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How to write custom mining functions

  1. Extend BaseTupleMiner
  2. Implement the mine method
  3. Inside mine, return a tuple of tensors

An example pair miner

from pytorch_metric_learning.miners import BaseTupleMiner
from pytorch_metric_learning.utils import loss_and_miner_utils as lmu

class ExamplePairMiner(BaseTupleMiner):
    def __init__(self, margin=0.1, **kwargs):
        super().__init__(**kwargs)
        self.margin = margin

    def mine(self, embeddings, labels, ref_emb, ref_labels):
        mat = self.distance(embeddings, ref_emb)
        a1, p, a2, n = lmu.get_all_pairs_indices(labels, ref_labels)
        pos_pairs = mat[a1, p]
        neg_pairs = mat[a2, n]
        pos_mask = (
            pos_pairs < self.margin
            if self.distance.is_inverted
            else pos_pairs > self.margin
        )
        neg_mask = (
            neg_pairs > self.margin
            if self.distance.is_inverted
            else neg_pairs < self.margin
        )
        return a1[pos_mask], p[pos_mask], a2[neg_mask], n[neg_mask]

The ExamplePairMiner does the following:

  • Computes the distance matrix between embeddings and ref_emb.
  • Finds the indices of all positive and negative pairs
  • Returns the indices of pairs that violate the margin

Example usage:

miner = ExamplePairMiner()
embeddings = torch.randn(128, 512)
labels = torch.randint(0, 10, size=(128,))
pairs = miner(embeddings, labels)

An example triplet miner

from pytorch_metric_learning.miners import BaseTupleMiner
from pytorch_metric_learning.utils import loss_and_miner_utils as lmu

class ExampleTripletMiner(BaseTupleMiner):
    def __init__(self, margin=0.1, **kwargs):
        super().__init__(**kwargs)
        self.margin = margin

    def mine(self, embeddings, labels, ref_emb, ref_labels):
        mat = self.distance(embeddings, ref_emb)
        a, p, n = lmu.get_all_triplets_indices(labels, ref_labels)
        pos_pairs = mat[a, p]
        neg_pairs = mat[a, n]
        triplet_margin = pos_pairs - neg_pairs if self.distance.is_inverted else neg_pairs - pos_pairs
        triplet_mask = triplet_margin <= self.margin
        return a[triplet_mask], p[triplet_mask], n[triplet_mask]

This miner works similarly to ExamplePairMiner, but finds triplets instead of pairs.

What is ref_emb?

The forward function of BaseTupleMiner has optional ref_emb and ref_labels arguments. The miner should return anchors from embeddings and positives and negatives from ref_emb. For example:

miner = ExamplePairMiner()
embeddings = torch.randn(128, 512)
labels = torch.randint(0, 10, size=(128,))
ref_emb = torch.randn(32, 512)
ref_labels = torch.randint(0, 10, size=(32,))
a1, p, a2, n = miner(embeddings, labels, ref_emb, ref_labels)
# a1 and a2 contain indices of "embeddings"
# p and n contain indices of "ref_emb"

Typically though, ref_emb and ref_labels are left to their default value of None, in which case they are set to embeddings and labels before being passed to the mine function.