轻量级网络论文：GhostNet: More Features from Cheap Operations及PyTorch其实现

轻量级网络论文：GhostNet: More Features from Cheap Operations及PyTorch其实现

深度学习论文: GhostNet: More Features from Cheap Operations及其PyTorch实现 GhostNet: More Features from Cheap Operations PDF: ​​​​​​​Motivation

作者分析了一些训练好的网络输出的特征图,发现其中存在大量冗余信息, 一些特征图可以可以由其他特征图经过一些简单的变化得到. 因此提出了能用更少参数提取更多特征的Ghost模块.

下图为输入图像经ResNet50产生的特征图，里面有许多成对的相似特征图

2 Ghost Module

Ghost模块将普通的卷积层分解为两个部分，第一部分包含了正常的卷积，但是卷积的数量会被严格控制。给在定第一部分的固有特征图之后，然后应用一系列简单的线性运算以生成更多特征图。

PyTorch代码:

class GhostModule(nn.Module): def __init__(self, in_channels,out_channels,s=2, kernel_size=1,stride=1, use_relu=True): super(GhostModule, self).__init__() intrinsic_channels = out_channels//s ghost_channels = intrinsic_channels * (s - 1) self.primary_conv = nn.Sequential( nn.Conv2d(in_channels=in_channels, out_channels=intrinsic_channels, kernel_size=kernel_size, stride=stride, padding=kernel_size // 2, bias=False), nn.BatchNorm2d(intrinsic_channels), nn.ReLU(inplace=True) if use_relu else nn.Sequential() ) self.cheap_op = DW_Conv3x3BNReLU(in_channels=intrinsic_channels, out_channels=ghost_channels, stride=stride,groups=intrinsic_channels) def forward(self, x): y = self.primary_conv(x) z = self.cheap_op(y) out = torch.cat([y, z], dim=1) return

复杂度分析:

与普通卷积神经网络相比，在不更改输出特征图大小的情况下，该Ghost模块中所需的参数总数和计算复杂度均已降低。

3 Ghost Bottlenecks

在Ghost模块的基础上，作者搭建了Ghost bottleneck来建立轻量化的模型。

PyTorch代码:

class GhostBottleneck(nn.Module): def __init__(self, in_channels,mid_channels, out_channels , kernel_size, stride, use_se, se_kernel_size=1): super(GhostBottleneck, self).__init__() self.stride = stride self.bottleneck = nn.Sequential( GhostModule(in_channels=in_channels,out_channels=mid_channels,kernel_size=1,use_relu=True), DW_Conv3x3BNReLU(in_channels=mid_channels, out_channels=mid_channels, stride=stride,groups=mid_channels) if self.stride>1 else nn.Sequential(), SqueezeAndExcite(mid_channels,mid_channels,se_kernel_size) if use_se else nn.Sequential(), GhostModule(in_channels=mid_channels, out_channels=out_channels, kernel_size=1, use_relu=False) ) if self.stride>1: self.shortcut = DW_Conv3x3BNReLU(in_channels=in_channels, out_channels=out_channels, stride=stride) else: self.shortcut = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1) def forward(self, x): out = self.bottleneck(x) residual = self.shortcut(x) out += residual return

4 GhostNet

PyTorch代码:

import torchimport torch.nn as nnimport torchvisiondef DW_Conv3x3BNReLU(in_channels,out_channels,stride,groups=1): return nn.Sequential( nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=stride, padding=1,groups=groups, bias=False), nn.BatchNorm2d(out_channels), nn.ReLU6(inplace=True) )class SqueezeAndExcite(nn.Module): def __init__(self, in_channels, out_channels, divide=4): super(SqueezeAndExcite, self).__init__() mid_channels = in_channels // divide self.pool = nn.AdaptiveAvgPool2d(1) self.SEblock = nn.Sequential( nn.Linear(in_features=in_channels, out_features=mid_channels), nn.ReLU6(inplace=True), nn.Linear(in_features=mid_channels, out_features=out_channels), nn.ReLU6(inplace=True), ) def forward(self, x): b, c, h, w = x.size() out = self.pool(x) out = out.view(b, -1) out = self.SEblock(out) out = out.view(b, c, 1, 1) return out * xclass GhostNet(nn.Module): def __init__(self, num_classes=1000): super(GhostNet, self).__init__() self.first_conv = nn.Sequential( nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=2, padding=1), nn.BatchNorm2d(16), nn.ReLU6(inplace=True), ) self.features = nn.Sequential( GhostBottleneck(in_channels=16, mid_channels=16, out_channels=16, kernel_size=3, stride=1, use_se=False), GhostBottleneck(in_channels=16, mid_channels=64, out_channels=24, kernel_size=3, stride=2, use_se=False), GhostBottleneck(in_channels=24, mid_channels=72, out_channels=24, kernel_size=3, stride=1, use_se=False), GhostBottleneck(in_channels=24, mid_channels=72, out_channels=40, kernel_size=5, stride=2, use_se=True, se_kernel_size=28), GhostBottleneck(in_channels=40, mid_channels=120, out_channels=40, kernel_size=5, stride=1, use_se=True, se_kernel_size=28), GhostBottleneck(in_channels=40, mid_channels=120, out_channels=40, kernel_size=5, stride=1, use_se=True, se_kernel_size=28), GhostBottleneck(in_channels=40, mid_channels=240, out_channels=80, kernel_size=3, stride=1, use_se=False), GhostBottleneck(in_channels=80, mid_channels=200, out_channels=80, kernel_size=3, stride=1, use_se=False), GhostBottleneck(in_channels=80, mid_channels=184, out_channels=80, kernel_size=3, stride=2, use_se=False), GhostBottleneck(in_channels=80, mid_channels=184, out_channels=80, kernel_size=3, stride=1, use_se=False), GhostBottleneck(in_channels=80, mid_channels=480, out_channels=112, kernel_size=3, stride=1, use_se=True, se_kernel_size=14), GhostBottleneck(in_channels=112, mid_channels=672, out_channels=112, kernel_size=3, stride=1, use_se=True, se_kernel_size=14), GhostBottleneck(in_channels=112, mid_channels=672, out_channels=160, kernel_size=5, stride=2, use_se=True,se_kernel_size=7), GhostBottleneck(in_channels=160, mid_channels=960, out_channels=160, kernel_size=5, stride=1, use_se=True,se_kernel_size=7), GhostBottleneck(in_channels=160, mid_channels=960, out_channels=160, kernel_size=5, stride=1, use_se=True,se_kernel_size=7), ) self.last_stage = nn.Sequential( nn.Conv2d(in_channels=160, out_channels=960, kernel_size=1, stride=1), nn.BatchNorm2d(960), nn.ReLU6(inplace=True), nn.AvgPool2d(kernel_size=7, stride=1), nn.Conv2d(in_channels=960, out_channels=1280, kernel_size=1, stride=1), nn.ReLU6(inplace=True), ) self.classifier = nn.Linear(in_features=1280,out_features=num_classes) def init_params(self): for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Linear) or isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) def forward(self, x): x = self.first_conv(x) x = self.features(x) x= self.last_stage(x) x = x.view(x.size(0), -1) out = self.classifier(x) return outif __name__ == '__main__': model = GhostNet() print(model) input = torch.randn(1, 3, 224, 224) out = model(input) print(out.shape)

5 Ablation

6 Visualization of Feature Maps

如下图可以看出Ghost其实使得同一个特征图中不同通道包含了不同的特征信息，增强了模型的表现力

深度学习论文: Agricultural Plant Cataloging and Establishment of a Data Framework from UAV-based Crop Images by Computer Vision Agricultural Plant Cataloging and Establishment of a Data Framework from UAV-based Crop Images by Computer Vision PDF: ​​​​​PyTorch代码: ​​概述

现代农业中基于无人机的图像检索能够收集大量的空间参考作物图像数据。然而，在大规模的实验中，无人机图像因包含复杂树冠结构中的大量农作物而受到影响。特别是对于时间效应的观察，这使得在几张图像上识别单个植物和提取相关信息变得非常复杂。

提出一个基于可理解的计算机视觉方法，对来自无人机的农作物图像进行自动化的时间和空间识别和个体化的工作流程，简称为 “cataloging”。并在两个真实世界的数据集上评估了该工作流程。

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