GoogLeNet代碼解讀

目錄

• • GoogLeNet代碼解讀
• 概述
• GooLeNet網絡結構圖
• • 1)從輸入到第一層inception
  • 2)從第2層inception到第4層inception
  • 3)從第5層inception到第7層inception
  • 4)從第8層inception到輸出
• GooLeNet架構搭建
• 代碼細節分析

概述

GooLeNet網絡結構圖

1)從輸入到第一層inception

2)從第2層inception到第4層inception

3)從第5層inception到第7層inception

4)從第8層inception到輸出

GooLeNet架構搭建

代碼細節分析

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 # 可供下載的googlenet預訓練模型名稱 __all__ = ['GoogLeNet','googlenet','GoogLeNetOutputs','_GoogLeNetOutputs'] # 預訓練權重下載 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)# 下載googlenet模型并加載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# 從輸入到第一層inception的卷積、池化處理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模塊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)# 輔助分類模塊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防止過擬合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:# 初始化權重和偏置參數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)# 給input增加一個維度并作中心化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# 構建googlenet網絡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模塊 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)# 輔助的inception模塊，用于分類 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# 將卷積、bn、激活封裝成一個函數，其實這里不封裝也行，分成3步來寫 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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