text: # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
  import numpy as np
  import fvcore.nn.weight_init as weight_init
  import torch
  import torch.nn.functional as F
  from torch import nn
  
  from detectron2.layers import (
      CNNBlockBase,
      Conv2d,
      DeformConv,
      ModulatedDeformConv,
      ShapeSpec,
      get_norm,
  )
  
  from detectron2.modeling.backbone import Backbone
  from detectron2.modeling import BACKBONE_REGISTRY
  
  __all__ = [
      "ResNetBlockBase",
      "BasicBlock",
      "BottleneckBlock",
      "DeformBottleneckBlock",
      "BasicStem",
      "ResNet",
      "make_stage",
      "build_two_stem_resnet_backbone",
  ]
  
  
  class BasicBlock(CNNBlockBase):
      """
      The basic residual block for ResNet-18 and ResNet-34 defined in :paper:`ResNet`,
      with two 3x3 conv layers and a projection shortcut if needed.
      """
  
      def __init__(self, in_channels, out_channels, *, stride=1, norm="BN"):
          """
          Args:
              in_channels (int): Number of input channels.
              out_channels (int): Number of output channels.
              stride (int): Stride for the first conv.
              norm (str or callable): normalization for all conv layers.
                  See :func:`layers.get_norm` for supported format.
          """
          super().__init__(in_channels, out_channels, stride)
  
          if in_channels != out_channels:
              self.shortcut = Conv2d(
                  in_channels,
                  out_channels,
                  kernel_size=1,
                  stride=stride,
                  bias=False,
                  norm=get_norm(norm, out_channels),
              )
          else:
              self.shortcut = None
  
          self.conv1 = Conv2d(
              in_channels,
              out_channels,
              kernel_size=3,
              stride=stride,
              padding=1,
              bias=False,
              norm=get_norm(norm, out_channels),
          )
  
          self.conv2 = Conv2d(
              out_channels,
              out_channels,
              kernel_size=3,
              stride=1,
              padding=1,
              bias=False,
              norm=get_norm(norm, out_channels),
          )
  
          for layer in [self.conv1, self.conv2, self.shortcut]:
              if layer is not None:  # shortcut can be None
                  weight_init.c2_msra_fill(layer)
  
      def forward(self, x):
          out = self.conv1(x)
          out = F.relu_(out)
          out = self.conv2(out)
  
          if self.shortcut is not None:
              shortcut = self.shortcut(x)
          else:
              shortcut = x
  
          out += shortcut
          out = F.relu_(out)
          return out
  
  
  class BottleneckBlock(CNNBlockBase):
      """
      The standard bottleneck residual block used by ResNet-50, 101 and 152
      defined in :paper:`ResNet`.  It contains 3 conv layers with kernels
      1x1, 3x3, 1x1, and a projection shortcut if needed.
      """
  
      def __init__(
          self,
          in_channels,
          out_channels,
          *,
          bottleneck_channels,
          stride=1,
          num_groups=1,
          norm="BN",
          stride_in_1x1=False,
          dilation=1,
      ):
          """
          Args:
              bottleneck_channels (int): number of output channels for the 3x3
                  "bottleneck" conv layers.
              num_groups (int): number of groups for the 3x3 conv layer.
              norm (str or callable): normalization for all conv layers.
                  See :func:`layers.get_norm` for supported format.
              stride_in_1x1 (bool): when stride>1, whether to put stride in the
                  first 1x1 convolution or the bottleneck 3x3 convolution.
              dilation (int): the dilation rate of the 3x3 conv layer.
          """
          super().__init__(in_channels, out_channels, stride)
  
          if in_channels != out_channels:
              self.shortcut = Conv2d(
                  in_channels,
                  out_channels,
                  kernel_size=1,
                  stride=stride,
                  bias=False,
                  norm=get_norm(norm, out_channels),
              )
          else:
              self.shortcut = None
  
          # The original MSRA ResNet models have stride in the first 1x1 conv
          # The subsequent fb.torch.resnet and Caffe2 ResNe[X]t implementations have
          # stride in the 3x3 conv
          stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride)
  
          self.conv1 = Conv2d(
              in_channels,
              bottleneck_channels,
              kernel_size=1,
              stride=stride_1x1,
              bias=False,
              norm=get_norm(norm, bottleneck_channels),
          )
  
          self.conv2 = Conv2d(
              bottleneck_channels,
              bottleneck_channels,
              kernel_size=3,
              stride=stride_3x3,
              padding=1 * dilation,
              bias=False,
              groups=num_groups,
              dilation=dilation,
              norm=get_norm(norm, bottleneck_channels),
          )
  
          self.conv3 = Conv2d(
              bottleneck_channels,
              out_channels,
              kernel_size=1,
              bias=False,
              norm=get_norm(norm, out_channels),
          )
  
          for layer in [self.conv1, self.conv2, self.conv3, self.shortcut]:
              if layer is not None:  # shortcut can be None
                  weight_init.c2_msra_fill(layer)
  
          # Zero-initialize the last normalization in each residual branch,
          # so that at the beginning, the residual branch starts with zeros,
          # and each residual block behaves like an identity.
          # See Sec 5.1 in "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour":
          # "For BN layers, the learnable scaling coefficient γ is initialized
          # to be 1, except for each residual block's last BN
          # where γ is initialized to be 0."
  
          # nn.init.constant_(self.conv3.norm.weight, 0)
          # TODO this somehow hurts performance when training GN models from scratch.
          # Add it as an option when we need to use this code to train a backbone.
  
      def forward(self, x):
          out = self.conv1(x)
          out = F.relu_(out)
  
          out = self.conv2(out)
          out = F.relu_(out)
  
          out = self.conv3(out)
  
          if self.shortcut is not None:
              shortcut = self.shortcut(x)
          else:
              shortcut = x
  
          out += shortcut
          out = F.relu_(out)
          return out
  
  
  class DeformBottleneckBlock(CNNBlockBase):
      """
      Similar to :class:`BottleneckBlock`, but with :paper:`deformable conv <deformconv>`
      in the 3x3 convolution.
      """
  
      def __init__(
          self,
          in_channels,
          out_channels,
          *,
          bottleneck_channels,
          stride=1,
          num_groups=1,
          norm="BN",
          stride_in_1x1=False,
          dilation=1,
          deform_modulated=False,
          deform_num_groups=1,
      ):
          super().__init__(in_channels, out_channels, stride)
          self.deform_modulated = deform_modulated
  
          if in_channels != out_channels:
              self.shortcut = Conv2d(
                  in_channels,
                  out_channels,
                  kernel_size=1,
                  stride=stride,
                  bias=False,
                  norm=get_norm(norm, out_channels),
              )
          else:
              self.shortcut = None
  
          stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride)
  
          self.conv1 = Conv2d(
              in_channels,
              bottleneck_channels,
              kernel_size=1,
              stride=stride_1x1,
              bias=False,
              norm=get_norm(norm, bottleneck_channels),
          )
  
          if deform_modulated:
              deform_conv_op = ModulatedDeformConv
              # offset channels are 2 or 3 (if with modulated) * kernel_size * kernel_size
              offset_channels = 27
          else:
              deform_conv_op = DeformConv
              offset_channels = 18
  
          self.conv2_offset = Conv2d(
              bottleneck_channels,
              offset_channels * deform_num_groups,
              kernel_size=3,
              stride=stride_3x3,
              padding=1 * dilation,
              dilation=dilation,
          )
          self.conv2 = deform_conv_op(
              bottleneck_channels,
              bottleneck_channels,
              kernel_size=3,
              stride=stride_3x3,
              padding=1 * dilation,
              bias=False,
              groups=num_groups,
              dilation=dilation,
              deformable_groups=deform_num_groups,
              norm=get_norm(norm, bottleneck_channels),
          )
  
          self.conv3 = Conv2d(
              bottleneck_channels,
              out_channels,
              kernel_size=1,
              bias=False,
              norm=get_norm(norm, out_channels),
          )
  
          for layer in [self.conv1, self.conv2, self.conv3, self.shortcut]:
              if layer is not None:  # shortcut can be None
                  weight_init.c2_msra_fill(layer)
  
          nn.init.constant_(self.conv2_offset.weight, 0)
          nn.init.constant_(self.conv2_offset.bias, 0)
  
      def forward(self, x):
          out = self.conv1(x)
          out = F.relu_(out)
  
          if self.deform_modulated:
              offset_mask = self.conv2_offset(out)
              offset_x, offset_y, mask = torch.chunk(offset_mask, 3, dim=1)
              offset = torch.cat((offset_x, offset_y), dim=1)
              mask = mask.sigmoid()
              out = self.conv2(out, offset, mask)
          else:
              offset = self.conv2_offset(out)
              out = self.conv2(out, offset)
          out = F.relu_(out)
  
          out = self.conv3(out)
  
          if self.shortcut is not None:
              shortcut = self.shortcut(x)
          else:
              shortcut = x
  
          out += shortcut
          out = F.relu_(out)
          return out
  
  
  class BasicStem(CNNBlockBase):
      """
      The standard ResNet stem (layers before the first residual block).
      """
  
      def __init__(self, in_channels=3, out_channels=64, norm="BN"):
          """
          Args:
              norm (str or callable): norm after the first conv layer.
                  See :func:`layers.get_norm` for supported format.
          """
          super().__init__(in_channels, out_channels, 4)
          self.in_channels = in_channels
          self.conv1 = Conv2d(
              in_channels,
              out_channels,
              kernel_size=7,
              stride=2,
              padding=3,
              bias=False,
              norm=get_norm(norm, out_channels),
          )
          weight_init.c2_msra_fill(self.conv1)
  
      def forward(self, x):
          x = self.conv1(x)
          x = F.relu_(x)
          x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
          return x
  
  
  class ResNet(Backbone):
      """
      Implement :paper:`ResNet`.
      """
  
      def __init__(self, stem, stem_pre, stages, num_classes=None, out_features=None):
          """
          Args:
              stem (nn.Module): a stem module
              stages (list[list[CNNBlockBase]]): several (typically 4) stages,
                  each contains multiple :class:`CNNBlockBase`.
              num_classes (None or int): if None, will not perform classification.
                  Otherwise, will create a linear layer.
              out_features (list[str]): name of the layers whose outputs should
                  be returned in forward. Can be anything in "stem", "linear", or "res2" ...
                  If None, will return the output of the last layer.
          """
          super().__init__()
          self.stem = stem
          self.stem_pre = stem_pre
          self.num_classes = num_classes
  
          current_stride = self.stem.stride
          self._out_feature_strides = {"stem": current_stride}
          self._out_feature_channels = {"stem": self.stem.out_channels}
  
          self.stages_and_names = []
          for i, blocks in enumerate(stages):
              assert len(blocks) > 0, len(blocks)
              for block in blocks:
                  assert isinstance(block, CNNBlockBase), block
  
              name = "res" + str(i + 2)
              stage = nn.Sequential(*blocks)
  
              self.add_module(name, stage)
              self.stages_and_names.append((stage, name))
  
              self._out_feature_strides[name] = current_stride = int(
                  current_stride * np.prod([k.stride for k in blocks])
              )
              self._out_feature_channels[name] = curr_channels = blocks[-1].out_channels
  
          if num_classes is not None:
              self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
              self.linear = nn.Linear(curr_channels, num_classes)
  
              # Sec 5.1 in "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour":
              # "The 1000-way fully-connected layer is initialized by
              # drawing weights from a zero-mean Gaussian with standard deviation of 0.01."
              nn.init.normal_(self.linear.weight, std=0.01)
              name = "linear"
  
          if out_features is None:
              out_features = [name]
          self._out_features = out_features
          assert len(self._out_features)
          children = [x[0] for x in self.named_children()]
          for out_feature in self._out_features:
              assert out_feature in children, "Available children: {}".format(", ".join(children))
  
      def forward(self, x, x_pre):
          """
          Args:
              x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.
  
          Returns:
              dict[str->Tensor]
          """
          assert x.dim() == 4, f"ResNet takes an input of shape (N, C, H, W). Got {x.shape} instead!"
          outputs = {}
          x = self.stem(x)
          x += self.stem_pre(x_pre)
          x /= 2
          if "stem" in self._out_features:
              outputs["stem"] = x
          for stage, name in self.stages_and_names:
              x = stage(x)
              if name in self._out_features:
                  outputs[name] = x
          if self.num_classes is not None:
              x = self.avgpool(x)
              x = torch.flatten(x, 1)
              x = self.linear(x)
              if "linear" in self._out_features:
                  outputs["linear"] = x
          return outputs
  
      def output_shape(self):
          return {
              name: ShapeSpec(
                  channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]
              )
              for name in self._out_features
          }
  
      def freeze(self, freeze_at=0):
          """
          Freeze the first several stages of the ResNet. Commonly used in
          fine-tuning.
  
          Layers that produce the same feature map spatial size are defined as one
          "stage" by :paper:`FPN`.
  
          Args:
              freeze_at (int): number of stages to freeze.
                  `1` means freezing the stem. `2` means freezing the stem and
                  one residual stage, etc.
  
          Returns:
              nn.Module: this ResNet itself
          """
          if freeze_at >= 1:
              self.stem.freeze()
              self.stem_pre.freeze()
          for idx, (stage, _) in enumerate(self.stages_and_names, start=2):
              if freeze_at >= idx:
                  for block in stage.children():
                      block.freeze()
          return self
  
      @staticmethod
      def make_stage(block_class, num_blocks, first_stride, *, in_channels, out_channels, **kwargs):
          """
          Create a list of blocks of the same type that forms one ResNet stage.
          Layers that produce the same feature map spatial size are defined as one
          "stage" by :paper:`FPN`.
  
          Args:
              block_class (type): a subclass of CNNBlockBase that's used to create all blocks in this
                  stage. A module of this type must not change spatial resolution of inputs unless its
                  stride != 1.
              num_blocks (int): number of blocks in this stage
              first_stride (int): the stride of the first block. The other blocks will have stride=1.
                  Therefore this is also the stride of the entire stage.
              in_channels (int): input channels of the entire stage.
              out_channels (int): output channels of **every block** in the stage.
              kwargs: other arguments passed to the constructor of `block_class`.
  
          Returns:
              list[nn.Module]: a list of block module.
          """
          assert "stride" not in kwargs, "Stride of blocks in make_stage cannot be changed."
          blocks = []
          for i in range(num_blocks):
              blocks.append(
                  block_class(
                      in_channels=in_channels,
                      out_channels=out_channels,
                      stride=first_stride if i == 0 else 1,
                      **kwargs,
                  )
              )
              in_channels = out_channels
          return blocks
  
  
  ResNetBlockBase = CNNBlockBase
  """
  Alias for backward compatibiltiy.
  """
  
  
  def make_stage(*args, **kwargs):
      """
      Deprecated alias for backward compatibiltiy.
      """
      return ResNet.make_stage(*args, **kwargs)
  
  
  @BACKBONE_REGISTRY.register()
  def build_two_stem_resnet_backbone(cfg, input_shape):
      """
      Create a ResNet instance from config.
  
      Returns:
          ResNet: a :class:`ResNet` instance.
      """
      # need registration of new blocks/stems?
      norm = cfg.MODEL.RESNETS.NORM
      stem = BasicStem(
          in_channels=input_shape.channels,
          out_channels=cfg.MODEL.RESNETS.STEM_OUT_CHANNELS,
          norm=norm,
      )
  
      stem_pre = BasicStem(
          in_channels=input_shape.channels,
          out_channels=cfg.MODEL.RESNETS.STEM_OUT_CHANNELS,
          norm=norm,
      )
  
      # fmt: off
      freeze_at           = cfg.MODEL.BACKBONE.FREEZE_AT
      out_features        = cfg.MODEL.RESNETS.OUT_FEATURES
      depth               = cfg.MODEL.RESNETS.DEPTH
      num_groups          = cfg.MODEL.RESNETS.NUM_GROUPS
      width_per_group     = cfg.MODEL.RESNETS.WIDTH_PER_GROUP
      bottleneck_channels = num_groups * width_per_group
      in_channels         = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS
      out_channels        = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
      stride_in_1x1       = cfg.MODEL.RESNETS.STRIDE_IN_1X1
      res5_dilation       = cfg.MODEL.RESNETS.RES5_DILATION
      deform_on_per_stage = cfg.MODEL.RESNETS.DEFORM_ON_PER_STAGE
      deform_modulated    = cfg.MODEL.RESNETS.DEFORM_MODULATED
      deform_num_groups   = cfg.MODEL.RESNETS.DEFORM_NUM_GROUPS
      # fmt: on
      assert res5_dilation in {1, 2}, "res5_dilation cannot be {}.".format(res5_dilation)
  
      num_blocks_per_stage = {
          18: [2, 2, 2, 2],
          34: [3, 4, 6, 3],
          50: [3, 4, 6, 3],
          101: [3, 4, 23, 3],
          152: [3, 8, 36, 3],
      }[depth]
  
      if depth in [18, 34]:
          assert out_channels == 64, "Must set MODEL.RESNETS.RES2_OUT_CHANNELS = 64 for R18/R34"
          assert not any(
              deform_on_per_stage
          ), "MODEL.RESNETS.DEFORM_ON_PER_STAGE unsupported for R18/R34"
          assert res5_dilation == 1, "Must set MODEL.RESNETS.RES5_DILATION = 1 for R18/R34"
          assert num_groups == 1, "Must set MODEL.RESNETS.NUM_GROUPS = 1 for R18/R34"
  
      stages = []
  
      # Avoid creating variables without gradients
      # It consumes extra memory and may cause allreduce to fail
      out_stage_idx = [{"res2": 2, "res3": 3, "res4": 4, "res5": 5}[f] for f in out_features]
      max_stage_idx = max(out_stage_idx)
      for idx, stage_idx in enumerate(range(2, max_stage_idx + 1)):
          dilation = res5_dilation if stage_idx == 5 else 1
          first_stride = 1 if idx == 0 or (stage_idx == 5 and dilation == 2) else 2
          stage_kargs = {
              "num_blocks": num_blocks_per_stage[idx],
              "first_stride": first_stride,
              "in_channels": in_channels,
              "out_channels": out_channels,
              "norm": norm,
          }
          # Use BasicBlock for R18 and R34.
          if depth in [18, 34]:
              stage_kargs["block_class"] = BasicBlock
          else:
              stage_kargs["bottleneck_channels"] = bottleneck_channels
              stage_kargs["stride_in_1x1"] = stride_in_1x1
              stage_kargs["dilation"] = dilation
              stage_kargs["num_groups"] = num_groups
              if deform_on_per_stage[idx]:
                  stage_kargs["block_class"] = DeformBottleneckBlock
                  stage_kargs["deform_modulated"] = deform_modulated
                  stage_kargs["deform_num_groups"] = deform_num_groups
              else:
                  stage_kargs["block_class"] = BottleneckBlock
          blocks = ResNet.make_stage(**stage_kargs)
          in_channels = out_channels
          out_channels *= 2
          bottleneck_channels *= 2
          stages.append(blocks)
      return ResNet(stem, stem_pre, stages, out_features=out_features).freeze(freeze_at)
  