Data augmentation with transforms in PyTorch - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of a composed transform on an image tensor

Given the following PyTorch code applying data augmentation transforms, what is the shape of the output tensor after the transforms?

PyTorch

import torch
from torchvision import transforms
from PIL import Image

transform = transforms.Compose([
    transforms.Resize((64, 64)),
    transforms.RandomHorizontalFlip(p=1.0),
    transforms.ToTensor()
])

# Simulate a PIL image as a tensor with shape (3, 100, 100)
# Normally transforms expect PIL images, so convert tensor to PIL Image
image = Image.fromarray((torch.rand(3, 100, 100).permute(1, 2, 0).mul(255).byte().numpy()))

output = transform(image)

print(output.shape)

A torch.Size([1, 3, 64, 64])

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

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

D torch.Size([3, 100, 100])

💡 Hint

Remember that Resize changes the image size and ToTensor converts to a tensor with channels first.

The Resize transform changes the image size to 64x64 pixels. The RandomHorizontalFlip flips the image horizontally but does not change shape. ToTensor converts the image to a tensor with shape (channels, height, width). So the output shape is (3, 64, 64).

❓ Model Choice

intermediate

Choosing the best augmentation for rotation invariance

You want to train a model to recognize objects regardless of their orientation. Which data augmentation transform should you add to your training pipeline to help the model learn rotation invariance?

A transforms.RandomRotation(degrees=45)

B transforms.RandomHorizontalFlip(p=0.5)

C transforms.ColorJitter(brightness=0.5)

D transforms.RandomCrop(size=32)

💡 Hint

Think about which transform changes the orientation of the image.

RandomRotation rotates the image by a random angle up to 45 degrees, helping the model learn to recognize objects in different orientations. HorizontalFlip flips the image but only horizontally, ColorJitter changes colors, and RandomCrop changes the size and position but not orientation.

❓ Hyperparameter

advanced

Effect of probability parameter in RandomHorizontalFlip

In the transform transforms.RandomHorizontalFlip(p=0.7), what does the parameter p=0.7 control?

A The probability that the image will be converted to grayscale

B The probability that the image will be flipped horizontally during augmentation

C The angle in degrees to flip the image

D The scale factor for resizing the image

💡 Hint

Think about what 'p' usually means in random transforms.

The parameter p in RandomHorizontalFlip sets the chance that the image will be flipped horizontally. Here, there is a 70% chance the image flips and 30% chance it stays the same.

🔧 Debug

advanced

Debugging a transform pipeline error

You have this transform pipeline: transform = transforms.Compose([ transforms.ToTensor(), transforms.RandomCrop(32), transforms.Normalize(mean=[0.5], std=[0.5]) ]) When you run it on a PIL image of size 28x28, you get an error. What is the cause?

A RandomCrop(32) fails because the image is smaller than the crop size

B ToTensor() must be after Normalize()

C Normalize expects 3 channels but the image has 1 channel

D Transforms.Compose requires a list of functions, not a list of transforms

💡 Hint

Check the image size compared to the crop size.

RandomCrop(32) tries to crop a 32x32 patch, but the input image is only 28x28, so it raises an error. To fix, either resize the image first or use a smaller crop size.

🧠 Conceptual

expert

Why use data augmentation in training deep learning models?

Which of the following best explains why data augmentation is important when training deep learning models?

A It removes noisy data points from the training set

B It reduces the training time by simplifying the dataset

C It guarantees the model will achieve 100% accuracy on the training set

D It artificially increases the size and diversity of the training data to reduce overfitting and improve generalization

💡 Hint

Think about how data augmentation affects the training data and model learning.

Data augmentation creates new variations of existing data, helping the model learn more robust features and reducing overfitting. It does not reduce training time or guarantee perfect accuracy.

Practice

1. What is the main purpose of using transforms.Compose in PyTorch data augmentation?

easy

A. To combine multiple image transformations into one pipeline

B. To train the model faster by skipping data loading

C. To convert images into numpy arrays

D. To save the augmented images to disk automatically

Solution

1. Step 1: Understand the role of transforms.Compose

   transforms.Compose is used to chain several image transformations so they apply sequentially to the input image.

2. Step 2: Identify the correct purpose

   It does not speed up training directly, convert images to numpy, or save images. Its main job is combining transformations.

3. Final Answer:

   To combine multiple image transformations into one pipeline -> Option A

4. Quick Check:

   transforms.Compose = combine transforms [OK]

Hint: Remember Compose chains transforms in order [OK]

Common Mistakes:

• Thinking Compose speeds up training
• Confusing Compose with image saving
• Assuming Compose converts image formats

2. Which of the following is the correct way to apply a horizontal flip and convert an image to a tensor using PyTorch transforms?

easy

A. transforms.Compose(transforms.RandomHorizontalFlip(), transforms.ToTensor())

B. transforms.Compose([transforms.RandomHorizontalFlip(), transforms.ToTensor()])

C. transforms.ToTensor(transforms.RandomHorizontalFlip())

D. transforms.RandomHorizontalFlip(transforms.ToTensor())

Solution

1. Step 1: Check the syntax for combining transforms

   PyTorch requires transforms to be passed as a list inside transforms.Compose([]).

2. Step 2: Validate each option

   transforms.Compose([transforms.RandomHorizontalFlip(), transforms.ToTensor()]) correctly uses a list inside Compose. The other options misuse function calls or pass arguments incorrectly.

3. Final Answer:

   transforms.Compose([transforms.RandomHorizontalFlip(), transforms.ToTensor()]) -> Option B

4. Quick Check:

   Compose needs list of transforms [OK]

Hint: Use Compose with a list of transforms inside brackets [OK]

Common Mistakes:

• Passing transforms as separate arguments instead of a list
• Calling transforms inside each other incorrectly
• Forgetting to convert images to tensor

3. Given the following code, what will be the shape of the output tensor after applying the transforms to a 3-channel 64x64 image?

transform = transforms.Compose([
    transforms.RandomRotation(90),
    transforms.ToTensor()
])
output = transform(image)

medium

A. [1, 64, 64]

B. [64, 64, 3]

C. [3, 64, 64]

D. [64, 3, 64]

Solution

1. Step 1: Understand the transform effects

   RandomRotation rotates the image but does not change its size or channels. ToTensor converts the image to a tensor with shape [channels, height, width].

2. Step 2: Determine output shape

   Input image is 3 channels, 64x64 pixels. After ToTensor, shape is [3, 64, 64]. Rotation keeps size same.

3. Final Answer:

   [3, 64, 64] -> Option C

4. Quick Check:

   ToTensor output shape = [channels, height, width] [OK]

Hint: ToTensor outputs [channels, height, width] shape [OK]

Common Mistakes:

• Confusing channel position in tensor shape
• Assuming rotation changes image size
• Thinking output is a numpy array shape

4. Identify the error in this PyTorch transform pipeline:

transform = transforms.Compose([
    transforms.RandomCrop(32),
    transforms.ToTensor,
    transforms.Normalize((0.5,), (0.5,))
])

medium

A. transforms.ToTensor is missing parentheses to call it

B. Normalize mean and std should be lists, not tuples

C. RandomCrop size should be a tuple, not an integer

D. Compose should not be used with Normalize

Solution

1. Step 1: Check each transform usage

   RandomCrop accepts an integer for size, so that is correct. Normalize accepts tuples for mean and std, so that is correct.

2. Step 2: Identify the missing parentheses

   transforms.ToTensor is a class, but it must be called as transforms.ToTensor() to create the transform instance.

3. Final Answer:

   transforms.ToTensor is missing parentheses to call it -> Option A

4. Quick Check:

   Call ToTensor() with parentheses [OK]

Hint: Always call transforms with parentheses [OK]

Common Mistakes:

• Forgetting parentheses on transform classes
• Thinking Normalize needs lists instead of tuples
• Misunderstanding RandomCrop size argument

5. You want to augment your training images by randomly flipping horizontally, rotating by up to 30 degrees, and normalizing with mean=0.5 and std=0.5 for each channel. Which transform pipeline correctly applies these steps in PyTorch?

hard

A. transforms.Compose([transforms.ToTensor(), transforms.RandomHorizontalFlip(), transforms.RandomRotation(30), transforms.Normalize((0.5,), (0.5,))])

B. transforms.Compose([transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), transforms.RandomHorizontalFlip(), transforms.RandomRotation(30), transforms.ToTensor()])

C. transforms.Compose([transforms.RandomRotation(30), transforms.RandomHorizontalFlip(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), transforms.ToTensor()])

D. transforms.Compose([transforms.RandomHorizontalFlip(), transforms.RandomRotation(30), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

Solution

1. Step 1: Order of transforms matters

   Data augmentation like flipping and rotation must happen before converting to tensor. Normalization happens after ToTensor.

2. Step 2: Check each option's order and parameters

   transforms.Compose([transforms.RandomHorizontalFlip(), transforms.RandomRotation(30), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) applies flip and rotation first, then ToTensor, then Normalize with correct mean/std for 3 channels. Others have wrong order or missing steps.

3. Final Answer:

   transforms.Compose([transforms.RandomHorizontalFlip(), transforms.RandomRotation(30), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) -> Option D

4. Quick Check:

   Augment before ToTensor, normalize after [OK]

Hint: Augment first, then ToTensor, then Normalize [OK]

Common Mistakes:

• Normalizing before ToTensor
• Applying augmentations after ToTensor
• Using wrong mean/std shapes for Normalize