formatting

pl_bolts/models/self_supervised/simclr/simclr_module.py

@@ -3,8 +3,7 @@
 
 import pytorch_lightning as pl
 import torch
-from pytorch_lightning.callbacks import LearningRateMonitor
-from pytorch_lightning.callbacks import ModelCheckpoint
+from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint
 from torch import nn
 from torch.nn import functional as F
 
@@ -250,7 +249,7 @@ def nt_xent_loss(self, out_1, out_2, temperature, eps=1e-6):
         neg = sim.sum(dim=-1)
 
         # from each row, subtract e^(1/temp) to remove similarity measure for x1.x1
-        row_sub = torch.Tensor(neg.shape).fill_(math.e ** (1 / temperature)).to(neg.device)
+        row_sub = torch.Tensor(neg.shape).fill_(math.e**(1 / temperature)).to(neg.device)
         neg = torch.clamp(neg - row_sub, min=eps)  # clamp for numerical stability
 
         # Positive similarity, pos becomes [2 * batch_size]

pl_bolts/models/self_supervised/simsiam/simsiam_module.py

@@ -3,9 +3,8 @@
 import pytorch_lightning as pl
 import torch
 from pytorch_lightning import seed_everything
+from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint
 from torch.nn import functional as F
-from pytorch_lightning.callbacks import LearningRateMonitor
-from pytorch_lightning.callbacks import ModelCheckpoint
 
 from pl_bolts.models.self_supervised.resnets import resnet18, resnet50
 from pl_bolts.models.self_supervised.simsiam.models import SiameseArm

pl_bolts/models/self_supervised/swav/swav_module.py

@@ -7,8 +7,7 @@
 import numpy as np
 import pytorch_lightning as pl
 import torch
-from pytorch_lightning.callbacks import LearningRateMonitor
-from pytorch_lightning.callbacks import ModelCheckpoint
+from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint
 from torch import distributed as dist
 from torch import nn
 

pl_bolts/optimizers/__init__.py

@@ -1,8 +1,6 @@
 from pl_bolts.optimizers.lars import LARS  # noqa: F401
-from pl_bolts.optimizers.lr_scheduler import (
-    LinearWarmupCosineAnnealingLR,  # noqa: F401
-    linear_warmup_decay,  # noqa: F401
-)
+from pl_bolts.optimizers.lr_scheduler import linear_warmup_decay  # noqa: F401
+from pl_bolts.optimizers.lr_scheduler import LinearWarmupCosineAnnealingLR  # noqa: F401
 
 __all__ = [
     "LARS",

pl_bolts/optimizers/lars.py

@@ -55,18 +55,33 @@ class LARS(Optimizer):
         and BYOL.
     """
 
-    def __init__(self, params, lr=required, momentum=0, dampening=0,
-                 weight_decay=0, nesterov=False, trust_coefficient=0.001, eps=1e-8):
+    def __init__(
+        self,
+        params,
+        lr=required,
+        momentum=0,
+        dampening=0,
+        weight_decay=0,
+        nesterov=False,
+        trust_coefficient=0.001,
+        eps=1e-8
+    ):
         if lr is not required and lr < 0.0:
             raise ValueError("Invalid learning rate: {}".format(lr))
         if momentum < 0.0:
             raise ValueError("Invalid momentum value: {}".format(momentum))
         if weight_decay < 0.0:
             raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
 
-        defaults = dict(lr=lr, momentum=momentum, dampening=dampening,
-                        weight_decay=weight_decay, nesterov=nesterov,
-                        trust_coefficient=trust_coefficient, eps=eps)
+        defaults = dict(
+            lr=lr,
+            momentum=momentum,
+            dampening=dampening,
+            weight_decay=weight_decay,
+            nesterov=nesterov,
+            trust_coefficient=trust_coefficient,
+            eps=eps
+        )
         if nesterov and (momentum <= 0 or dampening != 0):
             raise ValueError("Nesterov momentum requires a momentum and zero dampening")
 

pl_bolts/optimizers/lr_scheduler.py

@@ -136,9 +136,7 @@ def fn(step):
             # no decay
             return 1.0
 
-        progress = float(step - warmup_steps) / float(
-            max(1, total_steps - warmup_steps)
-        )
+        progress = float(step - warmup_steps) / float(max(1, total_steps - warmup_steps))
         if cosine:
             # cosine decay
             return 0.5 * (1.0 + math.cos(math.pi * progress))

tests/models/test_vision.py

@@ -1,8 +1,8 @@
 import pytest
 import pytorch_lightning as pl
 import torch
-from torch.utils.data import DataLoader
 from packaging import version
+from torch.utils.data import DataLoader
 
 from pl_bolts.datamodules import FashionMNISTDataModule, MNISTDataModule
 from pl_bolts.datasets import DummyDataset
@@ -17,8 +17,8 @@ def train_dataloader(self):
 
 
 @pytest.mark.skipif(
-    version.parse(pl.__version__) > version.parse("1.1.0"),
-    reason="igpt code not updated for latest lightning")
+    version.parse(pl.__version__) > version.parse("1.1.0"), reason="igpt code not updated for latest lightning"
+)
 def test_igpt(tmpdir, datadir):
     pl.seed_everything(0)
     dm = MNISTDataModule(data_dir=datadir, normalize=False)