python
import torch
import torch.nn as nn
def _make_divisible(ch, divisor=8, min_ch=None):
"""
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is divisible by 8
It can be seen here:
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
"""
if min_ch is None:
min_ch = divisor
new_ch = max(min_ch, int(ch + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_ch < 0.9 * ch:
new_ch += divisor
return new_ch
class ConvBNReLU(nn.Sequential):
def __init__(self, in_channel, out_channel, kernel_size=3, stride=1, groups=1):
padding = (kernel_size - 1) // 2
super(ConvBNReLU, self).__init__(
nn.Conv2d(in_channel, out_channel, kernel_size, stride, padding, groups=groups, bias=False),
nn.BatchNorm2d(out_channel),
nn.ReLU6(inplace=True)
)
class InvertedResidual(nn.Module):
def __init__(self, in_channel, out_channel, stride, expand_ration):
super(InvertedResidual, self).__init__()
hidden_channel = in_channel * expand_ration
self.use_shortcut = (stride==1 and in_channel==out_channel)
layers = []
if expand_ration != 1:
layers.append(ConvBNReLU(in_channel, hidden_channel, kernel_size=1))
#extent 添加多个元素
layers.extend([
#group=1 就是普通卷积， group=hidden_channel 就是depthwise卷积
ConvBNReLU(hidden_channel, hidden_channel, stride=stride, groups=hidden_channel),
nn.Conv2d(hidden_channel, out_channel, kernel_size=1, bias=False),
nn.BatchNorm2d(out_channel)
])
self.conv = nn.Sequential(*layers)
def forward(self, x):
if self.use_shortcut:
return x+self.conv(x)
else:
return self.conv(x)
class MobileNetV2(nn.Module):
def __init__(self, num_classes=1000, alpha=1.0, round_nearest=8):
super(MobileNetV2, self).__init__()
block = InvertedResidual
#使通道数32*alpha 变成最接近 round_nearest 的整倍数
input_channel = _make_divisible(32 * alpha, round_nearest)
last_channel = _make_divisible(1280 * alpha, round_nearest)
'''
t:扩展因子
c:输出特征矩阵深度channel
n:bottleneck的重复次数
s:步距
'''
inverted_residuals_setting =[
#t, c, n, s
[1, 16, 1, 1],
[6, 24, 2, 2],
[6, 32, 3, 2],
[6, 64, 4, 2],
[6, 96, 3, 1],
[6, 160, 3, 2],
[6, 320, 1, 1]
]
features = []
#conv1 layer
features.append(ConvBNReLU(3, input_channel, stride=2))
for t, c, n, s in inverted_residuals_setting:
output_channel = _make_divisible(c*alpha, round_nearest)
for i in range(n):
#每个n层bottleneck中只有第一层的stride=s， 剩余的层stride=1
stride = s if i == 0 else 1
features.append(block(input_channel, output_channel, stride, expand_ration=t))
input_channel = output_channel
features.append(ConvBNReLU(input_channel, last_channel, 1))
self.features = nn.Sequential(*features)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.classifier = nn.Sequential(
nn.Dropout(0.2),
nn.Linear(last_channel, num_classes)
)
# weight initialization
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
nn.init.zeros_(m.bias)
elif isinstance(m, nn.BatchNorm2d):
nn.init.ones_(m.weight)
nn.init.zeros_(m.bias)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
nn.init.zeros_(m.bias)
def forward(self, x):
x = self.features(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.classifier(x)
return x