所以我有一个训练数据集（用torch.utils.data.random_split创建），RGB图像大小为150 x150，共有7个类。有一个类不平衡，我用加权采样器修复了这个问题。但是，我也想添加图像增强到我创建的新数据中，以避免过度拟合，否则它们将是重复的图像。以下是我到目前为止所做的：

# define data augmentation transforms for training set
transform_train = transforms.Compose([
    transforms.RandomHorizontalFlip(),
    transforms.RandomResizedCrop(size=150, scale=(0.8, 1.0)),
    transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4),
    transforms.RandomRotation(degrees=15),
    transforms.ToTensor(),
])

# get the indices of examples in each class in the training set
class_indices = [[] for _ in range(7)]
for i in range(len(train_dataset)):
    _, label = train_dataset[i]
    class_indices[label].append(i)

# calculate the number of examples to sample from each class
max_class_size = max([len(class_indices[c]) for c in range(7)])
class_weights = [max_class_size / len(class_indices[c]) for c in range(7)]
num_samples = [int(class_weights[c] * len(class_indices[c])) for c in range(7)]

# create a WeightedRandomSampler to oversample the training set
sampler = data.WeightedRandomSampler(weights=class_weights, num_samples=sum(num_samples), replacement=True)
train_loader = data.DataLoader(train_dataset, batch_size=64, sampler=sampler)

# create new training set with oversampled examples
oversampled_train_dataset = data.Subset(train_dataset, indices=list(sampler))
oversampled_train_dataset.transform = transform_train

如您所见，当前我在末尾将转换应用于整个列车数据，这不是我想要的结果。在历元1得到的训练精度为1.0，而验证精度在0.25左右徘徊。它也不会随着历元的进展而提高/改变。在没有过采样的情况下，最终训练精度约为0.84，而验证精度约为0。71.
一个侧记，有大约20000图像在trainset之前过采样，所以为循环需要一段时间来运行，如果有优化我可以做，我会很感激的建议。谢谢