优秀的个人网站研究生培训机构排名
一、论文核心
在特征金字塔（FPN）中加入自适应结构特征融合模块，使其自适应地学习每个尺度特征图的融合空间权重
二、网络结构
其代码实现如下：
def add_conv(in_ch, out_ch, ksize, stride, leaky=True):"""Add a conv2d / batchnorm / leaky ReLU block.Args:in_ch (int): number of input channels of the convolution layer.out_ch (int): number of output channels of the convolution layer.ksize (int): kernel size of the convolution layer.stride (int): stride of the convolution layer.Returns:stage (Sequential) : Sequential layers composing a convolution block."""stage = nn.Sequential()pad = (ksize - 1) // 2stage.add_module('conv', nn.Conv2d(in_channels=in_ch,out_channels=out_ch, kernel_size=ksize, stride=stride,padding=pad, bias=False))stage.add_module('batch_norm', nn.BatchNorm2d(out_ch))if leaky:stage.add_module('leaky', nn.LeakyReLU(0.1))else:stage.add_module('relu6', nn.ReLU6(inplace=True))return stageclass ASFF(nn.Module):def __init__(self, level, rfb=False, vis=False):super(ASFF, self).__init__()self.level = levelself.dim = [512, 256, 256]self.inter_dim = self.dim[self.level]if level==0:self.stride_level_1 = add_conv(256, self.inter_dim, 3, 2)self.stride_level_2 = add_conv(256, self.inter_dim, 3, 2)self.expand = add_conv(self.inter_dim, 1024, 3, 1)elif level==1:self.compress_level_0 = add_conv(512, self.inter_dim, 1, 1)self.stride_level_2 = add_conv(256, self.inter_dim, 3, 2)self.expand = add_conv(self.inter_dim, 512, 3, 1)elif level==2:self.compress_level_0 = add_conv(512, self.inter_dim, 1, 1)self.expand = add_conv(self.inter_dim, 256, 3, 1)compress_c = 8 if rfb else 16 #when adding rfb, we use half number of channels to save memoryself.weight_level_0 = add_conv(self.inter_dim, compress_c, 1, 1)self.weight_level_1 = add_conv(self.inter_dim, compress_c, 1, 1)self.weight_level_2 = add_conv(self.inter_dim, compress_c, 1, 1)self.weight_levels = nn.Conv2d(compress_c*3, 3, kernel_size=1, stride=1, padding=0)self.vis= visdef forward(self, x_level_0, x_level_1, x_level_2):if self.level==0:level_0_resized = x_level_0level_1_resized = self.stride_level_1(x_level_1)level_2_downsampled_inter =F.max_pool2d(x_level_2, 3, stride=2, padding=1)level_2_resized = self.stride_level_2(level_2_downsampled_inter)elif self.level==1:level_0_compressed = self.compress_level_0(x_level_0)level_0_resized =F.interpolate(level_0_compressed, scale_factor=2, mode='nearest')level_1_resized =x_level_1level_2_resized =self.stride_level_2(x_level_2)elif self.level==2:level_0_compressed = self.compress_level_0(x_level_0)level_0_resized =F.interpolate(level_0_compressed, scale_factor=4, mode='nearest')level_1_resized =F.interpolate(x_level_1, scale_factor=2, mode='nearest')level_2_resized =x_level_2level_0_weight_v = self.weight_level_0(level_0_resized)level_1_weight_v = self.weight_level_1(level_1_resized)level_2_weight_v = self.weight_level_2(level_2_resized)levels_weight_v = torch.cat((level_0_weight_v, level_1_weight_v, level_2_weight_v),1)levels_weight = self.weight_levels(levels_weight_v)levels_weight = F.softmax(levels_weight, dim=1)fused_out_reduced = level_0_resized * levels_weight[:,0:1,:,:]+\level_1_resized * levels_weight[:,1:2,:,:]+\level_2_resized * levels_weight[:,2:,:,:]out = self.expand(fused_out_reduced)if self.vis:return out, levels_weight, fused_out_reduced.sum(dim=1)else:return out三、参考内容
ASFF:Learning Spatial Fusion for Single-Shot Object Detection
GitHub - GOATmessi8/ASFF: yolov3 with mobilenet v2 and ASFF