Implements the GIoU loss function.

GIoU loss was first introduced in the Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression. GIoU is an enhancement for models which use IoU in object detection.


gl = tfa.losses.GIoULoss()
boxes1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]])
boxes2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0]])
loss = gl(boxes1, boxes2)
<tf.Tensor: shape=(), dtype=float32, numpy=1.5041667>

Usage with tf.keras API:

model = tf.keras.Model()
model.compile('sgd', loss=tfa.losses.GIoULoss())

mode one of ['giou', 'iou'], decided to calculate GIoU or IoU loss.



Instantiates a Loss from its config (output of get_config()).

config Output of get_config().

A Loss instance.


View source

Returns the config dictionary for a Loss instance.


Invokes the Loss instance.

y_true Ground truth values. shape = [batch_size, d0, .. dN], except sparse loss functions such as sparse categorical crossentropy where shape = [batch_size, d0, .. dN-1]
y_pred The predicted values. shape = [batch_size, d0, .. dN]
sample_weight Optional sample_weight acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If sample_weight is a tensor of size [batch_size], then the total loss for each sample of the batch is rescaled by the corresponding element in the sample_weight vector. If the shape of sample_weight is [batch_size, d0, .. dN-1] (or can be broadcasted to this shape), then each loss element of y_pred is scaled by the corresponding value of sample_weight. (Note ondN-1: all loss functions reduce by 1 dimension, usually axis=-1.)

Weighted loss float Tensor. If reduction is NONE, this has shape [batch_size, d0, .. dN-1]; otherwise, it is scalar. (Note dN-1 because all loss functions reduce by 1 dimension, usually axis=-1.)

ValueError If the shape of sample_weight is invalid.