please use multiclass intead.

Example

>>> from torchmetrics import F1
>>> target = torch.tensor([0, 1, 2, 0, 1, 2])
>>> preds = torch.tensor([0, 2, 1, 0, 0, 1])
>>> f1 = F1(num_classes=3)
>>> f1(preds, target)
tensor(0.3333)

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

FBeta

class torchmetrics.FBeta(num_classes=None, beta=1.0, threshold=0.5, average='micro', mdmc_average=None, ignore_index=None, top_k=None, multiclass=None, compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None, multilabel=None)[source]

Computes F-score, specifically:

F_\beta = (1 + \beta^2) * \frac{\text{precision} * \text{recall}} {(\beta^2 * \text{precision}) + \text{recall}}

Where \beta is some positive real factor. Works with binary, multiclass, and multilabel data. Accepts probabilities from a model output or integer class values in prediction. Works with multi-dimensional preds and target.

Forward accepts

  • preds (float or long tensor): (N, ...) or (N, C, ...) where C is the number of classes

  • target (long tensor): (N, ...)

If preds and target are the same shape and preds is a float tensor, we use the self.threshold argument to convert into integer labels. This is the case for binary and multi-label probabilities.

If preds has an extra dimension as in the case of multi-class scores we perform an argmax on dim=1.

Parameters

  • num_classes (Optional[int]) – Number of classes. Necessary for 'macro', 'weighted' and None average methods.

  • beta (float) – Beta coefficient in the F measure.

  • threshold (float) – Threshold probability value for transforming probability predictions to binary (0,1) predictions, in the case of binary or multi-label inputs.

  • average (str) –

    Defines the reduction that is applied. Should be one of the following:

    • 'micro' [default]: Calculate the metric globally, across all samples and classes.

    • 'macro': Calculate the metric for each class separately, and average the metrics across classes (with equal weights for each class).

    • 'weighted': Calculate the metric for each class separately, and average the metrics across classes, weighting each class by its support (tp + fn).

    • 'none' or None: Calculate the metric for each class separately, and return the metric for every class.

    • 'samples': Calculate the metric for each sample, and average the metrics across samples (with equal weights for each sample).

    Note

    What is considered a sample in the multi-dimensional multi-class case depends on the value of mdmc_average.

  • mdmc_average (Optional[str]) –

    Defines how averaging is done for multi-dimensional multi-class inputs (on top of the average parameter). Should be one of the following:

    • None [default]: Should be left unchanged if your data is not multi-dimensional multi-class.

    • 'samplewise': In this case, the statistics are computed separately for each sample on the N axis, and then averaged over samples. The computation for each sample is done by treating the flattened extra axes ... (see Input types) as the N dimension within the sample, and computing the metric for the sample based on that.

    • 'global': In this case the N and ... dimensions of the inputs (see Input types) are flattened into a new N_X sample axis, i.e. the inputs are treated as if they were (N_X, C). From here on the average parameter applies as usual.

  • ignore_index (Optional[int]) – Integer specifying a target class to ignore. If given, this class index does not contribute to the returned score, regardless of reduction method. If an index is ignored, and average=None or 'none', the score for the ignored class will be returned as nan.

  • top_k (Optional[int]) –

    Number of highest probability entries for each sample to convert to 1s - relevant only for inputs with probability predictions. If this parameter is set for multi-label inputs, it will take precedence over threshold. For (multi-dim) multi-class inputs, this parameter defaults to 1.

    Should be left unset (None) for inputs with label predictions.

  • multiclass (Optional[bool]) – Used only in certain special cases, where you want to treat inputs as a different type than what they appear to be. See the parameter’s documentation section for a more detailed explanation and examples.

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False.

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

  • dist_sync_fn (Optional[Callable]) – Callback that performs the allgather operation on the metric state. When None, DDP will be used to perform the allgather.

  • multilabel (Optional[bool]) –

    Deprecated since version 0.3: Argument will not have any effect and will be removed in v0.4, please use multiclass intead.

Raises

ValueError – If average is none of "micro", "macro", "weighted", "none", None.

Example

>>> from torchmetrics import FBeta
>>> target = torch.tensor([0, 1, 2, 0, 1, 2])
>>> preds = torch.tensor([0, 2, 1, 0, 0, 1])
>>> f_beta = FBeta(num_classes=3, beta=0.5)
>>> f_beta(preds, target)
tensor(0.3333)

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

compute()[source]

Computes fbeta over state.

Return type

Tensor

HammingDistance

class torchmetrics.HammingDistance(threshold=0.5, compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None)[source]

Computes the average Hamming distance (also known as Hamming loss) between targets and predictions:

\text{Hamming distance} = \frac{1}{N \cdot L}\sum_i^N \sum_l^L 1(y_{il} \neq \hat{y_{il}})

Where y is a tensor of target values, \hat{y} is a tensor of predictions, and \bullet_{il} refers to the l-th label of the i-th sample of that tensor.

This is the same as 1-accuracy for binary data, while for all other types of inputs it treats each possible label separately - meaning that, for example, multi-class data is treated as if it were multi-label.

Accepts all input types listed in Input types.

Parameters

  • threshold (float) – Threshold probability value for transforming probability predictions to binary (0 or 1) predictions, in the case of binary or multi-label inputs.

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False.

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step.

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

  • dist_sync_fn (Optional[Callable]) – Callback that performs the allgather operation on the metric state. When None, DDP will be used to perform the all gather.

Raises

ValueError – If threshold is not between 0 and 1.

Example

>>> from torchmetrics import HammingDistance
>>> target = torch.tensor([[0, 1], [1, 1]])
>>> preds = torch.tensor([[0, 1], [0, 1]])
>>> hamming_distance = HammingDistance()
>>> hamming_distance(preds, target)
tensor(0.2500)

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

compute()[source]

Computes hamming distance based on inputs passed in to update previously.

Return type

Tensor

update(preds, target)[source]

Update state with predictions and targets. See Input types for more information on input types.

Parameters

  • preds (Tensor) – Predictions from model (probabilities, or labels)

  • target (Tensor) – Ground truth labels

Hinge

class torchmetrics.Hinge(squared=False, multiclass_mode=None, compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None)[source]

Computes the mean Hinge loss, typically used for Support Vector Machines (SVMs). In the binary case it is defined as:

\text{Hinge loss} = \max(0, 1 - y \times \hat{y})

Where y \in {-1, 1} is the target, and \hat{y} \in \mathbb{R} is the prediction.

In the multi-class case, when multiclass_mode=None (default), multiclass_mode=MulticlassMode.CRAMMER_SINGER or multiclass_mode="crammer-singer", this metric will compute the multi-class hinge loss defined by Crammer and Singer as:

\text{Hinge loss} = \max\left(0, 1 - \hat{y}_y + \max_{i \ne y} (\hat{y}_i)\right)

Where y \in {0, ..., \mathrm{C}} is the target class (where \mathrm{C} is the number of classes), and \hat{y} \in \mathbb{R}^\mathrm{C} is the predicted output per class.

In the multi-class case when multiclass_mode=MulticlassMode.ONE_VS_ALL or multiclass_mode='one-vs-all', this metric will use a one-vs-all approach to compute the hinge loss, giving a vector of C outputs where each entry pits that class against all remaining classes.

This metric can optionally output the mean of the squared hinge loss by setting squared=True

Only accepts inputs with preds shape of (N) (binary) or (N, C) (multi-class) and target shape of (N).

Parameters

  • squared (bool) – If True, this will compute the squared hinge loss. Otherwise, computes the regular hinge loss (default).

  • multiclass_mode (Union[str, MulticlassMode, None]) – Which approach to use for multi-class inputs (has no effect in the binary case). None (default), MulticlassMode.CRAMMER_SINGER or "crammer-singer", uses the Crammer Singer multi-class hinge loss. MulticlassMode.ONE_VS_ALL or "one-vs-all" computes the hinge loss in a one-vs-all fashion.

Raises

ValueError – If multiclass_mode is not: None, MulticlassMode.CRAMMER_SINGER, "crammer-singer", MulticlassMode.ONE_VS_ALL or "one-vs-all".

Example (binary case):
>>> import torch
>>> from torchmetrics import Hinge
>>> target = torch.tensor([0, 1, 1])
>>> preds = torch.tensor([-2.2, 2.4, 0.1])
>>> hinge = Hinge()
>>> hinge(preds, target)
tensor(0.3000)
Example (default / multiclass case):
>>> target = torch.tensor([0, 1, 2])
>>> preds = torch.tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]])
>>> hinge = Hinge()
>>> hinge(preds, target)
tensor(2.9000)
Example (multiclass example, one vs all mode):
>>> target = torch.tensor([0, 1, 2])
>>> preds = torch.tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]])
>>> hinge = Hinge(multiclass_mode="one-vs-all")
>>> hinge(preds, target)
tensor([2.2333, 1.5000, 1.2333])

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

compute()[source]

Override this method to compute the final metric value from state variables synchronized across the distributed backend.

Return type

Tensor

update(preds, target)[source]

Override this method to update the state variables of your metric class.

IoU

class torchmetrics.IoU(num_classes, ignore_index=None, absent_score=0.0, threshold=0.5, reduction='elementwise_mean', compute_on_step=True, dist_sync_on_step=False, process_group=None)[source]

Computes Intersection over union, or Jaccard index calculation:

J(A,B) = \frac{|A\cap B|}{|A\cup B|}

Where: A and B are both tensors of the same size, containing integer class values. They may be subject to conversion from input data (see description below). Note that it is different from box IoU.

Works with binary, multiclass and multi-label data. Accepts probabilities from a model output or integer class values in prediction. Works with multi-dimensional preds and target.

Forward accepts

  • preds (float or long tensor): (N, ...) or (N, C, ...) where C is the number of classes

  • target (long tensor): (N, ...)

If preds and target are the same shape and preds is a float tensor, we use the self.threshold argument to convert into integer labels. This is the case for binary and multi-label probabilities.

If preds has an extra dimension as in the case of multi-class scores we perform an argmax on dim=1.

Parameters

  • num_classes (int) – Number of classes in the dataset.

  • ignore_index (Optional[int]) – optional int specifying a target class to ignore. If given, this class index does not contribute to the returned score, regardless of reduction method. Has no effect if given an int that is not in the range [0, num_classes-1]. By default, no index is ignored, and all classes are used.

  • absent_score (float) – score to use for an individual class, if no instances of the class index were present in pred AND no instances of the class index were present in target. For example, if we have 3 classes, [0, 0] for pred, and [0, 2] for target, then class 1 would be assigned the absent_score.

  • threshold (float) – Threshold value for binary or multi-label probabilities.

  • reduction (str) –

    a method to reduce metric score over labels.

    • 'elementwise_mean': takes the mean (default)

    • 'sum': takes the sum

    • 'none': no reduction will be applied

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False.

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step.

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

Example

>>> from torchmetrics import IoU
>>> target = torch.randint(0, 2, (10, 25, 25))
>>> pred = torch.tensor(target)
>>> pred[2:5, 7:13, 9:15] = 1 - pred[2:5, 7:13, 9:15]
>>> iou = IoU(num_classes=2)
>>> iou(pred, target)
tensor(0.9660)

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

compute()[source]

Computes intersection over union (IoU)

Return type

Tensor

MatthewsCorrcoef

class torchmetrics.MatthewsCorrcoef(num_classes, threshold=0.5, compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None)[source]

Calculates Matthews correlation coefficient that measures the general correlation or quality of a classification. In the binary case it is defined as:

MCC = \frac{TP*TN - FP*FN}{\sqrt{(TP+FP)*(TP+FN)*(TN+FP)*(TN+FN)}}

where TP, TN, FP and FN are respectively the true postitives, true negatives, false positives and false negatives. Also works in the case of multi-label or multi-class input.

Note

This metric produces a multi-dimensional output, so it can not be directly logged.

Forward accepts

  • preds (float or long tensor): (N, ...) or (N, C, ...) where C is the number of classes

  • target (long tensor): (N, ...)

If preds and target are the same shape and preds is a float tensor, we use the self.threshold argument to convert into integer labels. This is the case for binary and multi-label probabilities.

If preds has an extra dimension as in the case of multi-class scores we perform an argmax on dim=1.

Parameters

  • num_classes (int) – Number of classes in the dataset.

  • threshold (float) – Threshold value for binary or multi-label probabilites. default: 0.5

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False. default: True

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step. default: False

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

  • dist_sync_fn (Optional[Callable]) – Callback that performs the allgather operation on the metric state. When None, DDP will be used to perform the allgather

Example

>>> from torchmetrics import MatthewsCorrcoef
>>> target = torch.tensor([1, 1, 0, 0])
>>> preds = torch.tensor([0, 1, 0, 0])
>>> matthews_corrcoef = MatthewsCorrcoef(num_classes=2)
>>> matthews_corrcoef(preds, target)
tensor(0.5774)

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

compute()[source]

Computes matthews correlation coefficient

Return type

Tensor

update(preds, target)[source]

Update state with predictions and targets.

Parameters

  • preds (Tensor) – Predictions from model

  • target (Tensor) – Ground truth values

Precision

class torchmetrics.Precision(num_classes=None, threshold=0.5, average='micro', mdmc_average=None, ignore_index=None, top_k=None, multiclass=None, compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None, multilabel=None, is_multiclass=None)[source]

Computes Precision:

\text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}}

Where \text{TP} and \text{FP} represent the number of true positives and false positives respecitively. With the use of top_k parameter, this metric can generalize to Precision@K.

The reduction method (how the precision scores are aggregated) is controlled by the average parameter, and additionally by the mdmc_average parameter in the multi-dimensional multi-class case. Accepts all inputs listed in Input types.

Parameters

  • num_classes (Optional[int]) – Number of classes. Necessary for 'macro', 'weighted' and None average methods.

  • threshold (float) – Threshold probability value for transforming probability predictions to binary (0,1) predictions, in the case of binary or multi-label inputs.

  • average (str) –

    Defines the reduction that is applied. Should be one of the following:

    • 'micro' [default]: Calculate the metric globally, across all samples and classes.

    • 'macro': Calculate the metric for each class separately, and average the metrics across classes (with equal weights for each class).

    • 'weighted': Calculate the metric for each class separately, and average the metrics across classes, weighting each class by its support (tp + fn).

    • 'none' or None: Calculate the metric for each class separately, and return the metric for every class.

    • 'samples': Calculate the metric for each sample, and average the metrics across samples (with equal weights for each sample).

    Note

    What is considered a sample in the multi-dimensional multi-class case depends on the value of mdmc_average.

  • mdmc_average (Optional[str]) –

    Defines how averaging is done for multi-dimensional multi-class inputs (on top of the average parameter). Should be one of the following:

    • None [default]: Should be left unchanged if your data is not multi-dimensional multi-class.

    • 'samplewise': In this case, the statistics are computed separately for each sample on the N axis, and then averaged over samples. The computation for each sample is done by treating the flattened extra axes ... (see Input types) as the N dimension within the sample, and computing the metric for the sample based on that.

    • 'global': In this case the N and ... dimensions of the inputs (see Input types) are flattened into a new N_X sample axis, i.e. the inputs are treated as if they were (N_X, C). From here on the average parameter applies as usual.

  • ignore_index (Optional[int]) – Integer specifying a target class to ignore. If given, this class index does not contribute to the returned score, regardless of reduction method. If an index is ignored, and average=None or 'none', the score for the ignored class will be returned as nan.

  • top_k (Optional[int]) –

    Number of highest probability entries for each sample to convert to 1s - relevant only for inputs with probability predictions. If this parameter is set for multi-label inputs, it will take precedence over threshold. For (multi-dim) multi-class inputs, this parameter defaults to 1.

    Should be left unset (None) for inputs with label predictions.

  • multiclass (Optional[bool]) – Used only in certain special cases, where you want to treat inputs as a different type than what they appear to be. See the parameter’s documentation section for a more detailed explanation and examples.

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False.

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

  • dist_sync_fn (Optional[Callable]) – Callback that performs the allgather operation on the metric state. When None, DDP will be used to perform the allgather.

  • multilabel (Optional[bool]) –

    Deprecated since version 0.3: Argument will not have any effect and will be removed in v0.4, please use multiclass intead.

  • is_multiclass (Optional[bool]) –

    Deprecated since version 0.3: Argument will not have any effect and will be removed in v0.4, please use multiclass intead.

Raises

ValueError – If average is none of "micro", "macro", "weighted", "samples", "none", None.

Example

>>> from torchmetrics import Precision
>>> preds  = torch.tensor([2, 0, 2, 1])
>>> target = torch.tensor([1, 1, 2, 0])
>>> precision = Precision(average='macro', num_classes=3)
>>> precision(preds, target)
tensor(0.1667)
>>> precision = Precision(average='micro')
>>> precision(preds, target)
tensor(0.2500)

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

compute()[source]

Computes the precision score based on inputs passed in to update previously.

Return type

Tensor

Returns

The shape of the returned tensor depends on the average parameter

  • If average in ['micro', 'macro', 'weighted', 'samples'], a one-element tensor will be returned

  • If average in ['none', None], the shape will be (C,), where C stands for the number of classes

PrecisionRecallCurve

class torchmetrics.PrecisionRecallCurve(num_classes=None, pos_label=None, compute_on_step=True, dist_sync_on_step=False, process_group=None)[source]

Computes precision-recall pairs for different thresholds. Works for both binary and multiclass problems. In the case of multiclass, the values will be calculated based on a one-vs-the-rest approach.

Forward accepts

  • preds (float tensor): (N, ...) (binary) or (N, C, ...) (multiclass) tensor with probabilities, where C is the number of classes.

  • target (long tensor): (N, ...) or (N, C, ...) with integer labels

Parameters

  • num_classes (Optional[int]) – integer with number of classes. Not nessesary to provide for binary problems.

  • pos_label (Optional[int]) – integer determining the positive class. Default is None which for binary problem is translate to 1. For multiclass problems this argument should not be set as we iteratively change it in the range [0,num_classes-1]

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False. default: True

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step. default: False

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

Example (binary case):
>>> from torchmetrics import PrecisionRecallCurve
>>> pred = torch.tensor([0, 1, 2, 3])
>>> target = torch.tensor([0, 1, 1, 0])
>>> pr_curve = PrecisionRecallCurve(pos_label=1)
>>> precision, recall, thresholds = pr_curve(pred, target)
>>> precision
tensor([0.6667, 0.5000, 0.0000, 1.0000])
>>> recall
tensor([1.0000, 0.5000, 0.0000, 0.0000])
>>> thresholds
tensor([1, 2, 3])
Example (multiclass case):
>>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
...                      [0.05, 0.75, 0.05, 0.05, 0.05],
...                      [0.05, 0.05, 0.75, 0.05, 0.05],
...                      [0.05, 0.05, 0.05, 0.75, 0.05]])
>>> target = torch.tensor([0, 1, 3, 2])
>>> pr_curve = PrecisionRecallCurve(num_classes=5)
>>> precision, recall, thresholds = pr_curve(pred, target)
>>> precision   
[tensor([1., 1.]), tensor([1., 1.]), tensor([0.2500, 0.0000, 1.0000]),
 tensor([0.2500, 0.0000, 1.0000]), tensor([0., 1.])]
>>> recall
[tensor([1., 0.]), tensor([1., 0.]), tensor([1., 0., 0.]), tensor([1., 0., 0.]), tensor([nan, 0.])]
>>> thresholds
[tensor([0.7500]), tensor([0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500])]

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

compute()[source]

Compute the precision-recall curve

Return type

Union[Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor], List[Tensor]]]

Returns

3-element tuple containing

precision:

tensor where element i is the precision of predictions with score >= thresholds[i] and the last element is 1. If multiclass, this is a list of such tensors, one for each class.

recall:

tensor where element i is the recall of predictions with score >= thresholds[i] and the last element is 0. If multiclass, this is a list of such tensors, one for each class.

thresholds:

Thresholds used for computing precision/recall scores

update(preds, target)[source]

Update state with predictions and targets.

Parameters

  • preds (Tensor) – Predictions from model

  • target (Tensor) – Ground truth values

Recall

class torchmetrics.Recall(num_classes=None, threshold=0.5, average='micro', mdmc_average=None, ignore_index=None, top_k=None, multiclass=None, compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None, multilabel=None, is_multiclass=None)[source]

Computes Recall:

\text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}}

Where \text{TP} and \text{FN} represent the number of true positives and false negatives respecitively. With the use of top_k parameter, this metric can generalize to Recall@K.

The reduction method (how the recall scores are aggregated) is controlled by the average parameter, and additionally by the mdmc_average parameter in the multi-dimensional multi-class case. Accepts all inputs listed in Input types.

Parameters

  • num_classes (Optional[int]) – Number of classes. Necessary for 'macro', 'weighted' and None average methods.

  • threshold (float) – Threshold probability value for transforming probability predictions to binary (0,1) predictions, in the case of binary or multi-label inputs.

  • average (str) –

    Defines the reduction that is applied. Should be one of the following:

    • 'micro' [default]: Calculate the metric globally, across all samples and classes.

    • 'macro': Calculate the metric for each class separately, and average the metrics across classes (with equal weights for each class).

    • 'weighted': Calculate the metric for each class separately, and average the metrics across classes, weighting each class by its support (tp + fn).

    • 'none' or None: Calculate the metric for each class separately, and return the metric for every class.

    • 'samples': Calculate the metric for each sample, and average the metrics across samples (with equal weights for each sample).

    Note

    What is considered a sample in the multi-dimensional multi-class case depends on the value of mdmc_average.

  • mdmc_average (Optional[str]) –

    Defines how averaging is done for multi-dimensional multi-class inputs (on top of the average parameter). Should be one of the following:

    • None [default]: Should be left unchanged if your data is not multi-dimensional multi-class.

    • 'samplewise': In this case, the statistics are computed separately for each sample on the N axis, and then averaged over samples. The computation for each sample is done by treating the flattened extra axes ... (see Input types) as the N dimension within the sample, and computing the metric for the sample based on that.

    • 'global': In this case the N and ... dimensions of the inputs (see Input types) are flattened into a new N_X sample axis, i.e. the inputs are treated as if they were (N_X, C). From here on the average parameter applies as usual.

  • ignore_index (Optional[int]) – Integer specifying a target class to ignore. If given, this class index does not contribute to the returned score, regardless of reduction method. If an index is ignored, and average=None or 'none', the score for the ignored class will be returned as nan.

  • top_k (Optional[int]) –

    Number of highest probability entries for each sample to convert to 1s - relevant only for inputs with probability predictions. If this parameter is set for multi-label inputs, it will take precedence over threshold. For (multi-dim) multi-class inputs, this parameter defaults to 1.

    Should be left unset (None) for inputs with label predictions.

  • multiclass (Optional[bool]) – Used only in certain special cases, where you want to treat inputs as a different type than what they appear to be. See the parameter’s documentation section for a more detailed explanation and examples.

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False.

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

  • dist_sync_fn (Optional[Callable]) – Callback that performs the allgather operation on the metric state. When None, DDP will be used to perform the allgather.

  • multilabel (Optional[bool]) –

    Deprecated since version 0.3: Argument will not have any effect and will be removed in v0.4, please use multiclass intead.

  • is_multiclass (Optional[bool]) –

    Deprecated since version 0.3: Argument will not have any effect and will be removed in v0.4, please use multiclass intead.

Raises

ValueError – If average is none of "micro", "macro", "weighted", "samples", "none", None.

Example

>>> from torchmetrics import Recall
>>> preds  = torch.tensor([2, 0, 2, 1])
>>> target = torch.tensor([1, 1, 2, 0])
>>> recall = Recall(average='macro', num_classes=3)
>>> recall(preds, target)
tensor(0.3333)
>>> recall = Recall(average='micro')
>>> recall(preds, target)
tensor(0.2500)

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

compute()[source]

Computes the recall score based on inputs passed in to update previously.

Return type

Tensor

Returns

The shape of the returned tensor depends on the average parameter

  • If average in ['micro', 'macro', 'weighted', 'samples'], a one-element tensor will be returned

  • If average in ['none', None], the shape will be (C,), where C stands for the number of classes

ROC

class torchmetrics.ROC(num_classes=None, pos_label=None, compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None)[source]

Computes the Receiver Operating Characteristic (ROC). Works for both binary, multiclass and multilabel problems. In the case of multiclass, the values will be calculated based on a one-vs-the-rest approach.

Forward accepts

  • preds (float tensor): (N, ...) (binary) or (N, C, ...) (multiclass/multilabel) tensor with probabilities, where C is the number of classes/labels.

  • target (long tensor): (N, ...) or (N, C, ...) with integer labels

Parameters

  • num_classes (Optional[int]) – integer with number of classes. Not nessesary to provide for binary problems.

  • pos_label (Optional[int]) – integer determining the positive class. Default is None which for binary problem is translate to 1. For multiclass problems this argument should not be set as we iteratively change it in the range [0,num_classes-1]

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False. default: True

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step. default: False

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

  • dist_sync_fn (Optional[Callable]) – Callback that performs the allgather operation on the metric state. When None, DDP will be used to perform the allgather

Example (binary case):
>>> from torchmetrics import ROC
>>> pred = torch.tensor([0, 1, 2, 3])
>>> target = torch.tensor([0, 1, 1, 1])
>>> roc = ROC(pos_label=1)
>>> fpr, tpr, thresholds = roc(pred, target)
>>> fpr
tensor([0., 0., 0., 0., 1.])
>>> tpr
tensor([0.0000, 0.3333, 0.6667, 1.0000, 1.0000])
>>> thresholds
tensor([4, 3, 2, 1, 0])
Example (multiclass case):
>>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05],
...                      [0.05, 0.75, 0.05, 0.05],
...                      [0.05, 0.05, 0.75, 0.05],
...                      [0.05, 0.05, 0.05, 0.75]])
>>> target = torch.tensor([0, 1, 3, 2])
>>> roc = ROC(num_classes=4)
>>> fpr, tpr, thresholds = roc(pred, target)
>>> fpr
[tensor([0., 0., 1.]), tensor([0., 0., 1.]), tensor([0.0000, 0.3333, 1.0000]), tensor([0.0000, 0.3333, 1.0000])]
>>> tpr
[tensor([0., 1., 1.]), tensor([0., 1., 1.]), tensor([0., 0., 1.]), tensor([0., 0., 1.])]
>>> thresholds 
[tensor([1.7500, 0.7500, 0.0500]),
 tensor([1.7500, 0.7500, 0.0500]),
 tensor([1.7500, 0.7500, 0.0500]),
 tensor([1.7500, 0.7500, 0.0500])]
Example (multilabel case):
>>> pred = torch.tensor([[0.8191, 0.3680, 0.1138],
...                      [0.3584, 0.7576, 0.1183],
...                      [0.2286, 0.3468, 0.1338],
...                      [0.8603, 0.0745, 0.1837]])
>>> target = torch.tensor([[1, 1, 0], [0, 1, 0], [0, 0, 0], [0, 1, 1]])
>>> roc = ROC(num_classes=3, pos_label=1)
>>> fpr, tpr, thresholds = roc(pred, target)
>>> fpr 
[tensor([0.0000, 0.3333, 0.3333, 0.6667, 1.0000]),
 tensor([0., 0., 0., 1., 1.]),
 tensor([0.0000, 0.0000, 0.3333, 0.6667, 1.0000])]
>>> tpr  
[tensor([0., 0., 1., 1., 1.]),
 tensor([0.0000, 0.3333, 0.6667, 0.6667, 1.0000]),
 tensor([0., 1., 1., 1., 1.])]
>>> thresholds 
[tensor([1.8603, 0.8603, 0.8191, 0.3584, 0.2286]),
 tensor([1.7576, 0.7576, 0.3680, 0.3468, 0.0745]),
 tensor([1.1837, 0.1837, 0.1338, 0.1183, 0.1138])]

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

compute()[source]

Compute the receiver operating characteristic

Return type

Union[Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor], List[Tensor]]]

Returns

3-element tuple containing

fpr:

tensor with false positive rates. If multiclass, this is a list of such tensors, one for each class.

tpr:

tensor with true positive rates. If multiclass, this is a list of such tensors, one for each class.

thresholds:

thresholds used for computing false- and true postive rates

update(preds, target)[source]

Update state with predictions and targets.

Parameters

  • preds (Tensor) – Predictions from model

  • target (Tensor) – Ground truth values

StatScores

class torchmetrics.StatScores(threshold=0.5, top_k=None, reduce='micro', num_classes=None, ignore_index=None, mdmc_reduce=None, multiclass=None, compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None, is_multiclass=None)[source]

Computes the number of true positives, false positives, true negatives, false negatives. Related to Type I and Type II errors and the confusion matrix.

The reduction method (how the statistics are aggregated) is controlled by the reduce parameter, and additionally by the mdmc_reduce parameter in the multi-dimensional multi-class case.

Accepts all inputs listed in Input types.

Parameters

  • threshold (float) – Threshold probability value for transforming probability predictions to binary (0 or 1) predictions, in the case of binary or multi-label inputs.

  • top_k (Optional[int]) –

    Number of highest probability entries for each sample to convert to 1s - relevant only for inputs with probability predictions. If this parameter is set for multi-label inputs, it will take precedence over threshold. For (multi-dim) multi-class inputs, this parameter defaults to 1.

    Should be left unset (None) for inputs with label predictions.

  • reduce (str) –

    Defines the reduction that is applied. Should be one of the following:

    • 'micro' [default]: Counts the statistics by summing over all [sample, class] combinations (globally). Each statistic is represented by a single integer.

    • 'macro': Counts the statistics for each class separately (over all samples). Each statistic is represented by a (C,) tensor. Requires num_classes to be set.

    • 'samples': Counts the statistics for each sample separately (over all classes). Each statistic is represented by a (N, ) 1d tensor.

    Note

    Wwhat is considered a sample in the multi-dimensional multi-class case depends on the value of mdmc_reduce.

  • num_classes (Optional[int]) – Number of classes. Necessary for (multi-dimensional) multi-class or multi-label data.

  • ignore_index (Optional[int]) – Specify a class (label) to ignore. If given, this class index does not contribute to the returned score, regardless of reduction method. If an index is ignored, and reduce='macro', the class statistics for the ignored class will all be returned as -1.

  • mdmc_reduce (Optional[str]) –

    Defines how the multi-dimensional multi-class inputs are handeled. Should be one of the following:

    • None [default]: Should be left unchanged if your data is not multi-dimensional multi-class (see Input types for the definition of input types).

    • 'samplewise': In this case, the statistics are computed separately for each sample on the N axis, and then the outputs are concatenated together. In each sample the extra axes ... are flattened to become the sub-sample axis, and statistics for each sample are computed by treating the sub-sample axis as the N axis for that sample.

    • 'global': In this case the N and ... dimensions of the inputs are flattened into a new N_X sample axis, i.e. the inputs are treated as if they were (N_X, C). From here on the reduce parameter applies as usual.

  • multiclass (Optional[bool]) – Used only in certain special cases, where you want to treat inputs as a different type than what they appear to be. See the parameter’s documentation section for a more detailed explanation and examples.

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False.

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

  • dist_sync_fn (Optional[Callable]) – Callback that performs the allgather operation on the metric state. When None, DDP will be used to perform the allgather.

  • is_multiclass (Optional[bool]) –

    Deprecated since version 0.3: Argument will not have any effect and will be removed in v0.4, please use multiclass intead.

Raises

  • ValueError – If threshold is not a float between 0 and 1.

  • ValueError – If reduce is none of "micro", "macro" or "samples".

  • ValueError – If mdmc_reduce is none of None, "samplewise", "global".

  • ValueError – If reduce is set to "macro" and num_classes is not provided.

  • ValueError – If num_classes is set and ignore_index is not in the range 0 <= ignore_index < num_classes.

Example

>>> from torchmetrics.classification import StatScores
>>> preds  = torch.tensor([1, 0, 2, 1])
>>> target = torch.tensor([1, 1, 2, 0])
>>> stat_scores = StatScores(reduce='macro', num_classes=3)
>>> stat_scores(preds, target)
tensor([[0, 1, 2, 1, 1],
        [1, 1, 1, 1, 2],
        [1, 0, 3, 0, 1]])
>>> stat_scores = StatScores(reduce='micro')
>>> stat_scores(preds, target)
tensor([2, 2, 6, 2, 4])

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

compute()[source]

Computes the stat scores based on inputs passed in to update previously.

Return type

Tensor

Returns

The metric returns a tensor of shape (..., 5), where the last dimension corresponds to [tp, fp, tn, fn, sup] (sup stands for support and equals tp + fn). The shape depends on the reduce and mdmc_reduce (in case of multi-dimensional multi-class data) parameters:

  • If the data is not multi-dimensional multi-class, then

    • If reduce='micro', the shape will be (5, )

    • If reduce='macro', the shape will be (C, 5), where C stands for the number of classes

    • If reduce='samples', the shape will be (N, 5), where N stands for the number of samples

  • If the data is multi-dimensional multi-class and mdmc_reduce='global', then

    • If reduce='micro', the shape will be (5, )

    • If reduce='macro', the shape will be (C, 5)

    • If reduce='samples', the shape will be (N*X, 5), where X stands for the product of sizes of all “extra” dimensions of the data (i.e. all dimensions except for C and N)

  • If the data is multi-dimensional multi-class and mdmc_reduce='samplewise', then

    • If reduce='micro', the shape will be (N, 5)

    • If reduce='macro', the shape will be (N, C, 5)

    • If reduce='samples', the shape will be (N, X, 5)

update(preds, target)[source]

Update state with predictions and targets. See Input types for more information on input types.

Parameters

  • preds (Tensor) – Predictions from model (probabilities or labels)

  • target (Tensor) – Ground truth values

Regression Metrics

ExplainedVariance

class torchmetrics.ExplainedVariance(multioutput='uniform_average', compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None)[source]

Computes explained variance:

\text{ExplainedVariance} = 1 - \frac{\text{Var}(y - \hat{y})}{\text{Var}(y)}

Where y is a tensor of target values, and \hat{y} is a tensor of predictions.

Forward accepts

  • preds (float tensor): (N,) or (N, ...) (multioutput)

  • target (long tensor): (N,) or (N, ...) (multioutput)

In the case of multioutput, as default the variances will be uniformly averaged over the additional dimensions. Please see argument multioutput for changing this behavior.

Parameters

  • multioutput (str) –

    Defines aggregation in the case of multiple output scores. Can be one of the following strings (default is ‘uniform_average’.):

    • ’raw_values’ returns full set of scores

    • ’uniform_average’ scores are uniformly averaged

    • ’variance_weighted’ scores are weighted by their individual variances

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False. default: True

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step. default: False

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

Raises

ValueError – If multioutput is not one of "raw_values", "uniform_average" or "variance_weighted".

Example

>>> from torchmetrics import ExplainedVariance
>>> target = torch.tensor([3, -0.5, 2, 7])
>>> preds = torch.tensor([2.5, 0.0, 2, 8])
>>> explained_variance = ExplainedVariance()
>>> explained_variance(preds, target)
tensor(0.9572)

>>> target = torch.tensor([[0.5, 1], [-1, 1], [7, -6]])
>>> preds = torch.tensor([[0, 2], [-1, 2], [8, -5]])
>>> explained_variance = ExplainedVariance(multioutput='raw_values')
>>> explained_variance(preds, target)
tensor([0.9677, 1.0000])

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

compute()[source]

Computes explained variance over state.

update(preds, target)[source]

Update state with predictions and targets.

Parameters

  • preds (Tensor) – Predictions from model

  • target (Tensor) – Ground truth values

MeanAbsoluteError

class torchmetrics.MeanAbsoluteError(compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None)[source]

Computes mean absolute error (MAE):

\text{MAE} = \frac{1}{N}\sum_i^N | y_i - \hat{y_i} |

Where y is a tensor of target values, and \hat{y} is a tensor of predictions.

Parameters

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False. default: True

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step. default: False

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

Example

>>> from torchmetrics import MeanAbsoluteError
>>> target = torch.tensor([3.0, -0.5, 2.0, 7.0])
>>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0])
>>> mean_absolute_error = MeanAbsoluteError()
>>> mean_absolute_error(preds, target)
tensor(0.5000)

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

compute()[source]

Computes mean absolute error over state.

update(preds, target)[source]

Update state with predictions and targets.

Parameters

  • preds (Tensor) – Predictions from model

  • target (Tensor) – Ground truth values

MeanSquaredError

class torchmetrics.MeanSquaredError(compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None)[source]

Computes mean squared error (MSE):

\text{MSE} = \frac{1}{N}\sum_i^N(y_i - \hat{y_i})^2

Where y is a tensor of target values, and \hat{y} is a tensor of predictions.

Parameters

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False. default: True

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step. default: False

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

Example

>>> from torchmetrics import MeanSquaredError
>>> target = torch.tensor([2.5, 5.0, 4.0, 8.0])
>>> preds = torch.tensor([3.0, 5.0, 2.5, 7.0])
>>> mean_squared_error = MeanSquaredError()
>>> mean_squared_error(preds, target)
tensor(0.8750)

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

compute()[source]

Computes mean squared error over state.

update(preds, target)[source]

Update state with predictions and targets.

Parameters

  • preds (Tensor) – Predictions from model

  • target (Tensor) – Ground truth values

MeanSquaredLogError

class torchmetrics.MeanSquaredLogError(compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None)[source]

Computes mean squared logarithmic error (MSLE):

\text{MSLE} = \frac{1}{N}\sum_i^N (\log_e(1 + y_i) - \log_e(1 + \hat{y_i}))^2

Where y is a tensor of target values, and \hat{y} is a tensor of predictions.

Parameters

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False. default: True

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step. default: False

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

Example

>>> from torchmetrics import MeanSquaredLogError
>>> target = torch.tensor([2.5, 5, 4, 8])
>>> preds = torch.tensor([3, 5, 2.5, 7])
>>> mean_squared_log_error = MeanSquaredLogError()
>>> mean_squared_log_error(preds, target)
tensor(0.0397)

Note

Half precision is only support on GPU for this metric

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

compute()[source]

Compute mean squared logarithmic error over state.

update(preds, target)[source]

Update state with predictions and targets.

Parameters

  • preds (Tensor) – Predictions from model

  • target (Tensor) – Ground truth values

PearsonCorrcoef

class torchmetrics.PearsonCorrcoef(compute_on_step=True, dist_sync_on_step=False, process_group=None)[source]

Computes pearson correlation coefficient:

P_{corr}(x,y) = \frac{cov(x,y)}{\sigma_x \sigma_y}

Where y is a tensor of target values, and x is a tensor of predictions.

Forward accepts

  • preds (float tensor): (N,)

  • target``(float tensor): ``(N,)

Parameters

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False. default: True

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step. default: False

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

Example

>>> from torchmetrics import PearsonCorrcoef
>>> target = torch.tensor([3, -0.5, 2, 7])
>>> preds = torch.tensor([2.5, 0.0, 2, 8])
>>> pearson = PearsonCorrcoef()
>>> pearson(preds, target)
tensor(0.9849)

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

compute()[source]

Computes pearson correlation coefficient over state.

update(preds, target)[source]

Update state with predictions and targets.

Parameters

  • preds (Tensor) – Predictions from model

  • target (Tensor) – Ground truth values

PSNR

class torchmetrics.PSNR(data_range=None, base=10.0, reduction='elementwise_mean', dim=None, compute_on_step=True, dist_sync_on_step=False, process_group=None)[source]

Computes peak signal-to-noise ratio (PSNR):

\text{PSNR}(I, J) = 10 * \log_{10} \left(\frac{\max(I)^2}{\text{MSE}(I, J)}\right)

Where \text{MSE} denotes the mean-squared-error function.

Parameters

  • data_range (Optional[float]) – the range of the data. If None, it is determined from the data (max - min). The data_range must be given when dim is not None.

  • base (float) – a base of a logarithm to use (default: 10)

  • reduction (str) –

    a method to reduce metric score over labels.

    • 'elementwise_mean': takes the mean (default)

    • 'sum': takes the sum

    • 'none': no reduction will be applied

  • dim (Union[int, Tuple[int, …], None]) – Dimensions to reduce PSNR scores over, provided as either an integer or a list of integers. Default is None meaning scores will be reduced across all dimensions and all batches.

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False. default: True

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step. default: False

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

Raises

ValueError – If dim is not None and data_range is not given.

Example

>>> from torchmetrics import PSNR
>>> psnr = PSNR()
>>> preds = torch.tensor([[0.0, 1.0], [2.0, 3.0]])
>>> target = torch.tensor([[3.0, 2.0], [1.0, 0.0]])
>>> psnr(preds, target)
tensor(2.5527)

Note

Half precision is only support on GPU for this metric

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

compute()[source]

Compute peak signal-to-noise ratio over state.

update(preds, target)[source]

Update state with predictions and targets.

Parameters

  • preds (Tensor) – Predictions from model

  • target (Tensor) – Ground truth values

R2Score

class torchmetrics.R2Score(num_outputs=1, adjusted=0, multioutput='uniform_average', compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None)[source]

Computes r2 score also known as coefficient of determination:

R^2 = 1 - \frac{SS_res}{SS_tot}

where SS_res=\sum_i (y_i - f(x_i))^2 is the sum of residual squares, and SS_tot=\sum_i (y_i - \bar{y})^2 is total sum of squares. Can also calculate adjusted r2 score given by

R^2_adj = 1 - \frac{(1-R^2)(n-1)}{n-k-1}

where the parameter k (the number of independent regressors) should be provided as the adjusted argument.

Forward accepts

  • preds (float tensor): (N,) or (N, M) (multioutput)

  • target (float tensor): (N,) or (N, M) (multioutput)

In the case of multioutput, as default the variances will be uniformly averaged over the additional dimensions. Please see argument multioutput for changing this behavior.

Parameters

  • num_outputs (int) – Number of outputs in multioutput setting (default is 1)

  • adjusted (int) – number of independent regressors for calculating adjusted r2 score. Default 0 (standard r2 score).

  • multioutput (str) –

    Defines aggregation in the case of multiple output scores. Can be one of the following strings (default is 'uniform_average'.):

    • 'raw_values' returns full set of scores

    • 'uniform_average' scores are uniformly averaged

    • 'variance_weighted' scores are weighted by their individual variances

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False. default: True

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step. default: False

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

Raises

  • ValueError – If adjusted parameter is not an integer larger or equal to 0.

  • ValueError – If multioutput is not one of "raw_values", "uniform_average" or "variance_weighted".

Example

>>> from torchmetrics import R2Score
>>> target = torch.tensor([3, -0.5, 2, 7])
>>> preds = torch.tensor([2.5, 0.0, 2, 8])
>>> r2score = R2Score()
>>> r2score(preds, target)
tensor(0.9486)

>>> target = torch.tensor([[0.5, 1], [-1, 1], [7, -6]])
>>> preds = torch.tensor([[0, 2], [-1, 2], [8, -5]])
>>> r2score = R2Score(num_outputs=2, multioutput='raw_values')
>>> r2score(preds, target)
tensor([0.9654, 0.9082])

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

compute()[source]

Computes r2 score over the metric states.

Return type

Tensor

update(preds, target)[source]

Update state with predictions and targets.

Parameters

  • preds (Tensor) – Predictions from model

  • target (Tensor) – Ground truth values

SpearmanCorrcoef

class torchmetrics.SpearmanCorrcoef(compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None)[source]

Computes spearmans rank correlation coefficient.

where rg_x and rg_y are the rank associated to the variables x and y. Spearmans correlations coefficient corresponds to the standard pearsons correlation coefficient calculated on the rank variables.

Parameters

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False. default: True

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step. default: False

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

  • dist_sync_fn (Optional[Callable]) – Callback that performs the allgather operation on the metric state. When None, DDP will be used to perform the allgather

Example

>>> from torchmetrics import SpearmanCorrcoef
>>> target = torch.tensor([3, -0.5, 2, 7])
>>> preds = torch.tensor([2.5, 0.0, 2, 8])
>>> spearman = SpearmanCorrcoef()
>>> spearman(preds, target)
tensor(1.0000)

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

compute()[source]

Computes spearmans correlation coefficient

update(preds, target)[source]

Update state with predictions and targets.

Parameters

  • preds (Tensor) – Predictions from model

  • target (Tensor) – Ground truth values

SSIM

class torchmetrics.SSIM(kernel_size=(11, 11), sigma=(1.5, 1.5), reduction='elementwise_mean', data_range=None, k1=0.01, k2=0.03, compute_on_step=True, dist_sync_on_step=False, process_group=None)[source]

Computes Structual Similarity Index Measure (SSIM).

Parameters

  • kernel_size (Sequence[int]) – size of the gaussian kernel (default: (11, 11))

  • sigma (Sequence[float]) – Standard deviation of the gaussian kernel (default: (1.5, 1.5))

  • reduction (str) –

    a method to reduce metric score over labels.

    • 'elementwise_mean': takes the mean (default)

    • 'sum': takes the sum

    • 'none': no reduction will be applied

  • data_range (Optional[float]) – Range of the image. If None, it is determined from the image (max - min)

  • k1 (float) – Parameter of SSIM. Default: 0.01

  • k2 (float) – Parameter of SSIM. Default: 0.03

Returns

Tensor with SSIM score

Example

>>> from torchmetrics import SSIM
>>> preds = torch.rand([16, 1, 16, 16])
>>> target = preds * 0.75
>>> ssim = SSIM()
>>> ssim(preds, target)
tensor(0.9219)

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

compute()[source]

Computes explained variance over state.

update(preds, target)[source]

Update state with predictions and targets.

Parameters

  • preds (Tensor) – Predictions from model

  • target (Tensor) – Ground truth values

Retrieval

Input details

For the purposes of retrieval metrics, inputs (indexes, predictions and targets) must have the same size (N stands for the batch size) and the following types:

indexes shape

indexes dtype

preds shape

preds dtype

target shape

target dtype

long

(N,…)

float

(N,…)

long or bool

(N,…)

Note

All dimensions are flattened at the beginning, so that, for example, a tensor of shape (N, M) is treated as (N * M, ).

In Information Retrieval you have a query that is compared with a variable number of documents. For each pair (Q_i, D_j), a score is computed that measures the relevance of document D w.r.t. query Q. Documents are then sorted by score and you hope that relevant documents are scored higher. target contains the labels for the documents (relevant or not).

Since a query may be compared with a variable number of documents, we use indexes to keep track of which scores belong to the set of pairs (Q_i, D_j) having the same query Q_i.

Note

Retrieval metrics are only intended to be used globally. This means that the average of the metric over each batch can be quite different from the metric computed on the whole dataset. For this reason, we suggest to compute the metric only when all the examples has been provided to the metric. When using Pytorch Lightning, we suggest to use on_step=False and on_epoch=True in self.log or to place the metric calculation in training_epoch_end, validation_epoch_end or test_epoch_end.

>>> from torchmetrics import RetrievalMAP
>>> # functional version works on a single query at a time
>>> from torchmetrics.functional import retrieval_average_precision

>>> # the first query was compared with two documents, the second with three
>>> indexes = torch.tensor([0, 0, 1, 1, 1])
>>> preds = torch.tensor([0.8, -0.4, 1.0, 1.4, 0.0])
>>> target = torch.tensor([0, 1, 0, 1, 1])

>>> map = RetrievalMAP() # or some other retrieval metric
>>> map(preds, target, indexes=indexes)
tensor(0.6667)

>>> # the previous instruction is roughly equivalent to
>>> res = []
>>> # iterate over indexes of first and second query
>>> for indexes in ([0, 1], [2, 3, 4]):
...     res.append(retrieval_average_precision(preds[indexes], target[indexes]))
>>> torch.stack(res).mean()
tensor(0.6667)

RetrievalMAP

class torchmetrics.RetrievalMAP(empty_target_action='neg', compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None)[source]

Computes Mean Average Precision.

Works with binary target data. Accepts float predictions from a model output.

Forward accepts

  • preds (float tensor): (N, ...)

  • target (long or bool tensor): (N, ...)

  • indexes (long tensor): (N, ...)

indexes, preds and target must have the same dimension. indexes indicate to which query a prediction belongs. Predictions will be first grouped by indexes and then MAP will be computed as the mean of the Average Precisions over each query.

Parameters

  • empty_target_action (str) –

    Specify what to do with queries that do not have at least a positive target. Choose from:

    • 'neg': those queries count as 0.0 (default)

    • 'pos': those queries count as 1.0

    • 'skip': skip those queries; if all queries are skipped, 0.0 is returned

    • 'error': raise a ValueError

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False. default: True

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step. default: False

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

  • dist_sync_fn (Optional[Callable]) – Callback that performs the allgather operation on the metric state. When None, DDP will be used to perform the allgather. default: None

Example

>>> from torchmetrics import RetrievalMAP
>>> indexes = tensor([0, 0, 0, 1, 1, 1, 1])
>>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2])
>>> target = tensor([False, False, True, False, True, False, True])
>>> map = RetrievalMAP()
>>> map(preds, target, indexes=indexes)
tensor(0.7917)

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

RetrievalMRR

class torchmetrics.RetrievalMRR(empty_target_action='neg', compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None)[source]

Computes Mean Reciprocal Rank.

Works with binary target data. Accepts float predictions from a model output.

Forward accepts

  • preds (float tensor): (N, ...)

  • target (long or bool tensor): (N, ...)

  • indexes (long tensor): (N, ...)

indexes, preds and target must have the same dimension. indexes indicate to which query a prediction belongs. Predictions will be first grouped by indexes and then MRR will be computed as the mean of the Reciprocal Rank over each query.

Parameters

  • empty_target_action (str) –

    Specify what to do with queries that do not have at least a positive target. Choose from:

    • 'neg': those queries count as 0.0 (default)

    • 'pos': those queries count as 1.0

    • 'skip': skip those queries; if all queries are skipped, 0.0 is returned

    • 'error': raise a ValueError

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False. default: True

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step. default: False

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

  • dist_sync_fn (Optional[Callable]) – Callback that performs the allgather operation on the metric state. When None, DDP will be used to perform the allgather. default: None

Example

>>> from torchmetrics import RetrievalMRR
>>> indexes = tensor([0, 0, 0, 1, 1, 1, 1])
>>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2])
>>> target = tensor([False, False, True, False, True, False, True])
>>> mrr = RetrievalMRR()
>>> mrr(preds, target, indexes=indexes)
tensor(0.7500)

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

RetrievalPrecision

class torchmetrics.RetrievalPrecision(empty_target_action='neg', compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None, k=None)[source]

Computes Precision.

Works with binary target data. Accepts float predictions from a model output.

Forward accepts:

  • preds (float tensor): (N, ...)

  • target (long or bool tensor): (N, ...)

  • indexes (long tensor): (N, ...)

indexes, preds and target must have the same dimension. indexes indicate to which query a prediction belongs. Predictions will be first grouped by indexes and then Precision will be computed as the mean of the Precision over each query.

Parameters

  • empty_target_action (str) –

    Specify what to do with queries that do not have at least a positive target. Choose from:

    • 'neg': those queries count as 0.0 (default)

    • 'pos': those queries count as 1.0

    • 'skip': skip those queries; if all queries are skipped, 0.0 is returned

    • 'error': raise a ValueError

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False. default: True

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step. default: False

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

  • dist_sync_fn (Optional[Callable]) – Callback that performs the allgather operation on the metric state. When None, DDP will be used to perform the allgather. default: None

  • k (Optional[int]) – consider only the top k elements for each query. default: None

Example

>>> from torchmetrics import RetrievalPrecision
>>> indexes = tensor([0, 0, 0, 1, 1, 1, 1])
>>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2])
>>> target = tensor([False, False, True, False, True, False, True])
>>> p2 = RetrievalPrecision(k=2)
>>> p2(preds, target, indexes=indexes)
tensor(0.5000)

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

RetrievalRecall

class torchmetrics.RetrievalRecall(empty_target_action='neg', compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None, k=None)[source]

Computes Recall.

Works with binary target data. Accepts float predictions from a model output.

Forward accepts:

  • preds (float tensor): (N, ...)

  • target (long or bool tensor): (N, ...)

  • indexes (long tensor): (N, ...)

indexes, preds and target must have the same dimension. indexes indicate to which query a prediction belongs. Predictions will be first grouped by indexes and then Recall will be computed as the mean of the Recall over each query.

Parameters

  • empty_target_action (str) –

    Specify what to do with queries that do not have at least a positive target. Choose from:

    • 'neg': those queries count as 0.0 (default)

    • 'pos': those queries count as 1.0

    • 'skip': skip those queries; if all queries are skipped, 0.0 is returned

    • 'error': raise a ValueError

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False. default: True

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step. default: False

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

  • dist_sync_fn (Optional[Callable]) – Callback that performs the allgather operation on the metric state. When None, DDP will be used to perform the allgather. default: None

  • k (Optional[int]) – consider only the top k elements for each query. default: None

Example

>>> from torchmetrics import RetrievalRecall
>>> indexes = tensor([0, 0, 0, 1, 1, 1, 1])
>>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2])
>>> target = tensor([False, False, True, False, True, False, True])
>>> r2 = RetrievalRecall(k=2)
>>> r2(preds, target, indexes=indexes)
tensor(0.7500)

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

RetrievalFallOut

class torchmetrics.RetrievalFallOut(empty_target_action='pos', compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None, k=None)[source]

Computes Fall-out.

Works with binary target data. Accepts float predictions from a model output.

Forward accepts:

  • preds (float tensor): (N, ...)

  • target (long or bool tensor): (N, ...)

  • indexes (long tensor): (N, ...)

indexes, preds and target must have the same dimension. indexes indicate to which query a prediction belongs. Predictions will be first grouped by indexes and then Fall-out will be computed as the mean of the Fall-out over each query.

Parameters

  • empty_target_action (str) –

    Specify what to do with queries that do not have at least a negative target. Choose from:

    • 'neg': those queries count as 0.0 (default)

    • 'pos': those queries count as 1.0

    • 'skip': skip those queries; if all queries are skipped, 0.0 is returned

    • 'error': raise a ValueError

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False. default: True

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step. default: False

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

  • dist_sync_fn (Optional[Callable]) – Callback that performs the allgather operation on the metric state. When None, DDP will be used to perform the allgather. default: None

  • k (Optional[int]) – consider only the top k elements for each query. default: None

Example

>>> from torchmetrics import RetrievalFallOut
>>> indexes = tensor([0, 0, 0, 1, 1, 1, 1])
>>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2])
>>> target = tensor([False, False, True, False, True, False, True])
>>> fo = RetrievalFallOut(k=2)
>>> fo(preds, target, indexes=indexes)
tensor(0.5000)

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

compute()[source]

First concat state indexes, preds and target since they were stored as lists. After that, compute list of groups that will help in keeping together predictions about the same query. Finally, for each group compute the _metric if the number of negative targets is at least 1, otherwise behave as specified by self.empty_target_action.

Return type

Tensor

RetrievalNormalizedDCG

class torchmetrics.RetrievalNormalizedDCG(empty_target_action='neg', compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None, k=None)[source]

Computes Normalized Discounted Cumulative Gain.

Works with binary or positive integer target data. Accepts float predictions from a model output.

Forward accepts:

  • preds (float tensor): (N, ...)

  • target (long or bool tensor): (N, ...)

  • indexes (long tensor): (N, ...)

indexes, preds and target must have the same dimension. indexes indicate to which query a prediction belongs. Predictions will be first grouped by indexes and then Normalized Discounted Cumulative Gain will be computed as the mean of the Normalized Discounted Cumulative Gain over each query.

Parameters

  • empty_target_action (str) –

    Specify what to do with queries that do not have at least a positive target. Choose from:

    • 'neg': those queries count as 0.0 (default)

    • 'pos': those queries count as 1.0

    • 'skip': skip those queries; if all queries are skipped, 0.0 is returned

    • 'error': raise a ValueError

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False. default: True

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step. default: False

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

  • dist_sync_fn (Optional[Callable]) – Callback that performs the allgather operation on the metric state. When None, DDP will be used to perform the allgather. default: None

  • k (Optional[int]) – consider only the top k elements for each query. default: None

Example

>>> from torchmetrics import RetrievalNormalizedDCG
>>> indexes = tensor([0, 0, 0, 1, 1, 1, 1])
>>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2])
>>> target = tensor([False, False, True, False, True, False, True])
>>> ndcg = RetrievalNormalizedDCG()
>>> ndcg(preds, target, indexes=indexes)
tensor(0.8467)

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

Wrappers

Modular wrapper metrics are not metrics in themself, but instead take a metric and alter the internal logic of the base metric.

class torchmetrics.BootStrapper(base_metric, num_bootstraps=10, mean=True, std=True, quantile=None, raw=False, sampling_strategy='poisson', compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None)[source]

Use to turn a metric into a bootstrapped metric that can automate the process of getting confidence intervals for metric values. This wrapper class basically keeps multiple copies of the same base metric in memory and whenever update or forward is called, all input tensors are resampled (with replacement) along the first dimension.

Parameters

  • base_metric (Metric) – base metric class to wrap

  • num_bootstraps (int) – number of copies to make of the base metric for bootstrapping

  • mean (bool) – if True return the mean of the bootstraps

  • std (bool) – if True return the standard diviation of the bootstraps

  • quantile (Union[float, Tensor, None]) – if given, returns the quantile of the bootstraps. Can only be used with pytorch version 1.6 or higher

  • raw (bool) – if True, return all bootstrapped values

  • sampling_strategy (str) – Determines how to produce bootstrapped samplings. Either 'poisson' or multinomial. If 'possion' is chosen, the number of times each sample will be included in the bootstrap will be given by n\sim Poisson(\lambda=1), which approximates the true bootstrap distribution when the number of samples is large. If 'multinomial' is chosen, we will apply true bootstrapping at the batch level to approximate bootstrapping over the hole dataset.

  • compute_on_step (bool) – Forward only calls update() and return None if this is set to False.

  • dist_sync_on_step (bool) – Synchronize metric state across processes at each forward() before returning the value at the step

  • process_group (Optional[Any]) – Specify the process group on which synchronization is called. default: None (which selects the entire world)

  • dist_sync_fn (Optional[Callable]) – Callback that performs the allgather operation on the metric state. When None, DDP will be used to perform the allgather.

Example::
>>> from pprint import pprint
>>> from torchmetrics import Accuracy, BootStrapper
>>> _ = torch.manual_seed(123)
>>> base_metric = Accuracy()
>>> bootstrap = BootStrapper(base_metric, num_bootstraps=20)
>>> bootstrap.update(torch.randint(5, (20,)), torch.randint(5, (20,)))
>>> output = bootstrap.compute()
>>> pprint(output)
{'mean': tensor(0.2205), 'std': tensor(0.0859)}
compute()[source]

Computes the bootstrapped metric values. Allways returns a dict of tensors, which can contain the following keys: mean, std, quantile and raw depending on how the class was initialized

Return type

Dict[str, Tensor]

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

Updates the state of the base metric. Any tensor passed in will be bootstrapped along dimension 0

Return type

None

Functional metrics

Classification Metrics

accuracy [func]

torchmetrics.functional.accuracy(preds, target, average='micro', mdmc_average='global', threshold=0.5, top_k=None, subset_accuracy=False, num_classes=None, multiclass=None, ignore_index=None)[source]

Computes Accuracy:

\text{Accuracy} = \frac{1}{N}\sum_i^N 1(y_i = \hat{y}_i)

Where y is a tensor of target values, and \hat{y} is a tensor of predictions.

For multi-class and multi-dimensional multi-class data with probability predictions, the parameter top_k generalizes this metric to a Top-K accuracy metric: for each sample the top-K highest probability items are considered to find the correct label.

For multi-label and multi-dimensional multi-class inputs, this metric computes the “global” accuracy by default, which counts all labels or sub-samples separately. This can be changed to subset accuracy (which requires all labels or sub-samples in the sample to be correctly predicted) by setting subset_accuracy=True.

Accepts all input types listed in Input types.

Parameters

  • preds (Tensor) – Predictions from model (probabilities, or labels)

  • target (Tensor) – Ground truth labels

  • average (str) –

    Defines the reduction that is applied. Should be one of the following:

    • 'micro' [default]: Calculate the metric globally, across all samples and classes.

    • 'macro': Calculate the metric for each class separately, and average the metrics across classes (with equal weights for each class).

    • 'weighted': Calculate the metric for each class separately, and average the metrics across classes, weighting each class by its support (tp + fn).

    • 'none' or None: Calculate the metric for each class separately, and return the metric for every class.

    • 'samples': Calculate the metric for each sample, and average the metrics across samples (with equal weights for each sample).

    Note

    What is considered a sample in the multi-dimensional multi-class case depends on the value of mdmc_average.

  • mdmc_average (Optional[str]) –

    Defines how averaging is done for multi-dimensional multi-class inputs (on top of the average parameter). Should be one of the following:

    • None [default]: Should be left unchanged if your data is not multi-dimensional multi-class.

    • 'samplewise': In this case, the statistics are computed separately for each sample on the N axis, and then averaged over samples. The computation for each sample is done by treating the flattened extra axes ... (see Input types) as the N dimension within the sample, and computing the metric for the sample based on that.

    • 'global': In this case the N and ... dimensions of the inputs (see Input types) are flattened into a new N_X sample axis, i.e. the inputs are treated as if they were (N_X, C). From here on the average parameter applies as usual.

  • num_classes (Optional[int]) – Number of classes. Necessary for 'macro', 'weighted' and None average methods.

  • threshold (float) – Threshold probability value for transforming probability predictions to binary (0,1) predictions, in the case of binary or multi-label inputs.

  • top_k (Optional[int]) –

    Number of highest probability predictions considered to find the correct label, relevant only for (multi-dimensional) multi-class inputs with probability predictions. The default value (None) will be interpreted as 1 for these inputs.

    Should be left at default (None) for all other types of inputs.

  • multiclass (Optional[bool]) – Used only in certain special cases, where you want to treat inputs as a different type than what they appear to be. See the parameter’s documentation section for a more detailed explanation and examples.

  • ignore_index (Optional[int]) – Integer specifying a target class to ignore. If given, this class index does not contribute to the returned score, regardless of reduction method. If an index is ignored, and average=None or 'none', the score for the ignored class will be returned as nan.

  • subset_accuracy (bool) –

    Whether to compute subset accuracy for multi-label and multi-dimensional multi-class inputs (has no effect for other input types).

    • For multi-label inputs, if the parameter is set to True, then all labels for each sample must be correctly predicted for the sample to count as correct. If it is set to False, then all labels are counted separately - this is equivalent to flattening inputs beforehand (i.e. preds = preds.flatten() and same for target).

    • For multi-dimensional multi-class inputs, if the parameter is set to True, then all sub-sample (on the extra axis) must be correct for the sample to be counted as correct. If it is set to False, then all sub-samples are counter separately - this is equivalent, in the case of label predictions, to flattening the inputs beforehand (i.e. preds = preds.flatten() and same for target). Note that the top_k parameter still applies in both cases, if set.

Raises

  • ValueError – If threshold is not a float between 0 and 1.

  • ValueError – If top_k parameter is set for multi-label inputs.

  • ValueError – If average is none of "micro", "macro", "weighted", "samples", "none", None.

  • ValueError – If mdmc_average is not one of None, "samplewise", "global".

  • ValueError – If average is set but num_classes is not provided.

  • ValueError – If num_classes is set and ignore_index is not in the range [0, num_classes).

  • ValueError – If top_k is not an integer larger than 0.

Example

>>> import torch
>>> from torchmetrics.functional import accuracy
>>> target = torch.tensor([0, 1, 2, 3])
>>> preds = torch.tensor([0, 2, 1, 3])
>>> accuracy(preds, target)
tensor(0.5000)

>>> target = torch.tensor([0, 1, 2])
>>> preds = torch.tensor([[0.1, 0.9, 0], [0.3, 0.1, 0.6], [0.2, 0.5, 0.3]])
>>> accuracy(preds, target, top_k=2)
tensor(0.6667)
Return type

Tensor

auc [func]

torchmetrics.functional.auc(x, y, reorder=False)[source]

Computes Area Under the Curve (AUC) using the trapezoidal rule

Parameters

  • x (Tensor) – x-coordinates

  • y (Tensor) – y-coordinates

  • reorder (bool) – if True, will reorder the arrays

Return type

Tensor

Returns

Tensor containing AUC score (float)

Raises

  • ValueError – If both x and y tensors are not 1d.

  • ValueError – If both x and y don’t have the same numnber of elements.

  • ValueError – If x tesnsor is neither increasing or decreasing.

Example

>>> from torchmetrics.functional import auc
>>> x = torch.tensor([0, 1, 2, 3])
>>> y = torch.tensor([0, 1, 2, 2])
>>> auc(x, y)
tensor(4.)
>>> auc(x, y, reorder=True)
tensor(4.)

auroc [func]

torchmetrics.functional.auroc(preds, target, num_classes=None, pos_label=None, average='macro', max_fpr=None, sample_weights=None)[source]

Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC)

Parameters

  • preds (Tensor) – predictions from model (logits or probabilities)

  • target (Tensor) – Ground truth labels

  • num_classes (Optional[int]) – integer with number of classes. Not nessesary to provide for binary problems.

  • pos_label (Optional[int]) – integer determining the positive class. Default is None which for binary problem is translate to 1. For multiclass problems this argument should not be set as we iteratively change it in the range [0,num_classes-1]

  • average (Optional[str]) –

    • 'micro' computes metric globally. Only works for multilabel problems

    • 'macro' computes metric for each class and uniformly averages them

    • 'weighted' computes metric for each class and does a weighted-average, where each class is weighted by their support (accounts for class imbalance)

    • None computes and returns the metric per class

  • max_fpr (Optional[float]) – If not None, calculates standardized partial AUC over the range [0, max_fpr]. Should be a float between 0 and 1.

  • sample_weights (Optional[Sequence]) – sample weights for each data point

Raises

  • ValueError – If max_fpr is not a float in the range (0, 1].

  • RuntimeError – If PyTorch version is below 1.6 since max_fpr requires torch.bucketize which is not available below 1.6.

  • ValueError – If max_fpr is not set to None and the mode is not binary since partial AUC computation is not available in multilabel/multiclass.

  • ValueError – If average is none of None, "macro" or "weighted".

Example (binary case):
>>> from torchmetrics.functional import auroc
>>> preds = torch.tensor([0.13, 0.26, 0.08, 0.19, 0.34])
>>> target = torch.tensor([0, 0, 1, 1, 1])
>>> auroc(preds, target, pos_label=1)
tensor(0.5000)
Example (multiclass case):
>>> preds = torch.tensor([[0.90, 0.05, 0.05],
...                       [0.05, 0.90, 0.05],
...                       [0.05, 0.05, 0.90],
...                       [0.85, 0.05, 0.10],
...                       [0.10, 0.10, 0.80]])
>>> target = torch.tensor([0, 1, 1, 2, 2])
>>> auroc(preds, target, num_classes=3)
tensor(0.7778)
Return type

Tensor

average_precision [func]

torchmetrics.functional.average_precision(preds, target, num_classes=None, pos_label=None, sample_weights=None)[source]

Computes the average precision score.

Parameters

  • preds (Tensor) – predictions from model (logits or probabilities)

  • target (Tensor) – ground truth values

  • num_classes (Optional[int]) – integer with number of classes. Not nessesary to provide for binary problems.

  • pos_label (Optional[int]) – integer determining the positive class. Default is None which for binary problem is translate to 1. For multiclass problems this argument should not be set as we iteratively change it in the range [0,num_classes-1]

  • sample_weights (Optional[Sequence]) – sample weights for each data point

Return type

Union[List[Tensor], Tensor]

Returns

tensor with average precision. If multiclass will return list of such tensors, one for each class

Example (binary case):
>>> from torchmetrics.functional import average_precision
>>> pred = torch.tensor([0, 1, 2, 3])
>>> target = torch.tensor([0, 1, 1, 1])
>>> average_precision(pred, target, pos_label=1)
tensor(1.)
Example (multiclass case):
>>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
...                      [0.05, 0.75, 0.05, 0.05, 0.05],
...                      [0.05, 0.05, 0.75, 0.05, 0.05],
...                      [0.05, 0.05, 0.05, 0.75, 0.05]])
>>> target = torch.tensor([0, 1, 3, 2])
>>> average_precision(pred, target, num_classes=5)
[tensor(1.), tensor(1.), tensor(0.2500), tensor(0.2500), tensor(nan)]

cohen_kappa [func]

torchmetrics.functional.cohen_kappa(preds, target, num_classes, weights=None, threshold=0.5)[source]

Calculates Cohen’s kappa score that measures inter-annotator agreement. It is defined as

\kappa = (p_o - p_e) / (1 - p_e)

where p_o is the empirical probability of agreement and p_e isg the expected agreement when both annotators assign labels randomly. Note that p_e is estimated using a per-annotator empirical prior over the class labels.

Parameters

  • preds (Tensor) – (float or long tensor), Either a (N, ...) tensor with labels or (N, C, ...) where C is the number of classes, tensor with labels/probabilities

  • target (Tensor) – target (long tensor), tensor with shape (N, ...) with ground true labels

  • num_classes (int) – Number of classes in the dataset.

  • weights (Optional[str]) – Weighting type to calculate the score. Choose from - None or 'none': no weighting - 'linear': linear weighting - 'quadratic': quadratic weighting

  • threshold (float) – Threshold value for binary or multi-label probabilities. default: 0.5

Example

>>> from torchmetrics.functional import cohen_kappa
>>> target = torch.tensor([1, 1, 0, 0])
>>> preds = torch.tensor([0, 1, 0, 0])
>>> cohen_kappa(preds, target, num_classes=2)
tensor(0.5000)
Return type

Tensor

confusion_matrix [func]

torchmetrics.functional.confusion_matrix(preds, target, num_classes, normalize=None, threshold=0.5, multilabel=False)[source]

Computes the confusion matrix. Works with binary, multiclass, and multilabel data. Accepts probabilities from a model output or integer class values in prediction. Works with multi-dimensional preds and target, but it should be noted that additional dimensions will be flattened.

If preds and target are the same shape and preds is a float tensor, we use the self.threshold argument to convert into integer labels. This is the case for binary and multi-label probabilities.

If preds has an extra dimension as in the case of multi-class scores we perform an argmax on dim=1.

If working with multilabel data, setting the is_multilabel argument to True will make sure that a confusion matrix gets calculated per label.

Parameters

  • preds (Tensor) – (float or long tensor), Either a (N, ...) tensor with labels or (N, C, ...) where C is the number of classes, tensor with labels/probabilities

  • target (Tensor) – target (long tensor), tensor with shape (N, ...) with ground true labels

  • num_classes (int) – Number of classes in the dataset.

  • normalize (Optional[str]) –

    Normalization mode for confusion matrix. Choose from

    • None or 'none': no normalization (default)

    • 'true': normalization over the targets (most commonly used)

    • 'pred': normalization over the predictions

    • 'all': normalization over the whole matrix

  • threshold (float) – Threshold value for binary or multi-label probabilities. default: 0.5

  • multilabel (bool) – determines if data is multilabel or not.

Example (binary data):
>>> from torchmetrics import ConfusionMatrix
>>> target = torch.tensor([1, 1, 0, 0])
>>> preds = torch.tensor([0, 1, 0, 0])
>>> confmat = ConfusionMatrix(num_classes=2)
>>> confmat(preds, target)
tensor([[2., 0.],
        [1., 1.]])
Example (multiclass data):
>>> target = torch.tensor([2, 1, 0, 0])
>>> preds = torch.tensor([2, 1, 0, 1])
>>> confmat = ConfusionMatrix(num_classes=3)
>>> confmat(preds, target)
tensor([[1., 1., 0.],
        [0., 1., 0.],
        [0., 0., 1.]])
Example (multilabel data):
>>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]])
>>> confmat = ConfusionMatrix(num_classes=3, multilabel=True)
>>> confmat(preds, target)  
tensor([[[1., 0.], [0., 1.]],
        [[1., 0.], [1., 0.]],
        [[0., 1.], [0., 1.]]])
Return type

Tensor

dice_score [func]

torchmetrics.functional.dice_score(preds, target, bg=False, nan_score=0.0, no_fg_score=0.0, reduction='elementwise_mean')[source]

Compute dice score from prediction scores

Parameters

  • preds (Tensor) – estimated probabilities

  • target (Tensor) – ground-truth labels

  • bg (bool) – whether to also compute dice for the background

  • nan_score (float) – score to return, if a NaN occurs during computation

  • no_fg_score (float) – score to return, if no foreground pixel was found in target

  • reduction (str) –

    a method to reduce metric score over labels.

    • 'elementwise_mean': takes the mean (default)

    • 'sum': takes the sum

    • 'none': no reduction will be applied

Return type

Tensor

Returns

Tensor containing dice score

Example

>>> from torchmetrics.functional import dice_score
>>> pred = torch.tensor([[0.85, 0.05, 0.05, 0.05],
...                      [0.05, 0.85, 0.05, 0.05],
...                      [0.05, 0.05, 0.85, 0.05],
...                      [0.05, 0.05, 0.05, 0.85]])
>>> target = torch.tensor([0, 1, 3, 2])
>>> dice_score(pred, target)
tensor(0.3333)

f1 [func]

torchmetrics.functional.f1(preds, target, beta=1.0, average='micro', mdmc_average=None, ignore_index=None, num_classes=None, threshold=0.5, top_k=None, multiclass=None, multilabel=None)[source]

Computes F1 metric. F1 metrics correspond to a equally weighted average of the precision and recall scores.

Works with binary, multiclass, and multilabel data. Accepts probabilities from a model output or integer class values in prediction. Works with multi-dimensional preds and target.

If preds and target are the same shape and preds is a float tensor, we use the self.threshold argument to convert into integer labels. This is the case for binary and multi-label probabilities.

If preds has an extra dimension as in the case of multi-class scores we perform an argmax on dim=1.

The reduction method (how the precision scores are aggregated) is controlled by the average parameter, and additionally by the mdmc_average parameter in the multi-dimensional multi-class case. Accepts all inputs listed in Input types.

Parameters

  • preds (Tensor) – Predictions from model (probabilities or labels)

  • target (Tensor) – Ground truth values

  • average (str) –

    Defines the reduction that is applied. Should be one of the following:

    • 'micro' [default]: Calculate the metric globally, across all samples and classes.

    • 'macro': Calculate the metric for each class separately, and average the metrics across classes (with equal weights for each class).

    • 'weighted': Calculate the metric for each class separately, and average the metrics across classes, weighting each class by its support (tp + fn).

    • 'none' or None: Calculate the metric for each class separately, and return the metric for every class.

    • 'samples': Calculate the metric for each sample, and average the metrics across samples (with equal weights for each sample).

    Note

    What is considered a sample in the multi-dimensional multi-class case depends on the value of mdmc_average.

  • mdmc_average (Optional[str]) –

    Defines how averaging is done for multi-dimensional multi-class inputs (on top of the average parameter). Should be one of the following:

    • None [default]: Should be left unchanged if your data is not multi-dimensional multi-class.

    • 'samplewise': In this case, the statistics are computed separately for each sample on the N axis, and then averaged over samples. The computation for each sample is done by treating the flattened extra axes ... (see Input types) as the N dimension within the sample, and computing the metric for the sample based on that.

    • 'global': In this case the N and ... dimensions of the inputs (see Input types) are flattened into a new N_X sample axis, i.e. the inputs are treated as if they were (N_X, C). From here on the average parameter applies as usual.

  • ignore_index (Optional[int]) – Integer specifying a target class to ignore. If given, this class index does not contribute to the returned score, regardless of reduction method. If an index is ignored, and average=None or 'none', the score for the ignored class will be returned as nan.

  • num_classes (Optional[int]) – Number of classes. Necessary for 'macro', 'weighted' and None average methods.

  • threshold (float) – Threshold probability value for transforming probability predictions to binary (0,1) predictions, in the case of binary or multi-label inputs.

  • top_k (Optional[int]) –

    Number of highest probability entries for each sample to convert to 1s - relevant only for inputs with probability predictions. If this parameter is set for multi-label inputs, it will take precedence over threshold. For (multi-dim) multi-class inputs, this parameter defaults to 1.

    Should be left unset (None) for inputs with label predictions.

  • multiclass (Optional[bool]) – Used only in certain special cases, where you want to treat inputs as a different type than what they appear to be. See the parameter’s documentation section for a more detailed explanation and examples.

  • multilabel (Optional[bool]) –

    Deprecated since version 0.3: Argument will not have any effect and will be removed in v0.4, please use multiclass intead.

Return type

Tensor

Returns

The shape of the returned tensor depends on the average parameter

  • If average in ['micro', 'macro', 'weighted', 'samples'], a one-element tensor will be returned

  • If average in ['none', None], the shape will be (C,), where C stands for the number of classes

Example

>>> from torchmetrics.functional import f1
>>> target = torch.tensor([0, 1, 2, 0, 1, 2])
>>> preds = torch.tensor([0, 2, 1, 0, 0, 1])
>>> f1(preds, target, num_classes=3)
tensor(0.3333)

fbeta [func]

torchmetrics.functional.fbeta(preds, target, beta=1.0, average='micro', mdmc_average=None, ignore_index=None, num_classes=None, threshold=0.5, top_k=None, multiclass=None, multilabel=None)[source]

Computes f_beta metric.

F_{\beta} = (1 + \beta^2) * \frac{\text{precision} * \text{recall}} {(\beta^2 * \text{precision}) + \text{recall}}

Works with binary, multiclass, and multilabel data. Accepts probabilities from a model output or integer class values in prediction. Works with multi-dimensional preds and target.

If preds and target are the same shape and preds is a float tensor, we use the self.threshold argument to convert into integer labels. This is the case for binary and multi-label probabilities.

If preds has an extra dimension as in the case of multi-class scores we perform an argmax on dim=1.

The reduction method (how the precision scores are aggregated) is controlled by the average parameter, and additionally by the mdmc_average parameter in the multi-dimensional multi-class case. Accepts all inputs listed in Input types.

Parameters

  • preds (Tensor) – Predictions from model (probabilities or labels)

  • target (Tensor) – Ground truth values

  • average (str) –

    Defines the reduction that is applied. Should be one of the following:

    • 'micro' [default]: Calculate the metric globally, across all samples and classes.

    • 'macro': Calculate the metric for each class separately, and average the metrics across classes (with equal weights for each class).

    • 'weighted': Calculate the metric for each class separately, and average the metrics across classes, weighting each class by its support (tp + fn).

    • 'none' or None: Calculate the metric for each class separately, and return the metric for every class.

    • 'samples': Calculate the metric for each sample, and average the metrics across samples (with equal weights for each sample).

    Note

    What is considered a sample in the multi-dimensional multi-class case depends on the value of mdmc_average.

  • mdmc_average (Optional[str]) –

    Defines how averaging is done for multi-dimensional multi-class inputs (on top of the average parameter). Should be one of the following:

    • None [default]: Should be left unchanged if your data is not multi-dimensional multi-class.

    • 'samplewise': In this case, the statistics are computed separately for each sample on the N axis, and then averaged over samples. The computation for each sample is done by treating the flattened extra axes ... (see Input types) as the N dimension within the sample, and computing the metric for the sample based on that.

    • 'global': In this case the N and ... dimensions of the inputs (see Input types) are flattened into a new N_X sample axis, i.e. the inputs are treated as if they were (N_X, C). From here on the average parameter applies as usual.

  • ignore_index (Optional[int]) – Integer specifying a target class to ignore. If given, this class index does not contribute to the returned score, regardless of reduction method. If an index is ignored, and average=None or 'none', the score for the ignored class will be returned as nan.

  • num_classes (Optional[int]) – Number of classes. Necessary for 'macro', 'weighted' and None average methods.

  • threshold (float) – Threshold probability value for transforming probability predictions to binary (0,1) predictions, in the case of binary or multi-label inputs.

  • top_k (Optional[int]) – Number of highest probability entries for each sample to convert to 1s - relevant only for inputs with probability predictions. If this parameter is set for multi-label inputs, it will take precedence over threshold. For (multi-dim) multi-class inputs, this parameter defaults to 1. Should be left unset (None) for inputs with label predictions.

  • multiclass (Optional[bool]) – Used only in certain special cases, where you want to treat inputs as a different type than what they appear to be. See the parameter’s documentation section for a more detailed explanation and examples.

  • multilabel (Optional[bool]) –

    Deprecated since version 0.3: Argument will not have any effect and will be removed in v0.4, please use multiclass intead.

Return type

Tensor

Returns

The shape of the returned tensor depends on the average parameter

  • If average in ['micro', 'macro', 'weighted', 'samples'], a one-element tensor will be returned

  • If average in ['none', None], the shape will be (C,), where C stands for the number of classes

Example

>>> from torchmetrics.functional import fbeta
>>> target = torch.tensor([0, 1, 2, 0, 1, 2])
>>> preds = torch.tensor([0, 2, 1, 0, 0, 1])
>>> fbeta(preds, target, num_classes=3, beta=0.5)
tensor(0.3333)

hamming_distance [func]

torchmetrics.functional.hamming_distance(preds, target, threshold=0.5)[source]

Computes the average Hamming distance (also known as Hamming loss) between targets and predictions:

\text{Hamming distance} = \frac{1}{N \cdot L} \sum_i^N \sum_l^L 1(y_{il} \neq \hat{y}_{il})

Where y is a tensor of target values, \hat{y} is a tensor of predictions, and \bullet_{il} refers to the l-th label of the i-th sample of that tensor.

This is the same as 1-accuracy for binary data, while for all other types of inputs it treats each possible label separately - meaning that, for example, multi-class data is treated as if it were multi-label.

Accepts all input types listed in Input types.

Parameters

  • preds (Tensor) – Predictions from model

  • target (Tensor) – Ground truth

  • threshold (float) – Threshold probability value for transforming probability predictions to binary (0 or 1) predictions, in the case of binary or multi-label inputs.

Example

>>> from torchmetrics.functional import hamming_distance
>>> target = torch.tensor([[0, 1], [1, 1]])
>>> preds = torch.tensor([[0, 1], [0, 1]])
>>> hamming_distance(preds, target)
tensor(0.2500)
Return type

Tensor

hinge [func]

torchmetrics.functional.hinge(preds, target, squared=False, multiclass_mode=None)[source]

Computes the mean Hinge loss, typically used for Support Vector Machines (SVMs). In the binary case it is defined as:

\text{Hinge loss} = \max(0, 1 - y \times \hat{y})

Where y \in {-1, 1} is the target, and \hat{y} \in \mathbb{R} is the prediction.

In the multi-class case, when multiclass_mode=None (default), multiclass_mode=MulticlassMode.CRAMMER_SINGER or multiclass_mode="crammer-singer", this metric will compute the multi-class hinge loss defined by Crammer and Singer as:

\text{Hinge loss} = \max\left(0, 1 - \hat{y}_y + \max_{i \ne y} (\hat{y}_i)\right)

Where y \in {0, ..., \mathrm{C}} is the target class (where \mathrm{C} is the number of classes), and \hat{y} \in \mathbb{R}^\mathrm{C} is the predicted output per class.

In the multi-class case when multiclass_mode=MulticlassMode.ONE_VS_ALL or multiclass_mode='one-vs-all', this metric will use a one-vs-all approach to compute the hinge loss, giving a vector of C outputs where each entry pits that class against all remaining classes.

This metric can optionally output the mean of the squared hinge loss by setting squared=True

Only accepts inputs with preds shape of (N) (binary) or (N, C) (multi-class) and target shape of (N).

Parameters

  • preds (Tensor) – Predictions from model (as float outputs from decision function).

  • target (Tensor) – Ground truth labels.

  • squared (bool) – If True, this will compute the squared hinge loss. Otherwise, computes the regular hinge loss (default).

  • multiclass_mode (Union[str, MulticlassMode, None]) – Which approach to use for multi-class inputs (has no effect in the binary case). None (default), MulticlassMode.CRAMMER_SINGER or "crammer-singer", uses the Crammer Singer multi-class hinge loss. MulticlassMode.ONE_VS_ALL or "one-vs-all" computes the hinge loss in a one-vs-all fashion.

Raises

  • ValueError – If preds shape is not of size (N) or (N, C).

  • ValueError – If target shape is not of size (N).

  • ValueError – If multiclass_mode is not: None, MulticlassMode.CRAMMER_SINGER, "crammer-singer", MulticlassMode.ONE_VS_ALL or "one-vs-all".

Example (binary case):
>>> import torch
>>> from torchmetrics.functional import hinge
>>> target = torch.tensor([0, 1, 1])
>>> preds = torch.tensor([-2.2, 2.4, 0.1])
>>> hinge(preds, target)
tensor(0.3000)
Example (default / multiclass case):
>>> target = torch.tensor([0, 1, 2])
>>> preds = torch.tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]])
>>> hinge(preds, target)
tensor(2.9000)
Example (multiclass example, one vs all mode):
>>> target = torch.tensor([0, 1, 2])
>>> preds = torch.tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]])
>>> hinge(preds, target, multiclass_mode="one-vs-all")
tensor([2.2333, 1.5000, 1.2333])
Return type

Tensor

iou [func]

torchmetrics.functional.iou(preds, target, ignore_index=None, absent_score=0.0, threshold=0.5, num_classes=None, reduction='elementwise_mean')[source]

Computes Intersection over union, or Jaccard index calculation:

J(A,B) = \frac{|A\cap B|}{|A\cup B|}

Where: A and B are both tensors of the same size, containing integer class values. They may be subject to conversion from input data (see description below).

Note that it is different from box IoU.

If preds and target are the same shape and preds is a float tensor, we use the self.threshold argument to convert into integer labels. This is the case for binary and multi-label probabilities.

If pred has an extra dimension as in the case of multi-class scores we perform an argmax on dim=1.

Parameters

  • preds (Tensor) – tensor containing predictions from model (probabilities, or labels) with shape [N, d1, d2, ...]

  • target (Tensor) – tensor containing ground truth labels with shape [N, d1, d2, ...]

  • ignore_index (Optional[int]) – optional int specifying a target class to ignore. If given, this class index does not contribute to the returned score, regardless of reduction method. Has no effect if given an int that is not in the range [0, num_classes-1], where num_classes is either given or derived from pred and target. By default, no index is ignored, and all classes are used.

  • absent_score (float) – score to use for an individual class, if no instances of the class index were present in pred AND no instances of the class index were present in target. For example, if we have 3 classes, [0, 0] for pred, and [0, 2] for target, then class 1 would be assigned the absent_score.

  • threshold (float) – Threshold value for binary or multi-label probabilities. default: 0.5

  • num_classes (Optional[int]) – Optionally specify the number of classes

  • reduction (str) –

    a method to reduce metric score over labels.

    • 'elementwise_mean': takes the mean (default)

    • 'sum': takes the sum

    • 'none': no reduction will be applied

Returns

Tensor containing single value if reduction is ‘elementwise_mean’, or number of classes if reduction is ‘none’

Return type

IoU score

Example

>>> from torchmetrics.functional import iou
>>> target = torch.randint(0, 2, (10, 25, 25))
>>> pred = torch.tensor(target)
>>> pred[2:5, 7:13, 9:15] = 1 - pred[2:5, 7:13, 9:15]
>>> iou(pred, target)
tensor(0.9660)

matthews_corrcoef [func]

torchmetrics.functional.matthews_corrcoef(preds, target, num_classes, threshold=0.5)[source]

Calculates Matthews correlation coefficient that measures the general correlation or quality of a classification. In the binary case it is defined as:

MCC = \frac{TP*TN - FP*FN}{\sqrt{(TP+FP)*(TP+FN)*(TN+FP)*(TN+FN)}}

where TP, TN, FP and FN are respectively the true postitives, true negatives, false positives and false negatives. Also works in the case of multi-label or multi-class input.

Parameters

  • preds (Tensor) – (float or long tensor), Either a (N, ...) tensor with labels or (N, C, ...) where C is the number of classes, tensor with labels/probabilities

  • target (Tensor) – target (long tensor), tensor with shape (N, ...) with ground true labels

  • num_classes (int) – Number of classes in the dataset.

  • threshold (float) – Threshold value for binary or multi-label probabilities. default: 0.5

Example

>>> from torchmetrics.functional import matthews_corrcoef
>>> target = torch.tensor([1, 1, 0, 0])
>>> preds = torch.tensor([0, 1, 0, 0])
>>> matthews_corrcoef(preds, target, num_classes=2)
tensor(0.5774)
Return type

Tensor

roc [func]

torchmetrics.functional.roc(preds, target, num_classes=None, pos_label=None, sample_weights=None)[source]

Computes the Receiver Operating Characteristic (ROC). Works with both binary, multiclass and multilabel input.

Parameters

  • preds (Tensor) – predictions from model (logits or probabilities)

  • target (Tensor) – ground truth values

  • num_classes (Optional[int]) – integer with number of classes. Not nessesary to provide for binary problems.

  • pos_label (Optional[int]) – integer determining the positive class. Default is None which for binary problem is translate to 1. For multiclass problems this argument should not be set as we iteratively change it in the range [0,num_classes-1]

  • sample_weights (Optional[Sequence]) – sample weights for each data point

Return type

Union[Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor], List[Tensor]]]

Returns

3-element tuple containing

fpr:

tensor with false positive rates. If multiclass or multilabel, this is a list of such tensors, one for each class/label.

tpr:

tensor with true positive rates. If multiclass or multilabel, this is a list of such tensors, one for each class/label.

thresholds:

tensor with thresholds used for computing false- and true postive rates If multiclass or multilabel, this is a list of such tensors, one for each class/label.

Example (binary case):
>>> from torchmetrics.functional import roc
>>> pred = torch.tensor([0, 1, 2, 3])
>>> target = torch.tensor([0, 1, 1, 1])
>>> fpr, tpr, thresholds = roc(pred, target, pos_label=1)
>>> fpr
tensor([0., 0., 0., 0., 1.])
>>> tpr
tensor([0.0000, 0.3333, 0.6667, 1.0000, 1.0000])
>>> thresholds
tensor([4, 3, 2, 1, 0])
Example (multiclass case):
>>> from torchmetrics.functional import roc
>>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05],
...                      [0.05, 0.75, 0.05, 0.05],
...                      [0.05, 0.05, 0.75, 0.05],
...                      [0.05, 0.05, 0.05, 0.75]])
>>> target = torch.tensor([0, 1, 3, 2])
>>> fpr, tpr, thresholds = roc(pred, target, num_classes=4)
>>> fpr
[tensor([0., 0., 1.]), tensor([0., 0., 1.]), tensor([0.0000, 0.3333, 1.0000]), tensor([0.0000, 0.3333, 1.0000])]
>>> tpr
[tensor([0., 1., 1.]), tensor([0., 1., 1.]), tensor([0., 0., 1.]), tensor([0., 0., 1.])]
>>> thresholds 
[tensor([1.7500, 0.7500, 0.0500]),
 tensor([1.7500, 0.7500, 0.0500]),
 tensor([1.7500, 0.7500, 0.0500]),
 tensor([1.7500, 0.7500, 0.0500])]
Example (multilabel case):
>>> from torchmetrics.functional import roc
>>> pred = torch.tensor([[0.8191, 0.3680, 0.1138],
...                      [0.3584, 0.7576, 0.1183],
...                      [0.2286, 0.3468, 0.1338],
...                      [0.8603, 0.0745, 0.1837]])
>>> target = torch.tensor([[1, 1, 0], [0, 1, 0], [0, 0, 0], [0, 1, 1]])
>>> fpr, tpr, thresholds = roc(pred, target, num_classes=3, pos_label=1)
>>> fpr 
[tensor([0.0000, 0.3333, 0.3333, 0.6667, 1.0000]),
 tensor([0., 0., 0., 1., 1.]),
 tensor([0.0000, 0.0000, 0.3333, 0.6667, 1.0000])]
>>> tpr
[tensor([0., 0., 1., 1., 1.]), tensor([0.0000, 0.3333, 0.6667, 0.6667, 1.0000]), tensor([0., 1., 1., 1., 1.])]
>>> thresholds 
[tensor([1.8603, 0.8603, 0.8191, 0.3584, 0.2286]),
 tensor([1.7576, 0.7576, 0.3680, 0.3468, 0.0745]),
 tensor([1.1837, 0.1837, 0.1338, 0.1183, 0.1138])]

precision [func]

torchmetrics.functional.precision(preds, target, average='micro', mdmc_average=None, ignore_index=None, num_classes=None, threshold=0.5, top_k=None, multiclass=None, multilabel=None, is_multiclass=None)[source]

Computes Precision:

\text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}}

Where \text{TP} and \text{FP} represent the number of true positives and false positives respecitively. With the use of top_k parameter, this metric can generalize to Precision@K.

The reduction method (how the precision scores are aggregated) is controlled by the average parameter, and additionally by the mdmc_average parameter in the multi-dimensional multi-class case. Accepts all inputs listed in Input types.

Parameters

  • preds (Tensor) – Predictions from model (probabilities or labels)

  • target (Tensor) – Ground truth values

  • average (str) –

    Defines the reduction that is applied. Should be one of the following:

    • 'micro' [default]: Calculate the metric globally, across all samples and classes.

    • 'macro': Calculate the metric for each class separately, and average the metrics across classes (with equal weights for each class).

    • 'weighted': Calculate the metric for each class separately, and average the metrics across classes, weighting each class by its support (tp + fn).

    • 'none' or None: Calculate the metric for each class separately, and return the metric for every class.

    • 'samples': Calculate the metric for each sample, and average the metrics across samples (with equal weights for each sample).

    Note

    What is considered a sample in the multi-dimensional multi-class case depends on the value of mdmc_average.

  • mdmc_average (Optional[str]) –

    Defines how averaging is done for multi-dimensional multi-class inputs (on top of the average parameter). Should be one of the following:

    • None [default]: Should be left unchanged if your data is not multi-dimensional multi-class.

    • 'samplewise': In this case, the statistics are computed separately for each sample on the N axis, and then averaged over samples. The computation for each sample is done by treating the flattened extra axes ... (see Input types) as the N dimension within the sample, and computing the metric for the sample based on that.

    • 'global': In this case the N and ... dimensions of the inputs (see Input types) are flattened into a new N_X sample axis, i.e. the inputs are treated as if they were (N_X, C). From here on the average parameter applies as usual.

  • ignore_index (Optional[int]) – Integer specifying a target class to ignore. If given, this class index does not contribute to the returned score, regardless of reduction method. If an index is ignored, and average=None or 'none', the score for the ignored class will be returned as nan.

  • num_classes (Optional[int]) – Number of classes. Necessary for 'macro', 'weighted' and None average methods.

  • threshold (float) – Threshold probability value for transforming probability predictions to binary (0,1) predictions, in the case of binary or multi-label inputs.

  • top_k (Optional[int]) –

    Number of highest probability entries for each sample to convert to 1s - relevant only for inputs with probability predictions. If this parameter is set for multi-label inputs, it will take precedence over threshold. For (multi-dim) multi-class inputs, this parameter defaults to 1.

    Should be left unset (None) for inputs with label predictions.

  • multiclass (Optional[bool]) – Used only in certain special cases, where you want to treat inputs as a different type than what they appear to be. See the parameter’s documentation section for a more detailed explanation and examples.

  • multilabel (Optional[bool]) –

    Deprecated since version 0.3: Argument will not have any effect and will be removed in v0.4, please use multiclass intead.

  • is_multiclass (Optional[bool]) –

    Deprecated since version 0.3: Argument will not have any effect and will be removed in v0.4, please use multiclass intead.

Return type

Tensor

Returns

The shape of the returned tensor depends on the average parameter

  • If average in ['micro', 'macro', 'weighted', 'samples'], a one-element tensor will be returned

  • If average in ['none', None], the shape will be (C,), where C stands for the number of classes

Raises

  • ValueError – If average is not one of "micro", "macro", "weighted", "samples", "none" or None.

  • ValueError – If mdmc_average is not one of None, "samplewise", "global".

  • ValueError – If average is set but num_classes is not provided.

  • ValueError – If num_classes is set and ignore_index is not in the range [0, num_classes).

Example

>>> from torchmetrics.functional import precision
>>> preds  = torch.tensor([2, 0, 2, 1])
>>> target = torch.tensor([1, 1, 2, 0])
>>> precision(preds, target, average='macro', num_classes=3)
tensor(0.1667)
>>> precision(preds, target, average='micro')
tensor(0.2500)

precision_recall [func]

torchmetrics.functional.precision_recall(preds, target, average='micro', mdmc_average=None, ignore_index=None, num_classes=None, threshold=0.5, top_k=None, multiclass=None, multilabel=None, is_multiclass=None)[source]

Computes Precision and Recall:

\text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}}

\text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}}

Where \text{TP}text{FN}` and \text{FP} represent the number of true positives, false negatives and false positives respecitively. With the use of top_k parameter, this metric can generalize to Recall@K and Precision@K.

The reduction method (how the recall scores are aggregated) is controlled by the average parameter, and additionally by the mdmc_average parameter in the multi-dimensional multi-class case. Accepts all inputs listed in Input types.

Parameters

  • preds (Tensor) – Predictions from model (probabilities, or labels)

  • target (Tensor) – Ground truth values

  • average (str) –

    Defines the reduction that is applied. Should be one of the following:

    • 'micro' [default]: Calculate the metric globally, across all samples and classes.

    • 'macro': Calculate the metric for each class separately, and average the metrics across classes (with equal weights for each class).

    • 'weighted': Calculate the metric for each class separately, and average the metrics across classes, weighting each class by its support (tp + fn).

    • 'none' or None: Calculate the metric for each class separately, and return the metric for every class.

    • 'samples': Calculate the metric for each sample, and average the metrics across samples (with equal weights for each sample).

    Note

    What is considered a sample in the multi-dimensional multi-class case depends on the value of mdmc_average.

  • mdmc_average (Optional[str]) –

    Defines how averaging is done for multi-dimensional multi-class inputs (on top of the average parameter). Should be one of the following:

    • None [default]: Should be left unchanged if your data is not multi-dimensional multi-class.

    • 'samplewise': In this case, the statistics are computed separately for each sample on the N axis, and then averaged over samples. The computation for each sample is done by treating the flattened extra axes ... (see Input types) as the N dimension within the sample, and computing the metric for the sample based on that.

    • 'global': In this case the N and ... dimensions of the inputs (see Input types) are flattened into a new N_X sample axis, i.e. the inputs are treated as if they were (N_X, C). From here on the average parameter applies as usual.

  • ignore_index (Optional[int]) – Integer specifying a target class to ignore. If given, this class index does not contribute to the returned score, regardless of reduction method. If an index is ignored, and average=None or 'none', the score for the ignored class will be returned as nan.

  • num_classes (Optional[int]) – Number of classes. Necessary for 'macro', 'weighted' and None average methods.

  • threshold (float) – Threshold probability value for transforming probability predictions to binary (0,1) predictions, in the case of binary or multi-label inputs

  • top_k (Optional[int]) –

    Number of highest probability entries for each sample to convert to 1s - relevant only for inputs with probability predictions. If this parameter is set for multi-label inputs, it will take precedence over threshold. For (multi-dim) multi-class inputs, this parameter defaults to 1.

    Should be left unset (None) for inputs with label predictions.

  • multiclass (Optional[bool]) – Used only in certain special cases, where you want to treat inputs as a different type than what they appear to be. See the parameter’s documentation section for a more detailed explanation and examples.

  • multilabel (Optional[bool]) –

    Deprecated since version 0.3: Argument will not have any effect and will be removed in v0.4, please use multiclass intead.

  • is_multiclass (Optional[bool]) –

    Deprecated since version 0.3: Argument will not have any effect and will be removed in v0.4, please use multiclass intead.

Returns

precision and recall. Their shape depends on the average parameter

  • If average in ['micro', 'macro', 'weighted', 'samples'], they are a single element tensor

  • If average in ['none', None], they are a tensor of shape (C, ), where C stands for the number of classes

Return type

The function returns a tuple with two elements

Raises

  • ValueError – If average is not one of "micro", "macro", "weighted", "samples", "none" or None.

  • ValueError – If mdmc_average is not one of None, "samplewise", "global".

  • ValueError – If average is set but num_classes is not provided.

  • ValueError – If num_classes is set and ignore_index is not in the range [0, num_classes).

Example

>>> from torchmetrics.functional import precision_recall
>>> preds  = torch.tensor([2, 0, 2, 1])
>>> target = torch.tensor([1, 1, 2, 0])
>>> precision_recall(preds, target, average='macro', num_classes=3)
(tensor(0.1667), tensor(0.3333))
>>> precision_recall(preds, target, average='micro')
(tensor(0.2500), tensor(0.2500))

precision_recall_curve [func]

torchmetrics.functional.precision_recall_curve(preds, target, num_classes=None, pos_label=None, sample_weights=None)[source]

Computes precision-recall pairs for different thresholds.

Parameters

  • preds (Tensor) – predictions from model (probabilities)

  • target (Tensor) – ground truth labels

  • num_classes (Optional[int]) – integer with number of classes. Not nessesary to provide for binary problems.

  • pos_label (Optional[int]) – integer determining the positive class. Default is None which for binary problem is translate to 1. For multiclass problems this argument should not be set as we iteratively change it in the range [0,num_classes-1]

  • sample_weights (Optional[Sequence]) – sample weights for each data point

Return type

Union[Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor], List[Tensor]]]

Returns

3-element tuple containing

precision:

tensor where element i is the precision of predictions with score >= thresholds[i] and the last element is 1. If multiclass, this is a list of such tensors, one for each class.

recall:

tensor where element i is the recall of predictions with score >= thresholds[i] and the last element is 0. If multiclass, this is a list of such tensors, one for each class.

thresholds:

Thresholds used for computing precision/recall scores

Raises

  • ValueError – If preds and target don’t have the same number of dimensions, or one additional dimension for preds.

  • ValueError – If the number of classes deduced from preds is not the same as the num_classes provided.

Example (binary case):
>>> from torchmetrics.functional import precision_recall_curve
>>> pred = torch.tensor([0, 1, 2, 3])
>>> target = torch.tensor([0, 1, 1, 0])
>>> precision, recall, thresholds = precision_recall_curve(pred, target, pos_label=1)
>>> precision
tensor([0.6667, 0.5000, 0.0000, 1.0000])
>>> recall
tensor([1.0000, 0.5000, 0.0000, 0.0000])
>>> thresholds
tensor([1, 2, 3])
Example (multiclass case):
>>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
...                      [0.05, 0.75, 0.05, 0.05, 0.05],
...                      [0.05, 0.05, 0.75, 0.05, 0.05],
...                      [0.05, 0.05, 0.05, 0.75, 0.05]])
>>> target = torch.tensor([0, 1, 3, 2])
>>> precision, recall, thresholds = precision_recall_curve(pred, target, num_classes=5)
>>> precision   
[tensor([1., 1.]), tensor([1., 1.]), tensor([0.2500, 0.0000, 1.0000]),
 tensor([0.2500, 0.0000, 1.0000]), tensor([0., 1.])]
>>> recall
[tensor([1., 0.]), tensor([1., 0.]), tensor([1., 0., 0.]), tensor([1., 0., 0.]), tensor([nan, 0.])]
>>> thresholds
[tensor([0.7500]), tensor([0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500])]

recall [func]

torchmetrics.functional.recall(preds, target, average='micro', mdmc_average=None, ignore_index=None, num_classes=None, threshold=0.5, top_k=None, multiclass=None, multilabel=None, is_multiclass=None)[source]

Computes Recall:

\text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}}

Where \text{TP} and \text{FN} represent the number of true positives and false negatives respecitively. With the use of top_k parameter, this metric can generalize to Recall@K.

The reduction method (how the recall scores are aggregated) is controlled by the average parameter, and additionally by the mdmc_average parameter in the multi-dimensional multi-class case. Accepts all inputs listed in Input types.

Parameters

  • preds (Tensor) – Predictions from model (probabilities, or labels)

  • target (Tensor) – Ground truth values

  • average (str) –

    Defines the reduction that is applied. Should be one of the following:

    • 'micro' [default]: Calculate the metric globally, across all samples and classes.

    • 'macro': Calculate the metric for each class separately, and average the metrics across classes (with equal weights for each class).

    • 'weighted': Calculate the metric for each class separately, and average the metrics across classes, weighting each class by its support (tp + fn).

    • 'none' or None: Calculate the metric for each class separately, and return the metric for every class.

    • 'samples': Calculate the metric for each sample, and average the metrics across samples (with equal weights for each sample).

    Note

    What is considered a sample in the multi-dimensional multi-class case depends on the value of mdmc_average.

  • mdmc_average (Optional[str]) –

    Defines how averaging is done for multi-dimensional multi-class inputs (on top of the average parameter). Should be one of the following:

    • None [default]: Should be left unchanged if your data is not multi-dimensional multi-class.

    • 'samplewise': In this case, the statistics are computed separately for each sample on the N axis, and then averaged over samples. The computation for each sample is done by treating the flattened extra axes ... (see Input types) as the N dimension within the sample, and computing the metric for the sample based on that.

    • 'global': In this case the N and ... dimensions of the inputs (see Input types) are flattened into a new N_X sample axis, i.e. the inputs are treated as if they were (N_X, C). From here on the average parameter applies as usual.

  • ignore_index (Optional[int]) – Integer specifying a target class to ignore. If given, this class index does not contribute to the returned score, regardless of reduction method. If an index is ignored, and average=None or 'none', the score for the ignored class will be returned as nan.

  • num_classes (Optional[int]) – Number of classes. Necessary for 'macro', 'weighted' and None average methods.

  • threshold (float) – Threshold probability value for transforming probability predictions to binary (0,1) predictions, in the case of binary or multi-label inputs

  • top_k (Optional[int]) –

    Number of highest probability entries for each sample to convert to 1s - relevant only for inputs with probability predictions. If this parameter is set for multi-label inputs, it will take precedence over threshold. For (multi-dim) multi-class inputs, this parameter defaults to 1.

    Should be left unset (None) for inputs with label predictions.

  • multiclass (Optional[bool]) – Used only in certain special cases, where you want to treat inputs as a different type than what they appear to be. See the parameter’s documentation section for a more detailed explanation and examples.

  • multilabel (Optional[bool]) –

    Deprecated since version 0.3: Argument will not have any effect and will be removed in v0.4, please use multiclass intead.

  • is_multiclass (Optional[bool]) –

    Deprecated since version 0.3: Argument will not have any effect and will be removed in v0.4, please use multiclass intead.

Return type

Tensor

Returns

The shape of the returned tensor depends on the average parameter

  • If average in ['micro', 'macro', 'weighted', 'samples'], a one-element tensor will be returned

  • If average in ['none', None], the shape will be (C,), where C stands for the number of classes

Raises

  • ValueError – If average is not one of "micro", "macro", "weighted", "samples", "none" or None.

  • ValueError – If mdmc_average is not one of None, "samplewise", "global".

  • ValueError – If average is set but num_classes is not provided.

  • ValueError – If num_classes is set and ignore_index is not in the range [0, num_classes).

Example

>>> from torchmetrics.functional import recall
>>> preds  = torch.tensor([2, 0, 2, 1])
>>> target = torch.tensor([1, 1, 2, 0])
>>> recall(preds, target, average='macro', num_classes=3)
tensor(0.3333)
>>> recall(preds, target, average='micro')
tensor(0.2500)

select_topk [func]

torchmetrics.utilities.data.select_topk(prob_tensor, topk=1, dim=1)[source]

Convert a probability tensor to binary by selecting top-k highest entries.

Parameters

  • prob_tensor (Tensor) – dense tensor of shape [..., C, ...], where C is in the position defined by the dim argument

  • topk (int) – number of highest entries to turn into 1s

  • dim (int) – dimension on which to compare entries

Return type

Tensor

Returns

A binary tensor of the same shape as the input tensor of type torch.int32

Example

>>> x = torch.tensor([[1.1, 2.0, 3.0], [2.0, 1.0, 0.5]])
>>> select_topk(x, topk=2)
tensor([[0, 1, 1],
        [1, 1, 0]], dtype=torch.int32)

stat_scores [func]

torchmetrics.functional.stat_scores(preds, target, reduce='micro', mdmc_reduce=None, num_classes=None, top_k=None, threshold=0.5, multiclass=None, ignore_index=None, is_multiclass=None)[source]

Computes the number of true positives, false positives, true negatives, false negatives. Related to Type I and Type II errors and the confusion matrix.

The reduction method (how the statistics are aggregated) is controlled by the reduce parameter, and additionally by the mdmc_reduce parameter in the multi-dimensional multi-class case. Accepts all inputs listed in Input types.

Parameters

  • preds (Tensor) – Predictions from model (probabilities or labels)

  • target (Tensor) – Ground truth values

  • threshold (float) – Threshold probability value for transforming probability predictions to binary (0 or 1) predictions, in the case of binary or multi-label inputs.

  • top_k (Optional[int]) –

    Number of highest probability entries for each sample to convert to 1s - relevant only for inputs with probability predictions. If this parameter is set for multi-label inputs, it will take precedence over threshold. For (multi-dim) multi-class inputs, this parameter defaults to 1.

    Should be left unset (None) for inputs with label predictions.

  • reduce (str) –

    Defines the reduction that is applied. Should be one of the following:

    • 'micro' [default]: Counts the statistics by summing over all [sample, class] combinations (globally). Each statistic is represented by a single integer.

    • 'macro': Counts the statistics for each class separately (over all samples). Each statistic is represented by a (C,) tensor. Requires num_classes to be set.

    • 'samples': Counts the statistics for each sample separately (over all classes). Each statistic is represented by a (N, ) 1d tensor.

    Note

    What is considered a sample in the multi-dimensional multi-class case depends on the value of mdmc_reduce.

  • num_classes (Optional[int]) – Number of classes. Necessary for (multi-dimensional) multi-class or multi-label data.

  • ignore_index (Optional[int]) – Specify a class (label) to ignore. If given, this class index does not contribute to the returned score, regardless of reduction method. If an index is ignored, and reduce='macro', the class statistics for the ignored class will all be returned as -1.

  • mdmc_reduce (Optional[str]) –

    Defines how the multi-dimensional multi-class inputs are handeled. Should be one of the following:

    • None [default]: Should be left unchanged if your data is not multi-dimensional multi-class (see Input types for the definition of input types).

    • 'samplewise': In this case, the statistics are computed separately for each sample on the N axis, and then the outputs are concatenated together. In each sample the extra axes ... are flattened to become the sub-sample axis, and statistics for each sample are computed by treating the sub-sample axis as the N axis for that sample.

    • 'global': In this case the N and ... dimensions of the inputs are flattened into a new N_X sample axis, i.e. the inputs are treated as if they were (N_X, C). From here on the reduce parameter applies as usual.

  • multiclass (Optional[bool]) – Used only in certain special cases, where you want to treat inputs as a different type than what they appear to be. See the parameter’s documentation section for a more detailed explanation and examples.

  • is_multiclass (Optional[bool]) –

    Deprecated since version 0.3: Argument will not have any effect and will be removed in v0.4, please use multiclass intead.

Return type

Tensor

Returns

The metric returns a tensor of shape (..., 5), where the last dimension corresponds to [tp, fp, tn, fn, sup] (sup stands for support and equals tp + fn). The shape depends on the reduce and mdmc_reduce (in case of multi-dimensional multi-class data) parameters:

  • If the data is not multi-dimensional multi-class, then

    • If reduce='micro', the shape will be (5, )

    • If reduce='macro', the shape will be (C, 5), where C stands for the number of classes

    • If reduce='samples', the shape will be (N, 5), where N stands for the number of samples

  • If the data is multi-dimensional multi-class and mdmc_reduce='global', then

    • If reduce='micro', the shape will be (5, )

    • If reduce='macro', the shape will be (C, 5)

    • If reduce='samples', the shape will be (N*X, 5), where X stands for the product of sizes of all “extra” dimensions of the data (i.e. all dimensions except for C and N)

  • If the data is multi-dimensional multi-class and mdmc_reduce='samplewise', then

    • If reduce='micro', the shape will be (N, 5)

    • If reduce='macro', the shape will be (N, C, 5)

    • If reduce='samples', the shape will be (N, X, 5)

Raises

  • ValueError – If reduce is none of "micro", "macro" or "samples".

  • ValueError – If mdmc_reduce is none of None, "samplewise", "global".

  • ValueError – If reduce is set to "macro" and num_classes is not provided.

  • ValueError – If num_classes is set and ignore_index is not in the range [0, num_classes).

  • ValueError – If ignore_index is used with binary data.

  • ValueError – If inputs are multi-dimensional multi-class and mdmc_reduce is not provided.

Example

>>> from torchmetrics.functional import stat_scores
>>> preds  = torch.tensor([1, 0, 2, 1])
>>> target = torch.tensor([1, 1, 2, 0])
>>> stat_scores(preds, target, reduce='macro', num_classes=3)
tensor([[0, 1, 2, 1, 1],
        [1, 1, 1, 1, 2],
        [1, 0, 3, 0, 1]])
>>> stat_scores(preds, target, reduce='micro')
tensor([2, 2, 6, 2, 4])

to_categorical [func]

torchmetrics.utilities.data.to_categorical(tensor, argmax_dim=1)[source]

Converts a tensor of probabilities to a dense label tensor

Parameters

  • tensor (Tensor) – probabilities to get the categorical label [N, d1, d2, …]

  • argmax_dim (int) – dimension to apply

Return type

Tensor

Returns

A tensor with categorical labels [N, d2, …]

Example

>>> x = torch.tensor([[0.2, 0.5], [0.9, 0.1]])
>>> to_categorical(x)
tensor([1, 0])

to_onehot [func]

torchmetrics.utilities.data.to_onehot(label_tensor, num_classes=None)[source]

Converts a dense label tensor to one-hot format

Parameters

  • label_tensor (Tensor) – dense label tensor, with shape [N, d1, d2, …]

  • num_classes (Optional[int]) – number of classes C

Return type

Tensor

Returns

A sparse label tensor with shape [N, C, d1, d2, …]

Example

>>> x = torch.tensor([1, 2, 3])
>>> to_onehot(x)
tensor([[0, 1, 0, 0],
        [0, 0, 1, 0],
        [0, 0, 0, 1]])

Regression Metrics

explained_variance [func]

torchmetrics.functional.explained_variance(preds, target, multioutput='uniform_average')[source]

Computes explained variance.

Parameters

  • preds (Tensor) – estimated labels

  • target (Tensor) – ground truth labels

  • multioutput (str) –

    Defines aggregation in the case of multiple output scores. Can be one of the following strings (default is ‘uniform_average’.):

    • ’raw_values’ returns full set of scores

    • ’uniform_average’ scores are uniformly averaged

    • ’variance_weighted’ scores are weighted by their individual variances

Example

>>> from torchmetrics.functional import explained_variance
>>> target = torch.tensor([3, -0.5, 2, 7])
>>> preds = torch.tensor([2.5, 0.0, 2, 8])
>>> explained_variance(preds, target)
tensor(0.9572)

>>> target = torch.tensor([[0.5, 1], [-1, 1], [7, -6]])
>>> preds = torch.tensor([[0, 2], [-1, 2], [8, -5]])
>>> explained_variance(preds, target, multioutput='raw_values')
tensor([0.9677, 1.0000])
Return type

Union[Tensor, Sequence[Tensor]]

image_gradients [func]

torchmetrics.functional.image_gradients(img)[source]

Computes the gradients of a given image using finite difference

Parameters

img (Tensor) – An (N, C, H, W) input tensor where C is the number of image channels

Return type

Tuple[Tensor, Tensor]

Returns

Tuple of (dy, dx) with each gradient of shape [N, C, H, W]

Raises

Example

>>> from torchmetrics.functional import image_gradients
>>> image = torch.arange(0, 1*1*5*5, dtype=torch.float32)
>>> image = torch.reshape(image, (1, 1, 5, 5))
>>> dy, dx = image_gradients(image)
>>> dy[0, 0, :, :]
tensor([[5., 5., 5., 5., 5.],
        [5., 5., 5., 5., 5.],
        [5., 5., 5., 5., 5.],
        [5., 5., 5., 5., 5.],
        [0., 0., 0., 0., 0.]])

Note

The implementation follows the 1-step finite difference method as followed by the TF implementation. The values are organized such that the gradient of [I(x+1, y)-[I(x, y)]] are at the (x, y) location

mean_absolute_error [func]

torchmetrics.functional.mean_absolute_error(preds, target)[source]

Computes mean absolute error

Parameters

  • preds (Tensor) – estimated labels

  • target (Tensor) – ground truth labels

Return type

Tensor

Returns

Tensor with MAE

Example

>>> from torchmetrics.functional import mean_absolute_error
>>> x = torch.tensor([0., 1, 2, 3])
>>> y = torch.tensor([0., 1, 2, 2])
>>> mean_absolute_error(x, y)
tensor(0.2500)

mean_squared_error [func]

torchmetrics.functional.mean_squared_error(preds, target)[source]

Computes mean squared error

Parameters

  • preds (Tensor) – estimated labels

  • target (Tensor) – ground truth labels

Return type

Tensor

Returns

Tensor with MSE

Example

>>> from torchmetrics.functional import mean_squared_error
>>> x = torch.tensor([0., 1, 2, 3])
>>> y = torch.tensor([0., 1, 2, 2])
>>> mean_squared_error(x, y)
tensor(0.2500)

mean_squared_log_error [func]

torchmetrics.functional.mean_squared_log_error(preds, target)[source]

Computes mean squared log error

Parameters

  • preds (Tensor) – estimated labels

  • target (Tensor) – ground truth labels

Return type

Tensor

Returns

Tensor with RMSLE

Example

>>> from torchmetrics.functional import mean_squared_log_error
>>> x = torch.tensor([0., 1, 2, 3])
>>> y = torch.tensor([0., 1, 2, 2])
>>> mean_squared_log_error(x, y)
tensor(0.0207)

Note

Half precision is only support on GPU for this metric

pearson_corrcoef [func]

torchmetrics.functional.pearson_corrcoef(preds, target)[source]

Computes pearson correlation coefficient.

Parameters

  • preds (Tensor) – estimated scores

  • target (Tensor) – ground truth scores

Example

>>> from torchmetrics.functional import pearson_corrcoef
>>> target = torch.tensor([3, -0.5, 2, 7])
>>> preds = torch.tensor([2.5, 0.0, 2, 8])
>>> pearson_corrcoef(preds, target)
tensor(0.9849)
Return type

Tensor

psnr [func]

torchmetrics.functional.psnr(preds, target, data_range=None, base=10.0, reduction='elementwise_mean', dim=None)[source]

Computes the peak signal-to-noise ratio

Parameters

  • preds (Tensor) – estimated signal

  • target (Tensor) – groun truth signal

  • data_range (Optional[float]) – the range of the data. If None, it is determined from the data (max - min). data_range must be given when dim is not None.

  • base (float) – a base of a logarithm to use (default: 10)

  • reduction (str) –

    a method to reduce metric score over labels.

    • 'elementwise_mean': takes the mean (default)

    • 'sum': takes the sum

    • 'none': no reduction will be applied

  • dim (Union[int, Tuple[int, …], None]) – Dimensions to reduce PSNR scores over provided as either an integer or a list of integers. Default is None meaning scores will be reduced across all dimensions.

Return type

Tensor

Returns

Tensor with PSNR score

Raises

ValueError – If dim is not None and data_range is not provided.

Example

>>> from torchmetrics.functional import psnr
>>> pred = torch.tensor([[0.0, 1.0], [2.0, 3.0]])
>>> target = torch.tensor([[3.0, 2.0], [1.0, 0.0]])
>>> psnr(pred, target)
tensor(2.5527)

Note

Half precision is only support on GPU for this metric

r2score [func]

torchmetrics.functional.r2score(preds, target, adjusted=0, multioutput='uniform_average')[source]

Computes r2 score also known as coefficient of determination:

R^2 = 1 - \frac{SS_res}{SS_tot}

where SS_res=\sum_i (y_i - f(x_i))^2 is the sum of residual squares, and SS_tot=\sum_i (y_i - \bar{y})^2 is total sum of squares. Can also calculate adjusted r2 score given by

R^2_adj = 1 - \frac{(1-R^2)(n-1)}{n-k-1}

where the parameter k (the number of independent regressors) should be provided as the adjusted argument.

Parameters

  • preds (Tensor) – estimated labels

  • target (Tensor) – ground truth labels

  • adjusted (int) – number of independent regressors for calculating adjusted r2 score. Default 0 (standard r2 score).

  • multioutput (str) –

    Defines aggregation in the case of multiple output scores. Can be one of the following strings (default is 'uniform_average'.):

    • 'raw_values' returns full set of scores

    • 'uniform_average' scores are uniformly averaged

    • 'variance_weighted' scores are weighted by their individual variances

Raises

  • ValueError – If both preds and targets are not 1D or 2D tensors.

  • ValueError – If len(preds) is less than 2 since at least 2 sampels are needed to calculate r2 score.

  • ValueError – If multioutput is not one of raw_values, uniform_average or variance_weighted.

  • ValueError – If adjusted is not an integer greater than 0.

Example

>>> from torchmetrics.functional import r2score
>>> target = torch.tensor([3, -0.5, 2, 7])
>>> preds = torch.tensor([2.5, 0.0, 2, 8])
>>> r2score(preds, target)
tensor(0.9486)

>>> target = torch.tensor([[0.5, 1], [-1, 1], [7, -6]])
>>> preds = torch.tensor([[0, 2], [-1, 2], [8, -5]])
>>> r2score(preds, target, multioutput='raw_values')
tensor([0.9654, 0.9082])
Return type

Tensor

spearman_corrcoef [func]

torchmetrics.functional.spearman_corrcoef(preds, target)[source]

where rg_x and rg_y are the rank associated to the variables x and y. Spearmans correlations coefficient corresponds to the standard pearsons correlation coefficient calculated on the rank variables.

Parameters

  • preds (Tensor) – estimated scores

  • target (Tensor) – ground truth scores

Example

>>> from torchmetrics.functional import spearman_corrcoef
>>> target = torch.tensor([3, -0.5, 2, 7])
>>> preds = torch.tensor([2.5, 0.0, 2, 8])
>>> spearman_corrcoef(preds, target)
tensor(1.0000)
Return type

Tensor

ssim [func]

torchmetrics.functional.ssim(preds, target, kernel_size=(11, 11), sigma=(1.5, 1.5), reduction='elementwise_mean', data_range=None, k1=0.01, k2=0.03)[source]

Computes Structual Similarity Index Measure

Parameters

  • preds (Tensor) – estimated image

  • target (Tensor) – ground truth image

  • kernel_size (Sequence[int]) – size of the gaussian kernel (default: (11, 11))

  • sigma (Sequence[float]) – Standard deviation of the gaussian kernel (default: (1.5, 1.5))

  • reduction (str) –

    a method to reduce metric score over labels.

    • 'elementwise_mean': takes the mean (default)

    • 'sum': takes the sum

    • 'none': no reduction will be applied

  • data_range (Optional[float]) – Range of the image. If None, it is determined from the image (max - min)

  • k1 (float) – Parameter of SSIM. Default: 0.01

  • k2 (float) – Parameter of SSIM. Default: 0.03

Return type

Tensor

Returns

Tensor with SSIM score

Raises

  • TypeError – If preds and target don’t have the same data type.

  • ValueError – If preds and target don’t have BxCxHxW shape.

  • ValueError – If the length of kernel_size or sigma is not 2.

  • ValueError – If one of the elements of kernel_size is not an odd positive number.

  • ValueError – If one of the elements of sigma is not a positive number.

Example

>>> from torchmetrics.functional import ssim
>>> preds = torch.rand([16, 1, 16, 16])
>>> target = preds * 0.75
>>> ssim(preds, target)
tensor(0.9219)

NLP

bleu_score [func]

torchmetrics.functional.bleu_score(translate_corpus, reference_corpus, n_gram=4, smooth=False)[source]

Calculate BLEU score of machine translated text with one or more references

Parameters

  • translate_corpus (Sequence[str]) – An iterable of machine translated corpus

  • reference_corpus (Sequence[str]) – An iterable of iterables of reference corpus

  • n_gram (int) – Gram value ranged from 1 to 4 (Default 4)

  • smooth (bool) – Whether or not to apply smoothing – Lin et al. 2004

Return type

Tensor

Returns

Tensor with BLEU Score

Example

>>> from torchmetrics.functional import bleu_score
>>> translate_corpus = ['the cat is on the mat'.split()]
>>> reference_corpus = [['there is a cat on the mat'.split(), 'a cat is on the mat'.split()]]
>>> bleu_score(translate_corpus, reference_corpus)
tensor(0.7598)

Pairwise

embedding_similarity [func]

torchmetrics.functional.embedding_similarity(batch, similarity='cosine', reduction='none', zero_diagonal=True)[source]

Computes representation similarity

Example

>>> from torchmetrics.functional import embedding_similarity
>>> embeddings = torch.tensor([[1., 2., 3., 4.], [1., 2., 3., 4.], [4., 5., 6., 7.]])
>>> embedding_similarity(embeddings)
tensor([[0.0000, 1.0000, 0.9759],
        [1.0000, 0.0000, 0.9759],
        [0.9759, 0.9759, 0.0000]])
Parameters

  • batch (Tensor) – (batch, dim)

  • similarity (str) – ‘dot’ or ‘cosine’

  • reduction (str) – ‘none’, ‘sum’, ‘mean’ (all along dim -1)

  • zero_diagonal (bool) – if True, the diagonals are set to zero

Return type

Tensor

Returns

A square matrix (batch, batch) with the similarity scores between all elements If sum or mean are used, then returns (b, 1) with the reduced value for each row

Retrieval

retrieval_average_precision [func]

torchmetrics.functional.retrieval_average_precision(preds, target)[source]

Computes average precision (for information retrieval), as explained here.

preds and target should be of the same shape and live on the same device. If no target is True, 0 is returned. target must be either bool or integers and preds must be float, otherwise an error is raised.

Parameters

  • preds (Tensor) – estimated probabilities of each document to be relevant.

  • target (Tensor) – ground truth about each document being relevant or not.

Return type

Tensor

Returns

a single-value tensor with the average precision (AP) of the predictions preds w.r.t. the labels target.

Example

>>> from torchmetrics.functional import retrieval_average_precision
>>> preds = tensor([0.2, 0.3, 0.5])
>>> target = tensor([True, False, True])
>>> retrieval_average_precision(preds, target)
tensor(0.8333)

retrieval_reciprocal_rank [func]

torchmetrics.functional.retrieval_reciprocal_rank(preds, target)[source]

Computes reciprocal rank (for information retrieval), as explained here.

preds and target should be of the same shape and live on the same device. If no target is True, 0 is returned. target must be either bool or integers and preds must be float, otherwise an error is raised.

Parameters

  • preds (Tensor) – estimated probabilities of each document to be relevant.

  • target (Tensor) – ground truth about each document being relevant or not.

Return type

Tensor

Returns

a single-value tensor with the reciprocal rank (RR) of the predictions preds wrt the labels target.

Example

>>> from torchmetrics.functional import retrieval_reciprocal_rank
>>> preds = torch.tensor([0.2, 0.3, 0.5])
>>> target = torch.tensor([False, True, False])
>>> retrieval_reciprocal_rank(preds, target)
tensor(0.5000)

retrieval_precision [func]

torchmetrics.functional.retrieval_precision(preds, target, k=None)[source]

Computes the precision metric (for information retrieval), as explained here. Precision is the fraction of relevant documents among all the retrieved documents.

preds and target should be of the same shape and live on the same device. If no target is True, 0 is returned. target must be either bool or integers and preds must be float, otherwise an error is raised. If you want to measure Precision@K, k must be a positive integer.

Parameters

  • preds (Tensor) – estimated probabilities of each document to be relevant.

  • target (Tensor) – ground truth about each document being relevant or not.

  • k (Optional[int]) – consider only the top k elements (default: None)

Return type

Tensor

Returns

a single-value tensor with the precision (at k) of the predictions preds w.r.t. the labels target.

Example

>>> preds = tensor([0.2, 0.3, 0.5])
>>> target = tensor([True, False, True])
>>> retrieval_precision(preds, target, k=2)
tensor(0.5000)

retrieval_recall [func]

torchmetrics.functional.retrieval_recall(preds, target, k=None)[source]

Computes the recall metric (for information retrieval), as explained here. Recall is the fraction of relevant documents retrieved among all the relevant documents.

preds and target should be of the same shape and live on the same device. If no target is True, 0 is returned. target must be either bool or integers and preds must be float, otherwise an error is raised. If you want to measure Recall@K, k must be a positive integer.

Parameters

  • preds (Tensor) – estimated probabilities of each document to be relevant.

  • target (Tensor) – ground truth about each document being relevant or not.

  • k (Optional[int]) – consider only the top k elements (default: None)

Return type

Tensor

Returns

a single-value tensor with the recall (at k) of the predictions preds w.r.t. the labels target.

Example

>>> from  torchmetrics.functional import retrieval_recall
>>> preds = tensor([0.2, 0.3, 0.5])
>>> target = tensor([True, False, True])
>>> retrieval_recall(preds, target, k=2)
tensor(0.5000)

retrieval_fall_out [func]

torchmetrics.functional.retrieval_fall_out(preds, target, k=None)[source]

Computes the Fall-out (for information retrieval), as explained here. Fall-out is the fraction of non-relevant documents retrieved among all the non-relevant documents.

preds and target should be of the same shape and live on the same device. If no target is True, 0 is returned. target must be either bool or integers and preds must be float, otherwise an error is raised. If you want to measure Fall-out@K, k must be a positive integer.

Parameters

  • preds (Tensor) – estimated probabilities of each document to be relevant.

  • target (Tensor) – ground truth about each document being relevant or not.

  • k (Optional[int]) – consider only the top k elements (default: None)

Return type

Tensor

Returns

a single-value tensor with the fall-out (at k) of the predictions preds w.r.t. the labels target.

Example

>>> from  torchmetrics.functional import retrieval_fall_out
>>> preds = tensor([0.2, 0.3, 0.5])
>>> target = tensor([True, False, True])
>>> retrieval_fall_out(preds, target, k=2)
tensor(1.)

retrieval_normalized_dcg [func]

torchmetrics.functional.retrieval_normalized_dcg(preds, target, k=None)[source]

Computes Normalized Discounted Cumulative Gain (for information retrieval), as explained here.

preds and target should be of the same shape and live on the same device. target must be either bool or integers and preds must be float, otherwise an error is raised.

Parameters

  • preds (Tensor) – estimated probabilities of each document to be relevant.

  • target (Tensor) – ground truth about each document relevance.

  • k (Optional[int]) – consider only the top k elements (default: None)

Return type

Tensor

Returns

a single-value tensor with the nDCG of the predictions preds w.r.t. the labels target.

Example

>>> from torchmetrics.functional import retrieval_normalized_dcg
>>> preds = torch.tensor([.1, .2, .3, 4, 70])
>>> target = torch.tensor([10, 0, 0, 1, 5])
>>> retrieval_normalized_dcg(preds, target)
tensor(0.6957)

Contributor Covenant Code of Conduct

Our Pledge

In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation.

Our Standards

Examples of behavior that contributes to creating a positive environment include:

  • Using welcoming and inclusive language

  • Being respectful of differing viewpoints and experiences

  • Gracefully accepting constructive criticism

  • Focusing on what is best for the community

  • Showing empathy towards other community members

Examples of unacceptable behavior by participants include:

  • The use of sexualized language or imagery and unwelcome sexual attention or advances

  • Trolling, insulting/derogatory comments, and personal or political attacks

  • Public or private harassment

  • Publishing others’ private information, such as a physical or electronic address, without explicit permission

  • Other conduct which could reasonably be considered inappropriate in a professional setting

Our Responsibilities

Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.

Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.

Scope

This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.

Enforcement

Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at waf2107@columbia.edu. All complaints will be reviewed and investigated and will result in a response that is deemed necessary and appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately.

Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project’s leadership.

Attribution

This Code of Conduct is adapted from the Contributor Covenant, version 1.4, available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html

For answers to common questions about this code of conduct, see https://www.contributor-covenant.org/faq

Contributing

Welcome to the Torchmetrics community! We’re building largest collection of native pytorch metrics, with the goal of reducing boilerplate and increasing reproducibility.

Contribution Types

We are always looking for help implementing new features or fixing bugs.

Bug Fixes:

  1. If you find a bug please submit a github issue.

    • Make sure the title explains the issue.

    • Describe your setup, what you are trying to do, expected vs. actual behaviour. Please add configs and code samples.

    • Add details on how to reproduce the issue - a minimal test case is always best, colab is also great. Note, that the sample code shall be minimal and if needed with publicly available data.

  2. Try to fix it or recommend a solution. We highly recommend to use test-driven approach:

    • Convert your minimal code example to a unit/integration test with assert on expected results.

    • Start by debugging the issue… You can run just this particular test in your IDE and draft a fix.

    • Verify that your test case fails on the master branch and only passes with the fix applied.

  3. Submit a PR!

Note, even if you do not find the solution, sending a PR with a test covering the issue is a valid contribution and we can help you or finish it with you :]

New Features:

  1. Submit a github issue - describe what is the motivation of such feature (adding the use case or an example is helpful).

  2. Let’s discuss to determine the feature scope.

  3. Submit a PR! We recommend test driven approach to adding new features as well:

    • Write a test for the functionality you want to add.

    • Write the functional code until the test passes.

  4. Add/update the relevant tests!

  • This PR is a good example for adding a new metric

Test cases:

Want to keep Torchmetrics healthy? Love seeing those green tests? So do we! How to we keep it that way? We write tests! We value tests contribution even more than new features. One of the core values of torchmetrics is that our users can trust our metric implementation. We can only guarantee this if our metrics are well tested.

Guidelines

Developments scripts

To build the documentation locally, simply execute the following commands from project root (only for Unix):

  • make clean cleans repo from temp/generated files

  • make docs builds documentation under docs/build/html

  • make test runs all project’s tests with coverage

Original code

All added or edited code shall be the own original work of the particular contributor. If you use some third-party implementation, all such blocks/functions/modules shall be properly referred and if possible also agreed by code’s author. For example - This code is inspired from http://.... In case you adding new dependencies, make sure that they are compatible with the actual Torchmetrics license (ie. dependencies should be at least as permissive as the Torchmetrics license).

Coding Style

  1. Use f-strings for output formation (except logging when we stay with lazy logging.info("Hello %s!", name).

  2. You can use pre-commit to make sure your code style is correct.

Documentation

We are using Sphinx with Napoleon extension. Moreover, we set Google style to follow with type convention.

See following short example of a sample function taking one position string and optional

from typing import Optional

def my_func(param_a: int, param_b: Optional[float] = None) -> str:
    """Sample function.

    Args:
        param_a: first parameter
        param_b: second parameter

    Return:
        sum of both numbers

    Example:
        Sample doctest example...
        >>> my_func(1, 2)
        3

    .. note:: If you want to add something.
    """
    p = param_b if param_b else 0
    return str(param_a + p)

When updating the docs make sure to build them first locally and visually inspect the html files (in the browser) for formatting errors. In certain cases, a missing blank line or a wrong indent can lead to a broken layout. Run these commands

make docs

and open docs/build/html/index.html in your browser.

Notes:

  • You need to have LaTeX installed for rendering math equations. You can for example install TeXLive by doing one of the following:

    • on Ubuntu (Linux) run apt-get install texlive or otherwise follow the instructions on the TeXLive website

    • use the RTD docker image

  • with PL used class meta you need to use python 3.7 or higher

When you send a PR the continuous integration will run tests and build the docs.

Testing

Local: Testing your work locally will help you speed up the process since it allows you to focus on particular (failing) test-cases. To setup a local development environment, install both local and test dependencies:

python -m pip install -r requirements/test.txt
python -m pip install pre-commit

You can run the full test-case in your terminal via this make script:

make test
# or natively
python -m pytest torchmetrics tests

Note: if your computer does not have multi-GPU nor TPU these tests are skipped.

GitHub Actions: For convenience, you can also use your own GHActions building which will be triggered with each commit. This is useful if you do not test against all required dependency versions.

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[0.3.2] - 2021-05-10

[0.3.2] - Added

  • Added is_differentiable property:

    • To AUC, AUROC, CohenKappa and AveragePrecision (#178)

    • To PearsonCorrCoef, SpearmanCorrcoef, R2Score and ExplainedVariance (#225)

[0.3.2] - Changed

  • MetricCollection should return metrics with prefix on items(), keys() (#209)

  • Calling compute before update will now give an warning (#164)

[0.3.2] - Removed

  • Removed numpy as dependency (#212)

[0.3.2] - Fixed

  • Fixed auc calculation and add tests (#197)

  • Fixed loading persisted metric states using load_state_dict() (#202)

  • Fixed PSNR not working with DDP (#214)

  • Fixed metric calculation with unequal batch sizes (#220)

  • Fixed metric concatenation for list states for zero-dim input (#229)

  • Fixed numerical instability in AUROC metric for large input (#230)

[0.3.1] - 2021-04-21

  • Cleaning remaining inconsistency and fix PL develop integration ( #191, #192, #193, #194 )

[0.3.0] - 2021-04-20

[0.3.0] - Added

  • Added BootStrapper to easily calculate confidence intervals for metrics (#101)

  • Added Binned metrics (#128)

  • Added metrics for Information Retrieval ((PL^5032)):

    • Added RetrievalMAP (PL^5032)

    • Added RetrievalMRR (#119)

    • Added RetrievalPrecision (#139)

    • Added RetrievalRecall (#146)

    • Added RetrievalNormalizedDCG (#160)

    • Added RetrievalFallOut (#161)

  • Added other metrics:

    • Added CohenKappa (#69)

    • Added MatthewsCorrcoef (#98)

    • Added PearsonCorrcoef (#157)

    • Added SpearmanCorrcoef (#158)

    • Added Hinge (#120)

  • Added average='micro' as an option in AUROC for multilabel problems (#110)

  • Added multilabel support to ROC metric (#114)

  • Added testing for half precision (#77, #135 )

  • Added AverageMeter for ad-hoc averages of values (#138)

  • Added prefix argument to MetricCollection (#70)

  • Added __getitem__ as metric arithmetic operation (#142)

  • Added property is_differentiable to metrics and test for differentiability (#154)

  • Added support for average, ignore_index and mdmc_average in Accuracy metric (#166)

  • Added postfix arg to MetricCollection (#188)

[0.3.0] - Changed

  • Changed ExplainedVariance from storing all preds/targets to tracking 5 statistics (#68)

  • Changed behaviour of confusionmatrix for multilabel data to better match multilabel_confusion_matrix from sklearn (#134)

  • Updated FBeta arguments (#111)

  • Changed reset method to use detach.clone() instead of deepcopy when resetting to default (#163)

  • Metrics passed as dict to MetricCollection will now always be in deterministic order (#173)

  • Allowed MetricCollection pass metrics as arguments (#176)

[0.3.0] - Deprecated

  • Rename argument is_multiclass -> multiclass (#162)

[0.3.0] - Removed

  • Prune remaining deprecated (#92)

[0.3.0] - Fixed

  • Fixed when _stable_1d_sort to work when n>=N (PL^6177)

  • Fixed _computed attribute not being correctly reset (#147)

  • Fixed to Blau score (#165)

  • Fixed backwards compatibility for logging with older version of pytorch-lightning (#182)

[0.2.0] - 2021-03-12

[0.2.0] - Changed

  • Decoupled PL dependency (#13)

  • Refactored functional - mimic the module-like structure: classification, regression, etc. (#16)

  • Refactored utilities - split to topics/submodules (#14)

  • Refactored MetricCollection (#19)

[0.2.0] - Removed

  • Removed deprecated metrics from PL base (#12, #15)

[0.1.0] - 2021-02-22

  • Added Accuracy metric now generalizes to Top-k accuracy for (multi-dimensional) multi-class inputs using the top_k parameter (PL^4838)

  • Added Accuracy metric now enables the computation of subset accuracy for multi-label or multi-dimensional multi-class inputs with the subset_accuracy parameter (PL^4838)

  • Added HammingDistance metric to compute the hamming distance (loss) (PL^4838)

  • Added StatScores metric to compute the number of true positives, false positives, true negatives and false negatives (PL^4839)

  • Added R2Score metric (PL^5241)

  • Added MetricCollection (PL^4318)

  • Added .clone() method to metrics (PL^4318)

  • Added IoU class interface (PL^4704)

  • The Recall and Precision metrics (and their functional counterparts recall and precision) can now be generalized to Recall@K and Precision@K with the use of top_k parameter (PL^4842)

  • Added compositional metrics (PL^5464)

  • Added AUC/AUROC class interface (PL^5479)

  • Added QuantizationAwareTraining callback (PL^5706)

  • Added ConfusionMatrix class interface (PL^4348)

  • Added multiclass AUROC metric (PL^4236)

  • Added PrecisionRecallCurve, ROC, AveragePrecision class metric (PL^4549)

  • Classification metrics overhaul (PL^4837)

  • Added F1 class metric (PL^4656)

  • Added metrics aggregation in Horovod and fixed early stopping (PL^3775)

  • Added persistent(mode) method to metrics, to enable and disable metric states being added to state_dict (PL^4482)

  • Added unification of regression metrics (PL^4166)

  • Added persistent flag to Metric.add_state (PL^4195)

  • Added classification metrics (PL^4043)

  • Added new Metrics API. (PL^3868, PL^3921)

  • Added EMB similarity (PL^3349)

  • Added SSIM metrics (PL^2671)

  • Added BLEU metrics (PL^2535)

Indices and tables

© Copyright Copyright (c) 2020-2021, PyTorchLightning et al... Revision 33aed0e1.

Built with Sphinx using a theme provided by Read the Docs.

User Guide

Metrics API references

Community

Read the Docs v: v0.3.2
Versions
latest
stable
v0.3.2
v0.3.1
v0.3.0
v0.2.0
v0.1.0
Downloads
On Read the Docs
Project Home
Builds
Free document hosting provided by Read the Docs.

To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy.