给出一个mobilenetv3代码示例,用torch实现
时间: 2023-04-04 10:05:10 浏览: 76
以下是一个使用 PyTorch 实现的 MobileNetV3 代码示例:
```python
import torch.nn as nn
import torch.nn.functional as F
class MobileNetV3(nn.Module):
def __init__(self, num_classes=1000, width_mult=1.0):
super(MobileNetV3, self).__init__()
# Define the MobileNetV3 architecture
self.conv1 = nn.Conv2d(3, int(16 * width_mult), kernel_size=3, stride=2, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(int(16 * width_mult))
self.hs1 = nn.ReLU6(inplace=True)
self.bottlenecks = nn.Sequential(
Bottleneck(int(16 * width_mult), int(16 * width_mult), 3, 2, False, width_mult),
Bottleneck(int(16 * width_mult), int(24 * width_mult), 3, 2, False, width_mult),
Bottleneck(int(24 * width_mult), int(24 * width_mult), 3, 1, False, width_mult),
Bottleneck(int(24 * width_mult), int(40 * width_mult), 5, 2, True, width_mult),
Bottleneck(int(40 * width_mult), int(40 * width_mult), 5, 1, True, width_mult),
Bottleneck(int(40 * width_mult), int(80 * width_mult), 3, 2, False, width_mult),
Bottleneck(int(80 * width_mult), int(80 * width_mult), 3, 1, False, width_mult),
Bottleneck(int(80 * width_mult), int(112 * width_mult), 5, 1, True, width_mult),
Bottleneck(int(112 * width_mult), int(112 * width_mult), 5, 1, True, width_mult),
Bottleneck(int(112 * width_mult), int(160 * width_mult), 5, 2, True, width_mult),
Bottleneck(int(160 * width_mult), int(160 * width_mult), 5, 1, True, width_mult),
Bottleneck(int(160 * width_mult), int(320 * width_mult), 3, 1, False, width_mult),
)
self.conv2 = nn.Conv2d(int(320 * width_mult), int(1280 * width_mult), kernel_size=1, stride=1, padding=0, bias=False)
self.bn2 = nn.BatchNorm2d(int(1280 * width_mult))
self.hs2 = nn.ReLU6(inplace=True)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.dropout = nn.Dropout(p=0.2, inplace=True)
self.fc = nn.Linear(int(1280 * width_mult), num_classes)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.hs1(x)
x = self.bottlenecks(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.hs2(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.dropout(x)
x = self.fc(x)
return x
class Bottleneck(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, use_se, width_mult):
super(Bottleneck, self).__init__()
# Define the Bottleneck architecture
mid_channels = int(out_channels / 6)
self.conv1 = nn.Conv2d(in_channels, mid_channels, kernel_size=1, stride=1, padding=0, bias=False)
self.bn1 = nn.BatchNorm2d(mid_channels)
self.hs1 = nn.ReLU6(inplace=True)
self.conv2 = nn.Conv2d(mid_channels, mid_channels, kernel_size=kernel_size, stride=stride, padding=kernel_size // 2, groups=mid_channels, bias=False)
self.bn2 = nn.BatchNorm2d(mid_channels)
self.hs2 = nn.ReLU6(inplace=True)
if use_se:
self.se = SEModule(mid_channels, width_mult)
else:
self.se = None
self.conv3 = nn.Conv2d(mid_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False)
self.bn3 = nn.BatchNorm2d(out_channels)
self.hs3 = nn.ReLU6(inplace=True)
self.use_res_connect = (stride == 1 and in_channels == out_channels)
def forward(self, x):
identity = x
x = self.conv1(x)
x = self.bn1(x)
x = self.hs1(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.hs2(x)
if self.se is not None:
x = self.se(x)
x = self.conv3(x)
x = self.bn3(x)
if self.use_res_connect:
x += identity
x = self.hs3(x)
return x
class SEModule(nn.Module):
def __init__(self, channels, width_mult):
super(SEModule, self).__init__()
# Define the Squeeze-and-Excitation (SE) module architecture
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc1 = nn.Conv2d(channels, int(channels * width_mult), kernel_size=1, stride=1, padding=0, bias=True)
self.hs1 = nn.ReLU6(inplace=True)
self.fc2 = nn.Conv2d(int(channels * width_mult), channels, kernel_size=1, stride=1, padding=0, bias=True)
self.hs2 = nn.Sigmoid()
def forward(self, x):
identity = x
x = self.avgpool(x)
x = self.fc1(x)
x = self.hs1(x)
x = self.fc2(x)
x = self.hs2(x)
x = identity * x
return x
```
这是一个基于 MobileNetV3 的轻量级卷积神经网络,可以用于图像分类等任务。