loss.py

from pytorch_metric_learning import losses, miners
from pytorch_metric_learning.distances import CosineSimilarity, DotProductSimilarity
loss_fn = losses.MultiSimilarityLoss(alpha=1.0, beta=50, base=0.0, distance=DotProductSimilarity())
miner = miners.MultiSimilarityMiner(epsilon=0.1, distance=CosineSimilarity())

#  The loss function call (this method will be called at each training iteration)
def loss_function(descriptors, labels):
    # we mine the pairs/triplets if there is an online mining strategy
    if miner is not None:
        miner_outputs = miner(descriptors, labels)
        loss = loss_fn(descriptors, labels, miner_outputs)
        # calculate the % of trivial pairs/triplets 
        # which do not contribute in the loss value
        nb_samples = descriptors.shape[0]
        nb_mined = len(set(miner_outputs[0].detach().cpu().numpy()))
        batch_acc = 1.0 - (nb_mined/nb_samples)

    else: # no online mining
        loss = loss_fn(descriptors, labels)
        batch_acc = 0.0
    return loss