给出mobilenetv3代码示例,用torch实现
时间: 2023-04-04 07:03:09 浏览: 149
以下是使用 PyTorch 实现 MobileNetV3 的代码示例:
```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__()
self.in_channels = int(16 * width_mult)
self.conv1 = nn.Conv2d(3, self.in_channels, kernel_size=3, stride=2, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(self.in_channels)
self.hs1 = nn.Hardswish()
self.bottlenecks = nn.Sequential(
Bottleneck(self.in_channels, int(16 * width_mult), 3, 1, False, False),
Bottleneck(int(16 * width_mult), int(24 * width_mult), 3, 2, False, False),
Bottleneck(int(24 * width_mult), int(24 * width_mult), 3, 1, False, False),
Bottleneck(int(24 * width_mult), int(40 * width_mult), 5, 2, True, False),
Bottleneck(int(40 * width_mult), int(40 * width_mult), 5, 1, True, False),
Bottleneck(int(40 * width_mult), int(40 * width_mult), 5, 1, True, False),
Bottleneck(int(40 * width_mult), int(80 * width_mult), 3, 2, False, True),
Bottleneck(int(80 * width_mult), int(80 * width_mult), 3, 1, False, True),
Bottleneck(int(80 * width_mult), int(80 * width_mult), 3, 1, False, True),
Bottleneck(int(80 * width_mult), int(112 * width_mult), 5, 1, True, True),
Bottleneck(int(112 * width_mult), int(112 * width_mult), 5, 1, True, True),
Bottleneck(int(112 * width_mult), int(160 * width_mult), 5, 2, True, True),
Bottleneck(int(160 * width_mult), int(160 * width_mult), 5, 1, True, True),
Bottleneck(int(160 * width_mult), int(160 * width_mult), 5, 1, True, True),
)
self.conv2 = nn.Conv2d(int(160 * width_mult), int(960 * width_mult), kernel_size=1, stride=1, bias=False)
self.bn2 = nn.BatchNorm2d(int(960 * width_mult))
self.hs2 = nn.Hardswish()
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.dropout = nn.Dropout(p=0.2, inplace=True)
self.fc = nn.Linear(int(960 * 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, use_hs):
super(Bottleneck, self).__init__()
self.stride = stride
self.use_se = use_se
self.use_hs = use_hs
mid_channels = int(in_channels / 4)
self.conv1 = nn.Conv2d(in_channels, mid_channels, kernel_size=1, stride=1, bias=False)
self.bn1 = nn.BatchNorm2d(mid_channels)
self.hs1 = nn.Hardswish() if use_hs else nn.ReLU(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.Hardswish() if use_hs else nn.ReLU(inplace=True)
if use_se:
self.se = SEBlock(mid_channels)
self.conv3 = nn.Conv2d(mid_channels, out_channels, kernel_size=1, stride=1, bias=False)
self.bn3 = nn.BatchNorm2d(out_channels)
self.hs3 = nn.Hardswish() if use_hs else nn.ReLU(inplace=True)
self.shortcut = nn.Sequential()
if stride == 1 and in_channels != out_channels:
self.shortcut = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False),
nn.BatchNorm2d(out_channels),
)
def forward(self, x):
out = self.conv1(x)
out = self.bn1(out)
out = self.hs1(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.hs2(out)
if self.use_se:
out = self.se(out)
out = self.conv3(out)
out = self.bn3(out)
out = self.hs3(out)
out += self.shortcut(x) if self.stride == 1 else out
return out
class SEBlock(nn.Module):
def __init__(self, channels, reduction=4):
super(SEBlock, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channels, channels // reduction, bias=False),
nn.ReLU(inplace=True),
nn.Linear(channels // reduction, channels, bias=False),
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.expand_as(x)
```
这是一个 MobileNetV3 的实现,可以用于图像分类任务。其中,`width_mult` 参数可以控制模型的宽度,可以根据实际情况进行调整。