ignite.metrics#

Metrics provide a way to compute various quantities of interest in an online fashion without having to store the entire output history of a model.

In practice a user needs to attach the metric instance to an engine. The metric value is then computed using the output of the engine’s process_function:

from ignite.engine import Engine
from ignite.metrics import Accuracy

def process_function(engine, batch):
    # ...
    return y_pred, y

engine = Engine(process_function)
metric = Accuracy()
metric.attach(engine, "accuracy")
# ...
state = engine.run(data)
print(f"Accuracy: {state.metrics['accuracy']}")

If the engine’s output is not in the format (y_pred, y) or {'y_pred': y_pred, 'y': y, ...}, the user can use the output_transform argument to transform it:

from ignite.engine import Engine
from ignite.metrics import Accuracy

def process_function(engine, batch):
    # ...
    return {'y_pred': y_pred, 'y_true': y, ...}

engine = Engine(process_function)

def output_transform(output):
    # `output` variable is returned by above `process_function`
    y_pred = output['y_pred']
    y = output['y_true']
    return y_pred, y  # output format is according to `Accuracy` docs

metric = Accuracy(output_transform=output_transform)
metric.attach(engine, "accuracy")
# ...
state = engine.run(data)
print(f"Accuracy: {state.metrics['accuracy']}")

Warning

Please, be careful when using lambda functions to setup multiple output_transform for multiple metrics

# Wrong
# metrics_group = [Accuracy(output_transform=lambda output: output[name]) for name in names]
# As lambda can not store `name` and all `output_transform` will use the last `name`

# A correct way. For example, using functools.partial
from functools import partial

def ot_func(output, name):
    return output[name]

metrics_group = [Accuracy(output_transform=partial(ot_func, name=name)) for name in names]

For more details, see here

Note

Most of implemented metrics are adapted to distributed computations and reduce their internal states across supported devices before computing metric value. This can be helpful to run the evaluation on multiple nodes/GPU instances/TPUs with a distributed data sampler. Following code snippet shows in detail how to use metrics:

device = f"cuda:{local_rank}"
model = torch.nn.parallel.DistributedDataParallel(model,
                                                  device_ids=[local_rank, ],
                                                  output_device=local_rank)
test_sampler = DistributedSampler(test_dataset)
test_loader = DataLoader(
    test_dataset,
    batch_size=batch_size,
    sampler=test_sampler,
    num_workers=num_workers,
    pin_memory=True
)

evaluator = create_supervised_evaluator(model, metrics={'accuracy': Accuracy()}, device=device)

Note

Metrics cannot be serialized using pickle module because the implementation is based on lambda functions. Therefore, use the third party library dill to overcome the limitation of pickle.

Reset, Update, Compute API#

User can also call directly the following methods on the metric:

reset() : resets internal variables and accumulators
update() : updates internal variables and accumulators with provided batch output (y_pred, y)
compute() : computes custom metric and return the result

This API gives a more fine-grained/custom usage on how to compute a metric. For example:

from ignite.metrics import Precision

# Define the metric
precision = Precision()

# Start accumulation:
for x, y in data:
    y_pred = model(x)
    precision.update((y_pred, y))

# Compute the result
print("Precision: ", precision.compute())

# Reset metric
precision.reset()

# Start new accumulation:
for x, y in data:
    y_pred = model(x)
    precision.update((y_pred, y))

# Compute new result
print("Precision: ", precision.compute())

Metric arithmetics#

Metrics could be combined together to form new metrics. This could be done through arithmetics, such as metric1 + metric2, use PyTorch operators, such as (metric1 + metric2).pow(2).mean(), or use a lambda function, such as MetricsLambda(lambda a, b: torch.mean(a + b), metric1, metric2).

For example:

from ignite.metrics import Precision, Recall

precision = Precision(average=False)
recall = Recall(average=False)
F1 = (precision * recall * 2 / (precision + recall)).mean()

Note

This example computes the mean of F1 across classes. To combine precision and recall to get F1 or other F metrics, we have to be careful that average=False, i.e. to use the unaveraged precision and recall, otherwise we will not be computing F-beta metrics.

Metrics also support indexing operation (if metric’s result is a vector/matrix/tensor). For example, this can be useful to compute mean metric (e.g. precision, recall or IoU) ignoring the background:

from ignite.metrics import ConfusionMatrix

cm = ConfusionMatrix(num_classes=10)
iou_metric = IoU(cm)
iou_no_bg_metric = iou_metric[:9]  # We assume that the background index is 9
mean_iou_no_bg_metric = iou_no_bg_metric.mean()
# mean_iou_no_bg_metric.compute() -> tensor(0.12345)

How to create a custom metric#

To create a custom metric one needs to create a new class inheriting from Metric and override three methods :

reset() : resets internal variables and accumulators
update() : updates internal variables and accumulators with provided batch output (y_pred, y)
compute() : computes custom metric and return the result

For example, we would like to implement for illustration purposes a multi-class accuracy metric with some specific condition (e.g. ignore user-defined classes):

from ignite.metrics import Metric
from ignite.exceptions import NotComputableError

# These decorators helps with distributed settings
from ignite.metrics.metric import sync_all_reduce, reinit__is_reduced


class CustomAccuracy(Metric):

    def __init__(self, ignored_class, output_transform=lambda x: x, device="cpu"):
        self.ignored_class = ignored_class
        self._num_correct = None
        self._num_examples = None
        super(CustomAccuracy, self).__init__(output_transform=output_transform, device=device)

    @reinit__is_reduced
    def reset(self):
        self._num_correct = torch.tensor(0, device=self._device)
        self._num_examples = 0
        super(CustomAccuracy, self).reset()

    @reinit__is_reduced
    def update(self, output):
        y_pred, y = output[0].detach(), output[1].detach()

        indices = torch.argmax(y_pred, dim=1)

        mask = (y != self.ignored_class)
        mask &= (indices != self.ignored_class)
        y = y[mask]
        indices = indices[mask]
        correct = torch.eq(indices, y).view(-1)

        self._num_correct += torch.sum(correct).to(self._device)
        self._num_examples += correct.shape[0]

    @sync_all_reduce("_num_examples", "_num_correct:SUM")
    def compute(self):
        if self._num_examples == 0:
            raise NotComputableError('CustomAccuracy must have at least one example before it can be computed.')
        return self._num_correct.item() / self._num_examples

We imported necessary classes as Metric, NotComputableError and decorators to adapt the metric for distributed setting. In reset method, we reset internal variables _num_correct and _num_examples which are used to compute the custom metric. In updated method we define how to update the internal variables. And finally in compute method, we compute metric value.

Notice that _num_correct is a tensor, since in update we accumulate tensor values. _num_examples is a python scalar since we accumulate normal integers. For differentiable metrics, you must detach the accumulated values before adding them to the internal variables.

We can check this implementation in a simple case:

import torch
torch.manual_seed(8)

m = CustomAccuracy(ignored_class=3)

batch_size = 4
num_classes = 5

y_pred = torch.rand(batch_size, num_classes)
y = torch.randint(0, num_classes, size=(batch_size, ))

m.update((y_pred, y))
res = m.compute()

print(y, torch.argmax(y_pred, dim=1))
# Out: tensor([2, 2, 2, 3]) tensor([2, 1, 0, 0])

print(m._num_correct, m._num_examples, res)
# Out: 1 3 0.3333333333333333

Metrics and its usages#

By default, Metrics are epoch-wise, it means

reset() is triggered every EPOCH_STARTED (See Events).
update() is triggered every ITERATION_COMPLETED.
compute() is triggered every EPOCH_COMPLETED.

Usages can be user defined by creating a class inheriting for MetricUsage. See the list below of usages.

Complete list of usages#

MetricUsage

EpochWise

BatchWise

BatchFiltered

Metrics and distributed computations#

In the above example, CustomAccuracy has reset, update, compute methods decorated with reinit__is_reduced(), sync_all_reduce(). The purpose of these features is to adapt metrics in distributed computations on supported backend and devices (see ignite.distributed for more details). More precisely, in the above example we added @sync_all_reduce("_num_examples", "_num_correct:SUM") over compute method. This means that when compute method is called, metric’s interal variables self._num_examples and self._num_correct:SUM are summed up over all participating devices. We specify the reduction operation self._num_correct:SUM or we keep the default self._num_examples as the default is SUM. We currently support four reduction operations (SUM, MAX, MIN, PRODUCT). Therefore, once collected, these internal variables can be used to compute the final metric value.

Complete list of metrics#

`Average`	Helper class to compute arithmetic average of a single variable.
`GeometricAverage`	Helper class to compute geometric average of a single variable.
`VariableAccumulation`	Single variable accumulator helper to compute (arithmetic, geometric, harmonic) average of a single variable.
`Accuracy`	Calculates the accuracy for binary, multiclass and multilabel data.
`confusion_matrix.ConfusionMatrix`	Calculates confusion matrix for multi-class data.
`ClassificationReport`	Build a text report showing the main classification metrics.
`DiceCoefficient`	Calculates Dice Coefficient for a given `ConfusionMatrix` metric.
`JaccardIndex`	Calculates the Jaccard Index using `ConfusionMatrix` metric.
`IoU`	Calculates Intersection over Union using `ConfusionMatrix` metric.
`mIoU`	Calculates mean Intersection over Union using `ConfusionMatrix` metric.
`EpochMetric`	Class for metrics that should be computed on the entire output history of a model.
`Fbeta`	Calculates F-beta score.
`Frequency`	Provides metrics for the number of examples processed per second.
`Loss`	Calculates the average loss according to the passed loss_fn.
`MeanAbsoluteError`	Calculates the mean absolute error.
`MeanPairwiseDistance`	Calculates the mean `PairwiseDistance`.
`MeanSquaredError`	Calculates the mean squared error.
`metric.Metric`	Base class for all Metrics.
`metrics_lambda.MetricsLambda`	Apply a function to other metrics to obtain a new metric.
`MultiLabelConfusionMatrix`	Calculates a confusion matrix for multi-labelled, multi-class data.
`precision.Precision`	Calculates precision for binary and multiclass data.
`PSNR`	Computes average Peak signal-to-noise ratio (PSNR).
`recall.Recall`	Calculates recall for binary and multiclass data.
`RootMeanSquaredError`	Calculates the root mean squared error.
`RunningAverage`	Compute running average of a metric or the output of process function.
`SSIM`	Computes Structual Similarity Index Measure
`TopKCategoricalAccuracy`	Calculates the top-k categorical accuracy.
`Bleu`	Calculates the BLEU score.
`Rouge`	Calculates the Rouge score for multiples Rouge-N and Rouge-L metrics.
`RougeL`	Calculates the Rouge-L score.
`RougeN`	Calculates the Rouge-N score.

Helpers for customizing metrics#

MetricUsage#

class ignite.metrics.metric.MetricUsage(started, completed, iteration_completed)[source]#

Base class for all usages of metrics.

A usage of metric defines the events when a metric starts to compute, updates and completes. Valid events are from Events.

Parameters

started (Events) – event when the metric starts to compute. This event will be associated to started().
completed (Events) – event when the metric completes. This event will be associated to completed().
iteration_completed (CallableEventWithFilter) – event when the metric updates. This event will be associated to iteration_completed().

EpochWise#

class ignite.metrics.metric.EpochWise[source]#

Epoch-wise usage of Metrics. It’s the default and most common usage of metrics.

Metric’s methods are triggered on the following engine events:

started() on every EPOCH_STARTED (See Events).
iteration_completed() on every ITERATION_COMPLETED.
completed() on every EPOCH_COMPLETED.

usage_name#

usage name string

Type: str

BatchWise#

class ignite.metrics.metric.BatchWise[source]#

Batch-wise usage of Metrics.

Metric’s methods are triggered on the following engine events:

started() on every ITERATION_STARTED (See Events).
iteration_completed() on every ITERATION_COMPLETED.
completed() on every ITERATION_COMPLETED.

usage_name#

usage name string

Type: str

BatchFiltered#

class ignite.metrics.metric.BatchFiltered(*args, **kwargs)[source]#

Batch filtered usage of Metrics. This usage is similar to epoch-wise but update event is filtered.

Metric’s methods are triggered on the following engine events:

started() on every EPOCH_STARTED (See Events).
iteration_completed() on filtered ITERATION_COMPLETED.
completed() on every EPOCH_COMPLETED.

Parameters

args (Any) – Positional arguments to setup ITERATION_COMPLETED
kwargs (Any) – Keyword arguments to setup ITERATION_COMPLETED handled by iteration_completed().

reinit__is_reduced#

ignite.metrics.metric.reinit__is_reduced(func)[source]#

Helper decorator for distributed configuration.

See ignite.metrics on how to use it.

Parameters: func (Callable) – A callable to reinit.
Return type: Callable

sync_all_reduce#

ignite.metrics.metric.sync_all_reduce(*attrs)[source]#

Helper decorator for distributed configuration to collect instance attribute value across all participating processes and apply the specified reduction operation.

See ignite.metrics on how to use it.

Parameters: attrs (Any) – attribute names of decorated class
Return type: Callable

Changed in version 0.4.5: - Ability to handle different reduction operations (SUM, MAX, MIN, PRODUCT).