From 0e591aab0845511f2bbc2d070b0e1d5935a74f30 Mon Sep 17 00:00:00 2001
From: manipopopo <pwmutantbread@gmail.com>
Date: Sun, 9 Aug 2020 09:57:43 +0000
Subject: [PATCH 1/9] Fix the progress bar for the sanity check

The original progress bar will always show
trainer.num_sanity_val_steps even if the length of the validation
DataLoader is less than trainer.num_sanity_val_steps.

The pytorch_lightning.trainer.data_loading._has_len is changed to
a public function has_len, which is called by
pytorch_lightning/callbacks/progress.py
---
 pytorch_lightning/callbacks/progress.py   | 10 ++++-
 pytorch_lightning/trainer/data_loading.py |  8 ++--
 tests/callbacks/test_progress_bar.py      | 47 +++++++++++++++++++++++
 tests/trainer/test_dataloaders.py         |  4 +-
 4 files changed, 62 insertions(+), 7 deletions(-)

diff --git a/pytorch_lightning/callbacks/progress.py b/pytorch_lightning/callbacks/progress.py
index c3cd9137c9ed7..ffdf90310f534 100644
--- a/pytorch_lightning/callbacks/progress.py
+++ b/pytorch_lightning/callbacks/progress.py
@@ -293,7 +293,15 @@ def init_test_tqdm(self) -> tqdm:
     def on_sanity_check_start(self, trainer, pl_module):
         super().on_sanity_check_start(trainer, pl_module)
         self.val_progress_bar = self.init_sanity_tqdm()
-        self.val_progress_bar.total = convert_inf(trainer.num_sanity_val_steps * len(trainer.val_dataloaders))
+
+        # There will be circular imports if we move the following import to the
+        # top of the file.
+        from pytorch_lightning.trainer.data_loading import has_len
+
+        self.val_progress_bar.total = sum(
+            min(trainer.num_sanity_val_steps,
+                len(d) if has_len(d) else float('inf'))
+            for d in trainer.val_dataloaders)
         self.main_progress_bar = tqdm(disable=True)  # dummy progress bar
 
     def on_sanity_check_end(self, trainer, pl_module):
diff --git a/pytorch_lightning/trainer/data_loading.py b/pytorch_lightning/trainer/data_loading.py
index 575e28354b5de..a4f239f379ed0 100644
--- a/pytorch_lightning/trainer/data_loading.py
+++ b/pytorch_lightning/trainer/data_loading.py
@@ -46,7 +46,7 @@ def _has_iterable_dataset(dataloader: DataLoader):
         and isinstance(dataloader.dataset, IterableDataset)
 
 
-def _has_len(dataloader: DataLoader) -> bool:
+def has_len(dataloader: DataLoader) -> bool:
     """ Checks if a given Dataloader has __len__ method implemented i.e. if
     it is a finite dataloader or infinite dataloader. """
 
@@ -195,7 +195,7 @@ def reset_train_dataloader(self, model: LightningModule) -> None:
         # automatically add samplers
         self.train_dataloader = self.auto_add_sampler(self.train_dataloader, train=True)
 
-        self.num_training_batches = len(self.train_dataloader) if _has_len(self.train_dataloader) else float('inf')
+        self.num_training_batches = len(self.train_dataloader) if has_len(self.train_dataloader) else float('inf')
         self._worker_check(self.train_dataloader, 'train dataloader')
 
         if isinstance(self.limit_train_batches, int) or self.limit_train_batches == 0.0:
@@ -219,7 +219,7 @@ def reset_train_dataloader(self, model: LightningModule) -> None:
                     f'to the number of the training batches ({self.num_training_batches}). '
                     'If you want to disable validation set `limit_val_batches` to 0.0 instead.')
         else:
-            if not _has_len(self.train_dataloader):
+            if not has_len(self.train_dataloader):
                 if self.val_check_interval == 1.0:
                     self.val_check_batch = float('inf')
                 else:
@@ -282,7 +282,7 @@ def _reset_eval_dataloader(
         # datasets could be none, 1 or 2+
         if len(dataloaders) != 0:
             for i, dataloader in enumerate(dataloaders):
-                num_batches = len(dataloader) if _has_len(dataloader) else float('inf')
+                num_batches = len(dataloader) if has_len(dataloader) else float('inf')
                 self._worker_check(dataloader, f'{mode} dataloader {i}')
 
                 # percent or num_steps
diff --git a/tests/callbacks/test_progress_bar.py b/tests/callbacks/test_progress_bar.py
index 713bdf3c3c2c4..276810d7c29ed 100644
--- a/tests/callbacks/test_progress_bar.py
+++ b/tests/callbacks/test_progress_bar.py
@@ -193,3 +193,50 @@ def on_test_batch_end(self, trainer, pl_module, batch, batch_idx, dataloader_idx
 
     trainer.test(model)
     assert progress_bar.test_batches_seen == progress_bar.total_test_batches
+
+
+@pytest.mark.parametrize('num_sanity_val_steps,num_val_dataloaders_batches', [
+    (-1, [10]),
+    (0, [10]),
+    (2, [10]),
+    (10, [2]),
+    (10, [2, 3]),
+    (10, [20, 3]),
+    (10, [20, 30]),
+    (10, [float('inf')]),
+    (10, [1, float('inf')]),
+])
+def test_sanity_check_progress_bar_total(tmpdir, num_sanity_val_steps,
+                                         num_val_dataloaders_batches):
+    """Test that the sanity_check progress finishes with the correct total steps processed."""
+
+    tmp_model = EvalModelTemplate(batch_size=1)
+    batch_size = len(tmp_model.dataloader(train=False, num_samples=1).dataset)
+    model = EvalModelTemplate(batch_size=batch_size)
+
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        progress_bar_refresh_rate=1,
+        limit_val_batches=(len(model.dataloader(train=False)) *
+                           len(num_val_dataloaders_batches)),
+        max_epochs=0,
+        num_sanity_val_steps=num_sanity_val_steps,
+    )
+
+    val_dataloaders = []
+    for num_samples in num_val_dataloaders_batches:
+        if num_samples == float('inf'):
+            val_dataloaders.append(model.val_dataloader__infinite())
+        else:
+            val_dataloaders.append(
+                model.dataloader(train=False, num_samples=num_samples))
+    trainer.fit(model, val_dataloaders=val_dataloaders)
+
+    # check val progress bar total is the number of steps sanity check runs
+    max_sanity_val_steps = (float('inf') if num_sanity_val_steps == -1 else
+                            num_sanity_val_steps)
+    num_sanity_check_run_steps = sum(
+        min(max_sanity_val_steps, num_val_dataloader_batches)
+        for num_val_dataloader_batches in num_val_dataloaders_batches)
+    val_progress_bar = trainer.progress_bar_callback.val_progress_bar
+    assert getattr(val_progress_bar, 'total', 0) == num_sanity_check_run_steps
diff --git a/tests/trainer/test_dataloaders.py b/tests/trainer/test_dataloaders.py
index d9e2500707fc8..0d58aea53698d 100644
--- a/tests/trainer/test_dataloaders.py
+++ b/tests/trainer/test_dataloaders.py
@@ -11,7 +11,7 @@
 
 import tests.base.develop_pipelines as tpipes
 from pytorch_lightning import Trainer, Callback
-from pytorch_lightning.trainer.data_loading import _has_iterable_dataset, _has_len
+from pytorch_lightning.trainer.data_loading import _has_iterable_dataset, has_len
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.base import EvalModelTemplate
 
@@ -637,7 +637,7 @@ def __len__(self):
             return len(original_dataset)
 
     dataloader = DataLoader(IterableWithLen(), batch_size=16)
-    assert _has_len(dataloader)
+    assert has_len(dataloader)
     assert _has_iterable_dataset(dataloader)
     trainer = Trainer(
         default_root_dir=tmpdir,

From 0a9342a5add1297208bd555f11c3a1c869e23564 Mon Sep 17 00:00:00 2001
From: manipopopo <pwmutantbread@gmail.com>
Date: Sun, 9 Aug 2020 10:25:09 +0000
Subject: [PATCH 2/9] Fix W504 line break after binary operator

---
 tests/callbacks/test_progress_bar.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/callbacks/test_progress_bar.py b/tests/callbacks/test_progress_bar.py
index 276810d7c29ed..9804be9f00450 100644
--- a/tests/callbacks/test_progress_bar.py
+++ b/tests/callbacks/test_progress_bar.py
@@ -214,11 +214,11 @@ def test_sanity_check_progress_bar_total(tmpdir, num_sanity_val_steps,
     batch_size = len(tmp_model.dataloader(train=False, num_samples=1).dataset)
     model = EvalModelTemplate(batch_size=batch_size)
 
+    num_dataloaders = len(num_val_dataloaders_batches)
     trainer = Trainer(
         default_root_dir=tmpdir,
         progress_bar_refresh_rate=1,
-        limit_val_batches=(len(model.dataloader(train=False)) *
-                           len(num_val_dataloaders_batches)),
+        limit_val_batches=len(model.dataloader(train=False)) * num_dataloaders,
         max_epochs=0,
         num_sanity_val_steps=num_sanity_val_steps,
     )

From f87074f07a06222e900e8440cdf21cf450c99b2a Mon Sep 17 00:00:00 2001
From: manipopopo <pwmutantbread@gmail.com>
Date: Tue, 11 Aug 2020 13:01:49 +0000
Subject: [PATCH 3/9] Move functions to pytorch_lightning.utilities.data

---
 pytorch_lightning/callbacks/progress.py   | 11 ++----
 pytorch_lightning/trainer/data_loading.py | 40 ++--------------------
 pytorch_lightning/utilities/data.py       | 41 +++++++++++++++++++++++
 tests/trainer/test_dataloaders.py         | 34 +------------------
 tests/utilities/test_data.py              | 39 +++++++++++++++++++++
 5 files changed, 86 insertions(+), 79 deletions(-)
 create mode 100644 pytorch_lightning/utilities/data.py
 create mode 100644 tests/utilities/test_data.py

diff --git a/pytorch_lightning/callbacks/progress.py b/pytorch_lightning/callbacks/progress.py
index ffdf90310f534..0475e2ec2cd1e 100644
--- a/pytorch_lightning/callbacks/progress.py
+++ b/pytorch_lightning/callbacks/progress.py
@@ -17,6 +17,7 @@
     from tqdm import tqdm
 
 from pytorch_lightning.callbacks import Callback
+from pytorch_lightning.utilities.data import has_len
 
 
 class ProgressBarBase(Callback):
@@ -293,15 +294,9 @@ def init_test_tqdm(self) -> tqdm:
     def on_sanity_check_start(self, trainer, pl_module):
         super().on_sanity_check_start(trainer, pl_module)
         self.val_progress_bar = self.init_sanity_tqdm()
-
-        # There will be circular imports if we move the following import to the
-        # top of the file.
-        from pytorch_lightning.trainer.data_loading import has_len
-
         self.val_progress_bar.total = sum(
-            min(trainer.num_sanity_val_steps,
-                len(d) if has_len(d) else float('inf'))
-            for d in trainer.val_dataloaders)
+            min(trainer.num_sanity_val_steps, len(d) if has_len(d) else float('inf')) for d in trainer.val_dataloaders
+        )
         self.main_progress_bar = tqdm(disable=True)  # dummy progress bar
 
     def on_sanity_check_end(self, trainer, pl_module):
diff --git a/pytorch_lightning/trainer/data_loading.py b/pytorch_lightning/trainer/data_loading.py
index a4f239f379ed0..8f3bd54d8a888 100644
--- a/pytorch_lightning/trainer/data_loading.py
+++ b/pytorch_lightning/trainer/data_loading.py
@@ -1,24 +1,17 @@
 import multiprocessing
 import platform
 from abc import ABC, abstractmethod
-from distutils.version import LooseVersion
 from typing import Union, List, Tuple, Callable, Optional
 
-import torch
 import torch.distributed as torch_distrib
 from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
 from torch.utils.data.distributed import DistributedSampler
 
 from pytorch_lightning.core import LightningModule
 from pytorch_lightning.utilities import rank_zero_warn
+from pytorch_lightning.utilities.data import has_iterable_dataset, has_len
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
-try:
-    from torch.utils.data import IterableDataset
-    ITERABLE_DATASET_EXISTS = True
-except ImportError:
-    ITERABLE_DATASET_EXISTS = False
-
 try:
     from apex import amp
 except ImportError:
@@ -41,35 +34,6 @@
     HOROVOD_AVAILABLE = True
 
 
-def _has_iterable_dataset(dataloader: DataLoader):
-    return ITERABLE_DATASET_EXISTS and hasattr(dataloader, 'dataset') \
-        and isinstance(dataloader.dataset, IterableDataset)
-
-
-def has_len(dataloader: DataLoader) -> bool:
-    """ Checks if a given Dataloader has __len__ method implemented i.e. if
-    it is a finite dataloader or infinite dataloader. """
-
-    try:
-        # try getting the length
-        if len(dataloader) == 0:
-            raise ValueError('`Dataloader` returned 0 length.'
-                             ' Please make sure that your Dataloader at least returns 1 batch')
-        has_len = True
-    except TypeError:
-        has_len = False
-    except NotImplementedError:  # e.g. raised by torchtext if a batch_size_fn is used
-        has_len = False
-
-    if has_len and _has_iterable_dataset(dataloader) and LooseVersion(torch.__version__) >= LooseVersion("1.4.0"):
-        rank_zero_warn(
-            'Your `IterableDataset` has `__len__` defined.'
-            ' In combination with multi-processing data loading (e.g. batch size > 1),'
-            ' this can lead to unintended side effects since the samples will be duplicated.'
-        )
-    return has_len
-
-
 class TrainerDataLoadingMixin(ABC):
 
     # this is just a summary on variables used in this abstract class,
@@ -131,7 +95,7 @@ def auto_add_sampler(self, dataloader: DataLoader, train: bool) -> DataLoader:
         # don't do anything if it's not a dataloader
         is_dataloader = isinstance(dataloader, DataLoader)
         # don't manipulate iterable datasets
-        is_iterable_ds = _has_iterable_dataset(dataloader)
+        is_iterable_ds = has_iterable_dataset(dataloader)
 
         if not is_dataloader or is_iterable_ds:
             return dataloader
diff --git a/pytorch_lightning/utilities/data.py b/pytorch_lightning/utilities/data.py
new file mode 100644
index 0000000000000..7569d68f00a9f
--- /dev/null
+++ b/pytorch_lightning/utilities/data.py
@@ -0,0 +1,41 @@
+from distutils.version import LooseVersion
+
+import torch
+from torch.utils.data import DataLoader
+
+from pytorch_lightning.utilities import rank_zero_warn
+
+try:
+    from torch.utils.data import IterableDataset
+    ITERABLE_DATASET_EXISTS = True
+except ImportError:
+    ITERABLE_DATASET_EXISTS = False
+
+
+def has_iterable_dataset(dataloader: DataLoader):
+    return ITERABLE_DATASET_EXISTS and hasattr(dataloader, 'dataset') \
+        and isinstance(dataloader.dataset, IterableDataset)
+
+
+def has_len(dataloader: DataLoader) -> bool:
+    """ Checks if a given Dataloader has __len__ method implemented i.e. if
+    it is a finite dataloader or infinite dataloader. """
+
+    try:
+        # try getting the length
+        if len(dataloader) == 0:
+            raise ValueError('`Dataloader` returned 0 length.'
+                             ' Please make sure that your Dataloader at least returns 1 batch')
+        has_len = True
+    except TypeError:
+        has_len = False
+    except NotImplementedError:  # e.g. raised by torchtext if a batch_size_fn is used
+        has_len = False
+
+    if has_len and has_iterable_dataset(dataloader) and LooseVersion(torch.__version__) >= LooseVersion("1.4.0"):
+        rank_zero_warn(
+            'Your `IterableDataset` has `__len__` defined.'
+            ' In combination with multi-processing data loading (e.g. batch size > 1),'
+            ' this can lead to unintended side effects since the samples will be duplicated.'
+        )
+    return has_len
diff --git a/tests/trainer/test_dataloaders.py b/tests/trainer/test_dataloaders.py
index 0d58aea53698d..52abb16478bca 100644
--- a/tests/trainer/test_dataloaders.py
+++ b/tests/trainer/test_dataloaders.py
@@ -4,14 +4,12 @@
 
 import pytest
 import torch
-from packaging.version import parse
 from torch.utils.data.dataloader import DataLoader
-from torch.utils.data.dataset import IterableDataset, Subset
+from torch.utils.data.dataset import Subset
 from torch.utils.data.distributed import DistributedSampler
 
 import tests.base.develop_pipelines as tpipes
 from pytorch_lightning import Trainer, Callback
-from pytorch_lightning.trainer.data_loading import _has_iterable_dataset, has_len
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.base import EvalModelTemplate
 
@@ -619,36 +617,6 @@ def test_warning_with_few_workers(mock, tmpdir, ckpt_path):
         trainer.test(**test_options)
 
 
-@pytest.mark.xfail(
-    parse(torch.__version__) < parse("1.4.0"),
-    reason="IterableDataset with __len__ before 1.4 raises",
-)
-def test_warning_with_iterable_dataset_and_len(tmpdir):
-    """ Tests that a warning messages is shown when an IterableDataset defines `__len__`. """
-    model = EvalModelTemplate()
-    original_dataset = model.train_dataloader().dataset
-
-    class IterableWithLen(IterableDataset):
-
-        def __iter__(self):
-            return iter(original_dataset)
-
-        def __len__(self):
-            return len(original_dataset)
-
-    dataloader = DataLoader(IterableWithLen(), batch_size=16)
-    assert has_len(dataloader)
-    assert _has_iterable_dataset(dataloader)
-    trainer = Trainer(
-        default_root_dir=tmpdir,
-        max_steps=3,
-    )
-    with pytest.warns(UserWarning, match='Your `IterableDataset` has `__len__` defined.'):
-        trainer.fit(model, train_dataloader=dataloader, val_dataloaders=[dataloader])
-    with pytest.warns(UserWarning, match='Your `IterableDataset` has `__len__` defined.'):
-        trainer.test(model, test_dataloaders=[dataloader])
-
-
 @pytest.mark.skipif(torch.cuda.device_count() < 2, reason='Test requires multiple GPUs')
 def test_dataloader_reinit_for_subclass(tmpdir):
 
diff --git a/tests/utilities/test_data.py b/tests/utilities/test_data.py
new file mode 100644
index 0000000000000..31b5ee5ceb566
--- /dev/null
+++ b/tests/utilities/test_data.py
@@ -0,0 +1,39 @@
+import pytest
+import torch
+from packaging.version import parse
+from torch.utils.data.dataloader import DataLoader
+from torch.utils.data.dataset import IterableDataset
+
+from pytorch_lightning import Trainer
+from pytorch_lightning.utilities.data import has_iterable_dataset, has_len
+from tests.base import EvalModelTemplate
+
+
+@pytest.mark.xfail(
+    parse(torch.__version__) < parse("1.4.0"),
+    reason="IterableDataset with __len__ before 1.4 raises",
+)
+def test_warning_with_iterable_dataset_and_len(tmpdir):
+    """ Tests that a warning messages is shown when an IterableDataset defines `__len__`. """
+    model = EvalModelTemplate()
+    original_dataset = model.train_dataloader().dataset
+
+    class IterableWithLen(IterableDataset):
+
+        def __iter__(self):
+            return iter(original_dataset)
+
+        def __len__(self):
+            return len(original_dataset)
+
+    dataloader = DataLoader(IterableWithLen(), batch_size=16)
+    assert has_len(dataloader)
+    assert has_iterable_dataset(dataloader)
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_steps=3,
+    )
+    with pytest.warns(UserWarning, match='Your `IterableDataset` has `__len__` defined.'):
+        trainer.fit(model, train_dataloader=dataloader, val_dataloaders=[dataloader])
+    with pytest.warns(UserWarning, match='Your `IterableDataset` has `__len__` defined.'):
+        trainer.test(model, test_dataloaders=[dataloader])

From 8b1fde44a08c88645a787df874a7b02c60185e78 Mon Sep 17 00:00:00 2001
From: manipopopo <pwmutantbread@gmail.com>
Date: Tue, 11 Aug 2020 13:48:20 +0000
Subject: [PATCH 4/9] Simplify test cases

---
 tests/callbacks/test_progress_bar.py | 29 +++++++++++-----------------
 1 file changed, 11 insertions(+), 18 deletions(-)

diff --git a/tests/callbacks/test_progress_bar.py b/tests/callbacks/test_progress_bar.py
index 9804be9f00450..96745e08ad8b1 100644
--- a/tests/callbacks/test_progress_bar.py
+++ b/tests/callbacks/test_progress_bar.py
@@ -195,19 +195,18 @@ def on_test_batch_end(self, trainer, pl_module, batch, batch_idx, dataloader_idx
     assert progress_bar.test_batches_seen == progress_bar.total_test_batches
 
 
-@pytest.mark.parametrize('num_sanity_val_steps,num_val_dataloaders_batches', [
-    (-1, [10]),
-    (0, [10]),
-    (2, [10]),
-    (10, [2]),
-    (10, [2, 3]),
-    (10, [20, 3]),
-    (10, [20, 30]),
-    (10, [float('inf')]),
-    (10, [1, float('inf')]),
+@pytest.mark.parametrize('num_sanity_val_steps,num_val_dataloaders_batches,num_sanity_check_run_steps', [
+    (-1, [10], 10),
+    (0, [10], 0),
+    (2, [10], 2),
+    (10, [2], 2),
+    (10, [2, 3], 5),
+    (10, [20, 3], 13),
+    (10, [20, 30], 20),
+    (10, [float('inf')], 10),
+    (10, [1, float('inf')], 11),
 ])
-def test_sanity_check_progress_bar_total(tmpdir, num_sanity_val_steps,
-                                         num_val_dataloaders_batches):
+def test_sanity_check_progress_bar_total(tmpdir, num_sanity_val_steps, num_val_dataloaders_batches, num_sanity_check_run_steps):
     """Test that the sanity_check progress finishes with the correct total steps processed."""
 
     tmp_model = EvalModelTemplate(batch_size=1)
@@ -232,11 +231,5 @@ def test_sanity_check_progress_bar_total(tmpdir, num_sanity_val_steps,
                 model.dataloader(train=False, num_samples=num_samples))
     trainer.fit(model, val_dataloaders=val_dataloaders)
 
-    # check val progress bar total is the number of steps sanity check runs
-    max_sanity_val_steps = (float('inf') if num_sanity_val_steps == -1 else
-                            num_sanity_val_steps)
-    num_sanity_check_run_steps = sum(
-        min(max_sanity_val_steps, num_val_dataloader_batches)
-        for num_val_dataloader_batches in num_val_dataloaders_batches)
     val_progress_bar = trainer.progress_bar_callback.val_progress_bar
     assert getattr(val_progress_bar, 'total', 0) == num_sanity_check_run_steps

From a6be8090b585f8abb114d4b3a624f59d6b4708c9 Mon Sep 17 00:00:00 2001
From: manipopopo <pwmutantbread@gmail.com>
Date: Tue, 11 Aug 2020 13:48:35 +0000
Subject: [PATCH 5/9] Update CHANGELOG

---
 CHANGELOG.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 6e639a5c53f6f..5390509b8c3db 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -138,6 +138,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed checkpointing to remote file paths ([#2925](https://github.com/PyTorchLightning/pytorch-lightning/pull/2925))
 
+- Fixed the total steps of the progress bar for the validation sanity check ([#2892](https://github.com/PyTorchLightning/pytorch-lightning/pull/2892))
+
 ## [0.8.5] - 2020-07-09
 
 ### Added

From 8041aa9b72324062fbcbda33d0a39e0d39a72051 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Adrian=20W=C3=A4lchli?= <aedu.waelchli@gmail.com>
Date: Tue, 11 Aug 2020 16:13:56 +0200
Subject: [PATCH 6/9] rename

---
 tests/callbacks/test_progress_bar.py | 6 +++---
 tests/utilities/test_data.py         | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/tests/callbacks/test_progress_bar.py b/tests/callbacks/test_progress_bar.py
index 96745e08ad8b1..347716a8eeb0e 100644
--- a/tests/callbacks/test_progress_bar.py
+++ b/tests/callbacks/test_progress_bar.py
@@ -195,7 +195,7 @@ def on_test_batch_end(self, trainer, pl_module, batch, batch_idx, dataloader_idx
     assert progress_bar.test_batches_seen == progress_bar.total_test_batches
 
 
-@pytest.mark.parametrize('num_sanity_val_steps,num_val_dataloaders_batches,num_sanity_check_run_steps', [
+@pytest.mark.parametrize('num_sanity_val_steps,num_val_dataloaders_batches,expected_num_steps', [
     (-1, [10], 10),
     (0, [10], 0),
     (2, [10], 2),
@@ -206,7 +206,7 @@ def on_test_batch_end(self, trainer, pl_module, batch, batch_idx, dataloader_idx
     (10, [float('inf')], 10),
     (10, [1, float('inf')], 11),
 ])
-def test_sanity_check_progress_bar_total(tmpdir, num_sanity_val_steps, num_val_dataloaders_batches, num_sanity_check_run_steps):
+def test_sanity_check_progress_bar_total(tmpdir, num_sanity_val_steps, num_val_dataloaders_batches, expected_num_steps):
     """Test that the sanity_check progress finishes with the correct total steps processed."""
 
     tmp_model = EvalModelTemplate(batch_size=1)
@@ -232,4 +232,4 @@ def test_sanity_check_progress_bar_total(tmpdir, num_sanity_val_steps, num_val_d
     trainer.fit(model, val_dataloaders=val_dataloaders)
 
     val_progress_bar = trainer.progress_bar_callback.val_progress_bar
-    assert getattr(val_progress_bar, 'total', 0) == num_sanity_check_run_steps
+    assert getattr(val_progress_bar, 'total', 0) == expected_num_steps
diff --git a/tests/utilities/test_data.py b/tests/utilities/test_data.py
index 31b5ee5ceb566..b0c31b68a561b 100644
--- a/tests/utilities/test_data.py
+++ b/tests/utilities/test_data.py
@@ -14,7 +14,7 @@
     reason="IterableDataset with __len__ before 1.4 raises",
 )
 def test_warning_with_iterable_dataset_and_len(tmpdir):
-    """ Tests that a warning messages is shown when an IterableDataset defines `__len__`. """
+    """ Tests that a warning message is shown when an IterableDataset defines `__len__`. """
     model = EvalModelTemplate()
     original_dataset = model.train_dataloader().dataset
 

From 117c1fe16f73437895cbac8bf4aebaa8b40b7e67 Mon Sep 17 00:00:00 2001
From: ananyahjha93 <ananya@pytorchlightning.ai>
Date: Tue, 11 Aug 2020 11:11:10 -0400
Subject: [PATCH 7/9] removed pep8 issue

---
 tests/callbacks/test_progress_bar.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/tests/callbacks/test_progress_bar.py b/tests/callbacks/test_progress_bar.py
index 347716a8eeb0e..023b5927699eb 100644
--- a/tests/callbacks/test_progress_bar.py
+++ b/tests/callbacks/test_progress_bar.py
@@ -206,7 +206,9 @@ def on_test_batch_end(self, trainer, pl_module, batch, batch_idx, dataloader_idx
     (10, [float('inf')], 10),
     (10, [1, float('inf')], 11),
 ])
-def test_sanity_check_progress_bar_total(tmpdir, num_sanity_val_steps, num_val_dataloaders_batches, expected_num_steps):
+def test_sanity_check_progress_bar_total(
+    tmpdir, num_sanity_val_steps, num_val_dataloaders_batches, expected_num_steps
+):
     """Test that the sanity_check progress finishes with the correct total steps processed."""
 
     tmp_model = EvalModelTemplate(batch_size=1)

From 5987308b7a6f07704a4e38d75c4405d86a99268e Mon Sep 17 00:00:00 2001
From: ananyahjha93 <ananya@pytorchlightning.ai>
Date: Thu, 13 Aug 2020 20:15:38 -0400
Subject: [PATCH 8/9] doc fix

---
 docs/source/debugging.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/debugging.rst b/docs/source/debugging.rst
index 56c96607bcc4b..df0b241293d23 100644
--- a/docs/source/debugging.rst
+++ b/docs/source/debugging.rst
@@ -1,7 +1,7 @@
 .. testsetup:: *
 
     from pytorch_lightning.trainer.trainer import Trainer
-    
+
  .. _debugging:
 
 Debugging

From e978a580dd37bc827cbd112af46eafafc388e04c Mon Sep 17 00:00:00 2001
From: ananyahjha93 <ananya@pytorchlightning.ai>
Date: Fri, 14 Aug 2020 13:35:41 -0400
Subject: [PATCH 9/9] doc

---
 docs/source/debugging.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/debugging.rst b/docs/source/debugging.rst
index df0b241293d23..19c75acae16e1 100644
--- a/docs/source/debugging.rst
+++ b/docs/source/debugging.rst
@@ -2,7 +2,7 @@
 
     from pytorch_lightning.trainer.trainer import Trainer
 
- .. _debugging:
+.. _debugging:
 
 Debugging
 =========