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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 RetinaHead(AnchorHead): |
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r"""An anchor-based head used in `RetinaNet |
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<https://arxiv.org/pdf/1708.02002.pdf>`_. |
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The head contains two subnetworks. The first classifies anchor boxes and |
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the second regresses deltas for the anchors. |
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Example: |
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>>> import torch |
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>>> self = RetinaHead(11, 7) |
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>>> x = torch.rand(1, 7, 32, 32) |
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>>> cls_score, bbox_pred = self.forward_single(x) |
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>>> # Each anchor predicts a score for each class except background |
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>>> cls_per_anchor = cls_score.shape[1] / self.num_anchors |
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>>> box_per_anchor = bbox_pred.shape[1] / self.num_anchors |
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>>> assert cls_per_anchor == (self.num_classes) |
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>>> assert box_per_anchor == 4 |
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""" |
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def __init__(self, |
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num_classes, |
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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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anchor_generator=dict( |
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type='AnchorGenerator', |
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octave_base_scale=4, |
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scales_per_octave=3, |
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ratios=[0.5, 1.0, 2.0], |
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strides=[8, 16, 32, 64, 128]), |
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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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super(RetinaHead, self).__init__( |
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num_classes, |
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in_channels, |
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anchor_generator=anchor_generator, |
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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.stacked_convs): |
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chn = self.in_channels if i == 0 else self.feat_channels |
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self.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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self.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.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: |
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normal_init(m.conv, std=0.01) |
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for m in self.reg_convs: |
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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_single(self, x): |
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"""Forward feature of a single scale level. |
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Args: |
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x (Tensor): Features of a single scale level. |
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Returns: |
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tuple: |
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cls_score (Tensor): Cls scores for a single scale level |
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the channels number is num_anchors * num_classes. |
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bbox_pred (Tensor): Box energies / deltas for a single scale |
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level, the channels number is num_anchors * 4. |
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""" |
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cls_feat = x |
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reg_feat = x |
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for cls_conv in self.cls_convs: |
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cls_feat = cls_conv(cls_feat) |
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for reg_conv in self.reg_convs: |
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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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return cls_score, bbox_pred |
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