open-mmlab / mmdetection3d

OpenMMLab's next-generation platform for general 3D object detection.
https://mmdetection3d.readthedocs.io/en/latest/
Apache License 2.0
5.06k stars 1.5k forks source link

error nuscenes_infos_train.pkl #2454

Open SMSajadi99 opened 1 year ago

SMSajadi99 commented 1 year ago

Hello, I received GitHub, I went to the folders according to your instructions, and its site is:

https://github.com/open-mmlab/mmdetection3d/blob/1.0/docs/en/datasets/nuscenes_det.md

The outputs are as follows: image image image

But I encountered this error

image

can you help me

SMSajadi99 commented 1 year ago

I am working with mini data, now the problem is that it calls the error sweeps/LIDAR_TOP twice

JingweiZhang12 commented 1 year ago

@SMSajadi99 We'll provide annotation files generated offline soon. You can try it first: nuscenes train split nuscenes val split nuscenes_mini train split nuscenes_mini val split

SMSajadi99 commented 1 year ago

Thanks so much

SMSajadi99 commented 1 year ago

@JingweiZhang12 Hi

Thank you very much for the data you sent.

But I want to start it on my system, on data mini. I will try to explain in detail so that we can fix the bug, thank you for your help.

nuscenes folder photo:

image

Installation libraries using Conda 23.1.0 and CUDA 11.7:

Package Version
absl-py 1.4.0
addict 2.4.0
anyio 3.6.2
appdirs 1.4.4
argon2-cffi 21.3.0
argon2-cffi-bindings 21.2.0
arrow 1.2.3
asttokens 2.2.1
attrs 22.2.0
backcall 0.2.0
beautifulsoup4 4.12.0
black 23.3.0
bleach 6.0.0
build 0.7.0
cachetools 5.3.0
ccimport 0.4.2
certifi 2022.12.7
cffi 1.15.1
charset-normalizer 3.1.0
click 8.1.3
colorama 0.4.6
comm 0.1.3
ConfigArgParse 1.5.3
contextlib2 21.6.0
contourpy 1.0.7
cumm-cu117 0.4.8
cycler 0.11.0
dash 2.9.2
dash-core-components 2.0.0
dash-html-components 2.0.0
dash-table 5.0.0
debugpy 1.6.6
decorator 5.1.1
defusedxml 0.7.1
descartes 1.1.0
docopt 0.6.2
exceptiongroup 1.1.1
executing 1.2.0
fastjsonschema 2.16.3
fire 0.5.0
flake8 6.0.0
Flask 2.2.3
flit_core 3.8.0
fonttools 4.39.3
fqdn 1.5.1
google-auth 2.17.1
google-auth-oauthlib 1.0.0
grpcio 1.53.0
h5py 3.8.0
idna 3.4
imageio 2.27.0
importlib-metadata 6.3.0
importlib-resources 5.12.0
iniconfig 2.0.0
inspect-it 0.3.2
ipykernel 6.22.0
ipython 8.12.0
ipython-genutils 0.2.0
ipywidgets 8.0.6
isoduration 20.11.0
itsdangerous 2.1.2
itypes 1.2.0
jedi 0.18.2
Jinja2 3.1.2
joblib 1.2.0
jsonpointer 2.3
jsonschema 4.17.3
jupyter 1.0.0
jupyter_client 8.1.0
jupyter-console 6.6.3
jupyter_core 5.3.0
jupyter-events 0.6.3
jupyter_server 2.5.0
jupyter_server_terminals 0.4.4
jupyterlab-pygments 0.2.2
jupyterlab-widgets 3.0.7
kiwisolver 1.4.4
lark 1.1.5
lazy_loader 0.2
llvmlite 0.36.0
loguru 0.6.0
lyft-dataset-sdk 0.0.8
Markdown 3.4.3
markdown-it-py 2.2.0
MarkupSafe 2.1.2
matplotlib 3.5.2
matplotlib-inline 0.1.6
mccabe 0.7.0
mdurl 0.1.2
mistune 2.0.5
mkl-fft 1.3.1
mkl-random 1.2.2
mkl-service 2.4.0
mmcv 2.0.0
mmdet 3.0.0
mmengine 0.7.2
model-index 0.1.11
mpi4py 3.0.3
msgpack 1.0.5
msgpack-numpy 0.4.8
multimethod 1.9.1
mypy-extensions 1.0.0
nbclassic 0.5.4
nbclient 0.7.2
nbconvert 7.2.10
nbformat 5.7.0
nest-asyncio 1.5.6
networkx 3.1
ninja 1.11.1
notebook 6.5.3
notebook_shim 0.2.2
numba 0.53.0
numpy 1.23.5
nuscenes-devkit 1.1.10
oauthlib 3.2.2
olefile 0.46
open3d 0.17.0
opencv-python 4.7.0.72
openmim 0.3.7
ordered-set 4.1.0
packaging 23.0
pandas 2.0.0
pandocfilters 1.5.0
parso 0.8.3
pathlib 1.0.1
pathspec 0.11.1
pccm 0.4.6
pep517 0.13.0
pickleshare 0.7.5
Pillow 9.5.0
pip 23.0.1
pip-tools 6.13.0
pkgutil_resolve_name 1.3.10
platformdirs 3.2.0
plotly 5.14.1
pluggy 1.0.0
portalocker 2.7.0
prometheus-client 0.16.0
prompt-toolkit 3.0.38
protobuf 4.22.1
psutil 5.9.4
pure-eval 0.2.2
pyasn1 0.4.8
pyasn1-modules 0.2.8
pybind11 2.10.4
pycocotools 2.0.6
pycodestyle 2.10.0
pycparser 2.21
pyflakes 3.0.1
Pygments 2.14.0
pyparsing 3.0.9
pyquaternion 0.9.9
pyrsistent 0.19.3
pytest 7.3.0
python-dateutil 2.8.2
python-json-logger 2.0.7
pytools 2022.1.14
pytz 2023.3
PyWavelets 1.4.1
pywin32 306
pywinpty 2.0.10
PyYAML 6.0
pyzmq 25.0.2
qtconsole 5.4.2
QtPy 2.3.1
regex 2023.3.23
requests 2.28.2
requests-oauthlib 1.3.1
rfc3339-validator 0.1.4
rfc3986-validator 0.1.1
rich 13.3.4
rsa 4.9
scikit-image 0.20.0
scikit-learn 1.2.2
scipy 1.9.1
Send2Trash 1.8.0
setuptools 65.6.3
Shapely 1.8.5
six 1.16.0
sniffio 1.3.0
soupsieve 2.4
spconv-cu117 2.3.6
stack-data 0.6.2
tabulate 0.9.0
tenacity 8.2.2
tensorboard 2.12.1
tensorboard-data-server 0.7.0
tensorboard-plugin-wit 1.8.1
tensorpack 0.11
termcolor 2.2.0
terminado 0.17.1
terminaltables 3.1.10
threadpoolctl 3.1.0
tifffile 2023.3.21
tinycss2 1.2.1
toml 0.10.2
tomli 2.0.1
torch 1.10.1
torchaudio 0.10.1
torchpack 0.3.1
torchvision 0.11.2
tornado 6.2
tqdm 4.65.0
traitlets 5.9.0
trimesh 2.35.39
typing 3.7.4.3
typing_extensions 4.4.0
tzdata 2023.3
uri-template 1.2.0
urllib3 1.26.15
wcwidth 0.2.6
webcolors 1.13
webencodings 0.5.1
websocket-client 1.5.1
Werkzeug 2.2.3
wheel 0.38.4
widgetsnbextension 4.0.7
win32-setctime 1.1.0
wincertstore 0.2
yapf 0.32.0
zipp 3.15.0

Now the main folder is as follows:

image image

Data folder:

image

Now I run the following code: Of course, I have to say two things

  1. In line 252 of the python creat_data.py file, I changed the version from v1.0 to v1.0-mini.

  2. I pulled out the creat_data file from the tools folder and put it in the mmdetection3d folder, because in the picture below it says that the file should be run in the tools folder, but in the file itself, it says to go to tools folder, which had this problem, in general, it ran fine with this. became.

image

Now the output is as follows:

image image image image image

Now I have to say two things:

  1. The newly created files in the data folder are as follows:

image

  1. If you look carefully, the path it is referring to writes the phrase sweeps/LIDAR_TOP twice, so it is clear that there is a problem. Untitled

Now, if possible, check this case, what should I do?

JingweiZhang12 commented 1 year ago

Fro the log, you could check the code of create_gt_databse. Maybe the path is repeated somewhere?

SMSajadi99 commented 1 year ago

Hi again @JingweiZhang12

I did this, created two separate folders, and put the data in them.

The productions were done without problems, but this was the output:

image

Now the problem that has arisen is that there are 4 files like the files you gave, for me there are 3 of which are shared and the other 2 are different and I don't have mini data in the pkl files. I was looking at the creat_gt_databasepython file and saw It produces exactly dbinfofiles, can you tell me the change that I have to make so that I can produce mini like you?

image

But I was looking at the python file nuscenes_converterand we saw that it is somehow forming this mini section.

image

Thank you for helping me

SMSajadi99 commented 1 year ago

Hi @JingweiZhang12 I typed the following code again with another method, but it didn't work like the files you sent me in the first question.

python create_data.py nuscenes --root-path ./data/nuscenes --version "v1.0-mini" --out-dir ./data/nuscenes --extra-tag nuscenes

what should i do

SMSajadi99 commented 1 year ago

Hi @JingweiZhang12, can you help me ?

Lcl159 commented 11 months ago

Hi, I have the same problem, have you solved it yet?

SMSajadi99 commented 11 months ago

Hi @Lcl159 , not yet Working with Dataset Mini is very difficult