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 lateralslaterals = [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 = 2else: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_shapeself.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 srcclass 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_dimnorm_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 featureattention: B X (HxW) X (HxW)"""x = self.pre_conv(x)m_batchsize, _, height, width = x.size()if pos is not None:x += posproj_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 + xout_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.pydef 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.pydef forward_train(self, inputs, pos, num_ins):# x_list是backbone+neck输出后的multi level feature mapx_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_maskz = 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 = Nonefeat_range = Noneelse: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.pydef 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 = ppos = label * h * w + r * w + cposes.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"):  # ignorex_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_maskm_batchsize = f_deep.size()[0]# with Timer("Elapsed time in ctnet_head: %f"):  # 0.3msz = 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"):  # 0.6msmask_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"):  # 0.2msseeds = self.ctdet_decode(hm, thr=hm_thr)if hack_seeds is not None:seeds = hack_seeds# with Timer("Elapsed time in stage2: %f"):  # 0.08mspos_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"):  # 0.8msif pos_tensor.size()[0] == 0:return [], hmelse:mask_params = params[:, :self.num_mask_params].gather(0, mask_pos_tensor)# with Timer("Elapsed time in mask_head: %f"):  #0.3msmasks = 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"):  # 0.25msregs = self.reg_head(reg_branch, reg_params, num_ins)else:regs = masksfeat_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'] = mseeds[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）