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import random
from collections.abc import Iterator
from typing import Union, Tuple
import numpy as np
from torch.utils import data
from utils.dataset import CASIAB
class TripletSampler(data.Sampler):
def __init__(
self,
data_source: Union[CASIAB],
batch_size: Tuple[int, int]
):
super().__init__(data_source)
self.metadata_labels = data_source.metadata['labels']
self.labels = data_source.labels
self.length = len(self.labels)
self.indexes = np.arange(0, self.length)
(self.pr, self.k) = batch_size
def __iter__(self) -> Iterator[int]:
while True:
sampled_indexes = []
# Sample pr subjects by sampling labels appeared in dataset
sampled_subjects = random.sample(self.metadata_labels, k=self.pr)
for label in sampled_subjects:
clips_from_subject = self.indexes[self.labels == label].tolist()
# Sample k clips from the subject without replacement if
# have enough clips, k more clips will sampled for
# disentanglement
k = self.k * 2
if len(clips_from_subject) >= k:
_sampled_indexes = random.sample(clips_from_subject, k=k)
else:
_sampled_indexes = random.choices(clips_from_subject, k=k)
sampled_indexes += _sampled_indexes
yield sampled_indexes
def __len__(self) -> int:
return self.length
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