Built-in datasets (torchvision.datasets) in PyTorch - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of loading CIFAR10 dataset

What is the output of the following code snippet when loading the CIFAR10 training dataset?

PyTorch

import torchvision.datasets as datasets
cifar10_train = datasets.CIFAR10(root='./data', train=True, download=False)
print(len(cifar10_train))

A 6000

B 60000

C 5000

D 50000

💡 Hint

Recall the number of training images in CIFAR10 dataset.

The CIFAR10 training dataset contains exactly 50,000 images. The code prints the length of the dataset, which is 50000.

❓ Model Choice

intermediate

Choosing dataset for handwritten digit recognition

Which torchvision built-in dataset is best suited for training a model to recognize handwritten digits?

A datasets.CIFAR10

B datasets.MNIST

C datasets.FashionMNIST

D datasets.ImageNet

💡 Hint

Think about which dataset contains handwritten digits.

MNIST contains grayscale images of handwritten digits 0-9, making it ideal for digit recognition tasks.

❓ Hyperparameter

advanced

Effect of 'transform' parameter in torchvision datasets

What is the effect of passing a torchvision.transforms.Compose object to the 'transform' parameter when loading a dataset like CIFAR10?

A It applies the specified transformations to each image when accessed from the dataset.

B It changes the dataset size by filtering images based on the transform.

C It downloads additional images matching the transform criteria.

D It converts the dataset labels to one-hot encoded vectors.

💡 Hint

Consider when and how transforms are applied in PyTorch datasets.

The 'transform' parameter applies the given transformations to each image on-the-fly when the image is accessed, without changing dataset size or labels.

❓ Metrics

advanced

Number of classes in FashionMNIST dataset

How many distinct classes does the FashionMNIST dataset contain?

A 50

B 100

C 10

D 5

💡 Hint

FashionMNIST has the same number of classes as MNIST.

FashionMNIST contains 10 classes representing different clothing items.

🔧 Debug

expert

Error raised when loading ImageNet without download

What error will be raised by the following code if the ImageNet dataset is not downloaded in the specified root folder? import torchvision.datasets as datasets imagenet = datasets.ImageNet(root='./data', split='train', download=False)

A RuntimeError

B FileNotFoundError

C ValueError

D TypeError

💡 Hint

Think about what happens if the dataset files are missing and download is False.

If the ImageNet dataset files are not found in the root directory and download is False, a RuntimeError is raised.

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