API References

DATA

Dataloaders

SimilarityDataLoader	Special version of DataLoader which works with similarity samples.
PairsSimilarityDataLoader	DataLoader designed to work with data represented as SimilarityPairSample.
GroupSimilarityDataLoader	DataLoader designed to work with data represented as SimilarityGroupSample.

Datasets

SimilarityGroupDataset

Wrapper, which converts standard dataset of classification task into dataset, compatible with GroupSimilarityDataLoader.

Samples

SimilarityGroupSample	Represent groups of similar objects all of which should match with one-another within the group.
SimilarityPairSample	Represents a pair of objects, their similarity and relationship with other pairs.

DISTANCES

Cosine	Compute cosine similarities (and its interpretation as distances).
DotProduct	Compute dot product similarities (and its interpretation as distances).
Euclidean	Compute Euclidean distances (and its interpretation as similarities).
Manhattan	Compute Manhattan distances (and its interpretation as similarities).

EVAL

Counters

AttachedMetric	Attach batch-wise metric to TrainableModel
Evaluator	Calculate metrics on the whole datasets

Group metrics

GroupMetric	Base class for group metrics
RetrievalRPrecision	Compute the retrieval R-precision score for group based data

Pair metrics

PairMetric	Base class for metrics computation for pair based data
RetrievalPrecision	Calculates retrieval precision@k for pair based datasets
RetrievalReciprocalRank	Calculates retrieval reciprocal rank for pair based datasets

Samplers

GroupSampler	Perform selection of embeddings and targets for group based tasks.
PairSampler	Perform selection of embeddings and targets for pairs based tasks.

LOSSES

Base

GroupLoss	Base class for group losses.
PairwiseLoss	Base class for pairwise losses.

Implementations

ArcFaceLoss	Additive Angular Margin Loss as defined in https://arxiv.org/abs/1801.07698
ContrastiveLoss	Contrastive loss.
MultipleNegativesRankingLoss	Implement Multiple Negatives Ranking Loss as described in https://arxiv.org/pdf/1705.00652.pdf
SoftmaxLoss	Regular cross-entropy loss.
TripletLoss	Implements Triplet Loss as defined in https://arxiv.org/abs/1503.03832
CircleLoss	Implements Circle Loss as defined in https://arxiv.org/abs/2002.10857.
FastAPLoss	FastAP Loss
CosFaceLoss	Large Margin Cosine Loss as defined in https://arxiv.org/pdf/1801.09414.pdf
CenterLoss	Center Loss as defined in the paper "A Discriminative Feature Learning Approach for Deep Face Recognition" (http://ydwen.github.io/papers/WenECCV16.pdf) It aims to minimize the intra-class variations while keeping the features of different classes separable.

Extras

PytorchMetricLearningWrapper

Provide a simple wrapper to be able to use losses and miners from pytorch-metric-learning.

MAIN

Quaterion

Fine-tuning entry point

TRAIN

TrainableModel

TrainableModel

Base class for models to be trained.

Cache

CacheConfig	Determine cache settings.
CacheType	Available tensor devices to be used for caching.

UTILS

TrainStage	Handle train stage.
get_triplet_mask	Creates a 3D mask of valid triplets for the batch-all strategy.
get_anchor_positive_mask	Creates a 2D mask of valid anchor-positive pairs.
get_anchor_negative_mask	Creates a 2D mask of valid anchor-negative pairs.