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

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

Author: pre-commit-ci[bot]

torch_points3d/core/instantiator.py

@@ -43,7 +43,7 @@ def litmodel(self, cfg: DictConfig) -> "PointCloudBaseModule":
 
     def model(self, cfg: DictConfig) -> "PointCloudBaseModel":
         return self.instantiate(cfg, self)
-        
+
     def backbone(self, cfg: DictConfig):
         return self.instantiate(cfg)
 

torch_points3d/models/base_model.py

@@ -1,5 +1,3 @@
-from typing import Any, Dict, Optional, Tuple, Union
-
 import torch.nn as nn
 
 from torch_points3d.core.instantiator import Instantiator
@@ -12,7 +10,7 @@ def __init__(self, instantiator: Instantiator):
         self.instantiator = instantiator
 
     def set_input(self, data):
-        raise(NotImplementedError("set_input needs to be defined!"))
-    
+        raise (NotImplementedError("set_input needs to be defined!"))
+
     def forward(self):
-        raise(NotImplementedError("forward needs to be defined!"))
+        raise (NotImplementedError("forward needs to be defined!"))

torch_points3d/models/segmentation/base_model.py

@@ -1,4 +1,3 @@
-from typing import Any, Dict, Optional, Tuple, Union
 from omegaconf import DictConfig
 
 import torch.nn as nn
@@ -11,10 +10,10 @@
 class SegmentationBaseModel(PointCloudBaseModel):
     def __init__(self, instantiator: Instantiator, num_classes: int, backbone: DictConfig):
         super().__init__(instantiator)
-        
+
         print(backbone)
-        self.backbone = self.instantiator.backbone(backbone)  
-             
+        self.backbone = self.instantiator.backbone(backbone)
+
         self.head = nn.Sequential(nn.Linear(self.backbone.output_nc, num_classes))
 
     def set_input(self, data):
@@ -24,7 +23,7 @@ def set_input(self, data):
             self.labels = data.y
         else:
             self.labels = None
-    
+
     def forward(self):
         features = self.backbone(self.input).x
         logits = self.head(features)

torch_points3d/tasks/base_model.py

@@ -1,11 +1,9 @@
 from typing import Any, Dict, Optional, Tuple, Union
 
 import pytorch_lightning as pl
-import torch
 from pytorch_lightning.utilities import rank_zero_info
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from omegaconf import DictConfig
-import torch.nn.functional as F
 
 from torch_points3d.core.instantiator import Instantiator
 from torch_points3d.core.config import OptimizerConfig, SchedulerConfig
@@ -17,7 +15,7 @@ def __init__(
         model: DictConfig,
         optimizer: OptimizerConfig,
         instantiator: Instantiator,
-        scheduler: SchedulerConfig = None, # scheduler shouldn't be required
+        scheduler: SchedulerConfig = None,  # scheduler shouldn't be required
     ):
         super().__init__()
         # some optimizers/schedulers need parameters only known dynamically
@@ -31,7 +29,7 @@ def __init__(
     def _init_model(self, model_cfg):
         print(model_cfg)
         self.model = self.instantiator.model(model_cfg)
-            
+
     def configure_optimizers(self) -> Dict:
         """Prepare optimizer and scheduler"""
         optims = {}

torch_points3d/trainer.py

@@ -30,12 +30,12 @@ def __init__(
                 "The instantiator did not return a DataModule instance." " Hydra hint: is `dataset._target_` defined?`"
             )
         self.data_module.setup("fit")
-        
+
         self.litmodel: PointCloudBaseModule = instantiator.litmodel(model)
         print(self.litmodel)
         self.trainer = instantiator.trainer(
             trainer,
-            logger=None, # eventually add logger config back in
+            logger=None,  # eventually add logger config back in
         )
 
     def train(self):