Readme

davnov134 · facebook-github-bot · commit 51de308b808b · 2021-02-02T05:45:41.000-08:00
Summary: Adds the readme file.

Reviewed By: nikhilaravi

Differential Revision: D25684459

fbshipit-source-id: f1aaa621a2a67c98d5fcfe33fe9bbfea8f95b537
diff --git a/projects/nerf/README.md b/projects/nerf/README.md
@@ -0,0 +1,79 @@
+Neural Radiance Fields in PyTorch3D
+===================================
+
+This project implements the Neural Radiance Fields (NeRF) from [1].
+
+Installation
+------------
+1) [Install PyTorch3D](https://github.com/facebookresearch/pytorch3d/blob/master/INSTALL.md)
+    - Note that this repo requires `PyTorch` version `>= v1.6.0` due to dependency on `torch.searchsorted`.
+
+2) Install other dependencies:
+    - [`visdom`](https://github.com/facebookresearch/visdom)
+    - [`hydra`](https://github.com/facebookresearch/hydra)
+    - [`Pillow`](https://python-pillow.org/)
+    - [`requests`](https://pypi.org/project/requests/)
+
+    E.g. using `pip`:
+    ```
+    pip install visdom
+    pip install hydra-core --upgrade
+    pip install Pillow
+    pip install requests
+    ```
+
+    Exporting videos further requires a working `ffmpeg`.
+
+Training NeRF
+-------------
+```
+python ./train_nerf.py --config-name lego
+```
+will train the model from [1] on the Lego dataset.
+
+Note that the script outputs visualizations to `Visdom`. In order to enable this, make sure to start the visdom server (before launching the training) with the following command:
+```
+python -m visdom.server
+```
+Note that training on the "lego" scene takes roughly 24 hours on a single Tesla V100.
+
+#### Training data
+Note that the `train_nerf.py` script will automatically download the relevant dataset in case it is missing.
+
+Testing NeRF
+------------
+```
+python ./test_nerf.py --config-name lego
+```
+Will load a trained model from the `./checkpoints` directory and evaluate it on the test split of the corresponding dataset (Lego in the case above).
+
+### Exporting multi-view video of the radience field
+Furthermore, the codebase supports generating videos of the neural radiance field.
+The following generates a turntable video of the Lego scene:
+```
+python ./test_nerf.py --config-name=lego test.mode='export_video'
+```
+Note that this requires a working `ffmpeg` for generating the video from exported frames.
+
+Additionally, note that generation of the video in the original resolution is quite slow. In order to speed up the process, one can decrease the resolution of the output video by setting the `data.image_size` flag:
+```
+python ./test_nerf.py --config-name=lego test.mode='export_video' data.image_size="[128,128]"
+```
+This will generate the video in a lower `128 x 128` resolution.
+
+
+Training & testing on other datasets
+------------------------------------
+Currently we support the following datasets:
+- lego `python ./train_nerf.py --config-name lego`
+- fern `python ./train_nerf.py --config-name fern`
+- pt3logo `python ./train_nerf.py --config-name pt3logo`
+
+The dataset files are located in the following public S3 bucket:
+https://dl.fbaipublicfiles.com/pytorch3d_nerf_data
+
+Attribution: `lego` and `fern` are data from the original code release of [1] in https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1, which are hosted under the CC-BY license (https://creativecommons.org/licenses/by/4.0/) The S3 bucket files contains the same images while the camera matrices have been adjusted to follow the PyTorch3D convention.
+
+
+#### References
+[1] Ben Mildenhall and Pratul P. Srinivasan and Matthew Tancik and Jonathan T. Barron and Ravi Ramamoorthi and Ren Ng, NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis, ECCV2020
diff --git a/projects/nerf/nerf/dataset.py b/projects/nerf/nerf/dataset.py
@@ -1,6 +1,6 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 import os
-from typing import Tuple, Optional, List
+from typing import List, Optional, Tuple
 
 import numpy as np
 import requests
@@ -9,6 +9,7 @@
 from pytorch3d.renderer import PerspectiveCameras
 from torch.utils.data import Dataset
 
+
 DEFAULT_DATA_ROOT = os.path.join(
     os.path.dirname(os.path.realpath(__file__)), "..", "data"
 )
diff --git a/projects/nerf/nerf/eval_video_utils.py b/projects/nerf/nerf/eval_video_utils.py
@@ -3,7 +3,7 @@
 from typing import Tuple
 
 import torch
-from pytorch3d.renderer import look_at_view_transform, PerspectiveCameras
+from pytorch3d.renderer import PerspectiveCameras, look_at_view_transform
 
 
 def generate_eval_video_cameras(
diff --git a/projects/nerf/nerf/nerf_renderer.py b/projects/nerf/nerf/nerf_renderer.py
@@ -1,5 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
-from typing import Tuple, List, Optional
+from typing import List, Optional, Tuple
 
 import torch
 from pytorch3d.renderer import ImplicitRenderer, ray_bundle_to_ray_points
@@ -11,7 +11,7 @@
 from .implicit_function import NeuralRadianceField
 from .raymarcher import EmissionAbsorptionNeRFRaymarcher
 from .raysampler import NeRFRaysampler, ProbabilisticRaysampler
-from .utils import sample_images_at_mc_locs, calc_psnr, calc_mse
+from .utils import calc_mse, calc_psnr, sample_images_at_mc_locs
 
 
 class RadianceFieldRenderer(torch.nn.Module):
diff --git a/projects/nerf/nerf/raysampler.py b/projects/nerf/nerf/raysampler.py
@@ -3,7 +3,7 @@
 from typing import List
 
 import torch
-from pytorch3d.renderer import RayBundle, NDCGridRaysampler, MonteCarloRaysampler
+from pytorch3d.renderer import MonteCarloRaysampler, NDCGridRaysampler, RayBundle
 from pytorch3d.renderer.cameras import CamerasBase
 
 from .utils import sample_pdf
diff --git a/projects/nerf/test_nerf.py b/projects/nerf/test_nerf.py
@@ -13,6 +13,7 @@
 from omegaconf import DictConfig
 from PIL import Image
 
+
 CONFIG_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), "configs")
 
 
diff --git a/projects/nerf/train_nerf.py b/projects/nerf/train_nerf.py
@@ -14,6 +14,7 @@
 from omegaconf import DictConfig
 from visdom import Visdom
 
+
 CONFIG_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), "configs")
 
 
