M2m中的采样

采样的完整代码

import torch
import numpy as np
from torchvision import datasets, transforms
from torch.utils.data import DataLoader, WeightedRandomSampler, SubsetRandomSampler

def get_oversampled_data(dataset, num_sample_per_class):
    """ Generate a list of indices that represents oversampling of the dataset. """
    targets = np.array(dataset.targets)
    class_sample_count = np.array([num_sample_per_class[target] for target in targets])
    weight = 1. / class_sample_count
    samples_weight = torch.from_numpy(weight)
    sampler = WeightedRandomSampler(samples_weight, len(samples_weight))
    return sampler

def get_val_test_data(dataset, num_test_samples):
    """ Split dataset into validation and test indices. """
    num_classes = 10
    targets = dataset.targets
    test_indices = []
    val_indices = []

    for i in range(num_classes):
        indices = [j for j, x in enumerate(targets) if x == i]
        np.random.shuffle(indices)
        val_indices.extend(indices[:num_test_samples])
        test_indices.extend(indices[num_test_samples:num_test_samples*2])

    return val_indices, test_indices

def get_oversampled(dataset_name, num_sample_per_class, batch_size, transform_train, transform_test):
    """ Create training and testing loaders with oversampling for imbalance. """
    dataset_class = datasets.__dict__[dataset_presets[dataset_name]['class']]
    dataset_train = dataset_class(root='./data', train=True, download=True, transform=transform_train)
    dataset_test = dataset_class(root='./data', train=False, download=True, transform=transform_test)

    # Oversampling
    sampler = get_oversampled_data(dataset_train, num_sample_per_class)
    train_loader = DataLoader(dataset_train, batch_size=batch_size, sampler=sampler)

    # Validation and Test split
    val_idx, test_idx = get_val_test_data(dataset_test, 1000)
    val_loader = DataLoader(dataset_test, batch_size=batch_size, sampler=SubsetRandomSampler(val_idx))
    test_loader = DataLoader(dataset_test, batch_size=batch_size, sampler=SubsetRandomSampler(test_idx))

    return train_loader, val_loader, test_loader

# Configuration and run
dataset_presets = {
    'cifar10': {'class': 'CIFAR10', 'num_classes': 10}
}
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])
num_sample_per_class = [500] * 10  # Pretend we want equal class distribution

train_loader, val_loader, test_loader = get_oversampled('cifar10', num_sample_per_class, 64, transform, transform)

# Print out some info from loaders
for i, (inputs, targets) in enumerate(train_loader):
    print(f'Batch {i}, Targets Counts: {torch.bincount(targets)}')
    if i == 1:  # Just show first two batches for demonstration
        break

`WeightedRandomSampler`类的`iter`

def __iter__(self) -> Iterator[int]:
    rand_tensor = torch.multinomial(self.weights, self.num_samples, self.replacement, generator=self.generator)
    return iter(rand_tensor.tolist())

方法功能：此方法实现了迭代器协议，允许WeightedRandomSampler对象在迭代中返回一系列随机选择的索引。

过采样的效果

在get_oversampled函数中，使用了WeightedRandomSampler来实现过采样的逻辑。这个过程虽然看起来是通过权重调整样本的选取概率，但实际上，通过这种方式也可以达到过采样的效果，尤其是当设置replacement=True时。让我们更详细地分析一下这一点：

权重的分配

权重是根据num_sample_per_class数组分配的，这个数组定义了每个类别希望被采样到的频率。在数据加载过程中，每个类别的样本将根据其在num_sample_per_class中对应的值获得一个权重。权重越大的类别在每次迭代中

被选中的概率也越大。这样，通过调整这些权重，我们可以控制模型在训练过程中看到的每个类别样本的频率，实现对类别不平衡的处理。

过采样的实现

在使用WeightedRandomSampler时，关键的参数是replacement：

如果replacement=True：这允许同一个样本在一次抽样中被多次选择，即进行了过采样。对于少数类的样本来说，即使它们在数据集中的绝对数量不多，也可以通过这种方式增加它们在每个训练批次中出现的次数，从而让模型更频繁地从这些少数类样本学习。
如果replacement=False：则每个样本只能被抽样一次，这通常用于不放回的抽样。在这种模式下，WeightedRandomSampler不会直接导致过采样，但可以用来确保每个类别在数据批次中都有均等的代表性，从而帮助模型学习到更平衡的特征。