code: from collections import OrderedDict 
  import torch
  from torch import Tensor
  import torch.nn as nn
  from torch.utils.model_zoo import load_url as load_state_dict_from_url
  from ptnetworks.ActivationTracker import ActivationTracker
  from typing import Type, Any, Callable, Union, List, Optional
  
  
  class ResNetCIFAR(nn.Module):
      def __init__(self,
          variant='resnet050', 
          n_classes=100, 
          pretrained=False, 
          freeze_features_until='', #exclusive
          no_gradient_required=False,
          enforce_batchnorm_requires_gradient=False,
          n_layers_to_be_removed_from_blocks=[],
          no_classifier=False,
          activation='relu',
          init_mode='kaiming_normal',
          statedict='',
          strict_loading=True):
          super().__init__()
  
          arg_dict = {
              'pretrained' : pretrained,
              'num_classes' : n_classes,
              'init_mode' : init_mode,
              'activation' : activation,
          }
  
          if variant == 'resnet018':
              self.embedded_model = resnet18(**arg_dict)
          elif variant == 'resnet034':
              self.embedded_model = resnet34(**arg_dict)
          elif variant == 'resnet050':
              self.embedded_model = resnet50(**arg_dict)
          elif variant == 'resnet101':
              self.embedded_model = resnet101(**arg_dict)
          elif variant == 'resnet152':
              self.embedded_model = resnet152(**arg_dict)
          elif variant == 'resnext050_32x4d':
              self.embedded_model = resnext50_32x4d(**arg_dict)
          elif variant == 'resnext101_32x8d':
              self.embedded_model = resnext101_32x8d(**arg_dict)
          elif variant == 'wide_resnet050_2':
              self.embedded_model = wide_resnet50_2(**arg_dict)
          elif variant == 'wide_resnet101_2':
              self.embedded_model = wide_resnet101_2(**arg_dict)
          else:
              print('select valid model variant')
  
          if no_classifier:
              self.embedded_model.classifier = nn.Identity()
  
          module_dict = OrderedDict([
                  ('classifier', self.embedded_model.classifier),
                  ('layer4', self.embedded_model.layer4),
                  ('layer3', self.embedded_model.layer3),
                  ('layer2', self.embedded_model.layer2),
                  ('layer1', self.embedded_model.layer1),
              ])
          
          if freeze_features_until:
              for param in self.embedded_model.parameters():
                  param.requires_grad = False
              
              if freeze_features_until not in module_dict:
                  raise ValueError("freeue_features_until does not match any network module")
              
              for key, module in module_dict.items():
                  for param in module.parameters():
                      param.requires_grad = True
                  if freeze_features_until == key:
                      break
  
          if n_layers_to_be_removed_from_blocks:
              modules = [
                  self.embedded_model.layer1,
                  self.embedded_model.layer2,
                  self.embedded_model.layer3,
                  self.embedded_model.layer4,
              ]
              for n_layers, layer in zip(n_layers_to_be_removed_from_blocks, modules):
                  for i in range(n_layers):
                      layer[-i-1] = nn.Identity()
  
          if statedict:
              pretrained_dict = torch.load(statedict, map_location=torch.device('cpu'))
              missing = self.load_state_dict(pretrained_dict, strict=strict_loading)
              print('Loading weights from statedict. Missing and unexpected keys:')
              print(missing)
  
          if enforce_batchnorm_requires_gradient:
              for m in self.embedded_model.modules():
                  if isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
                      for param in m.parameters():
                          param.requires_grad = True
  
          if no_gradient_required:
              for param in self.embedded_model.parameters():
                  param.requires_grad = False
                  
      def forward(self, batch):
          if isinstance(batch, dict) and 'data' in batch:
              logits = self.embedded_model(batch['data'])
              out = {'logits' : logits}
              return out
          else:
              return self.embedded_model(batch)
  
      def forward_features(self, batch, module=None):
          track_modules = ActivationTracker()
  
          assert isinstance(batch, dict) and 'data' in batch
          logits, activation_dict = track_modules.collect_stats(self.embedded_model, batch['data'], module)
          out = {'logits' : logits, 'activations' : activation_dict}
          return out
              
      def save(self, statedict_name):
          torch.save(self.state_dict(), statedict_name)
  
          
  MODEL_DIR = '/nfshome/linse/NO_INB_BACKUP/ModelZoo'
  
  
  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',
      'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
      'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
      'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
      'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
  }
  
  
  def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv2d:
      """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: int, out_planes: int, stride: int = 1) -> nn.Conv2d:
      """1x1 convolution"""
      return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
  
  
  class BasicBlock(nn.Module):
      expansion: int = 1
  
      def __init__(
          self,
          inplanes: int,
          planes: int,
          stride: int = 1,
          downsample: Optional[nn.Module] = None,
          groups: int = 1,
          base_width: int = 64,
          dilation: int = 1,
          norm_layer: Optional[Callable[..., nn.Module]] = None,
          activation_layer=nn.ReLU
      ) -> 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_1 = activation_layer(inplace=False)
          self.conv2 = conv3x3(planes, planes)
          self.bn2 = norm_layer(planes)
          self.downsample = downsample
          self.relu_2 = activation_layer(inplace=False)
          self.stride = stride
  
      def forward(self, x: Tensor) -> Tensor:
          identity = x
  
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu_1(out)
  
          out = self.conv2(out)
          out = self.bn2(out)
  
          if self.downsample is not None:
              identity = self.downsample(x)
  
          out += identity
          out = self.relu_2(out)
  
          return out
  
  
  class Bottleneck(nn.Module):
      # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
      # while original implementation places the stride at the first 1x1 convolution(self.conv1)
      # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
      # This variant is also known as ResNet V1.5 and improves accuracy according to
      # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
  
      expansion: int = 4
  
      def __init__(
          self,
          inplanes: int,
          planes: int,
          stride: int = 1,
          downsample: Optional[nn.Module] = None,
          groups: int = 1,
          base_width: int = 64,
          dilation: int = 1,
          norm_layer: Optional[Callable[..., nn.Module]] = None,
          activation_layer=nn.ReLU
      ) -> 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_1 = activation_layer(inplace=False)
          self.relu_2 = activation_layer(inplace=False)
          self.relu_3 = activation_layer(inplace=False)
          self.downsample = downsample
          self.stride = stride
  
      def forward(self, x: Tensor) -> Tensor:
          identity = x
  
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu_1(out)
  
          out = self.conv2(out)
          out = self.bn2(out)
          out = self.relu_2(out)
  
          out = self.conv3(out)
          out = self.bn3(out)
  
          if self.downsample is not None:
              identity = self.downsample(x)
  
          out += identity
          out = self.relu_3(out)
  
          return out
  
  
  class ResNet(nn.Module):
  
      def __init__(
          self,
          block: Type[Union[BasicBlock, Bottleneck]],
          layers: List[int],
          num_classes: int = 1000,
          zero_init_residual: bool = False,
          groups: int = 1,
          width_per_group: int = 64,
          replace_stride_with_dilation: Optional[List[bool]] = None,
          norm_layer: Optional[Callable[..., nn.Module]] = None,
          init_mode='kaiming_normal',
          activation='relu',
      ) -> None:
          super().__init__()
  
          self.ID = 'ResNet'
  
          if activation == 'relu':
              activation_layer = nn.ReLU
          elif activation == 'leaky_relu':
              activation_layer = nn.LeakyReLU
          self._activation_layer = activation_layer
  
          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
          #for CIFAR we choose a kernel size of 3 in the first convolutional layer
          self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=2, padding=3,
                                 bias=False)
          self.conv1.ID = self.ID + '_first_layer'
          self.bn1 = norm_layer(self.inplanes)
          self.relu = self._activation_layer(inplace=False)
          #we do not apply maxpooling after the first layer for CIFAR
          self.maxpool = nn.Identity() #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.classifier = nn.Linear(512 * block.expansion, num_classes)
  
          self.reinitialize(init_mode, activation, zero_init_residual)
  
  
      def reinitialize(self, init_mode, activation, zero_init_residual):
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  if init_mode == 'kaiming_normal':
                      nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity=activation)
                  elif init_mode == 'kaiming_uniform':
                      nn.init.kaiming_uniform_(m.weight, mode='fan_out', nonlinearity=activation)
                  elif init_mode == 'sparse':
                      nn.init.sparse_(m.weight, sparsity=0.1, std=0.01)
                  elif init_mode == 'orthogonal':
                      nn.init.orthogonal_(m.weight, gain=1)
              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)  # type: ignore[arg-type]
                  elif isinstance(m, BasicBlock):
                      nn.init.constant_(m.bn2.weight, 0)  # type: ignore[arg-type]
  
  
      def _make_layer(self, block: Type[Union[BasicBlock, Bottleneck]], planes: int, blocks: int,
                      stride: int = 1, dilate: bool = False) -> nn.Sequential:
          norm_layer = self._norm_layer
          downsample = None
          activation_layer = self._activation_layer
          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, activation_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, activation_layer=activation_layer))
  
          return nn.Sequential(*layers)
  
      def _forward_impl(self, x: Tensor) -> Tensor:
          # See note [TorchScript super()]
          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 = torch.flatten(x, 1)
          x = self.classifier(x)
  
          return x
  
      def forward(self, x: Tensor) -> Tensor:
          return self._forward_impl(x)
  
  
  def _resnet(
      arch: str,
      block: Type[Union[BasicBlock, Bottleneck]],
      layers: List[int],
      pretrained: bool,
      progress: bool,
      **kwargs: Any
  ) -> ResNet:
      model = ResNet(block, layers, **kwargs)
      if pretrained:
          state_dict = load_state_dict_from_url(model_urls[arch], progress=progress, model_dir=MODEL_DIR)
          model.load_state_dict(state_dict, strict=False)
      return model
  
  
  def resnet18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
      r"""ResNet-18 model from
      `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
  
      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: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
      r"""ResNet-34 model from
      `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
  
      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: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
      r"""ResNet-50 model from
      `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
  
      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: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
      r"""ResNet-101 model from
      `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
  
      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: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
      r"""ResNet-152 model from
      `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
  
      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(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
      r"""ResNeXt-50 32x4d model from
      `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
  
      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
      """
      kwargs['groups'] = 32
      kwargs['width_per_group'] = 4
      return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],
                     pretrained, progress, **kwargs)
  
  
  
  def resnext101_32x8d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
      r"""ResNeXt-101 32x8d model from
      `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
  
      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
      """
      kwargs['groups'] = 32
      kwargs['width_per_group'] = 8
      return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
                     pretrained, progress, **kwargs)
  
  
  
  def wide_resnet50_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
      r"""Wide ResNet-50-2 model from
      `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
  
      The model is the same as ResNet except for the bottleneck number of channels
      which is twice larger in every block. The number of channels in outer 1x1
      convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
      channels, and in Wide ResNet-50-2 has 2048-1024-2048.
  
      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
      """
      kwargs['width_per_group'] = 64 * 2
      return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3],
                     pretrained, progress, **kwargs)
  
  
  
  def wide_resnet101_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
      r"""Wide ResNet-101-2 model from
      `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
  
      The model is the same as ResNet except for the bottleneck number of channels
      which is twice larger in every block. The number of channels in outer 1x1
      convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
      channels, and in Wide ResNet-50-2 has 2048-1024-2048.
  
      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
      """
      kwargs['width_per_group'] = 64 * 2
      return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],
                     pretrained, progress, **kwargs)
apis: torch.nn.init.kaiming_normal_ torch.nn.Linear torch.nn.init.constant_ torch.nn.init.sparse_ torch.nn.AdaptiveAvgPool2d torch.flatten torch.nn.Conv2d torch.nn.Identity torch.nn.Sequential torch.utils.model_zoo.load_url torch.nn.init.kaiming_uniform_ torch.device torch.nn.init.orthogonal_