Resnet-18 作为 Faster R-CNN 的主干

Question

我用 pytorch 编码，我想使用resnet-18作为 Faster R-RCNN 的主干。当我打印resnet18 的结构时，这是输出：

>>import torch
>>import torchvision
>>import numpy as np
>>import torchvision.models as models

>>resnet18 = models.resnet18(pretrained=False)
>>print(resnet18)


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): BasicBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=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)
    )
    (1): BasicBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=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)
    )
  )
  (layer2): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=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)
      (downsample): Sequential(
        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=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)
    )
  )
  (layer3): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=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)
      (downsample): Sequential(
        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=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)
    )
  )
  (layer4): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=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)
      (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): BasicBlock(
      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=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)
    )
  )
  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
  (fc): Linear(in_features=512, out_features=1000, bias=True)
)

我的问题是，直到哪一层是特征提取器？是AdaptiveAvgPool2d应该快了R-CNN的骨干的一部分吗？

在本教程中，展示了如何用任意主干训练 Mask R-CNN，我想用 Faster R-CNN 做同样的事情，并用 resnet-18 训练一个 Faster R-CNN，但直到哪一层应该是一部分特征提取器对我来说很困惑。

我知道如何使用 resnet+Feature Pyramid Network 作为主干，我的问题是关于 resent。

Answer 1

如果我们想使用自适应平均池化的输出，我们对不同的 Resnet 使用此代码：

# backbone
        if backbone_name == 'resnet_18':
            resnet_net = torchvision.models.resnet18(pretrained=True)
            modules = list(resnet_net.children())[:-1]
            backbone = nn.Sequential(*modules)
            backbone.out_channels = 512
        elif backbone_name == 'resnet_34':
            resnet_net = torchvision.models.resnet34(pretrained=True)
            modules = list(resnet_net.children())[:-1]
            backbone = nn.Sequential(*modules)
            backbone.out_channels = 512
        elif backbone_name == 'resnet_50':
            resnet_net = torchvision.models.resnet50(pretrained=True)
            modules = list(resnet_net.children())[:-1]
            backbone = nn.Sequential(*modules)
            backbone.out_channels = 2048
        elif backbone_name == 'resnet_101':
            resnet_net = torchvision.models.resnet101(pretrained=True)
            modules = list(resnet_net.children())[:-1]
            backbone = nn.Sequential(*modules)
            backbone.out_channels = 2048
        elif backbone_name == 'resnet_152':
            resnet_net = torchvision.models.resnet152(pretrained=True)
            modules = list(resnet_net.children())[:-1]
            backbone = nn.Sequential(*modules)
            backbone.out_channels = 2048
        elif backbone_name == 'resnet_50_modified_stride_1':
            resnet_net = resnet50(pretrained=True)
            modules = list(resnet_net.children())[:-1]
            backbone = nn.Sequential(*modules)
            backbone.out_channels = 2048
        elif backbone_name == 'resnext101_32x8d':
            resnet_net = torchvision.models.resnext101_32x8d(pretrained=True)
            modules = list(resnet_net.children())[:-1]
            backbone = nn.Sequential(*modules)
            backbone.out_channels = 2048

如果我们想使用卷积特征图，我们使用以下代码：

 # backbone
        if backbone_name == 'resnet_18':
            resnet_net = torchvision.models.resnet18(pretrained=True)
            modules = list(resnet_net.children())[:-2]
            backbone = nn.Sequential(*modules)

        elif backbone_name == 'resnet_34':
            resnet_net = torchvision.models.resnet34(pretrained=True)
            modules = list(resnet_net.children())[:-2]
            backbone = nn.Sequential(*modules)

        elif backbone_name == 'resnet_50':
            resnet_net = torchvision.models.resnet50(pretrained=True)
            modules = list(resnet_net.children())[:-2]
            backbone = nn.Sequential(*modules)

        elif backbone_name == 'resnet_101':
            resnet_net = torchvision.models.resnet101(pretrained=True)
            modules = list(resnet_net.children())[:-2]
            backbone = nn.Sequential(*modules)

        elif backbone_name == 'resnet_152':
            resnet_net = torchvision.models.resnet152(pretrained=True)
            modules = list(resnet_net.children())[:-2]
            backbone = nn.Sequential(*modules)

        elif backbone_name == 'resnet_50_modified_stride_1':
            resnet_net = resnet50(pretrained=True)
            modules = list(resnet_net.children())[:-2]
            backbone = nn.Sequential(*modules)

        elif backbone_name == 'resnext101_32x8d':
            resnet_net = torchvision.models.resnext101_32x8d(pretrained=True)
            modules = list(resnet_net.children())[:-2]
            backbone = nn.Sequential(*modules)