· Deep Learning · 2 min read
📋 Prerequisites
- Basic knowledge of CNNs and PyTorch
🎯 What You'll Learn
- Understand dilation and upconvolution concepts
- Implement dilated convolutions in PyTorch
- Use transposed convolutions for upsampling in PyTorch
- Apply these techniques in segmentation and generation tasks
Introduction
Dilation and upconvolution (transposed convolution) extend CNN capabilities in PyTorch, helping capture large context or reconstruct higher-resolution feature maps.
1️⃣ Dilation in PyTorch
What is Dilation?
Dilation expands the receptive field by inserting spaces between kernel elements without increasing parameters, helping capture larger context.
Implementation:
import torch
import torch.nn as nn
dilated_conv = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, dilation=2, padding=2)
x = torch.randn(1, 3, 64, 64)
output = dilated_conv(x)
print("Dilated convolution output shape:", output.shape)Here:
✅ dilation=2 doubles the receptive field.
✅ padding=2 preserves the spatial dimension.
2️⃣ Upconvolution (Transposed Convolution) in PyTorch
What is Upconvolution?
Upconvolution (transposed convolution) learns how to upsample feature maps, increasing spatial dimensions, and is commonly used in:
✅ Semantic segmentation (U-Net, DeepLab).
✅ Image generation (GANs).
✅ Super-resolution.
Implementation:
upconv = nn.ConvTranspose2d(in_channels=16, out_channels=3, kernel_size=2, stride=2)
x_up = torch.randn(1, 16, 32, 32)
output_up = upconv(x_up)
print("Upconvolution output shape:", output_up.shape)Here:
✅ kernel_size=2, stride=2 will double the spatial dimensions.
3️⃣ Combined Example
# Dilated convolution followed by upconvolution
x = torch.randn(1, 3, 64, 64)
dilated_output = dilated_conv(x)
upconv = nn.ConvTranspose2d(in_channels=16, out_channels=3, kernel_size=2, stride=2)
upconv_output = upconv(dilated_output)
print("Dilated output shape:", dilated_output.shape)
print("Upconvolution output shape:", upconv_output.shape)4️⃣ Use Cases
✅ Segmentation: Preserve spatial resolution while expanding context.
✅ Generative models: Reconstruct images from feature maps.
✅ Super-resolution: Upsample low-resolution inputs effectively.
Conclusion
✅ Dilation helps capture a larger context efficiently.
✅ Upconvolution allows learnable upsampling for high-resolution tasks.
✅ Mastering these PyTorch techniques expands your toolkit for advanced deep learning projects.
What’s Next?
✅ Implement these techniques in your segmentation pipelines.
✅ Experiment with different dilation rates and transposed convolution configurations.
✅ Continue structured deep learning on superml.org.
Join the SuperML Community to share your experiments and projects.
Happy Building with PyTorch! 🚀