Bounding box handling in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the width of a bounding box.

PyTorch

width = bbox[[1]] - bbox[0]

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

A2

B4

C1

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong index for x_max.

Subtracting y-coordinates instead of x-coordinates.

The width of a bounding box is calculated by subtracting the x-coordinate of the left edge (bbox[0]) from the x-coordinate of the right edge (bbox[2]).

2fill in blank

medium

Complete the code to compute the area of a bounding box.

PyTorch

area = (bbox[2] - bbox[0]) * (bbox[[1]] - bbox[1])

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

A3

B2

C1

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong index for y_max.

Forgetting to subtract y_min from y_max.

The area is width times height. Width is bbox[2] - bbox[0], height is bbox[3] - bbox[1]. So the height uses index 3 for y_max.

3fill in blank

hard

Fix the error in the code to correctly compute the center of a bounding box.

PyTorch

center_x = (bbox[0] + bbox[[1]]) / 2

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

A3

B2

C1

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using y-coordinates instead of x-coordinates.

Using the wrong index for x_max.

The center x-coordinate is the average of x_min (bbox[0]) and x_max (bbox[2]). So the correct index is 2.

4fill in blank

hard

Fill both blanks to create a dictionary of bounding box widths and heights for a list of boxes.

PyTorch

sizes = {i: (bbox[[1]] - bbox[0], bbox[[2]] - bbox[1]) for i, bbox in enumerate(boxes)}

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

A2

B3

C1

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up x and y indices.

Using y_min or x_min incorrectly.

Width is x_max - x_min (bbox[2] - bbox[0]) and height is y_max - y_min (bbox[3] - bbox[1]).

5fill in blank

hard

Fill all three blanks to filter bounding boxes with area greater than 1000 and create a dictionary of their centers.

PyTorch

centers = {i: ((bbox[[1]] + bbox[0]) / 2, (bbox[[2]] + bbox[[3]]) / 2) for i, bbox in enumerate(boxes) if (bbox[2] - bbox[0]) * (bbox[3] - bbox[1]) > 1000}

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

A2

B1

C3

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up indices for y_min and y_max.

Using width or height instead of center coordinates.

The center x is average of x_min (bbox[0]) and x_max (bbox[2]). The center y is average of y_min (bbox[1]) and y_max (bbox[3]). So the indices are 2, 1, and 3 respectively.

Practice

1. What do the four numbers in a bounding box usually represent in PyTorch object detection tasks?

easy

A. Width, height, depth, and color

B. Center x, center y, width, height

C. Top-left corner x and y, width, height

D. Left, top, right, bottom coordinates of the box

Solution

1. Step 1: Understand bounding box format

   Bounding boxes in PyTorch detection models are often represented by four numbers indicating the edges: left (x_min), top (y_min), right (x_max), and bottom (y_max).

2. Step 2: Compare options with standard format

   Left, top, right, bottom coordinates of the box matches this format exactly, while others describe different formats or unrelated values.

3. Final Answer:

   Left, top, right, bottom coordinates of the box -> Option D

4. Quick Check:

   Bounding box = left, top, right, bottom [OK]

Hint: Remember bounding box edges are left, top, right, bottom [OK]

Common Mistakes:

• Confusing center coordinates with edges
• Mixing width and height with coordinates
• Assuming depth or color are part of bounding box

2. Which PyTorch tensor shape correctly represents a batch of 5 bounding boxes, each with 4 coordinates?

easy

A. torch.Size([5, 4])

B. torch.Size([4, 5])

C. torch.Size([5])

D. torch.Size([4])

Solution

1. Step 1: Identify bounding box tensor shape

   Each bounding box has 4 coordinates. For 5 boxes, the tensor shape should be (5, 4), meaning 5 rows (boxes) and 4 columns (coordinates).

2. Step 2: Match options with correct shape

   torch.Size([5, 4]) matches (5, 4). torch.Size([4, 5]) reverses dimensions, and C and D are one-dimensional, which is incorrect.

3. Final Answer:

   torch.Size([5, 4]) -> Option A

4. Quick Check:

   Batch size 5 and 4 coords = (5, 4) [OK]

Hint: Batch size first, coordinates second in tensor shape [OK]

Common Mistakes:

• Swapping batch and coordinate dimensions
• Using 1D tensor for multiple boxes
• Confusing number of boxes with number of coordinates

3. Given the following PyTorch code, what is the output?

import torch
boxes = torch.tensor([[1, 2, 4, 6], [0, 0, 3, 3]])
widths = boxes[:, 2] - boxes[:, 0]
heights = boxes[:, 3] - boxes[:, 1]
areas = widths * heights
print(areas.tolist())

medium

A. [10, 9]

B. [15, 9]

C. [12, 9]

D. [12, 6]

Solution

1. Step 1: Calculate widths and heights

   For box 1: width = 4 - 1 = 3, height = 6 - 2 = 4. For box 2: width = 3 - 0 = 3, height = 3 - 0 = 3.

2. Step 2: Calculate areas

   Area box 1 = 3 * 4 = 12, area box 2 = 3 * 3 = 9.

3. Final Answer:

   [12, 9] -> Option C

4. Quick Check:

   Area = width * height = [12, 9] [OK]

Hint: Area = (right-left) * (bottom-top) for each box [OK]

Common Mistakes:

• Subtracting coordinates in wrong order
• Mixing width with height values
• Forgetting to multiply widths and heights

4. Identify the error in this PyTorch code snippet for checking if points lie inside bounding boxes:

import torch
boxes = torch.tensor([1, 1, 5, 5])
points = torch.tensor([[3, 3], [6, 6]])
inside = (points[:, 0] >= boxes[:, 0]) & (points[:, 0] <= boxes[:, 2]) & (points[:, 1] >= boxes[:, 1]) & (points[:, 1] <= boxes[:, 3])
print(inside)

medium

A. The comparison between points and boxes tensors will fail due to shape mismatch

B. The logical AND operator '&' cannot be used with tensors

C. The code should use .all() to combine conditions

D. Points coordinates are reversed in the comparisons

Solution

1. Step 1: Check tensor shapes for broadcasting

   Boxes shape is (4,), points shape is (2,2). Comparing points[:, 0] (shape 2) with boxes[:, 0] causes shape mismatch as boxes is 1D.

2. Step 2: Understand broadcasting rules

   Direct element-wise comparison fails because points and boxes dimensions don't align for broadcasting in this code.

3. Final Answer:

   The comparison between points and boxes tensors will fail due to shape mismatch -> Option A

4. Quick Check:

   Shape mismatch causes error in element-wise comparison [OK]

Hint: Check tensor shapes before element-wise comparisons [OK]

Common Mistakes:

• Assuming PyTorch auto-broadcasts all shapes
• Using bitwise operators incorrectly
• Mixing coordinate order in comparisons

5. You want to convert bounding boxes from (left, top, right, bottom) format to (center_x, center_y, width, height) in PyTorch. Which code snippet correctly performs this conversion for a tensor boxes of shape (N, 4)?

hard

A. centers = boxes[:, :2] - boxes[:, 2:]
sizes = boxes[:, 2:] + boxes[:, :2]
converted = torch.cat([centers, sizes], dim=1)

B. centers = (boxes[:, :2] + boxes[:, 2:]) / 2
sizes = boxes[:, 2:] - boxes[:, :2]
converted = torch.cat([centers, sizes], dim=1)

C. centers = boxes[:, 2:] - boxes[:, :2]
sizes = (boxes[:, :2] + boxes[:, 2:]) / 2
converted = torch.cat([sizes, centers], dim=1)

D. centers = boxes[:, :2] * boxes[:, 2:]
sizes = boxes[:, 2:] / boxes[:, :2]
converted = torch.cat([centers, sizes], dim=1)

Solution

1. Step 1: Calculate center coordinates

   Center x and y are the average of left and right, top and bottom: (left + right)/2, (top + bottom)/2, which is (boxes[:, :2] + boxes[:, 2:]) / 2.

2. Step 2: Calculate width and height

   Width and height are right - left and bottom - top: boxes[:, 2:] - boxes[:, :2].

3. Step 3: Concatenate centers and sizes

   Combine centers and sizes along dimension 1 to get (center_x, center_y, width, height).

4. Final Answer:

   centers = (boxes[:, :2] + boxes[:, 2:]) / 2 sizes = boxes[:, 2:] - boxes[:, :2] converted = torch.cat([centers, sizes], dim=1) -> Option B

5. Quick Check:

   Center = avg edges, size = difference edges [OK]

Hint: Center = average edges; size = difference edges [OK]

Common Mistakes:

• Swapping center and size calculations
• Using addition instead of subtraction for sizes
• Concatenating in wrong order