balanced_batch_sampler.py

import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader, BatchSampler

class BalancedBatchSampler(BatchSampler):
    """
    BatchSampler - from a MNIST-like dataset, samples n_classes and within these classes samples n_samples.
    Returns batches of size n_classes * n_samples
    """

    def __init__(self, labels, n_classes, n_samples):
        # label: unique id per datapoint, e.g. path
        self.labels = labels
        self.labels_set = list(set(self.labels.numpy()))
        self.label_to_indices = {label: np.where(self.labels.numpy() == label)[0]
                                 for label in self.labels_set}
        for l in self.labels_set:
            np.random.shuffle(self.label_to_indices[l])
        self.used_label_indices_count = {label: 0 for label in self.labels_set}
        self.count = 0
        self.n_classes = n_classes
        self.n_samples = n_samples
        self.n_dataset = len(self.labels)
        self.batch_size = self.n_samples * self.n_classes

    def __iter__(self):
        self.count = 0
        while self.count + self.batch_size < self.n_dataset:
            classes = np.random.choice(self.labels_set, self.n_classes, replace=False)
            indices = []
            for class_ in classes:
                indices.extend(self.label_to_indices[class_][
                               self.used_label_indices_count[class_]:self.used_label_indices_count[
                                                                         class_] + self.n_samples])
                self.used_label_indices_count[class_] += self.n_samples
                if self.used_label_indices_count[class_] + self.n_samples > len(self.label_to_indices[class_]):
                    np.random.shuffle(self.label_to_indices[class_])
                    self.used_label_indices_count[class_] = 0
            yield indices
            self.count += self.n_classes * self.n_samples

    def __len__(self):
        return self.n_dataset // self.batch_size

# train_labels = torch.tensor([item[2] for item in train_dataset])
# train_dataloader = DataLoader(train_dataset, batch_sampler=train_sampler)

# test_labels = torch.tensor([item[2] for item in test_dataset])
# train_sampler = BalancedBatchSampler(train_labels, BATCH_SIZE, 1)
# test_sampler = BalancedBatchSampler(test_labels, BATCH_SIZE, 1)
# test_dataloader = DataLoader(test_dataset, batch_sampler=test_sampler)