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tf.keras.metrics.OneHotMeanIoU

Computes mean Intersection-Over-Union metric for one-hot encoded labels.

Inherits From: MeanIoU, IoU, Metric

tf.keras.metrics.OneHotMeanIoU(
    num_classes,
    name=None,
    dtype=None,
    ignore_class=None,
    sparse_y_pred=False,
    axis=-1
)

Formula:

iou = true_positives / (true_positives + false_positives + false_negatives)

Intersection-Over-Union is a common evaluation metric for semantic image segmentation.

To compute IoUs, the predictions are accumulated in a confusion matrix, weighted by sample_weight and the metric is then calculated from it.

If sample_weight is None, weights default to 1. Use sample_weight of 0 to mask values.

This class can be used to compute the mean IoU for multi-class classification tasks where the labels are one-hot encoded (the last axis should have one dimension per class). Note that the predictions should also have the same shape. To compute the mean IoU, first the labels and predictions are converted back into integer format by taking the argmax over the class axis. Then the same computation steps as for the base MeanIoU class apply.

Note, if there is only one channel in the labels and predictions, this class is the same as class MeanIoU. In this case, use MeanIoU instead.

Also, make sure that num_classes is equal to the number of classes in the data, to avoid a "labels out of bound" error when the confusion matrix is computed.

Args
`num_classes`	The possible number of labels the prediction task can have.
`name`	(Optional) string name of the metric instance.
`dtype`	(Optional) data type of the metric result.
`ignore_class`	Optional integer. The ID of a class to be ignored during metric computation. This is useful, for example, in segmentation problems featuring a "void" class (commonly -1 or 255) in segmentation maps. By default (`ignore_class=None`), all classes are considered.
`sparse_y_pred`	Whether predictions are encoded using natural numbers or probability distribution vectors. If `False`, the `argmax` function will be used to determine each sample's most likely associated label.
`axis`	(Optional) The dimension containing the logits. Defaults to `-1`.

Example:

y_true = np.array([[0, 0, 1], [1, 0, 0], [0, 1, 0], [1, 0, 0]])
y_pred = np.array([[0.2, 0.3, 0.5], [0.1, 0.2, 0.7], [0.5, 0.3, 0.1],
                      [0.1, 0.4, 0.5]])
sample_weight = [0.1, 0.2, 0.3, 0.4]
m = keras.metrics.OneHotMeanIoU(num_classes=3)
m.update_state(
    y_true=y_true, y_pred=y_pred, sample_weight=sample_weight)
# cm = [[0, 0, 0.2+0.4],
#       [0.3, 0, 0],
#       [0, 0, 0.1]]
# sum_row = [0.3, 0, 0.7], sum_col = [0.6, 0.3, 0.1]
# true_positives = [0, 0, 0.1]
# single_iou = true_positives / (sum_row + sum_col - true_positives))
# mean_iou = (0 + 0 + 0.1 / (0.7 + 0.1 - 0.1)) / 3
m.result()
0.048

Usage with compile() API:

model.compile(
    optimizer='sgd',
    loss='mse',
    metrics=[keras.metrics.OneHotMeanIoU(num_classes=3)])

Attributes
`dtype`
`variables`

Methods

`add_variable`

View source

add_variable(
    shape, initializer, dtype=None, aggregation='sum', name=None
)

`add_weight`

View source

add_weight(
    shape=(), initializer=None, dtype=None, name=None
)

`from_config`

View source

@classmethod
from_config(
    config
)

`get_config`

View source

get_config()

Return the serializable config of the metric.

`reset_state`

View source

reset_state()

Reset all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

`result`

View source

result()

Compute the intersection-over-union via the confusion matrix.

`stateless_reset_state`

View source

stateless_reset_state()

`stateless_result`

View source

stateless_result(
    metric_variables
)

`stateless_update_state`

View source

stateless_update_state(
    metric_variables, *args, **kwargs
)

`update_state`

View source

update_state(
    y_true, y_pred, sample_weight=None
)

Accumulates the confusion matrix statistics.

Args
`y_true`	The ground truth values.
`y_pred`	The predicted values.
`sample_weight`	Optional weighting of each example. Can be a `Tensor` whose rank is either 0, or the same as `y_true`, and must be broadcastable to `y_true`. Defaults to `1`.

Returns
Update op.

`call`

View source

__call__(
    *args, **kwargs
)

Call self as a function.

© 2022 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/keras/metrics/OneHotMeanIoU