GoogLeNet代码解读

目录

• • GoogLeNet代码解读
• 概述
• GooLeNet网络结构图
• • 1)从输入到第一层inception
  • 2)从第2层inception到第4层inception
  • 3)从第5层inception到第7层inception
  • 4)从第8层inception到输出
• GooLeNet架构搭建
• 代码细节分析

概述

GooLeNet网络结构图

1)从输入到第一层inception

2)从第2层inception到第4层inception

3)从第5层inception到第7层inception

4)从第8层inception到输出

GooLeNet架构搭建

代码细节分析

from collections import namedtuple import warnings import torch from torch import nn, Tensor import torch.nn.functional as F from .utils import load_state_dict_from_url from typing import Callable, Any, Optional, Tuple, List # 可供下载的googlenet预训练模型名称 __all__ = ['GoogLeNet','googlenet','GoogLeNetOutputs','_GoogLeNetOutputs'] # 预训练权重下载 model_urls = {'googlenet':'https://download.pytorch.org/models/googlenet-1378be20.pth',} GoogLeNetOutputs = namedtuple('GoogLeNetOutputs',['logits','aux_logits2','aux_logits1']) GoogLeNetOutputs.__annotations__ = {'logits': Tensor, 'aux_logits2': Optional[Tensor],'aux_logits1': Optional[Tensor]} _GoogLeNetOutputs = GoogLeNetOutputsdef googlenet(pretrained = False, progress = True, **kwargs):if pretrained:if 'transform_input' not in kwargs:kwargs['transform_input'] = Trueif 'aux_logits' not in kwargs:kwargs['aux_logits'] = Falseif kwargs['aux_logits']:warnings.warn('auxiliary heads in the pretrained googlenet model are NOT pretrained, ''so make sure to train them')orginal_aux_logits = kwargs['aux_logits']kwargs['aux_logits'] = Truekwargs['init_weights'] = Falsemodel = GoogLeNet(**kwargs)# 下载googlenet模型并加载state_dict = load_state_dict_from_url(model_urls['googlenet'],progress = progress)model.load_state_dict(state_dict)if not original_aux_logits:model.aux_logits = Falsemodel.aux1 = Nonemodel.aux2 = Nonereturn modelreturn GoogLeNet(**kwargs)class GoogLeNet(nn.Module):__constants__ = ['aux_logits','transform_input']def __init__(self,num_classes = 1000,aux_logits = True,trandform_input = False,init_weights = None,blocks = None):super(GoogLeNet,self).__init__()if blocks is None:blocks = [BasicConv2d, Inception, InceptionAux]if init_weights is None:warnings.warn('The default weight initialization of GoogleNet will be changed in future releases of ''torchvision. If you wish to keep the old behavior (which leads to long initialization times'' due to scipy/scipy#11299), please set init_weights=True.', FutureWarning)init_weights = Trueassert len(blocks)==3conv_block = blocks[0]inception_block = blocks[1]inception_aux_block = blocks[2]self.aux_logits = aux_logitsself.transform_input = transform_input# 从输入到第一层inception的卷积、池化处理self.conv1 = conv_block(3,64,kernel_size = 7, stride = 3, padding = 3)self.maxpool1 = nn.MaxPool2d(3,stride = 2, ceil_mode = True)self.conv2 = conv_block(64,64,kernel_size = 1)self.conv3 = conv_block(64,192,kernel_size = 3, padding = 1)self.maxpool2 = nn.MaxPool2d(3,stride = 2, ceil_mode = True)# 一系列的inception模块self.inception3a = inception_block(192,64,96,128,16,32,32)self.inception3b = inception_block(256, 128, 128, 192, 32, 96, 64)self.maxpool3 = nn.MaxPool2d(3, stride=2, ceil_mode=True)self.inception4a = inception_block(480, 192, 96, 208, 16, 48, 64)self.inception4b = inception_block(512, 160, 112, 224, 24, 64, 64)self.inception4c = inception_block(512, 128, 128, 256, 24, 64, 64)self.inception4d = inception_block(512, 112, 144, 288, 32, 64, 64)self.inception4e = inception_block(528, 256, 160, 320, 32, 128, 128)self.maxpool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)self.inception5a = inception_block(832, 256, 160, 320, 32, 128, 128)self.inception5b = inception_block(832, 384, 192, 384, 48, 128, 128)# 辅助分类模块if aux_logits:self.aux1 = inception_aux_block(512, num_classes)self.aux2 = inception_aux_block(528, num_classes)else:self.aux1 = None # type: ignore[assignment]self.aux2 = None # type: ignore[assignment]# 平均池化、dropout防止过拟合self.avgpool = nn.AdaptiveAvgPool2d((1, 1))self.dropout = nn.Dropout(0.2)self.fc = nn.Linear(1024, num_classes)if init_weights:self._initialize_weights()def _initialize_weights(self) -> None:# 初始化权重和偏置参数for m in self.modules():if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):import scipy.stats as statsX = stats.truncnorm(-2, 2, scale=0.01)values = torch.as_tensor(X.rvs(m.weight.numel()), dtype=m.weight.dtype)values = values.view(m.weight.size())with torch.no_grad():m.weight.copy_(values)elif isinstance(m, nn.BatchNorm2d):nn.init.constant_(m.weight, 1)nn.init.constant_(m.bias, 0)# 给input增加一个维度并作中心化def _transform_input(self, x: Tensor) -> Tensor:if self.transform_input:x_ch0 = torch.unsqueeze(x[:, 0], 1) * (0.229 / 0.5) + (0.485 - 0.5) / 0.5x_ch1 = torch.unsqueeze(x[:, 1], 1) * (0.224 / 0.5) + (0.456 - 0.5) / 0.5x_ch2 = torch.unsqueeze(x[:, 2], 1) * (0.225 / 0.5) + (0.406 - 0.5) / 0.5x = torch.cat((x_ch0, x_ch1, x_ch2), 1)return x# 构建googlenet网络def _forward(self, x: Tensor) -> Tuple[Tensor, Optional[Tensor], Optional[Tensor]]:# N x 3 x 224 x 224x = self.conv1(x)# N x 64 x 112 x 112x = self.maxpool1(x)# N x 64 x 56 x 56x = self.conv2(x)# N x 64 x 56 x 56x = self.conv3(x)# N x 192 x 56 x 56x = self.maxpool2(x)# N x 192 x 28 x 28x = self.inception3a(x)# N x 256 x 28 x 28x = self.inception3b(x)# N x 480 x 28 x 28x = self.maxpool3(x)# N x 480 x 14 x 14x = self.inception4a(x)# N x 512 x 14 x 14aux1: Optional[Tensor] = Noneif self.aux1 is not None:if self.training:aux1 = self.aux1(x)x = self.inception4b(x)# N x 512 x 14 x 14x = self.inception4c(x)# N x 512 x 14 x 14x = self.inception4d(x)# N x 528 x 14 x 14aux2: Optional[Tensor] = Noneif self.aux2 is not None:if self.training:aux2 = self.aux2(x)x = self.inception4e(x)# N x 832 x 14 x 14x = self.maxpool4(x)# N x 832 x 7 x 7x = self.inception5a(x)# N x 832 x 7 x 7x = self.inception5b(x)# N x 1024 x 7 x 7x = self.avgpool(x)# N x 1024 x 1 x 1x = torch.flatten(x, 1)# N x 1024x = self.dropout(x)x = self.fc(x)# N x 1000 (num_classes)return x, aux2, aux1@torch.jit.unuseddef eager_outputs(self, x: Tensor, aux2: Tensor, aux1: Optional[Tensor]) -> GoogLeNetOutputs:if self.training and self.aux_logits:return _GoogLeNetOutputs(x, aux2, aux1)else:return x # type: ignore[return-value]def forward(self, x: Tensor) -> GoogLeNetOutputs:x = self._transform_input(x)x, aux1, aux2 = self._forward(x)aux_defined = self.training and self.aux_logitsif torch.jit.is_scripting():if not aux_defined:warnings.warn("Scripted GoogleNet always returns GoogleNetOutputs Tuple")return GoogLeNetOutputs(x, aux2, aux1)else:return self.eager_outputs(x, aux2, aux1)# inception模块 class Inception(nn.Module):def __init__(self,in_channels: int,ch1x1: int,ch3x3red: int,ch3x3: int,ch5x5red: int,ch5x5: int,pool_proj: int,conv_block: Optional[Callable[..., nn.Module]] = None) -> None:super(Inception, self).__init__()if conv_block is None:conv_block = BasicConv2dself.branch1 = conv_block(in_channels, ch1x1, kernel_size=1)self.branch2 = nn.Sequential(conv_block(in_channels, ch3x3red, kernel_size=1),conv_block(ch3x3red, ch3x3, kernel_size=3, padding=1))self.branch3 = nn.Sequential(conv_block(in_channels, ch5x5red, kernel_size=1),# Here, kernel_size=3 instead of kernel_size=5 is a known bug.# Please see https://github.com/pytorch/vision/issues/906 for details.conv_block(ch5x5red, ch5x5, kernel_size=3, padding=1))self.branch4 = nn.Sequential(nn.MaxPool2d(kernel_size=3, stride=1, padding=1, ceil_mode=True),conv_block(in_channels, pool_proj, kernel_size=1))def _forward(self, x: Tensor) -> List[Tensor]:branch1 = self.branch1(x)branch2 = self.branch2(x)branch3 = self.branch3(x)branch4 = self.branch4(x)outputs = [branch1, branch2, branch3, branch4]return outputsdef forward(self, x: Tensor) -> Tensor:outputs = self._forward(x)return torch.cat(outputs, 1)# 辅助的inception模块，用于分类 class InceptionAux(nn.Module):def __init__(self,in_channels: int,num_classes: int,conv_block: Optional[Callable[..., nn.Module]] = None) -> None:super(InceptionAux, self).__init__()if conv_block is None:conv_block = BasicConv2dself.conv = conv_block(in_channels, 128, kernel_size=1)self.fc1 = nn.Linear(2048, 1024)self.fc2 = nn.Linear(1024, num_classes)def forward(self, x: Tensor) -> Tensor:# aux1: N x 512 x 14 x 14, aux2: N x 528 x 14 x 14x = F.adaptive_avg_pool2d(x, (4, 4))# aux1: N x 512 x 4 x 4, aux2: N x 528 x 4 x 4x = self.conv(x)# N x 128 x 4 x 4x = torch.flatten(x, 1)# N x 2048x = F.relu(self.fc1(x), inplace=True)# N x 1024x = F.dropout(x, 0.7, training=self.training)# N x 1024x = self.fc2(x)# N x 1000 (num_classes)return x# 将卷积、bn、激活封装成一个函数，其实这里不封装也行，分成3步来写 class BasicConv2d(nn.Module):def __init__(self,in_channels: int,out_channels: int,**kwargs: Any) -> None:super(BasicConv2d, self).__init__()self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)self.bn = nn.BatchNorm2d(out_channels, eps=0.001)def forward(self, x: Tensor) -> Tensor:x = self.conv(x)x = self.bn(x)return F.relu(x, inplace=True)

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