Trajectron-plus-plus/trajectron/model/model_registrar.py

import os
import torch
import torch.nn as nn


def get_model_device(model):
    return next(model.parameters()).device


class ModelRegistrar(nn.Module):
    def __init__(self, model_dir, device):
        super(ModelRegistrar, self).__init__()
        self.model_dict = nn.ModuleDict()
        self.model_dir = model_dir
        self.device = device

    def forward(self):
        raise NotImplementedError('Although ModelRegistrar is a nn.Module, it is only to store parameters.')

    def get_model(self, name, model_if_absent=None):
        # 4 cases: name in self.model_dict and model_if_absent is None         (OK)
        #          name in self.model_dict and model_if_absent is not None     (OK)
        #          name not in self.model_dict and model_if_absent is not None (OK)
        #          name not in self.model_dict and model_if_absent is None     (NOT OK)

        if name in self.model_dict:
            return self.model_dict[name]

        elif model_if_absent is not None:
            self.model_dict[name] = model_if_absent.to(self.device)
            return self.model_dict[name]

        else:
            raise ValueError(f'{name} was never initialized in this Registrar!')

    def get_name_match(self, name):
        ret_model_list = nn.ModuleList()
        for key in self.model_dict.keys():
            if name in key:
                ret_model_list.append(self.model_dict[key])
        return ret_model_list

    def get_all_but_name_match(self, name):
        ret_model_list = nn.ModuleList()
        for key in self.model_dict.keys():
            if name not in key:
                ret_model_list.append(self.model_dict[key])
        return ret_model_list

    def print_model_names(self):
        print(self.model_dict.keys())

    def save_models(self, curr_iter):
        # Create the model directiory if it's not present.
        save_path = os.path.join(self.model_dir,
                                 'model_registrar-%d.pt' % curr_iter)

        torch.save(self.model_dict, save_path)

    def load_models(self, iter_num):
        self.model_dict.clear()
        
        save_path = os.path.join(self.model_dir,
                                 'model_registrar-%d.pt' % iter_num)

        print('')
        print('Loading from ' + save_path)
        self.model_dict = torch.load(save_path, map_location=self.device)
        print('Loaded!')
        print('')

    def to(self, device):
        for name, model in self.model_dict.items():
            if get_model_device(model) != device:
                model.to(device)