GhostNet: More Features from Cheap Operations
一. 论文简介
论文可视化特征图如下所示,目的是找到更多的特征对(Ghost)
如何找到?
论文意思是保留内在特征(instrinsic feature),可以获得更多的特征对
如何判定?
未解释
![](https://img2020.cnblogs.com/blog/1033571/202009/1033571-20200904160242990-554886762.png)
二. 模块详解
2.1 整体结构介绍
- Ghost module
先进行正常卷积(代码用的
1*1
降低计算量),然后分组卷积,最后将输入特征和输出特征concat即可,关于卷积的一些操作如下链接所示:Point-wise / Depth-wise / group-convolution / Global Depthwise Convolution / Global Average Pooling
![](https://img2020.cnblogs.com/blog/1033571/202009/1033571-20200904160616149-241658589.png)
class GhostModule(nn.Module):
def __init__(self, inp, oup, kernel_size=1, ratio=2, dw_size=3, stride=1, relu=True):
super(GhostModule, self).__init__()
self.oup = oup
init_channels = math.ceil(oup / ratio)
new_channels = init_channels*(ratio-1)
# Custom Convolution
self.primary_conv = nn.Sequential(
nn.Conv2d(inp, init_channels, kernel_size, stride, kernel_size//2, bias=False),
nn.BatchNorm2d(init_channels),
nn.ReLU(inplace=True) if relu else nn.Sequential(),
)
# Depth-wise Convolution
self.cheap_operation = nn.Sequential(
nn.Conv2d(init_channels, new_channels, dw_size, 1, dw_size//2, groups=init_channels, bias=False),
nn.BatchNorm2d(new_channels),
nn.ReLU(inplace=True) if relu else nn.Sequential(),
)
def forward(self, x):
x1 = self.primary_conv(x)
x2 = self.cheap_operation(x1)
out = torch.cat([x1,x2], dim=1)
return out[:,:self.oup,:,:]
- GhostBottleneck
就是将Ghost module加入到resnet之内即可
![](https://img2020.cnblogs.com/blog/1033571/202009/1033571-20200904161510083-1022048399.png)
class GhostBottleneck(nn.Module):
""" Ghost bottleneck w/ optional SE"""
def __init__(self, in_chs, mid_chs, out_chs, dw_kernel_size=3,
stride=1, act_layer=nn.ReLU, se_ratio=0.):
super(GhostBottleneck, self).__init__()
has_se = se_ratio is not None and se_ratio > 0.
self.stride = stride
# Point-wise expansion
self.ghost1 = GhostModule(in_chs, mid_chs, relu=True)
# Depth-wise convolution
if self.stride > 1:
self.conv_dw = nn.Conv2d(mid_chs, mid_chs, dw_kernel_size, stride=stride,
padding=(dw_kernel_size-1)//2,
groups=mid_chs, bias=False)
self.bn_dw = nn.BatchNorm2d(mid_chs)
# Squeeze-and-excitation
if has_se:
self.se = SqueezeExcite(mid_chs, se_ratio=se_ratio)
else:
self.se = None
# Point-wise linear projection
self.ghost2 = GhostModule(mid_chs, out_chs, relu=False)
# shortcut
if (in_chs == out_chs and self.stride == 1):
self.shortcut = nn.Sequential()
else:
self.shortcut = nn.Sequential(
nn.Conv2d(in_chs, in_chs, dw_kernel_size, stride=stride,
padding=(dw_kernel_size-1)//2, groups=in_chs, bias=False),
nn.BatchNorm2d(in_chs),
nn.Conv2d(in_chs, out_chs, 1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(out_chs),
)
def forward(self, x):
residual = x
# 1st ghost bottleneck
x = self.ghost1(x)
# Depth-wise convolution
if self.stride > 1:
x = self.conv_dw(x)
x = self.bn_dw(x)
# Squeeze-and-excitation
if self.se is not None:
x = self.se(x)
# 2nd ghost bottleneck
x = self.ghost2(x)
x += self.shortcut(residual)
return x
- SE模块
Attention mode,通道使用不同权重
https://arxiv.org/abs/1709.01507
# SE module that attetion mode
class SqueezeExcite(nn.Module):
def __init__(self, in_chs, se_ratio=0.25, reduced_base_chs=None,
act_layer=nn.ReLU, gate_fn=hard_sigmoid, divisor=4, **_):
super(SqueezeExcite, self).__init__()
self.gate_fn = gate_fn
reduced_chs = _make_divisible((reduced_base_chs or in_chs) * se_ratio, divisor)
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv_reduce = nn.Conv2d(in_chs, reduced_chs, 1, bias=True)
self.act1 = act_layer(inplace=True)
self.conv_expand = nn.Conv2d(reduced_chs, in_chs, 1, bias=True)
def forward(self, x):
x_se = self.avg_pool(x)
x_se = self.conv_reduce(x_se)
x_se = self.act1(x_se)
x_se = self.conv_expand(x_se)
x = x * self.gate_fn(x_se)
return x