passing linting tests and fixed documentation

pyrad/data/dataloader.py

@@ -88,17 +88,19 @@ def setup_dataset_eval(config: DataConfig, test_mode: bool, device: str) -> Tupl
 class AbstractDataloaderV2(nn.Module):
     """Second version of the dataloader class
 
-    This version of the dataloader is designed to subsume both the train and eval dataloaders, especially since
-    this may contain learnable parameters which need to be shared across the train and test dataloaders. The idea
-    is that we have setup methods for train and eval separatley and this can be a combined train/eval if you want.
+    This version of the dataloader is designed to subsume both the train and eval dataloaders,
+    especially since this may contain learnable parameters which need to be shared across the train
+    and test dataloaders. The idea is that we have setup methods for train and eval separatley and
+    this can be a combined train/eval if you want.
 
-    The default should be for the train and eval dataloaders to be the same, but this can be overridden. This is
-    needed when there are learned parameters either in your data itself or in the way some of the data (that we will
-    pass to the renderer / Field) was generated. In these cases, we want these parameters to be accessible by
-    both the train and eval dataloaders, hence why you would want them to be in the same dataloader.
+    The default should be for the train and eval dataloaders to be the same, but this can be
+    overridden. This is needed when there are learned parameters either in your data itself or in
+    the way some of the data (that we will pass to the renderer / Field) was generated. In these
+    cases, we want these parameters to be accessible by both the train and eval dataloaders, hence
+    why you would want them to be in the same dataloader.
 
-    An instance where you may want to have only the eval dataloader is if you are doing evaluation and
-    don't have the dataset used to train the model.
+    An instance where you may want to have only the eval dataloader is if you are doing evaluation
+    and don't have the dataset used to train the model.
 
 
     Train Methods:
@@ -190,6 +192,23 @@ class AbstractCachedDataloaderV2(AbstractDataloaderV2):
     camera_to_world: torch.Tensor
     intrinsics: torch.Tensor
 
+    # These abstract methods are only copied over to appease the linter
+    @abstractmethod
+    def setup_train(self):
+        """Sets up the dataloader for training"""
+
+    @abstractmethod
+    def setup_eval(self):
+        """Sets up the dataloader for evaluation"""
+
+    @abstractmethod
+    def next_train(self) -> GenericDataContainer:
+        """Returns the next batch of data from the train dataloader"""
+
+    @abstractmethod
+    def next_eval(self) -> GenericDataContainer:
+        """Returns the next batch of data from the eval dataloader"""
+
 
 class TrainDataloader:
     """Training dataloader base class."""

pyrad/data/structs.py

@@ -20,9 +20,10 @@ from dataclasses import dataclass
 from typing import List, Optional
 
 import torch
-from pyrad.cameras.rays import RayBundle
 from torchtyping import TensorType
 
+from pyrad.cameras.rays import RayBundle
+
 
 @dataclass
 class PointCloud:

pyrad/utils/misc.py

@@ -92,12 +92,14 @@ def get_hash_str_from_dict(dictionary: Dict[str, Any]) -> str:
     return dhash.hexdigest()
 
 
-class IterableWrapper:
-    """A helper that will allow an instance of a class to return multiple kinds of iterables bound to different functions of that class.
+class IterableWrapper:  # pylint: disable=too-few-public-methods
+    """A helper that will allow an instance of a class to return multiple kinds of iterables bound
+    to different functions of that class.
 
-    To use this, pass in the instance of the class you want to have multiple kinds of iterables for, and pass in a
-    method belonging to that class that you want to be the __next__() method of the iterable. The resulting instantiated object will
-    be an iterable that will use the passed in class method as the __next__() method.
+    To use this, pass in the instance of the class you want to have multiple kinds of iterables for,
+    and pass in a method belonging to that class that you want to be the __next__() method of the
+    iterable. The resulting instantiated object will be an iterable that will use the passed in
+    class method as the __next__() method.
 
     Args:
         instance (object): instance class we are wrapping