在训练中更换数据采集器样本

Question

最初，数据加载器是用某些示例创建的。在训练过程中，我需要更换一个数据采集器中的样本。如何把它替换成数据采集器。

train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)


for sample,label in train_dataloader:
        prediction of model
        select misclassified samples and change them in train_dataloader but      how to change sample in train_dataloader

在培训过程中，需要对错误分类的样本进行修改。那么如何在train_dataloader中替换一个示例呢？

Answer 1

工艺每样

通常，您将通过创建继承torch.utils.data.Dataset的类并在__getitem__方法中进行处理，从而在示例级别处理数据。

from torch.utils.data import Dataset
class MyDataset(Dataset):
  def __init__(self, *args, **kwargs):
    super().__init__(*args, **kwargs)

  def __getitem__(self, i):
    # Call the __getitem__ from Dataset class
    sample = super().__getitem__(i)
    # ...Do some processing on the sample here
    return sample

train_data = MyDataset()
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)

工艺每批

可以使用collate_fn参数torch.utils.data.DataLoader来指定处理批处理的自定义函数。

from torch.utils.data import Dataset, DataLoader

def collate_fn(samples):
  # samples is a list of samples you get from the __getitem__ function of your torch.utils.data.Dataset instance
  # You can write here whatever processing you need before stacking samples into one batch of data
  batch = torch.stack(samples, dim=0)
  return batch

train_data = Dataset()
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size, collate_fn=collate_fn)