[pre-commit.ci] pre-commit suggestions (#958)

* [pre-commit.ci] pre-commit suggestions updates: - [github.com/PyCQA/docformatter: v1.5.0 → v1.5.1](PyCQA/docformatter@v1.5.0...v1.5.1) - [github.com/PyCQA/isort: 5.11.2 → 5.11.4](PyCQA/isort@5.11.2...5.11.4) * config --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Jirka Borovec <[email protected]> Co-authored-by: Jirka <[email protected]>
Lightning-Universe · Mar 28, 2023 · 05c305b · 05c305b
1 parent 679d728
commit 05c305b
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Showing 30 changed files with 13 additions and 49 deletions.
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -29,7 +29,7 @@ repos:
         name: Upgrade code
 
   - repo: https://github.com/PyCQA/docformatter
-    rev: v1.5.0
+    rev: v1.5.1
     hooks:
       - id: docformatter
         args: [--in-place, --wrap-summaries=115, --wrap-descriptions=120]
@@ -45,12 +45,12 @@ repos:
         exclude: CHANGELOG.md
 
   - repo: https://github.com/PyCQA/isort
-    rev: 5.11.2
+    rev: 5.12.0
     hooks:
       - id: isort
 
   - repo: https://github.com/psf/black
-    rev: 22.12.0
+    rev: 23.1.0
     hooks:
       - id: black
         name: Format code

diff --git a/pl_bolts/callbacks/data_monitor.py b/pl_bolts/callbacks/data_monitor.py
@@ -22,7 +22,6 @@
 
 @under_review()
 class DataMonitorBase(Callback):
-
     supported_loggers = (
         TensorBoardLogger,
         WandbLogger,
@@ -113,7 +112,6 @@ def _is_logger_available(self, logger: LightningLoggerBase) -> bool:
 
 @under_review()
 class ModuleDataMonitor(DataMonitorBase):
-
     GROUP_NAME_INPUT = "input"
     GROUP_NAME_OUTPUT = "output"
 
@@ -199,7 +197,6 @@ def hook(_: Module, inp: Sequence, out: Sequence) -> None:
 
 @under_review()
 class TrainingDataMonitor(DataMonitorBase):
-
     GROUP_NAME = "training_step"
 
     def __init__(self, log_every_n_steps: int = None):

diff --git a/pl_bolts/datamodules/experience_source.py b/pl_bolts/datamodules/experience_source.py
@@ -103,7 +103,6 @@ def runner(self, device: torch.device) -> Tuple[Experience]:
 
             # step through each env
             for env_idx, (env, action) in enumerate(zip(self.pool, actions)):
-
                 exp = self.env_step(env_idx, env, action)
                 history = self.histories[env_idx]
                 history.append(exp)

diff --git a/pl_bolts/datamodules/kitti_datamodule.py b/pl_bolts/datamodules/kitti_datamodule.py
@@ -19,7 +19,6 @@
 
 @under_review()
 class KittiDataModule(LightningDataModule):
-
     name = "kitti"
 
     def __init__(

diff --git a/pl_bolts/datamodules/sklearn_datamodule.py b/pl_bolts/datamodules/sklearn_datamodule.py
@@ -117,7 +117,6 @@ def __init__(
         *args,
         **kwargs,
     ) -> None:
-
         super().__init__(*args, **kwargs)
         self.num_workers = num_workers
         self.batch_size = batch_size

diff --git a/pl_bolts/datamodules/ssl_imagenet_datamodule.py b/pl_bolts/datamodules/ssl_imagenet_datamodule.py
@@ -18,7 +18,6 @@
 
 @under_review()
 class SSLImagenetDataModule(LightningDataModule):  # pragma: no cover
-
     name = "imagenet"
 
     def __init__(

diff --git a/pl_bolts/datamodules/vision_datamodule.py b/pl_bolts/datamodules/vision_datamodule.py
@@ -8,7 +8,6 @@
 
 
 class VisionDataModule(LightningDataModule):
-
     EXTRA_ARGS: dict = {}
     name: str = ""
     #: Dataset class to use

diff --git a/pl_bolts/datasets/base_dataset.py b/pl_bolts/datasets/base_dataset.py
@@ -16,7 +16,6 @@
 
 @under_review()
 class LightDataset(ABC, Dataset):
-
     data: Tensor
     targets: Tensor
     normalize: tuple

diff --git a/pl_bolts/datasets/ssl_amdim_datasets.py b/pl_bolts/datasets/ssl_amdim_datasets.py
@@ -30,7 +30,6 @@ def generate_train_val_split(cls, examples, labels, pct_val):
 
         cts = {x: 0 for x in range(nb_classes)}
         for img, class_idx in zip(examples, labels):
-
             # allow labeled
             if cts[class_idx] < nb_val_images:
                 val_x.append(img)
@@ -60,7 +59,6 @@ def select_nb_imgs_per_class(cls, examples, labels, nb_imgs_in_val):
 
         cts = {x: 0 for x in range(nb_classes)}
         for img_name, class_idx in zip(examples, labels):
-
             # allow labeled
             if cts[class_idx] < nb_imgs_in_val:
                 labeled.append(img_name)
@@ -76,7 +74,6 @@ def select_nb_imgs_per_class(cls, examples, labels, nb_imgs_in_val):
 
     @classmethod
     def deterministic_shuffle(cls, x, y):
-
         n = len(x)
         idxs = list(range(0, n))
         np.random.seed(1234)

diff --git a/pl_bolts/losses/self_supervised_learning.py b/pl_bolts/losses/self_supervised_learning.py
@@ -140,11 +140,8 @@ def forward(self, anchor_representations, positive_representations, mask_mat):
 
         # trick 2: tanh clip
         raw_scores = tanh_clip(raw_scores, clip_val=self.tclip)
-        """
-        pos_scores includes scores for all the positive samples
-        neg_scores includes scores for all the negative samples, with
-        scores for positive samples set to the min score (-self.tclip here)
-        """
+        """pos_scores includes scores for all the positive samples neg_scores includes scores for all the negative
+        samples, with scores for positive samples set to the min score (-self.tclip here)"""
         # ----------------------
         # EXTRACT POSITIVE SCORES
         # use the index mask to pull all the diagonals which are b1 x b1
@@ -337,8 +334,7 @@ def forward(self, anchor_maps, positive_maps):
 
         regularizer = 0
         losses = []
-        for (ai, pi) in self.map_indexes:
-
+        for ai, pi in self.map_indexes:
             # choose a random map
             if ai == -1:
                 ai = np.random.randint(0, len(anchor_maps))

diff --git a/pl_bolts/models/detection/faster_rcnn/faster_rcnn_module.py b/pl_bolts/models/detection/faster_rcnn/faster_rcnn_module.py
@@ -113,7 +113,6 @@ def forward(self, x):
         return self.model(x)
 
     def training_step(self, batch, batch_idx):
-
         images, targets = batch
         targets = [{k: v for k, v in t.items()} for t in targets]
 

diff --git a/pl_bolts/models/detection/retinanet/retinanet_module.py b/pl_bolts/models/detection/retinanet/retinanet_module.py
@@ -9,7 +9,6 @@
 from pl_bolts.utils.warnings import warn_missing_pkg
 
 if _TORCHVISION_AVAILABLE:
-
     from torchvision.models.detection.retinanet import RetinaNet as torchvision_RetinaNet
     from torchvision.models.detection.retinanet import RetinaNetHead, retinanet_resnet50_fpn
     from torchvision.ops import box_iou
@@ -97,7 +96,6 @@ def forward(self, x):
         return self.model(x)
 
     def training_step(self, batch, batch_idx):
-
         images, targets = batch
         targets = [{k: v for k, v in t.items()} for t in targets]
 

diff --git a/pl_bolts/models/gans/pix2pix/pix2pix_module.py b/pl_bolts/models/gans/pix2pix/pix2pix_module.py
@@ -18,7 +18,6 @@ def _weights_init(m):
 @under_review()
 class Pix2Pix(LightningModule):
     def __init__(self, in_channels, out_channels, learning_rate=0.0002, lambda_recon=200):
-
         super().__init__()
         self.save_hyperparameters()
 

diff --git a/pl_bolts/models/mnist_module.py b/pl_bolts/models/mnist_module.py
@@ -27,7 +27,6 @@ class LitMNIST(LightningModule):
     """
 
     def __init__(self, hidden_dim: int = 128, learning_rate: float = 1e-3, **kwargs: Any) -> None:
-
         if not _TORCHVISION_AVAILABLE:  # pragma: no cover
             raise ModuleNotFoundError("You want to use `torchvision` which is not installed yet.")
 

diff --git a/pl_bolts/models/rl/per_dqn_model.py b/pl_bolts/models/rl/per_dqn_model.py
@@ -91,7 +91,13 @@ def train_batch(
             states, actions, rewards, dones, new_states = samples
 
             for idx, _ in enumerate(dones):
-                yield (states[idx], actions[idx], rewards[idx], dones[idx], new_states[idx],), indices[
+                yield (
+                    states[idx],
+                    actions[idx],
+                    rewards[idx],
+                    dones[idx],
+                    new_states[idx],
+                ), indices[
                     idx
                 ], weights[idx]
 

diff --git a/pl_bolts/models/rl/reinforce_model.py b/pl_bolts/models/rl/reinforce_model.py
@@ -173,7 +173,6 @@ def train_batch(
         """
 
         while True:
-
             action = self.agent(self.state, self.device)
 
             next_state, reward, done, _ = self.env.step(action[0])

diff --git a/pl_bolts/models/rl/vanilla_policy_gradient_model.py b/pl_bolts/models/rl/vanilla_policy_gradient_model.py
@@ -132,7 +132,6 @@ def train_batch(
         """
 
         while True:
-
             action = self.agent(self.state, self.device)
 
             next_state, reward, done, _ = self.env.step(action[0])

diff --git a/pl_bolts/models/self_supervised/byol/byol_module.py b/pl_bolts/models/self_supervised/byol/byol_module.py
@@ -74,7 +74,6 @@ def __init__(
         initial_tau: float = 0.996,
         **kwargs: Any,
     ) -> None:
-
         super().__init__()
         self.save_hyperparameters(ignore="base_encoder")
 

diff --git a/pl_bolts/models/self_supervised/byol/models.py b/pl_bolts/models/self_supervised/byol/models.py
@@ -18,7 +18,6 @@ class MLP(nn.Module):
     """
 
     def __init__(self, input_dim: int = 2048, hidden_dim: int = 4096, output_dim: int = 256) -> None:
-
         super().__init__()
 
         self.model = nn.Sequential(
@@ -53,7 +52,6 @@ def __init__(
         projector_hidden_dim: int = 4096,
         projector_out_dim: int = 256,
     ) -> None:
-
         super().__init__()
 
         if isinstance(encoder, str):

diff --git a/pl_bolts/models/self_supervised/moco/moco2_module.py b/pl_bolts/models/self_supervised/moco/moco2_module.py
@@ -225,7 +225,6 @@ def forward(self, img_q, img_k, queue):
 
         # compute key features
         with torch.no_grad():  # no gradient to keys
-
             # shuffle for making use of BN
             if self._use_ddp(self.trainer):
                 img_k, idx_unshuffle = self._batch_shuffle_ddp(img_k)

diff --git a/pl_bolts/models/self_supervised/simclr/transforms.py b/pl_bolts/models/self_supervised/simclr/transforms.py
@@ -31,7 +31,6 @@ class SimCLRTrainDataTransform:
     def __init__(
         self, input_height: int = 224, gaussian_blur: bool = True, jitter_strength: float = 1.0, normalize=None
     ) -> None:
-
         if not _TORCHVISION_AVAILABLE:  # pragma: no cover
             raise ModuleNotFoundError("You want to use `transforms` from `torchvision` which is not installed yet.")
 
@@ -140,7 +139,6 @@ class SimCLRFinetuneTransform(SimCLRTrainDataTransform):
     def __init__(
         self, input_height: int = 224, jitter_strength: float = 1.0, normalize=None, eval_transform: bool = False
     ) -> None:
-
         super().__init__(
             normalize=normalize, input_height=input_height, gaussian_blur=None, jitter_strength=jitter_strength
         )

diff --git a/pl_bolts/models/self_supervised/simsiam/simsiam_module.py b/pl_bolts/models/self_supervised/simsiam/simsiam_module.py
@@ -75,7 +75,6 @@ def __init__(
         exclude_bn_bias: bool = False,
         **kwargs,
     ) -> None:
-
         super().__init__()
         self.save_hyperparameters(ignore="base_encoder")
 

diff --git a/pl_bolts/models/self_supervised/swav/transforms.py b/pl_bolts/models/self_supervised/swav/transforms.py
@@ -130,7 +130,6 @@ class SwAVFinetuneTransform:
     def __init__(
         self, input_height: int = 224, jitter_strength: float = 1.0, normalize=None, eval_transform: bool = False
     ) -> None:
-
         self.jitter_strength = jitter_strength
         self.input_height = input_height
         self.normalize = normalize

diff --git a/pl_bolts/models/vision/unet.py b/pl_bolts/models/vision/unet.py
@@ -32,7 +32,6 @@ def __init__(
         features_start: int = 64,
         bilinear: bool = False,
     ):
-
         if num_layers < 1:
             raise ValueError(f"num_layers = {num_layers}, expected: num_layers > 0")
 

diff --git a/pl_bolts/utils/arguments.py b/pl_bolts/utils/arguments.py
@@ -90,14 +90,11 @@ def gather_lit_args(cls: Any, root_cls: Optional[Any] = None) -> List[LitArg]:
     arguments: List[LitArg] = []
     argument_names = []
     for obj in inspect.getmro(cls):
-
         if obj is root_cls and len(arguments) > 0:
             break
 
         if issubclass(obj, root_cls):
-
             default_params = inspect.signature(obj.__init__).parameters  # type: ignore
-
             for arg in default_params:
                 arg_type = default_params[arg].annotation
                 arg_default = default_params[arg].default

diff --git a/tests/datamodules/test_experience_sources.py b/tests/datamodules/test_experience_sources.py
@@ -185,7 +185,6 @@ def test_source_is_done_2step_episode(self):
         self.source.histories[0].append(self.exp1)
 
         for idx, exp in enumerate(self.source.runner(self.device)):
-
             self.assertTrue(isinstance(exp, tuple))
 
             if idx == 0:
@@ -211,7 +210,6 @@ def test_source_is_done_metrics(self):
         history += [self.exp1, self.exp2, self.exp2]
 
         for idx, exp in enumerate(self.source.runner(self.device)):
-
             if idx == n_steps - 1:
                 self.assertEqual(self.source._total_rewards[0], 1)
                 self.assertEqual(self.source.total_steps[0], 1)

diff --git a/tests/datasets/test_datasets.py b/tests/datasets/test_datasets.py
@@ -29,7 +29,6 @@
 @pytest.mark.parametrize("batch_size,num_samples", [(16, 100), (1, 0)])
 def test_dummy_ds(catch_warnings, batch_size, num_samples):
     if num_samples > 0:
-
         ds = DummyDataset((1, 28, 28), (1,), num_samples=num_samples)
         dl = DataLoader(ds, batch_size=batch_size)
 

diff --git a/tests/losses/test_rl_loss.py b/tests/losses/test_rl_loss.py
@@ -13,7 +13,6 @@
 
 class TestRLLoss(TestCase):
     def setUp(self) -> None:
-
         self.state = torch.rand(32, 4, 84, 84)
         self.next_state = torch.rand(32, 4, 84, 84)
         self.action = torch.ones([32])

diff --git a/tests/models/rl/unit/test_agents.py b/tests/models/rl/unit/test_agents.py
@@ -31,7 +31,6 @@ def setUp(self) -> None:
         self.value_agent = ValueAgent(self.net, self.env.action_space.n)
 
     def test_value_agent(self):
-
         action = self.value_agent(self.state, self.device)
         self.assertIsInstance(action, list)
         self.assertIsInstance(action[0], int)

diff --git a/tests/utils/test_arguments.py b/tests/utils/test_arguments.py
@@ -7,7 +7,6 @@
 
 
 class DummyParentModel(LightningModule):
-
     name = "parent-model"
 
     def __init__(self, a: int, b: str, c: str = "parent_model_c"):
@@ -19,7 +18,6 @@ def forward(self, x):
 
 
 class DummyParentDataModule(LightningDataModule):
-
     name = "parent-dm"
 
     def __init__(self, d: str, c: str = "parent_dm_c"):
-Original file line number
+Diff line change
@@ Expand Up / @@ -27,7 +27,6 @@ class LitMNIST(LightningModule): @@
         """
         def __init__(self, hidden_dim: int = 128, learning_rate: float = 1e-3, **kwargs: Any) -> None:
             if not _TORCHVISION_AVAILABLE:  # pragma: no cover
                 raise ModuleNotFoundError("You want to use `torchvision` which is not installed yet.")
@@ Expand Down @@