Bugfix/lr finder

CHANGELOG.md

@@ -16,6 +16,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Reduction when `batch_size < num_gpus` ([#1609](https://github.com/PyTorchLightning/pytorch-lightning/pull/1609))
 
+- Updated LightningTemplateModel to look more like Colab example ([#1577](https://github.com/PyTorchLightning/pytorch-lightning/pull/1577))
+
 ### Deprecated
 
 ### Removed
@@ -30,6 +32,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed wandb logger `global_step` affects other loggers ([#1492](https://github.com/PyTorchLightning/pytorch-lightning/issues/1485))
 
+- Fixed bugs that prevent lr finder to be used together with early stopping and validation dataloaders ([#1676](https://github.com/PyTorchLightning/pytorch-lightning/pull/1676))
+
 ## [0.7.5] - 2020-04-27
 
 ### Changed
@@ -74,7 +78,6 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Defines shared proc. rank, remove rank from instances (e.g. loggers) ([#1408](https://github.com/PyTorchLightning/pytorch-lightning/pull/1408))
 - Updated semantic segmentation example with custom U-Net and logging ([#1371](https://github.com/PyTorchLightning/pytorch-lightning/pull/1371))
 - Disabled val and test shuffling ([#1600](https://github.com/PyTorchLightning/pytorch-lightning/pull/1600))
-- Updated LightningTemplateModel to look more like Colab example ([#1546](https://github.com/PyTorchLightning/pytorch-lightning/pull/1577))
 
 ### Deprecated
 

pytorch_lightning/trainer/lr_finder.py

@@ -53,6 +53,7 @@ def _run_lr_finder_internally(self, model: LightningModule):
     def lr_find(self,
                 model: LightningModule,
                 train_dataloader: Optional[DataLoader] = None,
+                val_dataloaders: Optional[DataLoader] = None,
                 min_lr: float = 1e-8,
                 max_lr: float = 1,
                 num_training: int = 100,
@@ -105,7 +106,7 @@ def lr_find(self,
         """
         save_path = os.path.join(self.default_root_dir, 'lr_find_temp.ckpt')
 
-        self._dump_params(model)
+        self._lr_finder_dump_params(model)
 
         # Prevent going into infinite loop
         self.auto_lr_find = False
@@ -129,8 +130,10 @@ def lr_find(self,
         # Accumulation of gradients
         self.accumulate_grad_batches = num_accumulation_steps
 
-        # Disable standard checkpoint
+        # Disable standard checkpoint & early stopping
         self.checkpoint_callback = False
+        self.early_stop_callback = None
+        self.enable_early_stop = False
 
         # Required for saving the model
         self.optimizers, self.schedulers = [], [],
@@ -150,7 +153,9 @@ def lr_find(self,
         model.configure_optimizers = lr_finder._get_new_optimizer(optimizers[0])
 
         # Fit, lr & loss logged in callback
-        self.fit(model, train_dataloader=train_dataloader)
+        self.fit(model,
+                 train_dataloader=train_dataloader,
+                 val_dataloaders=val_dataloaders)
 
         # Prompt if we stopped early
         if self.global_step != num_training:
@@ -165,13 +170,13 @@ def lr_find(self,
         os.remove(save_path)
 
         # Finish by resetting variables so trainer is ready to fit model
-        self._restore_params(model)
+        self._lr_finder_restore_params(model)
         if self.progress_bar_callback:
             self.progress_bar_callback.enable()
 
         return lr_finder
 
-    def _dump_params(self, model):
+    def _lr_finder_dump_params(self, model):
         # Prevent going into infinite loop
         self._params = {
             'auto_lr_find': self.auto_lr_find,
@@ -181,18 +186,22 @@ def _dump_params(self, model):
             'progress_bar_refresh_rate': self.progress_bar_refresh_rate,
             'accumulate_grad_batches': self.accumulate_grad_batches,
             'checkpoint_callback': self.checkpoint_callback,
+            'early_stop_callback': self.early_stop_callback,
+            'enable_early_stop': self.enable_early_stop,
             'progress_bar_callback': self.progress_bar_callback,
             'configure_optimizers': model.configure_optimizers,
         }
 
-    def _restore_params(self, model):
+    def _lr_finder_restore_params(self, model):
         self.auto_lr_find = self._params['auto_lr_find']
         self.logger = self._params['logger']
         self.callbacks = self._params['callbacks']
         self.max_steps = self._params['max_steps']
         self.progress_bar_refresh_rate = self._params['progress_bar_refresh_rate']
         self.accumulate_grad_batches = self._params['accumulate_grad_batches']
         self.checkpoint_callback = self._params['checkpoint_callback']
+        self.early_stop_callback = self._params['early_stop_callback']
+        self.enable_early_stop = self._params['enable_early_stop']
         self.progress_bar_callback = self._params['progress_bar_callback']
         model.configure_optimizers = self._params['configure_optimizers']
 

tests/trainer/test_lr_finder.py

@@ -82,8 +82,8 @@ class CurrentTestModel(
     )
 
     changed_attributes = ['callbacks', 'logger', 'max_steps', 'auto_lr_find',
-                          'progress_bar_refresh_rate',
-                          'accumulate_grad_batches',
+                          'progress_bar_refresh_rate', 'early_stop_callback',
+                          'accumulate_grad_batches', 'enable_early_stop',
                           'checkpoint_callback']
     attributes_before = {}
     for ca in changed_attributes: