Data loading with torchvision in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the torchvision datasets module.

Computer Vision

from torchvision import [1]

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

Amodels

Bdatasets

Ctransforms

Dutils

Attempts:

3 left

💡 Hint

Common Mistakes

Importing 'models' instead of 'datasets'.

Importing 'transforms' when datasets are needed.

The datasets module in torchvision provides easy access to popular vision datasets.

2fill in blank

medium

Complete the code to load the MNIST training dataset with torchvision.

Computer Vision

train_data = datasets.MNIST(root='./data', train=[1], download=True)

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

A1

BFalse

C'yes'

DTrue

Attempts:

3 left

💡 Hint

Common Mistakes

Using string 'yes' instead of boolean True.

Using 1 instead of True.

Setting train=True loads the training split of MNIST.

3fill in blank

hard

Fix the error in the code to apply a transform that converts images to tensors.

Computer Vision

from torchvision import transforms

transform = transforms.[1]()
train_data = datasets.MNIST(root='./data', train=True, download=True, transform=transform)

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

AToTensor

Bto_tensor

CtoTensor

DTensor

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase 'to_tensor' which does not exist.

Using 'Tensor' which is not a transform class.

The correct transform class is ToTensor with capital T and capital T in Tensor.

4fill in blank

hard

Fill both blanks to create a DataLoader for the training data with batch size 64 and shuffling enabled.

Computer Vision

from torch.utils.data import DataLoader

train_loader = DataLoader(train_data, batch_size=[1], shuffle=[2])

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

A64

BTrue

C32

DFalse

Attempts:

3 left

💡 Hint

Common Mistakes

Using batch size 32 instead of 64.

Setting shuffle to False which disables randomization.

Batch size 64 is common for training, and shuffle=True randomizes data order each epoch.

5fill in blank

hard

Fill all three blanks to create a transform pipeline that resizes images to 28x28, converts to tensor, and normalizes with mean 0.5 and std 0.5.

Computer Vision

transform = transforms.Compose([
    transforms.Resize(([1], [2])),
    transforms.ToTensor(),
    transforms.Normalize(mean=[[3]], std=[0.5])
])

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

A28

B0.5

C32

D0.0

Attempts:

3 left

💡 Hint

Common Mistakes

Using 32 instead of 28 for resize dimensions.

Using 0.0 for mean which is not balanced normalization.

Images are resized to 28x28 pixels, normalized with mean 0.5 and std 0.5 for balanced input.

Practice

1. What is the main purpose of using torchvision.datasets in a computer vision project?

easy

A. To easily download and load popular image datasets

B. To create neural network layers

C. To visualize images in a dataset

D. To perform mathematical operations on tensors

Solution

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

   It provides ready-to-use popular image datasets like CIFAR10, MNIST, etc., for easy loading.

2. Step 2: Differentiate from other torchvision modules

   Other modules handle transforms or models, but datasets focus on loading data.

3. Final Answer:

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

4. Quick Check:

   torchvision.datasets = load datasets [OK]

Hint: Datasets module is for loading data, not building models [OK]

Common Mistakes:

• Confusing datasets with model creation
• Thinking datasets handle image visualization
• Assuming datasets perform tensor math

2. Which of the following is the correct way to import the DataLoader class from torchvision?

easy

A. from torch.utils.data import DataLoader

B. from torchvision import DataLoader

C. import DataLoader from torchvision

D. from torchvision.datasets import DataLoader

Solution

1. Step 1: Recall the correct import path for DataLoader

   DataLoader is part of torch.utils.data, not torchvision directly.

2. Step 2: Check each option's syntax and source

   Only from torch.utils.data import DataLoader correctly imports DataLoader from torch.utils.data.

3. Final Answer:

   from torch.utils.data import DataLoader -> Option A

4. Quick Check:

   DataLoader import = torch.utils.data [OK]

Hint: DataLoader is in torch.utils.data, not torchvision [OK]

Common Mistakes:

• Importing DataLoader directly from torchvision
• Using incorrect import syntax
• Confusing datasets and DataLoader imports

3. What will be the output shape of images loaded from CIFAR10 dataset using torchvision if no transform is applied?

medium

A. [224, 224, 3]

B. [1, 28, 28]

C. [3, 32, 32]

D. [32, 32, 3]

Solution

1. Step 1: Recall CIFAR10 image dimensions

   CIFAR10 images are 32x32 pixels with 3 color channels (RGB).

2. Step 2: Understand PyTorch image tensor shape format

   PyTorch uses channel-first format: [channels, height, width], so shape is [3, 32, 32].

3. Final Answer:

   [3, 32, 32] -> Option C

4. Quick Check:

   CIFAR10 image shape = [3, 32, 32] [OK]

Hint: PyTorch images are channel-first: channels, height, width [OK]

Common Mistakes:

• Confusing channel order with height-width-channel
• Assuming grayscale images with 1 channel
• Mixing CIFAR10 size with MNIST or ImageNet

4. Identify the error in this code snippet for loading MNIST dataset with transforms:

from torchvision import datasets, transforms
transform = transforms.Compose([transforms.Resize(32), transforms.ToTensor()])
mnist_data = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
loader = DataLoader(mnist_data, batch_size=64, shuffle=True)

medium

A. batch_size must be 1 or 32 only

B. Missing import of DataLoader from torch.utils.data

C. MNIST dataset does not support transforms

D. Transforms.Resize cannot resize images

Solution

1. Step 1: Check imports for DataLoader usage

   DataLoader is used but not imported, causing a NameError.

2. Step 2: Verify other parts of the code

   Transforms.Resize and MNIST support transforms; batch_size can be any positive integer.

3. Final Answer:

   Missing import of DataLoader from torch.utils.data -> Option B

4. Quick Check:

   DataLoader must be imported before use [OK]

Hint: Always import DataLoader before using it [OK]

Common Mistakes:

• Forgetting to import DataLoader
• Thinking MNIST doesn't support transforms
• Assuming Resize is invalid for MNIST

5. You want to load CIFAR10 images resized to 64x64 pixels, normalized with mean=[0.5,0.5,0.5] and std=[0.5,0.5,0.5], and shuffled in batches of 128. Which code snippet correctly achieves this?

hard

A. transform = transforms.Compose([transforms.Resize((64,64)), transforms.ToTensor()])
data = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
loader = DataLoader(data, batch_size=128, shuffle=True)

B. transform = transforms.Compose([transforms.ToTensor(), transforms.Resize(64), transforms.Normalize([0.5]*3, [0.5]*3)])
data = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
loader = DataLoader(data, batch_size=128, shuffle=False)

C. transform = transforms.Compose([transforms.Resize(64), transforms.Normalize([0.5]*3, [0.5]*3)])
data = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
loader = DataLoader(data, batch_size=64, shuffle=True)

D. transform = transforms.Compose([transforms.Resize((64,64)), transforms.ToTensor(), transforms.Normalize([0.5]*3, [0.5]*3)])
data = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
loader = DataLoader(data, batch_size=128, shuffle=True)

Solution

1. Step 1: Check transform order and parameters

   Resize must be first with size (64,64), then ToTensor, then Normalize with correct mean and std.

2. Step 2: Verify DataLoader parameters

   Batch size is 128 and shuffle=True as required.

3. Step 3: Compare options

   transform = transforms.Compose([transforms.Resize((64,64)), transforms.ToTensor(), transforms.Normalize([0.5]*3, [0.5]*3)]) data = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform) loader = DataLoader(data, batch_size=128, shuffle=True) matches all requirements exactly; others have wrong order, missing steps, or wrong batch/shuffle.

4. Final Answer:

   transform = transforms.Compose([transforms.Resize((64,64)), transforms.ToTensor(), transforms.Normalize([0.5]*3, [0.5]*3)]) data = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform) loader = DataLoader(data, batch_size=128, shuffle=True) -> Option D

5. Quick Check:

   Resize->ToTensor->Normalize + batch=128 + shuffle=True [OK]

Hint: Resize first, then ToTensor, then Normalize; batch and shuffle as needed [OK]

Common Mistakes:

• Applying Normalize before ToTensor
• Using wrong Resize size or format
• Setting shuffle=False when shuffle=True needed
• Incorrect batch size