tfa.losses.GIoULoss
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Implements the GIoU loss function.
tfa.losses.GIoULoss(
mode: str = 'giou',
reduction: str = tf.keras.losses.Reduction.AUTO,
name: Optional[str] = 'giou_loss'
)
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.
Usage:
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)loss<tf.Tensor: shape=(), dtype=float32, numpy=1.5041667>
Usage with tf.keras API:
model = tf.keras.Model()model.compile('sgd', loss=tfa.losses.GIoULoss())
Args | |
|---|---|
mode
|
one of ['giou', 'iou'], decided to calculate GIoU or IoU loss. |
Methods
from_config
@classmethodfrom_config( config )
Instantiates a Loss from its config (output of get_config()).
| Args | |
|---|---|
config
|
Output of get_config().
|
| Returns | |
|---|---|
A Loss instance.
|
get_config
get_config()
Returns the config dictionary for a Loss instance.
__call__
__call__(
y_true, y_pred, sample_weight=None
)
Invokes the Loss instance.
| Args | |
|---|---|
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.)
|
| Returns | |
|---|---|
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.)
|
| Raises | |
|---|---|
ValueError
|
If the shape of sample_weight is invalid.
|