nn.MaxPool2d and nn.AvgPool2d in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a max pooling layer with a kernel size of 2.

PyTorch

import torch.nn as nn

pool = nn.[1](kernel_size=2)

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

AConv2d

BAvgPool2d

CMaxPool2d

DLinear

Attempts:

3 left

💡 Hint

Common Mistakes

Using nn.AvgPool2d instead of nn.MaxPool2d

Using nn.Conv2d which is for convolution, not pooling

The nn.MaxPool2d layer performs max pooling, which selects the maximum value in each window of the input.

2fill in blank

medium

Complete the code to create an average pooling layer with a stride of 2.

PyTorch

import torch.nn as nn

pool = nn.AvgPool2d(kernel_size=2, stride=[1])

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

A1

B3

C4

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Setting stride to 1 causes overlapping pooling windows

Using stride larger than kernel size can skip input regions

The stride controls how far the pooling window moves. Setting stride=2 moves the window by 2 pixels each step.

3fill in blank

hard

Fix the error in the code to apply max pooling to input tensor x.

PyTorch

import torch
import torch.nn as nn

x = torch.randn(1, 1, 4, 4)
pool = nn.MaxPool2d(kernel_size=2)
output = pool([1])

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

Ax

Bpool

Cnn

Dtorch

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the pooling layer itself instead of the input tensor

Passing the torch module instead of the input tensor

The pooling layer is a function that takes the input tensor x and returns the pooled output.

4fill in blank

hard

Fill both blanks to create an average pooling layer with kernel size 3 and stride 1.

PyTorch

import torch.nn as nn

pool = nn.[1](kernel_size=[2], stride=1)

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

AAvgPool2d

BMaxPool2d

C3

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Using MaxPool2d instead of AvgPool2d

Setting kernel size to 2 instead of 3

Use AvgPool2d for average pooling. The kernel size is set to 3 to cover 3x3 windows.

5fill in blank

hard

Fill all three blanks to create a max pooling layer with kernel size 2, stride 2, and padding 0.

PyTorch

import torch.nn as nn

pool = nn.[1](kernel_size=[2], stride=[3], padding=0)

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

AMaxPool2d

B2

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using AvgPool2d instead of MaxPool2d

Setting stride or kernel size incorrectly

Adding padding when not needed

The max pooling layer uses MaxPool2d with kernel size and stride both set to 2. Padding is zero to avoid adding extra pixels.

Practice

1. What is the main difference between nn.MaxPool2d and nn.AvgPool2d in PyTorch?

easy

A. nn.MaxPool2d selects the maximum value in each window, while nn.AvgPool2d computes the average value.

B. nn.MaxPool2d computes the average value, while nn.AvgPool2d selects the maximum value.

C. Both perform the same operation but on different input shapes.

D. nn.MaxPool2d increases data size, nn.AvgPool2d decreases it.

Solution

1. Step 1: Understand pooling operations

   nn.MaxPool2d picks the highest value in each sliding window, emphasizing strong features. nn.AvgPool2d calculates the average, smoothing the features.

2. Step 2: Compare their behavior

   Max pooling keeps the strongest signals, while average pooling provides a smoothed summary of the window.

3. Final Answer:

   nn.MaxPool2d selects the maximum value in each window, while nn.AvgPool2d computes the average value. -> Option A

4. Quick Check:

   MaxPool2d = max, AvgPool2d = average [OK]

Hint: MaxPool picks max; AvgPool averages values [OK]

Common Mistakes:

• Confusing max and average operations
• Thinking both increase data size
• Assuming they work on different input shapes

2. Which of the following is the correct way to create a 2D max pooling layer with a kernel size of 3 and stride of 2 in PyTorch?

easy

A. nn.AvgPool2d(kernel=3, stride=2)

B. nn.MaxPool2d(kernel_size=3, stride=2)

C. nn.MaxPool2d(stride=3, kernel_size=2)

D. nn.MaxPool2d(size=3, step=2)

Solution

1. Step 1: Check PyTorch pooling layer parameters

   The correct parameters for nn.MaxPool2d are kernel_size and stride. The order does not matter if named.

2. Step 2: Validate each option

   nn.MaxPool2d(kernel_size=3, stride=2) uses correct parameter names and values. nn.MaxPool2d(stride=3, kernel_size=2) swaps kernel_size and stride values incorrectly. nn.AvgPool2d(kernel=3, stride=2) uses AvgPool2d instead of MaxPool2d. nn.MaxPool2d(size=3, step=2) uses invalid parameter names.

3. Final Answer:

   nn.MaxPool2d(kernel_size=3, stride=2) -> Option B

4. Quick Check:

   Correct params: kernel_size, stride [OK]

Hint: Use kernel_size and stride parameters exactly [OK]

Common Mistakes:

• Using wrong parameter names like size or step
• Confusing MaxPool2d with AvgPool2d
• Swapping kernel_size and stride values

3. What is the output shape of the following PyTorch code snippet?

import torch
import torch.nn as nn

input_tensor = torch.randn(1, 1, 6, 6)
pool = nn.MaxPool2d(kernel_size=2, stride=2)
output = pool(input_tensor)
print(output.shape)

medium

A. torch.Size([1, 1, 2, 2])

B. torch.Size([1, 1, 6, 6])

C. torch.Size([1, 1, 4, 4])

D. torch.Size([1, 1, 3, 3])

Solution

1. Step 1: Understand input and pooling parameters

   Input shape is (batch=1, channels=1, height=6, width=6). Kernel size and stride are both 2.

2. Step 2: Calculate output dimensions

   Output height = floor((6 - 2) / 2) + 1 = floor(4 / 2) + 1 = 2 + 1 = 3. Similarly, output width = 3. So output shape is (1, 1, 3, 3).

3. Final Answer:

   torch.Size([1, 1, 3, 3]) -> Option D

4. Quick Check:

   Output size = floor((input - kernel)/stride)+1 [OK]

Hint: Output size = floor((input - kernel)/stride) + 1 [OK]

Common Mistakes:

• Forgetting to apply floor function
• Mixing up height and width calculations
• Assuming output size equals input size

4. Identify the error in the following PyTorch code using nn.AvgPool2d:

import torch
import torch.nn as nn

input_tensor = torch.randn(1, 1, 5, 5)
pool = nn.AvgPool2d(kernel_size=2, stride=3)
output = pool(input_tensor)
print(output.shape)

medium

A. No error; code runs correctly

B. Kernel size must be odd

C. Stride cannot be greater than kernel size

D. Input tensor shape is invalid

Solution

1. Step 1: Check parameter validity

   PyTorch allows stride to be different from kernel size, including stride > kernel size. Kernel size can be even or odd. Input tensor shape is valid.

2. Step 2: Confirm code runs without error

   Running this code produces a valid output shape without errors.

3. Final Answer:

   No error; code runs correctly -> Option A

4. Quick Check:

   Stride can differ from kernel size [OK]

Hint: Stride can be any positive int, not limited by kernel size [OK]

Common Mistakes:

• Assuming stride must be <= kernel size
• Thinking kernel size must be odd
• Believing input shape is invalid for pooling

5. You want to reduce the spatial size of a feature map from (1, 1, 10, 10) to (1, 1, 3, 3) using pooling layers. Which combination of nn.MaxPool2d or nn.AvgPool2d with kernel size and stride will achieve this output shape?

hard

A. Use nn.MaxPool2d with kernel_size=2, stride=2 twice sequentially

B. Use nn.AvgPool2d with kernel_size=4, stride=4

C. Use nn.MaxPool2d with kernel_size=3, stride=3

D. Use nn.AvgPool2d with kernel_size=5, stride=5

Solution

1. Step 1: Calculate output size for kernel_size=3, stride=3

   Output size = floor((10 - 3)/3) + 1 = floor(7/3) + 1 = 2 + 1 = 3, matching desired size.

2. Step 2: Check other options

   nn.AvgPool2d(kernel_size=4, stride=4): floor((10-4)/4)+1 = floor(6/4)+1 = 1 + 1 = 2 ≠ 3.
   nn.MaxPool2d(kernel_size=2, stride=2) twice: first floor((10-2)/2)+1 = 4 + 1 = 5, second floor((5-2)/2)+1 = 1 + 1 = 2 ≠ 3.
   nn.AvgPool2d(kernel_size=5, stride=5): floor((10-5)/5)+1 = 1 + 1 = 2 ≠ 3.

3. Final Answer:

   Use nn.MaxPool2d with kernel_size=3, stride=3 -> Option C

4. Quick Check:

   Output size = floor((input - kernel)/stride) + 1 [OK]

Hint: Output size = floor((input - kernel)/stride) + 1 [OK]

Common Mistakes:

• Ignoring floor function in output size calculation
• Assuming one pooling layer can't reduce to 3x3
• Confusing stride and kernel size effects