Image-to-image transformation in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load an image and convert it to a tensor for image-to-image transformation.

Prompt Engineering / GenAI

from PIL import Image
import torchvision.transforms as transforms

image = Image.open('input.jpg')
transform = transforms.Compose([
    transforms.ToTensor(),
])
tensor_image = [1](image)

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

AImage.open

Btransform

Ctransforms

DImage

Attempts:

3 left

💡 Hint

Common Mistakes

Using Image.open again instead of the transform.

Trying to call transforms directly without Compose.

The transform object applies the ToTensor() operation to convert the PIL image into a tensor.

2fill in blank

medium

Complete the code to define a simple convolutional layer for image-to-image transformation.

Prompt Engineering / GenAI

import torch.nn as nn

conv_layer = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=[1], padding=1)

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

A3

B1

C5

D7

Attempts:

3 left

💡 Hint

Common Mistakes

Using kernel size 1 which is too small to capture features.

Using kernel size 7 which is large and uncommon here.

A kernel size of 3 is common for convolutional layers to capture local features effectively.

3fill in blank

hard

Fix the error in the code to apply a ReLU activation after the convolutional layer.

Prompt Engineering / GenAI

import torch.nn.functional as F

output = F.[1](conv_layer(input_tensor))

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

Arelu

Bsigmoid

Csoftmax

Dtanh

Attempts:

3 left

💡 Hint

Common Mistakes

Using sigmoid which is less common for convolutional activations.

Using softmax which is for classification outputs.

ReLU is the correct activation function to apply after convolution for non-linearity in image tasks.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps image filenames to their transformed tensors if the image size is greater than 256.

Prompt Engineering / GenAI

image_tensors = {filename: [1] for filename, image in images.items() if image.size[0] [2] 256}

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

Atransform(image)

Btransform

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform without calling it as a function.

Using '<' instead of '>' in the condition.

We apply the transform to the image and filter images with width greater than 256 pixels.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps image names to their grayscale tensors if the height is less than 128.

Prompt Engineering / GenAI

grayscale_images = {name: [1](image.convert([2])) for name, image in dataset.items() if image.size[1] [3] 128}

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

Atransform

B'L'

C<

D'RGB'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'RGB' instead of 'L' for grayscale conversion.

Using '>' instead of '<' in the size condition.

We convert images to grayscale with 'L', apply the transform, and filter images with height less than 128.

Practice

1.

What is the main goal of image-to-image transformation in AI?

easy

A. To change an input image into a different output image automatically

B. To classify images into categories

C. To detect objects inside an image

D. To generate text from an image

Solution

1. Step 1: Understand the purpose of image-to-image transformation

   This technique changes one image into another, like coloring or style transfer.

2. Step 2: Compare with other image tasks

   Classification, detection, and text generation are different tasks, not image transformation.

3. Final Answer:

   To change an input image into a different output image automatically -> Option A

4. Quick Check:

   Image-to-image transformation = change image [OK]

Hint: Image-to-image means input image changes to output image [OK]

Common Mistakes:

• Confusing transformation with classification
• Thinking it detects objects instead of changing images
• Mixing it up with text generation from images

2.

Which of the following is the correct way to describe an image-to-image model's input and output?

Input: ?
Output: ?

easy

A. Input: Image, Output: Image

B. Input: Text, Output: Image

C. Input: Image, Output: Text

D. Input: Number, Output: Image

Solution

1. Step 1: Identify input type for image-to-image models

   These models take an image as input to transform it.

2. Step 2: Identify output type for image-to-image models

   The output is also an image, changed in style, color, or content.

3. Final Answer:

   Input: Image, Output: Image -> Option A

4. Quick Check:

   Input and output both images [OK]

Hint: Both input and output are images in image-to-image tasks [OK]

Common Mistakes:

• Confusing input as text or numbers
• Thinking output is text instead of image
• Mixing input/output types

3.

Consider this simplified Python code using a model for image-to-image transformation:

input_image = load_image('sketch.png')
output_image = model.transform(input_image)
save_image(output_image, 'colorized.png')
print(type(output_image))

What will be printed?

medium

A. <class 'str'>

B. <class 'numpy.ndarray'>

C. <class 'PIL.Image.Image'>

D. Error: model.transform is not defined

Solution

1. Step 1: Understand typical output type of image-to-image models

   Most models output images as numpy arrays representing pixel data.

2. Step 2: Check code for output type

   Since model.transform returns an image, it is usually a numpy.ndarray, not a PIL Image or string.

3. Final Answer:

   <class 'numpy.ndarray'> -> Option B

4. Quick Check:

   Model output image = numpy array [OK]

Hint: Model outputs image arrays, not strings or PIL objects [OK]

Common Mistakes:

• Assuming output is a string filename
• Confusing PIL Image with numpy array
• Expecting error without context

4.

Look at this code snippet for image-to-image transformation:

def transform_image(model, img_path):
    img = load_image(img_path)
    result = model.transform(img)
    return result

output = transform_image(my_model, 12345)
print(type(output))

What is the main error here?

medium

A. The function returns None instead of an image

B. The model.transform method does not exist

C. The image path should be a string, not a number

D. The print statement is missing parentheses

Solution

1. Step 1: Check the argument passed to load_image

   load_image expects a file path string, but 12345 is a number, causing an error.

2. Step 2: Verify other code parts

   model.transform and print syntax are correct; function returns result properly.

3. Final Answer:

   The image path should be a string, not a number -> Option C

4. Quick Check:

   Image path must be string [OK]

Hint: File paths must be strings, not numbers [OK]

Common Mistakes:

• Thinking model.transform is missing
• Ignoring argument type for image path
• Confusing print syntax in Python 3

5.

You want to build an image-to-image model that converts black-and-white sketches into colored images. Which approach is best?

A dataset has pairs of sketches and their colored versions.

hard

A. Train a text-to-image model with sketch descriptions

B. Use unsupervised clustering on sketches only

C. Apply image classification on sketches

D. Train a supervised model using paired sketch and color images

Solution

1. Step 1: Identify the task type

   Converting sketches to colored images is a paired image-to-image translation task.

2. Step 2: Choose the right training method

   Supervised learning with paired data (sketch and color image) is best to learn direct mapping.

3. Step 3: Evaluate other options

   Unsupervised clustering, text-to-image, and classification do not fit this paired transformation task.

4. Final Answer:

   Train a supervised model using paired sketch and color images -> Option D

5. Quick Check:

   Paired data needs supervised training [OK]

Hint: Use paired images for supervised training in image-to-image tasks [OK]

Common Mistakes:

• Choosing unsupervised methods without paired data
• Confusing text-to-image with image-to-image
• Using classification instead of transformation