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