Deep Learning Academy · 강의

사용자 지정 데이터 세트 클래스 작성하기

__len__과 __getitem__을 구현합니다

사용자 지정 데이터 세트 클래스 작성하기은(는) CoddyKit의 무료 Deep Learning Academy 강의입니다. 이것은 4개 중 1번째 강의입니다. 아래에서 전체 강의를 무료로 읽을 수 있으며, 내장 코드 에디터와 24/7 AI 튜터와 함께 브라우저에서 직접 실습할 수 있습니다. 이 강의는 Deep Learning Academy 학습 경로의 일부이며, 진행 상황이 웹과 CoddyKit 앱에 동기화됩니다. Deep Learning Academy 강의에는 총 4개의 강의가 포함되어 있습니다.

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Your Data Needs a Front Door

Before a model can learn, PyTorch needs a clean way to reach your samples one at a time. That front door is a Dataset class. 🚪

Start by Subclassing

You build a custom dataset by subclassing torch.utils.data.Dataset. PyTorch then knows exactly how to ask your object for data.

from torch.utils.data import Dataset

class MyData(Dataset):
    pass

Stash Your Data in init

The __init__ method runs once when you create the dataset. Use it to load file paths, arrays, or labels into the object's fields.

def __init__(self, X, y):
    self.X = X
    self.y = y

Two Methods Make It Work

A working dataset only needs two methods: __len__ to report its size and __getitem__ to fetch one sample. That is the whole contract.

len Counts Your Samples

The __len__ method returns how many samples you have. PyTorch reads this to know when an epoch ends and how far an index can go.

def __len__(self):
    return len(self.X)

getitem Returns One Sample

Given an index, __getitem__ returns a single sample, usually a feature and its label. This is where one row of data is handed over.

def __getitem__(self, idx):
    return self.X[idx], self.y[idx]

Return Tensors, Not Lists

__getitem__ should hand back tensors so the model can use them directly. Convert NumPy arrays or Python lists right here if needed.

import torch
x = torch.tensor(self.X[idx], dtype=torch.float32)

Lazy Loading for Big Data

For huge datasets, do not load everything in __init__. Instead read each file inside __getitem__ so only one sample sits in memory at a time.

Apply Transforms Per Sample

__getitem__ is the natural place to apply a transform, like resizing an image. Store the transform in __init__, then call it before returning.

if self.transform:
    x = self.transform(x)

Index It Like a List

Once built, your dataset behaves like a list. Calling len(ds) or ds[0] just triggers the two methods you defined. Test it before training.

ds = MyData(X, y)
print(len(ds), ds[0])

Now It Plugs Into Everything

This tidy interface is why a custom Dataset drops straight into a DataLoader. You write two methods and the rest of PyTorch just works.

Quick Check

Which method does PyTorch call to fetch a single sample by index?

Recap

A custom dataset subclasses Dataset and defines two methods: __len__ for its size and __getitem__ to return one sample. Two methods, full power. 🎉

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