详细解释这段代码 def init(self, args, model, env, logger): self.args = args self.device = th.device( "cuda" if th.cuda.is_available() and self.args.cuda else "cpu" ) self.logger = logger self.episodic = self.args.episodic if self.args.target: target_net = model(self.args).to(self.device) self.behaviour_net = model(self.args, target_net).to(self.device) else: self.behaviour_net = model(self.args).to(self.device) if self.args.replay: if not self.episodic: self.replay_buffer = TransReplayBuffer( int(self.args.replay_buffer_size) ) else: self.replay_buffer = EpisodeReplayBuffer( int(self.args.replay_buffer_size) ) self.env = env self.policy_optimizer = optim.RMSprop( self.behaviour_net.policy_dicts.parameters(), lr=args.policy_lrate, alpha=0.99, eps=1e-5 ) self.value_optimizer = optim.RMSprop( self.behaviour_net.value_dicts.parameters(), lr=args.value_lrate, alpha=0.99, eps=1e-5 ) if self.args.mixer: self.mixer_optimizer = optim.RMSprop( self.behaviour_net.mixer.parameters(), lr=args.mixer_lrate, alpha=0.99, eps=1e-5 ) self.init_action = th.zeros(1, self.args.agent_num, self.args.action_dim).to(self.device) self.steps = 0 self.episodes = 0 self.entr = self.args.entr
时间: 2024-04-27 09:21:34 浏览: 150
这段代码是一个类的初始化方法,接收四个参数args、model、env和logger。首先,将args、logger存储在类的属性中;然后,根据是否使用cuda,设置device属性为"cuda"或"cpu";若args中有target,则创建一个target_net模型,并将其移动到device上,同时创建一个behaviour_net模型,并将其移动到device上;否则,直接创建behaviour_net模型,并将其移动到device上。若args中有replay,则根据是否使用episodic,创建一个TransReplayBuffer或EpisodeReplayBuffer缓冲区,大小为args.replay_buffer_size;同时,将env赋值给类的env属性。接着,使用optim.RMSprop创建policy_optimizer、value_optimizer和mixer_optimizer(若args中有mixer),并分别将behaviour_net模型的policy_dicts、value_dicts和mixer参数作为优化器的参数。最后,初始化一些其他属性,如init_action、steps、episodes和entr。
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def train(cfg, args): # clear up residual cache from previous runs if torch.cuda.is_available(): torch.cuda.empty_cache() # main training / eval actions here # fix the seed for reproducibility if cfg.SEED is not None: torch.manual_seed(cfg.SEED) np.random.seed(cfg.SEED) random.seed(0) # setup training env including loggers logging_train_setup(args, cfg) logger = logging.get_logger("visual_prompt") train_loader, val_loader, test_loader = get_loaders(cfg, logger) logger.info("Constructing models...") model, cur_device = build_model(cfg) logger.info("Setting up Evalutator...") evaluator = Evaluator() logger.info("Setting up Trainer...") trainer = Trainer(cfg, model, evaluator, cur_device) if train_loader: trainer.train_classifier(train_loader, val_loader, test_loader) else: print("No train loader presented. Exit") if cfg.SOLVER.TOTAL_EPOCH == 0: trainer.eval_classifier(test_loader, "test", 0)
这是一个训练函数的代码,它接受两个参数:cfg 和 args。在函数中,首先清除之前运行的缓存,然后设置随机种子以便实现可重复性。接下来,设置日志记录器,获取数据加载器并构建模型。然后设置评估器和训练器,并调用训练器的 train_classifier 方法来训练分类器。如果没有提供训练数据加载器,则输出“没有训练加载器呈现。退出”。最后,如果 SOLVER.TOTAL_EPOCH 为 0,则调用训练器的 eval_classifier 方法在测试数据集上评估分类器。
def logging_train_setup(args, cfg) -> None: output_dir = cfg.OUTPUT_DIR if output_dir: PathManager.mkdirs(output_dir) logger = logging.setup_logging( cfg.NUM_GPUS, get_world_size(), output_dir, name="visual_prompt") # Log basic information about environment, cmdline arguments, and config rank = get_rank() logger.info( f"Rank of current process: {rank}. World size: {get_world_size()}") logger.info("Environment info:\n" + collect_env_info()) logger.info("Command line arguments: " + str(args)) if hasattr(args, "config_file") and args.config_file != "": logger.info( "Contents of args.config_file={}:\n{}".format( args.config_file, PathManager.open(args.config_file, "r").read() ) ) # Show the config logger.info("Training with config:") logger.info(pprint.pformat(cfg)) # cudnn benchmark has large overhead. # It shouldn't be used considering the small size of typical val set. if not (hasattr(args, "eval_only") and args.eval_only): torch.backends.cudnn.benchmark = cfg.CUDNN_BENCHMARK
这段代码是用来设置训练日志的。首先,它会创建一个输出目录。然后,它会使用logging模块设置日志,其中包括环境信息、命令行参数、配置信息和当前进程的排名等。如果有配置文件,它还会将配置文件的内容记录在日志中。接着,它会显示训练配置,并设置是否使用cudnn benchmark。如果args中有eval_only属性且为True,那么不会使用cudnn benchmark。
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