ebes.losses package
Submodules
ebes.losses.base module
- class ebes.losses.base.ModelLoss(*args, **kwargs)
Bases:
Module- forward(preds, _)
Define the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
ebes.losses.contrastive module
- class ebes.losses.contrastive.ContrastiveLoss(margin, pair_selector)
Bases:
ModuleContrastive loss
“Signature verification using a siamese time delay neural network”, NIPS 1993 https://papers.nips.cc/paper/769-signature-verification-using-a-siamese-time-delay-neural-network.pdf
- forward(embeddings, target)
Define the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
- class ebes.losses.contrastive.HardNegativePairSelector(neg_count=1)
Bases:
PairSelector- Generates all possible possitive pairs given labels and
neg_count hardest negative example for each example
- get_pairs(embeddings, labels)
- Return type:
tuple[Tensor,Tensor]
- class ebes.losses.contrastive.InfoNCELoss(temperature, pair_selector, angular_margin=0.0)
Bases:
ModuleInfoNCE Loss https://arxiv.org/abs/1807.03748
- forward(embeddings, target)
Define the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
- class ebes.losses.contrastive.PairSelector
Bases:
ABCStrategy to sample positive and negative embedding pairs.
- abstract get_pairs(embeddings, labels)
- Return type:
tuple[Tensor,Tensor]
- ebes.losses.contrastive.outer_pairwise_distance(a, b=None)
- Compute pairwise_distance of Tensors
A (size(A) = n x d, where n - rows count, d - vector size) and B (size(A) = m x d, where m - rows count, d - vector size)
- return matrix C (size n x m), such as
C_ij = distance(i-th row matrix A, j-th row matrix B)
if only one Tensor was given, computer pairwise distance to itself (B = A)
ebes.losses.multi_label module
- class ebes.losses.multi_label.MultiLabelBinaryCrossEntropyLoss
Bases:
Module- forward(logits, target)
Define the computation performed at every call.
Should be overridden by all subclasses. :rtype:
TensorNote
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
ebes.losses.neural_hawkes module
- class ebes.losses.neural_hawkes.NHLoss(*args, **kwargs)
Bases:
Module- forward(nh_return, _)
Define the computation performed at every call.
Should be overridden by all subclasses. :rtype:
TensorNote
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.