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output
dataset
checkpoint
sample
video
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock
# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
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# in version control.
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.pdm.toml
.pdm-python
.pdm-build/
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__pypackages__/
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celerybeat-schedule
celerybeat.pid
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*.sage.py
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/
mmpose
cv-mmap
config.toml
log
app.toml
*.prof
*.parquet
frpc.toml
configs/cvmmap/config_fl.toml
configs/cvmmap/config_fr.toml
npy.ipynb
play_pose_2d.ipynb
*.mp4
*.jpg
*.jpeg
temp_*
*.tar
*.tar.xz
*.tar.gz

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[project]
name = "template_project_alpha"
version = "0.1.0"
description = "a basic template project that has the ability to complete most of ML task with PyTorch and MMCV"
readme = "README.md"
requires-python = ">=3.12"
dependencies = [
"anyio>=4.9.0",
"awkward>=2.8.5",
"beartype>=0.21.0",
"click>=8.2.1",
"jaxtyping>=0.3.2",
"loguru>=0.7.3",
"mmcv",
"mmdet>=3.3.0",
"mmpose>=1.3.2",
"nats-py>=2.10.0",
"numba>=0.61.2",
"nvidia-nvimgcodec-cu12>=0.5.0.13",
"opencv-python>=4.12.0.88",
"orjson>=3.10.18",
"pyarrow>=20.0.0",
"pydantic>=2.11.7",
"pytorch3d",
"redis>=6.2.0",
"result>=0.17.0",
"tomli>=2.2.1",
"tomli-w>=1.2.0",
"torch>=2.7.0",
"torchvision>=0.22.0",
"ultralytics>=8.3.166",
"xtcocotools",
]
[dependency-groups]
dev = ["jupyterlab>=4.4.4", "pip", "psutil>=7.0.0", "setuptools"]
[tool.uv]
no-build-isolation-package = ["chumpy", "xtcocotools"]
[tool.uv.sources]
torch = [
{ index = "pytorch-cpu", marker = "sys_platform != 'linux'" },
{ index = "pytorch-cu128", marker = "sys_platform == 'linux'" },
]
torchvision = [
{ index = "pytorch-cpu", marker = "sys_platform != 'linux'" },
{ index = "pytorch-cu128", marker = "sys_platform == 'linux'" },
]
# built with cu128
mmcv = { path = "misc/mmcv-2.2.0-cp312-cp312-linux_x86_64.whl" }
xtcocotools = { path = "misc/xtcocotools-1.14.3-cp312-cp312-linux_x86_64.whl" }
pytorch3d = { path = "misc/pytorch3d-0.7.8-cp312-cp312-linux_x86_64.whl" }
[[tool.uv.index]]
name = "pytorch-cpu"
url = "https://download.pytorch.org/whl/cpu"
explicit = true
[[tool.uv.index]]
name = "pytorch-cu128"
url = "https://download.pytorch.org/whl/cu128"
explicit = true

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