fixed most linting errors

.vscode/settings.json

@@ -11,6 +11,7 @@
         "--line-length=120"
     ],
     "python.linting.pylintEnabled": true,
+    "python.linting.pylintArgs": [],
     "python.linting.flake8Enabled": false,
     "python.linting.enabled": true,
     "python.testing.pytestArgs": [

configs/config.yaml

@@ -1,7 +1,3 @@
-# defaults:
-#   - experiment: lego_bulldozer
-#   - dataset: blender
-
 # OPTIONS FOR DIFFERENT NERF VARIANTS
 # ----
 # sampler options
@@ -28,7 +24,8 @@ loss:
 
 # dataset options
 dataset:
-  dataset: blender # type of dataset
+  data_directory: /path/to/data
+  dataset_type: blender # type of dataset
   scene: lego
   num_workers: 4
 

configs/experiment/lego_bulldozer.yaml

@@ -1,2 +0,0 @@
-name: lego_bulldozer
-feature_size: 5

mattport/nerf/dataset/base.py

@@ -1,6 +1,12 @@
+"""
+The dataset baseclass.
+"""
+
 from abc import abstractmethod
 import torch
 
+from mattport.nerf.cameras import Camera
+
 
 class Dataset(torch.utils.data.Dataset):
     """_summary_
@@ -12,20 +18,51 @@ class Dataset(torch.utils.data.Dataset):
     def __init__(
         self,
         data_directory: str,
-    ) -> None:
+        dataset_type: str,
+        scene: str
+    ):
+        """_summary_
+
+        Args:
+            data_directory (str): _description_
+            type (str): _description_
+            scene (str): _description_
+        """
         super().__init__()
-        pass
+        self.data_directory = data_directory
+        self.dataset_type = dataset_type
+        self.scene = scene
+
 
     def __len__(self):
         pass
 
     @abstractmethod
     def get_image(self, idx):
-        pass
+        """_summary_
+
+        Args:
+            idx (_type_): _description_
+
+        Raises:
+            NotImplementedError: _description_
+        """
+        raise NotImplementedError
 
     @abstractmethod
-    def get_pose(self, idx):
-        pass
+    def get_camera(self, idx) -> Camera:
+        """_summary_
+
+        Args:
+            idx (_type_): _description_
+
+        Raises:
+            NotImplementedError: _description_
+
+        Returns:
+            Camera: _description_
+        """
+        raise NotImplementedError
 
     def __getitem__(self, idx):
         raise NotImplementedError

mattport/nerf/dataset/build.py

@@ -1,8 +1,12 @@
-from argon2 import DEFAULT_RANDOM_SALT_LENGTH
-from mattport.nerf.dataset.base import Dataset
+"""
+Builds the dataset
+"""
 import importlib
 import logging
 
+from mattport.nerf.dataset.base import Dataset
+
+
 def build_dataset(config: dict) -> Dataset:
     """Returns the dataset according to the specified config.
     # TODO(ethan): change the type from dict to be a dataclass
@@ -11,7 +15,12 @@ def build_dataset(config: dict) -> Dataset:
         config (dict): _description_
     """
 
+    # TODO(ethan): better handle this with configs
     module = importlib.import_module("mattport.nerf.dataset.base")
-    dataset = module.Dataset(**config)
+    dataset = module.Dataset(
+        data_directory=config.data_directory,
+        dataset_type=config.dataset_type,
+        scene=config.scene,
+    )
     logging.info(dataset)
-    return dataset
+    return dataset

mattport/nerf/graph.py

@@ -1,6 +1,17 @@
+"""
+The Graph module contains all trainable parameters.
+"""
+from dataclasses import dataclass
 from torch import nn
 
 
+@dataclass
+class GraphInputs:
+    """Datastucture to encode the inputs to the graph."""
+
+    points: float
+
+
 class Graph(nn.Module):
     """_summary_
 
@@ -8,8 +19,10 @@ class Graph(nn.Module):
         nn (_type_): _description_
     """
 
-    def __init__(self, config) -> None:
+    def __init__(self) -> None:
         super().__init__()
-        self.config = config
+        raise NotImplementedError
 
-    
+    def forward(self):
+        """_summary_"""
+        raise NotImplementedError

mattport/nerf/optimizer.py

@@ -1,21 +1,26 @@
+"""
+Optimizer class.
+"""
+
 from torch import optim
 
 
 class Optimizer:
     """_summary_"""
 
-    def __init__(
-        self, 
-        params: dict,
-        lr: float) -> None:
+    def __init__(self, params: dict, lr: float):
         """
         Args:
             params (dict): _description_
             lr (float): Learning rate.
         """
         self.optimizer = optim.Adam(params)
+        self.lr = lr
 
     def test(self):
-        raise NotImplementedError
+        """_summary_
 
-        
+        Raises:
+            NotImplementedError: _description_
+        """
+        raise NotImplementedError

mattport/nerf/trainer.py

@@ -1,15 +1,20 @@
-from abc import abstractmethod
-import logging
-import torch
-
+"""
+Code to train model.
+"""
+from mattport.nerf.dataset.build import build_dataset
 from mattport.nerf.optimizer import Optimizer
 from mattport.nerf.graph import Graph
-from mattport.nerf.dataset.build import build_dataset
 
 
 class Trainer(object):
+    """Training class"""
+
     def __init__(self, config: dict):
         self.config = config
+        self.train_dataset = None
+        self.test_dataset = None
+        self.graph = None
+        self.optimizer = None
 
     def setup_dataset(self):
         """_summary_
@@ -20,13 +25,11 @@ class Trainer(object):
     def setup_graph(self):
         """_summary_
         """
-        self.graph = Graph(
-            encodings_config=self.config.encodings,
-            renderer_config=self.config.renderer,
-            loss_config=self.config.loss
-        )
+        self.graph = Graph()
 
     def setup_optimizer(self):
+        """_summary_
+        """
         self.optimizer = Optimizer(params=self.graph.parameters(), **self.config.optimizer)
 
     def load_checkpoint(self):
@@ -47,12 +50,12 @@ class Trainer(object):
 
     def train(self):
         """_summary_"""
-        pass
+        raise NotImplementedError
 
     def train_epoch(self):
         """_summary_"""
-        pass
+        raise NotImplementedError
 
     def train_iteration(self):
         """_summary_"""
-        pass
+        raise NotImplementedError

scripts/run_train_nerf.py

@@ -1,21 +1,23 @@
-import os
-from typing import Dict
-from mattport.nerf.trainer import Trainer
+"""
+run_train_nerf.py
+"""
 import logging
 import hydra
 from omegaconf import DictConfig
 
+from mattport.nerf.trainer import Trainer
+
+
 logging.basicConfig(format="[%(filename)s:%(lineno)d] %(message)s", level=logging.DEBUG)
 
 
 @hydra.main(config_path="../configs", config_name="config.yaml")
 def main(config: DictConfig):
+    """Main function."""
     trainer = Trainer(config)
-    print("trainer!")
-
     trainer.setup_dataset()
-    # trainer.setup_graph()
-    # trainer.setup_optimizer()
+    trainer.setup_graph()
+    trainer.setup_optimizer()
 
 
 if __name__ == "__main__":