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| import torch.nn as nn | |
| from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init | |
| from ..builder import HEADS | |
| from .anchor_head import AnchorHead | |
| class RetinaSepBNHead(AnchorHead): | |
| """"RetinaHead with separate BN. | |
| In RetinaHead, conv/norm layers are shared across different FPN levels, | |
| while in RetinaSepBNHead, conv layers are shared across different FPN | |
| levels, but BN layers are separated. | |
| """ | |
| def __init__(self, | |
| num_classes, | |
| num_ins, | |
| in_channels, | |
| stacked_convs=4, | |
| conv_cfg=None, | |
| norm_cfg=None, | |
| **kwargs): | |
| self.stacked_convs = stacked_convs | |
| self.conv_cfg = conv_cfg | |
| self.norm_cfg = norm_cfg | |
| self.num_ins = num_ins | |
| super(RetinaSepBNHead, self).__init__(num_classes, in_channels, | |
| **kwargs) | |
| def _init_layers(self): | |
| """Initialize layers of the head.""" | |
| self.relu = nn.ReLU(inplace=True) | |
| self.cls_convs = nn.ModuleList() | |
| self.reg_convs = nn.ModuleList() | |
| for i in range(self.num_ins): | |
| cls_convs = nn.ModuleList() | |
| reg_convs = nn.ModuleList() | |
| for i in range(self.stacked_convs): | |
| chn = self.in_channels if i == 0 else self.feat_channels | |
| cls_convs.append( | |
| ConvModule( | |
| chn, | |
| self.feat_channels, | |
| 3, | |
| stride=1, | |
| padding=1, | |
| conv_cfg=self.conv_cfg, | |
| norm_cfg=self.norm_cfg)) | |
| reg_convs.append( | |
| ConvModule( | |
| chn, | |
| self.feat_channels, | |
| 3, | |
| stride=1, | |
| padding=1, | |
| conv_cfg=self.conv_cfg, | |
| norm_cfg=self.norm_cfg)) | |
| self.cls_convs.append(cls_convs) | |
| self.reg_convs.append(reg_convs) | |
| for i in range(self.stacked_convs): | |
| for j in range(1, self.num_ins): | |
| self.cls_convs[j][i].conv = self.cls_convs[0][i].conv | |
| self.reg_convs[j][i].conv = self.reg_convs[0][i].conv | |
| self.retina_cls = nn.Conv2d( | |
| self.feat_channels, | |
| self.num_anchors * self.cls_out_channels, | |
| 3, | |
| padding=1) | |
| self.retina_reg = nn.Conv2d( | |
| self.feat_channels, self.num_anchors * 4, 3, padding=1) | |
| def init_weights(self): | |
| """Initialize weights of the head.""" | |
| for m in self.cls_convs[0]: | |
| normal_init(m.conv, std=0.01) | |
| for m in self.reg_convs[0]: | |
| normal_init(m.conv, std=0.01) | |
| bias_cls = bias_init_with_prob(0.01) | |
| normal_init(self.retina_cls, std=0.01, bias=bias_cls) | |
| normal_init(self.retina_reg, std=0.01) | |
| def forward(self, feats): | |
| """Forward features from the upstream network. | |
| Args: | |
| feats (tuple[Tensor]): Features from the upstream network, each is | |
| a 4D-tensor. | |
| Returns: | |
| tuple: Usually a tuple of classification scores and bbox prediction | |
| cls_scores (list[Tensor]): Classification scores for all scale | |
| levels, each is a 4D-tensor, the channels number is | |
| num_anchors * num_classes. | |
| bbox_preds (list[Tensor]): Box energies / deltas for all scale | |
| levels, each is a 4D-tensor, the channels number is | |
| num_anchors * 4. | |
| """ | |
| cls_scores = [] | |
| bbox_preds = [] | |
| for i, x in enumerate(feats): | |
| cls_feat = feats[i] | |
| reg_feat = feats[i] | |
| for cls_conv in self.cls_convs[i]: | |
| cls_feat = cls_conv(cls_feat) | |
| for reg_conv in self.reg_convs[i]: | |
| reg_feat = reg_conv(reg_feat) | |
| cls_score = self.retina_cls(cls_feat) | |
| bbox_pred = self.retina_reg(reg_feat) | |
| cls_scores.append(cls_score) | |
| bbox_preds.append(bbox_pred) | |
| return cls_scores, bbox_preds | |