[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

pyproject.toml

@@ -1,2 +1,2 @@
 [tool.black]
-line-length = 120
+line-length = 120

torch_points3d/core/instantiator.py

@@ -47,9 +47,7 @@ def model(
     ) -> "TaskTransformer":
         if model_data_kwargs is None:
             model_data_kwargs = {}
-        model_data_kwargs = dict(
-            model_data_kwargs
-        )  # avoid ConfigKeyError: Key 'tokenizer' is not in struct`
+        model_data_kwargs = dict(model_data_kwargs)  # avoid ConfigKeyError: Key 'tokenizer' is not in struct`
 
         # use `model_data_kwargs` to pass `tokenizer` and `pipeline_kwargs`
         # as not all models might contain these parameters.
@@ -60,33 +58,25 @@ def model(
 
         return self.instantiate(cfg, instantiator=self, **model_data_kwargs)
 
-    def optimizer(
-        self, model: torch.nn.Module, cfg: DictConfig
-    ) -> torch.optim.Optimizer:
+    def optimizer(self, model: torch.nn.Module, cfg: DictConfig) -> torch.optim.Optimizer:
         no_decay = ["bias", "LayerNorm.weight"]
         grouped_parameters = [
             {
                 "params": [
-                    p
-                    for n, p in model.named_parameters()
-                    if not any(nd in n for nd in no_decay) and p.requires_grad
+                    p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay) and p.requires_grad
                 ],
                 "weight_decay": cfg.weight_decay,
             },
             {
                 "params": [
-                    p
-                    for n, p in model.named_parameters()
-                    if any(nd in n for nd in no_decay) and p.requires_grad
+                    p for n, p in model.named_parameters() if any(nd in n for nd in no_decay) and p.requires_grad
                 ],
                 "weight_decay": 0.0,
             },
         ]
         return self.instantiate(cfg, grouped_parameters)
 
-    def scheduler(
-        self, cfg: DictConfig, optimizer: torch.optim.Optimizer
-    ) -> torch.optim.lr_scheduler._LRScheduler:
+    def scheduler(self, cfg: DictConfig, optimizer: torch.optim.Optimizer) -> torch.optim.lr_scheduler._LRScheduler:
         return self.instantiate(cfg, optimizer=optimizer)
 
     def data_module(

train.py

@@ -8,9 +8,7 @@
 
 @hydra.main(config_path="conf", config_name="config")
 def main(cfg):
-    OmegaConf.set_struct(
-        cfg, False
-    )  # This allows getattr and hasattr methods to function correctly
+    OmegaConf.set_struct(cfg, False)  # This allows getattr and hasattr methods to function correctly
     if cfg.pretty_print:
         print(OmegaConf.to_yaml(cfg))