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View Code? Open in Web Editor NEWAn unofficial pytorch implementation of BakedSDF
Home Page: https://bakedsdf.github.io/
License: MIT License
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question about train bakedsdf-blender
Hi, Thanks for your great job! When I use the bakedsdf-blender.yaml config parameters execute the following code，
python launch.py --config configs/bakedsdf-colmap.yaml --gpu 0 --train dataset.root_dir=$1 \ --resume_weights_only --resume latest
The output result is incorrect. Can you tell me what might be causing it?
About Loss val
Why is the loss value during training nan, but the psnr value is normal during evaluation?
OutOfMemoryError : how to reduce GPU memory footprint ?
when I try to run :
python launch.py --config configs/bakedsdf-colmap.yaml --gpu 0 --train
I get this type of error in my GPU GeForce RTX 2070 (with 8 Gb of VRAM)
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.10 GiB (GPU 0; 7.79 GiB total capacity; 3.30 GiB already allocated; 341.44 MiB free; 5.82 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
Epoch 0: : 16it [02:12, 8.29s/it, loss=1.99, train/inv_s=20.10, train/num_rays=1226.0]
- How can I change config to reduce GPU memory footprint ?
- What is the minim amount of VRAM required ?
How to render a trajectory video?
The code is stuck.
Thanks for your great work. I tried to run your code on my data but the code is stuck here:
python launch.py --config configs/bakedsdf-colmap.yaml --gpu 0 --train dataset.root_dir=./data/360_v2/garden/
Global seed set to 42
/home/zh/.conda/envs/bakedsdf/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
Using 16bit None Automatic Mixed Precision (AMP)
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
`Trainer(limit_train_batches=1.0)` was configured so 100% of the batches per epoch will be used..
[rank: 0] Global seed set to 42
Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/1
----------------------------------------------------------------------------------------------------
distributed_backend=nccl
All distributed processes registered. Starting with 1 processes
----------------------------------------------------------------------------------------------------
You are using a CUDA device ('NVIDIA GeForce RTX 4090') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
| Name | Type | Params
----------------------------------------
0 | model | BakedSDFModel | 41.9 M
----------------------------------------
41.9 M Trainable params
0 Non-trainable params
41.9 M Total params
83.865 Total estimated model params size (MB)
Epoch 0: : 0it [00:00, ?it/s]
When I cancel this task, it exits from this line:
Epoch 0: : 0it [00:00, ?it/s]^C^C/home/zh/.conda/envs/bakedsdf/lib/python3.8/site-packages/pytorch_lightning/trainer/call.py:48: UserWarning: Detected KeyboardInterrupt, attempting graceful shutdown...
rank_zero_warn("Detected KeyboardInterrupt, attempting graceful shutdown...")
This is my environment:
# Name Version Build Channel
_libgcc_mutex 0.1 main defaults
_openmp_mutex 5.1 1_gnu defaults
absl-py 2.0.0 pypi_0 pypi
antlr4-python3-runtime 4.9.3 pypi_0 pypi
ca-certificates 2023.12.12 h06a4308_0 defaults
cachetools 5.3.2 pypi_0 pypi
certifi 2023.11.17 pypi_0 pypi
charset-normalizer 3.3.2 pypi_0 pypi
contourpy 1.1.1 pypi_0 pypi
cycler 0.12.1 pypi_0 pypi
filelock 3.13.1 pypi_0 pypi
fonttools 4.47.0 pypi_0 pypi
fsspec 2023.12.2 pypi_0 pypi
google-auth 2.26.1 pypi_0 pypi
google-auth-oauthlib 1.0.0 pypi_0 pypi
grpcio 1.60.0 pypi_0 pypi
idna 3.6 pypi_0 pypi
imageio 2.33.1 pypi_0 pypi
imageio-ffmpeg 0.4.9 pypi_0 pypi
importlib-metadata 7.0.1 pypi_0 pypi
importlib-resources 6.1.1 pypi_0 pypi
jinja2 3.1.2 pypi_0 pypi
kiwisolver 1.4.5 pypi_0 pypi
ld_impl_linux-64 2.38 h1181459_1 defaults
libffi 3.4.4 h6a678d5_0 defaults
libgcc-ng 11.2.0 h1234567_1 defaults
libgomp 11.2.0 h1234567_1 defaults
libstdcxx-ng 11.2.0 h1234567_1 defaults
lightning-utilities 0.10.0 pypi_0 pypi
markdown 3.5.1 pypi_0 pypi
markdown-it-py 3.0.0 pypi_0 pypi
markupsafe 2.1.3 pypi_0 pypi
matplotlib 3.7.4 pypi_0 pypi
mdurl 0.1.2 pypi_0 pypi
mpmath 1.3.0 pypi_0 pypi
ncurses 6.4 h6a678d5_0 defaults
nerfacc 0.3.3 pypi_0 pypi
networkx 3.1 pypi_0 pypi
ninja 1.11.1.1 pypi_0 pypi
numpy 1.24.4 pypi_0 pypi
nvidia-cublas-cu12 12.1.3.1 pypi_0 pypi
nvidia-cuda-cupti-cu12 12.1.105 pypi_0 pypi
nvidia-cuda-nvrtc-cu12 12.1.105 pypi_0 pypi
nvidia-cuda-runtime-cu12 12.1.105 pypi_0 pypi
nvidia-cudnn-cu12 8.9.2.26 pypi_0 pypi
nvidia-cufft-cu12 11.0.2.54 pypi_0 pypi
nvidia-curand-cu12 10.3.2.106 pypi_0 pypi
nvidia-cusolver-cu12 11.4.5.107 pypi_0 pypi
nvidia-cusparse-cu12 12.1.0.106 pypi_0 pypi
nvidia-nccl-cu12 2.18.1 pypi_0 pypi
nvidia-nvjitlink-cu12 12.3.101 pypi_0 pypi
nvidia-nvtx-cu12 12.1.105 pypi_0 pypi
oauthlib 3.2.2 pypi_0 pypi
omegaconf 2.2.3 pypi_0 pypi
opencv-python 4.9.0.80 pypi_0 pypi
openssl 3.0.12 h7f8727e_0 defaults
packaging 23.2 pypi_0 pypi
pillow 10.2.0 pypi_0 pypi
pip 23.3.1 py38h06a4308_0 defaults
protobuf 4.25.1 pypi_0 pypi
pyasn1 0.5.1 pypi_0 pypi
pyasn1-modules 0.3.0 pypi_0 pypi
pybind11 2.11.1 pypi_0 pypi
pygments 2.17.2 pypi_0 pypi
pymcubes 0.1.4 pypi_0 pypi
pyparsing 3.1.1 pypi_0 pypi
pyransac3d 0.6.0 pypi_0 pypi
python 3.8.18 h955ad1f_0 defaults
python-dateutil 2.8.2 pypi_0 pypi
pytorch-lightning 1.9.5 pypi_0 pypi
pyyaml 6.0.1 pypi_0 pypi
readline 8.2 h5eee18b_0 defaults
requests 2.31.0 pypi_0 pypi
requests-oauthlib 1.3.1 pypi_0 pypi
rich 13.7.0 pypi_0 pypi
rsa 4.9 pypi_0 pypi
scipy 1.10.1 pypi_0 pypi
setuptools 68.2.2 py38h06a4308_0 defaults
six 1.16.0 pypi_0 pypi
sqlite 3.41.2 h5eee18b_0 defaults
sympy 1.12 pypi_0 pypi
tensorboard 2.14.0 pypi_0 pypi
tensorboard-data-server 0.7.2 pypi_0 pypi
tinycudann 1.7 pypi_0 pypi
tk 8.6.12 h1ccaba5_0 defaults
torch 2.1.2 pypi_0 pypi
torch-efficient-distloss 0.1.3 pypi_0 pypi
torchmetrics 1.2.1 pypi_0 pypi
torchvision 0.16.2 pypi_0 pypi
tqdm 4.66.1 pypi_0 pypi
trimesh 4.0.8 pypi_0 pypi
triton 2.1.0 pypi_0 pypi
typing-extensions 4.9.0 pypi_0 pypi
urllib3 2.1.0 pypi_0 pypi
werkzeug 3.0.1 pypi_0 pypi
wheel 0.41.2 py38h06a4308_0 defaults
xz 5.4.5 h5eee18b_0 defaults
zipp 3.17.0 pypi_0 pypi
zlib 1.2.13 h5eee18b_0 defaults
Could you give me a hand : )
When run "python BakedSDF2FBX.py glbfile", error!
step:
- python launch.py --config configs/bakedsdf-blender.yaml --gpu 0 --train --exp_dir /home/disk2/out/torch-bakedsdf/ dataset.root_dir=/home/disk2/data/nerf_synthetic/lego/ name=nues-blender-lego
- python export.py --exp_dir /home/disk2/out/torch-bakedsdf/nues-blender-lego/@20231123-153121/ --output-dir /home/disk2/out/torch-bakedsdf/nues-blender-lego/@20231123-153121/
- python BakedSDF2FBX.py /home/disk2/out/torch-bakedsdf/nues-blender-lego/@20231123-153121/nues-blender-lego.glb
output：
其他方法重建的mesh，如何实现只学习表征
您好：
请问其他方法重建的mesh，如colmap方法重建的mesh模型，如何学习其外观信息，实现mesh纹理的生成。
即输入为mesh和colmap姿态，利用torch-bakedsdf，最终如何生成带纹理的mesh模型。
It appears that the Install command does not work properly
pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
pip install -r requirements.txt
appears to give me result of
cuda-nn/#subdirectory=bindings/torch
Collecting git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch q-build-jx3n58h2
Cloning https://github.com/NVlabs/tiny-cuda-nn/ to c:\users\sydian\appdata\local\temp\pip-re 'C:\Users\Sydian\AppData\Local\Temp\pip-req-build-jx3n58h2'q-build-jx3n58h2 a39ce44
Running command git clone --filter=blob:none --quiet https://github.com/NVlabs/tiny-cuda-nn/ 'C:\Users\Sydian\AppData\Local\Temp\pip-req-build-jx3n58h2'
Resolved https://github.com/NVlabs/tiny-cuda-nn/ to commit e02068459c4c36ba8b6fc40e312a301dca39ce44
Running command git submodule update --init --recursive -q
Preparing metadata (setup.py) ... error
error: subprocess-exited-with-error
× python setup.py egg_info did not run successfully.
│ exit code: 1
╰─> [8 lines of output]
C:\Users\Sydian\AppData\Local\Temp\pip-req-build-jx3n58h2\bindings/torch\setup.py:5: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
from pkg_resources import parse_version
Traceback (most recent call last):
File "<string>", line 2, in <module>
File "<pip-setuptools-caller>", line 34, in <module>
File "C:\Users\Sydian\AppData\Local\Temp\pip-req-build-jx3n58h2\bindings/torch\setup.py", line 9, in <module>
ModuleNotFoundError: No module named 'torch'
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed
× Encountered error while generating package metadata.
╰─> See above for output.
note: This is an issue with the package mentioned above, not pip.
hint: See above for details.
Newly created environment through conda. Python=3.10
Error while building nerfacc
I got an error when I train the model using python launch.py --config configs/neus-colmap.yaml --gpu 0 --train:
nvcc fatal : Value 'c++17' is not defined for option 'std'
ninja: build stopped: subcommand failed.
command fails
Hi, and thank you for making this code available.
I am trying to run on the mip360 garden dataset, and when I run:
python launch.py --config configs/neus-colmap.yaml --gpu 0 --train dataset.root_dir=D://NERF//BakedSDF//torch-bakedsdf-main//torch-bakedsdf-main//load//unbounded360//garden//
I see this error:
D:\NERF\BakedSDF\torch-bakedsdf-main\torch-bakedsdf-main>python launch.py --config configs/neus-colmap.yaml --gpu 0 --train
Global seed set to 42
Using 16bit native Automatic Mixed Precision (AMP)
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
`Trainer(limit_train_batches=1.0)` was configured so 100% of the batches per epoch will be used..
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
fatal: not a git repository (or any of the parent directories): .git
D:\NERF\BakedSDF\torch-bakedsdf-main\torch-bakedsdf-main\utils\callbacks.py:76: UserWarning: Code snapshot is not saved. Please make sure you have git installed and are in a git repository.
rank_zero_warn("Code snapshot is not saved. Please make sure you have git installed and are in a git repository.")
| Name | Type | Params
------------------------------------
0 | model | NeuSModel | 28.0 M
------------------------------------
28.0 M Trainable params
0 Non-trainable params
28.0 M Total params
55.913 Total estimated model params size (MB)
Traceback (most recent call last):
File "D:\NERF\BakedSDF\torch-bakedsdf-main\torch-bakedsdf-main\launch.py", line 130, in <module>
main()
File "D:\NERF\BakedSDF\torch-bakedsdf-main\torch-bakedsdf-main\launch.py", line 119, in main
trainer.fit(system, datamodule=dm)
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 770, in fit
self._call_and_handle_interrupt(
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 723, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 811, in _fit_impl
results = self._run(model, ckpt_path=self.ckpt_path)
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 1236, in _run
results = self._run_stage()
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 1323, in _run_stage
return self._run_train()
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 1353, in _run_train
self.fit_loop.run()
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\loops\base.py", line 204, in run
self.advance(*args, **kwargs)
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\loops\fit_loop.py", line 266, in advance
self._outputs = self.epoch_loop.run(self._data_fetcher)
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\loops\base.py", line 204, in run
self.advance(*args, **kwargs)
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\loops\epoch\training_epoch_loop.py", line 208, in advance
batch_output = self.batch_loop.run(batch, batch_idx)
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\loops\base.py", line 204, in run
self.advance(*args, **kwargs)
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\loops\batch\training_batch_loop.py", line 88, in advance
outputs = self.optimizer_loop.run(split_batch, optimizers, batch_idx)
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\loops\base.py", line 204, in run
self.advance(*args, **kwargs)
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\loops\optimization\optimizer_loop.py", line 203, in advance
result = self._run_optimization(
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\loops\optimization\optimizer_loop.py", line 256, in _run_optimization
self._optimizer_step(optimizer, opt_idx, batch_idx, closure)
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\loops\optimization\optimizer_loop.py", line 369, in _optimizer_step
self.trainer._call_lightning_module_hook(
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 1595, in _call_lightning_module_hook
output = fn(*args, **kwargs)
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\core\lightning.py", line 1646, in optimizer_step
optimizer.step(closure=optimizer_closure)
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\core\optimizer.py", line 168, in step
step_output = self._strategy.optimizer_step(self._optimizer, self._optimizer_idx, closure, **kwargs)
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\strategies\strategy.py", line 193, in optimizer_step
return self.precision_plugin.optimizer_step(model, optimizer, opt_idx, closure, **kwargs)
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\plugins\precision\native_amp.py", line 85, in optimizer_step
closure_result = closure()
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\loops\optimization\optimizer_loop.py", line 148, in __call__
self._result = self.closure(*args, **kwargs)
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\loops\optimization\optimizer_loop.py", line 134, in closure
step_output = self._step_fn()
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\loops\optimization\optimizer_loop.py", line 427, in _training_step
training_step_output = self.trainer._call_strategy_hook("training_step", *step_kwargs.values())
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 1765, in _call_strategy_hook
output = fn(*args, **kwargs)
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\strategies\dp.py", line 125, in training_step
return self.model(*args, **kwargs)
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\torch\nn\parallel\data_parallel.py", line 169, in forward
return self.module(*inputs[0], **kwargs[0])
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\overrides\data_parallel.py", line 64, in forward
output = super().forward(*inputs, **kwargs)
File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\overrides\base.py", line 82, in forward
output = self.module.training_step(*inputs, **kwargs)
File "D:\NERF\BakedSDF\torch-bakedsdf-main\torch-bakedsdf-main\systems\neus.py", line 95, in training_step
train_num_rays = int(self.train_num_rays * (self.train_num_samples / out['num_samples_full'].sum().item()))
ZeroDivisionError: division by zero
Epoch 0: : 0it [02:25, ?it/s]
[W C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\torch\csrc\CudaIPCTypes.cpp:15] Producer process has been terminated before all shared CUDA tensors released. See Note [Sharing CUDA tensors]
What might be causing this?
Thank you!
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