ConvFCBBoxHead类定义在\mmdet\models\roi_heads\bbox_heads\convfc_bbox_head.py中,其作用是对共享特征层进行卷积和全连接操作,然后在forward到BBoxHead类中,而且也继承自BBoxHead类.convfc_bbox_head.py还包含了Shared2FCBBoxHead和Shared4Conv1FCBBoxHead两个类.
import torch.nn as nn from mmcv.cnn import ConvModule from mmdet.models.builder import HEADS from .bbox_head import BBoxHead @HEADS.register_module() class ConvFCBBoxHead(BBoxHead): r"""More general bbox head, with shared conv and fc layers and two optional separated branches. .. code-block:: none /-> cls convs -> cls fcs -> cls shared convs -> shared fcs \-> reg convs -> reg fcs -> reg """ # noqa: W605 def __init__(self, num_shared_convs=0, num_shared_fcs=0, num_cls_convs=0, num_cls_fcs=0, num_reg_convs=0, num_reg_fcs=0, conv_out_channels=256, fc_out_channels=1024, conv_cfg=None, norm_cfg=None, *args, **kwargs): super(ConvFCBBoxHead, self).__init__(*args, **kwargs) assert (num_shared_convs + num_shared_fcs + num_cls_convs + num_cls_fcs + num_reg_convs + num_reg_fcs > 0) if num_cls_convs > 0 or num_reg_convs > 0: assert num_shared_fcs == 0 if not self.with_cls: assert num_cls_convs == 0 and num_cls_fcs == 0 if not self.with_reg: assert num_reg_convs == 0 and num_reg_fcs == 0 self.num_shared_convs = num_shared_convs self.num_shared_fcs = num_shared_fcs self.num_cls_convs = num_cls_convs self.num_cls_fcs = num_cls_fcs self.num_reg_convs = num_reg_convs self.num_reg_fcs = num_reg_fcs self.conv_out_channels = conv_out_channels self.fc_out_channels = fc_out_channels self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg # add shared convs and fcs self.shared_convs, self.shared_fcs, last_layer_dim = self._add_conv_fc_branch( self.num_shared_convs, self.num_shared_fcs, self.in_channels, True) self.shared_out_channels = last_layer_dim # add cls specific branch self.cls_convs, self.cls_fcs, self.cls_last_dim = self._add_conv_fc_branch( self.num_cls_convs, self.num_cls_fcs, self.shared_out_channels) # add reg specific branch self.reg_convs, self.reg_fcs, self.reg_last_dim = self._add_conv_fc_branch( self.num_reg_convs, self.num_reg_fcs, self.shared_out_channels) if self.num_shared_fcs == 0 and not self.with_avg_pool: if self.num_cls_fcs == 0: self.cls_last_dim *= self.roi_feat_area if self.num_reg_fcs == 0: self.reg_last_dim *= self.roi_feat_area self.relu = nn.ReLU(inplace=False) # reconstruct fc_cls and fc_reg since input channels are changed if self.with_cls: self.fc_cls = nn.Linear(self.cls_last_dim, self.num_classes + 1) if self.with_reg: out_dim_reg = (4 if self.reg_class_agnostic else 4 * self.num_classes) self.fc_reg = nn.Linear(self.reg_last_dim, out_dim_reg) def _add_conv_fc_branch(self, num_branch_convs, num_branch_fcs, in_channels, is_shared=False): """Add shared or separable branch. convs -> avg pool (optional) -> fcs """ last_layer_dim = in_channels # add branch specific conv layers branch_convs = nn.ModuleList() if num_branch_convs > 0: for i in range(num_branch_convs): conv_in_channels = ( last_layer_dim if i == 0 else self.conv_out_channels) branch_convs.append( ConvModule( conv_in_channels, self.conv_out_channels, 3, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg)) last_layer_dim = self.conv_out_channels # add branch specific fc layers branch_fcs = nn.ModuleList() if num_branch_fcs > 0: # for shared branch, only consider self.with_avg_pool # for separated branches, also consider self.num_shared_fcs if (is_shared or self.num_shared_fcs == 0) and not self.with_avg_pool: last_layer_dim *= self.roi_feat_area for i in range(num_branch_fcs): fc_in_channels = ( last_layer_dim if i == 0 else self.fc_out_channels) branch_fcs.append( nn.Linear(fc_in_channels, self.fc_out_channels)) last_layer_dim = self.fc_out_channels return branch_convs, branch_fcs, last_layer_dim def init_weights(self): super(ConvFCBBoxHead, self).init_weights() # conv layers are already initialized by ConvModule for module_list in [self.shared_fcs, self.cls_fcs, self.reg_fcs]: for m in module_list.modules(): if isinstance(m, nn.Linear): nn.init.xavier_uniform_(m.weight) nn.init.constant_(m.bias, 0) def forward(self, x): # shared part if self.num_shared_convs > 0: for conv in self.shared_convs: x = conv(x) if self.num_shared_fcs > 0: if self.with_avg_pool: x = self.avg_pool(x) x = x.flatten(1) for fc in self.shared_fcs: x = self.relu(fc(x)) # separate branches x_cls = x x_reg = x for conv in self.cls_convs: x_cls = conv(x_cls) if x_cls.dim() > 2: if self.with_avg_pool: x_cls = self.avg_pool(x_cls) x_cls = x_cls.flatten(1) for fc in self.cls_fcs: x_cls = self.relu(fc(x_cls)) for conv in self.reg_convs: x_reg = conv(x_reg) if x_reg.dim() > 2: if self.with_avg_pool: x_reg = self.avg_pool(x_reg) x_reg = x_reg.flatten(1) for fc in self.reg_fcs: x_reg = self.relu(fc(x_reg)) cls_score = self.fc_cls(x_cls) if self.with_cls else None bbox_pred = self.fc_reg(x_reg) if self.with_reg else None return cls_score, bbox_pred @HEADS.register_module() class Shared2FCBBoxHead(ConvFCBBoxHead): def __init__(self, fc_out_channels=1024, *args, **kwargs): super(Shared2FCBBoxHead, self).__init__( num_shared_convs=0, num_shared_fcs=2, num_cls_convs=0, num_cls_fcs=0, num_reg_convs=0, num_reg_fcs=0, fc_out_channels=fc_out_channels, *args, **kwargs) @HEADS.register_module() class Shared4Conv1FCBBoxHead(ConvFCBBoxHead): def __init__(self, fc_out_channels=1024, *args, **kwargs): super(Shared4Conv1FCBBoxHead, self).__init__( num_shared_convs=4, num_shared_fcs=1, num_cls_convs=0, num_cls_fcs=0, num_reg_convs=0, num_reg_fcs=0, fc_out_channels=fc_out_channels, *args, **kwargs)
主要的函数有:
(1) __init__():初始化函数,主要参数是各层的数量;
(2) _add_conv_fc_branch():增加卷积或全连接层;
(3) init_weights():初始化权重;
(4) forward():前向传播;
Shared2FCBBoxHead和Shared4Conv1FCBBoxHead类继承自ConvFCBBoxHead类,主要参数如下:
(1) num_shared_convs:共享卷积层数量;
(2) num_shared_fcs:共享全连接层数量;
(3) num_cls_convs:分类卷积层数量;
(4) num_cls_fcs:分类全连接层数量;
(5) num_reg_convs:回归卷积层的数量;
(6) num_reg_fcs:回归全连接层的数量;
(7) fc_out_channels:全连接层后输出层的数量,默认值为1024.
更改这些参数的值,就可以构建不同结构的模型,还是非常方便的.
MMDetection源码解析：Faster RCNN(7)--ConvFCBBoxHead,Shared2FCBBoxHead和Shared4Conv1FCBBoxHead类