fix #2386

pytorch_lightning/core/saving.py

@@ -171,6 +171,7 @@ def load_from_checkpoint(
 
     @classmethod
     def _load_model_state(cls, checkpoint: Dict[str, Any], *cls_args, **cls_kwargs):
+        cls_spec = inspect.getfullargspec(cls.__init__)
         # pass in the values we saved automatically
         if cls.CHECKPOINT_HYPER_PARAMS_KEY in checkpoint:
             model_args = {}
@@ -184,7 +185,6 @@ def _load_model_state(cls, checkpoint: Dict[str, Any], *cls_args, **cls_kwargs):
                 model_args = checkpoint[cls.CHECKPOINT_HYPER_PARAMS_TYPE](model_args)
 
             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()
 
@@ -199,8 +199,11 @@ def _load_model_state(cls, checkpoint: Dict[str, Any], *cls_args, **cls_kwargs):
             else:
                 cls_args = (model_args,) + cls_args
 
-        # load the state_dict on the model automatically
+        # 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):
@@ -238,13 +240,19 @@ def __init__(self, *args, dict_conf=OmegaConf.create(dict(my_param='something'))
         self.save_hyperparameters()
 
 
+class NoArgsSubClassEvalModel(EvalModelTemplate):
+    def __init__(self):
+        super().__init__()
+
+
 @pytest.mark.parametrize("cls", [
     EvalModelTemplate,
     SubClassEvalModel,
     SubSubClassEvalModel,
     AggSubClassEvalModel,
     UnconventionalArgsEvalModel,
     DictConfSubClassEvalModel,
+    # NoArgsSubClassEvalModel,
 ])
 def test_collect_init_arguments(tmpdir, cls):
     """ Test that the model automatically saves the arguments passed into the constructor """
@@ -425,3 +433,39 @@ def test_hparams_save_yaml(tmpdir):
 
     save_hparams_to_yaml(path_yaml, OmegaConf.create(hparams))
     assert load_hparams_from_yaml(path_yaml) == hparams
+
+
+def test_nohparams_train_test(tmpdir):
+
+    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)
+
+    model = SimpleNoArgsModel()
+    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)