4
0
mirror of https://github.com/nerfstudio-project/nerfstudio.git synced 2026-11-11 10:34:29 +08:00
nerfstudio/nerfstudio/model_components/scene_colliders.py
Terrance Wang b17268e83b
[WIP] dreamfusion implementation (#1008)
* starting the framework for dreamfusion

* Removed view direction from RGB to calculate albedo

* Started diffusion

* Added dreamfusion to CLI, removed direction embeddings

* Normals in dreamfusion

* Lambertian shading added

* Train normals in Dreamfusion

* Stable diffusion from Stable Dreamfusion

* Fix normal orientation loss

* Configurable light direction sampling

* adding density initalization at origin

* V1 datamanager

* Don't train normals for first 1k iters

* SDS loss added to dreamfusion

* Dreamfusion pipeline

* Background MLP and SphereCollider

* Normals prediction training fixed

* Sigmoid on background, prompt in config

* Shading hyperparameter adjustements

* Shading hyperparameter tuning

* Location based prompting, density initialization fades, LR change

* Cleaned imports, more config options

* Tuning, more config changes

* remove unused sampler, normalize predicted normals from network

* change position encoding to direction encoding for background color mlp

* Updating dreamfusion to use custom trainer, fixed bug, multi-gpu support (put diffusion on one specific device), merged with main

* Hard coding less, offloading to config

* Appeasing the linter

* First pass opacity / sparsity losses

* Small patches and configs

* Accidentally pushed logs

* Removing logs for real this time

* Transmittance loss bug fix

* 12 GB Dreamfusion!

* Lint

* Remove unecissary collider changes

* Start SD cleanup

* Cleanup SD code

* adding overhead location prompting

* Use lambertian renderer

* turn grad accum off by default, decrease background mlp size, add overhead text embedding

* Cleanup

* Fix normals vis

* change uniform sampler to prediction network, add option to jit unet for faster sds_loss

* Use shaders

* cleaning up jit code

* add warning if using non jit unet

* add proposal network optimizer to config

* speed up render and cleanup code

* Clean up datamanager

* Add distortion loss

* fix wanb

* adding interpolated location sampling

* Update configs

* interpolated location prompting option, lowering init density, transmittance loss interpolation flipped

* rename alphas mult to alphas loss mult

* Mess around with normals

* add option to use random bg color + bg mlp

* Remove Dreamfusion Trainer (#1423)

* Dreamfusion trainer no longer needed

* Added typing for grad scaler

* SDS return loss

* Fix CFG

* Cleanup

* Update configs

* Fix opacity loss, use unform sampling first round of prop

* add prompt edditing to viewer

* Choose your own diffusion

* remove dreamfusion pipeline file

* merging + remove dreamfusion pipeline file

* Update configs

* add stable diff utils

* cleanup

* cleanup

* Fix test

* positional prompting bugfix

* Warmups

* tune

* lint

* Remove dataparser

* merging + text embedding bugfix

* Set data path to launch viewer for DreamFusion (#1776)

* Set data path to launch viewer for DreamFusion

* Apply formatter

* Fallback to 'base_dir' if data manager has no path

* Make if statement non-inline

* Reformat

---------

Co-authored-by: Matthew Tancik <mattt4001@gmail.com>

* add deepfloyd option first try

* merging

* merging from main

* merging

* removing comments, cleaning code, making stable diff utils usable for deepfloyd, remove deepfloyd text encoder after use

* undo chagnes in data folder

* cleaning code, removing unneccesary changes from main, removed normals prediction network

* remove old scripts files, rename generative pipeline flag to no_input_data, revert to default histogram padding in proposal sampler

* changing pipeline flag

* move no input data flag from pipeline to datamanager

* undo config and merge in deepfloyd from main

* merging grad scaler pipeline addition

* merging + setting proposal net scheduler

* revert nerfacto config change

* typing + linting

* rename dreamfusion datamanager to random camras datamanager, check datamanager typing in pipeline and trainer

* fix test import

* fix test import

* changing datamanager check in trainer, add typing in random cameras datamanager

* add Tensor to randomcamerasdatamanager

* remove old comments

* resolve tyro warning

* update fields to use tcnn

* lint

* add packages for deepfloyd

* add versions

* docs first draft

* rename to generfacto, clean docs

* fix import error

* minor changes

* fix orientation loss bug, retune params

* cleanup

* typing

* trying docs fix

* fixing generative package imports

* fix pyright

* docs fix

* merge w main

* docs minor change

* tuning + bugfixes

* remove torch.backends

---------

Co-authored-by: Abhik Ahuja <ahujaabhik@berkeley.edu>
Co-authored-by: David McAllister <davidrmc2@gmail.com>
Co-authored-by: Jake Austin <jake-austin@berkeley.edu>
Co-authored-by: Matthew Tancik <tancik@berkeley.edu>
Co-authored-by: Matt Tancik <mattt4001@gmail.com>
Co-authored-by: SeungWoo Yoo <dreamy1534@gmail.com>
Co-authored-by: ayaanzhaque <ayaanzhaque@gmail.com>
Co-authored-by: Ayaan Haque <ayaanzhaque@berkeley.edu>
2023-06-17 23:34:04 -07:00

188 lines
6.5 KiB
Python

# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Scene Colliders
"""
from __future__ import annotations
import torch
from jaxtyping import Float
from torch import Tensor, nn
from nerfstudio.cameras.rays import RayBundle
from nerfstudio.data.scene_box import SceneBox
class SceneCollider(nn.Module):
"""Module for setting near and far values for rays."""
def __init__(self, **kwargs) -> None:
self.kwargs = kwargs
super().__init__()
def set_nears_and_fars(self, ray_bundle: RayBundle) -> RayBundle:
"""To be implemented."""
raise NotImplementedError
def forward(self, ray_bundle: RayBundle) -> RayBundle:
"""Sets the nears and fars if they are not set already."""
if ray_bundle.nears is not None and ray_bundle.fars is not None:
return ray_bundle
return self.set_nears_and_fars(ray_bundle)
class AABBBoxCollider(SceneCollider):
"""Module for colliding rays with the scene box to compute near and far values.
Args:
scene_box: scene box to apply to dataset
"""
def __init__(self, scene_box: SceneBox, near_plane: float = 0.0, **kwargs) -> None:
super().__init__(**kwargs)
self.scene_box = scene_box
self.near_plane = near_plane
def _intersect_with_aabb(
self, rays_o: Float[Tensor, "num_rays 3"], rays_d: Float[Tensor, "num_rays 3"], aabb: Float[Tensor, "2 3"]
):
"""Returns collection of valid rays within a specified near/far bounding box along with a mask
specifying which rays are valid
Args:
rays_o: (num_rays, 3) ray origins
rays_d: (num_rays, 3) ray directions
aabb: (2, 3) This is [min point (x,y,z), max point (x,y,z)]
"""
# avoid divide by zero
dir_fraction = 1.0 / (rays_d + 1e-6)
# x
t1 = (aabb[0, 0] - rays_o[:, 0:1]) * dir_fraction[:, 0:1]
t2 = (aabb[1, 0] - rays_o[:, 0:1]) * dir_fraction[:, 0:1]
# y
t3 = (aabb[0, 1] - rays_o[:, 1:2]) * dir_fraction[:, 1:2]
t4 = (aabb[1, 1] - rays_o[:, 1:2]) * dir_fraction[:, 1:2]
# z
t5 = (aabb[0, 2] - rays_o[:, 2:3]) * dir_fraction[:, 2:3]
t6 = (aabb[1, 2] - rays_o[:, 2:3]) * dir_fraction[:, 2:3]
nears = torch.max(
torch.cat([torch.minimum(t1, t2), torch.minimum(t3, t4), torch.minimum(t5, t6)], dim=1), dim=1
).values
fars = torch.min(
torch.cat([torch.maximum(t1, t2), torch.maximum(t3, t4), torch.maximum(t5, t6)], dim=1), dim=1
).values
# clamp to near plane
near_plane = self.near_plane if self.training else 0
nears = torch.clamp(nears, min=near_plane)
fars = torch.maximum(fars, nears + 1e-6)
return nears, fars
def set_nears_and_fars(self, ray_bundle: RayBundle) -> RayBundle:
"""Intersects the rays with the scene box and updates the near and far values.
Populates nears and fars fields and returns the ray_bundle.
Args:
ray_bundle: specified ray bundle to operate on
"""
aabb = self.scene_box.aabb
nears, fars = self._intersect_with_aabb(ray_bundle.origins, ray_bundle.directions, aabb)
ray_bundle.nears = nears[..., None]
ray_bundle.fars = fars[..., None]
return ray_bundle
def _intersect_with_sphere(
rays_o: torch.Tensor, rays_d: torch.Tensor, center: torch.Tensor, radius: float = 1.0, near_plane: float = 0.0
):
a = (rays_d * rays_d).sum(dim=-1, keepdim=True)
b = 2 * (rays_o - center) * rays_d
b = b.sum(dim=-1, keepdim=True)
c = (rays_o - center) * (rays_o - center)
c = c.sum(dim=-1, keepdim=True) - radius**2
# clamp to near plane
nears = (-b - torch.sqrt(torch.square(b) - 4 * a * c)) / (2 * a)
fars = (-b + torch.sqrt(torch.square(b) - 4 * a * c)) / (2 * a)
nears = torch.clamp(nears, min=near_plane)
fars = torch.maximum(fars, nears + 1e-6)
nears = torch.nan_to_num(nears, nan=0.0)
fars = torch.nan_to_num(fars, nan=0.0)
return nears, fars
class SphereCollider(SceneCollider):
"""Module for colliding rays with the scene box to compute near and far values.
Args:
center: center of sphere to intersect [3]
radius: radius of sphere to intersect
near_plane: near plane to clamp to
"""
def __init__(self, center: torch.Tensor, radius: float, near_plane: float = 0.0, **kwargs) -> None:
super().__init__(**kwargs)
self.center = center
self.radius = radius
self.near_plane = near_plane
def set_nears_and_fars(self, ray_bundle: RayBundle) -> RayBundle:
"""Intersects the rays with the scene box and updates the near and far values.
Populates nears and fars fields and returns the ray_bundle.
Args:
ray_bundle: specified ray bundle to operate on
"""
self.center = self.center.to(ray_bundle.origins.device)
near_plane = self.near_plane if self.training else 0
nears, fars = _intersect_with_sphere(
rays_o=ray_bundle.origins,
rays_d=ray_bundle.directions,
center=self.center,
radius=self.radius,
near_plane=near_plane,
)
ray_bundle.nears = nears
ray_bundle.fars = fars
return ray_bundle
class NearFarCollider(SceneCollider):
"""Sets the nears and fars with fixed values.
Args:
near_plane: distance to near plane
far_plane: distance to far plane
"""
def __init__(self, near_plane: float, far_plane: float, **kwargs) -> None:
self.near_plane = near_plane
self.far_plane = far_plane
super().__init__(**kwargs)
def set_nears_and_fars(self, ray_bundle: RayBundle) -> RayBundle:
ones = torch.ones_like(ray_bundle.origins[..., 0:1])
near_plane = self.near_plane if self.training else 0
ray_bundle.nears = ones * near_plane
ray_bundle.fars = ones * self.far_plane
return ray_bundle