def reset_parameters(self): for sa_module in self.sa_modules: sa_module.reset_parameters(xavier_uniform) for fp_module in self.fp_modules: fp_module.reset_parameters(xavier_uniform) self.mlp_seg.reset_parameters(xavier_uniform) set_bn(self, momentum=0.01)
def reset_parameters(self): # default initialization self.mlp_local.reset_parameters(xavier_uniform) self.mlp_seg.reset_parameters(xavier_uniform) self.conv_seg.reset_parameters(xavier_uniform) # set batch normalization to 0.01 as default set_bn(self, momentum=0.01)
def reset_parameters(self): for edge_conv in self.edge_convs: edge_conv.reset_parameters(xavier_uniform) self.mlp_local.reset_parameters(xavier_uniform) self.mlp_global.reset_parameters(xavier_uniform) xavier_uniform(self.classifier) set_bn(self, momentum=0.01)
def reset_parameters(self): # default initialization in original implementation self.mlp_local.reset_parameters(xavier_uniform) self.mlp_global.reset_parameters(xavier_uniform) xavier_uniform(self.classifier) # set batch normalization to 0.01 as default set_bn(self, momentum=0.01)
def reset_parameters(self): # default initialization self.backbone1.reset_parameters() self.backbone2.reset_parameters() self.head.reset_parameters() # set batch normalization to 0.01 as default set_bn(self, momentum=0.01)
def reset_parameters(self): for edge_conv in self.edge_convs: edge_conv.reset_parameters(xavier_uniform) self.mlp_label.reset_parameters(xavier_uniform) self.mlp_local.reset_parameters(xavier_uniform) self.mlp_seg.reset_parameters(xavier_uniform) self.conv_seg.reset_parameters(xavier_uniform) xavier_uniform(self.seg_logit) set_bn(self, momentum=0.01)
def reset_parameters(self): # default initialization self.mlp_local.reset_parameters(xavier_uniform) self.mlp_seg.reset_parameters(xavier_uniform) self.conv_seg.reset_parameters(xavier_uniform) if self.cls_logit is not None: self.mlp_cls.reset_parameters(xavier_uniform) xavier_uniform(self.cls_logit) xavier_uniform(self.seg_logit) # set batch normalization to 0.01 as default set_bn(self, momentum=0.01)
def reset_parameters(self): # default initialization in original implementation self.p1.reset_parameters() self.p2.reset_parameters() self.p3.reset_parameters() self.p4.reset_parameters() xavier_uniform(self.classifier1) xavier_uniform(self.classifier2) xavier_uniform(self.classifier3) xavier_uniform(self.classifier22) xavier_uniform(self.classifier4) xavier_uniform(self.classifier8) xavier_uniform(self.classifier9) self.mlp_local8.reset_parameters(xavier_uniform) self.mlp_global8.reset_parameters(xavier_uniform) self.mlp_local9.reset_parameters(xavier_uniform) self.mlp_global9.reset_parameters(xavier_uniform) self.mlp_global22.reset_parameters(xavier_uniform) set_bn(self, momentum=0.01)
def reset_parameters(self): for sa_module in self.sa_modules: sa_module.reset_parameters(xavier_uniform) self.mlp_global.reset_parameters(xavier_uniform) xavier_uniform(self.classifier) set_bn(self, momentum=0.01)
def reset_parameters(self): #xavier_uniform(self.classifier) self.mlp_local.reset_parameters(xavier_uniform) self.conv1d.reset_parameters(xavier_uniform) set_bn(self, momentum=0.01)
def reset_parameters(self): #xavier_uniform(self.ins_logit) self.mlp_local.reset_parameters(xavier_uniform) set_bn(self, momentum=0.01)