fixed hooks

pytorch_lightning/models/trainer.py

@@ -86,7 +86,7 @@ class Trainer(TrainerIO):
             self.test_percent_check = overfit_pct
 
     def __is_function_implemented(self, f_name):
-        f_op = getattr(self, f_name, None)
+        f_op = getattr(self.model, f_name, None)
         return callable(f_op)
 
     @property
@@ -266,10 +266,6 @@ class Trainer(TrainerIO):
                 if met_batch_limit:
                     break
 
-                # give model a chance to end epoch early
-                if self.model.should_stop_epoch(data_batch):
-                    break
-
                 # ---------------
                 # RUN TRAIN STEP
                 # ---------------
@@ -331,7 +327,9 @@ class Trainer(TrainerIO):
 
         # hook
         if self.__is_function_implemented('on_batch_start'):
-            self.model.on_batch_start()
+            response = self.model.on_batch_start(data_batch)
+            if response == -1:
+                return
 
         if self.enable_tqdm:
             self.prog_bar.update(1)

pytorch_lightning/root_module/hooks.py

@@ -1,7 +1,7 @@
 import torch
 
 class ModelHooks(torch.nn.Module):
-    def on_batch_start(self):
+    def on_batch_start(self, data_batch):
         pass
 
     def on_batch_end(self):
@@ -19,5 +19,3 @@ class ModelHooks(torch.nn.Module):
     def on_post_performance_check(self):
         pass
 
-    def should_stop_epoch(self, data_batch):
-        return False