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:param stride:步长，设置为1时图片的大小不变，设置为2时，图片的面积变为原来的四分之一

:param expand_ratio:放大的倍率

:return:

“”"

def init(self, inp, oup, stride, expand_ratio, fused):

super(MBConv, self).init()

assert stride in [1, 2]

hidden_dim = round(inp * expand_ratio)

self.identity = stride == 1 and inp == oup

if fused:

self.conv = nn.Sequential(

fused

nn.Conv2d(inp, hidden_dim, 3, stride, 1, bias=False),

nn.BatchNorm2d(hidden_dim),

SiLU(),

SELayer(inp, hidden_dim),

pw-linear

nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),

nn.BatchNorm2d(oup),

)

else:

self.conv = nn.Sequential(

pw

nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),

nn.BatchNorm2d(hidden_dim),

SiLU(),

dw

nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),

nn.BatchNorm2d(hidden_dim),

SiLU(),

SELayer(inp, hidden_dim),

pw-linear

nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),

nn.BatchNorm2d(oup),

)

def forward(self, x):

if self.identity:

return x + self.conv(x)

else:

return self.conv(x)

主体模块

---

class EfficientNetv2(nn.Module):

def init(self, cfgs, num_classes=1000, width_mult=1.):

super(EfficientNetv2, self).init()

self.cfgs = cfgs

building first layer

input_channel = _make_divisible(24 * width_mult, 8)

layers = [conv_3x3_bn(3, input_channel, 2)]

building inverted residual blocks

block = MBConv

for t, c, n, s, fused in self.cfgs:

output_channel = _make_divisible(c * width_mult, 8)

for i in range(n):

layers.append(block(input_channel, output_channel, s if i == 0 else 1, t, fused))

input_channel = output_channel

self.features = nn.Sequential(*layers)

building last several layers

output_channel = _make_divisible(1792 * width_mult, 8) if width_mult > 1.0 else 1792

self.conv = conv_1x1_bn(input_channel, output_channel)

self.avgpool = nn.AdaptiveAvgPool2d((1, 1))

self.classifier = nn.Linear(output_channel, num_classes)

self._initialize_weights()

def forward(self, x):

x = self.features(x)

x = self.conv(x)

x = self.avgpool(x)

x = x.view(x.size(0), -1)

x = self.classifier(x)

return x

def _initialize_weights(self):

for m in self.modules():

if isinstance(m, nn.Conv2d):

n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels

m.weight.data.normal_(0, math.sqrt(2. / n))

if m.bias is not None:

m.bias.data.zero_()

elif isinstance(m, nn.BatchNorm2d):

m.weight.data.fill_(1)

m.bias.data.zero_()

elif isinstance(m, nn.Linear):

m.weight.data.normal_(0, 0.001)

m.bias.data.zero_()

理解这段代码，我们还需要了解输入参数cfgs，以efficientnetv2_s为例：

def efficientnetv2_s(**kwargs):

“”"

Constructs a EfficientNetV2-S model

“”"

cfgs = [

t, c, n, s, fused

[1, 24, 2, 1, 1],

[4, 48, 4, 2, 1],

[4, 64, 4, 2, 1],

[4, 128, 6, 2, 0],

[6, 160, 9, 1, 0],

[6, 272, 15, 2, 0],

]

return EfficientNetv2(cfgs, **kwargs)

第一列“t”指的是MBConv模块和Fused-MBConv模块第一个输入后放大的倍率。

第二列“c”,channel,指的是输出的channel。

第三列“n”，指定的是MBConv模块和Fused-MBConv模块堆叠的个数。

第四列“s”，指的是卷积的步长，步长为1，图片的大小不变，步长为图片的面积缩小为原来的四分之一，实现降维。

第五列“fused”，选择MBConv模块或Fused-MBConv模块，为1这是Fused-MBConv模块，0则是MBConv模块，对应了前面摘要提过了，在浅层用Fused-MBConv代替MBConv。

完整代码

====

import torch

import torch.nn as nn

import math

all = [‘efficientnetv2_s’, ‘efficientnetv2_m’, ‘efficientnetv2_l’, ‘efficientnetv2_xl’]

from torchsummary import summary

#这个函数的目的是确保Channel能被8整除。

def _make_divisible(v, divisor, min_value=None):

“”"

这个函数的目的是确保Channel能被8整除。

:param v:

:param divisor:

:param min_value:

:return:

“”"

if min_value is None:

min_value = divisor

new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)

Make sure that round down does not go down by more than 10%.

if new_v < 0.9 * v:

new_v += divisor

return new_v

SiLU (Swish) activation function

if hasattr(nn, ‘SiLU’):

SiLU = nn.SiLU

else:

For compatibility with old PyTorch versions

class SiLU(nn.Module):

def forward(self, x):

return x * torch.sigmoid(x)

class SELayer(nn.Module):

def init(self, inp, oup, reduction=4):

super(SELayer, self).init()

self.avg_pool = nn.AdaptiveAvgPool2d(1)

self.fc = nn.Sequential(

nn.Linear(oup, _make_divisible(inp // reduction, 8)),

SiLU(),

nn.Linear(_make_divisible(inp // reduction, 8), oup),

nn.Sigmoid()

)

def forward(self, x):

b, c, _, _ = x.size()

y = self.avg_pool(x).view(b, c)

y = self.fc(y).view(b, c, 1, 1)

return x * y

def conv_3x3_bn(inp, oup, stride):

return nn.Sequential(

nn.Conv2d(inp, oup, 3, stride, 1, bias=False),

nn.BatchNorm2d(oup),

SiLU()

)

def conv_1x1_bn(inp, oup):

return nn.Sequential(

nn.Conv2d(inp, oup, 1, 1, 0, bias=False),

nn.BatchNorm2d(oup),

SiLU()

)

class MBConv(nn.Module):

“”"

定义MBConv模块和Fused-MBConv模块，将fused设置为1或True是Fused-MBConv，否则是MBConv

:param inp:输入的channel

:param oup:输出的channel

:param stride:步长，设置为1时图片的大小不变，设置为2时，图片的面积变为原来的四分之一

:param expand_ratio:放大的倍率

:return:

“”"

def init(self, inp, oup, stride, expand_ratio, fused):

super(MBConv, self).init()

assert stride in [1, 2]

hidden_dim = round(inp * expand_ratio)

self.identity = stride == 1 and inp == oup

if fused:

self.conv = nn.Sequential(

fused

nn.Conv2d(inp, hidden_dim, 3, stride, 1, bias=False),

nn.BatchNorm2d(hidden_dim),

SiLU(),

SELayer(inp, hidden_dim),

pw-linear

nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),

nn.BatchNorm2d(oup),

)

else:

self.conv = nn.Sequential(

pw

nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),

nn.BatchNorm2d(hidden_dim),

SiLU(),

dw

nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),

nn.BatchNorm2d(hidden_dim),

SiLU(),

SELayer(inp, hidden_dim),

pw-linear

nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),

nn.BatchNorm2d(oup),

)

def forward(self, x):

if self.identity:

return x + self.conv(x)

else:

return self.conv(x)

class EfficientNetv2(nn.Module):

def init(self, cfgs, num_classes=1000, width_mult=1.):

super(EfficientNetv2, self).init()

self.cfgs = cfgs

building first layer

input_channel = _make_divisible(24 * width_mult, 8)

layers = [conv_3x3_bn(3, input_channel, 2)]

building inverted residual blocks

block = MBConv

for t, c, n, s, fused in self.cfgs:

output_channel = _make_divisible(c * width_mult, 8)

for i in range(n):

layers.append(block(input_channel, output_channel, s if i == 0 else 1, t, fused))

input_channel = output_channel

self.features = nn.Sequential(*layers)

building last several layers

output_channel = _make_divisible(1792 * width_mult, 8) if width_mult > 1.0 else 1792

self.conv = conv_1x1_bn(input_channel, output_channel)

self.avgpool = nn.AdaptiveAvgPool2d((1, 1))

self.classifier = nn.Linear(output_channel, num_classes)

self._initialize_weights()

def forward(self, x):

x = self.features(x)

x = self.conv(x)

x = self.avgpool(x)

x = x.view(x.size(0), -1)

x = self.classifier(x)

return x

def _initialize_weights(self):

for m in self.modules():

if isinstance(m, nn.Conv2d):

n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels

m.weight.data.normal_(0, math.sqrt(2. / n))

if m.bias is not None:

m.bias.data.zero_()

网上学习资料一大堆，但如果学到的知识不成体系，遇到问题时只是浅尝辄止，不再深入研究，那么很难做到真正的技术提升。

需要这份系统化学习资料的朋友，可以戳这里获取

一个人可以走的很快，但一群人才能走的更远！不论你是正从事IT行业的老鸟或是对IT行业感兴趣的新人，都欢迎加入我们的的圈子（技术交流、学习资源、职场吐槽、大厂内推、面试辅导），让我们一起学习成长！