content: # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
  """
  Backbone modules.
  """
  from collections import OrderedDict
  
  import torch
  import torch.nn.functional as F
  import torchvision
  from torch import nn
  from torchvision.models._utils import IntermediateLayerGetter
  from typing import Dict, List
  
  from util.misc import NestedTensor, is_main_process
  
  from .position_encoding import build_position_encoding
  
  import IPython
  e = IPython.embed
  
  class FrozenBatchNorm2d(torch.nn.Module):
      """
      BatchNorm2d where the batch statistics and the affine parameters are fixed.
  
      Copy-paste from torchvision.misc.ops with added eps before rqsrt,
      without which any other policy_models than torchvision.policy_models.resnet[18,34,50,101]
      produce nans.
      """
  
      def __init__(self, n):
          super(FrozenBatchNorm2d, self).__init__()
          self.register_buffer("weight", torch.ones(n))
          self.register_buffer("bias", torch.zeros(n))
          self.register_buffer("running_mean", torch.zeros(n))
          self.register_buffer("running_var", torch.ones(n))
  
      def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
                                missing_keys, unexpected_keys, error_msgs):
          num_batches_tracked_key = prefix + 'num_batches_tracked'
          if num_batches_tracked_key in state_dict:
              del state_dict[num_batches_tracked_key]
  
          super(FrozenBatchNorm2d, self)._load_from_state_dict(
              state_dict, prefix, local_metadata, strict,
              missing_keys, unexpected_keys, error_msgs)
  
      def forward(self, x):
          # move reshapes to the beginning
          # to make it fuser-friendly
          w = self.weight.reshape(1, -1, 1, 1)
          b = self.bias.reshape(1, -1, 1, 1)
          rv = self.running_var.reshape(1, -1, 1, 1)
          rm = self.running_mean.reshape(1, -1, 1, 1)
          eps = 1e-5
          scale = w * (rv + eps).rsqrt()
          bias = b - rm * scale
          return x * scale + bias
  
  
  class BackboneBase(nn.Module):
  
      def __init__(self, backbone: nn.Module, train_backbone: bool, num_channels: int, return_interm_layers: bool):
          super().__init__()
          # for name, parameter in backbone.named_parameters(): # only train later layers # TODO do we want this?
          #     if not train_backbone or 'layer2' not in name and 'layer3' not in name and 'layer4' not in name:
          #         parameter.requires_grad_(False)
          if return_interm_layers:
              return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"}
          else:
              return_layers = {'layer4': "0"}
          self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)
          self.num_channels = num_channels
  
      def forward(self, tensor):
          xs = self.body(tensor)
          return xs
          # out: Dict[str, NestedTensor] = {}
          # for name, x in xs.items():
          #     m = tensor_list.mask
          #     assert m is not None
          #     mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
          #     out[name] = NestedTensor(x, mask)
          # return out
  
  
  class Backbone(BackboneBase):
      """ResNet backbone with frozen BatchNorm."""
      def __init__(self, name: str,
                   train_backbone: bool,
                   return_interm_layers: bool,
                   dilation: bool):
          backbone = getattr(torchvision.models, name)(
              replace_stride_with_dilation=[False, False, dilation],
              pretrained=is_main_process(), norm_layer=FrozenBatchNorm2d) # pretrained # TODO do we want frozen batch_norm??
          num_channels = 512 if name in ('resnet18', 'resnet34') else 2048
          super().__init__(backbone, train_backbone, num_channels, return_interm_layers)
  
  
  class Joiner(nn.Sequential):
      def __init__(self, backbone, position_embedding):
          super().__init__(backbone, position_embedding)
  
      def forward(self, tensor_list: NestedTensor):
          xs = self[0](tensor_list)
          out: List[NestedTensor] = []
          pos = []
          for name, x in xs.items():
              out.append(x)
              # position encoding
              pos.append(self[1](x).to(x.dtype))
  
          return out, pos
  
  
  def build_backbone(args):
      position_embedding = build_position_encoding(args)
      train_backbone = args.lr_backbone > 0
      return_interm_layers = args.masks
      backbone = Backbone(args.backbone, train_backbone, return_interm_layers, args.dilation)
      model = Joiner(backbone, position_embedding)
      model.num_channels = backbone.num_channels
      return model
  