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- # Copyright (c) OpenMMLab. All rights reserved.
- 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
-
-
- @HEADS.register_module()
- 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,
- init_cfg=None,
- **kwargs):
- assert init_cfg is None, 'To prevent abnormal initialization ' \
- 'behavior, init_cfg is not allowed to be set'
- 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, init_cfg=init_cfg, **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_base_priors * self.cls_out_channels,
- 3,
- padding=1)
- self.retina_reg = nn.Conv2d(
- self.feat_channels, self.num_base_priors * 4, 3, padding=1)
-
- def init_weights(self):
- """Initialize weights of the head."""
- super(RetinaSepBNHead, self).init_weights()
- 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
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