def add_params(self, params, module, prefix='', is_dcn_module=None):
        """Add all parameters of module to the params list.

        The parameters of the given module will be added to the list of param
        groups, with specific rules defined by paramwise_cfg.
        Args:
            params (list[dict]): A list of param groups, it will be modified
                in place.
            module (nn.Module): The module to be added.
            prefix (str): The prefix of the module
            is_dcn_module (int|float|None): If the current module is a
                submodule of DCN, `is_dcn_module` will be passed to
                control conv_offset layer's learning rate. Defaults to None.
        """
        parameter_groups = {}
        print(self.paramwise_cfg)
        num_layers = self.paramwise_cfg.get('num_layers') + 2
        layer_decay_rate = self.paramwise_cfg.get('layer_decay_rate')
        print('Build LayerDecayOptimizerConstructor %f - %d' %
              (layer_decay_rate, num_layers))
        weight_decay = self.base_wd

        for name, param in module.named_parameters():
            if not param.requires_grad:
                continue  # frozen weights
            if len(param.shape) == 1 or name.endswith('.bias') \
                    or name in ('pos_embed', 'cls_token'):
                # or "relative_position_bias_table" in name:
                group_name = 'no_decay'
                this_weight_decay = 0.
            else:
                group_name = 'decay'
                this_weight_decay = weight_decay

            layer_id = get_num_layer_for_vit(name, num_layers)
            group_name = 'layer_%d_%s' % (layer_id, group_name)

            if group_name not in parameter_groups:
                scale = layer_decay_rate**(num_layers - layer_id - 1)

                parameter_groups[group_name] = {
                    'weight_decay': this_weight_decay,
                    'params': [],
                    'param_names': [],
                    'lr_scale': scale,
                    'group_name': group_name,
                    'lr': scale * self.base_lr,
                }

            parameter_groups[group_name]['params'].append(param)
            parameter_groups[group_name]['param_names'].append(name)
        rank, _ = get_dist_info()
        if rank == 0:
            to_display = {}
            for key in parameter_groups:
                to_display[key] = {
                    'param_names': parameter_groups[key]['param_names'],
                    'lr_scale': parameter_groups[key]['lr_scale'],
                    'lr': parameter_groups[key]['lr'],
                    'weight_decay': parameter_groups[key]['weight_decay'],
                }
            print('Param groups = %s' % json.dumps(to_display, indent=2))

        # state_dict = module.state_dict()
        # for group_name in parameter_groups:
        #     group = parameter_groups[group_name]
        #     for name in group["param_names"]:
        #         group["params"].append(state_dict[name])
        params.extend(parameter_groups.values())
Add all parameters of module to the params list.

        The parameters of the given module will be added to the list of param
        groups, with specific rules defined by paramwise_cfg.
        Args:
            params (list[dict]): A list of param groups, it will be modified
                in place.
            module (nn.Module): The module to be added.
            prefix (str): The prefix of the module
            is_dcn_module (int|float|None): If the current module is a
                submodule of DCN, `is_dcn_module` will be passed to
                control conv_offset layer's learning rate. Defaults to None.
        
EVA-01/seg/mmcv_custom/layer_decay_optimizer_constructor.py