if args.checkpoint: if args.last: ckpt_path = args.dir_result + '/' + args.project_name + '/ckpts/best_{}.pth'.format(str(seed_num)) elif args.best: ckpt_path = args.dir_result + '/' + args.project_name + '/ckpts/best_{}.pth'.format(str(seed_num)) checkpoint = torch.load(ckpt_path, map_location=device) model.load_state_dict(checkpoint['model']) logger.best_auc = checkpoint['score'] start_epoch = checkpoint['epoch'] del checkpoint else: logger.best_auc = 0 start_epoch = 1
时间: 2024-04-07 13:28:04 浏览: 129
这段代码是用来加载模型训练过程中保存的 checkpoint 文件的,其中包含了模型的状态字典、当前训练的 epoch 数以及最佳的验证集 AUC 值等信息。如果在训练时设置了 `args.checkpoint` 为 True,则会加载保存的 checkpoint 文件;否则,会将 `logger.best_auc` 初始化为 0,`start_epoch` 初始化为 1。其中,`args.last` 和 `args.best` 用于指定加载最后一个 checkpoint 文件还是最佳的 checkpoint 文件。
相关问题
下面这段代码的作用是什么:def ovssc_inference( data_pickle_path: str, model_ckpt_path: str, dump_path: str = "visualization/", ): args = config_parser().parse_args( args=["--load", model_ckpt_path, "--file_path", data_pickle_path] ) with open(os.path.dirname(args.load) + "/args.pkl", "rb") as file: exp_args = pickle.load(file) for arg in vars(exp_args): if any(arg == s for s in ["device", "file_path", "load"]): continue setattr(args, arg, getattr(exp_args, arg)) args.domain_randomization = False scene_bounds = tuple(args.scene_bounds) logging.info("Preparing batch")
这段代码的作用是进行 OVSSC 推理,其中 data_pickle_path 是数据 pickle 文件的路径,model_ckpt_path 是模型的 checkpoint 文件路径,dump_path 是可视化结果的保存路径。代码中还加载了模型的参数,并设置了一些参数,最后进行了批处理。
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor class LossCallBack(LossMonitor): """ Monitor the loss in training. If the loss in NAN or INF terminating training. """ def __init__(self, has_trained_epoch=0, per_print_times=per_print_steps): super(LossCallBack, self).__init__() self.has_trained_epoch = has_trained_epoch self._per_print_times = per_print_times def step_end(self, run_context): cb_params = run_context.original_args() loss = cb_params.net_outputs if isinstance(loss, (tuple, list)): if isinstance(loss[0], ms.Tensor) and isinstance(loss[0].asnumpy(), np.ndarray): loss = loss[0] if isinstance(loss, ms.Tensor) and isinstance(loss.asnumpy(), np.ndarray): loss = np.mean(loss.asnumpy()) cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1 if isinstance(loss, float) and (np.isnan(loss) or np.isinf(loss)): raise ValueError("epoch: {} step: {}. Invalid loss, terminating training.".format( cb_params.cur_epoch_num, cur_step_in_epoch)) if self._per_print_times != 0 and cb_params.cur_step_num % self._per_print_times == 0: # pylint: disable=line-too-long print("epoch: %s step: %s, loss is %s" % (cb_params.cur_epoch_num + int(self.has_trained_epoch), cur_step_in_epoch, loss), flush=True) time_cb = TimeMonitor(data_size=step_size) loss_cb = LossCallBack(has_trained_epoch=0) cb = [time_cb, loss_cb] ckpt_save_dir = cfg['output_dir'] device_target = context.get_context('device_target') if cfg['save_checkpoint']: config_ck = CheckpointConfig(save_checkpoint_steps=save_ckpt_num*step_size, keep_checkpoint_max=10) # config_ck = CheckpointConfig(save_checkpoint_steps=5*step_size, keep_checkpoint_max=10) ckpt_cb = ModelCheckpoint(prefix="resnet", directory=ckpt_save_dir, config=config_ck) cb += [ckpt_cb]
这段代码定义了一些回调函数,用于在训练过程中监控和保存模型。
首先,定义了一个名为LossCallBack的类,继承自LossMonitor回调类。它重写了step_end方法,在每个训练步骤结束时监控损失值。如果损失值为NaN或INF,将抛出ValueError以终止训练。如果_per_print_times参数不为0且当前步骤数是_per_print_times的倍数,将打印当前的训练损失值。
然后,创建了一个TimeMonitor回调实例和一个LossCallBack回调实例。TimeMonitor用于监控训练时间,LossCallBack用于监控训练损失值。
接着,创建了一个回调列表cb,并将time_cb和loss_cb添加到列表中。同时,获取配置文件中的ckpt_save_dir和device_target。
如果配置文件中的save_checkpoint为True,则创建一个CheckpointConfig实例config_ck,用于配置模型保存的参数(保存间隔、最大保存个数等)。然后,创建一个ModelCheckpoint回调实例ckpt_cb,并将其添加到回调列表cb中。
最后,返回回调列表cb,用于在训练过程中使用。
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