Nndocs (#301)

Also nuke modules we don't need/use

---------

Signed-off-by: Francis Williams <francis@fwilliams.info>

Author: fwilliams

docs/index.rst

@@ -66,9 +66,9 @@ spatial intelligence research and applications.
 fVDB Reality Capture Toolbox
 --------------------------------
 
-In addition to the core fVDB library, we also provide the `fVDB Reality Capture <https://fvdb.ai/fvdb-reality-capture>`_ toolbox,
+In addition to the core fVDB library, we also provide the `fVDB Reality Capture <https://fvdb.ai/reality-capture>`_ toolbox,
 which is a collection of tools and utilities for 3D reconstruction and scene understanding using fVDB. Analogous to how `torchvision <https://pytorch.org/vision/stable/index.html>`_
-provides datasets, models, and transforms for computer vision tasks, `fVDB Reality Capture <https://fvdb.ai/fvdb-reality-capture>`_ provides datasets, models, and
+provides datasets, models, and transforms for computer vision tasks, `fVDB Reality Capture <https://fvdb.ai/reality-capture>`_ provides datasets, models, and
 algorithms for 3D reconstruction from sensor data.
 
 .. toctree::

fvdb/nn/__init__.py

@@ -2,26 +2,13 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 from .modules import (
-    CELU,
-    ELU,
-    GELU,
-    SELU,
     AvgPool,
     BatchNorm,
-    Dropout,
     GroupNorm,
-    InjectFromGrid,
-    LeakyReLU,
-    Linear,
     MaxPool,
-    ReLU,
-    Sequential,
-    Sigmoid,
-    SiLU,
     SparseConv3d,
     SparseConvTranspose3d,
     SyncBatchNorm,
-    Tanh,
     UpsamplingNearest,
 )
 from .simple_unet import (
@@ -39,20 +26,9 @@
 __all__ = [
     "AvgPool",
     "BatchNorm",
-    "CELU",
-    "Dropout",
-    "ELU",
-    "GELU",
     "GroupNorm",
     "InjectFromGrid",
-    "LeakyReLU",
-    "Linear",
     "MaxPool",
-    "ReLU",
-    "SELU",
-    "Sequential",
-    "Sigmoid",
-    "SiLU",
     "SimpleUNet",
     "SimpleUNetBasicBlock",
     "SimpleUNetBottleneck",
@@ -65,6 +41,5 @@
     "SparseConv3d",
     "SparseConvTranspose3d",
     "SyncBatchNorm",
-    "Tanh",
     "UpsamplingNearest",
 ]