Kernel size, stride, padding in PyTorch

Introduction

Kernel size, stride, and padding control how a filter moves over an image in convolution. They help decide the output size and what parts of the image the model looks at.

When building a convolutional neural network to process images.

When you want to control how much the filter moves each step over the input.

When you want to keep the output image size the same as the input.

When you want to reduce the output size to focus on important features.

When you want to avoid losing edge information in images.

Syntax

PyTorch

torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0)

kernel_size is the size of the filter (e.g., 3 means 3x3).

stride is how many pixels the filter moves each step (default is 1).

padding adds pixels around the input edges (default is 0).

Examples

A 3x3 filter, stride 1, no padding.

PyTorch

conv = torch.nn.Conv2d(1, 10, kernel_size=3)

A 5x5 filter that moves 2 pixels each step, reducing output size.

PyTorch

conv = torch.nn.Conv2d(1, 10, kernel_size=5, stride=2)

A 3x3 filter with padding 1 to keep output size same as input.

PyTorch

conv = torch.nn.Conv2d(1, 10, kernel_size=3, padding=1)

Sample Model

This code shows how kernel size, stride, and padding affect output size and values. We use a simple 5x5 input and set all weights to 1 for clarity.

PyTorch

import torch
import torch.nn as nn

# Create a sample input: batch size 1, 1 channel, 5x5 image
input_tensor = torch.arange(25, dtype=torch.float32).reshape(1, 1, 5, 5)

# Define conv layers with different kernel_size, stride, padding
conv1 = nn.Conv2d(1, 1, kernel_size=3, stride=1, padding=0)
conv2 = nn.Conv2d(1, 1, kernel_size=3, stride=2, padding=0)
conv3 = nn.Conv2d(1, 1, kernel_size=3, stride=1, padding=1)

# Initialize weights and bias to 1 for easy understanding
for conv in [conv1, conv2, conv3]:
    nn.init.constant_(conv.weight, 1.0)
    nn.init.constant_(conv.bias, 0.0)

# Apply convolutions
output1 = conv1(input_tensor)
output2 = conv2(input_tensor)
output3 = conv3(input_tensor)

# Print shapes and outputs
print(f"Output1 shape: {output1.shape}")
print(output1)
print(f"Output2 shape: {output2.shape}")
print(output2)
print(f"Output3 shape: {output3.shape}")
print(output3)

Important Notes

Padding helps keep the output size the same as input when using stride 1.

Stride greater than 1 reduces output size by skipping positions.

Kernel size controls the area the filter looks at each step.

Summary

Kernel size is the filter size that scans the input.

Stride controls how far the filter moves each step.

Padding adds pixels around input to control output size and edge info.

Practice

1. What does the stride parameter control in a convolutional layer in PyTorch?

easy

A. How far the filter moves on the input each step

B. The size of the filter scanning the input

C. The number of filters used in the layer

D. The amount of zero padding added around the input

Solution

1. Step 1: Understand stride in convolution

   Stride defines the step size the filter moves when scanning the input image or feature map.

2. Step 2: Differentiate stride from other parameters

   Kernel size is the filter size, padding adds pixels around input, and number of filters controls output depth.

3. Final Answer:

   How far the filter moves on the input each step -> Option A

4. Quick Check:

   Stride = step size of filter movement [OK]

Hint: Stride = filter step size, not size or padding [OK]

Common Mistakes:

• Confusing stride with kernel size
• Thinking stride controls padding
• Mixing stride with number of filters

2. Which of the following is the correct way to create a 2D convolutional layer in PyTorch with kernel size 3, stride 2, and padding 1?

easy

A. nn.Conv2d(in_channels=1, out_channels=10, kernel_size=3, stride=2, padding=1)

B. nn.Conv2d(1, 10, kernel=3, stride=2, pad=1)

C. nn.Conv2d(1, 10, kernel_size=3, stride=1, padding=2)

D. nn.Conv2d(in_channels=1, out_channels=10, kernel_size=2, stride=2, padding=1)

Solution

1. Step 1: Check PyTorch Conv2d parameter names

   Correct parameters are in_channels, out_channels, kernel_size, stride, and padding.

2. Step 2: Match values to question

   Kernel size=3, stride=2, padding=1 matches only nn.Conv2d(in_channels=1, out_channels=10, kernel_size=3, stride=2, padding=1) exactly.

3. Final Answer:

   nn.Conv2d(in_channels=1, out_channels=10, kernel_size=3, stride=2, padding=1) -> Option A

4. Quick Check:

   Correct parameter names and values used [OK]

Hint: Use exact PyTorch parameter names: kernel_size, stride, padding [OK]

Common Mistakes:

• Using wrong parameter names like kernel or pad
• Mixing stride and padding values
• Wrong kernel size or stride values

3. Given an input tensor of size (1, 1, 7, 7), a Conv2d layer with kernel_size=3, stride=2, and padding=1 is applied. What is the output spatial size (height and width)?

medium

A. (1, 1, 3, 3)

B. (1, 1, 5, 5)

C. (1, 1, 2, 2)

D. (1, 1, 4, 4)

Solution

1. Step 1: Use output size formula for Conv2d

   Output size = floor((Input + 2*padding - kernel_size)/stride) + 1

2. Step 2: Calculate output height and width

   Input=7, padding=1, kernel=3, stride=2
   Output = floor((7 + 2*1 - 3)/2) + 1 = floor((7 + 2 - 3)/2) + 1 = floor(6/2) + 1 = 3 + 1 = 4

3. Final Answer:

   (1, 1, 4, 4) -> Option D

4. Quick Check:

   Output size formula applied correctly [OK]

Hint: Apply formula: floor((I+2P-K)/S)+1 for each dimension [OK]

Common Mistakes:

• Forgetting to add padding twice
• Using ceil instead of floor
• Mixing stride and kernel size in formula

4. You wrote this PyTorch code but get an error:

nn.Conv2d(1, 10, kernel_size=3, stride=2, padding=0)

on input size (1, 1, 1, 1). What is the likely cause?

medium

A. Kernel size must be even number

B. Padding is too small for the input size causing negative output dimension

C. Stride value must be 1 for kernel size 3

D. Input channels and output channels are swapped

Solution

1. Step 1: Check output size with given parameters

   Output size = floor((1 + 2*0 - 3)/2) + 1 = floor((-2)/2) + 1 = floor(-1) + 1 = -1 + 1 = 0 (invalid)

2. Step 2: Consider if padding causes error

   Padding=0 on small input (1x1) causes the calculated output size to be zero, which PyTorch raises as a runtime error due to insufficient padding for the kernel size.

3. Final Answer:

   Padding is too small for the input size causing negative output dimension -> Option B

4. Quick Check:

   Padding too small -> invalid output size [OK]

Hint: Check if padding is too small for input size [OK]

Common Mistakes:

• Assuming stride must be 1 for kernel 3
• Thinking kernel size must be even
• Swapping input and output channels

5. You want to keep the output size the same as the input size (7x7) after a Conv2d layer with kernel_size=5 and stride=1. What padding value should you use?

hard

A. 1

B. 0

C. 2

D. 3

Solution

1. Step 1: Use formula for output size with stride=1

   Output size = Input size if padding = (kernel_size - 1) / 2

2. Step 2: Calculate padding

   Padding = (5 - 1) / 2 = 4 / 2 = 2

3. Final Answer:

   2 -> Option C

4. Quick Check:

   Padding = (kernel_size - 1)/2 keeps size same [OK]

Hint: Padding = (kernel_size - 1) / 2 for same size with stride 1 [OK]

Common Mistakes:

• Using padding 0 or 1 incorrectly
• Forgetting stride must be 1 for same size
• Using padding larger than needed