Built-in datasets (torchvision.datasets) in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the CIFAR10 dataset from torchvision.datasets.

PyTorch

from torchvision.datasets import [1]

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

AFashionMNIST

BMNIST

CCIFAR10

DImageNet

Attempts:

3 left

💡 Hint

Common Mistakes

Importing MNIST instead of CIFAR10

Using ImageNet which is not in torchvision.datasets by default

Confusing FashionMNIST with CIFAR10

The CIFAR10 dataset is imported from torchvision.datasets using from torchvision.datasets import CIFAR10.

2fill in blank

medium

Complete the code to download the CIFAR10 training dataset with transforms.

PyTorch

train_dataset = CIFAR10(root='./data', train=[1], download=True, transform=None)

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

ATrue

BFalse

C'train'

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Setting train to False loads the test set

Using string 'train' instead of boolean True

Passing 1 instead of True

Setting train=True loads the training split of CIFAR10.

3fill in blank

hard

Fix the error in the code to apply a ToTensor transform to the CIFAR10 dataset.

PyTorch

from torchvision.transforms import [1]
train_dataset = CIFAR10(root='./data', train=True, download=True, transform=ToTensor())

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

ACompose

BNormalize

CResize

DToTensor

Attempts:

3 left

💡 Hint

Common Mistakes

Importing Compose instead of ToTensor

Using Normalize without ToTensor first

Trying to resize images instead of converting to tensor

The ToTensor transform converts images to PyTorch tensors and must be imported from torchvision.transforms.

4fill in blank

hard

Fill both blanks to create a DataLoader for the training dataset with batch size 32 and shuffling enabled.

PyTorch

from torch.utils.data import DataLoader
train_loader = DataLoader(train_dataset, batch_size=[1], shuffle=[2])

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

A32

BTrue

CFalse

D64

Attempts:

3 left

💡 Hint

Common Mistakes

Setting shuffle to False which disables random order

Using batch size 64 which is valid but not the intended answer

Confusing batch_size and shuffle parameters

Batch size 32 and shuffle=True are common settings for training data loaders.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps class indices to class names from CIFAR10.

PyTorch

class_to_name = [1]: [2] for [3] in range(len(train_dataset.classes))}

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

Ai

Btrain_dataset.classes[i]

Didx

Attempts:

3 left

💡 Hint

Common Mistakes

Using idx instead of i inconsistently

Swapping keys and values

Using range without len()

This dictionary comprehension uses the index i to map to the class name at train_dataset.classes[i].

Practice

1. What is the main purpose of using torchvision.datasets in PyTorch?

easy

A. To visualize model training progress

B. To create custom neural network layers

C. To easily download and load popular image datasets

D. To optimize model hyperparameters automatically

Solution

1. Step 1: Understand the role of torchvision.datasets

   It provides built-in access to popular image datasets like CIFAR10, MNIST, etc., handling download and loading.

2. Step 2: Compare with other options

   Options A, B, and D relate to different PyTorch functionalities, not dataset loading.

3. Final Answer:

   To easily download and load popular image datasets -> Option C

4. Quick Check:

   torchvision.datasets = dataset loading [OK]

Hint: Remember: torchvision.datasets = easy dataset access [OK]

Common Mistakes:

• Confusing datasets with model layers
• Thinking it handles training visualization
• Assuming it tunes hyperparameters

2. Which of the following is the correct way to import the CIFAR10 dataset from torchvision?

easy

A. from torchvision.datasets import CIFAR10

B. import torchvision.datasets.CIFAR10

C. from torchvision import CIFAR10

D. import CIFAR10 from torchvision.datasets

Solution

1. Step 1: Recall Python import syntax for modules and classes

   The correct syntax to import a class from a module is: from module import Class.

2. Step 2: Apply to torchvision.datasets and CIFAR10

   So, from torchvision.datasets import CIFAR10 is correct. Other options have syntax errors or wrong order.

3. Final Answer:

   from torchvision.datasets import CIFAR10 -> Option A

4. Quick Check:

   Correct import syntax = from torchvision.datasets import CIFAR10 [OK]

Hint: Use 'from module import Class' for dataset imports [OK]

Common Mistakes:

• Using dot notation in import statement incorrectly
• Swapping import order
• Trying to import dataset directly without module

3. What will be the output shape of images loaded from the MNIST dataset using torchvision.datasets with default settings?

medium

A. (28, 28)

B. (1, 28, 28)

C. (3, 28, 28)

D. (32, 32, 3)

Solution

1. Step 1: Recall MNIST image format

   MNIST images are grayscale with size 28x28 pixels.

2. Step 2: Understand PyTorch tensor shape for images

   PyTorch uses channel-first format: (channels, height, width). Grayscale images have 1 channel, so shape is (1, 28, 28).

3. Final Answer:

   (1, 28, 28) -> Option B

4. Quick Check:

   MNIST images = grayscale 28x28 = (1,28,28) [OK]

Hint: PyTorch images: channels first, grayscale = 1 channel [OK]

Common Mistakes:

• Assuming shape is height x width only
• Confusing RGB (3 channels) with grayscale
• Using (28, 28) without channel dimension

4. Consider this code snippet:

from torchvision.datasets import CIFAR10
train_data = CIFAR10(root='./data', train=True, download=True)
print(len(train_data))

What error will occur if download=True is omitted and data is not already downloaded?

medium

A. SyntaxError: Missing download argument

B. No error, data loads automatically

C. FileNotFoundError: No such file or directory

D. RuntimeError: Dataset not found and download=False

Solution

1. Step 1: Understand CIFAR10 dataset download behavior

   If download=False (default) and data is missing, it raises an error because it won't download automatically.

2. Step 2: Identify the error type

   The error raised is a RuntimeError indicating dataset not found and download flag is False.

3. Final Answer:

   RuntimeError: Dataset not found and download=False -> Option D

4. Quick Check:

   Missing data + download=False = RuntimeError [OK]

Hint: Always set download=True first time to avoid RuntimeError [OK]

Common Mistakes:

• Expecting automatic download without flag
• Confusing RuntimeError with SyntaxError
• Assuming FileNotFoundError is raised

5. You want to load the CIFAR10 training dataset with images normalized to mean=0.5 and std=0.5 for each RGB channel. Which code snippet correctly applies this transform using torchvision?

hard

A. from torchvision.datasets import CIFAR10
from torchvision.transforms import Compose, ToTensor, Normalize
transform = Compose([ToTensor(), Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))])
data = CIFAR10(root='./data', train=True, download=True, transform=transform)

B. from torchvision.datasets import CIFAR10
transform = Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))
data = CIFAR10(root='./data', train=True, download=True, transform=transform)

C. from torchvision.datasets import CIFAR10
from torchvision.transforms import ToTensor
transform = ToTensor()
data = CIFAR10(root='./data', train=True, download=True, transform=transform)
data = Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))

D. from torchvision.datasets import CIFAR10
from torchvision.transforms import Compose, Normalize
transform = Compose([Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))])
data = CIFAR10(root='./data', train=True, download=True, transform=transform)

Solution

1. Step 1: Understand required transforms for normalization

   Images must first be converted to tensors with ToTensor() before normalization.

2. Step 2: Check transform composition

   Use Compose to chain ToTensor() and Normalize() in order.

3. Step 3: Verify each option

   from torchvision.datasets import CIFAR10 from torchvision.transforms import Compose, ToTensor, Normalize transform = Compose([ToTensor(), Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))]) data = CIFAR10(root='./data', train=True, download=True, transform=transform) correctly composes both transforms and passes to dataset. Others miss ToTensor() or apply transforms incorrectly.

4. Final Answer:

   Option A code snippet with Compose, ToTensor, Normalize -> Option A

5. Quick Check:

   Normalize after ToTensor using Compose = from torchvision.datasets import CIFAR10 from torchvision.transforms import Compose, ToTensor, Normalize transform = Compose([ToTensor(), Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))]) data = CIFAR10(root='./data', train=True, download=True, transform=transform) [OK]

Hint: Always use ToTensor before Normalize in Compose [OK]

Common Mistakes:

• Applying Normalize without ToTensor first
• Not using Compose to chain transforms
• Applying transforms outside dataset argument