采用 WeightedRandomSampler:
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 dataset_train = datasets.ImageFolder(traindir) # For unbalanced dataset we create a weighted sampler
weights = make_weights_for_balanced_classes(dataset_train.imgs, len(dataset_train.classes)) weights = torch.DoubleTensor(weights) sampler = torch.utils.data.sampler.WeightedRandomSampler(weights, len(weights)) train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=args.batch_size, shuffle = True, sampler = sampler, num_workers=args.workers, pin_memory=True) Reference: Balanced Sampling between classes with torchvision DataLoader
参考方法2: 作者给出了均匀采样和非均匀采样的差别
