MixNet
【摘要】
import torch
import torch.nn as nn
class HardSwish(nn.Module):
def __init__(self, inplace=True):
...
import torch
import torch.nn as nn
class HardSwish(nn.Module):
def __init__(self, inplace=True):
super(HardSwish, self).__init__()
self.relu6 = nn.ReLU6(inplace)
def forward(self, x):
return x*self.relu6(x+3)/6
def ConvBNActivation(in_channels,out_channels,kernel_size,stride,activate):
return nn.Sequential(
nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=(kernel_size-1)//2),
nn.BatchNorm2d(out_channels),
nn.ReLU6(inplace=True) if activate == 'relu' else HardSwish(inplace=True)
)
def Conv1x1BNActivation(in_channels,out_channels,activate):
return nn.Sequential(
nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1),
nn.BatchNorm2d(out_channels),
nn.ReLU6(inplace=True) if activate == 'relu' else HardSwish(inplace=True)
)
def Conv1x1BN(in_channels,out_channels):
return nn.Sequential(
nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1),
nn.BatchNorm2d(out_channels)
)
class MDConv(nn.Module):
def __init__(self, nchannels, kernel_sizes, stride):
super(MDConv,self).__init__()
self.nchannels = nchannels
self.groups = len(kernel_sizes)
self.split_channels = [nchannels // self.groups for _ in range(self.groups)]
self.split_channels[0] += nchannels - sum(self.split_channels)
self.layers = []
for i in range(self.groups):
self.layers.append(nn.Conv2d(in_channels=self.split_channels[i],out_channels=self.split_channels[i],
kernel_size=kernel_sizes[i], stride=stride,padding=int(kernel_sizes[i]//2), groups=self.split_channels[i]))
def forward(self, x):
split_x = torch.split(x, self.split_channels, dim=1)
outputs = [layer(sp_x) for layer,sp_x in zip(self.layers, split_x)]
return torch.cat(outputs, dim=1)
class SqueezeAndExcite(nn.Module):
def __init__(self, nchannels, squeeze_channels, se_ratio=1):
super(SqueezeAndExcite, self).__init__()
squeeze_channels = int(squeeze_channels * se_ratio)
self.SEblock = nn.Sequential(
nn.Conv2d(in_channels=nchannels, out_channels=squeeze_channels, kernel_size=1, stride=1, padding=0),
nn.ReLU6(inplace=True),
nn.Conv2d(in_channels=squeeze_channels, out_channels=nchannels, kernel_size=1, stride=1, padding=0),
nn.Sigmoid(),
)
def forward(self, x):
out = torch.mean(x, (2, 3), keepdim=True)
out = self.SEblock(out)
return out * x
class MDConvBlock(nn.Module):
def __init__(self, in_channels,out_channels, kernel_sizes, stride,expand_ratio, activate='relu', se_ratio=1):
super(MDConvBlock,self).__init__()
self.stride = stride
self.se_ratio = se_ratio
mid_channels = in_channels * expand_ratio
self.expand_conv = Conv1x1BNActivation(in_channels, mid_channels, activate)
self.md_conv = nn.Sequential(
# in_channels,out_channels,groups, kernel_sizes, strides
MDConv(mid_channels, kernel_sizes, stride),
nn.BatchNorm2d(mid_channels),
nn.ReLU6(inplace=True) if activate == 'relu' else HardSwish(inplace=True),
)
if self.se_ratio > 0:
self.squeeze_excite = SqueezeAndExcite(mid_channels, in_channels)
self.projection_conv = Conv1x1BN(mid_channels,out_channels)
def forward(self, x):
x = self.expand_conv(x)
x = self.md_conv(x)
if self.se_ratio > 0:
x = self.squeeze_excite(x)
out = self.projection_conv(x)
return out
class MixNet(nn.Module):
mixnet_s = [(16, 16, [3], 1, 1, 'ReLU', 0.0),
(16, 24, [3], 2, 6, 'ReLU', 0.0),
(24, 24, [3], 1, 3, 'ReLU', 0.0),
(24, 40, [3, 5, 7], 2, 6, 'Swish', 0.5),
(40, 40, [3, 5], 1, 6, 'Swish', 0.5),
(40, 40, [3, 5], 1, 6, 'Swish', 0.5),
(40, 40, [3, 5], 1, 6, 'Swish', 0.5),
(40, 80, [3, 5, 7], 2, 6, 'Swish', 0.25),
(80, 80, [3, 5], 1, 6, 'Swish', 0.25),
(80, 80, [3, 5], 1, 6, 'Swish', 0.25),
(80, 120, [3, 5, 7], 1, 6, 'Swish', 0.5),
(120, 120, [3, 5, 7, 9], 1, 3, 'Swish', 0.5),
(120, 120, [3, 5, 7, 9], 1, 3, 'Swish', 0.5),
(120, 200, [3, 5, 7, 9, 11], 2, 6, 'Swish', 0.5),
(200, 200, [3, 5, 7, 9], 1, 6, 'Swish', 0.5),
(200, 200, [3, 5, 7, 9], 1, 6, 'Swish', 0.5)
]
mixnet_m = [(24, 24, [3], 1, 1, 'ReLU', 0.0),
(24, 32, [3, 5, 7], 2, 6, 'ReLU', 0.0),
(32, 32, [3], 1, 3, 'ReLU', 0.0),
(32, 40, [3, 5, 7, 9], 2, 6, 'Swish', 0.5),
(40, 40, [3, 5], 1, 6, 'Swish', 0.5),
(40, 40, [3, 5], 1, 6, 'Swish', 0.5),
(40, 40, [3, 5], 1, 6, 'Swish', 0.5),
(40, 80, [3, 5, 7], 2, 6, 'Swish', 0.25),
(80, 80, [3, 5, 7, 9], 1, 6, 'Swish', 0.25),
(80, 80, [3, 5, 7, 9], 1, 6, 'Swish', 0.25),
(80, 80, [3, 5, 7, 9], 1, 6, 'Swish', 0.25),
(80, 120, [3], 1, 6, 'Swish', 0.5),
(120, 120, [3, 5, 7, 9], 1, 3, 'Swish', 0.5),
(120, 120, [3, 5, 7, 9], 1, 3, 'Swish', 0.5),
(120, 120, [3, 5, 7, 9], 1, 3, 'Swish', 0.5),
(120, 200, [3, 5, 7, 9], 2, 6, 'Swish', 0.5),
(200, 200, [3, 5, 7, 9], 1, 6, 'Swish', 0.5),
(200, 200, [3, 5, 7, 9], 1, 6, 'Swish', 0.5),
(200, 200, [3, 5, 7, 9], 1, 6, 'Swish', 0.5)]
def __init__(self, type='mixnet_s'):
super(MixNet,self).__init__()
if type == 'mixnet_s':
config = self.mixnet_s
stem_channels = 16
elif type == 'mixnet_m':
config = self.mixnet_m
stem_channels = 24
self.stem = nn.Sequential(
nn.Conv2d(in_channels=3,out_channels=stem_channels,kernel_size=3,stride=2,padding=1),
nn.BatchNorm2d(stem_channels),
HardSwish(inplace=True),
)
layers = []
for in_channels, out_channels, kernel_sizes, stride, expand_ratio, activate, se_ratio in config:
layers.append(MDConvBlock(
in_channels,
out_channels,
kernel_sizes=kernel_sizes,
stride=stride,
expand_ratio=expand_ratio,
activate=activate,
se_ratio=se_ratio
))
self.bottleneck = nn.Sequential(*layers)
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.BatchNorm2d) or isinstance(m, nn.Linear):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def forward(self, x):
x = self.stem(x)
out = self.bottleneck(x)
return out
if __name__ == '__main__':
model = MixNet(type ='mixnet_m')
print(model)
input = torch.randn(1, 3, 224, 224)
out = model(input)
print(out.shape)
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文章来源: wanghao.blog.csdn.net，作者：AI浩，版权归原作者所有，如需转载，请联系作者。
原文链接：wanghao.blog.csdn.net/article/details/121607274
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