Compose transforms in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a composed transform that converts images to tensors.

PyTorch

transform = transforms.Compose([transforms.To[1]()])

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

ATensor

BImage

CPIL

DArray

Attempts:

3 left

💡 Hint

Common Mistakes

Using ToImage instead of ToTensor.

Using ToPIL which converts tensors back to images.

The ToTensor transform converts a PIL image or numpy array to a PyTorch tensor.

2fill in blank

medium

Complete the code to normalize images with mean 0.5 and std 0.5 using Compose.

PyTorch

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

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

A0.0

B1.0

C0.5

D0.25

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean 0.0 which does not center the data properly.

Using std 1.0 which does not scale the data.

The mean and std values are set to 0.5 to normalize image pixel values between -1 and 1.

3fill in blank

hard

Fix the error in the Compose transform that should resize images to 128x128 pixels.

PyTorch

transform = transforms.Compose([transforms.Resize([1])])

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

A128

B(128, 128)

C[128, 128]

D128, 128

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a single integer which resizes the smaller edge only.

Using a list instead of a tuple.

The Resize transform expects a tuple for width and height, so (128, 128) is correct.

4fill in blank

hard

Fill both blanks to create a Compose transform that converts images to tensors and then randomly flips them horizontally.

PyTorch

transform = transforms.Compose([transforms.[1](), transforms.[2](p=0.5)])

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

AToTensor

BRandomCrop

CRandomHorizontalFlip

DNormalize

Attempts:

3 left

💡 Hint

Common Mistakes

Using RandomCrop instead of RandomHorizontalFlip.

Normalizing before converting to tensor.

First convert images to tensors with ToTensor, then apply random horizontal flip with RandomHorizontalFlip.

5fill in blank

hard

Fill all three blanks to create a Compose transform that resizes images to 64x64, converts to tensor, and normalizes with mean 0.5 and std 0.5.

PyTorch

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

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

A(64, 64)

BToTensor

C0.5

DRandomVerticalFlip

Attempts:

3 left

💡 Hint

Common Mistakes

Using RandomVerticalFlip instead of ToTensor.

Passing size as a list instead of a tuple.

Resize images to (64, 64), convert to tensor with ToTensor, then normalize with mean and std 0.5.

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