Add INTERRUPTED state, improve tests, move state switching from callb…

…ack to a trainer.

pytorch_lightning/trainer/states.py

@@ -1,31 +1,9 @@
-from enum import Enum, auto
-
-from pytorch_lightning import Callback
+from enum import Enum
 
 
 class TrainerState(Enum):
-    """ State which is set to the Trainer to indicate what is being executed. """
-    INITIALIZE = auto()
-    RUNNING = auto()
-    FINISHED = auto()
-
-
-class _TrainerStateSwitcher(Callback):
-    """ Special callback used by the Trainer. This callback sets proper
-        state to the trainer depending on what is being executed.
-    """
-
-    def on_init_start(self, trainer):
-        trainer.state = TrainerState.INITIALIZE
-
-    def on_init_end(self, trainer):
-        trainer.state = TrainerState.INITIALIZE
-
-    def setup(self, trainer, stage: str):
-        trainer.state = TrainerState.RUNNING
-
-    def teardown(self, trainer, stage: str):
-        trainer.state = TrainerState.FINISHED
-
-    def on_keyboard_interrupt(self, trainer, pl_module):
-        trainer.state = TrainerState.FINISHED
+    """ State which is set in the Trainer to indicate what is currently or was executed. """
+    INITIALIZE = 'INITIALIZE'
+    RUNNING = 'RUNNING'
+    FINISHED = 'FINISHED'
+    INTERRUPTED = 'INTERRUPTED'

pytorch_lightning/trainer/trainer.py

@@ -26,7 +26,7 @@
 from pytorch_lightning.trainer.logging import TrainerLoggingMixin
 from pytorch_lightning.trainer.model_hooks import TrainerModelHooksMixin
 from pytorch_lightning.trainer.optimizers import TrainerOptimizersMixin
-from pytorch_lightning.trainer.states import _TrainerStateSwitcher, TrainerState
+from pytorch_lightning.trainer.states import TrainerState
 from pytorch_lightning.trainer.supporters import TensorRunningAccum
 from pytorch_lightning.trainer.training_io import TrainerIOMixin
 from pytorch_lightning.trainer.training_loop import TrainerTrainLoopMixin
@@ -419,8 +419,6 @@ def __init__(
         # init callbacks
         self.callbacks = callbacks or []
 
-        self.callbacks.append(_TrainerStateSwitcher())
-
         # configure early stop callback
         # creates a default one if none passed in
         early_stop_callback = self.configure_early_stopping(early_stop_callback)
@@ -914,6 +912,8 @@ def fit(
         # check that model is configured correctly
         self.check_model_configuration(model)
 
+        self.state = TrainerState.RUNNING
+
         # callbacks
         self.on_fit_start()
         if self.is_function_implemented('on_fit_start', model):
@@ -1031,6 +1031,8 @@ def fit(
         if self.is_function_implemented('teardown'):
             model.teardown('fit')
 
+        if self.state != TrainerState.INTERRUPTED:
+            self.state = TrainerState.FINISHED
         # return 1 when finished
         # used for testing or when we need to know that training succeeded
         return results or 1
@@ -1246,6 +1248,8 @@ def test(
         if self.is_function_implemented('setup', model_ref):
             model_ref.setup('test')
 
+        self.state = TrainerState.RUNNING
+
         # if user requests the best checkpoint but we don't have it, error
         if model is None and ckpt_path == 'best' and self.checkpoint_callback.save_top_k <= 0:
             raise MisconfigurationException(
@@ -1295,6 +1299,9 @@ def test(
             model_ref = self.get_model()
             model_ref.teardown('test')
 
+        if self.state != TrainerState.INTERRUPTED:
+            self.state = TrainerState.FINISHED
+
         return results
 
     def check_model_configuration(self, model: LightningModule):

pytorch_lightning/trainer/training_loop.py

@@ -159,6 +159,7 @@ def training_step(self, batch, batch_idx):
 from pytorch_lightning.callbacks import ModelCheckpoint
 from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.loggers import LightningLoggerBase
+from pytorch_lightning.trainer.states import TrainerState
 from pytorch_lightning.trainer.supporters import TensorRunningAccum
 from pytorch_lightning.utilities import rank_zero_warn, NATIVE_AMP_AVALAIBLE
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
@@ -240,6 +241,7 @@ class TrainerTrainLoopMixin(ABC):
     terminate_on_nan: bool
     tpu_id: int
     interactive_ddp_procs: ...
+    state: TrainerState
 
     # Callback system
     callbacks: List[Callback]
@@ -397,6 +399,7 @@ def train(self):
             # user could press ctrl+c many times... only shutdown once
             if not self.interrupted:
                 self.interrupted = True
+                self.state = TrainerState.INTERRUPTED
                 self.on_keyboard_interrupt()
 
                 self.run_training_teardown()

tests/trainer/test_states.py

@@ -1,4 +1,4 @@
-from pytorch_lightning import Trainer
+from pytorch_lightning import Trainer, Callback
 from pytorch_lightning.trainer.states import TrainerState
 from tests.base import EvalModelTemplate
 
@@ -15,6 +15,35 @@ def test_initialize_state(tmpdir):
     assert trainer.state == TrainerState.INITIALIZE
 
 
+def test_running_state_during_fit(tmpdir):
+    """
+        Tests that state is set to RUNNING during fit
+    """
+
+    class StateSnapshotCallback(Callback):
+        def __init__(self):
+            super().__init__()
+            self.trainer_state = None
+
+        def on_batch_start(self, trainer, pl_module):
+            self.trainer_state = trainer.state
+
+    hparams = EvalModelTemplate.get_default_hparams()
+    model = EvalModelTemplate(**hparams)
+
+    snapshot_callback = StateSnapshotCallback()
+
+    trainer = Trainer(
+        callbacks=[snapshot_callback],
+        default_root_dir=tmpdir,
+        fast_dev_run=True,
+    )
+
+    trainer.fit(model)
+
+    assert snapshot_callback.trainer_state == TrainerState.RUNNING
+
+
 def test_finished_state_after_fit(tmpdir):
     """
         Tests that state is FINISHED after fit
@@ -32,6 +61,35 @@ def test_finished_state_after_fit(tmpdir):
     assert trainer.state == TrainerState.FINISHED
 
 
+def test_running_state_during_test(tmpdir):
+    """
+        Tests that state is set to RUNNING during test
+    """
+
+    class StateSnapshotCallback(Callback):
+        def __init__(self):
+            super().__init__()
+            self.trainer_state = None
+
+        def on_test_batch_start(self, trainer, pl_module):
+            self.trainer_state = trainer.state
+
+    hparams = EvalModelTemplate.get_default_hparams()
+    model = EvalModelTemplate(**hparams)
+
+    snapshot_callback = StateSnapshotCallback()
+
+    trainer = Trainer(
+        callbacks=[snapshot_callback],
+        default_root_dir=tmpdir,
+        fast_dev_run=True,
+    )
+
+    trainer.test(model)
+
+    assert snapshot_callback.trainer_state == TrainerState.RUNNING
+
+
 def test_finished_state_after_test(tmpdir):
     """
         Tests that state is FINISHED after fit
@@ -47,3 +105,28 @@ def test_finished_state_after_test(tmpdir):
     trainer.test(model)
 
     assert trainer.state == TrainerState.FINISHED
+
+
+def test_interrupt_state_on_keyboard_interrupt(tmpdir):
+    """
+        Tests that state is set to INTERRUPTED on KeyboardInterrupt
+    """
+    hparams = EvalModelTemplate.get_default_hparams()
+    model = EvalModelTemplate(**hparams)
+
+    class InterruptCallback(Callback):
+        def __init__(self):
+            super().__init__()
+
+        def on_batch_start(self, trainer, pl_module):
+            raise KeyboardInterrupt
+
+    trainer = Trainer(
+        callbacks=[InterruptCallback()],
+        default_root_dir=tmpdir,
+        fast_dev_run=True,
+    )
+
+    trainer.fit(model)
+
+    assert trainer.state == TrainerState.INTERRUPTED