def test_module_exists():
"""Test if the some 3rd party libs are available."""
assert _module_available("torch")
assert _module_available("torch.nn.parallel")
assert not _module_available("torch.nn.asdf")
assert not _module_available("asdf")
assert not _module_available("asdf.bla.asdf")
import logging
import os
from argparse import Namespace
from typing import Any, Dict, Optional, Union
import torch
from torch import is_tensor
import pytorch_lightning as pl
from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_experiment
from pytorch_lightning.utilities import _module_available, rank_zero_only
from pytorch_lightning.utilities.exceptions import MisconfigurationException
log = logging.getLogger(__name__)
_COMET_AVAILABLE = _module_available("comet_ml")
if _COMET_AVAILABLE:
import comet_ml
from comet_ml import ExistingExperiment as CometExistingExperiment
from comet_ml import Experiment as CometExperiment
from comet_ml import OfflineExperiment as CometOfflineExperiment
try:
from comet_ml.api import API
except ImportError: # pragma: no-cover
# For more information, see: https://www.comet.ml/docs/python-sdk/releases/#release-300
from comet_ml.papi import API # pragma: no-cover
else:
# needed for test mocks, these tests shall be updated
comet_ml = None
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Test Tube Logger
----------------
"""
from argparse import Namespace
from typing import Any, Dict, Optional, Union
from pytorch_lightning.core.lightning import LightningModule
from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_experiment
from pytorch_lightning.utilities import _module_available
from pytorch_lightning.utilities.distributed import rank_zero_only, rank_zero_warn
_TESTTUBE_AVAILABLE = _module_available("test_tube")
if _TESTTUBE_AVAILABLE:
from test_tube import Experiment
else:
Experiment = None
class TestTubeLogger(LightningLoggerBase):
r"""
Log to local file system in `TensorBoard <https://www.tensorflow.org/tensorboard>`_ format
but using a nicer folder structure (see `full docs <https://williamfalcon.github.io/test-tube>`_).
Install it with pip:
.. code-block:: bash
>>> _compare_version("torch", operator.ge, "0.1")
True
"""
try:
pkg = importlib.import_module(package)
except (ModuleNotFoundError, DistributionNotFound):
return False
try:
pkg_version = Version(pkg.__version__)
except TypeError:
# this is mock by sphinx, so it shall return True ro generate all summaries
return True
return op(pkg_version, Version(version))
_NATIVE_AMP_AVAILABLE: bool = _module_available("torch.cuda.amp") and hasattr(
torch.cuda.amp, "autocast")
_TORCHVISION_AVAILABLE: bool = _module_available("torchvision")
_GYM_AVAILABLE: bool = _module_available("gym")
_SKLEARN_AVAILABLE: bool = _module_available("sklearn")
_PIL_AVAILABLE: bool = _module_available("PIL")
_OPENCV_AVAILABLE: bool = _module_available("cv2")
_WANDB_AVAILABLE: bool = _module_available("wandb")
_MATPLOTLIB_AVAILABLE: bool = _module_available("matplotlib")
_TORCHVISION_LESS_THAN_0_9_1: bool = _compare_version("torchvision",
operator.lt, "0.9.1")
_PL_GREATER_EQUAL_1_4 = _compare_version("pytorch_lightning", operator.ge,
"1.4.0")
_PL_GREATER_EQUAL_1_4_5 = _compare_version("pytorch_lightning", operator.ge,
"1.4.5")
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from argparse import Namespace
from typing import Any, Dict, Optional, Type, Union
from pytorch_lightning.callbacks import Callback
from pytorch_lightning.core.datamodule import LightningDataModule
from pytorch_lightning.core.lightning import LightningModule
from pytorch_lightning.trainer.trainer import Trainer
from pytorch_lightning.utilities import _module_available
from pytorch_lightning.utilities.seed import seed_everything
_JSONARGPARSE_AVAILABLE = _module_available("jsonargparse")
if _JSONARGPARSE_AVAILABLE:
from jsonargparse import ActionConfigFile, ArgumentParser
else:
ArgumentParser = object
class LightningArgumentParser(ArgumentParser):
"""Extension of jsonargparse's ArgumentParser for pytorch-lightning"""
def __init__(self, *args, parse_as_dict: bool = True, **kwargs) -> None:
"""Initialize argument parser that supports configuration file input
For full details of accepted arguments see `ArgumentParser.__init__
<https://jsonargparse.readthedocs.io/en/stable/#jsonargparse.core.ArgumentParser.__init__>`_.
"""
if not _JSONARGPARSE_AVAILABLE:
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import Optional
import torch
from pytorch_lightning.core.lightning import LightningModule
from pytorch_lightning.plugins.ddp_plugin import DDPPlugin
from pytorch_lightning.utilities import _module_available, RPC_AVAILABLE
DEFAULT_RPC_TIMEOUT_SEC = 60.
if RPC_AVAILABLE:
from torch.distributed import rpc
if _module_available("torch.distributed.rpc.constants") and hasattr(torch.distributed.rpc.constants, "DEFAULT_RPC_TIMEOUT_SEC"):
from torch.distributed.rpc.constants import DEFAULT_RPC_TIMEOUT_SEC
class RPCPlugin(DDPPlugin):
"""
Backbone for RPC Plugins built on top of DDP.
RPC introduces different communication behaviour than DDP. Unlike DDP, processes potentially are not
required to run the same code as the main process.
This leads to edge cases where logic needs to be re-defined. This class contains special cases
that need to be addressed when using RPC communication when building custom RPC Plugins.
"""
def __init__(self, rpc_timeout_sec: float = DEFAULT_RPC_TIMEOUT_SEC, **kwargs):
self.rpc_timeout_sec = rpc_timeout_sec
self.rpc_initialized = False
# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import operator
from pytorch_lightning.utilities import _module_available
from pytorch_lightning.utilities.imports import _compare_version
_BOLTS_AVAILABLE = _module_available("pl_bolts") and _compare_version(
"pl_bolts", operator.ge, "0.4.0")
_BOLTS_GREATER_EQUAL_0_5_0 = _module_available(
"pl_bolts") and _compare_version("pl_bolts", operator.ge, "0.5.0")
_WANDB_AVAILABLE = _module_available("wandb")
import os
from pytorch_lightning.utilities import _module_available
_EXAMPLES_ROOT = os.path.dirname(__file__)
_PACKAGE_ROOT = os.path.dirname(_EXAMPLES_ROOT)
_DATASETS_PATH = os.path.join(_PACKAGE_ROOT, 'Datasets')
_TORCHVISION_AVAILABLE = _module_available("torchvision")
_DALI_AVAILABLE = _module_available("nvidia.dali")
LIGHTNING_LOGO = """
####
###########
####################
############################
#####################################
##############################################
######################### ###################
####################### ###################
#################### ####################
################## #####################
################ ######################
##################### #################
###################### ###################
##################### #####################
#################### #######################
################### #########################
##############################################
#####################################
############################
# Copyright 2020 The PyTorch Lightning team and The HuggingFace Team. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from pytorch_lightning.utilities import _module_available
nltk = None
if _module_available('nltk'):
import nltk
nltk.download("punkt", quiet=True)
def add_newline_to_end_of_each_sentence(x: str) -> str:
"""This was added to get rougeLsum scores matching published rougeL scores for BART and PEGASUS."""
re.sub("<n>", "", x) # remove pegasus newline char
assert nltk, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(x))
import json
import os
from pathlib import Path
import pytest
import torch
from PIL import Image
from pytorch_lightning.utilities import _module_available
from torchvision import transforms as T
from flash.vision.detection.data import ImageDetectionData
_COCO_AVAILABLE = _module_available("pycocotools")
if _COCO_AVAILABLE:
from pycocotools.coco import COCO
def _create_dummy_coco_json(dummy_json_path):
dummy_json = {
"images": [{
"id": 0,
'width': 1920,
'height': 1080,
'file_name': 'sample_one.png',
}, {
"id": 1,
"width": 1920,
"height": 1080,
"file_name": "sample_two.png",
}],
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional
import torch
from torch.utils.data import DataLoader
from pytorch_lightning.core.datamodule import LightningDataModule
from pytorch_lightning.utilities import _module_available
from tests.helpers.datasets import MNIST, SklearnDataset, TrialMNIST
_SKLEARN_AVAILABLE = _module_available("sklearn")
if _SKLEARN_AVAILABLE:
from sklearn.datasets import make_classification, make_regression
from sklearn.model_selection import train_test_split
else:
make_classification = None
make_regression = None
train_test_split = None
class MNISTDataModule(LightningDataModule):
def __init__(self, data_dir: str = "./", batch_size: int = 32, use_trials: bool = False) -> None:
super().__init__()
self.data_dir = data_dir
self.batch_size = batch_size
import torch
from pytorch_lightning.utilities import _module_available
_NATIVE_AMP_AVAILABLE = _module_available("torch.cuda.amp") and hasattr(
torch.cuda.amp, "autocast")
_TORCHVISION_AVAILABLE = _module_available("torchvision")
_GYM_AVAILABLE = _module_available("gym")
_SKLEARN_AVAILABLE = _module_available("sklearn")
_PIL_AVAILABLE = _module_available("PIL")
_OPENCV_AVAILABLE = _module_available("cv2")
# Copyright 2020 The PyTorch Lightning team and The HuggingFace Team. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from pytorch_lightning.utilities import _module_available
nltk = None
if _module_available("nltk"):
import nltk
nltk.download("punkt", quiet=True)
def add_newline_to_end_of_each_sentence(x: str) -> str:
"""This was added to get rougeLsum scores matching published rougeL scores for BART and PEGASUS."""
re.sub("<n>", "", x) # remove pegasus newline char
assert nltk, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(x))
from importlib.util import find_spec
import torch
from pytorch_lightning.utilities import _module_available
_NATIVE_AMP_AVAILABLE = _module_available("torch.cuda.amp") and hasattr(
torch.cuda.amp, "autocast")
_TORCHVISION_AVAILABLE = find_spec("torchvision") is not None
_GYM_AVAILABLE = find_spec("gym") is not None
_SKLEARN_AVAILABLE = find_spec("sklearn") is not None
_PIL_AVAILABLE = find_spec("PIL") is not None
_OPENCV_AVAILABLE = find_spec("cv2") is not None
MLflow Logger
-------------
"""
import logging
import os
import re
from argparse import Namespace
from time import time
from typing import Any, Dict, Optional, Union
from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_experiment
from pytorch_lightning.utilities import _module_available, rank_zero_only, rank_zero_warn
log = logging.getLogger(__name__)
LOCAL_FILE_URI_PREFIX = "file:"
_MLFLOW_AVAILABLE = _module_available("mlflow")
try:
import mlflow
from mlflow.tracking import context, MlflowClient
from mlflow.utils.mlflow_tags import MLFLOW_RUN_NAME
# todo: there seems to be still some remaining import error with Conda env
except ImportError:
_MLFLOW_AVAILABLE = False
mlflow, MlflowClient, context = None, None, None
MLFLOW_RUN_NAME = "mlflow.runName"
# before v1.1.0
if hasattr(context, "resolve_tags"):
from mlflow.tracking.context import resolve_tags
"""
Neptune Logger
--------------
"""
import logging
from argparse import Namespace
from typing import Any, Dict, Iterable, Optional, Union
import torch
from torch import is_tensor
from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_experiment
from pytorch_lightning.utilities import _module_available, rank_zero_only
log = logging.getLogger(__name__)
_NEPTUNE_AVAILABLE = _module_available("neptune")
if _NEPTUNE_AVAILABLE:
import neptune
from neptune.experiments import Experiment
else:
# needed for test mocks, these tests shall be updated
neptune, Experiment = None, None
class NeptuneLogger(LightningLoggerBase):
r"""
Log using `Neptune <https://neptune.ai>`_.
Install it with pip:
"""
Weights and Biases Logger
-------------------------
"""
import os
from argparse import Namespace
from typing import Any, Dict, Optional, Union
import torch.nn as nn
from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_experiment
from pytorch_lightning.utilities import _module_available, rank_zero_only
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.warnings import WarningCache
_WANDB_AVAILABLE = _module_available("wandb")
try:
import wandb
from wandb.wandb_run import Run
except ImportError:
# needed for test mocks, these tests shall be updated
wandb, Run = None, None
class WandbLogger(LightningLoggerBase):
r"""
Log using `Weights and Biases <https://www.wandb.com/>`_.
Install it with pip:
from six.moves import urllib
from pytorch_lightning.utilities import _module_available
# TorchVision hotfix https://github.com/pytorch/vision/issues/1938
opener = urllib.request.build_opener()
opener.addheaders = [('User-agent', 'Mozilla/5.0')]
urllib.request.install_opener(opener)
_EXAMPLES_ROOT = os.path.dirname(__file__)
_PACKAGE_ROOT = os.path.dirname(_EXAMPLES_ROOT)
_DATASETS_PATH = os.path.join(_PACKAGE_ROOT, 'Datasets')
_TORCHVISION_MNIST_AVAILABLE = not bool(
os.environ.get("PL_USE_MOCKED_MNIST", False))
_DALI_AVAILABLE = _module_available("nvidia.dali")
if _TORCHVISION_MNIST_AVAILABLE:
try:
from torchvision.datasets.mnist import MNIST
MNIST(_DATASETS_PATH, download=True)
except HTTPError:
_TORCHVISION_MNIST_AVAILABLE = False
LIGHTNING_LOGO = """
####
###########
####################
############################
#####################################
##############################################
Compare package version with some requirements
>>> _compare_version("torch", operator.ge, "0.1")
True
"""
try:
pkg = importlib.import_module(package)
except (ModuleNotFoundError, DistributionNotFound):
return False
try:
pkg_version = Version(pkg.__version__)
except TypeError:
# this is mock by sphinx, so it shall return True ro generate all summaries
return True
return op(pkg_version, Version(version))
_NATIVE_AMP_AVAILABLE: bool = _module_available("torch.cuda.amp") and hasattr(
torch.cuda.amp, "autocast")
_TORCHVISION_AVAILABLE: bool = _module_available("torchvision")
_GYM_AVAILABLE: bool = _module_available("gym")
_SKLEARN_AVAILABLE: bool = _module_available("sklearn")
_PIL_AVAILABLE: bool = _module_available("PIL")
_OPENCV_AVAILABLE: bool = _module_available("cv2")
_WANDB_AVAILABLE: bool = _module_available("wandb")
_MATPLOTLIB_AVAILABLE: bool = _module_available("matplotlib")
_TORCHVISION_LESS_THAN_0_9_1: bool = _compare_version("torchvision",
operator.lt, "0.9.1")
__all__ = ["BatchGradientVerification"]
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch.nn as nn
import pytorch_lightning as pl
from pytorch_lightning.overrides.base import _LightningModuleWrapperBase, unwrap_lightning_module
from pytorch_lightning.utilities import _IS_WINDOWS, _module_available
_FAIRSCALE_AVAILABLE = not _IS_WINDOWS and _module_available("fairscale.nn")
if _FAIRSCALE_AVAILABLE:
from fairscale.nn.data_parallel.sharded_ddp import ShardedDataParallel
class LightningShardedDataParallel(_LightningModuleWrapperBase):
# Just do this for later docstrings
pass
def unwrap_lightning_module_sharded(
wrapped_model: nn.Module) -> "pl.LightningModule":
model = wrapped_model
if isinstance(model, ShardedDataParallel):
model = model.module
return unwrap_lightning_module(model)
