在 PyTorch 中使用 WeightedRandomSampler

Question

我需要在 PyTorch 中实现多标签图像分类模型。然而我的数据不平衡，所以我使用WeightedRandomSamplerPyTorch 中的创建自定义数据加载器。但是当我迭代自定义数据加载器时，我收到错误：IndexError: list index out of range

使用此链接实现了以下代码：https://discuss.pytorch.org/t/balanced-sampling- Between-classes-with-torchvision-dataloader/2703/3?u=surajsubramanian

def make_weights_for_balanced_classes(images, nclasses):                        
    count = [0] * nclasses                                                      
    for item in images:                                                         
        count[item[1]] += 1                                                     
    weight_per_class = [0.] * nclasses                                      
    N = float(sum(count))                                                   
    for i in range(nclasses):                                                   
        weight_per_class[i] = N/float(count[i])                                 
    weight = [0] * len(images)                                              
    for idx, val in enumerate(images):                                          
        weight[idx] = weight_per_class[val[1]]                                  
    return weight 

weights = make_weights_for_balanced_classes(train_dataset.imgs, len(full_dataset.classes))
weights = torch.DoubleTensor(weights)
sampler = WeightedRandomSampler(weights, len(weights))

train_loader = DataLoader(train_dataset, batch_size=4,sampler = sampler, pin_memory=True)   

根据/sf/answers/4256944681/中的答案，以下是我更新的代码。但是当我创建一个数据加载器时，loader = DataLoader(full_dataset, batch_size=4, sampler=sampler)也len(loader)返回 1。

class_counts = [1691, 743, 2278, 1271]
num_samples = np.sum(class_counts)
labels = [tag for _,tag in full_dataset.imgs] 

class_weights = [num_samples/class_counts[i] for i in range(len(class_counts)]
weights = [class_weights[labels[i]] for i in range(num_samples)]
sampler = WeightedRandomSampler(torch.DoubleTensor(weights), num_samples)

预先非常感谢！

我根据下面接受的答案添加了一个实用函数：

def sampler_(dataset):
    dataset_counts = imageCount(dataset)
    num_samples = sum(dataset_counts)
    labels = [tag for _,tag in dataset]

    class_weights = [num_samples/dataset_counts[i] for i in range(n_classes)]
    weights = [class_weights[labels[i]] for i in range(num_samples)]
    sampler = WeightedRandomSampler(torch.DoubleTensor(weights), int(num_samples))
    return sampler

imageCount 函数查找数据集中每个类别的图像数量。数据集中的每一行都包含图像和类，因此我们考虑元组中的第二个元素。

def imageCount(dataset):
    image_count = [0]*(n_classes)
    for img in dataset:
        image_count[img[1]] += 1
    return image_count

Answer 1

该代码看起来有点复杂......您可以尝试以下操作：

#Let there be 9 samples and 1 sample in class 0 and 1 respectively
class_counts = [9.0, 1.0]
num_samples = sum(class_counts)
labels = [0, 0,..., 0, 1] #corresponding labels of samples

class_weights = [num_samples/class_counts[i] for i in range(len(class_counts))]
weights = [class_weights[labels[i]] for i in range(int(num_samples))]
sampler = WeightedRandomSampler(torch.DoubleTensor(weights), int(num_samples))

Answer 2

Here is an alternative solution:

import numpy as np
from torch.utils.data.sampler import WeightedRandomSampler

counts = np.bincount(y)
labels_weights = 1. / counts
weights = labels_weights[y]
WeightedRandomSampler(weights, len(weights))

where y is a list of labels corresponding to each sample, has shape (n_samples,) and are encoded [0, ..., n_classes].

weights won't add up to 1, which is ok according to the official docs.