fix setup call while testing

CHANGELOG.md

@@ -25,6 +25,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
+- Fixed setup call while testing ([#2624](https://github.com/PyTorchLightning/pytorch-lightning/pull/2624))
+
 - Fixed Horovod backend to scale LR schedlers with the optimizer ([#2626](https://github.com/PyTorchLightning/pytorch-lightning/pull/2626))
 
 - Fixed `dtype` and `device` properties not getting updated in submodules ([#2657](https://github.com/PyTorchLightning/pytorch-lightning/pull/2657))

pytorch_lightning/trainer/distrib_data_parallel.py

@@ -506,8 +506,8 @@ def ddp_train(self, process_idx, q, model, is_master=False, proc_offset=0):
         model.init_ddp_connection(self.global_rank, self.world_size, self.is_slurm_managing_tasks)
 
         # call setup after the ddp process has connected
-        self.setup('fit')
-        if self.is_function_implemented('setup', model):
+        if not self.testing:
+            self.setup('fit')
             model.setup('fit')
 
         # on world_size=0 let everyone know training is starting

pytorch_lightning/trainer/distrib_parts.py

@@ -167,8 +167,8 @@ def __transfer_batch_to_device(self, batch: Any, device: torch.device):
 
     def single_gpu_train(self, model):
         # call setup
-        self.setup('fit')
-        if self.is_function_implemented('setup', model):
+        if not self.testing:
+            self.setup('fit')
             model.setup('fit')
 
         model.cuda(self.root_gpu)
@@ -189,8 +189,8 @@ def single_gpu_train(self, model):
 
     def tpu_train(self, tpu_core_idx, model):
         # call setup after the ddp process has connected
-        self.setup('fit')
-        if self.is_function_implemented('setup', model):
+        if not self.testing:
+            self.setup('fit')
             model.setup('fit')
 
         # put model on tpu
@@ -229,8 +229,8 @@ def tpu_train(self, tpu_core_idx, model):
 
     def dp_train(self, model):
         # call setup after the ddp process has connected
-        self.setup('fit')
-        if self.is_function_implemented('setup', model):
+        if not self.testing:
+            self.setup('fit')
             model.setup('fit')
 
         model.cuda(self.root_gpu)
@@ -275,8 +275,8 @@ def dp_train(self, model):
 
     def horovod_train(self, model):
         # call setup after the ddp process has connected
-        self.setup('fit')
-        if self.is_function_implemented('setup', model):
+        if not self.testing:
+            self.setup('fit')
             model.setup('fit')
 
         if torch.cuda.is_available() and self.on_gpu:

pytorch_lightning/trainer/trainer.py

@@ -1054,8 +1054,8 @@ def fit(
                 raise MisconfigurationException('amp + cpu is not supported.  Please use a GPU option')
 
             # call setup after the ddp process has connected
-            self.setup('fit')
-            if self.is_function_implemented('setup', model):
+            if not self.testing:
+                self.setup('fit')
                 model.setup('fit')
 
             # CHOOSE OPTIMIZER
@@ -1328,8 +1328,7 @@ def test(
 
     def __test_using_best_weights(self, ckpt_path, test_dataloaders):
         model = self.get_model()
-        if self.is_function_implemented('setup', model):
-            model.setup('test')
+        model.setup('test')
 
         # if user requests the best checkpoint but we don't have it, error
         if ckpt_path == 'best' and self.checkpoint_callback.save_top_k <= 0:
@@ -1373,8 +1372,7 @@ def __test_using_best_weights(self, ckpt_path, test_dataloaders):
 
     def __test_given_model(self, model, test_dataloaders):
         # setup hook
-        if self.is_function_implemented('setup', model):
-            model.setup('test')
+        model.setup('test')
 
         # attach data
         if test_dataloaders is not None:

tests/trainer/test_trainer.py

@@ -980,3 +980,34 @@ def test_trainer_pickle(tmpdir):
     )
     pickle.dumps(trainer)
     cloudpickle.dumps(trainer)
+
+
+def test_trainer_setup_call(tmpdir):
+    """Test setup call with fit and test call."""
+
+    class CurrentModel(EvalModelTemplate):
+
+        def setup(self, stage):
+            self.stage = stage
+
+    class TrainerSubclass(Trainer):
+
+        def setup(self, stage):
+            self.stage = stage
+
+    model = CurrentModel()
+
+    # fit model
+    trainer = TrainerSubclass(
+        default_root_dir=tmpdir,
+        max_epochs=1,
+        checkpoint_callback=False
+    )
+
+    trainer.fit(model)
+    assert trainer.stage == 'fit'
+    assert trainer.get_model().stage == 'fit'
+
+    trainer.test(ckpt_path=None)
+    assert trainer.stage == 'test'
+    assert trainer.get_model().stage == 'test'