made load from checkpoint flexible (#1307)

* made load from checkpoint flexible

* made load from checkpoint flexible

* made load from checkpoint flexible

Author: williamFalcon

CHANGELOG.md

@@ -21,6 +21,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
+- Enhanced load_from_checkpoint to also forward params to the model ([#1307](https://github.com/PyTorchLightning/pytorch-lightning/pull/1307))
 - Made `evalaute` method private >> `Trainer._evaluate(...)`. ([#1260](https://github.com/PyTorchLightning/pytorch-lightning/pull/1260))
 
 ### Deprecated

docs/source/weights_loading.rst

@@ -84,17 +84,50 @@ To save your own checkpoint call:
 Checkpoint Loading
 ------------------
 
-To load a model along with its weights, biases and hyperparameters use following method:
+To load a model along with its weights, biases and hyperparameters use following method.
 
 .. code-block:: python
 
     model = MyLightingModule.load_from_checkpoint(PATH)
     model.eval()
     y_hat = model(x)
 
-A LightningModule is no different than a nn.Module. This means you can load it and use it for
-predictions as you would a nn.Module.
+The above only works if you used `hparams` in your model definition
 
+.. code-block:: python
+
+    class MyModel(pl.LightningModule):
+
+        def __init__(self, hparams):
+            self.hparams = hparams
+            self.l1 = nn.Linear(hparams.in_dim, hparams.out_dim)
+
+But if you don't and instead pass individual parameters
+
+.. code-block:: python
+
+    class MyModel(pl.LightningModule):
+
+        def __init__(self, in_dim, out_dim):
+            self.l1 = nn.Linear(in_dim, out_dim)
+
+you can restore the model like this
+
+.. code-block:: python
+
+    model = MyModel.load_from_checkpoint(PATH, in_dim=128, out_dim=10)
+
+
+Restoring Training State
+------------------------
+
+If you don't just want to load weights, but instead restore the full training,
+do the following:
+
+.. code-block:: python
+
+   model = LitModel()
+   trainer = Trainer(resume_from_checkpoint='some/path/to/my_checkpoint.ckpt')
 
-.. note:: To restore the trainer state as well use
-    :meth:`pytorch_lightning.trainer.trainer.Trainer.resume_from_checkpoint`.
+   # automatically restores model, epoch, step, LR schedulers, apex, etc...
+   trainer.fit(model)

pytorch_lightning/core/lightning.py

@@ -1324,6 +1324,7 @@ def load_from_checkpoint(
             checkpoint_path: str,
             map_location: Optional[Union[Dict[str, str], str, torch.device, int, Callable]] = None,
             tags_csv: Optional[str] = None,
+            *args, **kwargs
     ) -> 'LightningModule':
         r"""
 
@@ -1346,6 +1347,7 @@ def __init__(self, hparams):
 
         Args:
             checkpoint_path: Path to checkpoint.
+            model_args: Any keyword args needed to init the model.
             map_location:
                 If your checkpoint saved a GPU model and you now load on CPUs
                 or a different number of GPUs, use this to map to the new setup.
@@ -1387,6 +1389,14 @@ def __init__(self, hparams):
                     tags_csv='/path/to/hparams_file.csv'
                 )
 
+                # or load passing whatever args the model takes to load
+                MyLightningModule.load_from_checkpoint(
+                    'path/to/checkpoint.ckpt',
+                    learning_rate=0.1,
+                    layers=2,
+                    pretrained_model=some_model
+                )
+
                 # predict
                 pretrained_model.eval()
                 pretrained_model.freeze()
@@ -1403,11 +1413,11 @@ def __init__(self, hparams):
             hparams.__setattr__('on_gpu', False)
             checkpoint['hparams'] = vars(hparams)
 
-        model = cls._load_model_state(checkpoint)
+        model = cls._load_model_state(checkpoint, *args, **kwargs)
         return model
 
     @classmethod
-    def _load_model_state(cls, checkpoint: Dict[str, Any]) -> 'LightningModule':
+    def _load_model_state(cls, checkpoint: Dict[str, Any], *args, **kwargs) -> 'LightningModule':
         cls_takes_hparams = 'hparams' in inspect.signature(cls.__init__).parameters
         ckpt_hparams = checkpoint.get('hparams')
 
@@ -1433,7 +1443,10 @@ def _load_model_state(cls, checkpoint: Dict[str, Any]) -> 'LightningModule':
 
         # load the state_dict on the model automatically
         model_args = [hparams] if hparams else []
-        model = cls(*model_args)
+        if len(model_args) > 0:
+            model = cls(*model_args)
+        else:
+            model = cls(*args, **kwargs)
         model.load_state_dict(checkpoint['state_dict'])
 
         # give model a chance to load something