nn.Conv2d layers in PyTorch - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output shape after Conv2d layer

What is the output shape of the tensor after applying this Conv2d layer?

Input tensor shape: (batch_size=1, channels=3, height=32, width=32)
Layer: nn.Conv2d(in_channels=3, out_channels=6, kernel_size=5, stride=1, padding=0)

PyTorch

import torch
import torch.nn as nn

conv = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=5, stride=1, padding=0)
input_tensor = torch.randn(1, 3, 32, 32)
output = conv(input_tensor)
print(output.shape)

A (1, 6, 27, 27)

B (1, 3, 28, 28)

C (1, 6, 28, 28)

D (1, 6, 32, 32)

💡 Hint

Remember the formula for output size: (W - K + 2P) / S + 1

The output height and width are calculated as (32 - 5 + 0) / 1 + 1 = 28. The output channels are 6 as set by out_channels.

❓ Model Choice

intermediate

Choosing Conv2d parameters for same input-output size

You want a Conv2d layer that keeps the input height and width the same after convolution.
Input shape: (batch_size=1, channels=3, height=28, width=28)
Which Conv2d layer parameters will achieve this?

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

B nn.Conv2d(3, 10, kernel_size=5, stride=1, padding=0)

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

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

💡 Hint

Padding can help keep the size same if chosen correctly.

Using kernel_size=3, stride=1, and padding=1 keeps the height and width unchanged because (28 - 3 + 2*1)/1 + 1 = 28.

❓ Hyperparameter

advanced

Effect of stride on Conv2d output size

Given an input tensor of shape (1, 3, 64, 64), which Conv2d layer will produce an output with height and width equal to 16?

A nn.Conv2d(3, 8, kernel_size=3, stride=4, padding=0)

B nn.Conv2d(3, 8, kernel_size=5, stride=2, padding=0)

C nn.Conv2d(3, 8, kernel_size=3, stride=3, padding=1)

D nn.Conv2d(3, 8, kernel_size=7, stride=4, padding=0)

💡 Hint

Use the formula: output = (W - K + 2P)/S + 1 and solve for output=16.

For option A: (64 - 3 + 0)/4 + 1 = 15.25 + 1 = 16.25, which floors to 16 in PyTorch. Others produce different sizes.

🔧 Debug

advanced

Identifying error in Conv2d layer usage

What error will this code raise when run?

conv = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=3)
input_tensor = torch.randn(1, 1, 28, 28)
output = conv(input_tensor)

PyTorch

import torch
import torch.nn as nn
conv = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=3)
input_tensor = torch.randn(1, 1, 28, 28)
output = conv(input_tensor)

A TypeError: Conv2d() missing required positional argument 'stride'

B RuntimeError: Given groups=1, weight of size [6, 3, 3, 3], expected input[1, 1, 28, 28] to have 3 channels, but got 1 channels instead

C ValueError: kernel_size must be odd

D No error, runs successfully

💡 Hint

Check if input channels match the Conv2d in_channels parameter.

The input tensor has 1 channel but Conv2d expects 3 channels, causing a runtime error.

🧠 Conceptual

expert

Understanding dilation effect in Conv2d

How does increasing the dilation parameter in nn.Conv2d affect the receptive field and output size?

Choose the correct statement.

A Dilation has no effect on receptive field or output size, only on computation speed

B Increasing dilation decreases the receptive field and increases output size

C Increasing dilation increases output size but keeps receptive field constant

D Increasing dilation increases the receptive field without changing the kernel size, and reduces the output size if padding is not adjusted

💡 Hint

Think of dilation as spacing out kernel elements, making the filter cover a larger area.

Dilation spaces out kernel elements, increasing receptive field. Without adjusting padding, output size shrinks because effective kernel size grows.

Practice

1. What does the nn.Conv2d layer in PyTorch primarily do?

easy

A. It increases the image size by adding pixels.

B. It slides filters over images to find patterns.

C. It converts images to grayscale.

D. It sorts images by color intensity.

Solution

1. Step 1: Understand the role of convolution layers

   Convolution layers slide small filters over input images to detect features like edges or textures.

2. Step 2: Match the function to the options

   Only It slides filters over images to find patterns. correctly describes this sliding filter action, while others describe unrelated image operations.

3. Final Answer:

   It slides filters over images to find patterns. -> Option B

4. Quick Check:

   Convolution = sliding filters [OK]

Hint: Conv2d = sliding filters over images to find features [OK]

Common Mistakes:

• Thinking Conv2d changes image size by adding pixels
• Confusing Conv2d with image color adjustments
• Assuming Conv2d sorts or rearranges pixels

2. Which of the following is the correct way to create a Conv2d layer with 3 input channels, 16 output channels, and a 3x3 kernel in PyTorch?

easy

A. nn.Conv2d(3, 16, kernel_size=3)

B. nn.Conv2d(16, 3, kernel_size=3)

C. nn.Conv2d(3, 16, kernel=3)

D. nn.Conv2d(input=3, output=16, size=3)

Solution

1. Step 1: Recall Conv2d constructor parameters

   The correct order is nn.Conv2d(in_channels, out_channels, kernel_size).

2. Step 2: Check each option

   nn.Conv2d(3, 16, kernel_size=3) matches the correct parameter order and uses the correct keyword for kernel size. The other options have wrong parameter order or incorrect keywords.

3. Final Answer:

   nn.Conv2d(3, 16, kernel_size=3) -> Option A

4. Quick Check:

   Conv2d(in, out, kernel_size) = A [OK]

Hint: Remember Conv2d(in_channels, out_channels, kernel_size) [OK]

Common Mistakes:

• Swapping input and output channels
• Using wrong parameter names like 'kernel' instead of 'kernel_size'
• Passing parameters as keywords not supported by Conv2d

3. What will be the output shape of the following PyTorch Conv2d layer when applied to an input tensor of shape (1, 3, 32, 32)?

conv = nn.Conv2d(3, 6, kernel_size=5)
output = conv(torch.randn(1, 3, 32, 32))
print(output.shape)

medium

A. torch.Size([1, 3, 28, 28])

B. torch.Size([1, 6, 32, 32])

C. torch.Size([6, 3, 28, 28])

D. torch.Size([1, 6, 28, 28])

Solution

1. Step 1: Calculate output spatial size

   Output size = (Input size - Kernel size + 1) = (32 - 5 + 1) = 28 for both height and width.

2. Step 2: Determine output channels and batch size

   Output channels = 6, batch size = 1, so output shape is (1, 6, 28, 28).

3. Final Answer:

   torch.Size([1, 6, 28, 28]) -> Option D

4. Quick Check:

   Output shape = (batch, out_channels, 28, 28) [OK]

Hint: Output size = input - kernel + 1 if stride=1, padding=0 [OK]

Common Mistakes:

• Assuming output size equals input size without padding
• Mixing up input and output channels in shape
• Forgetting batch size dimension

4. Identify the error in this Conv2d layer definition:

conv = nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=3)
output = conv(torch.randn(1, 3, 28, 28))
print(output.shape)

medium

A. Stride cannot be 2 in Conv2d.

B. Input tensor shape is incorrect for 3 input channels.

C. Padding is too large causing output size to increase unexpectedly.

D. Kernel size must be an odd number.

Solution

1. Step 1: Calculate output size with given parameters

   Output size formula: floor((Input + 2*padding - kernel_size)/stride) + 1 = floor((28 + 6 - 3)/2) + 1 = floor(31/2) + 1 = 15 + 1 = 16.

2. Step 2: Understand padding effect

   Padding=3 is large for kernel=3, causing output spatial size to increase unexpectedly, which is unusual and may cause unexpected behavior.

3. Final Answer:

   Padding is too large causing output size to increase unexpectedly. -> Option C

4. Quick Check:

   Large padding inflates output size [OK]

Hint: Check padding size relative to kernel size for output shape [OK]

Common Mistakes:

• Thinking stride=2 is invalid
• Assuming input shape is wrong for 3 channels
• Believing kernel size must be odd always

5. You want to design a Conv2d layer that keeps the input image size (28x28) unchanged after convolution with a 5x5 kernel and stride 1. Which padding value should you use?

hard

A. Padding = 2

B. Padding = 1

C. Padding = 0

D. Padding = 3

Solution

1. Step 1: Use output size formula for Conv2d

   Output size = floor((Input + 2*padding - kernel_size)/stride) + 1. We want output = input = 28, stride=1, kernel=5.

2. Step 2: Solve for padding

   28 = (28 + 2*padding - 5) + 1 -> 28 = 24 + 2*padding -> 2*padding = 4 -> padding = 2.

3. Final Answer:

   Padding = 2 -> Option A

4. Quick Check:

   Padding 2 keeps size with 5x5 kernel [OK]

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

Common Mistakes:

• Using zero padding and expecting same size
• Choosing padding less than 2 for 5x5 kernel
• Confusing stride effect with padding