Triplet Loss Utility for Pytorch Library.
TripletTorch is a small pytorch utility for triplet loss projects. It provides simple way to create custom triplet datasets and common triplet mining loss techniques.
Install the module using the pip utility ( may require to run as sudo ).
pip3 install triplettorchfrom triplettorch import TripletDataset
# Create a triplet dataset given:
# * labels : array of label ( class ) for each sample of the dataset
# * data_fn : method to access data for a given index in the dataset
# * size : number of samples in the dataset
# * n_sample: number of sample per draw ( to increase probability to
# contain valid triplets in a batch )
# Do not forget to concatenate batch dimension and sample dimension
# when used with a DataLoader as TripletDataset[ idx ] returns a
# ( batch_size, n_sample, ... ) dimension tensor for labels and data
dataset = TripletDataset( labels, data_fn, size, n_sample )from triplettorch import AllTripletMiner, HardNegativeTripletMiner
# Define the triplet mining loss given:
# * margin: the margin float value from the triplet loss definition
miner = AllTripletMiner( .5 ).cuda( )
miner = HardNegativeTripletMiner( .5 ).cuda( )
# Use the loss in training given:
# * labels : array of label ( class ) for each sample of the batch
# * embeddings: output of the neural network for each sample of the batch
# Returns two values:
# * loss : triplet loss value
# * frac_pos: fraction of positive triplets
# None ( None HardNegativeTripletMiner )
loss, frac_pos = miner( labels, embeddings )The repository provides an example application with the MNIST dataset.
