YOLOV5增加AFPN

对大佬的改进进行学习过程中发现的问题及解决，原文

yolov5增加AFPN-全新特征融合模块AFPN，效果完胜PAFPN_athrunsunny的博客-优快云博客

1.common.py加入  导入 import torch.nn.functional as F

class Upsample(nn.Module):
    def __init__(self, in_channels, out_channels, scale_factor=2):
        super(Upsample, self).__init__()
 
        self.upsample = nn.Sequential(
            Conv(in_channels, out_channels, 1),
            nn.Upsample(scale_factor=scale_factor, mode='bilinear')
        )
 
        # carafe
        # from mmcv.ops import CARAFEPack
        # self.upsample = nn.Sequential(
        #     BasicConv(in_channels, out_channels, 1),
        #     CARAFEPack(out_channels, scale_factor=scale_factor)
        # )
 
    def forward(self, x):
        x = self.upsample(x)
 
        return x
 
class Downsample(nn.Module):
    def __init__(self, in_channels, out_channels,scale_factor=2):
        super(Downsample, self).__init__()
 
        self.downsample = nn.Sequential(
            Conv(in_channels, out_channels, scale_factor, scale_factor, 0)
        )
 
    def forward(self, x):
        x = self.downsample(x)
 
        return x
 
class ASFF_2(nn.Module):
    def __init__(self, inter_dim=512,level=0,channel=[64,128]):
        super(ASF