View source on GitHub |
Computes the Intersection-Over-Union metric for one-hot encoded labels.
Inherits From: IoU
, Metric
, Layer
, Module
tf.keras.metrics.OneHotIoU(
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.
This class can be used to compute 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 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 IoU
class apply.
Note, if there is only one channel in the labels and predictions, this class
is the same as class IoU
. In this case, use IoU
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.
Standalone usage:
y_true = tf.constant([[0, 0, 1], [1, 0, 0], [0, 1, 0], [1, 0, 0]])
y_pred = tf.constant([[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 = metrics.OneHotIoU(num_classes=3, target_class_ids=[0, 2])
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 / (0.3 + 0.6 - 0) + 0.1 / (0.7 + 0.1 - 0.1)) / 2
m.result().numpy()
0.071
Usage with compile()
API:
model.compile(
optimizer='sgd',
loss='mse',
metrics=[tf.keras.metrics.OneHotIoU(num_classes=3, target_class_id=[1])])
Methods
merge_state
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
reset_state()
Resets all of the metric state variables.
This function is called between epochs/steps, when a metric is evaluated during training.
result
result()
Compute the intersection-over-union via the confusion matrix.
update_state
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. |