fix loading with hparams

CHANGELOG.md

@@ -44,6 +44,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed loading past checkpoints from v0.7.x ([#2405](https://github.com/PyTorchLightning/pytorch-lightning/pull/2405))
 
+- Fixed loading model without arguments ([#2403](https://github.com/PyTorchLightning/pytorch-lightning/pull/2403))
+
 ## [0.8.1] - 2020-06-19
 
 ### Fixed

pytorch_lightning/core/saving.py

@@ -171,6 +171,8 @@ def load_from_checkpoint(
 
     @classmethod
     def _load_model_state(cls, checkpoint: Dict[str, Any], *cls_args, **cls_kwargs):
+        cls_spec = inspect.getfullargspec(cls.__init__)
+        cls_init_args_name = inspect.signature(cls).parameters.keys()
         # pass in the values we saved automatically
         if cls.CHECKPOINT_HYPER_PARAMS_KEY in checkpoint:
             model_args = {}
@@ -183,23 +185,25 @@ def _load_model_state(cls, checkpoint: Dict[str, Any], *cls_args, **cls_kwargs):
             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__)
-            kwargs_identifier = cls_spec.varkw
-            cls_init_args_name = inspect.signature(cls).parameters.keys()
 
             if args_name == '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 cls_init_args_name:
                     cls_kwargs.update({args_name: model_args})
             else:
                 cls_args = (model_args,) + cls_args
 
-        # load the state_dict on the model automatically
+        if not cls_spec.varkw:
+            # filter kwargs according to class init unless it allows any argument via kwargs
+            cls_kwargs = {k: v for k, v in cls_kwargs.items() if k in cls_init_args_name}
+
+        # prevent passing positional arguments if class does not accept any
+        if len(cls_spec.args) <= 1 and not cls_spec.kwonlyargs:
+            cls_args, cls_kwargs = [], {}
         model = cls(*cls_args, **cls_kwargs)
+        # load the state_dict on the model automatically
         model.load_state_dict(checkpoint['state_dict'])
 
         # give model a chance to load something

tests/models/test_hparams.py

@@ -6,11 +6,13 @@
 import pytest
 import torch
 from omegaconf import OmegaConf, Container
+from torch.nn import functional as F
+from torch.utils.data import DataLoader
 
 from pytorch_lightning import Trainer, LightningModule
 from pytorch_lightning.core.saving import save_hparams_to_yaml, load_hparams_from_yaml
 from pytorch_lightning.utilities import AttributeDict
-from tests.base import EvalModelTemplate
+from tests.base import EvalModelTemplate, TrialMNIST
 
 
 class SaveHparamsModel(EvalModelTemplate):
@@ -103,16 +105,16 @@ def test_explicit_args_hparams(tmpdir):
     """
 
     # define model
-    class TestModel(EvalModelTemplate):
+    class LocalModel(EvalModelTemplate):
         def __init__(self, test_arg, test_arg2):
             super().__init__()
             self.save_hyperparameters('test_arg', 'test_arg2')
 
-    model = TestModel(test_arg=14, test_arg2=90)
+    model = LocalModel(test_arg=14, test_arg2=90)
 
     # run standard test suite
-    raw_checkpoint_path = _run_standard_hparams_test(tmpdir, model, TestModel)
-    model = TestModel.load_from_checkpoint(raw_checkpoint_path, test_arg2=120)
+    raw_checkpoint_path = _run_standard_hparams_test(tmpdir, model, LocalModel)
+    model = LocalModel.load_from_checkpoint(raw_checkpoint_path, test_arg2=120)
 
     # config specific tests
     assert model.hparams.test_arg2 == 120
@@ -124,16 +126,16 @@ def test_implicit_args_hparams(tmpdir):
     """
 
     # define model
-    class TestModel(EvalModelTemplate):
+    class LocalModel(EvalModelTemplate):
         def __init__(self, test_arg, test_arg2):
             super().__init__()
             self.save_hyperparameters()
 
-    model = TestModel(test_arg=14, test_arg2=90)
+    model = LocalModel(test_arg=14, test_arg2=90)
 
     # run standard test suite
-    raw_checkpoint_path = _run_standard_hparams_test(tmpdir, model, TestModel)
-    model = TestModel.load_from_checkpoint(raw_checkpoint_path, test_arg2=120)
+    raw_checkpoint_path = _run_standard_hparams_test(tmpdir, model, LocalModel)
+    model = LocalModel.load_from_checkpoint(raw_checkpoint_path, test_arg2=120)
 
     # config specific tests
     assert model.hparams.test_arg2 == 120
@@ -145,12 +147,12 @@ def test_explicit_missing_args_hparams(tmpdir):
     """
 
     # define model
-    class TestModel(EvalModelTemplate):
+    class LocalModel(EvalModelTemplate):
         def __init__(self, test_arg, test_arg2):
             super().__init__()
             self.save_hyperparameters('test_arg')
 
-    model = TestModel(test_arg=14, test_arg2=90)
+    model = LocalModel(test_arg=14, test_arg2=90)
 
     # test proper property assignments
     assert model.hparams.test_arg == 14
@@ -166,7 +168,7 @@ def __init__(self, test_arg, test_arg2):
     assert raw_checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_KEY]['test_arg'] == 14
 
     # verify that model loads correctly
-    model = TestModel.load_from_checkpoint(raw_checkpoint_path, test_arg2=123)
+    model = LocalModel.load_from_checkpoint(raw_checkpoint_path, test_arg2=123)
     assert model.hparams.test_arg == 14
     assert 'test_arg2' not in model.hparams  # test_arg2 is not registered in class init
 
@@ -427,3 +429,71 @@ def test_hparams_save_yaml(tmpdir):
 
     save_hparams_to_yaml(path_yaml, OmegaConf.create(hparams))
     assert load_hparams_from_yaml(path_yaml) == hparams
+
+
+class NoArgsSubClassEvalModel(EvalModelTemplate):
+    def __init__(self):
+        super().__init__()
+
+
+class SimpleNoArgsModel(LightningModule):
+    def __init__(self):
+        super().__init__()
+        self.l1 = torch.nn.Linear(28 * 28, 10)
+
+    def forward(self, x):
+        return torch.relu(self.l1(x.view(x.size(0), -1)))
+
+    def training_step(self, batch, batch_nb):
+        x, y = batch
+        loss = F.cross_entropy(self(x), y)
+        return {'loss': loss, 'log': {'train_loss': loss}}
+
+    def test_step(self, batch, batch_nb):
+        x, y = batch
+        loss = F.cross_entropy(self(x), y)
+        return {'loss': loss, 'log': {'train_loss': loss}}
+
+    def configure_optimizers(self):
+        return torch.optim.Adam(self.parameters(), lr=0.02)
+
+
+@pytest.mark.parametrize("cls", [
+    SimpleNoArgsModel,
+    NoArgsSubClassEvalModel,
+])
+def test_model_nohparams_train_test(tmpdir, cls):
+    """Test models that do not tae any argument in init."""
+
+    model = cls()
+    trainer = Trainer(
+        max_epochs=1,
+        default_root_dir=tmpdir,
+    )
+
+    train_loader = DataLoader(TrialMNIST(os.getcwd(), train=True, download=True), batch_size=32)
+    trainer.fit(model, train_loader)
+
+    test_loader = DataLoader(TrialMNIST(os.getcwd(), train=False, download=True), batch_size=32)
+    trainer.test(test_dataloaders=test_loader)
+
+
+def test_model_ignores_non_exist_kwargument(tmpdir):
+    """Test that the model takes only valid class arguments."""
+
+    class LocalModel(EvalModelTemplate):
+        def __init__(self, batch_size=15):
+            super().__init__(batch_size=batch_size)
+            self.save_hyperparameters()
+
+    model = LocalModel()
+    assert model.hparams.batch_size == 15
+
+    # verify that the checkpoint saved the correct values
+    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1)
+    trainer.fit(model)
+
+    # verify that we can overwrite whatever we want
+    raw_checkpoint_path = _raw_checkpoint_path(trainer)
+    model = LocalModel.load_from_checkpoint(raw_checkpoint_path, non_exist_kwarg=99)
+    assert 'non_exist_kwarg' not in model.hparams