text: import pdb
  import torch
  import numpy as np
  import torch.nn as nn
  import torch.nn.functional as F
  import torchvision.models as tmodels
  
  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)
  
  class BasicBlock(nn.Module):
      expansion = 1
  
      def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None):
          super(BasicBlock, self).__init__()
          self.conv1 = conv3x3(inplanes, planes, stride)
          self.bn1 = nn.BatchNorm2d(planes)
          self.relu = nn.ReLU(inplace=True)
          self.conv2 = conv3x3(planes, planes)
          self.bn2 = nn.BatchNorm2d(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, dilation=1, downsample=None):
          super(Bottleneck, self).__init__()
          self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
          self.bn1 = nn.BatchNorm2d(planes)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, dilation=dilation, padding=dilation, bias=False)
          self.bn2 = nn.BatchNorm2d(planes)
          self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
          self.bn3 = nn.BatchNorm2d(planes * self.expansion)
          self.relu = nn.ReLU(inplace=True)
          self.downsample = downsample
          self.stride = stride
  
      def forward(self, x):
          residual = 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:
              residual = self.downsample(x)
  
          out += residual
          out = self.relu(out)
  
          return out
  
  class DilatedResNet(nn.Module):
  
      def __init__(self, block, layers, strides=[1,2,2,2], dilations=[1,1,1,1]):
          self.inplanes = 64
          super(DilatedResNet, self).__init__()
          self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
          self.bn1 = nn.BatchNorm2d(64)
          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], stride=strides[0], dilation=dilations[0])
          self.layer2 = self._make_layer(block, 128, layers[1], stride=strides[1], dilation=dilations[1])
          self.layer3 = self._make_layer(block, 256, layers[2], stride=strides[2], dilation=dilations[2])
          self.layer4 = self._make_layer(block, 512, layers[3], stride=strides[3], dilation=dilations[3])
  
          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.init.constant_(m.weight, 1)
                  nn.init.constant_(m.bias, 0)
  
      def _make_layer(self, block, planes, blocks, stride=1, dilation=1):
          downsample = None
          if stride != 1 or self.inplanes != planes * block.expansion:
              downsample = nn.Sequential(
                  nn.Conv2d(self.inplanes, planes * block.expansion,
                            kernel_size=1, stride=stride, bias=False),
                  nn.BatchNorm2d(planes * block.expansion),
              )
  
          layers = []
          layers.append(block(self.inplanes, planes, stride=stride, dilation=dilation, downsample=downsample))
          self.inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(self.inplanes, planes))
  
          return nn.Sequential(*layers)
  
      def train(self, mode):
          return super(DilatedResNet, self).train(False)
  
      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)
          return x
  
  class DilatedResNetBackbone(nn.Module):
      def __init__(self, block, layers, strides=[1,2,2,2], dilations=[1,1,1,1], pretrained=None):
          super(DilatedResNetBackbone, self).__init__()
          rnet = DilatedResNet(block, layers, strides, dilations)
  
          pretrained = {k:v for k,v in pretrained.items() if 'fc' not in k}
          rnet.load_state_dict(pretrained)
          self.rnet = rnet
          for param in self.rnet.parameters():
              param.requires_grad = False
  
          self.spatial_dim = int(56//np.prod(strides))
          self.feat_dim = 2048
  
      def forward(self, x):
          with torch.no_grad():
              x = self.rnet(x)
          return x
  
  class R8(nn.Module):
      def __init__(self, block, layers, strides=[1,2,2,2], dilations=[1,1,1,1]):
          super(DilatedResNetBackbone, self).__init__()
          rnet = DilatedResNet(block, layers, strides, dilations)
  
          pretrained = {k:v for k,v in pretrained.items() if 'fc' not in k}
          rnet.load_state_dict(pretrained)
          self.rnet = rnet
          for param in self.rnet.parameters():
              param.requires_grad = False
  
          self.spatial_dim = int(56//np.prod(strides))
          self.feat_dim = 2048
  
      def forward(self, x):
          with torch.no_grad():
              x = self.rnet(x)
          return x
  
  def r50_n7():
      wts = tmodels.resnet50(pretrained=True).state_dict()
      net = DilatedResNetBackbone(Bottleneck, [3, 4, 6, 3], strides=[1,2,2,2], dilations=[1,1,1,1], pretrained=wts)
      return net
  
  def dr50_n28():
      wts = tmodels.resnet50(pretrained=True).state_dict()
      net = DilatedResNetBackbone(Bottleneck, [3, 4, 6, 3], strides=[1,2,1,1], dilations=[1,1,2,4], pretrained=wts)
      return net
  
  def dr50_n56(pretrained=True):
      wts = tmodels.resnet50(pretrained=pretrained).state_dict()
      net = DilatedResNetBackbone(Bottleneck, [3, 4, 6, 3], strides=[1,1,1,1], dilations=[1,2,4,8], pretrained=wts)
      return net
  
  def r8():
      net = DilatedResNet(Bottleneck, [1,1,1,1], strides=[1,2,2,2], dilations=[1,1,1,1])
      net.load_state_dict(tmodels.resnet101(pretrained=True).state_dict(), strict=False)
      net.feat_dim = 2048
      net.spatial_dim = 7
      return net
  
  class AVDResNet(nn.Module):
      def __init__(self, pretrained=True):
          super().__init__()
          resnet50 = dr50_n56(pretrained=pretrained)
          self.main = nn.Sequential(
                                  resnet50.rnet.conv1,
                                  resnet50.rnet.bn1,
                                  resnet50.rnet.relu,
                                  resnet50.rnet.maxpool,
                                  resnet50.rnet.layer1,
                                  resnet50.rnet.layer2,
                                  nn.Conv2d(512, 32, 1, 1)
                             )
  
      def forward(self, x):
          out = self.main(x)
          return out
  