text: #!/usr/bin/env python
  # -*- encoding: utf-8 -*-
  # Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
  
  import torch
  import torch.nn as nn
  
  from .darknet import CSPDarknet
  from .network_blocks import BaseConv, CSPLayer, DWConv
  
  
  class YOLOPAFPN(nn.Module):
      """
      YOLOv3 model. Darknet 53 is the default backbone of this model.
      """
  
      def __init__(
          self, depth=1.0, width=1.0, in_features=("dark3", "dark4", "dark5"),
          in_channels=[256, 512, 1024], depthwise=False, act="silu",
      ):
          super().__init__()
          self.backbone = CSPDarknet(depth, width, depthwise=depthwise, act=act)
          self.in_features = in_features
          self.in_channels = in_channels
          Conv = DWConv if depthwise else BaseConv
  
          self.upsample = nn.Upsample(scale_factor=2, mode="nearest")
          self.lateral_conv0 = BaseConv(
              int(in_channels[2] * width), int(in_channels[1] * width), 1, 1, act=act
          )
          self.C3_p4 = CSPLayer(
              int(2 * in_channels[1] * width),
              int(in_channels[1] * width),
              round(3 * depth),
              False,
              depthwise=depthwise,
              act=act,
          )  # cat
  
          self.reduce_conv1 = BaseConv(
              int(in_channels[1] * width), int(in_channels[0] * width), 1, 1, act=act
          )
          self.C3_p3 = CSPLayer(
              int(2 * in_channels[0] * width),
              int(in_channels[0] * width),
              round(3 * depth),
              False,
              depthwise=depthwise,
              act=act,
          )
  
          # bottom-up conv
          self.bu_conv2 = Conv(
              int(in_channels[0] * width), int(in_channels[0] * width), 3, 2, act=act
          )
          self.C3_n3 = CSPLayer(
              int(2 * in_channels[0] * width),
              int(in_channels[1] * width),
              round(3 * depth),
              False,
              depthwise=depthwise,
              act=act,
          )
  
          # bottom-up conv
          self.bu_conv1 = Conv(
              int(in_channels[1] * width), int(in_channels[1] * width), 3, 2, act=act
          )
          self.C3_n4 = CSPLayer(
              int(2 * in_channels[1] * width),
              int(in_channels[2] * width),
              round(3 * depth),
              False,
              depthwise=depthwise,
              act=act,
          )
  
      def forward(self, input):
          """
          Args:
              inputs: input images.
  
          Returns:
              Tuple[Tensor]: FPN feature.
          """
  
          #  backbone
          out_features = self.backbone(input)
          features = [out_features[f] for f in self.in_features]
          [x2, x1, x0] = features
  
          fpn_out0 = self.lateral_conv0(x0)  # 1024->512/32
          f_out0 = self.upsample(fpn_out0)  # 512/16
          f_out0 = torch.cat([f_out0, x1], 1)  # 512->1024/16
          f_out0 = self.C3_p4(f_out0)  # 1024->512/16
  
          fpn_out1 = self.reduce_conv1(f_out0)  # 512->256/16
          f_out1 = self.upsample(fpn_out1)  # 256/8
          f_out1 = torch.cat([f_out1, x2], 1)  # 256->512/8
          pan_out2 = self.C3_p3(f_out1)  # 512->256/8
  
          p_out1 = self.bu_conv2(pan_out2)  # 256->256/16
          p_out1 = torch.cat([p_out1, fpn_out1], 1)  # 256->512/16
          pan_out1 = self.C3_n3(p_out1)  # 512->512/16
  
          p_out0 = self.bu_conv1(pan_out1)  # 512->512/32
          p_out0 = torch.cat([p_out0, fpn_out0], 1)  # 512->1024/32
          pan_out0 = self.C3_n4(p_out0)  # 1024->1024/32
  
          outputs = (pan_out2, pan_out1, pan_out0)
          return outputs
  