Data augmentation with transforms in PyTorch - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is data augmentation in machine learning?

Data augmentation is a technique to increase the diversity of training data by applying random transformations like rotation, flipping, or scaling. This helps models learn better and avoid overfitting.

beginner

Name three common image transformations used in PyTorch for data augmentation.

Common transformations include RandomHorizontalFlip (flips image horizontally), RandomRotation (rotates image by a random angle), and ColorJitter (changes brightness, contrast, saturation).

intermediate

How does RandomHorizontalFlip help improve model performance?

RandomHorizontalFlip flips images horizontally at random during training. This teaches the model to recognize objects regardless of left-right orientation, improving generalization.

intermediate

Explain the role of Compose in PyTorch transforms.

Compose lets you chain multiple transformations together. For example, you can rotate, flip, and normalize images in one step by passing a list of transforms to Compose.

intermediate

What is the difference between transforms applied during training and testing?

During training, random transforms are applied to augment data and improve robustness. During testing, deterministic transforms like resizing and normalization are used to keep evaluation consistent.

Which PyTorch transform randomly flips an image horizontally?

A RandomHorizontalFlip

B RandomRotation

C ColorJitter

D Normalize

RandomHorizontalFlip flips images horizontally at random during training.

What does the Compose transform do in PyTorch?

A Applies a single transformation

B Chains multiple transformations together

C Normalizes images

D Loads images from disk

Compose chains multiple transforms so they apply sequentially.

Why do we apply data augmentation only during training?

A To make the model robust by showing varied data

B To speed up training

C To increase test accuracy

D To reduce dataset size

Augmentation shows varied data to the model, helping it generalize better.

Which transform would you use to randomly change image brightness?

A ToTensor

B RandomRotation

C RandomCrop

D ColorJitter

ColorJitter can randomly change brightness, contrast, and saturation.

What is a common transform applied during both training and testing?

A RandomHorizontalFlip

B RandomRotation

C Normalization

D ColorJitter

Normalization is applied during testing to keep input consistent.

Describe how you would set up data augmentation for training an image classifier using PyTorch transforms.

Think about the sequence of transformations to increase data variety.

Explain why data augmentation helps prevent overfitting in machine learning models.

Consider how seeing different versions of data affects learning.

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