fix loading model with kwargs

.github/PULL_REQUEST_TEMPLATE.md

@@ -11,7 +11,7 @@ Fixes # (issue)
 
 - [ ] Was this discussed/approved via a Github issue? (no need for typos and docs improvements)
 - [ ] Did you read the [contributor guideline](https://github.com/PyTorchLightning/pytorch-lightning/blob/master/.github/CONTRIBUTING.md), Pull Request section?
-- [ ] Did you make sure your PR does only one thing, instead of bundling different changes together? Otherwise, we ask you create a separate PR for every change.
+- [ ] Did you make sure your PR does only one thing, instead of bundling different changes together? Otherwise, we ask you to create a separate PR for every change.
 - [ ] Did you make sure to update the documentation with your changes?
 - [ ] Did you write any new necessary tests? 
 - [ ] Did you verify new and existing tests pass locally with your changes?

CHANGELOG.md

@@ -36,6 +36,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed lost compatibility with custom datatypes implementing `.to` ([#2335](https://github.com/PyTorchLightning/pytorch-lightning/pull/2335))
 
+- Fixed loading model with kwargs ([#2387](https://github.com/PyTorchLightning/pytorch-lightning/pull/2387))
+
 ## [0.8.1] - 2020-06-19
 
 ### Fixed

pytorch_lightning/core/saving.py

@@ -170,7 +170,7 @@ def load_from_checkpoint(
         return model
 
     @classmethod
-    def _load_model_state(cls, checkpoint: Dict[str, Any], *args, **kwargs):
+    def _load_model_state(cls, checkpoint: Dict[str, Any], *cls_args, **cls_kwargs):
         # pass in the values we saved automatically
         if cls.CHECKPOINT_HYPER_PARAMS_KEY in checkpoint:
             model_args = {}
@@ -184,19 +184,23 @@ def _load_model_state(cls, checkpoint: Dict[str, Any], *args, **kwargs):
                 model_args = checkpoint[cls.CHECKPOINT_HYPER_PARAMS_TYPE](model_args)
 
             args_name = checkpoint.get(cls.CHECKPOINT_HYPER_PARAMS_NAME)
-            init_args_name = inspect.signature(cls).parameters.keys()
+            cls_spec = inspect.getfullargspec(cls.__init__)
+            kwargs_identifier = cls_spec.varkw
+            cls_init_args_name = inspect.signature(cls).parameters.keys()
 
             if args_name == 'kwargs':
-                cls_kwargs = {k: v for k, v in model_args.items() if k in init_args_name}
-                kwargs.update(**cls_kwargs)
+                # in case the class cannot take any extra argument filter only the possible
+                if not kwargs_identifier:
+                    model_args = {k: v for k, v in model_args.items() if k in cls_init_args_name}
+                cls_kwargs.update(**model_args)
             elif args_name:
-                if args_name in init_args_name:
-                    kwargs.update({args_name: model_args})
+                if args_name in cls_init_args_name:
+                    cls_kwargs.update({args_name: model_args})
             else:
-                args = (model_args, ) + args
+                cls_args = (model_args,) + cls_args
 
         # load the state_dict on the model automatically
-        model = cls(*args, **kwargs)
+        model = cls(*cls_args, **cls_kwargs)
         model.load_state_dict(checkpoint['state_dict'])
 
         # give model a chance to load something

pytorch_lightning/trainer/training_io.py

@@ -324,7 +324,7 @@ def dump_checkpoint(self, weights_only: bool = False) -> dict:
         checkpoint = {
             'epoch': self.current_epoch + 1,
             'global_step': self.global_step + 1,
-            'pytorch-ligthning_version': pytorch_lightning.__version__,
+            'pytorch-lightning_version': pytorch_lightning.__version__,
         }
 
         if not weights_only:

tests/base/model_template.py

@@ -36,19 +36,19 @@ class EvalModelTemplate(
     >>> model = EvalModelTemplate()
     """
 
-    def __init__(self,
-                 *args,
-                 drop_prob: float = 0.2,
-                 batch_size: int = 32,
-                 in_features: int = 28 * 28,
-                 learning_rate: float = 0.001 * 8,
-                 optimizer_name: str = 'adam',
-                 data_root: str = PATH_DATASETS,
-                 out_features: int = 10,
-                 hidden_dim: int = 1000,
-                 b1: float = 0.5,
-                 b2: float = 0.999,
-                 **kwargs) -> object:
+    def __init__(
+            self,
+            drop_prob: float = 0.2,
+            batch_size: int = 32,
+            in_features: int = 28 * 28,
+            learning_rate: float = 0.001 * 8,
+            optimizer_name: str = 'adam',
+            data_root: str = PATH_DATASETS,
+            out_features: int = 10,
+            hidden_dim: int = 1000,
+            b1: float = 0.5,
+            b2: float = 0.999
+    ):
         # init superclass
         super().__init__()
         self.save_hyperparameters()

tests/models/test_hparams.py

@@ -275,16 +275,15 @@ def test_collect_init_arguments(tmpdir, cls):
     assert raw_checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_KEY]['batch_size'] == 179
 
     # verify that model loads correctly
-    # TODO: uncomment and get it pass
-    # model = cls.load_from_checkpoint(raw_checkpoint_path)
-    # assert model.hparams.batch_size == 179
-    #
-    # if isinstance(model, AggSubClassEvalModel):
-    #     assert isinstance(model.hparams.my_loss, torch.nn.CrossEntropyLoss)
-    #
-    # if isinstance(model, DictConfSubClassEvalModel):
-    #     assert isinstance(model.hparams.dict_conf, Container)
-    #     assert model.hparams.dict_conf == 'anything'
+    model = cls.load_from_checkpoint(raw_checkpoint_path)
+    assert model.hparams.batch_size == 179
+
+    if isinstance(model, AggSubClassEvalModel):
+        assert isinstance(model.hparams.my_loss, torch.nn.CrossEntropyLoss)
+
+    if isinstance(model, DictConfSubClassEvalModel):
+        assert isinstance(model.hparams.dict_conf, Container)
+        assert model.hparams.dict_conf['my_param'] == 'anything'
 
     # verify that we can overwrite whatever we want
     model = cls.load_from_checkpoint(raw_checkpoint_path, batch_size=99)

tests/test_deprecated.py

@@ -128,9 +128,8 @@ def test_end(self, outputs):
 
 
 def test_tbd_remove_in_v1_0_0_model_hooks():
-    hparams = EvalModelTemplate.get_default_hparams()
 
-    model = ModelVer0_6(hparams)
+    model = ModelVer0_6()
 
     with pytest.deprecated_call(match='v1.0'):
         trainer = Trainer(logger=False)
@@ -143,7 +142,7 @@ def test_tbd_remove_in_v1_0_0_model_hooks():
         result = trainer._evaluate(model, dataloaders=[[None]], max_batches=1)
     assert result == {'val_loss': torch.tensor(0.6)}
 
-    model = ModelVer0_7(hparams)
+    model = ModelVer0_7()
 
     with pytest.deprecated_call(match='v1.0'):
         trainer = Trainer(logger=False)

tests/trainer/test_lr_finder.py

@@ -182,7 +182,7 @@ def test_suggestion_with_non_finite_values(tmpdir):
     """ Test that non-finite values does not alter results """
 
     hparams = EvalModelTemplate.get_default_hparams()
-    model = EvalModelTemplate(hparams)
+    model = EvalModelTemplate(**hparams)
 
     # logger file to get meta
     trainer = Trainer(

tests/trainer/test_optimizers.py

@@ -231,7 +231,7 @@ def configure_optimizers(self):
             return config
 
     hparams = EvalModelTemplate.get_default_hparams()
-    model = CurrentModel(hparams)
+    model = CurrentModel(**hparams)
 
     # fit model
     trainer = Trainer(