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import random
from collections.abc import Iterator
from typing import Union
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']
metadata_conditions = data_source.metadata['conditions']
self.subsets = {}
for condition in metadata_conditions:
pre, _ = condition.split('-')
if self.subsets.get(pre, None) is None:
self.subsets[pre] = []
self.subsets[pre].append(condition)
self.num_subsets = len(self.subsets)
self.num_seq = {pre: len(seq) for (pre, seq) in self.subsets.items()}
self.min_num_seq = min(self.num_seq.values())
self.labels = data_source.labels
self.conditions = data_source.conditions
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:
mask = self.labels == label
# Fix unbalanced datasets
if self.num_subsets > 1:
condition_mask = np.zeros(self.conditions.shape, dtype=bool)
for num, conditions_ in zip(
self.num_seq.values(), self.subsets.values()
):
if num > self.min_num_seq:
conditions = random.sample(
conditions_, self.min_num_seq
)
else:
conditions = conditions_
for condition in conditions:
condition_mask |= self.conditions == condition
mask &= condition_mask
clips = self.indexes[mask].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) >= k:
_sampled_indexes = random.sample(clips, k=k)
else:
_sampled_indexes = random.choices(clips, k=k)
sampled_indexes += _sampled_indexes
yield sampled_indexes
def __len__(self) -> int:
return self.length
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