PyTorch GhostNet: A Lightweight Convolutional Neural Network
 have played a pivotal role in the field of computer vision, achieving state-of-the-art performance across various tasks such as image classification, object detection, and semantic segmentation. However, as CNNs become deeper and more complex, they tend to require more computational resources and memory, making it challenging to deploy them on resource-constrained devices such as mobile phones or embedded systems.
To address this issue, researchers have been developing lightweight CNN architectures that aim to achieve high accuracy with fewer parameters and computations. One such architecture is GhostNet, which was introduced in the paper "GhostNet: More Features from Cheap Operations" by Han et al. It offers a good trade-off between model size, computation cost, and accuracy, making it suitable for real-time applications on low-power devices.
In this article, we will explore the principles behind GhostNet and implement it using PyTorch, a popular deep learning framework.
Ghost Module
The GhostNet architecture introduces a new building block called the Ghost Module. It consists of two parallel branches: the primary branch and the cheap branch. The primary branch performs a standard convolution operation, while the cheap branch aims to capture additional features at a lower computational cost.
The Ghost Module operates as follows:
- The input tensor is first passed through the primary branch, which applies a 1x1 convolution followed by a 3x3 depthwise separable convolution. This branch captures the main features of the input.
import torch
import torch.nn as nn
class GhostModule(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, groups=1, ratio=2):
super(GhostModule, self).__init__()
self.primary_conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels // ratio, kernel_size, stride, padding, groups=groups, bias=False),
nn.BatchNorm2d(out_channels // ratio),
nn.ReLU(inplace=True)
)
self.cheap_conv = nn.Sequential(
nn.Conv2d(out_channels // ratio, out_channels, kernel_size, stride, padding, groups=out_channels // ratio, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
def forward(self, x):
primary_features = self.primary_conv(x)
cheap_features = self.cheap_conv(primary_features)
return torch.cat((primary_features, cheap_features), 1)
- The output from the primary branch is then concatenated with the output from the cheap branch, resulting in a tensor with double the number of channels.
GhostNet Architecture
The GhostNet architecture is built upon the MobileNetV3 backbone, which consists of inverted residual blocks with linear bottlenecks. GhostNet replaces the standard convolutions in MobileNetV3 with Ghost Modules to reduce computation cost and model size.
Here is a simplified implementation of the GhostNet architecture:
import torch
import torch.nn as nn
class GhostNet(nn.Module):
def __init__(self, num_classes=1000):
super(GhostNet, self).__init__()
self.stage1 = nn.Sequential(
nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(16),
nn.ReLU(inplace=True),
GhostModule(16, 16, kernel_size=1, stride=1, padding=0),
nn.Conv2d(32, 16, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(16),
nn.ReLU(inplace=True),
GhostModule(16, 48, kernel_size=3, stride=2, padding=1),
nn.Conv2d(96, 24, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(24),
nn.ReLU(inplace=True)
)
# Add more stages and classifiers...
self.classifier = nn.Linear(960, num_classes)
def forward(self, x):
x = self.stage1(x)
# Apply other stages...
x = x.mean([2, 3]) # Global average pooling
x = self.classifier(x)
return x
Conclusion
In this article, we introduced GhostNet, a lightweight CNN architecture that achieves a good balance between model size, computation cost, and accuracy. We explored the Ghost Module and implemented the GhostNet architecture using PyTorch. GhostNet is a valuable addition to the field of computer vision, as it enables real-time applications on resource-constrained devices without compromising performance.
By leveraging lightweight architectures like GhostNet, we can make deep learning models more accessible and deployable on a wide range of devices, opening up new possibilities for various computer vision tasks.
