GoogLeNet网络的Pytorch实现

1.文章原文地址

Going deeper with convolutions

2.文章摘要

我们提出了一种代号为Inception的深度卷积神经网络，它在ILSVRC2014的分类和检测任务上都取得当前最佳成绩。这种结构的主要特点是提高了网络内部计算资源的利用率。这是通过精心的设计实现的，它允许增加网络的深度和宽度，同时保持计算预算不变。为了提高效果，这个网络的架构确定是基于Hebbian原则和多尺度处理的直觉。其中一个典型的实例用于提交到ILSVRC2014上，我们称之为GoogLeNet,它是一个22层的深度网络，该网络的效果通过分类和检测任务来加以评估。

3.网络结构

4.Pytorch实现

 import warnings
 from collections import namedtuple
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from torch.utils.model_zoo import load_url as load_state_dict_from_url
 from torchsummary import summary

 __all__ = ['GoogLeNet', 'googlenet']

 model_urls = {
     # GoogLeNet ported from TensorFlow
     'googlenet': 'https://download.pytorch.org/models/googlenet-1378be20.pth',
 }

 _GoogLeNetOuputs = namedtuple('GoogLeNetOuputs', ['logits', 'aux_logits2', 'aux_logits1'])

 def googlenet(pretrained=False, progress=True, **kwargs):
     r"""GoogLeNet (Inception v1) model architecture from
     `"Going Deeper with Convolutions" <http://arxiv.org/abs/1409.4842>`_.
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
         aux_logits (bool): If True, adds two auxiliary branches that can improve training.
             Default: *False* when pretrained is True otherwise *True*
         transform_input (bool): If True, preprocesses the input according to the method with which it
             was trained on ImageNet. Default: *False*
     """
     if pretrained:
         if 'transform_input' not in kwargs:
             kwargs['transform_input'] = True
         if 'aux_logits' not in kwargs:
             kwargs['aux_logits'] = False
         if kwargs['aux_logits']:
             warnings.warn('auxiliary heads in the pretrained googlenet model are NOT pretrained, '
                           'so make sure to train them')
         original_aux_logits = kwargs['aux_logits']
         kwargs['aux_logits'] = True
         kwargs['init_weights'] = False
         model = GoogLeNet(**kwargs)
         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 = False
             del model.aux1, model.aux2
         return model

     return GoogLeNet(**kwargs)

 class GoogLeNet(nn.Module):

     def __init__(self, num_classes=1000, aux_logits=True, transform_input=False, init_weights=True):
         super(GoogLeNet, self).__init__()
         self.aux_logits = aux_logits
         self.transform_input = transform_input

         self.conv1 = BasicConv2d(3, 64, kernel_size=7, stride=2, padding=3)
         self.maxpool1 = nn.MaxPool2d(3, stride=2, ceil_mode=True)   #向上取整
         self.conv2 = BasicConv2d(64, 64, kernel_size=1)
         self.conv3 = BasicConv2d(64, 192, kernel_size=3, padding=1)
         self.maxpool2 = nn.MaxPool2d(3, stride=2, ceil_mode=True)

         self.inception3a = Inception(192, 64, 96, 128, 16, 32, 32)
         self.inception3b = Inception(256, 128, 128, 192, 32, 96, 64)
         self.maxpool3 = nn.MaxPool2d(3, stride=2, ceil_mode=True)

         self.inception4a = Inception(480, 192, 96, 208, 16, 48, 64)
         self.inception4b = Inception(512, 160, 112, 224, 24, 64, 64)
         self.inception4c = Inception(512, 128, 128, 256, 24, 64, 64)
         self.inception4d = Inception(512, 112, 144, 288, 32, 64, 64)
         self.inception4e = Inception(528, 256, 160, 320, 32, 128, 128)
         self.maxpool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)

         self.inception5a = Inception(832, 256, 160, 320, 32, 128, 128)
         self.inception5b = Inception(832, 384, 192, 384, 48, 128, 128)

         if aux_logits:
             self.aux1 = InceptionAux(512, num_classes)
             self.aux2 = InceptionAux(528, num_classes)

         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):
         for m in self.modules():
             if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
                 import scipy.stats as stats
                 X = 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)

     def forward(self, x):
         if self.transform_input:
             x_ch0 = torch.unsqueeze(x[:, 0], 1) * (0.229 / 0.5) + (0.485 - 0.5) / 0.5
             x_ch1 = torch.unsqueeze(x[:, 1], 1) * (0.224 / 0.5) + (0.456 - 0.5) / 0.5
             x_ch2 = torch.unsqueeze(x[:, 2], 1) * (0.225 / 0.5) + (0.406 - 0.5) / 0.5
             x = torch.cat((x_ch0, x_ch1, x_ch2), 1)

         # N x 3 x 224 x 224
         x = self.conv1(x)
         # N x 64 x 112 x 112
         x = self.maxpool1(x)
         # N x 64 x 56 x 56
         x = self.conv2(x)
         # N x 64 x 56 x 56
         x = self.conv3(x)
         # N x 192 x 56 x 56
         x = self.maxpool2(x)

         # N x 192 x 28 x 28
         x = self.inception3a(x)
         # N x 256 x 28 x 28
         x = self.inception3b(x)
         # N x 480 x 28 x 28
         x = self.maxpool3(x)
         # N x 480 x 14 x 14
         x = self.inception4a(x)
         # N x 512 x 14 x 14
         if self.training and self.aux_logits:
             aux1 = self.aux1(x)

         x = self.inception4b(x)
         # N x 512 x 14 x 14
         x = self.inception4c(x)
         # N x 512 x 14 x 14
         x = self.inception4d(x)
         # N x 528 x 14 x 14
         if self.training and self.aux_logits:
             aux2 = self.aux2(x)

         x = self.inception4e(x)
         # N x 832 x 14 x 14
         x = self.maxpool4(x)
         # N x 832 x 7 x 7
         x = self.inception5a(x)
         # N x 832 x 7 x 7
         x = self.inception5b(x)
         # N x 1024 x 7 x 7

         x = self.avgpool(x)
         # N x 1024 x 1 x 1
         x = x.view(x.size(0), -1)
         # N x 1024
         x = self.dropout(x)
         x = self.fc(x)
         # N x 1000 (num_classes)
         if self.training and self.aux_logits:
             return _GoogLeNetOuputs(x, aux2, aux1)
         return x

 class Inception(nn.Module):     #Inception模块

     def __init__(self, in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj):
         super(Inception, self).__init__()

         self.branch1 = BasicConv2d(in_channels, ch1x1, kernel_size=1)

         self.branch2 = nn.Sequential(
             BasicConv2d(in_channels, ch3x3red, kernel_size=1),
             BasicConv2d(ch3x3red, ch3x3, kernel_size=3, padding=1)
         )

         self.branch3 = nn.Sequential(
             BasicConv2d(in_channels, ch5x5red, kernel_size=1),
             BasicConv2d(ch5x5red, ch5x5, kernel_size=3, padding=1)
         )

         self.branch4 = nn.Sequential(
             nn.MaxPool2d(kernel_size=3, stride=1, padding=1, ceil_mode=True),
             BasicConv2d(in_channels, pool_proj, kernel_size=1)
         )

     def forward(self, x):
         branch1 = self.branch1(x)
         branch2 = self.branch2(x)
         branch3 = self.branch3(x)
         branch4 = self.branch4(x)

         outputs = [branch1, branch2, branch3, branch4]
         return torch.cat(outputs, 1)

 class InceptionAux(nn.Module):      #辅助分支

     def __init__(self, in_channels, num_classes):
         super(InceptionAux, self).__init__()
         self.conv = BasicConv2d(in_channels, 128, kernel_size=1)

         self.fc1 = nn.Linear(2048, 1024)
         self.fc2 = nn.Linear(1024, num_classes)

     def forward(self, x):
         # aux1: N x 512 x 14 x 14, aux2: N x 528 x 14 x 14
         x = F.adaptive_avg_pool2d(x, (4, 4))
         # aux1: N x 512 x 4 x 4, aux2: N x 528 x 4 x 4
         x = self.conv(x)
         # N x 128 x 4 x 4
         x = x.view(x.size(0), -1)
         # N x 2048
         x = F.relu(self.fc1(x), inplace=True)
         # N x 1024
         x = F.dropout(x, 0.7, training=self.training)
         # N x 1024
         x = self.fc2(x)
         # N x num_classes

         return x

 class BasicConv2d(nn.Module):       #Conv2d+BN+Relu

     def __init__(self, in_channels, out_channels, **kwargs):
         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):
         x = self.conv(x)
         x = self.bn(x)
         return F.relu(x, inplace=True)

 if __name__=="__main__":
     model=googlenet()
     print(model,(3,224,224))

参考

https://github.com/pytorch/vision/tree/master/torchvision/models