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使用 collate_fn 处理可变长度输入

填充并堆叠长度不齐的样本

使用 collate_fn 处理可变长度输入 是 CoddyKit 上的免费 Deep Learning Academy 课时。这是第 3 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 Deep Learning Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Deep Learning Academy 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

When Samples Don't Match

Stacking into a batch needs every sample the same shape. But sentences and audio clips have different lengths, so the default collate step fails. 🧩

What collate_fn Does

The DataLoader gathers a list of samples and passes them to collate_fn, which merges them into one batch. By default it simply stacks tensors.

Ragged Inputs Break Stacking

Try to stack a length-5 and a length-8 sequence and PyTorch raises a shape error. Ragged lengths are exactly the case a custom collate must handle.

Write Your Own collate_fn

You pass a function to the DataLoader's collate_fn argument. It receives a list of samples and returns whatever batch shape your model expects.

loader = DataLoader(ds, batch_size=4, collate_fn=my_collate)

Step One: Split the List

Inside your function, unzip the list of pairs into separate sequences and labels. Now you can treat each group on its own before merging.

def my_collate(batch):
    seqs, labels = zip(*batch)

Pad to the Longest

The trick for variable lengths is padding: extend every sequence to the longest one with a filler value, so they finally share a shape.

pad_sequence Does It for You

PyTorch ships pad_sequence, which pads a list of tensors to equal length and stacks them. Set batch_first so the batch dimension comes first.

from torch.nn.utils.rnn import pad_sequence
padded = pad_sequence(seqs, batch_first=True)

Remember the Real Lengths

Padding adds fake tokens, so also return each sequence's true length. Your model uses these to ignore the padded positions during the forward pass.

lengths = torch.tensor([len(s) for s in seqs])

Stack the Labels

Labels are usually fixed size, so a normal stack works for them. Return the padded inputs, the lengths, and the stacked labels together.

labels = torch.stack(labels)
return padded, lengths, labels

Mask Out the Padding

Later you build a mask from the lengths so the loss and attention skip padded slots. Padding fills shape without polluting the gradients.

One Function, Any Shape

With a custom collate_fn, the same DataLoader handles text, audio, and graphs. You control exactly how loose samples become one tidy batch.

Quick Check

Why do variable-length sequences need a custom collate_fn?

Recap

A custom collate_fn turns a list of uneven samples into one batch, usually by padding sequences to equal length and tracking their real sizes. 🎉

常见问题解答

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