fix loading past checpoints (#2405)

* fix #2334

* chlog

CHANGELOG.md

@@ -42,6 +42,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed loading model with kwargs ([#2387](https://github.com/PyTorchLightning/pytorch-lightning/pull/2387))
 
+- Fixed loading past checkpoints from v0.7.x ([#2405](https://github.com/PyTorchLightning/pytorch-lightning/pull/2405))
+
 ## [0.8.1] - 2020-06-19
 
 ### Fixed

pytorch_lightning/core/saving.py

@@ -180,8 +180,7 @@ def _load_model_state(cls, checkpoint: Dict[str, Any], *cls_args, **cls_kwargs):
                 if hparam_key in checkpoint:
                     model_args.update(checkpoint[hparam_key])
 
-            if cls.CHECKPOINT_HYPER_PARAMS_TYPE in checkpoint:
-                model_args = checkpoint[cls.CHECKPOINT_HYPER_PARAMS_TYPE](model_args)
+            model_args = _convert_loaded_hparams(model_args, checkpoint.get(cls.CHECKPOINT_HYPER_PARAMS_TYPE))
 
             args_name = checkpoint.get(cls.CHECKPOINT_HYPER_PARAMS_NAME)
             cls_spec = inspect.getfullargspec(cls.__init__)
@@ -248,6 +247,18 @@ def on_hpc_load(self, checkpoint: Dict[str, Any]) -> None:
         """
 
 
+def _convert_loaded_hparams(model_args: dict, hparams_type: Union[Callable, str] = None) -> object:
+    """Convert hparams according given type in callable or string (past) format"""
+    # if not hparams type define
+    if not hparams_type:
+        return model_args
+    # if past checkpoint loaded, convert str to callable
+    if isinstance(hparams_type, str):
+        hparams_type = AttributeDict
+    # convert hparams
+    return hparams_type(model_args)
+
+
 def update_hparams(hparams: dict, updates: dict) -> None:
     """
     Overrides hparams with new values

tests/models/test_hparams.py

@@ -268,6 +268,7 @@ def test_collect_init_arguments(tmpdir, cls):
     # verify that the checkpoint saved the correct values
     trainer = Trainer(default_root_dir=tmpdir, max_epochs=2, overfit_batches=0.5)
     trainer.fit(model)
+
     raw_checkpoint_path = _raw_checkpoint_path(trainer)
 
     raw_checkpoint = torch.load(raw_checkpoint_path)
@@ -391,6 +392,7 @@ def test_load_past_checkpoint(tmpdir, past_key):
     raw_checkpoint_path = _raw_checkpoint_path(trainer)
     raw_checkpoint = torch.load(raw_checkpoint_path)
     raw_checkpoint[past_key] = raw_checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_KEY]
+    raw_checkpoint['hparams_type'] = 'Namespace'
     raw_checkpoint[past_key]['batch_size'] = -17
     del raw_checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_KEY]
     # save back the checkpoint