def init_weights(self): normal_init(self.deform_conv, std=0.01)
def init_weights(self): """Initialize weights of classification layer.""" normal_init(self.conv_seg, mean=0, std=0.01)
def init_weights(self): """Initialize model weights.""" normal_init(self.conv, mean=0, std=0.001, bias=0)
def init_weights(self): for m in self.ins_convs_x: normal_init(m.conv, std=0.01) for m in self.ins_convs_y: normal_init(m.conv, std=0.01) for m in self.cate_convs: normal_init(m.conv, std=0.01) bias_ins = bias_init_with_prob(0.01) for m in self.dsolo_ins_list_x: normal_init(m, std=0.01, bias=bias_ins) for m in self.dsolo_ins_list_y: normal_init(m, std=0.01, bias=bias_ins) bias_cate = bias_init_with_prob(0.01) normal_init(self.dsolo_cate, std=0.01, bias=bias_cate)
def init_weights(self): normal_init(self.conv_cls, std=0.01) normal_init(self.conv_reg, std=0.01)
def init_weights(self): normal_init(self.conv_offset, std=0.1) normal_init(self.conv_adaption, std=0.01)
def init_weights(self): for m in self.cls_convs: normal_init(m.conv, std=0.01) for m in self.reg_convs: normal_init(m.conv, std=0.01) bias_cls = bias_init_with_prob(0.01) normal_init(self.fcos_cls, std=0.01, bias=bias_cls) normal_init(self.fcos_reg, std=0.01) normal_init(self.fcos_centerness, std=0.01) normal_init(self.sip_cof, std=0.01) normal_init(self.sip_mask_lat, std=0.01) normal_init(self.sip_mask_lat0, std=0.01) self.feat_align.init_weights() for m in self.track_convs: normal_init(m.conv, std=0.01)
def init_weights(self): for m in self.feature_adaptation: normal_init(m.conv, std=0.01) normal_init(self.regression_conv, std=0.01)
def init_weights(self): """Initialize weights of the head.""" for m in self.convs_pred: normal_init(m, std=0.01)
def init_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): normal_init(m, std=0.01)
def init_weights(self): """Initialize weights of the head.""" for m in self.cls_convs: normal_init(m.conv, std=0.01) for m in self.reg_convs: normal_init(m.conv, std=0.01) bias_cls = bias_init_with_prob(0.01) normal_init(self.reppoints_cls_conv, std=0.01) normal_init(self.reppoints_cls_out, std=0.01, bias=bias_cls) normal_init(self.reppoints_pts_init_conv, std=0.01) normal_init(self.reppoints_pts_init_out, std=0.01) normal_init(self.reppoints_pts_refine_conv, std=0.01) normal_init(self.reppoints_pts_refine_out, std=0.01)
def init_weights(self): """Initialize weights of the head.""" super(FSAFHead, self).init_weights() # The positive bias in self.retina_reg conv is to prevent predicted \ # bbox with 0 area normal_init(self.retina_reg, std=0.01, bias=0.25)
def init_weights(self): """Initialize weights of the head.""" super().init_weights() normal_init(self.conv_centerness, std=0.01)
def init_weights(self): for m in self.fam_reg_convs: normal_init(m.conv, std=0.01) for m in self.fam_cls_convs: normal_init(m.conv, std=0.01) bias_cls = bias_init_with_prob(0.01) normal_init(self.fam_reg, std=0.01) normal_init(self.fam_cls, std=0.01, bias=bias_cls) if self.align_conv_type == 'AlignConv': self.align_conv.init_weights() elif self.align_conv_type == 'Conv': normal_init(self.align_conv, std=0.01) else: normal_init(self.align_conv_offset, std=0.1) normal_init(self.align_conv, std=0.01) normal_init(self.or_conv, std=0.01) for m in self.odm_reg_convs: normal_init(m.conv, std=0.01) for m in self.odm_cls_convs: normal_init(m.conv, std=0.01) normal_init(self.odm_cls, std=0.01, bias=bias_cls) normal_init(self.odm_reg, std=0.01)
def init_weights(self): normal_init(self.rpn_conv, std=0.01) super(GARPNHead, self).init_weights()
def init_weights(self): """Initialize the weights of head.""" bias_cls = bias_init_with_prob(0.01) normal_init(self.conv_cls, std=0.01, bias=bias_cls) normal_init(self.conv_reg, std=0.01)
def init_weights(self): """Initialize weights of the head.""" for m in self.f_1_convs: normal_init(m.conv, std=0.01) for m in self.f_2_convs: normal_init(m.conv, std=0.01) for m in self.f_r_convs: normal_init(m.conv, std=0.01) bias_cls = bias_init_with_prob(0.01) normal_init(self.f_1_retina, std=0.01, bias=bias_cls) normal_init(self.f_2_retina, std=0.01, bias=bias_cls) normal_init(self.f_mil_retina, std=0.01, bias=bias_cls) normal_init(self.f_r_retina, std=0.01)
def _init_conv(m): classname = m.__class__.__name__ if classname.find('Conv') != -1: normal_init(m, std=0.01)
def init_weights(self): for m in self.cls_convs: normal_init(m.conv, std=0.01) for m in self.reg_convs: normal_init(m.conv, std=0.01) for m in self.emb_convs: normal_init(m.conv, std=0.01) bias_cls = bias_init_with_prob(0.01) normal_init(self.yolact_cls, std=0.01, bias=bias_cls) normal_init(self.yolact_reg, std=0.01) normal_init(self.yolact_emb, std=0.01)
def init_weights(self): for m in self.cls_convs: normal_init(m.conv, std=0.01) for m in self.reg_convs: normal_init(m.conv, std=0.01) self.feature_adaption_cls.init_weights() self.feature_adaption_reg.init_weights() bias_cls = bias_init_with_prob(0.01) normal_init(self.conv_loc, std=0.01, bias=bias_cls) normal_init(self.conv_shape, std=0.01) normal_init(self.retina_cls, std=0.01, bias=bias_cls) normal_init(self.retina_reg, std=0.01)
def init_weights(self): """Initiate the parameters from scratch.""" normal_init(self.fc_cls, std=self.init_std)
def init_weights(self): for m in self.cls_convs: normal_init(m.conv, std=0.01) for m in self.reg_convs: normal_init(m.conv, std=0.01) for m in self.offset_convs: normal_init(m.conv, std=0.01) bias_cls = bias_init_with_prob(0.01) normal_init(self.csp_cls, std=0.01, bias=bias_cls) normal_init(self.csp_reg, std=0.01) normal_init(self.csp_offset, std=0.01)
def init_weights(self): normal_init(self.fc, mean=0, std=0.01, bias=0)
def init_weights(self): for m in self.cls_convs: normal_init(m.conv, std=0.01) for m in self.reg_convs: normal_init(m.conv, std=0.01) for m in self.sac_convs: normal_init(m.conv, std=0.01) bias_cls = bias_init_with_prob(0.01) normal_init(self.fcos_cls, std=0.01, bias=bias_cls) normal_init(self.fcos_reg, std=0.01) normal_init(self.fcos_saccade_score, std=0.01)
def init_weights(self): normal_init(self.rpn_conv, std=0.01) normal_init(self.rpn_cls, std=0.01) normal_init(self.rpn_reg, std=0.01)
def init_weights(self): for m in self.cls_convs: normal_init(m.conv, std=0.01) for m in self.reg_convs: normal_init(m.conv, std=0.01) for m in self.reg_convs_3d: normal_init(m.conv, std=0.01) bias_cls = bias_init_with_prob(0.01) normal_init(self.fcos_cls, std=0.01, bias=bias_cls) normal_init(self.fcos_reg, std=0.01) normal_init(self.fcos_reg_3d, std=0.01) normal_init(self.fcos_centerness, std=0.01)
def init_weights(self): """Initialize weights of the head.""" normal_init(self.rpn_conv, std=0.01) normal_init(self.rpn_cls, std=0.01) normal_init(self.rpn_reg, std=0.01)
def init_weights(self, init_linear='normal'): normal_init(self.predictor, std=0.01)
def _init_layers(self): self.cls_convs = nn.ModuleList() self.reg_convs = nn.ModuleList() for i in range(self.stacked_convs - 1): chn = self.in_channels if i == 0 else self.feat_channels self.cls_convs.append( ConvModule(chn, self.feat_channels, 3, stride=1, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, bias=self.norm_cfg is None)) for i in range(self.stacked_convs): chn = self.in_channels if i == 0 else self.feat_channels self.reg_convs.append( ConvModule(chn, self.feat_channels, 3, stride=1, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, bias=self.norm_cfg is None)) self.fcos_cls = nn.Conv2d(self.feat_channels, self.cls_out_channels, 3, padding=1) self.fcos_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1) self.fcos_centerness = nn.Conv2d(self.feat_channels, 1, 3, padding=1) self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides]) self.nc = 32 ###########instance############## self.feat_align = FeatureAlign(self.feat_channels, self.feat_channels, 3, flag_norm=self.norm_cfg is not None) self.sip_cof = nn.Conv2d(self.feat_channels, self.nc * 4, 3, padding=1) self.sip_mask_lat = nn.Conv2d(512, self.nc, 3, padding=1) self.sip_mask_lat0 = nn.Conv2d(768, 512, 1, padding=0) if self.rescoring_flag: self.convs_scoring = [] channels = [1, 16, 16, 16, 32, 64, 128] for i in range(6): in_channels = channels[i] out_channels = channels[i + 1] stride = 2 if i == 0 else 2 padding = 0 self.convs_scoring.append( ConvModule(in_channels, out_channels, 3, stride=stride, padding=padding, bias=True)) self.convs_scoring = nn.Sequential(*self.convs_scoring) self.mask_scoring = nn.Conv2d(128, self.num_classes - 1, 1) for m in self.convs_scoring: kaiming_init(m.conv) normal_init(self.mask_scoring, std=0.001) self.relu = nn.ReLU(inplace=True) self.crop_cuda = CropSplit(2) self.crop_gt_cuda = CropSplitGt(2) self.init_weights()
def init_weights(self): """Initialize last classification layer of MaskPointHead, conv layers are already initialized by ConvModule""" normal_init(self.fc_logits, std=0.001)