__getitem__ and __len__ in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define the __len__ method that returns the size of the dataset.

PyTorch

def __len__(self):
    return [1]

Drag options to blanks, or click blank then click option'

Aself.data

Bself.length

Clen(self.data)

Dlen(self)

Attempts:

3 left

💡 Hint

Common Mistakes

Returning self.data directly instead of its length.

Using len(self) which causes infinite recursion.

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

2fill in blank

medium

Complete the code to define the __getitem__ method that returns the data item at the given index.

PyTorch

def __getitem__(self, index):
    return [1]

Drag options to blanks, or click blank then click option'

Aself.data.get(index)

Bself[index]

Cself.get(index)

Dself.data[index]

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to call self.get(index) which is not defined.

Using self[index] which causes recursion.

The __getitem__ method should return the item at index from self.data, which is a list or similar container.

3fill in blank

hard

Fix the error in the __getitem__ method to correctly return the label for the given index.

PyTorch

def __getitem__(self, index):
    data = self.data[index]
    label = self.labels[1]
    return data, label

Drag options to blanks, or click blank then click option'

A[index]

B(index)

C{index}

D.index

Attempts:

3 left

💡 Hint

Common Mistakes

Using parentheses which calls the object as a function.

Using braces which is invalid syntax for indexing.

To access an element by index in a list, use square brackets []. So self.labels[index] is correct.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each word to its length only if the length is greater than 3.

PyTorch

lengths = {word: [1] for word in words if len(word) [2] 3}

Drag options to blanks, or click blank then click option'

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using < instead of > in the condition.

Mapping to word instead of its length.

The dictionary comprehension maps each word to its length using len(word). The condition filters words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each uppercase key to its value only if the value is positive.

PyTorch

result = [1]: [2] for k, v in data.items() if v [3] 0}

Drag options to blanks, or click blank then click option'

Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using k instead of k.upper() for keys.

Using < instead of > in the condition.

The comprehension uses k.upper() to convert keys to uppercase, maps to value v, and filters for values greater than zero using >.

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