Python Pytorch-在使用Dataloader之前连接数据集

我试图加载两个数据集，并将它们用于培训

包版本：python 3.7； pytorch 1.3.1

可以单独创建数据加载程序，并按顺序对其进行培训：

from torch.utils.data import DataLoader, ConcatDataset


train_loader_modelnet = DataLoader(ModelNet(args.modelnet_root, categories=args.modelnet_categories,split='train', transform=transform_modelnet, device=args.device),batch_size=args.batch_size, shuffle=True)

train_loader_mydata = DataLoader(MyDataset(args.customdata_root, categories=args.mydata_categories, split='train', device=args.device),batch_size=args.batch_size, shuffle=True)

for e in range(args.epochs):
    for idx, batch in enumerate(tqdm(train_loader_modelnet)):
        # training on dataset1
    for idx, batch in enumerate(tqdm(train_loader_custom)):
        # training on dataset2

注意：MyDataset是一个自定义的数据集类，它实现了

def\uu len\uuuuuuuuuuuuuuuuuuuuuuuuuuuuuself:

def\uuuuuuuuuuuuuuuuuuuuuuuuu getitem（self，index）：

。由于上面的配置工作正常，这似乎是一个可行的实现

但理想情况下，我希望将它们组合成一个dataloader对象。我根据pytorch文档进行了尝试：

train_modelnet = ModelNet(args.modelnet_root, categories=args.modelnet_categories,
                          split='train', transform=transform_modelnet, device=args.device)
train_mydata = CloudDataset(args.customdata_root, categories=args.mydata_categories,
                             split='train', device=args.device)
train_loader = torch.utils.data.ConcatDataset(train_modelnet, train_customdata)

for e in range(args.epochs):
    for idx, batch in enumerate(tqdm(train_loader)):
        # training on combined

但是，在随机批处理中，我得到了以下“参数0中的元素X应为张量，但得到了元组”类型的错误。任何帮助都将不胜感激

>   40%|████      | 53/131 [01:03<02:00,  1.55s/it]
>  Traceback (mostrecent call last):   File
> "/home/chris/Programs/pycharm-anaconda-2019.3.4/plugins/python/helpers/pydev/pydevd.py",
> line 1434, in _exec
>     pydev_imports.execfile(file, globals, locals)  # execute the script   File
> "/home/chris/Programs/pycharm-anaconda-2019.3.4/plugins/python/helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile
>     exec(compile(contents+"\n", file, 'exec'), glob, loc)   File "/home/chris/Documents/4yp/Data/my_kaolin/Classification/pointcloud_classification_combinedset.py",
> line 83, in <module>
>     for idx, batch in enumerate(tqdm(train_loader)):   File "/home/chris/anaconda3/envs/4YP/lib/python3.7/site-packages/tqdm/std.py",
> line 1107, in __iter__
>     for obj in iterable:   File "/home/chris/anaconda3/envs/4YP/lib/python3.7/site-packages/torch/utils/data/dataloader.py",
> line 346, in __next__
>     data = self._dataset_fetcher.fetch(index)  # may raise StopIteration   File
> "/home/chris/anaconda3/envs/4YP/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py",
> line 47, in fetch
>     return self.collate_fn(data)   File "/home/chris/anaconda3/envs/4YP/lib/python3.7/site-packages/torch/utils/data/_utils/collate.py",
> line 79, in default_collate
>     return [default_collate(samples) for samples in transposed]   File "/home/chris/anaconda3/envs/4YP/lib/python3.7/site-packages/torch/utils/data/_utils/collate.py",
> line 79, in <listcomp>
>     return [default_collate(samples) for samples in transposed]   File "/home/chris/anaconda3/envs/4YP/lib/python3.7/site-packages/torch/utils/data/_utils/collate.py",
> line 55, in default_collate
>     return torch.stack(batch, 0, out=out) TypeError: expected Tensor as element 3 in argument 0, but got tuple  

---

我猜这两个数据集有时返回不同的类型。当数据是张量时，火炬把它们叠加起来，它们最好是相同的形状。如果它们有点像字符串，torch会把它们做成元组。这听起来像是你的一个数据集有时返回的不是张量的东西。我会在您的数据集的输出上添加一些断言，以检查它是否在做您想要的事情，或者使用

pdb

深入@Leopd的答案，您可以使用PyTorch提供的

collate\u fn

。其思想是，在

collate\u fn

中，您将定义示例应如何堆叠以生成批次。由于您使用的是torch 1.3.1，请确保您看到的是正确版本的

---

让我知道这是否有帮助，或者您是否有任何后续问题：）

如果我答对了您的问题：您有如下train和dev套件（及其相应的装载机）

train_set = CustomDataset(...)
train_loader = DataLoader(dataset=train_set, ...)
dev_set = CustomDataset(...)
dev_loader = DataLoader(dataset=dev_set, ...)

您希望将它们连接起来，以便使用train+dev作为培训数据，对吗？如果是这样，您只需拨打：

train_dev_sets = torch.utils.data.ConcatDataset([train_set, dev_set])
train_dev_loader = DataLoader(dataset=train_dev_sets, ...)

列车开发装载机是包含两组数据的装载机

现在，请确保数据具有相同的形状和类型，即相同数量的要素，或相同的类别/编号等

train_dev_sets = torch.utils.data.ConcatDataset([train_set, dev_set])
train_dev_loader = DataLoader(dataset=train_dev_sets, ...)