​

 

该论文主要关注点：

  • 网络层之间的信息流动-the flow of information through the network layers
  • 残差构造模块-the residual building block
  • 投影捷径-the projection shortcut

该论文主要贡献：

  • 提出了一种新的残差网络。该网络提供了一个更好的信息流动的路径，使得网络更易于优化。
  • 改善了投影捷径，减少了信息的损失。所谓的投影捷径，是指当前残差块和下一个残差块的维度不一致时使用的跳跃连接。
  • 提出了一个构造模块增加空间通道，以学习到更加强大的空间模式
  • 与ResNet相比，在没有增加模型复杂度的情况下，在六个数据集上都取得了提升

直接看代码：


【论文笔记】Improved Residual Networks for Image and Video Recognition（ResNet新变体：IResNet）_卷积【论文笔记】Improved Residual Networks for Image and Video Recognition（ResNet新变体：IResNet）_2d_02


import torch
import torch.nn as nn
import os
from div.download_from_url import download_from_url

try:
    from torch.hub import _get_torch_home
    torch_cache_home = _get_torch_home()
except ImportError:
    torch_cache_home = os.path.expanduser(
        os.getenv('TORCH_HOME', os.path.join(
            os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch')))
default_cache_path = os.path.join(torch_cache_home, 'pretrained')

__all__ = ['iResNet', 'iresnet18', 'iresnet34', 'iresnet50', 'iresnet101',
           'iresnet152', 'iresnet200', 'iresnet302', 'iresnet404', 'iresnet1001']

model_urls = {
    'iresnet18': 'Trained model not available yet!!',
    'iresnet34': 'Trained model not available yet!!',
    'iresnet50': 'https://drive.google.com/uc?export=download&id=1Waw3ob8KPXCY9iCLdAD6RUA0nvVguc6K',
    'iresnet101': 'https://drive.google.com/uc?export=download&id=1cZ4XhwZfUOm_o0WZvenknHIqgeqkY34y',
    'iresnet152': 'https://drive.google.com/uc?export=download&id=10heFLYX7VNlaSrDy4SZbdOOV9xwzwyli',
    'iresnet200': 'https://drive.google.com/uc?export=download&id=1Ao-f--jNU7MYPqSW8UMonXVrq3mkLRpW',
    'iresnet302': 'https://drive.google.com/uc?export=download&id=1UcyvLhLzORJZBUQDNJdsx3USCloXZT6V',
    'iresnet404': 'https://drive.google.com/uc?export=download&id=1hEOHErsD6AF1b3qQi56mgxvYDneTvMIq',
    'iresnet1001': 'Trained model not available yet!!',
}


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


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, norm_layer=None,
                 start_block=False, end_block=False, exclude_bn0=False):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        if not start_block and not exclude_bn0:
            self.bn0 = norm_layer(inplanes)

        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)

        if start_block:
            self.bn2 = norm_layer(planes)

        if end_block:
            self.bn2 = norm_layer(planes)

        self.downsample = downsample
        self.stride = stride

        self.start_block = start_block
        self.end_block = end_block
        self.exclude_bn0 = exclude_bn0

    def forward(self, x):
        identity = x

        if self.start_block:
            out = self.conv1(x)
        elif self.exclude_bn0:
            out = self.relu(x)
            out = self.conv1(out)
        else:
            out = self.bn0(x)
            out = self.relu(out)
            out = self.conv1(out)

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

        out = self.conv2(out)

        if self.start_block:
            out = self.bn2(out)

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

        out += identity

        if self.end_block:
            out = self.bn2(out)
            out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, norm_layer=None,
                 start_block=False, end_block=False, exclude_bn0=False):
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1

        if not start_block and not exclude_bn0:
            self.bn0 = norm_layer(inplanes)

        self.conv1 = conv1x1(inplanes, planes)
        self.bn1 = norm_layer(planes)
        self.conv2 = conv3x3(planes, planes, stride)
        self.bn2 = norm_layer(planes)
        self.conv3 = conv1x1(planes, planes * self.expansion)

        if start_block:
            self.bn3 = norm_layer(planes * self.expansion)

        if end_block:
            self.bn3 = norm_layer(planes * self.expansion)

        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

        self.start_block = start_block
        self.end_block = end_block
        self.exclude_bn0 = exclude_bn0

    def forward(self, x):
        identity = x

        if self.start_block:
            out = self.conv1(x)
        elif self.exclude_bn0:
            out = self.relu(x)
            out = self.conv1(out)
        else:
            out = self.bn0(x)
            out = self.relu(out)
            out = self.conv1(out)

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

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

        out = self.conv3(out)

        if self.start_block:
            out = self.bn3(out)

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

        out += identity

        if self.end_block:
            out = self.bn3(out)
            out = self.relu(out)

        return out


class iResNet(nn.Module):

    def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, norm_layer=None, dropout_prob0=0.0):
        super(iResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self.inplanes = 64
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = norm_layer(64)
        self.relu = nn.ReLU(inplace=True)
        self.layer1 = self._make_layer(block, 64, layers[0], stride=2, norm_layer=norm_layer)
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2, norm_layer=norm_layer)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2, norm_layer=norm_layer)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2, norm_layer=norm_layer)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))

        if dropout_prob0 > 0.0:
            self.dp = nn.Dropout(dropout_prob0, inplace=True)
            print("Using Dropout with the prob to set to 0 of: ", dropout_prob0)
        else:
            self.dp = None

        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, norm_layer=None):
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        downsample = None
        if stride != 1 and self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.MaxPool2d(kernel_size=3, stride=stride, padding=1),
                conv1x1(self.inplanes, planes * block.expansion),
                norm_layer(planes * block.expansion),
            )
        elif self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion),
                norm_layer(planes * block.expansion),
            )
        elif stride != 1:
            downsample = nn.MaxPool2d(kernel_size=3, stride=stride, padding=1)

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, norm_layer,
                            start_block=True))
        self.inplanes = planes * block.expansion
        exclude_bn0 = True
        for _ in range(1, (blocks-1)):
            layers.append(block(self.inplanes, planes, norm_layer=norm_layer,
                                exclude_bn0=exclude_bn0))
            exclude_bn0 = False

        layers.append(block(self.inplanes, planes, norm_layer=norm_layer, end_block=True, exclude_bn0=exclude_bn0))

        return nn.Sequential(*layers)

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

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

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

        if self.dp is not None:
            x = self.dp(x)

        x = self.fc(x)

        return x


def iresnet18(pretrained=False, **kwargs):
    """Constructs a iResNet-18 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = iResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
    if pretrained:
        os.makedirs(default_cache_path, exist_ok=True)
        model.load_state_dict(torch.load(download_from_url(model_urls['iresnet18'],
                                                           root=default_cache_path)))
    return model


def iresnet34(pretrained=False, **kwargs):
    """Constructs a iResNet-34 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = iResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
    if pretrained:
        os.makedirs(default_cache_path, exist_ok=True)
        model.load_state_dict(torch.load(download_from_url(model_urls['iresnet34'],
                                                           root=default_cache_path)))
    return model


def iresnet50(pretrained=False, **kwargs):
    """Constructs a iResNet-50 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = iResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
    if pretrained:
        os.makedirs(default_cache_path, exist_ok=True)
        model.load_state_dict(torch.load(download_from_url(model_urls['iresnet50'],
                                                           root=default_cache_path)))
    return model


def iresnet101(pretrained=False, **kwargs):
    """Constructs a iResNet-101 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = iResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
    if pretrained:
        os.makedirs(default_cache_path, exist_ok=True)
        model.load_state_dict(torch.load(download_from_url(model_urls['iresnet101'],
                                                           root=default_cache_path)))
    return model


def iresnet152(pretrained=False, **kwargs):
    """Constructs a iResNet-152 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = iResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
    if pretrained:
        os.makedirs(default_cache_path, exist_ok=True)
        model.load_state_dict(torch.load(download_from_url(model_urls['iresnet152'],
                                                           root=default_cache_path)))
    return model


def iresnet200(pretrained=False, **kwargs):
    """Constructs a iResNet-200 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = iResNet(Bottleneck, [3, 24, 36, 3], **kwargs)
    if pretrained:
        os.makedirs(default_cache_path, exist_ok=True)
        model.load_state_dict(torch.load(download_from_url(model_urls['iresnet200'],
                                                           root=default_cache_path)))
    return model


def iresnet302(pretrained=False, **kwargs):
    """Constructs a iResNet-302 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = iResNet(Bottleneck, [4,  34, 58, 4], **kwargs)
    if pretrained:
        os.makedirs(default_cache_path, exist_ok=True)
        model.load_state_dict(torch.load(download_from_url(model_urls['iresnet302'],
                                                           root=default_cache_path)))
    return model


def iresnet404(pretrained=False, **kwargs):
    """Constructs a iResNet-404 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = iResNet(Bottleneck, [4,  46, 80, 4], **kwargs)
    if pretrained:
        os.makedirs(default_cache_path, exist_ok=True)
        model.load_state_dict(torch.load(download_from_url(model_urls['iresnet404'],
                                                           root=default_cache_path)))
    return model


def iresnet1001(pretrained=False, **kwargs):
    """Constructs a iResNet-1001 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = iResNet(Bottleneck, [4,  155, 170, 4], **kwargs)
    if pretrained:
        os.makedirs(default_cache_path, exist_ok=True)
        model.load_state_dict(torch.load(download_from_url(model_urls['iresnet1001'],
                                                           root=default_cache_path)))
    return model

View Code

代码太长了，就折叠起来了。

看下iresnet50()的输出：



iResNet(
  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (relu): ReLU(inplace=True)
  (layer1): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
        (1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
      (bn0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
  )
  (layer2): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
        (1): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
      (bn0): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (relu): ReLU(inplace=True)
    )
    (3): Bottleneck(
      (bn0): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
  )
  (layer3): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
        (1): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
      (bn0): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (relu): ReLU(inplace=True)
    )
    (3): Bottleneck(
      (bn0): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (relu): ReLU(inplace=True)
    )
    (4): Bottleneck(
      (bn0): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (relu): ReLU(inplace=True)
    )
    (5): Bottleneck(
      (bn0): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
  )
  (layer4): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
        (1): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (2): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
      (bn0): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
  )
  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
  (fc): Linear(in_features=2048, out_features=1000, bias=True)
)


然后是原始resnet50的输出：



ResNet(
  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (relu): ReLU(inplace=True)
  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
  (layer1): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
  )
  (layer2): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (3): Bottleneck(
      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
  )
  (layer3): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (3): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (4): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (5): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
  )
  (layer4): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
  )
  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
  (fc): Linear(in_features=2048, out_features=1000, bias=True)
)


我们对照着看下iresnet50和resnet50有什么异同：

（1）第一个不同点

在iresnet50中：

【论文笔记】Improved Residual Networks for Image and Video Recognition（ResNet新变体：IResNet）_卷积_03

在resnet50中：

【论文笔记】Improved Residual Networks for Image and Video Recognition（ResNet新变体：IResNet）_论文_04

iresnet50与resnet50相比：少了一个MaxPool2d。 

（2） 第一个相同点

【论文笔记】Improved Residual Networks for Image and Video Recognition（ResNet新变体：IResNet）_卷积_05

这里是每一组中的第0个bottleneckl。特别需要注意最后的一个ReLU。中间的两个ReLU由于是在前向传播过程中计算的，因此在打印模型结构的时候省略了。而且iresnet最后主干路上是没有ReLU激活函数的。

（3） 第二个不同点

在iresnet60中：

【论文笔记】Improved Residual Networks for Image and Video Recognition（ResNet新变体：IResNet）_权值_06

在resnet50中：

【论文笔记】Improved Residual Networks for Image and Video Recognition（ResNet新变体：IResNet）_2d_07

当某残差块和下一层残差块维度不一致时，iresnet50中跳跃连接进行了改动。

（4）第三个不同点

咋irenet50中：

【论文笔记】Improved Residual Networks for Image and Video Recognition（ResNet新变体：IResNet）_权值_08

在resnet50中：

【论文笔记】Improved Residual Networks for Image and Video Recognition（ResNet新变体：IResNet）_ide_09

这里是每一组中的第1个bottleneck。与resnet50相比，iresnet50这conv3之后没有bn3层 。同样的，这里的主干路上也没有ReLU。

（5）第四个不同点

在Iresnet50中：

【论文笔记】Improved Residual Networks for Image and Video Recognition（ResNet新变体：IResNet）_权值_10

在第1个bottleneck之后的botleneck使用的就是上述所示的结构，即bn+relu+conv的组合。在主干路上同样没有ReLU。

（6） 第五个不同点

每一组中的最后一个bottleneck，在（5）的基础上在主干路上增加一个BN+ReLU。

构造残差块的方式就如Fig 1所示：

【论文笔记】Improved Residual Networks for Image and Video Recognition（ResNet新变体：IResNet）_论文_11

针对于downsampling的改进就是下图所示：

 【论文笔记】Improved Residual Networks for Image and Video Recognition（ResNet新变体：IResNet）_论文_12

除此之外，还借鉴了ResNeXt的思想，同时将bottleneck中通道的增减顺序颠倒过来，即先利用1×1卷积增加通道，最后在进行减少通道。

【论文笔记】Improved Residual Networks for Image and Video Recognition（ResNet新变体：IResNet）_ide_13

使用了ResGrou block的网络结构如下所示：

【论文笔记】Improved Residual Networks for Image and Video Recognition（ResNet新变体：IResNet）_论文_14

知道了其实怎么做的，接下来就是解决为什么它要这么做？

1、为什么在原始ResNet中每一组残差块的前中部分的主干上都不使用ReLU？

这个位置的ReLU会让负权值清零，对信息传播带来负面影响，特别是在网络刚开始训练的时候，会存在很多负权值、

2、pre-act比原始ResNet更易于优化，为什么还要对pre-act ResNet的模块进行改动？

（1）直连通道上没有BN，使得全信号通道上没有归一化，增加了学习的难度

（2）四个残差组之间都是以1个1×1卷积结束，几个block之间缺少非线性，限制了学习的能力。

因此在每一组中最后一个bottleneck的主干上加一个ReLU+BN。

3、为什么要对下采样层进行改进？

因为使用1×1的步长为2的卷积会丢失75%的重要信息，而余下的25%的信息也没有设计什么有意义的标准，会对主干路上的流信息造成负面影响。

4、为什么使用本文的方式进行下采样层的改造？

改造后将考虑来自特征映射的所有信息，并在下一步中选择激活度最高的元素，减少了信息的损失。同时，conv3×3可以视为软下采样，3×3max pooling可以视为硬下采样，两种方式优势可以互补。硬下采样有助于选择激活度最高的元素，软下采样有助于不丢失所有空间信息，有利于更好的定位。

5、为什么要设计ResGroup?

原始bottleneck中先使用1×1卷积减少通道数目，再使用3×3卷积操作，最后使用1×1卷积增加通道数目。而botleneck的核心是3×3卷积， 3×3卷积可操作的通道数目太少会削弱网络的学习能力，因此考虑先增加通道，最后再进行减少。为了减少参数和浮点计算量采用分组卷积的方式。

最终结果就不一一说明了，直接上图：

【论文笔记】Improved Residual Networks for Image and Video Recognition（ResNet新变体：IResNet）_论文_15

【论文笔记】Improved Residual Networks for Image and Video Recognition（ResNet新变体：IResNet）_论文_16

【论文笔记】Improved Residual Networks for Image and Video Recognition（ResNet新变体：IResNet）_卷积_17

【论文笔记】Improved Residual Networks for Image and Video Recognition（ResNet新变体：IResNet）_权值_18

【论文笔记】Improved Residual Networks for Image and Video Recognition（ResNet新变体：IResNet）_权值_19

 

后面还有一些就不粘了，感兴趣的可以去看原论文。需要注意的是该方法成功唉cifar10/cifar100上训练出了一个3002层的网络，在imagenet上成功训练出404层的网络。虽然一味的增加网络的深度并不是有效的，但是在将来的某一天，更深的网络也许会找到其适合的任务。

 

如有错误，欢迎指出。