Merge pull request #706 from torch-points3d/precommit-fix

Fix formatting over entire repo

.devcontainer/Dockerfile.cpu

@@ -2,4 +2,4 @@ FROM principialabs/torch-points3d:latest-cpu
 
 COPY .devcontainer/setup.sh setup.sh
 RUN bash setup.sh
-ENV PATH="${PATH}:$HOME/.poetry/bin"
+ENV PATH="${PATH}:$HOME/.poetry/bin"

.devcontainer/Dockerfile.gpu

@@ -1,4 +1,4 @@
 FROM principialabs/torch-points3d:latest-gpu
 
 COPY .devcontainer/setup.sh setup.sh
-RUN bash setup.sh
+RUN bash setup.sh

.devcontainer/devcontainer.json

@@ -26,4 +26,4 @@
 	"extensions": [
 		"ms-python.python"
 	]
-}
+}

.github/workflows/pythonapp.yml

@@ -45,7 +45,7 @@ jobs:
       - name: Static checks with flake8 and mypy
         run: |
           source ${pythonEnv}/bin/activate
-          pip install flake8 mypy  # Dev tools
+          pip install flake8 mypy==0.910  # Dev tools
           make staticchecks
         env:
           pythonEnv: /home/runner/.cache/${{ matrix.python-version }}

eval.py

@@ -9,7 +9,7 @@ def main(cfg):
     OmegaConf.set_struct(cfg, False)  # This allows getattr and hasattr methods to function correctly
     if cfg.pretty_print:
         print(OmegaConf.to_yaml(cfg))
-    
+
     trainer = Trainer(cfg)
     trainer.eval()
     #

examples/pointnet2_classification_forward.py

@@ -17,7 +17,12 @@
 # Batch(batch=[2], pos=[2, 1024, 3], x=[2, 1024, 5])
 
 
-model = PointNet2(architecture="encoder", input_nc=input_nc, num_layers=3, output_nc=num_classes,)
+model = PointNet2(
+    architecture="encoder",
+    input_nc=input_nc,
+    num_layers=3,
+    output_nc=num_classes,
+)
 
 res = model(data)
 print(res)

examples/pointnet_segmentation_forward.py

@@ -2,6 +2,7 @@
 import torch
 
 from utils import load_local_torchpoints3d
+
 load_local_torchpoints3d()
 
 from torch_points3d.models.segmentation.pointnet import PointNet
@@ -21,9 +22,9 @@
 data = Batch.from_data_list([data, data])
 
 print(data)
-#Batch(batch=[1000], pos=[1000, 3], x=[1000, 3])
+# Batch(batch=[1000], pos=[1000, 3], x=[1000, 3])
 
-pointnet = PointNet(OmegaConf.create({'conv_type': 'PARTIAL_DENSE'}))
+pointnet = PointNet(OmegaConf.create({"conv_type": "PARTIAL_DENSE"}))
 
 pointnet.set_input(data, "cpu")
 data_out = pointnet.forward()
@@ -43,11 +44,11 @@
 data = SimpleBatch.from_data_list([data, data])
 
 print(data)
-#SimpleBatch(pos=[2, 500, 3], x=[2, 500, 3])
+# SimpleBatch(pos=[2, 500, 3], x=[2, 500, 3])
 
-pointnet = PointNet(OmegaConf.create({'conv_type': 'DENSE'}))
+pointnet = PointNet(OmegaConf.create({"conv_type": "DENSE"}))
 
 pointnet.set_input(data, "cpu")
 data_out = pointnet.forward()
 print(data_out.shape)
-#torch.Size([2, 500, 4])
+# torch.Size([2, 500, 4])

examples/utils.py

@@ -12,4 +12,4 @@ def load_local_torchpoints3d():
     spec = importlib.util.spec_from_file_location(MODULE_NAME, MODULE_PATH)
     module = importlib.util.module_from_spec(spec)
     sys.modules[spec.name] = module
-    spec.loader.exec_module(module)
+    spec.loader.exec_module(module)

forward_scripts/forward.py

@@ -88,7 +88,11 @@ def main(cfg):
     # Set dataloaders
     dataset = instantiate_dataset(checkpoint.data_config)
     dataset.create_dataloaders(
-        model, cfg.batch_size, cfg.shuffle, cfg.num_workers, False,
+        model,
+        cfg.batch_size,
+        cfg.shuffle,
+        cfg.num_workers,
+        False,
     )
     log.info(dataset)