Data transforms in PyTorch - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output shape after applying this transform?

Consider a PyTorch tensor representing an image of shape (3, 64, 64). We apply the following transform:

transform = torchvision.transforms.Compose([torchvision.transforms.Resize(32), torchvision.transforms.CenterCrop(28)])

What will be the shape of the resulting tensor after applying transform?

PyTorch

import torch
import torchvision.transforms as T

img = torch.randn(3, 64, 64)
transform = T.Compose([T.Resize(32), T.CenterCrop(28)])
result = transform(img)
print(result.shape)

A torch.Size([3, 64, 64])

B torch.Size([3, 32, 32])

C torch.Size([1, 28, 28])

D torch.Size([3, 28, 28])

💡 Hint

Resize changes the smaller edge to 32, then CenterCrop cuts a 28x28 square from the center.

Resize changes the image so the smaller edge is 32, keeping aspect ratio. Since input is square 64x64, it becomes 32x32. Then CenterCrop crops a 28x28 patch from the center. The channel dimension (3) stays the same.

❓ Model Choice

intermediate

Which transform is best to normalize image pixels to [0,1] range?

You have image tensors with pixel values from 0 to 255. You want to normalize them to the range [0,1] before feeding into a model. Which PyTorch transform should you use?

A torchvision.transforms.Normalize(mean=[0.5], std=[0.5])

B torchvision.transforms.ToTensor()

C torchvision.transforms.Lambda(lambda x: x / 255)

D torchvision.transforms.Resize(256)

💡 Hint

One transform converts PIL images or numpy arrays to tensors and scales pixels to [0,1].

ToTensor() converts images to float tensors and scales pixel values from [0,255] to [0,1]. Normalize changes mean and std but does not scale 0-255 to 0-1.

❓ Hyperparameter

advanced

Choosing mean and std for Normalize transform

You want to normalize RGB images using torchvision.transforms.Normalize. Which values for mean and std are correct if your images have pixel values in [0,1] and you want zero mean and unit variance per channel?

A mean=[0, 0, 0], std=[1, 1, 1]

B mean=[1, 1, 1], std=[0, 0, 0]

C mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]

D mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]

💡 Hint

Zero mean and unit variance means subtract 0 and divide by 1.

Normalize subtracts mean and divides by std. To keep zero mean and unit variance, use mean=0 and std=1 for each channel.

🔧 Debug

advanced

Why does this transform code raise an error?

This code raises an error when applied to a tensor image. Why?

transform = torchvision.transforms.Compose([torchvision.transforms.RandomHorizontalFlip(), torchvision.transforms.ToTensor()])

Assume input is a PyTorch tensor image.

PyTorch

import torch
import torchvision.transforms as T
img = torch.randn(3, 64, 64)
transform = T.Compose([T.RandomHorizontalFlip(), T.ToTensor()])
result = transform(img)

A RandomHorizontalFlip expects a PIL image or numpy array, not a tensor

B ToTensor must be applied before RandomHorizontalFlip

C RandomHorizontalFlip requires the tensor to be 4D, but input is 3D

D The code runs without error

💡 Hint

Check the expected input types for RandomHorizontalFlip.

RandomHorizontalFlip works on PIL images or numpy arrays, not on PyTorch tensors. Applying it directly to a tensor causes an error.

🧠 Conceptual

expert

Why use data transforms during training but not always during testing?

In machine learning, why do we often apply random data transforms (like flips, crops) during training but use fixed transforms during testing?

A Random transforms are only compatible with GPUs, fixed transforms work on CPUs

B Random transforms reduce training time, fixed transforms speed up testing

C Random transforms increase training data diversity to improve generalization, while testing uses fixed transforms for consistent evaluation

D Random transforms normalize data, fixed transforms augment data

💡 Hint

Think about how randomness affects learning and evaluation.

Random transforms create varied training examples to help the model learn robust features. During testing, fixed transforms ensure the evaluation is stable and reproducible.