mobilenetv3模块代码

### MobilenetV3 模块源码实现 为了实现 MobileNetV3 模块并将其应用于 YOLOv8 模型中，以下是基于 PyTorch 的 MobileNetV3 源码实现： #### 1. 定义基本组件 首先定义一些基础构建模块，这些模块构成了 MobileNetV3 的核心部分。 ```python import torch.nn as nn import torch class H_Swish(nn.Module): def __init__(self, inplace=True): super(H_Swish, self).__init__() self.relu = nn.ReLU6(inplace=inplace) def forward(self, x): return x * self.relu(x + 3.) / 6. class H_Sigmoid(nn.Module): def __init__(self, inplace=True): super(H_Sigmoid, self).__init__() self.relu = nn.ReLU6(inplace=inplace) def forward(self, x): return self.relu(x + 3.) / 6. ``` #### 2. 构建 Bottleneck 层 Bottleneck 是 MobileNetV3 中的关键层之一，它实现了高效的特征提取。 ```python class SEBlock(nn.Module): def __init__(self, channel, reduction=4): super(SEBlock, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.fc = nn.Sequential( nn.Linear(channel, channel // reduction, bias=False), nn.ReLU(inplace=True), nn.Linear(channel // reduction, channel, bias=False), H_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) class Bottleneck(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride, exp_size, se=False, nl='RE'): super(Bottleneck, self).__init__() assert stride in [1, 2] padding = (kernel_size - 1) // 2 if nl == "HS": act = H_Swish elif nl == "RE": act = nn.ReLU self.use_res_connect = stride == 1 and in_channels == out_channels layers = [] if in_channels != exp_size: layers.append(nn.Conv2d(in_channels, exp_size, 1, 1, 0, bias=False)) layers.append(nn.BatchNorm2d(exp_size)) layers.append(act(inplace=True)) layers.extend([ nn.Conv2d(exp_size, exp_size, kernel_size, stride, padding, groups=exp_size, bias=False), nn.BatchNorm2d(exp_size), act(inplace=True)]) if se: layers.append(SEBlock(exp_size)) layers.extend([ nn.Conv2d(exp_size, out_channels, 1, 1, 0, bias=False), nn.BatchNorm2d(out_channels)]) self.conv = nn.Sequential(*layers) def forward(self, x): if self.use_res_connect: return x + self.conv(x) else: return self.conv(x) ``` #### 3. 组合完整的 MobileNetV3 结构 最后组合上述各部分形成完整的 MobileNetV3 网络架构。 ```python cfgs_large = [ # k, t, c, SE, NL, s [3, 16, 16, False, 'RE', 1], [3, 64, 24, False, 'RE', 2], [3, 72, 24, False, 'RE', 1], [5, 72, 40, True, 'RE', 2], [5, 120, 40, True, 'RE', 1], [5, 120, 40, True, 'RE', 1], [3, 240, 80, False, 'HS', 2], [3, 200, 80, False, 'HS', 1], [3, 184, 80, False, 'HS', 1], [3, 184, 80, False, 'HS', 1], [3, 480, 112, True, 'HS', 1], [3, 672, 112, True, 'HS', 1], [5, 672, 160, True, 'HS', 2], [5, 960, 160, True, 'HS', 1], ] class MobileNetV3_Large(nn.Module): def __init__(self, num_classes=1000): super(MobileNetV3_Large, self).__init__() hidden_dim = 16 layers = [] first_conv_output = 16 layers.append(nn.Sequential( nn.Conv2d(3, first_conv_output, 3, 2, 1, bias=False), nn.BatchNorm2d(first_conv_output), H_Swish())) input_channel = first_conv_output for k, exp_size, c, use_se, nl, s in cfgs_large: output_channel = c layers.append(Bottleneck(input_channel, output_channel, k, s, exp_size, use_se, nl)) input_channel = output_channel last_conv_input = input_channel last_conv_output = 960 layers.append(nn.Sequential( nn.Conv2d(last_conv_input, last_conv_output, 1, 1, 0, bias=False), nn.BatchNorm2d(last_conv_output), H_Swish())) self.features = nn.Sequential(*layers) self.classifier = nn.Sequential( nn.Linear(last_conv_output, 1280), H_Swish(), nn.Dropout(0.2), nn.Linear(1280, num_classes) ) def forward(self, x): x = self.features(x) x = x.mean([2, 3]) # global average pooling x = self.classifier(x) return x ``` 此代码展示了如何创建一个适用于图像分类任务的 MobileNetV3-Large 版本模型。对于特定应用如目标检测（YOLOv8），可能还需要进一步调整输入/输出维度以及某些内部参数设置以适应具体需求[^2]。