quaterion.loss.triplet_loss module

class TripletLoss(margin: Optional[float] = 1.0, distance_metric_name: Distance = Distance.COSINE, mining: Optional[str] = 'hard')[source]

Bases: GroupLoss

Implements Triplet Loss as defined in https://arxiv.org/abs/1503.03832

It supports batch-all and batch-hard strategies for online triplet mining.

Parameters:

• margin – Margin value to push negative examples apart. Optional, defaults to 0.5.

• distance_metric_name – Name of the distance function, e.g., Distance. Optional, defaults to COSINE.

• mining (str, optional) – Triplet mining strategy. One of “all”, “hard”. Defaults to “hard”.

forward(embeddings: Tensor, groups: LongTensor) → Tensor[source]

Calculates Triplet Loss with specified embeddings and labels.

Parameters:

• embeddings (Tensor) – Batch of embeddings. Shape: (batch_size, embedding_dim)

• groups (torch.LongTensor) – Batch of labels associated with embeddings. Shape: (batch_size,)

Returns:

torch.Tensor – Scalar loss value.

get_config_dict()[source]

Config used in saving and loading purposes.

Config object has to be JSON-serializable.

Returns:

Dict[str, Any] – JSON-serializable dict of params

training: bool