def add_params()

in semantic_segmentation/mmcv_custom/layer_decay_optimizer_constructor.py [0:0]

• 46 lines of code
• 11 McCabe index (conditional complexity)

    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
        decay_rate = self.paramwise_cfg.get('decay_rate')
        decay_type = self.paramwise_cfg.get('decay_type', "layer_wise")
        print("Build LearningRateDecayOptimizerConstructor %s %f - %d" % (decay_type, 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'):
                group_name = "no_decay"
                this_weight_decay = 0.
            else:
                group_name = "decay"
                this_weight_decay = weight_decay

            if decay_type == "layer_wise":
                layer_id = get_num_layer_layer_wise(name, self.paramwise_cfg.get('num_layers'))
            elif decay_type == "stage_wise":
                layer_id = get_num_layer_stage_wise(name, num_layers)
                
            group_name = "layer_%d_%s" % (layer_id, group_name)

            if group_name not in parameter_groups:
                scale = 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))
        
        params.extend(parameter_groups.values())