Compose transforms in PyTorch - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of Compose in PyTorch transforms?

Compose lets you combine multiple image transformations into one sequence. It applies each transform one after another to the input data.

beginner

How do you use Compose to apply a resize and then convert an image to a tensor?

transform = Compose([Resize((128, 128)), ToTensor()])

This creates a transform that first resizes images to 128x128 pixels, then converts them to tensors.

intermediate

What happens if you change the order of transforms inside Compose?

The transforms are applied in the order you list them. Changing order can change the final output. For example, Normalize() before ToTensor() will cause an error.

intermediate

Can Compose include custom transforms?

<p>Yes! You can write your own transform as a class or function and include it inside <code>Compose</code> along with built-in transforms.</p>

beginner

What is the output of applying Compose([Resize((64,64)), ToTensor()]) to an image?

The output is a PyTorch tensor representing the image resized to 64x64 pixels, ready for model input.

What does Compose do in PyTorch transforms?

A Loads images from disk

B Creates a new dataset

C Trains a neural network

D Chains multiple transforms to apply in sequence

Compose chains multiple transforms so they run one after another on the input.

Which of these is a correct way to use Compose?

A Compose([Resize((128,128)), ToTensor()])

B Compose([ToTensor(), Resize((128,128))])

C Compose(Resize((128,128)), ToTensor())

D Compose(ToTensor())

Transforms must be inside a list passed to Compose. Also, resizing should come before converting to tensor.

What type of object does ToTensor() produce?

A NumPy array

B PyTorch tensor

C Python list

D PIL Image

ToTensor() converts images to PyTorch tensors.

If you want to add a custom transform to Compose, what must it do?

A Train a model

B Load data from disk

C Return a transformed image or tensor

D Print the image

Custom transforms must take input and return the transformed output to work in Compose.

What happens if you put Normalize() before ToTensor() in Compose?

A Normalize will fail because input is a PIL image, not a tensor

B It works fine

C The image will be normalized twice

D The image will convert back to tensor

Normalize() expects a PyTorch tensor, so putting it before ToTensor() causes an error.

Explain how Compose helps when preparing images for a PyTorch model.

Think about how you get images ready step-by-step.

Describe what could go wrong if you change the order of transforms inside Compose.

Consider what happens if you convert to tensor too early.

Practice

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

easy

A. To chain multiple image transforms into one step

B. To train a neural network model

C. To load datasets from disk

D. To visualize images in a grid

Solution

1. Step 1: Understand Compose functionality

   Compose is used to combine several image transformations so they run one after another.

2. Step 2: Compare with other options

   Training models, loading datasets, and visualization are different tasks not handled by Compose.

3. Final Answer:

   To chain multiple image transforms into one step -> Option A

4. Quick Check:

   Compose chains transforms = C [OK]

Hint: Compose chains transforms in order for easy preprocessing [OK]

Common Mistakes:

• Thinking Compose trains models
• Confusing Compose with dataset loading
• Assuming Compose visualizes images

2. Which of the following is the correct way to create a Compose transform that converts images to tensor and normalizes them?

easy

A. transforms.Compose(transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,)))

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

C. transforms.Compose{transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))}

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

Solution

1. Step 1: Check Compose syntax

   Compose takes a list of transforms inside square brackets [].

2. Step 2: Verify order and syntax

   transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]) uses correct list syntax and order: ToTensor first, then Normalize. transforms.Compose(transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))) misses list brackets, C uses curly braces, D reverses order which is incorrect.

3. Final Answer:

   transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]) -> Option B

4. Quick Check:

   Compose needs list and correct order = A [OK]

Hint: Use square brackets [] inside Compose and correct transform order [OK]

Common Mistakes:

• Omitting list brackets in Compose
• Using curly braces instead of brackets
• Reversing transform order

3. What will be the output shape of the tensor after applying this Compose transform on a 28x28 grayscale image?

transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Resize((14, 14))
])
img_tensor = transform(image)

medium

A. [3, 14, 14]

B. [1, 28, 28]

C. [1, 14, 14]

D. [3, 28, 28]

Solution

1. Step 1: Understand ToTensor output

   For a grayscale image, ToTensor() converts it to a tensor with shape [channels, height, width] where channels=1.

2. Step 2: Apply Resize transform

   Resize((14,14)) changes height and width to 14 each, channels remain 1.

3. Final Answer:

   [1, 14, 14] -> Option C

4. Quick Check:

   Grayscale tensor shape after resize = [1,14,14] [OK]

Hint: Grayscale images have 1 channel; Resize changes height and width [OK]

Common Mistakes:

• Assuming 3 channels for grayscale
• Ignoring Resize effect on shape
• Confusing height and width order

4. Identify the error in this Compose transform code:

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

medium

A. ToTensor should be replaced with ToPILImage

B. Normalize parameters are incorrect

C. Compose requires a tuple, not a list

D. Normalize should come after ToTensor

Solution

1. Step 1: Check transform order

   Normalize expects a tensor input, so ToTensor() must come first to convert image to tensor.

2. Step 2: Validate other options

   Parameters for Normalize are correct, Compose accepts list, and ToPILImage is not needed here.

3. Final Answer:

   Normalize should come after ToTensor -> Option D

4. Quick Check:

   Normalize after ToTensor = A [OK]

Hint: Always put ToTensor before Normalize in Compose [OK]

Common Mistakes:

• Placing Normalize before ToTensor
• Confusing list and tuple for Compose
• Replacing ToTensor incorrectly

5. You want to apply a Compose transform that first randomly flips an image horizontally, then converts it to a tensor, and finally normalizes it. Which code snippet correctly implements this?

hard

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

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

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

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

Solution

1. Step 1: Order of transforms

   RandomHorizontalFlip works on PIL images, so it must come before ToTensor which converts to tensor.

2. Step 2: Normalize after ToTensor

   Normalize requires tensor input, so it must come after ToTensor.

3. Step 3: Verify options

   transforms.Compose([transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]) has correct order: flip, then tensor, then normalize. Others have wrong order causing errors or wrong behavior.

4. Final Answer:

   transforms.Compose([transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]) -> Option A

5. Quick Check:

   Flip before ToTensor, Normalize last = D [OK]

Hint: Flip before ToTensor; Normalize last in Compose chain [OK]

Common Mistakes:

• Placing Normalize before ToTensor
• Applying flip after ToTensor (wrong input type)
• Mixing order causing runtime errors