PyTorch Lightning加载checkpoint报IsADirectoryError排查

请问如下训练函数为何会触发IsADirectoryError报错：

def train_graph_classifier(model_name, **model_kwargs):
  pl.seed_everything(42)

  # 创建带生成回调的PyTorch Lightning训练器
  root_dir = os.path.join('/home/predictor2', "GraphLevel" + model_name)
  os.makedirs(root_dir, exist_ok=True)
  trainer = pl.Trainer(default_root_dir=root_dir,
                     callbacks=[ModelCheckpoint(save_weights_only=True, mode="max", monitor="val_acc")],
                     gpus=1 if str(device).startswith("cuda") else 0,
                     max_epochs=500,
                     progress_bar_refresh_rate=0)
  trainer.logger._default_hp_metric = None # 不需要的可选日志参数

  # 检查是否存在预训练模型，存在则直接加载跳过训练
  pretrained_filename = os.path.join('/home/predictor2', f"GraphLevel{model_name}.ckpt")
  if os.path.isfile(pretrained_filename):
    print("Found pretrained model, loading...")
    model = GraphLevelGNN.load_from_checkpoint(pretrained_filename)
  else:
    pl.seed_everything(42)
    model = GraphLevelGNN(c_in=dataset.num_node_features, 
                          c_out=1 if dataset.num_classes==2 else dataset.num_classes,  #change
                          **model_kwargs)
    trainer.fit(model, graph_train_loader, graph_val_loader)
    model = GraphLevelGNN.load_from_checkpoint(trainer.checkpoint_callback.best_model_path)

  # 在验证集、测试集上测试最优模型
  train_result = trainer.test(model, graph_train_loader, verbose=False)
  test_result = trainer.test(model, graph_test_loader, verbose=False)
  result = {"test": test_result[0]['test_acc'], "train": train_result[0]['test_acc']} 
  return model, result

函数运行返回的错误栈如下：

Traceback (most recent call last):
  File "stability_v3_alternative_net.py", line 604, in <module>
    dp_rate=0.2)
  File "stability_v3_alternative_net.py", line 591, in train_graph_classifier
    model = GraphLevelGNN.load_from_checkpoint(trainer.checkpoint_callback.best_model_path)
  File "/root/miniconda3/lib/python3.7/site-packages/pytorch_lightning/core/saving.py", line 139, in load_from_checkpoint
    checkpoint = pl_load(checkpoint_path, map_location=lambda storage, loc: storage)
  File "/root/miniconda3/lib/python3.7/site-packages/pytorch_lightning/utilities/cloud_io.py", line 46, in load
    with fs.open(path_or_url, "rb") as f:
  File "/root/miniconda3/lib/python3.7/site-packages/fsspec/spec.py", line 1043, in open
    **kwargs,
  File "/root/miniconda3/lib/python3.7/site-packages/fsspec/implementations/local.py", line 159, in _open
    return LocalFileOpener(path, mode, fs=self, **kwargs)
  File "/root/miniconda3/lib/python3.7/site-packages/fsspec/implementations/local.py", line 254, in __init__
    self._open()
  File "/root/miniconda3/lib/python3.7/site-packages/fsspec/implementations/local.py", line 259, in _open
    self.f = open(self.path, mode=self.mode)
IsADirectoryError: [Errno 21] Is a directory: '/home/predictor'

其中/home/predictor是当前工作目录，已特意创建predictor2目录存储训练相关文件，即便把代码中predictor2替换为predictor仍会触发相同错误。已知错误含义是程序读取文件时传入的路径为目录而非目标文件，但代码中没有显式引用工作目录/home/predictor，无法定位触发点，代码参考公开PyTorch GNN教程示例编写，需要排查问题原因与解决方案。

报错核心是trainer.checkpoint_callback.best_model_path返回了目录路径/home/predictor而非具体的ckpt文件路径，由两类常见问题触发：

• 检查点保存逻辑未生效：配置的ModelCheckpoint监控指标为val_acc，如果验证步骤未正常运行、或val_acc指标没有被正确记录到日志中，检查点回调不会生成任何有效模型文件，此时best_model_path会被默认赋值为当前工作目录，加载时自然触发目录错误。
• PyTorch Lightning版本兼容问题：你当前使用的是适配Python3.7的1.x旧版本PL，如果不给ModelCheckpoint显式指定dirpath参数，回调不会自动继承Trainer的default_root_dir作为检查点存储路径，会回退到当前工作目录，未生成有效检查点时就会直接返回目录路径。

按优先级依次排查修改：

• 给ModelCheckpoint显式指定检查点存储目录，避免路径回退：

  # 先定义检查点专属存储目录
  ckpt_dir = os.path.join(root_dir, "checkpoints")
  os.makedirs(ckpt_dir, exist_ok=True)
  # 初始化回调时显式传入路径、文件名规则参数
  checkpoint_callback = ModelCheckpoint(
      dirpath=ckpt_dir,
      filename="best-model-{epoch:02d}-{val_acc:.3f}",
      save_weights_only=True,
      mode="max",
      monitor="val_acc"
  )
  trainer = pl.Trainer(
      default_root_dir=root_dir,
      callbacks=[checkpoint_callback],
      gpus=1 if str(device).startswith("cuda") else 0,
      max_epochs=500,
      progress_bar_refresh_rate=0
  )
  

• 确认验证流程正常，val_acc指标被正确记录：检查验证步validation_step中是否正确调用self.log("val_acc", acc, prog_bar=True)，确保日志记录的指标名和ModelCheckpoint的monitor参数完全一致，无拼写错误。
• 加载最优模型前增加路径校验，提前拦截无效路径问题：

  trainer.fit(model, graph_train_loader, graph_val_loader)
  best_ckpt_path = trainer.checkpoint_callback.best_model_path
  # 确认路径是有效文件再执行加载
  assert os.path.isfile(best_ckpt_path), f"最佳模型路径无效，当前值为{best_ckpt_path}，请检查检查点保存逻辑"
  model = GraphLevelGNN.load_from_checkpoint(best_ckpt_path)
  

内容的提问来源于stack exchange，提问作者Slowat_Kela