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‎CHANGELOG.md‎

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@@ -40,12 +40,6 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
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- Support number for logging with `sync_dist=True` ([#5080](https://github.com/PyTorchLightning/pytorch-lightning/pull/5080)
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- Added offset logging step when resuming for Wandb logger ([#5050](https://github.com/PyTorchLightning/pytorch-lightning/pull/5050)
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### Changed
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### Deprecated
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### Removed
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- `enable_pl_optimizer=False` by default to temporarily fix AMP issues ([#5163](https://github.com/PyTorchLightning/pytorch-lightning/pull/5163)

‎pl_examples/__init__.py‎

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@@ -2,12 +2,12 @@
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from pytorch_lightning.utilities import _module_available
44

5-
EXAMPLES_ROOT = os.path.dirname(__file__)
6-
PACKAGE_ROOT = os.path.dirname(EXAMPLES_ROOT)
7-
DATASETS_PATH = os.path.join(PACKAGE_ROOT, 'Datasets')
5+
_EXAMPLES_ROOT = os.path.dirname(__file__)
6+
_PACKAGE_ROOT = os.path.dirname(_EXAMPLES_ROOT)
7+
_DATASETS_PATH = os.path.join(_PACKAGE_ROOT, 'Datasets')
88

9-
TORCHVISION_AVAILABLE = _module_available("torchvision")
10-
DALI_AVAILABLE = _module_available("nvidia.dali")
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_TORCHVISION_AVAILABLE = _module_available("torchvision")
10+
_DALI_AVAILABLE = _module_available("nvidia.dali")
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LIGHTNING_LOGO = """

‎pl_examples/basic_examples/autoencoder.py‎

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@@ -21,9 +21,9 @@
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from torch.utils.data import random_split
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import pytorch_lightning as pl
24-
from pl_examples import TORCHVISION_AVAILABLE, cli_lightning_logo
24+
from pl_examples import _TORCHVISION_AVAILABLE, cli_lightning_logo
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26-
if TORCHVISION_AVAILABLE:
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if _TORCHVISION_AVAILABLE:
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from torchvision.datasets.mnist import MNIST
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from torchvision import transforms
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else:

‎pl_examples/basic_examples/backbone_image_classifier.py‎

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@@ -19,9 +19,9 @@
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from torch.utils.data import DataLoader, random_split
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import pytorch_lightning as pl
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from pl_examples import DATASETS_PATH, TORCHVISION_AVAILABLE, cli_lightning_logo
22+
from pl_examples import _DATASETS_PATH, _TORCHVISION_AVAILABLE, cli_lightning_logo
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24-
if TORCHVISION_AVAILABLE:
24+
if _TORCHVISION_AVAILABLE:
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from torchvision.datasets.mnist import MNIST
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from torchvision import transforms
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else:
@@ -111,8 +111,8 @@ def cli_main():
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# ------------
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# data
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# ------------
114-
dataset = MNIST(DATASETS_PATH, train=True, download=True, transform=transforms.ToTensor())
115-
mnist_test = MNIST(DATASETS_PATH, train=False, download=True, transform=transforms.ToTensor())
114+
dataset = MNIST(_DATASETS_PATH, train=True, download=True, transform=transforms.ToTensor())
115+
mnist_test = MNIST(_DATASETS_PATH, train=False, download=True, transform=transforms.ToTensor())
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mnist_train, mnist_val = random_split(dataset, [55000, 5000])
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train_loader = DataLoader(mnist_train, batch_size=args.batch_size)

‎pl_examples/basic_examples/dali_image_classifier.py‎

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@@ -23,15 +23,15 @@
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from torch.utils.data import random_split
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import pytorch_lightning as pl
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from pl_examples import TORCHVISION_AVAILABLE, DALI_AVAILABLE, cli_lightning_logo
26+
from pl_examples import _TORCHVISION_AVAILABLE, _DALI_AVAILABLE, cli_lightning_logo
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28-
if TORCHVISION_AVAILABLE:
28+
if _TORCHVISION_AVAILABLE:
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from torchvision.datasets.mnist import MNIST
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from torchvision import transforms
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else:
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from tests.base.datasets import MNIST
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if DALI_AVAILABLE:
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if _DALI_AVAILABLE:
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from nvidia.dali import ops
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from nvidia.dali.pipeline import Pipeline
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from nvidia.dali.plugin.pytorch import DALIClassificationIterator
@@ -166,7 +166,7 @@ def add_model_specific_args(parent_parser):
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def cli_main():
169-
if not DALI_AVAILABLE:
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if not _DALI_AVAILABLE:
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return
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pl.seed_everything(1234)

‎pl_examples/basic_examples/mnist_datamodule.py‎

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@@ -16,10 +16,10 @@
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from torch.utils.data import DataLoader, random_split
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from pl_examples import DATASETS_PATH, TORCHVISION_AVAILABLE
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from pl_examples import _DATASETS_PATH, _TORCHVISION_AVAILABLE
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from pytorch_lightning import LightningDataModule
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if TORCHVISION_AVAILABLE:
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if _TORCHVISION_AVAILABLE:
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from torchvision import transforms as transform_lib
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from torchvision.datasets import MNIST
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else:
@@ -38,7 +38,7 @@ class MNISTDataModule(LightningDataModule):
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3939
def __init__(
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self,
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data_dir: str = DATASETS_PATH,
41+
data_dir: str = _DATASETS_PATH,
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val_split: int = 5000,
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num_workers: int = 16,
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normalize: bool = False,
@@ -123,7 +123,7 @@ def test_dataloader(self):
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124124
@property
125125
def default_transforms(self):
126-
if not TORCHVISION_AVAILABLE:
126+
if not _TORCHVISION_AVAILABLE:
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return None
128128
if self.normalize:
129129
mnist_transforms = transform_lib.Compose(

‎pl_examples/domain_templates/semantic_segmentation.py‎

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@@ -67,8 +67,8 @@ class KITTI(Dataset):
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encoded using `encode_segmap`, and given `transform` (if any) are applied to the image only
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(mask does not usually require transforms, but they can be implemented in a similar way).
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70-
>>> from pl_examples import DATASETS_PATH
71-
>>> dataset_path = os.path.join(DATASETS_PATH, "Kitti")
70+
>>> from pl_examples import _DATASETS_PATH
71+
>>> dataset_path = os.path.join(_DATASETS_PATH, "Kitti")
7272
>>> _create_synth_kitti_dataset(dataset_path, image_dims=(1024, 512))
7373
>>> KITTI(dataset_path, 'train') # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
7474
<...semantic_segmentation.KITTI object at ...>
@@ -161,8 +161,8 @@ class SegModel(pl.LightningModule):
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Adam optimizer is used along with Cosine Annealing learning rate scheduler.
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164-
>>> from pl_examples import DATASETS_PATH
165-
>>> dataset_path = os.path.join(DATASETS_PATH, "Kitti")
164+
>>> from pl_examples import _DATASETS_PATH
165+
>>> dataset_path = os.path.join(_DATASETS_PATH, "Kitti")
166166
>>> _create_synth_kitti_dataset(dataset_path, image_dims=(1024, 512))
167167
>>> SegModel(dataset_path) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
168168
SegModel(

‎pl_examples/test_examples.py‎

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@@ -19,7 +19,7 @@
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import pytest
2020
import torch
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22-
from pl_examples import DALI_AVAILABLE
22+
from pl_examples import _DALI_AVAILABLE
2323

2424
ARGS_DEFAULT = """
2525
--default_root_dir %(tmpdir)s \
@@ -104,7 +104,7 @@ def test_examples_cpu(tmpdir, import_cli, cli_args):
104104
module.cli_main()
105105

106106

107-
@pytest.mark.skipif(not DALI_AVAILABLE, reason="Nvidia DALI required")
107+
@pytest.mark.skipif(not _DALI_AVAILABLE, reason="Nvidia DALI required")
108108
@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
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@pytest.mark.skipif(platform.system() != 'Linux', reason='Only applies to Linux platform.')
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@pytest.mark.parametrize('cli_args', [ARGS_GPU])

‎pytorch_lightning/__init__.py‎

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_logger.addHandler(python_logging.StreamHandler())
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_logger.setLevel(python_logging.INFO)
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43-
PACKAGE_ROOT = os.path.dirname(__file__)
44-
PROJECT_ROOT = os.path.dirname(PACKAGE_ROOT)
43+
_PACKAGE_ROOT = os.path.dirname(__file__)
44+
_PROJECT_ROOT = os.path.dirname(_PACKAGE_ROOT)
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4646
try:
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# This variable is injected in the __builtins__ by the build

‎pytorch_lightning/loggers/wandb.py‎

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@@ -162,7 +162,9 @@ def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) ->
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163163
metrics = self._add_prefix(metrics)
164164
if step is not None and step + self._step_offset < self.experiment.step:
165-
self.warning_cache.warn('Trying to log at a previous step. Use `commit=False` when logging metrics manually.')
165+
self.warning_cache.warn(
166+
'Trying to log at a previous step. Use `commit=False` when logging metrics manually.'
167+
)
166168
self.experiment.log(metrics, step=(step + self._step_offset) if step is not None else None)
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@property

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