diable val and test shuffling (#1600)

* diable val and test shuffling

* diable val and test shuffling

* diable val and test shuffling

* diable val and test shuffling

* log

* condition

* shuffle

* refactor

Co-authored-by: J. Borovec <jirka.borovec@seznam.cz>

CHANGELOG.md

@@ -46,6 +46,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Updated semantic segmentation example with custom u-net and logging ([#1371](https://github.com/PyTorchLightning/pytorch-lightning/pull/1371))
 
+- Diabled val and test shuffling ([#1600](https://github.com/PyTorchLightning/pytorch-lightning/pull/1600))
+
 
 ### Deprecated
 

pytorch_lightning/core/lightning.py

@@ -1349,7 +1349,7 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
                     loader = torch.utils.data.DataLoader(
                         dataset=dataset,
                         batch_size=self.hparams.batch_size,
-                        shuffle=True
+                        shuffle=False
                     )
 
                     return loader
@@ -1394,7 +1394,7 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
                     loader = torch.utils.data.DataLoader(
                         dataset=dataset,
                         batch_size=self.hparams.batch_size,
-                        shuffle=True
+                        shuffle=False
                     )
 
                     return loader

pytorch_lightning/trainer/data_loading.py

@@ -3,7 +3,7 @@ from abc import ABC, abstractmethod
 from typing import Union, List, Tuple, Callable
 
 import torch.distributed as torch_distrib
-from torch.utils.data import DataLoader
+from torch.utils.data import DataLoader, RandomSampler
 from torch.utils.data.distributed import DistributedSampler
 
 from pytorch_lightning.core import LightningModule
@@ -195,8 +195,7 @@ class TrainerDataLoadingMixin(ABC):
                 self.val_check_batch = int(self.num_training_batches * self.val_check_interval)
                 self.val_check_batch = max(1, self.val_check_batch)
 
-    def _reset_eval_dataloader(self, model: LightningModule,
-                               mode: str) -> Tuple[int, List[DataLoader]]:
+    def _reset_eval_dataloader(self, model: LightningModule, mode: str) -> Tuple[int, List[DataLoader]]:
         """Generic method to reset a dataloader for evaluation.
 
         Args:
@@ -211,6 +210,13 @@ class TrainerDataLoadingMixin(ABC):
         if not isinstance(dataloaders, list):
             dataloaders = [dataloaders]
 
+        # shuffling in val and test set is bad practice
+        for loader in dataloaders:
+            if mode in ('val', 'test') and hasattr(loader, 'sampler') and isinstance(loader.sampler, RandomSampler):
+                raise MisconfigurationException(
+                    f'Your {mode}_dataloader has shuffle=True, it is best practice to turn'
+                    ' this off for validation and test dataloaders.')
+
         # add samplers
         dataloaders = [self.auto_add_sampler(dl, train=False) for dl in dataloaders if dl]
 

tests/base/eval_model_template.py

@@ -10,13 +10,14 @@ from tests.base.eval_model_test_epoch_ends import TestEpochEndVariations
 from tests.base.eval_model_test_steps import TestStepVariations
 from tests.base.eval_model_train_dataloaders import TrainDataloaderVariations
 from tests.base.eval_model_train_steps import TrainingStepVariations
-from tests.base.eval_model_utils import ModelTemplateUtils
+from tests.base.eval_model_utils import ModelTemplateUtils, ModelTemplateData
 from tests.base.eval_model_valid_dataloaders import ValDataloaderVariations
 from tests.base.eval_model_valid_epoch_ends import ValidationEpochEndVariations
 from tests.base.eval_model_valid_steps import ValidationStepVariations
 
 
 class EvalModelTemplate(
+    ModelTemplateData,
     ModelTemplateUtils,
     TrainingStepVariations,
     ValidationStepVariations,

tests/base/eval_model_utils.py

@@ -3,7 +3,8 @@ from torch.utils.data import DataLoader
 from tests.base.datasets import TrialMNIST
 
 
-class ModelTemplateUtils:
+class ModelTemplateData:
+    hparams: ...
 
     def dataloader(self, train):
         dataset = TrialMNIST(root=self.hparams.data_root, train=train, download=True)
@@ -11,10 +12,14 @@ class ModelTemplateUtils:
         loader = DataLoader(
             dataset=dataset,
             batch_size=self.hparams.batch_size,
-            shuffle=True
+            # test and valid shall not be shuffled
+            shuffle=train,
         )
         return loader
 
+
+class ModelTemplateUtils:
+
     def get_output_metric(self, output, name):
         if isinstance(output, dict):
             val = output[name]

tests/base/models.py

@@ -149,7 +149,7 @@ class TestModelBase(LightningModule):
         loader = DataLoader(
             dataset=dataset,
             batch_size=batch_size,
-            shuffle=True
+            shuffle=train
         )
 
         return loader