Fix linting errors throughout repo

.github/workflows/code_checks.yml

@@ -31,6 +31,8 @@ jobs:
         options: "--check --verbose --line-length=120"
     - name: Python Pylint GitHub Action
       uses: vcoder4c/git_pylint@v3
+      with:
+        options: "--max-line-length=120 --generated-members=numpy.*,torch.*,cv2.*,cv.* --good-names=i,j,x,y,lr --disable=too-many-arguments
       env:
         GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
     - name: Test with pytest

.vscode/settings.json

@@ -19,7 +19,9 @@
     "python.testing.unittestEnabled": false,
     "python.testing.pytestEnabled": true,
     "python.linting.pylintArgs": [
-        "--generated-members=numpy.* ,torch.* ,cv2.* , cv.*",
-        "--max-line-length=120"
+        "--generated-members=numpy.*,torch.*,cv2.*,cv.*",
+        "--max-line-length=120",
+        "--good-names=i,j,x,y,lr",
+        "--disable=too-many-arguments"
     ],
 }

mattport/nerf/modules/__init__.py

@@ -1,2 +1,3 @@
+"""init modules"""
 from .encoding import Encoding
 from .mlp import MLP

mattport/nerf/modules/encoding.py

@@ -131,8 +131,8 @@ class RFFEncoding(Encoding):
         Returns:
             TensorType[..., "output_dim"]: Output values will be between -1 and 1
         """
-        B = torch.normal(mean=0, std=self.scale, size=(self.in_dim, self.num_frequencies))
-        scaled_inputs = in_tensor @ B  # [..., "num_frequencies"]
+        b_matrix = torch.normal(mean=0, std=self.scale, size=(self.in_dim, self.num_frequencies))
+        scaled_inputs = in_tensor @ b_matrix  # [..., "num_frequencies"]
         encoded_inputs = torch.cat([torch.sin(scaled_inputs), torch.cos(scaled_inputs)], axis=-1)
         return encoded_inputs
 

mattport/nerf/modules/mlp.py

@@ -1,3 +1,6 @@
+"""
+Multi Layer Perceptron
+"""
 from typing import Optional
 
 from torch import nn

mattport/nerf/optimizer.py

@@ -24,3 +24,7 @@ class Optimizer:
             NotImplementedError: _description_
         """
         raise NotImplementedError
+
+    def test2(self):
+        """Summary"""
+        raise NotImplementedError

mattport/nerf/sampler.py

@@ -2,11 +2,12 @@
 Collection of sampling strategies
 """
 from typing import NamedTuple
-from torchtyping import TensorType
-from torch import nn
-import torch
-import nerf.cameras as cameras
 
+import torch
+from torch import nn
+from torchtyping import TensorType
+
+from mattport.nerf import cameras
 
 RaySamples = NamedTuple(
     "RaySamples", [("locations", TensorType[..., "num_samples", 3]), ("deltas", TensorType[..., "num_samples"])]

mattport/nerf/trainer.py

@@ -3,10 +3,9 @@ Code to train model.
 """
 from mattport.nerf.dataset.build import build_dataset
 from mattport.nerf.optimizer import Optimizer
-from mattport.nerf.graph import Graph
 
 
-class Trainer(object):
+class Trainer:
     """Training class"""
 
     def __init__(self, config: dict):
@@ -23,7 +22,8 @@ class Trainer(object):
 
     def setup_graph(self):
         """_summary_"""
-        self.graph = Graph()
+        # self.graph = Graph()
+        raise NotImplementedError
 
     def setup_optimizer(self):
         """_summary_"""