isort

pytorch_lightning/accelerators/base_backend.py

@@ -1,10 +1,11 @@
-import torch
+import math
 from typing import Any
-from pytorch_lightning.utilities.apply_func import move_data_to_device
+
+import torch
+
 from pytorch_lightning.utilities import AMPType, rank_zero_warn
+from pytorch_lightning.utilities.apply_func import move_data_to_device
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-import math
-
 
 try:
     from apex import amp

pytorch_lightning/accelerators/cpu_backend.py

@@ -12,9 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import torch
-from pytorch_lightning.utilities.exceptions import MisconfigurationException
+
 from pytorch_lightning.accelerators.base_backend import Accelerator
 from pytorch_lightning.utilities import AMPType, rank_zero_warn
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
 
 class CPUBackend(Accelerator):

pytorch_lightning/accelerators/ddp_base_backend.py

@@ -13,20 +13,21 @@
 # limitations under the License
 import os
 import re
-import torch
 
-from pytorch_lightning.utilities import AMPType
-from pytorch_lightning.accelerators.base_backend import Accelerator
+import torch
 import torch.distributed as torch_distrib
 import torch.distributed as dist
-from pytorch_lightning.utilities.cloud_io import atomic_save
-from pytorch_lightning.utilities.distributed import rank_zero_warn, rank_zero_only
+
 from pytorch_lightning import _logger as log
+from pytorch_lightning.accelerators.base_backend import Accelerator
+from pytorch_lightning.utilities import AMPType
+from pytorch_lightning.utilities.cloud_io import atomic_save
+from pytorch_lightning.utilities.distributed import rank_zero_only, rank_zero_warn
 from pytorch_lightning.utilities.seed import seed_everything
 
 try:
-    from hydra.utils import to_absolute_path, get_original_cwd
     from hydra.core.hydra_config import HydraConfig
+    from hydra.utils import get_original_cwd, to_absolute_path
 except ImportError:
     HYDRA_AVAILABLE = False
 else:

pytorch_lightning/accelerators/dp_backend.py

@@ -15,12 +15,12 @@
 import torch
 from torch import optim
 
+from pytorch_lightning.accelerators.base_backend import Accelerator
 from pytorch_lightning.core import LightningModule
+from pytorch_lightning.core.step_result import Result
 from pytorch_lightning.overrides.data_parallel import LightningDataParallel
 from pytorch_lightning.utilities import AMPType
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from pytorch_lightning.core.step_result import Result
-from pytorch_lightning.accelerators.base_backend import Accelerator
 
 
 class DataParallelBackend(Accelerator):

pytorch_lightning/accelerators/gpu_backend.py

@@ -13,8 +13,9 @@
 # limitations under the License.
 
 import torch
-from pytorch_lightning.utilities import AMPType
+
 from pytorch_lightning.accelerators.base_backend import Accelerator
+from pytorch_lightning.utilities import AMPType
 
 
 class GPUBackend(Accelerator):

pytorch_lightning/accelerators/horovod_backend.py

@@ -12,11 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from contextlib import ExitStack
+
 import torch
-from pytorch_lightning.utilities import AMPType
+from torch.optim.lr_scheduler import _LRScheduler
+
 from pytorch_lightning.accelerators.base_backend import Accelerator
+from pytorch_lightning.utilities import AMPType
 from pytorch_lightning.utilities.distributed import rank_zero_only
-from torch.optim.lr_scheduler import _LRScheduler
 
 try:
     import horovod.torch as hvd

pytorch_lightning/accelerators/tpu_backend.py

@@ -19,17 +19,17 @@
 import torch.multiprocessing as mp
 
 from pytorch_lightning import _logger as log
-from pytorch_lightning.core import LightningModule
-from pytorch_lightning.utilities import rank_zero_info, rank_zero_only, rank_zero_warn, AMPType
-from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.accelerators.base_backend import Accelerator
+from pytorch_lightning.core import LightningModule
+from pytorch_lightning.utilities import AMPType, rank_zero_info, rank_zero_only, rank_zero_warn
 from pytorch_lightning.utilities.cloud_io import atomic_save
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
 try:
     import torch_xla
     import torch_xla.core.xla_model as xm
-    import torch_xla.distributed.xla_multiprocessing as xmp
     import torch_xla.distributed.parallel_loader as xla_pl
+    import torch_xla.distributed.xla_multiprocessing as xmp
 except ImportError:
     XLA_AVAILABLE = False
 else:

pytorch_lightning/callbacks/model_checkpoint.py

@@ -542,4 +542,4 @@ def to_yaml(self, filepath: Optional[Union[str, Path]] = None):
         if filepath is None:
             filepath = os.path.join(self.dirpath, "best_k_models.yaml")
         with open(filepath, "w") as fp:
-            yaml.dump(best_k, fp)
+            yaml.dump(best_k, fp)

pytorch_lightning/core/lightning.py

@@ -23,6 +23,11 @@
 
 import torch
 import torch.distributed as torch_distrib
+from torch import ScriptModule, Tensor
+from torch.nn import Module
+from torch.nn.parallel import DistributedDataParallel
+from torch.optim.optimizer import Optimizer
+
 from pytorch_lightning import _logger as log
 from pytorch_lightning.core.grads import GradInformation
 from pytorch_lightning.core.hooks import DataHooks, ModelHooks
@@ -32,16 +37,7 @@
 from pytorch_lightning.overrides.data_parallel import LightningDistributedDataParallel
 from pytorch_lightning.utilities import rank_zero_warn
 from pytorch_lightning.utilities.device_dtype_mixin import DeviceDtypeModuleMixin
-from pytorch_lightning.utilities.parsing import (
-    AttributeDict,
-    collect_init_args,
-    get_init_args,
-)
-from torch import ScriptModule, Tensor
-from torch.nn import Module
-from torch.nn.parallel import DistributedDataParallel
-from torch.optim.optimizer import Optimizer
-
+from pytorch_lightning.utilities.parsing import AttributeDict, collect_init_args, get_init_args
 
 try:
     import torch_xla.core.xla_model as xm

pytorch_lightning/trainer/connectors/precision_connector.py

@@ -13,9 +13,9 @@
 # limitations under the License.
 
 from pytorch_lightning import _logger as log
-from pytorch_lightning.utilities import APEX_AVAILABLE, NATIVE_AMP_AVALAIBLE, rank_zero_warn, AMPType
-from pytorch_lightning.plugins.native_amp import NativeAMP
 from pytorch_lightning.plugins.apex import ApexPlugin
+from pytorch_lightning.plugins.native_amp import NativeAMP
+from pytorch_lightning.utilities import APEX_AVAILABLE, NATIVE_AMP_AVALAIBLE, AMPType, rank_zero_warn
 
 
 class PrecisionConnector: