tf.keras.metrics.IoU

Computes the Intersection-Over-Union metric for specific target classes.

Inherits From: Metric, Layer, Module

tf.keras.metrics.IoU(
    num_classes: int,
    target_class_ids: Union[List[int], Tuple[int, ...]],
    name=None,
    dtype=None
)

General definition and computation:

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

For an individual class, the IoU metric is defined as follows:

iou = true_positives / (true_positives + false_positives + false_negatives)

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.

Note, this class first computes IoUs for all individual classes, then returns the mean of IoUs for the classes that are specified by target_class_ids. If target_class_ids has only one id value, the IoU of that specific class is returned.

num_classes The possible number of labels the prediction task can have. A confusion matrix of dimension = [num_classes, num_classes] will be allocated to accumulate predictions from which the metric is calculated.
target_class_ids A tuple or list of target class ids for which the metric is returned. To compute IoU for a specific class, a list (or tuple) of a single id value should be provided.
name (Optional) string name of the metric instance.
dtype (Optional) data type of the metric result.

Standalone usage:

# cm = [[1, 1],
#        [1, 1]]
# sum_row = [2, 2], sum_col = [2, 2], true_positives = [1, 1]
# iou = true_positives / (sum_row + sum_col - true_positives))
# iou = [0.33, 0.33]
m = tf.keras.metrics.IoU(num_classes=2, target_class_id=[0])
m.update_state([0, 0, 1, 1], [0, 1, 0, 1])
m.result().numpy()
0.33333334
 
m.reset_state()
m.update_state([0, 0, 1, 1], [0, 1, 0, 1],
               sample_weight=[0.3, 0.3, 0.3, 0.1])
# cm = [[0.3, 0.3],
#        [0.3, 0.1]]
# sum_row = [0.6, 0.4], sum_col = [0.6, 0.4], true_positives = [0.3, 0.1]
# iou = [0.33, 0.14]
m.result().numpy()
0.33

Usage with compile() API:

model.compile(
  optimizer='sgd',
  loss='mse',
  metrics=[tf.keras.metrics.IoU(num_classes=2, target_class_id=[0])])

Methods

merge_state

View source

merge_state(
    metrics
)

Merges the state from one or more metrics.

This method can be used by distributed systems to merge the state computed by different metric instances. Typically the state will be stored in the form of the metric's weights. For example, a tf.keras.metrics.Mean metric contains a list of two weight values: a total and a count. If there were two instances of a tf.keras.metrics.Accuracy that each independently aggregated partial state for an overall accuracy calculation, these two metric's states could be combined as follows:

m1 = tf.keras.metrics.Accuracy()
_ = m1.update_state([[1], [2]], [[0], [2]])
 
m2 = tf.keras.metrics.Accuracy()
_ = m2.update_state([[3], [4]], [[3], [4]])
 
m2.merge_state([m1])
m2.result().numpy()
0.75

Args
metrics an iterable of metrics. The metrics must have compatible state.

Raises
ValueError If the provided iterable does not contain metrics matching the metric's required specifications.

reset_state

View source

reset_state()

Resets 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.

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. Defaults to 1. Can be a Tensor whose rank is either 0, or the same rank as y_true, and must be broadcastable to y_true.

Returns
Update op.