MoGA_A（B、C）代码理解
class MoGaA(nn.Module):
def __init__(self, n_class=1000, input_size=224):
super(MoGaA, self).__init__()
assert input_size % 32 == 0 # 输入数据要是32的整数倍
# 参数配置
mb_config = [
# expansion, out_channel, kernel_size, stride, act(0 RE 1 Hs), se
# 扩张倍数，输出通道数，卷积大小，步长，激活函数（relu 或 Hswish），se模块
[6, 24, 5, 2, 0, 0], # 5x5卷积
[6, 24, 7, 1, 0, 0], # 7x7卷积
[6, 40, 3, 2, 0, 0], # 3x3卷积
[6, 40, 3, 1, 0, 1], # 3x3卷积 se
[3, 40, 3, 1, 0, 1], # 3x3卷积 se
[6, 80, 3, 2, 1, 1], # 3x3卷积 Hs se
[6, 80, 3, 1, 1, 0], # 3x3卷积 Hs
[6, 80, 7, 1, 1, 0], # 7x7卷积 Hs
[3, 80, 7, 1, 1, 1], # 7x7卷积 Hs se
[6, 112, 7, 1, 1, 0], # 7x7卷积 Hs
[6, 112, 3, 1, 1, 0], # 3x3卷积 Hs
[6, 160, 3, 2, 1, 0], # 3x3卷积 Hs
[6, 160, 5, 1, 1, 1], # 5x5卷积 Hs se
[6, 160, 5, 1, 1, 1], # 5x5卷积 Hs se
]
first_filter = 16 # 第一个通道数
second_filter = 16 # 第二个通道数
second_last_filter = 960 # 倒数第二个通道数
last_channel = 1280 # 最后一个通道数
self.last_channel = last_channel # 1280
self.stem = stem(3, first_filter, 2) # 第一个模块
self.separable_conv = separable_conv(first_filter, second_filter) # 第二个模块 可分离卷积
self.mb_module = list() # mb模块list
input_channel = second_filter # 输入通道数16
for t, c, k, s, a, se in mb_config: # 遍历配置参数
# 通道数扩张率t，输出通道数c，卷积核大小k，步长s，激活函数选择a，se模块选择se
output_channel = c # 输出通道数24
act = nn.ReLU(inplace=True) if a == 0 else Hswish(inplace=True) # 激活函数选择
self.mb_module.append(
InvertedResidual(input_channel, output_channel, k, s, expand_ratio=t, act=act, se=se != 0))
input_channel = output_channel # 更新输入通道数
self.mb_module = nn.Sequential(*self.mb_module) # 类型转换为nn.Sequential
self.conv_before_pooling = conv_before_pooling(input_channel, second_last_filter) # 在pool前的卷积操作
self.global_pooling = nn.AdaptiveAvgPool2d(1) # 自适应均值池化，输出大小为（1，1）
self.conv_head = conv_head(second_last_filter, last_channel) # 在pool后的卷积操作
self.classifier = classifier(last_channel, n_class) # 分类器
self._initialize_weights() # 初始化
def forward(self, x):
x = self.stem(x)
x = self.separable_conv(x)
x = self.mb_module(x)
x = self.conv_before_pooling(x)
x = self.global_pooling(x)
x = self.conv_head(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
def _initialize_weights(self):
# 初始化参数
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):
n = m.weight.size(0) # fan-out
init_range = 1.0 / math.sqrt(n)
m.weight.data.uniform_(-init_range, init_range)
m.bias.data.zero_()
MoGaB参数设置
mb_config = [
# expansion, out_channel, kernel_size, stride, act(0 RE 1 Hs), se
[3, 24, 3, 2, 0, 0],
[3, 24, 3, 1, 0, 0],
[6, 40, 7, 2, 0, 0],
[3, 40, 3, 1, 0, 0],
[6, 40, 5, 1, 0, 0],
[6, 80, 3, 2, 1, 1],
[6, 80, 5, 1, 1, 1],
[3, 80, 3, 1, 1, 0],
[6, 80, 7, 1, 1, 1],
[6, 112, 7, 1, 1, 0],
[3, 112, 5, 1, 1, 0],
[6, 160, 7, 2, 1, 1],
[6, 160, 7, 1, 1, 1],
[6, 160, 3, 1, 1, 1],
]
MoGaC参数设置
mb_config = [
# expansion, out_channel, kernel_size, stride, act(0 RE 1 Hs), se
[3, 24, 5, 2, 0, 0],
[3, 24, 3, 1, 0, 0],
[3, 40, 5, 2, 0, 0],
[3, 40, 3, 1, 0, 0],
[3, 40, 5, 1, 0, 0],
[3, 80, 5, 2, 1, 0],
[6, 80, 5, 1, 1, 1],
[3, 80, 5, 1, 1, 0],
[3, 80, 5, 1, 1, 0],
[6, 112, 3, 1, 1, 0],
[6, 112, 3, 1, 1, 1],
[6, 160, 3, 2, 1, 1],
[6, 160, 3, 1, 1, 1],
[6, 160, 3, 1, 1, 1],
]
def stem(inp, oup, stride):
# 输入通道数，输出通道数，步长
return nn.Sequential(
nn.Conv2d(inp, oup, 3, stride, 1, bias=False), # 3x3卷积
nn.BatchNorm2d(oup),
# nn.ReLU6(inplace=True)
Hswish() # Hs激活函数
)
def separable_conv(inp, oup):
# 可分离卷积
return nn.Sequential(
nn.Conv2d(inp, inp, 3, 1, 1, groups=inp, bias=False), # 3x3分离卷积
nn.BatchNorm2d(inp),
nn.ReLU(inplace=True),
nn.Conv2d(inp, oup, 1, 1, 0, bias=False), # 1x1卷积
nn.BatchNorm2d(oup),
)
class InvertedResidual(nn.Module):
def __init__(self, inp, oup, kernel_size, stride, expand_ratio, act, se):
super(InvertedResidual, self).__init__()
assert stride in [1, 2]
self.stride = stride
self.act = act
self.se = se
padding = kernel_size // 2 # padding补零
hidden_dim = round(inp * expand_ratio) # 中间隐藏层的通道数，扩增expand_ratio倍
self.use_res_connect = self.stride == 1 and inp == oup # 步长为1且输入与输出通道数相等，则用残差
self.conv1 = nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False) # 1x1卷积
self.bn1 = nn.BatchNorm2d(hidden_dim)
self.conv2 = nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, padding, groups=hidden_dim, bias=False) # kernel_size x kernel_size 分离卷积
self.bn2 = nn.BatchNorm2d(hidden_dim)
if self.se: # se模块
self.mid_se = SEModule(hidden_dim, act)
self.conv3 = nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False)
self.bn3 = nn.BatchNorm2d(oup)
def forward(self, x):
inputs = x # 原始输入
x = self.conv1(x)
x = self.bn1(x)
x = self.act(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.act(x)
if self.se: # se模块计算
x = self.mid_se(x)
x = self.conv3(x)
x = self.bn3(x)
if self.use_res_connect: # 残差连接
return inputs + x
else:
return x
class SEModule(nn.Module):
# se模块
def __init__(self, channel, act, reduction=4):
# reduction = 4 中间层数除以4
super(SEModule, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1) # 自适应均值池化，输出大小为（1，1）
self.conv = nn.Sequential(
nn.Conv2d(channel, channel // reduction, 1, 1, 0, bias=True), # 1x1卷积
act
)
self.fc = nn.Sequential(
nn.Conv2d(channel // reduction, channel, 1, 1, 0, bias=True), # 1x1卷积
Hsigmoid() #激活函数
)
def forward(self, x):
y = self.avg_pool(x)
y = self.conv(y)
y = self.fc(y)
return torch.mul(x, y) # torch.mul是矩阵x和y对应位相乘
class Hswish(nn.Module):
# Hswish激活函数
def __init__(self, inplace=True):
super(Hswish, self).__init__()
self.inplace = inplace
def forward(self, x):
return x * F.relu6(x + 3., inplace=self.inplace) / 6. # 与Hsigmoid相比多乘了个x
class Hsigmoid(nn.Module):
# Hsigmoid激活函数
def __init__(self, inplace=True):
super(Hsigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
return F.relu6(x + 3., inplace=self.inplace) / 6.
def conv_before_pooling(inp, oup):
return nn.Sequential(
nn.Conv2d(inp, oup, 1, 1, 0, bias=False), # 1x1卷积
nn.BatchNorm2d(oup),
# nn.ReLU6(inplace=True)
Hswish()
)
def conv_head(inp, oup):
return nn.Sequential(
nn.Conv2d(inp, oup, 1, bias=False), # 1x1卷积
Hswish(inplace=True),
nn.Dropout2d(0.2) # dropout层
)
def classifier(inp, nclass):
# 分类器
return nn.Linear(inp, nclass)
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