Table of Contents
Shortcuts

ConfusionMatrix#

class ignite.metrics.confusion_matrix.ConfusionMatrix(num_classes, average=None, output_transform=<function ConfusionMatrix.<lambda>>, device=device(type='cpu'))[source]#

Calculates confusion matrix for multi-class data.

  • update must receive output of the form (y_pred, y) or {'y_pred': y_pred, 'y': y}.

  • y_pred must contain logits and has the following shape (batch_size, num_classes, …). If you are doing binary classification, see Note for an example on how to get this.

  • y should have the following shape (batch_size, …) and contains ground-truth class indices with or without the background class. During the computation, argmax of y_pred is taken to determine predicted classes.

Parameters

  • num_classes (int) – Number of classes, should be > 1. See notes for more details.

  • average (Optional[str]) – confusion matrix values averaging schema: None, “samples”, “recall”, “precision”. Default is None. If average=”samples” then confusion matrix values are normalized by the number of seen samples. If average=”recall” then confusion matrix values are normalized such that diagonal values represent class recalls. If average=”precision” then confusion matrix values are normalized such that diagonal values represent class precisions.

  • output_transform (Callable) – a callable that is used to transform the Engine’s process_function’s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs.

  • device (Union[str, torch.device]) – specifies which device updates are accumulated on. Setting the metric’s device to be the same as your update arguments ensures the update method is non-blocking. By default, CPU.

Note

In case of the targets y in (batch_size, …) format, target indices between 0 and num_classes only contribute to the confusion matrix and others are neglected. For example, if num_classes=20 and target index equal 255 is encountered, then it is filtered out.

If you are doing binary classification with a single output unit, you may have to transform your network output, so that you have one value for each class. E.g. you can transform your network output into a one-hot vector with:

def binary_one_hot_output_transform(output):
    y_pred, y = output
    y_pred = torch.sigmoid(y_pred).round().long()
    y_pred = ignite.utils.to_onehot(y_pred, 2)
    y = y.long()
    return y_pred, y

metrics = {
    "confusion_matrix": ConfusionMatrix(2, output_transform=binary_one_hot_output_transform),
}

evaluator = create_supervised_evaluator(
    model, metrics=metrics, output_transform=lambda x, y, y_pred: (y_pred, y)
)

Methods

attach

Attaches current metric to provided engine.

completed

Helper method to compute metric’s value and put into the engine.

compute

Computes the metric based on it’s accumulated state.

detach

Detaches current metric from the engine and no metric’s computation is done during the run.

is_attached

Checks if current metric is attached to provided engine.

iteration_completed

Helper method to update metric’s computation.

normalize

Normalize given matrix with given average.

reset

Resets the metric to it’s initial state.

started

Helper method to start data gathering for metric’s computation.

update

Updates the metric’s state using the passed batch output.
attach(engine, name, usage=<ignite.metrics.metric.EpochWise object>)#

Attaches current metric to provided engine. On the end of engine’s run, engine.state.metrics dictionary will contain computed metric’s value under provided name.

Parameters
Return type

None

Example:

metric = ...
metric.attach(engine, "mymetric")

assert "mymetric" in engine.run(data).metrics

assert metric.is_attached(engine)

Example with usage:

metric = ...
metric.attach(engine, "mymetric", usage=BatchWise.usage_name)

assert "mymetric" in engine.run(data).metrics

assert metric.is_attached(engine, usage=BatchWise.usage_name)
completed(engine, name)#

Helper method to compute metric’s value and put into the engine. It is automatically attached to the engine with attach(). If metrics’ value is torch tensor, it is explicitly sent to CPU device.

Parameters

  • engine (ignite.engine.engine.Engine) – the engine to which the metric must be attached

  • name (str) – the name of the metric used as key in dict engine.state.metrics

Return type

None

Changed in version 0.4.3: Added dict in metrics results.

Changed in version 0.4.5: metric’s value is put on CPU if torch tensor.

compute()[source]#

Computes the metric based on it’s accumulated state.

By default, this is called at the end of each epoch.

Returns

the actual quantity of interest. However, if a Mapping is returned, it will be (shallow) flattened into engine.state.metrics when completed() is called.

Return type

Any

Raises

NotComputableError – raised when the metric cannot be computed.

detach(engine, usage=<ignite.metrics.metric.EpochWise object>)#

Detaches current metric from the engine and no metric’s computation is done during the run. This method in conjunction with attach() can be useful if several metrics need to be computed with different periods. For example, one metric is computed every training epoch and another metric (e.g. more expensive one) is done every n-th training epoch.

Parameters
Return type

None

Example:

metric = ...
engine = ...
metric.detach(engine)

assert "mymetric" not in engine.run(data).metrics

assert not metric.is_attached(engine)

Example with usage:

metric = ...
engine = ...
metric.detach(engine, usage="batch_wise")

assert "mymetric" not in engine.run(data).metrics

assert not metric.is_attached(engine, usage="batch_wise")
is_attached(engine, usage=<ignite.metrics.metric.EpochWise object>)#

Checks if current metric is attached to provided engine. If attached, metric’s computed value is written to engine.state.metrics dictionary.

Parameters
Return type

bool

iteration_completed(engine)#

Helper method to update metric’s computation. It is automatically attached to the engine with attach().

Note

engine.state.output is used to compute metric values. The majority of implemented metrics accepts the following formats for engine.state.output: (y_pred, y) or {'y_pred': y_pred, 'y': y}. y_pred and y can be torch tensors or list of tensors/numbers if applicable.

Parameters

engine (ignite.engine.engine.Engine) – the engine to which the metric must be attached

Return type

None

Changed in version 0.4.5: y_pred and y can be torch tensors or list of tensors/numbers

static normalize(matrix, average)[source]#

Normalize given matrix with given average.

Parameters
Return type

torch.Tensor

reset()[source]#

Resets the metric to it’s initial state.

By default, this is called at the start of each epoch.

Return type

None

started(engine)#

Helper method to start data gathering for metric’s computation. It is automatically attached to the engine with attach().

Parameters

engine (ignite.engine.engine.Engine) – the engine to which the metric must be attached

Return type

None

update(output)[source]#

Updates the metric’s state using the passed batch output.

By default, this is called once for each batch.

Parameters

output (Sequence[torch.Tensor]) – the is the output from the engine’s process function.

Return type

None