Merge pull request #5 from orperel/operel/imports_docs_cleanups

import cleanups, some missing core docs

Author: orperel

app/main.py

@@ -13,7 +13,7 @@
     from wisp.trainers import *
     from wisp.config_parser import parse_options, argparse_to_str, get_modules_from_config, \
         get_optimizer_from_config
-    from wisp.framework.state import WispState
+    from wisp.framework import WispState
 
     # Usual boilerplate
     parser = parse_options(return_parser=True)

app/main_interactive.py

@@ -17,7 +17,7 @@
     from wisp.trainers import *
     from wisp.config_parser import parse_options, argparse_to_str, get_modules_from_config, \
         get_optimizer_from_config
-    from wisp.framework.state import WispState
+    from wisp.framework import WispState
 
     # Usual boilerplate
     parser = parse_options(return_parser=True)
@@ -38,9 +38,9 @@
                                       scene_state=scene_state)
 
     if not os.environ.get('WISP_HEADLESS') == '1':
-        from wisp.renderer.app.optimizer_renderer import OptimizationRenderer
+        from wisp.renderer.app.optimization_app import OptimizationApp
         scene_state.renderer.device = trainer.device  # Use same device for trainer and renderer
-        renderer = OptimizationRenderer(wisp_state=scene_state,
+        renderer = OptimizationApp(wisp_state=scene_state,
                                         trainer_step_func=trainer.iterate,
                                         experiment_name="wisp trainer")
         renderer.run()

configs/ngp_nerf.yaml

@@ -20,7 +20,7 @@ optimizer:
 
 dataset:
     dataset_type: 'multiview'
-    multiview_dataset_format: 'rtmv'
+    multiview_dataset_format: 'standard'
     num_rays_sampled_per_img: 4096
     mip: 2
     bg_color: 'white'

setup.py

@@ -17,8 +17,8 @@
 from torch.utils.cpp_extension import BuildExtension, CppExtension, CUDAExtension, CUDA_HOME
 
 PACKAGE_NAME = 'wisp'
-DESCRIPTION = 'neural fields research'
-URL = 'https://gitlab-master.nvidia.com/ttakikawa/solr'
+DESCRIPTION = 'Kaolin-Wisp: A PyTorch library for performing research on neural fields'
+URL = 'https://github.com/NVIDIAGameWorks/kaolin-wisp'
 AUTHOR = 'Towaki Takikawa'
 LICENSE = 'NVIDIA Source Code License'
 version = '0.1.0'

wisp/accelstructs/octree_as.py

@@ -7,22 +7,16 @@
 # license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
 
 import torch
-import torch.nn.functional as F
-import torch.nn as nn
-import numpy as np
-import logging as log
-
 import wisp.ops.mesh as mesh_ops
-
-from wisp.ops.perf import PerfTimer
-from wisp.ops.debug import PsDebugger
-
+from wisp.utils import PsDebugger, PerfTimer
 import wisp.ops.spc as wisp_spc_ops
 import kaolin.ops.spc as spc_ops
 import kaolin.render.spc as spc_render
 
+
 class OctreeAS(object):
-    """Octree accelstruct class implemented using Kaolin SPC.
+    """Octree bottom-level acceleration structure class implemented using Kaolin SPC.
+       Can be used to to quickly query cells occupancy, and trace rays against the volume.
     """
 
     def __init__(self):
@@ -126,8 +120,9 @@ def query(self, coords, level=None, with_parents=False):
         return spc_ops.unbatched_query(self.octree, self.prefix, coords, level, with_parents)
 
     def raytrace(self, rays, level=None, with_exit=False):
-        """Traces rays against the SPC structure.
-            
+        """Traces rays against the SPC structure, returning all intersections along the ray with the SPC points
+        (SPC points are quantized, and can be interpreted as octree cell centers or corners).
+
         Args:
             rays (wisp.core.Rays): Ray origins and directions of shape [batch, 3].
             level (int) : The level of the octree to raytrace. If None, traces the highest level.

wisp/config_parser.py

@@ -11,18 +11,17 @@
 import argparse
 import pprint
 import yaml
-
 import torch
-
 from wisp.datasets import *
-from wisp.core import Pipeline
+from wisp.models import Pipeline
 from wisp.models.nefs import *
 from wisp.models.grids import *
 from wisp.tracers import *
-from wisp.ops.ray import *
+from wisp.datasets.transforms import *
 
 str2optim = {m.lower(): getattr(torch.optim, m) for m in dir(torch.optim) if m[0].isupper()}
 
+
 def parse_options(return_parser=False):
     """Function used to parse options.
     

wisp/core/__init__.py

@@ -7,9 +7,8 @@
 # license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
 
 from .primitives import PrimitivesPack
-from .render_buffer import RenderBuffer
 from .rays import Rays
-from .pipeline import Pipeline
 from .channels import *
 from .channel_fn import *
 from .colors import *
+from .render_buffer import RenderBuffer