车道线检测CondLaneNet论文和源码解读

CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution

Paper：https://arxiv.org/pdf/2105.05003.pdf

code：GitHub - aliyun/conditional-lane-detection

论文解读：

一、摘要

这项工作作为车道线检测任务，比较新颖的是检测头head。并不同于常规的基于bbox进行目标检测，这项工作采用的是检测关键点构造mask，输出形式类似instance segmentation。

二、网络结构

• backbone采用的是普通的CNN，比如ResNet；
• neck采用的是TransformerFPN，实际上就是考虑到车道线比较长，需要全局注意力，因此就在基础FPN构造金字塔之前对backbone输出的feature进行了Transformer的self-attention操作
• head分为两部分：
  • Proposal head用于检测车道线实例，并为每个实例生成动态的卷积核参数；
  • Conditional shape head利用Proposal head步骤生成的动态卷积核参数和conditional卷积确定车道线的point set。然后根据这些point set进行连线得到最后的车道线结果。

代码解析：

代码基于mmdetection框架(v2.0.0)开发。在config/condlanenet/里可以看到有三个文件夹，分别对应作者在三个数据集CurveLanes、CULane、TuSimple上的配置。它们之间最大的区别在于针对CurveLanes设计了RIM。下面我重点分析一下它们共同的一些模块：

backbone

采用的是resnet，根据模型的大小可能选择resnet18到resnet101不等

neck

这里采用的是TransConvFPN，在mmdet/models/necks/trans_fpn.py

跟FPN不同点主要在于多了个transformer操作。动机是觉得车道线比较细长，需要有self-attention这样non-local的结构。

也就是在resnet和FPN的中间多了一个transformer模块。

  ## TransConvFPN 不重要的代码部分已省略
     def forward(self, src):
        assert len(src) >= len(self.in_channels)
        src = list(src)
        if self.attention:
            trans_feat = self.trans_head(src[self.trans_idx])
        else:
            trans_feat = src[self.trans_idx]
        inputs = src[:-1]
        inputs.append(trans_feat)
        if len(inputs) > len(self.in_channels):
            for _ in range(len(inputs) - len(self.in_channels)):
                del inputs[0]
        ## 下面内容跟FPN一致
        # build laterals
        laterals = [
            lateral_conv(inputs[i + self.start_level])
            for i, lateral_conv in enumerate(self.lateral_convs)
        ]
        ## 省略

 ## 在TransConvFPN的__init__里
if self.attention:
    self.trans_head = TransConvEncoderModule(**trans_cfg)
 
class TransConvEncoderModule(nn.Module):
    def __init__(self, in_dim, attn_in_dims, attn_out_dims, strides, ratios, downscale=True, pos_shape=None):
        super(TransConvEncoderModule, self).__init__()
        if downscale:
            stride = 2
        else:
            stride = 1
        # self.first_conv = ConvModule(in_dim, 2*in_dim, kernel_size=3, stride=stride, padding=1)
        # self.final_conv = ConvModule(attn_out_dims[-1], attn_out_dims[-1], kernel_size=3, stride=1, padding=1)
        attn_layers = []
        for dim1, dim2, stride, ratio in zip(attn_in_dims, attn_out_dims, strides, ratios):
            attn_layers.append(AttentionLayer(dim1, dim2, ratio, stride))
        if pos_shape is not None:
            self.attn_layers = nn.ModuleList(attn_layers)
        else:
            self.attn_layers = nn.Sequential(*attn_layers)
        self.pos_shape = pos_shape
        self.pos_embeds = []
        if pos_shape is not None:
            for dim in attn_out_dims:
                pos_embed = build_position_encoding(dim, pos_shape).cuda()
                self.pos_embeds.append(pos_embed)
    
    def forward(self, src):
        # src = self.first_conv(src)
        if self.pos_shape is None:
            src = self.attn_layers(src)
        else:
            for layer, pos in zip(self.attn_layers, self.pos_embeds):
                src = layer(src, pos.to(src.device))
        # src = self.final_conv(src)
        return src
 
class AttentionLayer(nn.Module):
    """ Position attention module"""
 
    def __init__(self, in_dim, out_dim, ratio=4, stride=1):
        super(AttentionLayer, self).__init__()
        self.chanel_in = in_dim
        norm_cfg = dict(type='BN', requires_grad=True)
        act_cfg = dict(type='ReLU')
        self.pre_conv = ConvModule(
            in_dim,
            out_dim,
            kernel_size=3,
            stride=stride,
            padding=1,
            norm_cfg=norm_cfg,
            act_cfg=act_cfg,
            inplace=False)
        self.query_conv = nn.Conv2d(
            in_channels=out_dim, out_channels=out_dim // ratio, kernel_size=1)
        self.key_conv = nn.Conv2d(
            in_channels=out_dim, out_channels=out_dim // ratio, kernel_size=1)
        self.value_conv = nn.Conv2d(
            in_channels=out_dim, out_channels=out_dim, kernel_size=1)
        self.final_conv = ConvModule(
            out_dim,
            out_dim,
            kernel_size=3,
            padding=1,
            norm_cfg=norm_cfg,
            act_cfg=act_cfg)
        self.softmax = nn.Softmax(dim=-1)
        self.gamma = nn.Parameter(torch.zeros(1))
 
    def forward(self, x, pos=None):
        """
            inputs :
                x : inpput feature maps( B X C X H X W)
            returns :
                out : attention value + input feature
                attention: B X (HxW) X (HxW)
        """
        x = self.pre_conv(x)
        m_batchsize, _, height, width = x.size()
        if pos is not None:
            x += pos
        proj_query = self.query_conv(x).view(m_batchsize, -1,
                                             width * height).permute(0, 2, 1)
        proj_key = self.key_conv(x).view(m_batchsize, -1, width * height)
 
        energy = torch.bmm(proj_query, proj_key)
        attention = self.softmax(energy)
        attention = attention.permute(0, 2, 1)
        proj_value = self.value_conv(x).view(m_batchsize, -1, width * height)
        out = torch.bmm(proj_value, attention)
        out = out.view(m_batchsize, -1, height, width)
        proj_value = proj_value.view(m_batchsize, -1, height, width)
        out_feat = self.gamma * out + x
        out_feat = self.final_conv(out_feat)
        return out_feat

head

用的是CondLaneHead，在mmdet/models/dense_heads/condlanenet_head.py

需要重点分析，跟一般的检测任务差别很大：

首先这个CondLaneHead类的forward方法是直接调用了forward_test，因此要从model去看到neck输出后具体调用的是head的什么函数

    
    # mmdet/models/detectors/condlanenet.py
    def forward(self, img, img_metas=None, return_loss=True, **kwargs):
        ...
        if img_metas is None:
            return self.test_inference(img)
        elif return_loss:
            return self.forward_train(img, img_metas, **kwargs)
        else:
            return self.forward_test(img, img_metas, **kwargs)
 
    def forward_train(self, img, img_metas, **kwargs):
        ...
        if self.head:
            outputs = self.bbox_head.forward_train(output, poses, num_ins)
        ...
 
    def forward_test(self,
                     img,
                     img_metas,
                     benchmark=False,
                     hack_seeds=None,
                     **kwargs):
        ...
        if self.head:
            seeds, hm = self.bbox_head.forward_test(output, hack_seeds,
                                                    kwargs['thr'])
        ...

 所以实际上head的forward是没用到的，直接去看head的forward_train和forward_test就行

forward_train

    # mmdet/models/dense_heads/condlanenet_head.py
    def forward_train(self, inputs, pos, num_ins):
        # x_list是backbone+neck输出后的multi level feature map
        x_list = list(inputs)
        # 这里根据hm_idx参数来取某个level 的feature map,用它去生成heat_map
        # mask同理
        f_hm = x_list[self.hm_idx]
 
        f_mask = x_list[self.mask_idx]
        m_batchsize = f_hm.size()[0]
 
        # f_mask
        z = self.ctnet_head(f_hm)
        hm, params = z['hm'], z['params']
        h_hm, w_hm = hm.size()[2:]
        h_mask, w_mask = f_mask.size()[2:]
        params = params.view(m_batchsize, self.num_classes, -1, h_hm, w_hm)
        mask_branch = self.mask_branch(f_mask)
        reg_branch = mask_branch
        # reg_branch = self.reg_branch(f_mask)
        params = params.permute(0, 1, 3, 4,
                                2).contiguous().view(-1, self.num_gen_params)
 
        pos_tensor = torch.from_numpy(np.array(pos)).long().to(
            params.device).unsqueeze(1)
 
        pos_tensor = pos_tensor.expand(-1, self.num_gen_params)
        mask_pos_tensor = pos_tensor[:, :self.num_mask_params]
        reg_pos_tensor = pos_tensor[:, self.num_mask_params:]
        if pos_tensor.size()[0] == 0:
            masks = None
            feat_range = None
        else:
            mask_params = params[:, :self.num_mask_params].gather(
                0, mask_pos_tensor)
            masks = self.mask_head(mask_branch, mask_params, num_ins)
            if self.regression:
                reg_params = params[:, self.num_mask_params:].gather(
                    0, reg_pos_tensor)
                regs = self.reg_head(reg_branch, reg_params, num_ins)
            else:
                regs = masks
            # regs = regs.view(sum(num_ins), 1, h_mask, w_mask)
            feat_range = masks.permute(0, 1, 3,
                                       2).view(sum(num_ins), w_mask, h_mask)
            feat_range = self.mlp(feat_range)
        return hm, regs, masks, feat_range, [mask_branch, reg_branch]

forward_test

    # mmdet/models/dense_heads/condlanenet_head.py
    def forward_test(
            self,
            inputs,
            hack_seeds=None,
            hm_thr=0.3,
    ):
 
        def parse_pos(seeds, batchsize, num_classes, h, w, device):
            pos_list = [[p['coord'], p['id_class'] - 1] for p in seeds]
            poses = []
            for p in pos_list:
                [c, r], label = p
                pos = label * h * w + r * w + c
                poses.append(pos)
            poses = torch.from_numpy(np.array(
                poses, np.long)).long().to(device).unsqueeze(1)
            return poses
 
        # with Timer("Elapsed time in stage1: %f"):  # ignore
        x_list = list(inputs)
        f_hm = x_list[self.hm_idx]
        f_mask = x_list[self.mask_idx]
        m_batchsize = f_hm.size()[0]
        f_deep = f_mask
        m_batchsize = f_deep.size()[0]
        # with Timer("Elapsed time in ctnet_head: %f"):  # 0.3ms
        z = self.ctnet_head(f_hm)
        h_hm, w_hm = f_hm.size()[2:]
        h_mask, w_mask = f_mask.size()[2:]
        hm, params = z['hm'], z['params']
        hm = torch.clamp(hm.sigmoid(), min=1e-4, max=1 - 1e-4)
        params = params.view(m_batchsize, self.num_classes, -1, h_hm, w_hm)
        # with Timer("Elapsed time in two branch: %f"):  # 0.6ms
        mask_branch = self.mask_branch(f_mask)
        reg_branch = mask_branch
        # reg_branch = self.reg_branch(f_mask)
        params = params.permute(0, 1, 3, 4,
                                2).contiguous().view(-1, self.num_gen_params)
 
        batch_size, num_classes, h, w = hm.size()
        # with Timer("Elapsed time in ct decode: %f"):  # 0.2ms
        seeds = self.ctdet_decode(hm, thr=hm_thr)
        if hack_seeds is not None:
            seeds = hack_seeds
        # with Timer("Elapsed time in stage2: %f"):  # 0.08ms
        pos_tensor = parse_pos(seeds, batch_size, num_classes, h, w, hm.device)
        pos_tensor = pos_tensor.expand(-1, self.num_gen_params)
        num_ins = [pos_tensor.size()[0]]
        mask_pos_tensor = pos_tensor[:, :self.num_mask_params]
        if self.regression:
            reg_pos_tensor = pos_tensor[:, self.num_mask_params:]
        # with Timer("Elapsed time in stage3: %f"):  # 0.8ms
        if pos_tensor.size()[0] == 0:
            return [], hm
        else:
            mask_params = params[:, :self.num_mask_params].gather(
                0, mask_pos_tensor)
            # with Timer("Elapsed time in mask_head: %f"):  #0.3ms
            masks = self.mask_head(mask_branch, mask_params, num_ins)
            if self.regression:
                reg_params = params[:, self.num_mask_params:].gather(
                    0, reg_pos_tensor)
                # with Timer("Elapsed time in reg_head: %f"):  # 0.25ms
                regs = self.reg_head(reg_branch, reg_params, num_ins)
            else:
                regs = masks
            feat_range = masks.permute(0, 1, 3,
                                       2).view(sum(num_ins), w_mask, h_mask)
            feat_range = self.mlp(feat_range)
            for i in range(len(seeds)):
                seeds[i]['reg'] = regs[0, i:i + 1, :, :]
                m = masks[0, i:i + 1, :, :]
                seeds[i]['mask'] = m
                seeds[i]['range'] = feat_range[i:i + 1]
            return seeds, hm

可以发现，这部分的操作跟论文中描述的差不多。

（等我具体有时间再慢慢弄来看，最近很忙）