config = pickle.load(config_f) assert (config.model_ind == given_config.model_ind) config.restart = True config.restart_from_best = given_config.restart_from_best # copy over new num_epochs and lr schedule config.num_epochs = given_config.num_epochs config.lr_schedule = given_config.lr_schedule else: print("Config: %s" % config_to_str(config)) # Model ------------------------------------------------------------------------ dataloaders, mapping_assignment_dataloader, mapping_test_dataloader = \ cluster_create_dataloaders(config) net = archs.__dict__[config.arch](config) if config.restart: model_path = os.path.join(config.out_dir, net_name) net.load_state_dict( torch.load(model_path, map_location=lambda storage, loc: storage)) net.cuda() net = torch.nn.DataParallel(net) net.train() optimiser = get_opt(config.opt)(net.module.parameters(), lr=config.lr) if config.restart: optimiser.load_state_dict( torch.load(os.path.join(config.out_dir, opt_name)))
config = pickle.load(config_f) assert config.model_ind == given_config.model_ind config.restart = True config.restart_from_best = given_config.restart_from_best # copy over new num_epochs and lr schedule config.num_epochs = given_config.num_epochs config.lr_schedule = given_config.lr_schedule else: print("Config: %s" % config_to_str(config)) # Model ------------------------------------------------------------------------ dataloaders, mapping_assignment_dataloader, mapping_test_dataloader = cluster_create_dataloaders( config ) net = archs.__dict__[config.arch](config) # type: ignore if config.restart: assert net_name is not None model_path = os.path.join(config.out_dir, net_name) net.load_state_dict( torch.load(model_path, map_location=lambda storage, loc: storage) ) net.cuda() net = torch.nn.DataParallel(net) net.train() optimiser = get_opt(config.opt)(net.module.parameters(), lr=config.lr) if config.restart: