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| import copy | |
| import torch.nn as nn | |
| from mmcv.cnn import (ConvModule, Scale, bias_init_with_prob, | |
| caffe2_xavier_init, normal_init) | |
| from mmdet.models.dense_heads.fcos_head import FCOSHead | |
| from ..builder import HEADS | |
| class NASFCOSHead(FCOSHead): | |
| """Anchor-free head used in `NASFCOS <https://arxiv.org/abs/1906.04423>`_. | |
| It is quite similar with FCOS head, except for the searched structure of | |
| classification branch and bbox regression branch, where a structure of | |
| "dconv3x3, conv3x3, dconv3x3, conv1x1" is utilized instead. | |
| """ | |
| def _init_layers(self): | |
| """Initialize layers of the head.""" | |
| dconv3x3_config = dict( | |
| type='DCNv2', | |
| kernel_size=3, | |
| use_bias=True, | |
| deform_groups=2, | |
| padding=1) | |
| conv3x3_config = dict(type='Conv', kernel_size=3, padding=1) | |
| conv1x1_config = dict(type='Conv', kernel_size=1) | |
| self.arch_config = [ | |
| dconv3x3_config, conv3x3_config, dconv3x3_config, conv1x1_config | |
| ] | |
| self.cls_convs = nn.ModuleList() | |
| self.reg_convs = nn.ModuleList() | |
| for i, op_ in enumerate(self.arch_config): | |
| op = copy.deepcopy(op_) | |
| chn = self.in_channels if i == 0 else self.feat_channels | |
| assert isinstance(op, dict) | |
| use_bias = op.pop('use_bias', False) | |
| padding = op.pop('padding', 0) | |
| kernel_size = op.pop('kernel_size') | |
| module = ConvModule( | |
| chn, | |
| self.feat_channels, | |
| kernel_size, | |
| stride=1, | |
| padding=padding, | |
| norm_cfg=self.norm_cfg, | |
| bias=use_bias, | |
| conv_cfg=op) | |
| self.cls_convs.append(copy.deepcopy(module)) | |
| self.reg_convs.append(copy.deepcopy(module)) | |
| self.conv_cls = nn.Conv2d( | |
| self.feat_channels, self.cls_out_channels, 3, padding=1) | |
| self.conv_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1) | |
| self.conv_centerness = nn.Conv2d(self.feat_channels, 1, 3, padding=1) | |
| self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides]) | |
| def init_weights(self): | |
| """Initialize weights of the head.""" | |
| # retinanet_bias_init | |
| bias_cls = bias_init_with_prob(0.01) | |
| normal_init(self.conv_reg, std=0.01) | |
| normal_init(self.conv_centerness, std=0.01) | |
| normal_init(self.conv_cls, std=0.01, bias=bias_cls) | |
| for branch in [self.cls_convs, self.reg_convs]: | |
| for module in branch.modules(): | |
| if isinstance(module, ConvModule) \ | |
| and isinstance(module.conv, nn.Conv2d): | |
| caffe2_xavier_init(module.conv) | |