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View Code? Open in Web Editor NEW[CVPR 2024] 4K4D: Real-Time 4D View Synthesis at 4K Resolution
Home Page: https://zju3dv.github.io/4k4d/
License: Other
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Questions about the mask loss in the paper
In the paper, why is the sum of the products of the predicted and ground truth values used? Why don't we use the L1 or L2 loss?
古德古德
nice！
No mask directory in Mobile-stage dataset
I have downloaded the Mobile-Stage dataset, but I was unable to find the masks directory after extracting the data. The masks directory seems to be essential data for rendering using the pre-trained models of the Mobile-Stage dataset, and I was unable to perform rendering. Could you tell me how to obtain the mask data?
EasyVolcap can't import xxx
Your work is very stunning!
But when i test with the gui demo, the error appears that
"ImportError: cannot import name 'DptLossType' from 'easyvolcap.utils.loss_utils' (xxx/EasyVolcap/easyvolcap/utils/loss_utils.py)".
I checked the code in loss_utils.py and 'DptLossType' truly didn't exist.
Did i miss some steps in setting up the environment?
Thank you!
Are there any detailed steps for training our own data?
This is great work. We hope to use 4K4D to reconstruct our own scenes.I'd like to know how long it will take for the training code to be updated?
Get in trouble with pytorch3d 's installing and tiny-cuda-nn 's compiling.
Here is my cmd log. I rent a linux machine with rtx 3080 on AutoDL.com, because i only have a windows laptop with rtx 3070.
- pytorch3d seems don't match the version of pytorch and python.
(easyvolcap) root@autodl-container-ecb6119b52-cd4c9d8e:~/4K4D# conda install pytorch3d -c pytorch3d
Channels:
- pytorch3d
- https://mirrors.ustc.edu.cn/anaconda/pkgs/main
- https://mirrors.ustc.edu.cn/anaconda/pkgs/free
- defaults
Platform: linux-64
Collecting package metadata (repodata.json): done
Solving environment: failed
LibMambaUnsatisfiableError: Encountered problems while solving:
- nothing provides pytorch 1.5.1 needed by pytorch3d-0.2.5-py36_cu101_pyt151
Could not solve for environment specs
The following packages are incompatible
├─ pin-1 is installable and it requires
│ └─ python 3.12.* , which can be installed;
└─ pytorch3d is not installable because there are no viable options
├─ pytorch3d [0.2.5|0.3.0|0.4.0] would require
│ └─ python >=3.6,<3.7.0a0 , which conflicts with any installable versions previously reported;
├─ pytorch3d [0.2.5|0.3.0|0.4.0] would require
│ └─ pytorch 1.5.1 , which does not exist (perhaps a missing channel);
├─ pytorch3d [0.2.5|0.3.0|0.4.0] would require
│ └─ pytorch 1.6.0 , which does not exist (perhaps a missing channel);
├─ pytorch3d [0.2.5|0.3.0|0.4.0] would require
│ └─ python >=3.7,<3.8.0a0 , which conflicts with any installable versions previously reported;
├─ pytorch3d [0.2.5|0.3.0|0.4.0] would require
│ └─ python >=3.8,<3.9.0a0 , which conflicts with any installable versions previously reported;
├─ pytorch3d [0.3.0|0.4.0] would require
│ └─ pytorch 1.7.0 , which does not exist (perhaps a missing channel);
├─ pytorch3d 0.4.0 would require
│ └─ python >=3.9,<3.10.0a0 , which conflicts with any installable versions previously reported;
├─ pytorch3d [0.5.0|0.6.0|0.6.1] would require
│ └─ python_abi 3.6.* _cp36m, which does not exist (perhaps a missing channel);
├─ pytorch3d [0.5.0|0.6.0|...|0.7.4] would require
│ └─ cudatoolkit >=11.1,<11.2 , which does not exist (perhaps a missing channel);
├─ pytorch3d [0.5.0|0.6.0|...|0.7.1] would require
│ └─ python_abi 3.7. _cp37m, which does not exist (perhaps a missing channel);
├─ pytorch3d [0.5.0|0.6.0|...|0.7.5] would require
│ └─ python_abi 3.8. _cp38, which does not exist (perhaps a missing channel);
├─ pytorch3d [0.5.0|0.6.1|...|0.7.5] would require
│ └─ python_abi 3.9. _cp39, which does not exist (perhaps a missing channel);
├─ pytorch3d [0.6.2|0.7.0|...|0.7.5] would require
│ └─ python_abi 3.10. _cp310, which does not exist (perhaps a missing channel);
├─ pytorch3d [0.6.2|0.7.0|...|0.7.4] would require
│ └─ cudatoolkit >=11.5,<11.6 , which does not exist (perhaps a missing channel);
├─ pytorch3d [0.7.0|0.7.1|...|0.7.5] would require
│ └─ cudatoolkit >=11.6,<11.7 , which does not exist (perhaps a missing channel);
└─ pytorch3d 0.7.5 would require
└─ python_abi 3.11. *_cp311, which does not exist (perhaps a missing channel).
2.tiny-cuda-nn compiling failed , seems that it can't find <cusolverDn. h> which is a file in CUDA toolkit.
It‘s worth mentioning that, with python 3.9 +pytorch 1.12.1 +cuda11.6 , I can install pytorch3d , but can't compile tiny-cuda-nn.
If I just follow your "envirment.yml", conda will install python 3.12 +pytorch 2.2.1 +cuda12.1 , in this case , I can't install pytorch3d.
And i still don't get open3d installed.
By the way , I wonder if i can use GUI viewer in the rented linux machine（ubuntu 20.04） , it don't have GUI.
I have run nerfstudio and instantNGP in my windows laptop successfully. But when I try to install easyvolcap in windows , seems that some dependencies are not support windows system. Is there any way to get 4K4D run on my windows laptop? Although it only have a RTX 3070 with 8GB VRAM : )
Do you have some solutions or suggestions for my problem? Thanks a lot !
version `GLIBC_2.33' not found
Hello, I have a problem and need your help.
I successfully executed evc -t test -c configs/projects/realtime4dv/rendering/4k4d_0013_01.yaml,configs/specs/eval.yaml,configs/specs/vf0.yaml on NVIDIA GeForce RTX 3090, but when I executed it on NVIDIA GeForce RTX 4090, I encountered the following problem:
error log
(4k4d) xuankai@kemove-ESC8000-G4:~/code/4k4d$ evc -t test -c configs/projects/realtime4dv/rendering/4k4d_0013_01.yaml,configs/specs/eval.yaml,configs/specs/vf0.yaml
╭─────────────────────────────────────────────────── Traceback (most recent call last) ────────────────────────────────────────────────────╮
│ /home/xuankai/code/4k4d/easyvolcap/runners/__init__.py:7 in <module> │
│ │
│ ❱ 7 │ │ exec(f'from . import {module}') │
│ in <module>:1 │
│ │
│ /home/xuankai/code/4k4d/easyvolcap/runners/volumetric_video_viewer.py:5 in <module> │
│ │
│ ❱ 5 import glm │
│ │
│ /home/xuankai/anaconda3/envs/4k4d/lib/python3.9/site-packages/glm/__init__.py:1 in <module> │
│ │
│ ❱ 1 from ._glm import * │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
ImportError: /lib/x86_64-linux-gnu/libc.so.6: version `GLIBC_2.33' not found (required by /home/xuankai/anaconda3/envs/4k4d/lib/python3.9/site-packages/glm/_glm.so)
2024-04-06 14:54:22.506165 __main__ -> preflight: Starting experiment: 4k4d_0013_01, command: test main.py:80
2024-04-06 easyvolcap.dataloader… Preparing vhulls data/renbody/0013_01/surfs VAL 100% ━━━━━━━━━━━━━━━━━━━━━━ 1/1 0:00:00 < 0:00:00 ? it/s v…
14:54:24.010105 -> load_vhulls:
2024-04-06 easyvolcap.utils.d… Loading mask bytes for data/renbody/0013_01/masks VAL 100% ━━━━━━━━━━━━━━━━━━━ 60/60 0:00:00 < 0:00:00 128.4 it/s p…
14:54:24.652740 ->
load_resize_undist…
2024-04-06 easyvolcap.util… Loading imgs bytes for data/renbody/0013_01/images_calib VAL 100% ━━━━━━━━━━━━━━━━━ 60/60 0:00:01 < 0:00:00 49.52 it/s p…
14:54:26.643625 ->
load_resize_und…
2024-04-06 easyvolcap.utils.dat… Cropping msks imgs for data/renbody/0013_01 VAL 100% ━━━━━━━━━━━━━━━━━━━━━ 60/60 0:00:00 < 0:00:00 255.4 it/s p…
14:54:27.012361 ->
decode_crop_fill_ims…
2024-04-06 14:54:27.459624 easyvolcap.runners.visualizers.volumetric_video_visualizer -> __init__: Visualization output: volumetric_video_visualizer.py:80
data/result/4k4d_0013_01/{RENDER}
2024-04-06 14:54:27.465893 easyvolcap.runners.recorders -> __init__: Saved config file to recorders.py:105
data/record/4k4d_0013_01/4k4d_0013_01_1712386467.yaml
2024-04-06 14:54:27.467401 __main__ -> launcher: Launching runner for experiment: 4k4d_0013_01 main.py:50
2024-04-06 14:54:32.777724 easyvolcap.models.samplers.supe… Caching rgbw and center 100% ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1/1 0:00:03 < 0:00:00 ? it/s s…
-> _load_state_dict_post_hook:
2024-04-06 14:54:32.797353 easyvolcap.models.samplers.… Computing features for caching 100% ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1/1 0:00:00 < 0:00:00 ? it/s s…
->
_load_state_dict_post_hook:
2024-04-06 14:54:32.884327 easyvolcap.models.samplers.su… Caching spherical harmonics 100% ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1/1 0:00:00 < 0:00:00 ? it/s s…
-> _load_state_dict_post_hook:
2024-04-06 14:54:32.906269 easyvolcap.models.samplers.supe… Caching radius and alpha 100% ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1/1 0:00:00 < 0:00:00 ? it/s s…
-> precompute_geometry:
2024-04-06 14:54:32.912684 easyvolcap.utils.net_utils -> load_network: Loaded network data/trained_model/4k4d_0013_01/1599.npz at epoch -1 net_utils.py:436
2024-04-06 14:54:34.722552 easyvolcap.utils.console_utils -> inner: Runtime exception: /lib/x86_64-linux-gnu/libc.so.6: version console_utils.py:391
`GLIBC_2.33' not found (required by
/home/xuankai/anaconda3/envs/4k4d/lib/python3.9/site-packages/glm/_glm.so)
╭─────────────────────────────────────────────────── Traceback (most recent call last) ────────────────────────────────────────────────────╮
│ /home/xuankai/code/4k4d/easyvolcap/utils/console_utils.py:388 in inner │
│ │
│ ❱ 388 │ │ │ │ return func(*args, **kwargs) │
│ │
│ /home/xuankai/code/4k4d/easyvolcap/scripts/main.py:272 in main │
│ │
│ ❱ 272 │ else: globals()[args.type](cfg) # invoke this (call callable_from_cfg -> call_from_cfg) │
│ │
│ /home/xuankai/code/4k4d/easyvolcap/engine/registry.py:56 in inner │
│ │
│ ❱ 56 │ │ return call_from_cfg(func, cfg) │
│ │
│ /home/xuankai/code/4k4d/easyvolcap/engine/registry.py:47 in call_from_cfg │
│ │
│ ❱ 47 │ return func(**call_args) │
│ │
│ /home/xuankai/code/4k4d/easyvolcap/scripts/main.py:181 in test │
│ │
│ ❱ 181 │ launcher(**kwargs, runner_function=runner.test, runner_object=runner) │
│ │
│ /home/xuankai/code/4k4d/easyvolcap/scripts/main.py:52 in launcher │
│ │
│ ❱ 52 │ runner_function() │
│ │
│ /home/xuankai/code/4k4d/easyvolcap/runners/volumetric_video_runner.py:275 in test │
│ │
│ ❱ 275 │ │ self.test_epoch(epoch) │
│ │
│ /home/xuankai/code/4k4d/easyvolcap/runners/volumetric_video_runner.py:428 in test_epoch │
│ │
│ ❱ 428 │ │ for _ in test_generator: pass # the actual calling │
│ │
│ /home/xuankai/code/4k4d/easyvolcap/runners/volumetric_video_runner.py:439 in test_generator │
│ │
│ ❱ 439 │ │ │ │ output: dotdict = self.model(batch) │
│ │
│ /home/xuankai/anaconda3/envs/4k4d/lib/python3.9/site-packages/torch/nn/modules/module.py:1501 in _call_impl │
│ │
│ ❱ 1501 │ │ │ return forward_call(*args, **kwargs) │
│ │
│ /home/xuankai/code/4k4d/easyvolcap/models/volumetric_video_model.py:245 in forward │
│ │
│ ❱ 245 │ │ output = rendering_function(*input, batch=batch) │
│ │
│ /home/xuankai/code/4k4d/easyvolcap/models/volumetric_video_model.py:104 in render_rays │
│ │
│ ❱ 104 │ │ xyz, dir, t, dist = self.sampler.sample(ray_o, ray_d, near, far, t, batch) # B, P, S │
│ │
│ /home/xuankai/code/4k4d/easyvolcap/utils/net_utils.py:95 in sample │
│ │
│ ❱ 95 │ │ │ self.forward(batch) │
│ │
│ /home/xuankai/code/4k4d/easyvolcap/models/samplers/super_charged_r4dv.py:514 in forward │
│ │
│ ❱ 514 │ │ rgb, acc, dpt = self.render_points(xyz, rgb, rad, occ, batch) # almost always use render_cudagl │
│ │
│ /home/xuankai/code/4k4d/easyvolcap/models/samplers/point_planes_sampler.py:410 in render_points │
│ │
│ ❱ 410 │ │ │ if self.use_cudagl: return self.render_cudagl(*args, **kwargs) │
│ │
│ /home/xuankai/code/4k4d/easyvolcap/models/samplers/point_planes_sampler.py:517 in render_cudagl │
│ │
│ ❱ 517 │ │ from easyvolcap.utils.gl_utils import HardwareRendering │
│ │
│ /home/xuankai/code/4k4d/easyvolcap/utils/gl_utils.py:9 in <module> │
│ │
│ ❱ 9 import glm │
│ │
│ /home/xuankai/anaconda3/envs/4k4d/lib/python3.9/site-packages/glm/__init__.py:1 in <module> │
│ │
│ ❱ 1 from ._glm import * │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
ImportError: /lib/x86_64-linux-gnu/libc.so.6: version `GLIBC_2.33' not found (required by /home/xuankai/anaconda3/envs/4k4d/lib/python3.9/site-packages/glm/_glm.so)
2024-04-06 14:54:35.272045 easyvolcap.runners.volumetric_video_runner -> 0% ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0/3 0:00:02 < -:--:-- ? it/s v…
test_generator:
*** /lib/x86_64-linux-gnu/libc.so.6: version `GLIBC_2.33' not found (required by /home/xuankai/anaconda3/envs/4k4d/lib/python3.9/site-packages/glm/_glm.so)
> /home/xuankai/anaconda3/envs/4k4d/lib/python3.9/site-packages/glm/__init__.py(1)<module>()
----> 1 from ._glm import *
2 __version__ = version()
(Pdbr) exit
I checked the GLIBC versions of the two servers and found that the GLIBC versions of the two servers were the same:
Can you give me any advice? Thank you so much for all your help!
Minimal Dataset extract_images 解帧发绿
python scripts/realtime4dv/extract_images.py --data_root data/renbody/0013_01解帧得到的图片发绿：
NHR, renbody 都存在这个问题，zju_mocap则没有出现这个问题。
ffmpeg安装方式：
sudo add-apt-repository ppa:savoury1/ffmpeg4
sudo apt update
sudo apt install ffmpegCan it render poses that have never appeared before?
Great research. I have a question for you: 4k4D seems to perform well in free-viewpoint rendering of videos, and the majority of the demo is focused on human bodies. Could this capability potentially extend to another similar task, such as creating animatable human bodies from a video segment, which can be generalized to any pose and undergo free-viewpoint rendering, akin to Animatable NeRF? I'm curious if there are any upcoming demos in this direction. Thank you.
About the release time of the code
Hi, thanks for the great job !!!
I'm quite curious about the exact release time of your source code.
Will you release the code in next month (November)? I can't wait to try it!
Looking forward to your reply !
Is there any plan to add the training code?
Thanks for sharing！I was so interested in your job that I wanted to try it on my own data.
Error during rendering
Command: evc -t test -c configs/projects/realtime4dv/rendering/4k4d_rxy_demo.yaml,configs/specs/eval.yaml,configs/specs/spiral.yaml,configs/specs/ibr.yaml,configs/specs/vf0.yaml
Error如下：
Error log
2024-02-01 22:51:17.099045 easyvolcap.runners -> <module>: Failed to import submodule volumetric_video_viewer of __init__.py:9
/DATA/DATA1/rxy_code/projects/202312_demo/EasyVolcap/easyvolcap/runners/__init__.py
╭──────────────────────────────────────────── Traceback (most recent call last) ─────────────────────────────────────────────╮
│ /DATA/DATA1/rxy_code/projects/202312_demo/EasyVolcap/easyvolcap/runners/__init__.py:6 in <module> │
│ │
│ ❱ 6 │ │ exec(f'from . import {module}') │
│ in <module>:1 │
│ │
│ /DATA/DATA1/rxy_code/projects/202312_demo/EasyVolcap/easyvolcap/runners/volumetric_video_viewer.py:21 in <module> │
│ │
│ ❱ 21 from imgui_bundle import imgui_color_text_edit as ed │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
ModuleNotFoundError: No module named 'imgui_bundle'
2024-02-01 22:51:17.143225 easyvolcap.scripts.main -> preflight: Starting experiment: 4k4d_rxy_demo, command: test main.py:80
2024-02-01 easyvolca… Loading mask bytes for data/rxy/rxy_demo/masks VAL 100% ━━━━━━━━━━━ 4/4 0:00:00 < 0:00:00 258.9 it/s p…
22:51:17.… ->
load_resi…
2024-02-01 easyvolca… Loading imgs bytes for data/rxy/rxy_demo/images VAL 100% ━━━━━━━━━━ 4/4 0:00:00 < 0:00:00 403.3 it/s p…
22:51:18.… ->
load_resi…
2024-02-01 22:51:18.886432 easyvolcap.utils.console_utils -> inner: Runtime exception: OpenCV(4.9.0) console_utils.py:341
/io/opencv/modules/imgcodecs/src/loadsave.cpp:1121: error: (-215:Assertion
failed) !image.empty() in function 'imencode'
╭──────────────────────────────────────────── Traceback (most recent call last) ─────────────────────────────────────────────╮
│ /DATA/DATA1/rxy_code/projects/202312_demo/EasyVolcap/easyvolcap/utils/console_utils.py:338 in inner │
│ │
│ ❱ 338 │ │ │ return func(*args, **kwargs) │
│ │
│ /DATA/DATA1/rxy_code/projects/202312_demo/EasyVolcap/easyvolcap/engine/registry.py:56 in inner │
│ │
│ ❱ 56 │ │ return call_from_cfg(func, cfg) │
│ │
│ /DATA/DATA1/rxy_code/projects/202312_demo/EasyVolcap/easyvolcap/engine/registry.py:47 in call_from_cfg │
│ │
│ ❱ 47 │ return func(**call_args) │
│ │
│ /DATA/DATA1/rxy_code/projects/202312_demo/EasyVolcap/easyvolcap/scripts/main.py:158 in test │
│ │
│ ❱ 158 │ val_dataloader: "VolumetricVideoDataloader" = DATALOADERS.build(val_dataloader_cfg) # reuse the validataion │
│ │
│ /DATA/DATA1/rxy_code/projects/202312_demo/EasyVolcap/easyvolcap/engine/registry.py:300 in build │
│ │
│ ❱ 300 │ │ return self.build_func(*args, **kwargs, registry=self) │
│ │
│ /DATA/DATA1/rxy_code/projects/202312_demo/EasyVolcap/easyvolcap/engine/registry.py:131 in build_from_cfg │
│ │
│ ❱ 131 │ return call_from_cfg(obj_cls, args) │
│ │
│ /DATA/DATA1/rxy_code/projects/202312_demo/EasyVolcap/easyvolcap/engine/registry.py:47 in call_from_cfg │
│ │
│ ❱ 47 │ return func(**call_args) │
│ │
│ /DATA/DATA1/rxy_code/projects/202312_demo/EasyVolcap/easyvolcap/dataloaders/volumetric_video_dataloader.py:94 in __init__ │
│ │
│ ❱ 94 │ │ dataset: VolumetricVideoDataset = DATASETS.build(dataset_cfg) │
│ │
│ /DATA/DATA1/rxy_code/projects/202312_demo/EasyVolcap/easyvolcap/engine/registry.py:300 in build │
│ │
│ ❱ 300 │ │ return self.build_func(*args, **kwargs, registry=self) │
│ │
│ /DATA/DATA1/rxy_code/projects/202312_demo/EasyVolcap/easyvolcap/engine/registry.py:131 in build_from_cfg │
│ │
│ ❱ 131 │ return call_from_cfg(obj_cls, args) │
│ │
│ /DATA/DATA1/rxy_code/projects/202312_demo/EasyVolcap/easyvolcap/engine/registry.py:47 in call_from_cfg │
│ │
│ ❱ 47 │ return func(**call_args) │
│ │
│ /DATA/DATA1/rxy_code/projects/202312_demo/EasyVolcap/easyvolcap/dataloaders/datasets/image_based_inference_dataset.py:33 i │
│ │
│ ❱ 33 │ │ call_from_cfg(super().__init__, kwargs) # will have prepared other parts of the dataset (interpolation or o │
│ │
│ /DATA/DATA1/rxy_code/projects/202312_demo/EasyVolcap/easyvolcap/engine/registry.py:47 in call_from_cfg │
│ │
│ ❱ 47 │ return func(**call_args) │
│ │
│ /DATA/DATA1/rxy_code/projects/202312_demo/EasyVolcap/easyvolcap/dataloaders/datasets/volumetric_video_inference_dataset.py │
│ │
│ ❱ 56 │ │ call_from_cfg(super().__init__, kwargs) │
│ │
│ /DATA/DATA1/rxy_code/projects/202312_demo/EasyVolcap/easyvolcap/engine/registry.py:47 in call_from_cfg │
│ │
│ ❱ 47 │ return func(**call_args) │
│ │
│ /DATA/DATA1/rxy_code/projects/202312_demo/EasyVolcap/easyvolcap/dataloaders/datasets/volumetric_video_dataset.py:272 in __ │
│ │
│ ❱ 272 │ │ │ self.load_bytes() # load image bytes (also load vhulls) │
│ │
│ /DATA/DATA1/rxy_code/projects/202312_demo/EasyVolcap/easyvolcap/dataloaders/datasets/image_based_inference_dataset.py:70 i │
│ │
│ ❱ 70 │ │ return VolumetricVideoDataset.load_bytes(self) # store images │
│ │
│ /DATA/DATA1/rxy_code/projects/202312_demo/EasyVolcap/easyvolcap/dataloaders/datasets/volumetric_video_dataset.py:472 in lo │
│ │
│ ❱ 472 │ │ │ │ decode_crop_fill_ims_bytes(self.ims_bytes, self.mks_bytes, self.Ks.numpy(), self.Rs.numpy(), self.T │
│ │
│ /DATA/DATA1/rxy_code/projects/202312_demo/EasyVolcap/easyvolcap/utils/data_utils.py:1679 in decode_crop_fill_ims_bytes │
│ │
│ ❱ 1679 │ out = parallel_execution(list(ims_bytes), list(mks_bytes), list(Ks), list(Rs), list(Ts), list(bounds), │
│ │
│ /DATA/DATA1/rxy_code/projects/202312_demo/EasyVolcap/easyvolcap/utils/parallel_utils.py:69 in parallel_execution │
│ │
│ ❱ 69 │ │ │ │ results.append(async_result.get()) # will sync the corresponding thread │
│ │
│ /home/jj/.conda/envs/sadtalker/lib/python3.8/multiprocessing/pool.py:771 in get │
│ │
│ ❱ 771 │ │ │ raise self._value │
│ │
│ /home/jj/.conda/envs/sadtalker/lib/python3.8/multiprocessing/pool.py:125 in worker │
│ │
│ ❱ 125 │ │ │ result = (True, func(*args, **kwds)) │
│ │
│ /DATA/DATA1/rxy_code/projects/202312_demo/EasyVolcap/easyvolcap/utils/data_utils.py:1663 in decode_crop_fill_im_bytes │
│ │
│ ❱ 1663 │ im_bytes = cv2.imencode(encode_ext, img, [cv2.IMWRITE_JPEG_QUALITY, jpeg_quality, cv2.IMWRITE_PNG_COMPRESSION, │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
error: OpenCV(4.9.0) /io/opencv/modules/imgcodecs/src/loadsave.cpp:1121: error: (-215:Assertion failed) !image.empty() in function 'imencode'
2024-02-01 easyvolcap.… Cropping msks imgs for data/rxy/rxy_demo VAL 25% ━━━╺━━━━━━━━━ 1/4 0:00:01 < -:--:-- ? it/s p…
22:51:19.806… ->
decode_crop…
*** OpenCV(4.9.0) /io/opencv/modules/imgcodecs/src/loadsave.cpp:1121: error: (-215:Assertion failed) !image.empty() in
function 'imencode'
> /DATA/DATA1/rxy_code/projects/202312_demo/EasyVolcap/easyvolcap/utils/data_utils.py(1663)decode_crop_fill_im_bytes()
1661 K[1, 2] -= y
1662
-> 1663 im_bytes = cv2.imencode(encode_ext, img, [cv2.IMWRITE_JPEG_QUALITY, jpeg_quality, cv2.IMWRITE_PNG_COMPRESSION, png
1664 mk_bytes = cv2.imencode(encode_ext, msk, [cv2.IMWRITE_JPEG_QUALITY, jpeg_quality, cv2.IMWRITE_PNG_COMPRESSION, png
1665 return im_bytes, mk_bytes, K, h, w, x, yerror in imgui_bundle
Hello, your work is very interesting！
I execute evc -t gui -c configs/projects/realtime4dv/rendering/4k4d_0013_01.yaml,configs/specs/video.yaml on the Minimal Dataset.
I have the following error:
then, I followed the prompts to install imgui_bundle. however, I have a new problem:
`(4k4d) xuankai@com4-X780-G30:~/code/4K4D$ pip install imgui_bundle
Collecting imgui_bundle
Using cached imgui-bundle-1.3.0.tar.gz (36.8 MB)
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing metadata (pyproject.toml) ... done
Requirement already satisfied: numpy>=1.15 in /home/xuankai/anaconda3/envs/4k4d/lib/python3.9/site-packages (from imgui_bundle) (1.26.4)
Collecting munch>=2.0.0 (from imgui_bundle)
Using cached munch-4.0.0-py2.py3-none-any.whl.metadata (5.9 kB)
Collecting glfw>2.5 (from imgui_bundle)
Using cached glfw-2.7.0-py2.py27.py3.py30.py31.py32.py33.py34.py35.py36.py37.py38-none-manylinux2014_x86_64.whl.metadata (5.4 kB)
Collecting PyOpenGL>=3.0 (from imgui_bundle)
Using cached PyOpenGL-3.1.7-py3-none-any.whl.metadata (3.2 kB)
Requirement already satisfied: pillow>=9.0.0 in /home/xuankai/anaconda3/envs/4k4d/lib/python3.9/site-packages (from imgui_bundle) (10.2.0)
Using cached glfw-2.7.0-py2.py27.py3.py30.py31.py32.py33.py34.py35.py36.py37.py38-none-manylinux2014_x86_64.whl (211 kB)
Using cached munch-4.0.0-py2.py3-none-any.whl (9.9 kB)
Using cached PyOpenGL-3.1.7-py3-none-any.whl (2.4 MB)
Building wheels for collected packages: imgui_bundle
Building wheel for imgui_bundle (pyproject.toml) ... error
error: subprocess-exited-with-error
× Building wheel for imgui_bundle (pyproject.toml) did not run successfully.
│ exit code: 1
╰─> [118 lines of output]
--------------------------------------------------------------------------------
-- Trying 'Ninja' generator
--------------------------------
---------------------------
----------------------
-----------------
------------
-------
--
CMake Deprecation Warning at CMakeLists.txt:1 (cmake_minimum_required):
Compatibility with CMake < 3.5 will be removed from a future version of
CMake.
Update the VERSION argument <min> value or use a ...<max> suffix to tell
CMake that the project does not need compatibility with older versions.
Not searching for unused variables given on the command line.
-- The C compiler identification is GNU 9.4.0
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Check for working C compiler: /usr/bin/cc - skipped
-- Detecting C compile features
-- Detecting C compile features - done
-- The CXX compiler identification is GNU 9.4.0
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Check for working CXX compiler: /usr/bin/c++ - skipped
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Configuring done (2.6s)
-- Generating done (0.0s)
-- Build files have been written to: /tmp/pip-install-10dc4lcj/imgui-bundle_c98c9f82c8004e88b0d20ceb0adac3a6/_cmake_test_compile/build
--
-------
------------
-----------------
----------------------
---------------------------
--------------------------------
-- Trying 'Ninja' generator - success
--------------------------------------------------------------------------------
Configuring Project
Working directory:
/tmp/pip-install-10dc4lcj/imgui-bundle_c98c9f82c8004e88b0d20ceb0adac3a6/_skbuild/linux-x86_64-3.9/cmake-build
Command:
/tmp/pip-build-env-wz5irisp/overlay/lib/python3.9/site-packages/cmake/data/bin/cmake /tmp/pip-install-10dc4lcj/imgui-bundle_c98c9f82c8004e88b0d20ceb0adac3a6 -G Ninja -DCMAKE_MAKE_PROGRAM:FILEPATH=/tmp/pip-build-env-wz5irisp/overlay/lib/python3.9/site-packages/ninja/data/bin/ninja --no-warn-unused-cli -DCMAKE_INSTALL_PREFIX:PATH=/tmp/pip-install-10dc4lcj/imgui-bundle_c98c9f82c8004e88b0d20ceb0adac3a6/_skbuild/linux-x86_64-3.9/cmake-install/bindings/imgui_bundle -DPYTHON_VERSION_STRING:STRING=3.9.18 -DSKBUILD:INTERNAL=TRUE -DCMAKE_MODULE_PATH:PATH=/tmp/pip-build-env-wz5irisp/overlay/lib/python3.9/site-packages/skbuild/resources/cmake -DPYTHON_EXECUTABLE:PATH=/home/xuankai/anaconda3/envs/4k4d/bin/python -DPYTHON_INCLUDE_DIR:PATH=/home/xuankai/anaconda3/envs/4k4d/include/python3.9 -DPYTHON_LIBRARY:PATH=/home/xuankai/anaconda3/envs/4k4d/lib/libpython3.9.so -DPython_EXECUTABLE:PATH=/home/xuankai/anaconda3/envs/4k4d/bin/python -DPython_ROOT_DIR:PATH=/home/xuankai/anaconda3/envs/4k4d -DPython_FIND_REGISTRY:STRING=NEVER -DPython_INCLUDE_DIR:PATH=/home/xuankai/anaconda3/envs/4k4d/include/python3.9 -DPython3_EXECUTABLE:PATH=/home/xuankai/anaconda3/envs/4k4d/bin/python -DPython3_ROOT_DIR:PATH=/home/xuankai/anaconda3/envs/4k4d -DPython3_FIND_REGISTRY:STRING=NEVER -DPython3_INCLUDE_DIR:PATH=/home/xuankai/anaconda3/envs/4k4d/include/python3.9 -DCMAKE_MAKE_PROGRAM:FILEPATH=/tmp/pip-build-env-wz5irisp/overlay/lib/python3.9/site-packages/ninja/data/bin/ninja -DCMAKE_BUILD_TYPE:STRING=Release
Not searching for unused variables given on the command line.
-- The C compiler identification is GNU 9.4.0
-- The CXX compiler identification is GNU 9.4.0
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Check for working C compiler: /usr/bin/cc - skipped
-- Detecting C compile features
-- Detecting C compile features - done
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Check for working CXX compiler: /usr/bin/c++ - skipped
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- _him_check_if_no_backend_selected return ON
-- Cleaning bindings/imgui_bundle before pip build
CMake Warning (dev) at /tmp/pip-build-env-wz5irisp/overlay/lib/python3.9/site-packages/pybind11/share/cmake/pybind11/FindPythonLibsNew.cmake:101 (message):
Policy CMP0148 is not set: The FindPythonInterp and FindPythonLibs modules
are removed. Run "cmake --help-policy CMP0148" for policy details. Use
the cmake_policy command to set the policy and suppress this warning, or
preferably upgrade to using FindPython, either by calling it explicitly
before pybind11, or by setting PYBIND11_FINDPYTHON ON before pybind11.
Call Stack (most recent call first):
/tmp/pip-build-env-wz5irisp/overlay/lib/python3.9/site-packages/pybind11/share/cmake/pybind11/pybind11Tools.cmake:50 (find_package)
/tmp/pip-build-env-wz5irisp/overlay/lib/python3.9/site-packages/pybind11/share/cmake/pybind11/pybind11Common.cmake:192 (include)
/tmp/pip-build-env-wz5irisp/overlay/lib/python3.9/site-packages/pybind11/share/cmake/pybind11/pybind11Config.cmake:250 (include)
cmake/find_pybind11.cmake:20 (find_package)
CMakeLists.txt:329 (find_pybind11)
This warning is for project developers. Use -Wno-dev to suppress it.
'/home/xuankai/anaconda3/envs/4k4d/bin/python' '-c' 'import pybind11; print(pybind11.get_cmake_dir())'
-- Found PythonInterp: /home/xuankai/anaconda3/envs/4k4d/bin/python (found suitable version "3.9.18", minimum required is "3.6")
-- Found PythonLibs: /home/xuankai/anaconda3/envs/4k4d/lib/libpython3.9.so
-- Performing Test HAS_FLTO
-- Performing Test HAS_FLTO - Success
-- Found pybind11: /tmp/pip-build-env-wz5irisp/overlay/lib/python3.9/site-packages/pybind11/include (found version "2.12.0")
-- Performing Test CMAKE_HAVE_LIBC_PTHREAD
-- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Failed
-- Looking for pthread_create in pthreads
-- Looking for pthread_create in pthreads - not found
-- Looking for pthread_create in pthread
-- Looking for pthread_create in pthread - found
-- Found Threads: TRUE
-- Using X11 for window creation
CMake Error at /tmp/pip-build-env-wz5irisp/overlay/lib/python3.9/site-packages/cmake/data/share/cmake-3.29/Modules/FindPackageHandleStandardArgs.cmake:230 (message):
Could NOT find X11 (missing: X11_X11_INCLUDE_PATH X11_X11_LIB)
Call Stack (most recent call first):
/tmp/pip-build-env-wz5irisp/overlay/lib/python3.9/site-packages/cmake/data/share/cmake-3.29/Modules/FindPackageHandleStandardArgs.cmake:600 (_FPHSA_FAILURE_MESSAGE)
/tmp/pip-build-env-wz5irisp/overlay/lib/python3.9/site-packages/cmake/data/share/cmake-3.29/Modules/FindX11.cmake:676 (find_package_handle_standard_args)
external/glfw/glfw/CMakeLists.txt:208 (find_package)
-- Configuring incomplete, errors occurred!
Traceback (most recent call last):
File "/tmp/pip-build-env-wz5irisp/overlay/lib/python3.9/site-packages/skbuild/setuptools_wrap.py", line 666, in setup
env = cmkr.configure(
File "/tmp/pip-build-env-wz5irisp/overlay/lib/python3.9/site-packages/skbuild/cmaker.py", line 357, in configure
raise SKBuildError(msg)
An error occurred while configuring with CMake.
Command:
/tmp/pip-build-env-wz5irisp/overlay/lib/python3.9/site-packages/cmake/data/bin/cmake /tmp/pip-install-10dc4lcj/imgui-bundle_c98c9f82c8004e88b0d20ceb0adac3a6 -G Ninja -DCMAKE_MAKE_PROGRAM:FILEPATH=/tmp/pip-build-env-wz5irisp/overlay/lib/python3.9/site-packages/ninja/data/bin/ninja --no-warn-unused-cli -DCMAKE_INSTALL_PREFIX:PATH=/tmp/pip-install-10dc4lcj/imgui-bundle_c98c9f82c8004e88b0d20ceb0adac3a6/_skbuild/linux-x86_64-3.9/cmake-install/bindings/imgui_bundle -DPYTHON_VERSION_STRING:STRING=3.9.18 -DSKBUILD:INTERNAL=TRUE -DCMAKE_MODULE_PATH:PATH=/tmp/pip-build-env-wz5irisp/overlay/lib/python3.9/site-packages/skbuild/resources/cmake -DPYTHON_EXECUTABLE:PATH=/home/xuankai/anaconda3/envs/4k4d/bin/python -DPYTHON_INCLUDE_DIR:PATH=/home/xuankai/anaconda3/envs/4k4d/include/python3.9 -DPYTHON_LIBRARY:PATH=/home/xuankai/anaconda3/envs/4k4d/lib/libpython3.9.so -DPython_EXECUTABLE:PATH=/home/xuankai/anaconda3/envs/4k4d/bin/python -DPython_ROOT_DIR:PATH=/home/xuankai/anaconda3/envs/4k4d -DPython_FIND_REGISTRY:STRING=NEVER -DPython_INCLUDE_DIR:PATH=/home/xuankai/anaconda3/envs/4k4d/include/python3.9 -DPython3_EXECUTABLE:PATH=/home/xuankai/anaconda3/envs/4k4d/bin/python -DPython3_ROOT_DIR:PATH=/home/xuankai/anaconda3/envs/4k4d -DPython3_FIND_REGISTRY:STRING=NEVER -DPython3_INCLUDE_DIR:PATH=/home/xuankai/anaconda3/envs/4k4d/include/python3.9 -DCMAKE_MAKE_PROGRAM:FILEPATH=/tmp/pip-build-env-wz5irisp/overlay/lib/python3.9/site-packages/ninja/data/bin/ninja -DCMAKE_BUILD_TYPE:STRING=Release
Source directory:
/tmp/pip-install-10dc4lcj/imgui-bundle_c98c9f82c8004e88b0d20ceb0adac3a6
Working directory:
/tmp/pip-install-10dc4lcj/imgui-bundle_c98c9f82c8004e88b0d20ceb0adac3a6/_skbuild/linux-x86_64-3.9/cmake-build
Please see CMake's output for more information.
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for imgui_bundle
Failed to build imgui_bundle
ERROR: Could not build wheels for imgui_bundle, which is required to install pyproject.toml-based projects`
then, I tried to install imgui_bundle through https://github.com/pthom/imgui_bundle, But this asks for Python >=3.10.
I wonder if you have any advice on the problems I am facing? Thank you very much!
How to convert CamPose.inf to extri.yml?
About real-time rendering
Great work!
I wonder whether 4k4d can achieve real-time effect like the demo in GPS Gaussian ? If possible, can our algorithm still achieve such amazing framerates and rendering effect?
CUDA out of memory
Hi. Allow me to thank you for the other day's support.
I was trying out Rendering With Minimal Dataset (Only Encoded Videos) using the script file you provided, but I encountered RuntimeError: CUDA out of memory. However, the memory size displayed under Including non-PyTorch memory is a very large 17179869184.00 GiB, so I'm not sure if my PC's GPU is insufficient or if I've made some mistake in the settings. Is it possible to run 4K4D on a GPU with 8GB of memory?
Environment
- OS: Ubuntu 22.04.3 LTS (Windows11 Pro WSL2)
- Anaconda: conda 23.7.4
- GPU: NVIDIA GeForce RTX 3060 Ti (Memory 8GB)
Command
python scripts/realtime4dv/extract_images.py --data_root data/renbody/0012_01
python scripts/realtime4dv/extract_masks.py --data_root data/renbody/0012_01
evc -t gui -c configs/projects/realtime4dv/rendering/4k4d_0012_01.yaml,configs/specs/video.yaml
Log
(easyvolcap) miu@garnet:~/mmd/4K4D$ evc -t gui -c configs/projects/realtime4dv/rendering/4k4d_0012_01.yaml,configs/specs/video.yaml
2023-12-23 08:46:38.110584 easyvolcap.scripts.main -> preflight: Starting experiment: 4k4d_0012_01, command: main.py:80 gui
2023-… easyv… Loading mask bytes for data/renbody/0012_01/masks_libx265 VAL 100% ━━━━━━ 9,000… 0:00:… < 0:00:… 170.9 p…08:47… -> it/s
load_…
2023-… easyv… Loading imgs bytes for data/renbody/0012_01/images_libx265 VAL 100% ━━━━━━ 9,000… 0:01:… < 0:00… 46.42 p…08:49… -> it/s
load_…
2023-12… easyvol… Cropping msks imgs for data/renbody/0012_01 VAL 100% ━━━━━━━━━ 9,000/9… 0:00:34 < 0:00:00 252.9 p…08:49:3… -> it/s
decode_…
2023-12-23 08:49:41.726240 easyvolcap.runners.visualizers.volumetric_video_visualizer volumetric_video_visualizer.py:76 -> __init__: Visualization output:
data/result/4k4d_0012_01/{RENDER,DEPTH}
2023-12-23 08:49:41.728334 easyvolcap.runners.recorders -> __init__: Saved config file to recorders.py:105 data/record/4k4d_0012_01/4k4d_0012_01_1703288981.yaml
2023-12-23 08:49:45.815186 easyvolcap.utils.console_utils -> inner: Runtime exception: The console_utils.py:337 following operation failed in the TorchScript interpreter.
Traceback of TorchScript (most recent call last):
File "/home/miu/mmd/4K4D/easyvolcap/utils/enerf_utils.py", line 94, in
sample_geometry_feature_image
# -> B, S, P, 4
xyz1 = (xyz1[..., None, :, :] @ src_exts.mT)
xyzs = xyz1[..., :3] @ src_ixts.mT # B, S, P, 3 @ B, S, 3, 3
~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
xy = xyzs[..., :-1] / (xyzs[..., -1:] + 1e-8) # B, S, P, 2
x, y = xy.chunk(2, dim=-1) # B, S, P, 1
RuntimeError: CUDA out of memory. Tried to allocate 180.00 MiB. GPU 0
has a total capacty of 8.00 GiB of which 0 bytes is free. Including
non-PyTorch memory, this process has 17179869184.00 GiB memory in use.
Of the allocated memory 7.80 GiB is allocated by PyTorch, and 89.26 MiB
is reserved by PyTorch but unallocated. If reserved but unallocated
memory is large try setting max_split_size_mb to avoid fragmentation.
See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
╭───────────────────────────────────────── Traceback (most recent call last) ──────────────────────────────────────────╮│ /home/miu/mmd/4K4D/easyvolcap/utils/console_utils.py:334 in inner ││ ││ ❱ 334 │ │ │ return func(*args, **kwargs) ││ ││ /home/miu/mmd/4K4D/easyvolcap/engine/registry.py:56 in inner ││ ││ ❱ 56 │ │ return call_from_cfg(func, cfg) ││ ││ /home/miu/mmd/4K4D/easyvolcap/engine/registry.py:47 in call_from_cfg ││ ││ ❱ 47 │ return func(**call_args) ││ ││ /home/miu/mmd/4K4D/easyvolcap/scripts/main.py:119 in gui ││ ││ ❱ 119 │ viewer: "VolumetricVideoViewer" = RUNNERS.build(viewer_cfg, runner=runner) # will start the window ││ ││ /home/miu/mmd/4K4D/easyvolcap/engine/registry.py:300 in build ││ ││ ❱ 300 │ │ return self.build_func(*args, **kwargs, registry=self) ││ ││ /home/miu/mmd/4K4D/easyvolcap/engine/registry.py:131 in build_from_cfg ││ ││ ❱ 131 │ return call_from_cfg(obj_cls, args) ││ ││ /home/miu/mmd/4K4D/easyvolcap/engine/registry.py:47 in call_from_cfg ││ ││ ❱ 47 │ return func(**call_args) ││ ││ /home/miu/mmd/4K4D/easyvolcap/runners/volumetric_video_viewer.py:113 in __init__ ││ ││ ❱ 113 │ │ self.epoch = self.runner.load_network() # load weights only (without optimizer states) ││ ││ /home/miu/mmd/4K4D/easyvolcap/runners/volumetric_video_runner.py:179 in load_network ││ ││ ❱ 179 │ │ epoch = load_network(model=self.model, # only loading the network, without recorder? ││ ││ /home/miu/mmd/4K4D/easyvolcap/utils/net_utils.py:2657 in load_network ││ ││ ❱ 2657 │ (model if not isinstance(model, DDP) else model.module).load_state_dict(pretrained_model, strict=strict) ││ ││ /home/miu/anaconda3/envs/easyvolcap/lib/python3.11/site-packages/torch/nn/modules/module.py:2138 in load_state_dict ││ ││ ❱ 2138 │ │ load(self, state_dict) ││ ││ /home/miu/anaconda3/envs/easyvolcap/lib/python3.11/site-packages/torch/nn/modules/module.py:2126 in load ││ ││ ❱ 2126 │ │ │ │ │ load(child, child_state_dict, child_prefix) ││ ││ /home/miu/anaconda3/envs/easyvolcap/lib/python3.11/site-packages/torch/nn/modules/module.py:2131 in load ││ ││ ❱ 2131 │ │ │ │ out = hook(module, incompatible_keys) ││ ││ /home/miu/anaconda3/envs/easyvolcap/lib/python3.11/site-packages/torch/utils/_contextlib.py:115 in decorate_context ││ ││ ❱ 115 │ │ │ return func(*args, **kwargs) ││ ││ /home/miu/mmd/4K4D/easyvolcap/models/samplers/super_charged_r4dv.py:375 in _load_state_dict_post_hook ││ ││ ❱ 375 │ │ │ rgbw, cent = l_average_single_frame(i) ││ ││ /home/miu/anaconda3/envs/easyvolcap/lib/python3.11/site-packages/torch/utils/_contextlib.py:115 in decorate_context ││ ││ ❱ 115 │ │ │ return func(*args, **kwargs) ││ ││ /home/miu/mmd/4K4D/easyvolcap/models/samplers/super_charged_r4dv.py:148 in average_single_frame ││ ││ ❱ 148 │ ibrs_rgbs = sample_geometry_feature_image( │╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯RuntimeError: The following operation failed in the TorchScript interpreter.
Traceback of TorchScript (most recent call last):
File "/home/miu/mmd/4K4D/easyvolcap/utils/enerf_utils.py", line 94, in sample_geometry_feature_image
# -> B, S, P, 4
xyz1 = (xyz1[..., None, :, :] @ src_exts.mT)
xyzs = xyz1[..., :3] @ src_ixts.mT # B, S, P, 3 @ B, S, 3, 3
~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
xy = xyzs[..., :-1] / (xyzs[..., -1:] + 1e-8) # B, S, P, 2
x, y = xy.chunk(2, dim=-1) # B, S, P, 1
RuntimeError: CUDA out of memory. Tried to allocate 180.00 MiB. GPU 0 has a total capacty of 8.00 GiB of which 0 bytes is free. Including non-PyTorch memory, this process has 17179869184.00 GiB memory in use. Of the allocated memory 7.80 GiB is allocated by PyTorch, and 89.26 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
2023-12-23 easyvolcap.model… Caching rgbw and center 0% ━━━━━━━━━━━━━━━━━ 0/150 0:00:03 < -:--:-- ? it/s s…08:49:46.335766 ->
_load_state_dict…
*** The following operation failed in the TorchScript interpreter.
Traceback of TorchScript (most recent call last):
File "/home/miu/mmd/4K4D/easyvolcap/utils/enerf_utils.py", line 94, in sample_geometry_feature_image
# -> B, S, P, 4
xyz1 = (xyz1[..., None, :, :] @ src_exts.mT)
xyzs = xyz1[..., :3] @ src_ixts.mT # B, S, P, 3 @ B, S, 3, 3
~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
xy = xyzs[..., :-1] / (xyzs[..., -1:] + 1e-8) # B, S, P, 2
x, y = xy.chunk(2, dim=-1) # B, S, P, 1
RuntimeError: CUDA out of memory. Tried to allocate 180.00 MiB. GPU 0 has a total capacty of 8.00 GiB of which 0 bytes
is free. Including non-PyTorch memory, this process has 17179869184.00 GiB memory in use. Of the allocated memory 7.80
GiB is allocated by PyTorch, and 89.26 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory islarge try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and
PYTORCH_CUDA_ALLOC_CONF
> /home/miu/mmd/4K4D/easyvolcap/models/samplers/super_charged_r4dv.py(148)average_single_frame()
146
147 # Compute projected color of every image, using the original size image
--> 148 ibrs_rgbs = sample_geometry_feature_image(
149 xyz,
150 src_feat_inps,
How long does it take to train a model
How long does it take to extract the model? and how long does it take to generate a new action?
thanks a lot
Test view
I run evc -t test -c configs/projects/realtime4dv/rendering/4k4d_sport1.yaml,configs/specs/eval.yaml in NHR dataset and find that the code test 0th, 25th, and 50th input views (200 frames). Is this consistent with the experiment in the paper? In addtion, the code(basketball) test 0th, 26th, and 52th input views(194 frames).
Originally posted by @GuaGod in zju3dv/EasyVolcap#17
Bad results than expected.
Hello,
I followed the instructions in the readme, and I tested your results with NHR basketball dataset, but I got a bad result:
bad.results.mp4
What should I do to get a good result?
How to get a 3d object from a new video?
Hello, thank you for the great work. I was wondering if it was possible with the current implementation to pass as input 2/3 videos from a new scene and get a 3d animation file that could be rendered using Blender for example?
Question about KPlanes result of DyNeRF dataset?
May I ask whether your k-planes results are from orginal paper results or from your own actual runs to get? I trained the K-planes hybrid version using the DyNeRF dataset, following K-planes config file without making any other changes to the configuration. There are difference between the results in the paper and my results. sarafridov/K-Planes#30 (comment)
An error occurred(Segmentation fault) while loading the model
Extraction of depth map and normal map
Hi,
Thank you for the great work!
I'm assuming that we can render depth and normal map from the learned point clouds.
Could you give any tips for extracting depth and normal map from the learned models?
Thanks,
Bradley
Licensing
Great work! 🥇 🤯
How will the licensing look?
easyvolcap.utils.console_utils -> inner: Runtime exception: 'SuperChargedR4DV is not in the samplers registry'
Hi, thanks for your great job!
I tried to visualize the result of NHR basketball dataset, but it came with the following error:
easyvolcap.utils.console_utils -> inner: Runtime exception: 'SuperChargedR4DV is not in the samplers registry'
Hope for your reply!
training speed
Hello, I have a strange problem with train time.
I executed evc-train -c configs/exps/4k4d/4k4d_0013_01_r4.yaml,configs/specs/static.yaml,configs/specs/tiny.yaml exp_name=4k4d_0013_01_r4_static on NVIDIA GeForce RTX 4090.
But it takes me about 40 minutes to train single-frame.
It's even more serious when I executed evc-train -c configs/exps/4k4d/4k4d_0013_01_r4.yaml. it takes me about 4 days to train all frames (NVIDIA GeForce RTX 4090).
Moreover, I also observed a strange phenomenon during my training. When I ran a 4k4d training experiment on the 4090, the gpustat command showed that there were two experiments running.
(The same is true on 4090)
In addition, the psnr of the training results of 4k4d_0013_01_r4_static also failed to reach about 30.
Can you give me any advice? Thank you so much for all your help!
where is the preprocess scripts for DNA-Rendering dataset?
Thanks for your nice work and Could you please inform me where the script for preprocessing the camera and SMPL-X parameters within the DNA-Rendering dataset can be found?
Are there any plans to release missing files?
Thanks for the great work !
I see that the following two files have not been released yet, but is there any plan to release them in the future?
thanks a lot
Nice Work!
Thanks for sharing the result. I have a couple following questions:
- Why use depth peeling? There are many other ways of sorting depth.
- Why use space carving for generating point cloud? What is the advantage?
- Do you think it would converge faster if include any shape aware terms like human body detection?
- Using temporal info is expensive, but what if just us them as simple consistency check for adjacent frames
- Does it handle reflection well since you enabled view-dependent training?
- Other methods could be tried in the future.
Runtime exception: Could not find {optimized/intri.yml,optimized/extri.yml}
Could not initialize OpenGL context
I have download the [pretrained models], and place them into data/trained_model (e.g. data/trained_model/4k4d_0008_01/1599.npz, data/trained_model/4k4d_0008_01_r4/latest.pt and data/trained_model/4k4d_0008_01_mb/-1.npz)
and I have download the [Minimal Dataset],
run the code
# For foreground datasets with masks and masked images (DNA-Rendering, NHR, ZJU-Mocap)
python scripts/realtime4dv/extract_images.py --data_root data/renbody/0008_01
python scripts/realtime4dv/extract_masks.py --data_root data/renbody/0008_01
it perform well.
I run the code:
evc-gui -c configs/projects/realtime4dv/rendering/4k4d_0008_01.yaml
the error is
How to solve this problem?
Error in opengl
Hi, I want to run this project on a cloud server through mobaxterm.
When I run the cmd evc -t gui -c configs/projects/realtime4dv/rendering/4k4d_actor5_6.yaml,configs/specs/video.yaml,get a error in function def common_opengl_options().
Could you please give me me some advice about the error. Hope your reply.
The log
╭─────────────────────────────────────────────────── Traceback (most recent call last) ────────────────────────────────────────────────────╮
│ /data/shaenli/4K4D/easyvolcap/utils/console_utils.py:338 in inner │
│ │
│ ❱ 338 │ │ │ return func(*args, **kwargs) │
│ │
│ /data/shaenli/4K4D/easyvolcap/engine/registry.py:56 in inner │
│ │
│ ❱ 56 │ │ return call_from_cfg(func, cfg) │
│ │
│ /data/shaenli/4K4D/easyvolcap/engine/registry.py:47 in call_from_cfg │
│ │
│ ❱ 47 │ return func(**call_args) │
│ │
│ /data/shaenli/4K4D/easyvolcap/scripts/main.py:119 in gui │
│ │
│ ❱ 119 │ viewer: "VolumetricVideoViewer" = RUNNERS.build(viewer_cfg, runner=runner) # will start the window │
│ │
│ /data/shaenli/4K4D/easyvolcap/engine/registry.py:300 in build │
│ │
│ ❱ 300 │ │ return self.build_func(*args, **kwargs, registry=self) │
│ │
│ /data/shaenli/4K4D/easyvolcap/engine/registry.py:131 in build_from_cfg │
│ │
│ ❱ 131 │ return call_from_cfg(obj_cls, args) │
│ │
│ /data/shaenli/4K4D/easyvolcap/engine/registry.py:47 in call_from_cfg │
│ │
│ ❱ 47 │ return func(**call_args) │
│ │
│ /data/shaenli/4K4D/easyvolcap/runners/volumetric_video_viewer.py:123 in __init__ │
│ │
│ ❱ 123 │ │ self.init_opengl() │
│ │
│ /data/shaenli/4K4D/easyvolcap/runners/volumetric_video_viewer.py:1247 in init_opengl │
│ │
│ ❱ 1247 │ │ common_opengl_options() │
│ │
│ /data/shaenli/4K4D/easyvolcap/utils/gl_utils.py:65 in common_opengl_options │
│ │
│ ❱ 65 │ gl.glEnable(gl.GL_ALPHA_TEST) │
│ │
│ /root/miniconda3/lib/python3.11/site-packages/OpenGL/error.py:228 in glCheckError │
│ │
│ ❱ 228 │ │ │ │ │ raise GLError( │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
GLError: GLError(
err = 1280,
baseOperation = glEnable,
cArguments = (GL_ALPHA_TEST,)
)
*** GLError(
err = 1280,
baseOperation = glEnable,
cArguments = (GL_ALPHA_TEST,)
)
> /root/miniconda3/lib/python3.11/site-packages/OpenGL/error.py(228)glCheckError()
226 err = self._currentChecker()
227 if err != self._noErrorResult:
--> 228 raise GLError(
229 err,
230 result,
environment
- OS：Ubuntu 22.04.2 LTS
- CUDA：11.8
# packages in environment at /root/miniconda3:
#
# Name Version Build Channel
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 2_gnu conda-forge
absl-py 2.0.0 pypi_0 pypi
addict 2.4.0 pypi_0 pypi
ansi2html 1.9.1 pypi_0 pypi
antlr4-python3-runtime 4.9.3 pypi_0 pypi
anyio 4.2.0 pypi_0 pypi
aom 3.7.1 h59595ed_0 conda-forge
archspec 0.2.1 pyhd3eb1b0_0
argon2-cffi 23.1.0 pypi_0 pypi
argon2-cffi-bindings 21.2.0 pypi_0 pypi
arrow 1.3.0 pypi_0 pypi
asttokens 2.4.1 pypi_0 pypi
async-lru 2.0.4 pypi_0 pypi
attrs 23.2.0 pypi_0 pypi
autopep8 2.0.4 pypi_0 pypi
babel 2.14.0 pypi_0 pypi
beautifulsoup4 4.12.2 pypi_0 pypi
black 23.12.1 pypi_0 pypi
bleach 6.1.0 pypi_0 pypi
blessed 1.20.0 pypi_0 pypi
blinker 1.7.0 pypi_0 pypi
boltons 23.1.1 pyhd8ed1ab_0 conda-forge
brotli-python 1.1.0 py311hb755f60_1 conda-forge
bzip2 1.0.8 h7b6447c_0
c-ares 1.24.0 hd590300_0 conda-forge
ca-certificates 2023.12.12 h06a4308_0
cachetools 5.3.2 pypi_0 pypi
cairo 1.18.0 h3faef2a_0 conda-forge
calmsize 0.1.3 pypi_0 pypi
certifi 2023.11.17 py311h06a4308_0
cffi 1.16.0 py311hb3a22ac_0 conda-forge
charset-normalizer 2.0.4 pyhd3eb1b0_0
chumpy 0.70 pypi_0 pypi
clang-format 17.0.6 pypi_0 pypi
click 8.1.7 pypi_0 pypi
cloudpickle 3.0.0 pypi_0 pypi
cmake 3.28.1 hcfe8598_0 conda-forge
colorama 0.4.6 pyhd8ed1ab_0 conda-forge
comm 0.2.1 pypi_0 pypi
commentjson 0.9.0 pypi_0 pypi
conda 23.11.0 py311h06a4308_0
conda-content-trust 0.2.0 pyhd8ed1ab_0 conda-forge
conda-libmamba-solver 23.12.0 pyhd3eb1b0_1
conda-package-handling 2.2.0 pyh38be061_0 conda-forge
conda-package-streaming 0.9.0 pyhd8ed1ab_0 conda-forge
configargparse 1.7 pypi_0 pypi
contourpy 1.2.0 pypi_0 pypi
cryptography 41.0.7 py311hcb13ee4_1 conda-forge
cuda-cudart 12.1.105 0 nvidia
cuda-cupti 12.1.105 0 nvidia
cuda-libraries 12.1.0 0 nvidia
cuda-nvrtc 12.1.105 0 nvidia
cuda-nvtx 12.1.105 0 nvidia
cuda-opencl 12.3.101 0 nvidia
cuda-python 12.3.0 pypi_0 pypi
cuda-runtime 12.1.0 0 nvidia
curl 8.5.0 hca28451_0 conda-forge
cycler 0.12.1 pypi_0 pypi
dash 2.14.2 pypi_0 pypi
dash-core-components 2.0.0 pypi_0 pypi
dash-html-components 2.0.0 pypi_0 pypi
dash-table 5.0.0 pypi_0 pypi
dav1d 1.2.1 hd590300_0 conda-forge
debugpy 1.8.0 pypi_0 pypi
decorator 5.1.1 pypi_0 pypi
defusedxml 0.7.1 pypi_0 pypi
detectron2 0.6 pypi_0 pypi
diff-gauss 1.0.5 pypi_0 pypi
distro 1.9.0 pyhd8ed1ab_0 conda-forge
docstring-parser 0.15 pypi_0 pypi
dotmap 1.3.30 pypi_0 pypi
easyvolcap 0.0.0 pypi_0 pypi
einops 0.7.0 pypi_0 pypi
et-xmlfile 1.1.0 pypi_0 pypi
executing 2.0.1 pypi_0 pypi
expat 2.5.0 hcb278e6_1 conda-forge
fast-autocomplete 0.9.0 pypi_0 pypi
fastjsonschema 2.19.1 pypi_0 pypi
ffmpeg 6.1.1 gpl_h186bccc_100 conda-forge
filelock 3.9.0 pypi_0 pypi
flask 3.0.0 pypi_0 pypi
flatbuffers 23.5.26 pypi_0 pypi
fmt 10.1.1 h00ab1b0_1 conda-forge
font-ttf-dejavu-sans-mono 2.37 hab24e00_0 conda-forge
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fontconfig 2.14.2 h14ed4e7_0 conda-forge
fonts-conda-ecosystem 1 0 conda-forge
fonts-conda-forge 1 0 conda-forge
fonttools 4.47.0 pypi_0 pypi
fqdn 1.5.1 pypi_0 pypi
freetype 2.12.1 h267a509_2 conda-forge
freetype-py 2.4.0 pypi_0 pypi
fribidi 1.0.10 h36c2ea0_0 conda-forge
fsspec 2023.12.2 pypi_0 pypi
func-timeout 4.3.5 pypi_0 pypi
fvcore 0.1.5.post20221221 pypi_0 pypi
gettext 0.21.1 h27087fc_0 conda-forge
git 2.43.0 pl5321h7bc287a_0 conda-forge
glfw 2.6.4 pypi_0 pypi
gmp 6.3.0 h59595ed_0 conda-forge
gnutls 3.7.9 hb077bed_0 conda-forge
google-auth 2.26.1 pypi_0 pypi
google-auth-oauthlib 1.2.0 pypi_0 pypi
gpustat 1.2.dev19+gf4cd639 pypi_0 pypi
graphite2 1.3.13 h58526e2_1001 conda-forge
grpcio 1.60.0 pypi_0 pypi
h5py 3.10.0 pypi_0 pypi
harfbuzz 8.3.0 h3d44ed6_0 conda-forge
human-body-prior 2.2.2.0 pypi_0 pypi
hydra-core 1.3.2 pypi_0 pypi
icu 73.2 h59595ed_0 conda-forge
idna 3.6 pyhd8ed1ab_0 conda-forge
imageio 2.33.1 pypi_0 pypi
imgui-bundle 1.1.0 pypi_0 pypi
importlib-metadata 7.0.1 pypi_0 pypi
iopath 0.1.9 pypi_0 pypi
ipdb 0.13.13 pypi_0 pypi
ipykernel 6.28.0 pypi_0 pypi
ipython 8.19.0 pypi_0 pypi
ipywidgets 8.1.1 pypi_0 pypi
isoduration 20.11.0 pypi_0 pypi
itsdangerous 2.1.2 pypi_0 pypi
jedi 0.19.1 pypi_0 pypi
jinja2 3.1.2 pypi_0 pypi
joblib 1.3.2 pypi_0 pypi
json5 0.9.14 pypi_0 pypi
jsonpatch 1.32 pyhd3eb1b0_0
jsonpointer 2.1 pyhd3eb1b0_0
jsonschema 4.20.0 pypi_0 pypi
jsonschema-specifications 2023.12.1 pypi_0 pypi
jupyter 1.0.0 pypi_0 pypi
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jupyter-console 6.6.3 pypi_0 pypi
jupyter-core 5.7.0 pypi_0 pypi
jupyter-events 0.9.0 pypi_0 pypi
jupyter-lsp 2.2.1 pypi_0 pypi
jupyter-server 2.12.2 pypi_0 pypi
jupyter-server-terminals 0.5.1 pypi_0 pypi
jupyterlab 4.0.10 pypi_0 pypi
jupyterlab-pygments 0.3.0 pypi_0 pypi
jupyterlab-server 2.25.2 pypi_0 pypi
jupyterlab-widgets 3.0.9 pypi_0 pypi
keyutils 1.6.1 h166bdaf_0 conda-forge
kiwisolver 1.4.5 pypi_0 pypi
kornia 0.7.1 pypi_0 pypi
krb5 1.21.2 h659d440_0 conda-forge
lame 3.100 h166bdaf_1003 conda-forge
lark-parser 0.7.8 pypi_0 pypi
lazy-loader 0.3 pypi_0 pypi
ld_impl_linux-64 2.38 h1181459_1
libarchive 3.7.2 h2aa1ff5_1 conda-forge
libass 0.17.1 h8fe9dca_1 conda-forge
libcublas 12.1.0.26 0 nvidia
libcufft 11.0.2.4 0 nvidia
libcufile 1.8.1.2 0 nvidia
libcurand 10.3.4.107 0 nvidia
libcurl 8.5.0 hca28451_0 conda-forge
libcusolver 11.4.4.55 0 nvidia
libcusparse 12.0.2.55 0 nvidia
libdrm 2.4.114 h166bdaf_0 conda-forge
libedit 3.1.20230828 h5eee18b_0
libev 4.33 h7f8727e_1
libevent 2.1.10 h28343ad_4 conda-forge
libexpat 2.5.0 hcb278e6_1 conda-forge
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libglib 2.78.3 h783c2da_0 conda-forge
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libiconv 1.17 hd590300_2 conda-forge
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lpips 0.1.4 pypi_0 pypi
lz4-c 1.9.4 h6a678d5_0
lzo 2.10 h516909a_1000 conda-forge
mamba 1.5.6 py311h3072747_0 conda-forge
markdown 3.5.1 pypi_0 pypi
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markupsafe 2.1.3 pypi_0 pypi
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memory-tempfile 2.2.3 pypi_0 pypi
menuinst 2.0.1 py311h38be061_0 conda-forge
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msgpack 1.0.7 pypi_0 pypi
munch 4.0.0 pypi_0 pypi
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nettle 3.9.1 h7ab15ed_0 conda-forge
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notebook 7.0.6 pypi_0 pypi
notebook-shim 0.2.3 pypi_0 pypi
numpy 1.24.1 pypi_0 pypi
nvdiffrast 0.3.1 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
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nvidia-cusolver-cu12 11.4.5.107 pypi_0 pypi
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nvidia-ml-py 12.535.133 pypi_0 pypi
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nvidia-nvjitlink-cu12 12.3.101 pypi_0 pypi
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oauthlib 3.2.2 pypi_0 pypi
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packaging 23.2 pyhd8ed1ab_0 conda-forge
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pcre2 10.42 hebb0a14_0
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perl 5.32.1 7_hd590300_perl5 conda-forge
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pip 23.3.2 pyhd8ed1ab_0 conda-forge
pixman 0.42.2 h59595ed_0 conda-forge
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portalocker 2.8.2 pypi_0 pypi
prometheus-client 0.19.0 pypi_0 pypi
prompt-toolkit 3.0.43 pypi_0 pypi
protobuf 3.20.3 pypi_0 pypi
psutil 5.9.7 pypi_0 pypi
pthread-stubs 0.4 h36c2ea0_1001 conda-forge
ptyprocess 0.7.0 pypi_0 pypi
pure-eval 0.2.2 pypi_0 pypi
pyasn1 0.5.1 pypi_0 pypi
pyasn1-modules 0.3.0 pypi_0 pypi
pybind11-abi 4 hd3eb1b0_1
pycocotools 2.0.7 pypi_0 pypi
pycodestyle 2.11.1 pypi_0 pypi
pycosat 0.6.6 py311h459d7ec_0 conda-forge
pycparser 2.21 pyhd3eb1b0_0
pyglet 2.0.10 pypi_0 pypi
pyglm 2.7.1 pypi_0 pypi
pygments 2.17.2 pypi_0 pypi
pymcubes 0.1.4 pypi_0 pypi
pymeshlab 2023.12 pypi_0 pypi
pyntcloud 0.3.1 pypi_0 pypi
pyopengl 3.1.0 pypi_0 pypi
pyopenssl 23.3.0 pyhd8ed1ab_0 conda-forge
pyparsing 3.1.1 pypi_0 pypi
pyperclip 1.8.2 pypi_0 pypi
pyquaternion 0.9.9 pypi_0 pypi
pyrender 0.1.45 pypi_0 pypi
pysocks 1.7.1 pyha2e5f31_6 conda-forge
python 3.11.7 hab00c5b_1_cpython conda-forge
python-dateutil 2.8.2 pypi_0 pypi
python-json-logger 2.0.7 pypi_0 pypi
python_abi 3.11 4_cp311 conda-forge
pytorch-cuda 12.1 ha16c6d3_5 pytorch
pytorch-memlab 0.3.0 pypi_0 pypi
pytorch-msssim 1.0.0 pypi_0 pypi
pytorch3d 0.7.5 pypi_0 pypi
pyturbojpeg 1.7.3 pypi_0 pypi
pytz 2023.3.post1 pypi_0 pypi
pyyaml 6.0.1 pypi_0 pypi
pyzmq 25.1.2 pypi_0 pypi
qtconsole 5.5.1 pypi_0 pypi
qtpy 2.4.1 pypi_0 pypi
readline 8.2 h5eee18b_0
referencing 0.32.0 pypi_0 pypi
reproc 14.2.4 h295c915_1
reproc-cpp 14.2.4 h295c915_1
requests 2.31.0 pyhd8ed1ab_0 conda-forge
requests-oauthlib 1.3.1 pypi_0 pypi
retrying 1.3.4 pypi_0 pypi
rfc3339-validator 0.1.4 pypi_0 pypi
rfc3986-validator 0.1.1 pypi_0 pypi
rhash 1.4.4 hd590300_0 conda-forge
rich 13.7.0 pypi_0 pypi
rpds-py 0.16.2 pypi_0 pypi
rsa 4.9 pypi_0 pypi
ruamel.yaml 0.18.5 py311h459d7ec_0 conda-forge
ruamel.yaml.clib 0.2.7 py311h459d7ec_2 conda-forge
scikit-image 0.22.0 pypi_0 pypi
scikit-learn 1.3.2 pypi_0 pypi
scipy 1.11.4 pypi_0 pypi
send2trash 1.8.2 pypi_0 pypi
setuptools 69.0.3 pyhd8ed1ab_0 conda-forge
shtab 1.6.5 pypi_0 pypi
simple-knn 0.0.0 pypi_0 pypi
six 1.16.0 pypi_0 pypi
smplx 0.1.28 pypi_0 pypi
sniffio 1.3.0 pypi_0 pypi
sounddevice 0.4.6 pypi_0 pypi
soupsieve 2.5 pypi_0 pypi
sqlite 3.41.2 h5eee18b_0
stack-data 0.6.3 pypi_0 pypi
svt-av1 1.8.0 h59595ed_0 conda-forge
sympy 1.12 pypi_0 pypi
tabulate 0.9.0 pypi_0 pypi
tenacity 8.2.3 pypi_0 pypi
tensorboard 2.15.1 pypi_0 pypi
tensorboard-data-server 0.7.2 pypi_0 pypi
tensorboardx 2.6.2.2 pypi_0 pypi
termcolor 2.4.0 pypi_0 pypi
terminado 0.18.0 pypi_0 pypi
threadpoolctl 3.2.0 pypi_0 pypi
tifffile 2023.12.9 pypi_0 pypi
timg 1.1.6 pypi_0 pypi
tinycss2 1.2.1 pypi_0 pypi
tinycudann 1.7 pypi_0 pypi
tk 8.6.13 noxft_h4845f30_101 conda-forge
tmux 3.3 h385fc29_0 conda-forge
tomli 2.0.1 pypi_0 pypi
torch 2.1.0+cu118 pypi_0 pypi
torch-scatter 2.1.2 pypi_0 pypi
torch-tb-profiler 0.4.3 pypi_0 pypi
torchaudio 2.1.0+cu118 pypi_0 pypi
torchdiffeq 0.2.3 pypi_0 pypi
torchmcubes 0.1.0 pypi_0 pypi
torchvision 0.16.0+cu118 pypi_0 pypi
tornado 6.4 pypi_0 pypi
tqdm 4.66.1 pyhd8ed1ab_0 conda-forge
traitlets 5.14.1 pypi_0 pypi
trimesh 4.0.8 pypi_0 pypi
triton 2.1.0 pypi_0 pypi
truststore 0.8.0 pyhd8ed1ab_0 conda-forge
types-python-dateutil 2.8.19.14 pypi_0 pypi
typing-extensions 4.4.0 pypi_0 pypi
tyro 0.6.3 pypi_0 pypi
tzdata 2023.4 pypi_0 pypi
ujson 5.9.0 pypi_0 pypi
uri-template 1.3.0 pypi_0 pypi
urllib3 2.1.0 pyhd8ed1ab_0 conda-forge
vim 9.0.2059 py311pl5321hf9d0b55_1 conda-forge
wcwidth 0.2.12 pypi_0 pypi
webcolors 1.13 pypi_0 pypi
webencodings 0.5.1 pypi_0 pypi
websocket-client 1.7.0 pypi_0 pypi
werkzeug 3.0.1 pypi_0 pypi
wheel 0.42.0 pyhd8ed1ab_0 conda-forge
widgetsnbextension 4.0.9 pypi_0 pypi
x264 1!164.3095 h166bdaf_2 conda-forge
x265 3.5 h924138e_3 conda-forge
xatlas 0.0.8 pypi_0 pypi
xorg-fixesproto 5.0 h7f98852_1002 conda-forge
xorg-kbproto 1.0.7 h7f98852_1002 conda-forge
xorg-libice 1.1.1 hd590300_0 conda-forge
xorg-libsm 1.2.4 h7391055_0 conda-forge
xorg-libx11 1.8.7 h8ee46fc_0 conda-forge
xorg-libxau 1.0.11 hd590300_0 conda-forge
xorg-libxdmcp 1.1.3 h7f98852_0 conda-forge
xorg-libxext 1.3.4 h0b41bf4_2 conda-forge
xorg-libxfixes 5.0.3 h7f98852_1004 conda-forge
xorg-libxrender 0.9.11 hd590300_0 conda-forge
xorg-libxt 1.3.0 hd590300_1 conda-forge
xorg-renderproto 0.11.1 h7f98852_1002 conda-forge
xorg-xextproto 7.3.0 h0b41bf4_1003 conda-forge
xorg-xproto 7.0.31 h7f98852_1007 conda-forge
xz 5.4.5 h5eee18b_0
yacs 0.1.8 pypi_0 pypi
yaml-cpp 0.8.0 h6a678d5_0
yapf 0.40.2 pypi_0 pypi
zipp 3.17.0 pypi_0 pypi
zlib 1.2.13 hd590300_5 conda-forge
zstandard 0.22.0 py311haa97af0_0 conda-forge
zstd 1.5.5 hc292b87_0
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