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
@classmethod
from_config(
config
)
Instantiates a Loss
from its config (output of get_config()
).
Args |
config
|
Output of get_config() .
|
get_config
View source
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.
|