ResNet网络的Pytorch实现

1.文章原文地址

Deep Residual Learning for  Image Recognition

2.文章摘要

神经网络的层次越深越难训练。我们提出了一个残差学习框架来简化网络的训练，这些网络比之前使用的网络都要深的多。我们明确地将层变为学习关于层输入的残差函数，而不是学习未参考的函数。我们提供了综合的实验证据来表明这个残差网络更容易优化，以及通过极大提升网络深度可以获得更好的准确率。在ImageNet数据集上，我们评估了残差网络，该网络有152层，层数是VGG网络的8倍，但是有更低的复杂度。几个残差网络的集成在ImageNet数据集上取得了3.57%错误率。这个结果在ILSVRC2015分类任务上取得第一名的成绩。我们也使用了100和1000层网络用在了数据集CIFAR-10上加以分析。

在许多视觉识别任务中，表征的深度是至关重要的。仅仅通过极端深的表征，我们在COCO目标检测数据集上得到了28%的相对提高。深度残差网络是我们提交到ILSVRC & COCO2015竞赛的网络基础，在这里我们获得了ImageNet检测任务、ImageNet定位任务，COCO检测任务和COCO分割任务的第一名。

3.网络结构

4.Pytorch实现

 import torch.nn as nn
 from  torch.utils.model_zoo import load_url as load_state_dict_from_url

 __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
            'resnet152', 'resnext50_32x4d', 'resnext101_32x8d']

 model_urls = {
     'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
     'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
     'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
     'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
     'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
 }

 def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
     """3x3 convolution with padding"""
     return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                      padding=dilation, groups=groups, bias=False, dilation=dilation)

 def conv1x1(in_planes, out_planes, stride=1):
     """1x1 convolution"""
     return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)

 class BasicBlock(nn.Module):
     expansion = 1

     def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                  base_width=64, dilation=1, norm_layer=None):
         super(BasicBlock, self).__init__()
         if norm_layer is None:
             norm_layer = nn.BatchNorm2d
         if groups != 1 or base_width != 64:
             raise ValueError('BasicBlock only supports groups=1 and base_width=64')
         if dilation > 1:
             raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
         # Both self.conv1 and self.downsample layers downsample the input when stride != 1
         self.conv1 = conv3x3(inplanes, planes, stride)
         self.bn1 = norm_layer(planes)
         self.relu = nn.ReLU(inplace=True)
         self.conv2 = conv3x3(planes, planes)
         self.bn2 = norm_layer(planes)
         self.downsample = downsample
         self.stride = stride

     def forward(self, x):
         identity = x

         out = self.conv1(x)
         out = self.bn1(out)
         out = self.relu(out)

         out = self.conv2(out)
         out = self.bn2(out)

         if self.downsample is not None:
             identity = self.downsample(x)

         out += identity
         out = self.relu(out)

         return out

 class Bottleneck(nn.Module):
     expansion = 4

     def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                  base_width=64, dilation=1, norm_layer=None):
         super(Bottleneck, self).__init__()
         if norm_layer is None:
             norm_layer = nn.BatchNorm2d
         width = int(planes * (base_width / 64.)) * groups
         # Both self.conv2 and self.downsample layers downsample the input when stride != 1
         self.conv1 = conv1x1(inplanes, width)
         self.bn1 = norm_layer(width)
         self.conv2 = conv3x3(width, width, stride, groups, dilation)
         self.bn2 = norm_layer(width)
         self.conv3 = conv1x1(width, planes * self.expansion)
         self.bn3 = norm_layer(planes * self.expansion)
         self.relu = nn.ReLU(inplace=True)
         self.downsample = downsample
         self.stride = stride

     def forward(self, x):
         identity = x

         out = self.conv1(x)
         out = self.bn1(out)
         out = self.relu(out)

         out = self.conv2(out)
         out = self.bn2(out)
         out = self.relu(out)

         out = self.conv3(out)
         out = self.bn3(out)

         if self.downsample is not None:
             identity = self.downsample(x)

         out += identity
         out = self.relu(out)

         return out

 class ResNet(nn.Module):

     def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
                  groups=1, width_per_group=64, replace_stride_with_dilation=None,
                  norm_layer=None):
         super(ResNet, self).__init__()
         if norm_layer is None:
             norm_layer = nn.BatchNorm2d
         self._norm_layer = norm_layer

         self.inplanes = 64
         self.dilation = 1
         if replace_stride_with_dilation is None:
             # each element in the tuple indicates if we should replace
             # the 2x2 stride with a dilated convolution instead
             replace_stride_with_dilation = [False, False, False]
         if len(replace_stride_with_dilation) != 3:
             raise ValueError("replace_stride_with_dilation should be None "
                              "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
         self.groups = groups
         self.base_width = width_per_group
         self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
                                bias=False)
         self.bn1 = norm_layer(self.inplanes)
         self.relu = nn.ReLU(inplace=True)
         self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
         self.layer1 = self._make_layer(block, 64, layers[0])
         self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
                                        dilate=replace_stride_with_dilation[0])
         self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                        dilate=replace_stride_with_dilation[1])
         self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                        dilate=replace_stride_with_dilation[2])
         self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
         self.fc = nn.Linear(512 * block.expansion, num_classes)

         for m in self.modules():
             if isinstance(m, nn.Conv2d):
                 nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
             elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                 nn.init.constant_(m.weight, 1)
                 nn.init.constant_(m.bias, 0)

         # Zero-initialize the last BN in each residual branch,
         # so that the residual branch starts with zeros, and each residual block behaves like an identity.
         # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
         if zero_init_residual:
             for m in self.modules():
                 if isinstance(m, Bottleneck):
                     nn.init.constant_(m.bn3.weight, 0)
                 elif isinstance(m, BasicBlock):
                     nn.init.constant_(m.bn2.weight, 0)

     def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
         norm_layer = self._norm_layer
         downsample = None
         previous_dilation = self.dilation
         if dilate:
             self.dilation *= stride
             stride = 1
         if stride != 1 or self.inplanes != planes * block.expansion:
             downsample = nn.Sequential(
                 conv1x1(self.inplanes, planes * block.expansion, stride),
                 norm_layer(planes * block.expansion),
             )

         layers = []
         layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                             self.base_width, previous_dilation, norm_layer))
         self.inplanes = planes * block.expansion
         for _ in range(1, blocks):
             layers.append(block(self.inplanes, planes, groups=self.groups,
                                 base_width=self.base_width, dilation=self.dilation,
                                 norm_layer=norm_layer))

         return nn.Sequential(*layers)

     def forward(self, x):
         x = self.conv1(x)
         x = self.bn1(x)
         x = self.relu(x)
         x = self.maxpool(x)

         x = self.layer1(x)
         x = self.layer2(x)
         x = self.layer3(x)
         x = self.layer4(x)

         x = self.avgpool(x)
         x = x.reshape(x.size(0), -1)
         x = self.fc(x)

         return x

 def _resnet(arch, inplanes, planes, pretrained, progress, **kwargs):
     model = ResNet(inplanes, planes, **kwargs)
     if pretrained:
         state_dict = load_state_dict_from_url(model_urls[arch],
                                               progress=progress)
         model.load_state_dict(state_dict)
     return model

 def resnet18(pretrained=False, progress=True, **kwargs):
     """Constructs a ResNet-18 model.
     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
     """
     return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
                    **kwargs)

 def resnet34(pretrained=False, progress=True, **kwargs):
     """Constructs a ResNet-34 model.
     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
     """
     return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
                    **kwargs)

 def resnet50(pretrained=False, progress=True, **kwargs):
     """Constructs a ResNet-50 model.
     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
     """
     return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
                    **kwargs)

 def resnet101(pretrained=False, progress=True, **kwargs):
     """Constructs a ResNet-101 model.
     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
     """
     return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
                    **kwargs)

 def resnet152(pretrained=False, progress=True, **kwargs):
     """Constructs a ResNet-152 model.
     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
     """
     return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
                    **kwargs)

 def resnext50_32x4d(**kwargs):
     kwargs['groups'] = 32
     kwargs['width_per_group'] = 4
     return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],
                    pretrained=False, progress=True, **kwargs)

 def resnext101_32x8d(**kwargs):
     kwargs['groups'] = 32
     kwargs['width_per_group'] = 8
     return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
                    pretrained=False, progress=True, **kwargs)

参考

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