__getitem__ and __len__ in PyTorch - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of __getitem__ in a PyTorch Dataset

Consider this PyTorch Dataset class. What will be the output of dataset[2]?

PyTorch

import torch
from torch.utils.data import Dataset

class SimpleDataset(Dataset):
    def __init__(self):
        self.data = [10, 20, 30, 40, 50]
    def __len__(self):
        return len(self.data)
    def __getitem__(self, idx):
        return self.data[idx] * 2

dataset = SimpleDataset()
output = dataset[2]

A 30

B 60

C 40

D IndexError

💡 Hint

Remember __getitem__ returns data at index multiplied by 2.

The __getitem__ method returns self.data[idx] * 2. At index 2, self.data[2] is 30, so output is 60.

❓ Model Choice

intermediate

Choosing __len__ for a Custom Dataset

You have a dataset class with a list of 1000 images. Which implementation of __len__ correctly returns the dataset size?

A

def __len__(self):
    return 1000

B

def __len__(self):
    return self.images[0]

C

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

D

def __len__(self):
    return self.images

💡 Hint

The length should reflect the number of items in the images list.

The __len__ method should return the number of items in the dataset, which is the length of the images list.

🔧 Debug

advanced

Debugging __getitem__ Index Error

This Dataset class raises an IndexError when accessing dataset[5]. Why?

PyTorch

class MyDataset:
    def __init__(self):
        self.data = [1, 2, 3, 4, 5]
    def __len__(self):
        return len(self.data)
    def __getitem__(self, idx):
        return self.data[idx + 1]

dataset = MyDataset()
output = dataset[5]

A Because data list is empty

B Because __len__ returns wrong length

C Because __getitem__ returns a list instead of a number

D Because __getitem__ accesses index 6 which is out of range

💡 Hint

Check how the index is used inside __getitem__.

dataset[5] calls __getitem__(5), which returns self.data[6]. Since data has length 5, index 6 is out of range causing IndexError.

❓ Hyperparameter

advanced

Effect of __len__ on DataLoader Batching

If a Dataset's __len__ returns 50 but actually contains 100 samples, what happens when using a DataLoader with batch size 10?

A DataLoader will load only 50 samples in 5 batches

B DataLoader will load all 100 samples in 10 batches

C DataLoader will raise a ValueError due to mismatch

D DataLoader will load 100 samples but batch size will be ignored

💡 Hint

DataLoader uses __len__ to know dataset size.

DataLoader relies on __len__ to know how many samples to load. If __len__ returns 50, it will load only 50 samples in 5 batches of 10.

🧠 Conceptual

expert

Why Implement Both __getitem__ and __len__ in PyTorch Dataset?

Why is it important to implement both __getitem__ and __len__ methods in a PyTorch Dataset class?

A Because DataLoader uses __len__ to know dataset size and __getitem__ to fetch samples by index

B Because __getitem__ initializes the dataset and __len__ trains the model

C Because __len__ loads data and __getitem__ shuffles it

D Because __len__ is used only for printing and __getitem__ is optional

💡 Hint

Think about how DataLoader interacts with Dataset.

DataLoader calls __len__ to know how many samples exist and __getitem__ to get each sample by index during training.

Practice

1. What does the __len__ method do in a PyTorch dataset class?

easy

A. It saves the dataset to disk.

B. It returns one data item by index.

C. It returns the total number of items in the dataset.

D. It trains the model on the dataset.

Solution

1. Step 1: Understand the role of __len__ in datasets

   The __len__ method tells PyTorch how many items are in the dataset so it knows the dataset size.

2. Step 2: Differentiate from __getitem__

   Unlike __getitem__ which returns one item, __len__ returns the total count of items.

3. Final Answer:

   It returns the total number of items in the dataset. -> Option C

4. Quick Check:

   __len__ = total items [OK]

Hint: Remember: length means total count, not single item [OK]

Common Mistakes:

• Confusing __len__ with __getitem__
• Thinking __len__ returns data samples
• Assuming __len__ trains the model

2. Which of the following is the correct way to define the __getitem__ method in a PyTorch dataset class?

easy

A. def __getitem__(self, index, batch): return self.data[index:batch]

B. def __getitem__(self, index): return self.data[index]

C. def __getitem__(self): return self.data

D. def __getitem__(index): return self.data[index]

Solution

1. Step 1: Check method signature requirements

   The __getitem__ method must accept self and an index parameter to return one item.

2. Step 2: Validate return statement

   Returning self.data[index] correctly fetches the item at the given index.

3. Final Answer:

   def __getitem__(self, index): return self.data[index] -> Option B

4. Quick Check:

   __getitem__ needs self and index [OK]

Hint: Always include self and index parameters in __getitem__ [OK]

Common Mistakes:

• Missing self parameter
• Adding extra parameters not supported
• Returning whole data instead of one item

3. Given this dataset class, what will print(len(dataset)) output?

class MyDataset:
    def __init__(self):
        self.data = [10, 20, 30, 40]
    def __len__(self):
        return len(self.data)
    def __getitem__(self, idx):
        return self.data[idx]

dataset = MyDataset()

medium

A. 4

B. 3

C. Error

D. 40

Solution

1. Step 1: Check the data list length

   The list self.data contains 4 elements: 10, 20, 30, 40.

2. Step 2: Understand __len__ return value

   The __len__ method returns len(self.data), which is 4.

3. Final Answer:

   4 -> Option A

4. Quick Check:

   len(data list) = 4 [OK]

Hint: len(dataset) calls __len__, returns data list length [OK]

Common Mistakes:

• Confusing length with last element value
• Expecting __getitem__ output instead
• Forgetting to call len() properly

4. Identify the error in this PyTorch dataset class:

class Dataset:
    def __init__(self, data):
        self.data = data
    def __len__(self):
        return self.data
    def __getitem__(self, idx):
        return self.data[idx]

medium

A. __len__ should return an integer, not the data list

B. __getitem__ is missing the self parameter

C. __init__ should not accept data as argument

D. __getitem__ should return the whole data list

Solution

1. Step 1: Check the return type of __len__

   The __len__ method returns self.data, which is a list, not an integer.

2. Step 2: Understand expected __len__ behavior

   __len__ must return the number of items (an integer), so it should return len(self.data).

3. Final Answer:

   __len__ should return an integer, not the data list -> Option A

4. Quick Check:

   __len__ returns int length, not list [OK]

Hint: __len__ must return count, not the data itself [OK]

Common Mistakes:

• Returning data list instead of its length
• Forgetting to use len() inside __len__
• Misdefining __getitem__ parameters

5. You want to create a PyTorch dataset that returns the square of numbers from 0 to 9. Which implementation correctly uses __getitem__ and __len__?

hard

A. class SquareDataset:
def __len__(self):
return 9
def __getitem__(self, idx):
return idx ** 2

B. class SquareDataset:
def __len__(self):
return 10
def __getitem__(self, idx):
return idx + idx

C. class SquareDataset:
def __len__(self):
return 10
def __getitem__(self):
return idx * idx

D. class SquareDataset:
def __len__(self):
return 10
def __getitem__(self, idx):
return idx * idx

Solution

1. Step 1: Check the length method

   Since numbers 0 to 9 means 10 items, __len__ should return 10.

2. Step 2: Verify __getitem__ returns square

   The method must accept idx and return idx * idx to give squares.

3. Final Answer:

   class SquareDataset: def __len__(self): return 10 def __getitem__(self, idx): return idx * idx -> Option D

4. Quick Check:

   Length 10, item = idx squared [OK]

Hint: Length matches count; __getitem__ returns square [OK]

Common Mistakes:

• Returning wrong length (9 instead of 10)
• Missing idx parameter in __getitem__
• Returning sum instead of square