EMIFF

1. 论文：https://arxiv.org/abs/2303.10975

2. 代码：https://github.com/Bosszhe/EMIFF

​ 本文提出了一种新的基于摄像机的三维检测框架，增强型多尺度图像特征融合（EMIFF）。虽然EMIFF的输入是2D图像，但是它的neck层的结构设计应该普适于点云的3D目标检测，同时其中的MFC等模块可以简单地被替换成更先进的其他组件。

​ 为了充分利用车辆和基础设施的整体视角，本文提出了多尺度交叉注意MCA（包含了MFC和MFS）和相机感知通道掩蔽CCM模块，以在尺度、空间和通道（MFC尺度级增强、MFS空间级增强、CCM通道级增强）级别增强基础设施和车辆特征，从而纠正相机异步引入的姿态误差。我们还引入了一个特征压缩FC模块，该模块具有信道和空间压缩块，以提高传输效率。

MFC

​ MFC模块首先应用于多尺度特征。由于姿态误差会导致2D平面上投影位置和地面真实位置之间的位移，我们对每个比例特征应用DCN，以允许每个像素获得其周围的空间信息。然后，通过UpConv块将不同尺度的特征上采样到相同的尺寸。

class double_conv(nn.Module):

    def __init__(self, in_ch, out_ch):
        super(double_conv, self).__init__()

        self.conv = nn.Sequential(
            nn.Conv2d(in_ch, out_ch, 3, stride=1, padding=1),
            nn.BatchNorm2d(out_ch),
            nn.ReLU(),
            nn.Conv2d(out_ch, out_ch, 3, stride=1, padding=1),
            nn.BatchNorm2d(out_ch),
            nn.ReLU()
        )

    def forward(self, x):
        x = self.conv(x)
        return x
    
class DCN_Up_Conv_List(nn.Module):

    def __init__(self, neck_dcn, channels):
        super(DCN_Up_Conv_List, self).__init__()


        self.upconv0 = nn.Sequential(
            double_conv(channels,channels),
        )

        self.upconv1 = nn.Sequential(
            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
            double_conv(channels,channels),
        )
        self.upconv2 = nn.Sequential(
            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
            double_conv(channels,channels),
            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
            double_conv(channels,channels),
        )
        self.upconv3 = nn.Sequential(
            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
            double_conv(channels,channels),
            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
            double_conv(channels,channels),
            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
            double_conv(channels,channels),
        )

        self.dcn0 = build_neck(neck_dcn)
        self.dcn1 = build_neck(neck_dcn)
        self.dcn2 = build_neck(neck_dcn)
        self.dcn3 = build_neck(neck_dcn)

    def forward(self, x):
        assert x.__len__() == 4
        x0 = self.dcn0(x[0])
        x0 = self.upconv0(x0)

        x1 = self.dcn1(x[1])
        x1 = self.upconv1(x1)

        x2 = self.dcn2(x[2])
        x2 = self.upconv2(x2)

        x3 = self.dcn3(x[3])
        x3 = self.upconv3(x3)

        return [x0,x1,x2,x3]

MFS

​ MFS应用MeanPooling操作获得不同尺度的基础设施特征的表示，而不同尺度的车辆特征首先通过mean操作融合，然后通过MeanPooling进行细化。为了寻找不同尺度下车辆特征和基础设施特征之间的相关性，交叉注意应用于基础设施表示作为关键，车辆表示作为查询，生成每个尺度m的注意权重 m inf。我们计算特征^fM inf和权重 m inf之间的乘积。MCA的最终输出是增强的基础设施图像特征finf和车辆图像特征fveh。

def attention(query, key, mask=None, dropout=None):

    # from IPython import embed
    # embed()

    "Compute 'Scaled Dot Product Attention'"
    d_k = query.size(-1)
    scores = torch.matmul(query, key.transpose(-2, -1)) 
            / math.sqrt(d_k)
    if mask is not None:
        scores = scores.masked_fill(mask == 0, -1e9)
    p_attn = F.softmax(scores, dim = -1)
    if dropout is not None:
        p_attn = dropout(p_attn)
    return p_attn

def extract_img_feat(self, img, img_metas):
    """Extract features from images."""
    bs = img.shape[0]
    img_v = img[:,0,...]
    img_i = img[:,1,...]

    x_v = self.backbone_v(img_v)
    x_v = self.neck_v(x_v)
    x_v = self.dcn_up_conv_v(list(x_v))
    x_v_tensor = torch.stack(x_v).permute(1,0,2,3,4)
    x_v_out = torch.mean(x_v_tensor,dim=1)

    x_i = self.backbone_i(img_i)
    x_i = self.neck_i(x_i)
    # from IPython import embed
    # embed(header='compress')

    # Add compression encoder-decoder
   服务器托管 x_i = self.inf_compressor(x_i)

    x_i = self.dcn_up_conv_i(list(x_i))
    x_i_tensor = torch.stack(x_i).permute(1,0,2,3,4)

    # query.shape[B,C]
    # key.shape[B,N_levels,C]
    query = torch.mean(x_v_out,dim=(-2,-1))[:,None,:]
    key = torch.mean(x_i_tensor,dim=(-2,-1))
    weights_i = attention(query,key).squeeze(1)

    # print('attention_weights',weights_i)

    x_i_out = (weights_i[:,:,None,None,None] * x_i_tensor).sum(dim=1)

    return tuple((x_v_out, x_i_out))

CCM

​ CCM将学习一个通道掩码来衡量通道之间的重要性。由于不同的通道表示不同距离的目标信息，这些信息与相机参数密切相关，因此将相机参数作为先验来增强图像特征是直观的。首先，将摄像机的内、外特性拉伸成一维并进行连接。然后，使用MLP将它们放大到特征的维数C，以生成通道掩模Mveh/inf。最后，Mveh/inf用于在通道方向上重新加权图像特征fveh/inf，并获得结果f’veh/inf。

class CCMNet(nn.Module):
    def __init__(self, in_channels, mid_channels, context_channels, reduction_ratio=1):
        super(CCMNet, self).__init__()
        self.reduce_conv = nn.Sequential(
            nn.Conv2d(in_channels,
                      mid_channels,
                      kernel_size=3,
                      stride=1,
                      padding=1),
            nn.BatchNorm2d(mid_channels),
            nn.ReLU(inplace=True),
        )
        self.context_conv = nn.Conv2d(mid_channels,
                                      context_channels,
                                      kernel_size=1,
                                      stride=1,
                                      padding=0)
        self.bn = nn.BatchNorm1d(16)
        self.context_mlp = Mlp(16, mid_channels, mid_channels)
        self.context_se = SE_Inception_Layer(mid_channels,reduction_ratio=reduction_ratio)  # NOTE: add camera-aware

        # self.context_se = CASELayer(mid_channels,reduction_ratio=8)  # NOTE: add camera-aware
    
    def ida_mat_cal(self,img_meta):
        img_scale_factor = (img_meta['scale_factor'][:2]
                if 'scale_factor' in img_meta.keys() else 1)

        img_shape = img_meta['img_shape'][:2]
        orig_h, orig_w = img_shape

        ida_rot = torch.eye(2)
        ida_tran = torch.zeros(2)

        ida_rot *=服务器托管 img_scale_factor
        # ida_tran -= torch.Tensor(crop[:2])
        if 'flip' in img_meta.keys() and img_meta['flip']:
            A = torch.Tensor([[-1, 0], [0, 1]])
            b = torch.Tensor([orig_w, 0])
            ida_rot = A.matmul(ida_rot)
            ida_tran = A.matmul(ida_tran) + b

        ida_mat = ida_rot.new_zeros(4, 4)
        ida_mat[3, 3] = 1
        ida_mat[2, 2] = 1
        ida_mat[:2, :2] = ida_rot
        ida_mat[:2, 3] = ida_tran

        return ida_mat

    def forward(self, x_v, x_i, img_metas):
        # x [bs,num_cams,C,H,W]
        bs, C, H, W = x_v.shape
        num_cams = 2

        x = torch.stack((x_v,x_i),dim=1).reshape(-1, C, H, W)

        extrinsic_v_list = list()
        extrinsic_i_list = list()
        intrinsic_v_list = list()
        intrinsic_i_list = list()
        for img_meta in img_metas:

            extrinsic_v = torch.Tensor(img_meta['lidar2img']['extrinsic'][0])
            extrinsic_i = torch.Tensor(img_meta['lidar2img']['extrinsic'][1])
            intrinsic_v = torch.Tensor(img_meta['lidar2img']['intrinsic'][0])
            intrinsic_i = torch.Tensor(img_meta['lidar2img']['intrinsic'][1])
            # from IPython import embed
            # embed(header='ida')
            ida_mat = self.ida_mat_cal(img_meta)

            intrinsic_v = ida_mat @ intrinsic_v
            intrinsic_i = ida_mat @ intrinsic_i

            extrinsic_v_list.append(extrinsic_v)
            extrinsic_i_list.append(extrinsic_i)
            intrinsic_v_list.append(intrinsic_v)
            intrinsic_i_list.append(intrinsic_i)

        extrinsic_v = torch.stack(extrinsic_v_list)
        extrinsic_i = torch.stack(extrinsic_i_list)
        intrinsic_v = torch.stack(intrinsic_v_list)
        intrinsic_i = torch.stack(intrinsic_i_list)

        extrinsic = torch.stack((extrinsic_v,extrinsic_i),dim=1) 
        intrinsic = torch.stack((intrinsic_v,intrinsic_i),dim=1) 

        in_mlp = torch.stack(
                    (
                        intrinsic[..., 0, 0],
                        intrinsic[..., 1, 1],
                        intrinsic[..., 0, 2],
                        intrinsic[ ..., 1, 2],
                    ),
                    dim=-1
                )

        # from IPython import embed
        # embed(header='DCMNet')
        ex_mlp = extrinsic[...,:3,:].view(bs,num_cams,-1)
        mlp_input = torch.cat((in_mlp,ex_mlp),dim=-1)
        mlp_input = mlp_input.reshape(-1,mlp_input.shape[-1]).to(x.device)

        mlp_input = self.bn(mlp_input)
        x = self.reduce_conv(x)
        # context_se = self.context_mlp(mlp_input)[..., None, None]
        context_se = self.context_mlp(mlp_input)
        context = self.context_se(x, context_se)
        context = self.context_conv(context)

        context = context.reshape(bs,num_cams,C,H,W)
        x_v_out = context[:,0,...]
        x_i_out = context[:,1,...]

        # from IPython import embed
        # embed(header='DCMNet end')
        return tuple((x_v_out, x_i_out))

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