replace maskrcnn-benchmark nms with torchvision nms
This commit is contained in:
Zhongdao
parent
1cb8cee836
commit
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46 changed files with 112372 additions and 111016 deletions
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.gitignore
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.gitignore
vendored
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@ -1,112 +1,112 @@
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results/
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results/
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weights/
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weights/
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data/mot17
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data/mot17
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data/mot19
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data/mot19
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tmp/
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tmp/
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external/
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external/
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=======
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=======
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# Byte-compiled / optimized / DLL files
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# Byte-compiled / optimized / DLL files
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__pycache__/
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__pycache__/
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*.py[cod]
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*.py[cod]
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*$py.class
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*$py.class
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# C extensions
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# C extensions
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*.so
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*.so
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!utils/*.so
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!utils/*.so
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# Distribution / packaging
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# Distribution / packaging
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.Python
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.Python
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build/
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build/
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develop-eggs/
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develop-eggs/
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dist/
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dist/
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downloads/
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downloads/
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eggs/
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eggs/
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.eggs/
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.eggs/
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lib/
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lib/
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lib64/
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lib64/
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parts/
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parts/
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sdist/
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sdist/
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var/
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var/
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wheels/
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wheels/
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*.egg-info/
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*.egg-info/
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.installed.cfg
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.installed.cfg
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*.egg
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*.egg
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MANIFEST
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MANIFEST
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# PyInstaller
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# PyInstaller
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# Usually these files are written by a python script from a template
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.manifest
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*.spec
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*.spec
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# Installer logs
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# Installer logs
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pip-log.txt
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pip-log.txt
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pip-delete-this-directory.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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# Unit test / coverage reports
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htmlcov/
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htmlcov/
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.tox/
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.tox/
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.coverage
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.coverage
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.coverage.*
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.coverage.*
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.cache
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.cache
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nosetests.xml
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nosetests.xml
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coverage.xml
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coverage.xml
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*.cover
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*.cover
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.hypothesis/
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.hypothesis/
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.pytest_cache/
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.pytest_cache/
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# Translations
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# Translations
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*.mo
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*.mo
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*.pot
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*.pot
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# Django stuff:
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# Django stuff:
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*.log
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*.log
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local_settings.py
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local_settings.py
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db.sqlite3
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db.sqlite3
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# Flask stuff:
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# Flask stuff:
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instance/
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instance/
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.webassets-cache
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.webassets-cache
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# Scrapy stuff:
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# Scrapy stuff:
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.scrapy
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.scrapy
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# Sphinx documentation
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# Sphinx documentation
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docs/_build/
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docs/_build/
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# PyBuilder
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# PyBuilder
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target/
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target/
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# Jupyter Notebook
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# Jupyter Notebook
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.ipynb_checkpoints
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.ipynb_checkpoints
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# pyenv
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# pyenv
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.python-version
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.python-version
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# celery beat schedule file
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# celery beat schedule file
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celerybeat-schedule
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celerybeat-schedule
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# SageMath parsed files
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# SageMath parsed files
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*.sage.py
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*.sage.py
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# Environments
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# Environments
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.env
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.env
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.venv
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.venv
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env/
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env/
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venv/
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venv/
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ENV/
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ENV/
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env.bak/
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env.bak/
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venv.bak/
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venv.bak/
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# Spyder project settings
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# Spyder project settings
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.spyderproject
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.spyderproject
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.spyproject
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.spyproject
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# Rope project settings
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# Rope project settings
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.ropeproject
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.ropeproject
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# mkdocs documentation
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# mkdocs documentation
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/site
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/site
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# mypy
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# mypy
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.mypy_cache/
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.mypy_cache/
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352
DATASET_ZOO.md
352
DATASET_ZOO.md
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@ -1,176 +1,176 @@
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# Dataset Zoo
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# Dataset Zoo
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We provide several relevant datasets for training and evaluating the Joint Detection and Embedding (JDE) model.
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We provide several relevant datasets for training and evaluating the Joint Detection and Embedding (JDE) model.
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Annotations are provided in a unified format. If you want to use these datasets, please **follow their licenses**,
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Annotations are provided in a unified format. If you want to use these datasets, please **follow their licenses**,
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and if you use these datasets in your research, please cite the original work (you can find the bibtex in the bottom).
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and if you use these datasets in your research, please cite the original work (you can find the bibtex in the bottom).
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## Data Format
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## Data Format
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All the dataset has the following structrue:
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All the dataset has the following structrue:
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```
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```
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Caltech
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Caltech
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|——————images
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|——————images
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| └——————00001.jpg
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| └——————00001.jpg
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| |—————— ...
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| |—————— ...
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| └——————0000N.jpg
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| └——————0000N.jpg
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└——————labels_with_ids
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└——————labels_with_ids
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└——————00001.txt
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└——————00001.txt
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|—————— ...
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|—————— ...
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└——————0000N.txt
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└——————0000N.txt
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```
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```
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Every image corresponds to an annation text. Given an image path,
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Every image corresponds to an annation text. Given an image path,
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the annotation text path can be easily generated by replacing the string `images` with `labels_with_ids` and replacing `.jpg` with `.txt`.
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the annotation text path can be easily generated by replacing the string `images` with `labels_with_ids` and replacing `.jpg` with `.txt`.
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In the annotation text, each line is a bounding box and has the following format,
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In the annotation text, each line is a bounding box and has the following format,
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```
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```
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[class] [identity] [x_center] [y_center] [width] [height]
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[class] [identity] [x_center] [y_center] [width] [height]
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```
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```
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The field `[class]` is not used in this project since we only care about a single class, i.e., pedestrian here.
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The field `[class]` is not used in this project since we only care about a single class, i.e., pedestrian here.
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The field `[identity]` is an integer from `0` to `num_identities - 1`, or `-1` if this box has no identity annotation.
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The field `[identity]` is an integer from `0` to `num_identities - 1`, or `-1` if this box has no identity annotation.
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***Note** that the values of `[x_center] [y_center] [width] [height]` are normalized by the width/height of the image, so they are float numbers ranging from 0 to 1.
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***Note** that the values of `[x_center] [y_center] [width] [height]` are normalized by the width/height of the image, so they are float numbers ranging from 0 to 1.
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## Download
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## Download
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### Caltech Pedestrian
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### Caltech Pedestrian
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Baidu NetDisk:
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Baidu NetDisk:
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[[0]](https://pan.baidu.com/s/1sYBXXvQaXZ8TuNwQxMcAgg)
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[[0]](https://pan.baidu.com/s/1sYBXXvQaXZ8TuNwQxMcAgg)
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[[1]](https://pan.baidu.com/s/1lVO7YBzagex1xlzqPksaPw)
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[[1]](https://pan.baidu.com/s/1lVO7YBzagex1xlzqPksaPw)
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[[2]](https://pan.baidu.com/s/1PZXxxy_lrswaqTVg0GuHWg)
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[[2]](https://pan.baidu.com/s/1PZXxxy_lrswaqTVg0GuHWg)
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[[3]](https://pan.baidu.com/s/1M93NCo_E6naeYPpykmaNgA)
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[[3]](https://pan.baidu.com/s/1M93NCo_E6naeYPpykmaNgA)
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[[4]](https://pan.baidu.com/s/1ZXCdPNXfwbxQ4xCbVu5Dtw)
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[[4]](https://pan.baidu.com/s/1ZXCdPNXfwbxQ4xCbVu5Dtw)
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[[5]](https://pan.baidu.com/s/1kcZkh1tcEiBEJqnDtYuejg)
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[[5]](https://pan.baidu.com/s/1kcZkh1tcEiBEJqnDtYuejg)
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[[6]](https://pan.baidu.com/s/1sDjhtgdFrzR60KKxSjNb2A)
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[[6]](https://pan.baidu.com/s/1sDjhtgdFrzR60KKxSjNb2A)
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[[7]](https://pan.baidu.com/s/18Zvp_d33qj1pmutFDUbJyw)
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[[7]](https://pan.baidu.com/s/18Zvp_d33qj1pmutFDUbJyw)
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Google Drive: [[annotation]](https://drive.google.com/file/d/1h8vxl_6tgi9QVYoer9XcY9YwNB32TE5k/view?usp=sharing) ,
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Google Drive: [[annotation]](https://drive.google.com/file/d/1h8vxl_6tgi9QVYoer9XcY9YwNB32TE5k/view?usp=sharing) ,
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please download all the `.tar` file from [this page](http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/datasets/USA/) and unzip the images under `Caltech/images`
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please download all the `.tar` file from [this page](http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/datasets/USA/) and unzip the images under `Caltech/images`
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Original dataset webpage: [CaltechPedestrians](http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/)
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Original dataset webpage: [CaltechPedestrians](http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/)
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### CityPersons
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### CityPersons
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Baidu NetDisk:
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Baidu NetDisk:
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[[0]](https://pan.baidu.com/s/1g24doGOdkKqmbgbJf03vsw)
|
[[0]](https://pan.baidu.com/s/1g24doGOdkKqmbgbJf03vsw)
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[[1]](https://pan.baidu.com/s/1mqDF9M5MdD3MGxSfe0ENsA)
|
[[1]](https://pan.baidu.com/s/1mqDF9M5MdD3MGxSfe0ENsA)
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[[2]](https://pan.baidu.com/s/1Qrbh9lQUaEORCIlfI25wdA)
|
[[2]](https://pan.baidu.com/s/1Qrbh9lQUaEORCIlfI25wdA)
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[[3]](https://pan.baidu.com/s/1lw7shaffBgARDuk8mkkHhw)
|
[[3]](https://pan.baidu.com/s/1lw7shaffBgARDuk8mkkHhw)
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|
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Google Drive:
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Google Drive:
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[[0]](https://drive.google.com/file/d/1DgLHqEkQUOj63mCrS_0UGFEM9BG8sIZs/view?usp=sharing)
|
[[0]](https://drive.google.com/file/d/1DgLHqEkQUOj63mCrS_0UGFEM9BG8sIZs/view?usp=sharing)
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[[1]](https://drive.google.com/file/d/1BH9Xz59UImIGUdYwUR-cnP1g7Ton_LcZ/view?usp=sharing)
|
[[1]](https://drive.google.com/file/d/1BH9Xz59UImIGUdYwUR-cnP1g7Ton_LcZ/view?usp=sharing)
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[[2]](https://drive.google.com/file/d/1q_OltirP68YFvRWgYkBHLEFSUayjkKYE/view?usp=sharing)
|
[[2]](https://drive.google.com/file/d/1q_OltirP68YFvRWgYkBHLEFSUayjkKYE/view?usp=sharing)
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[[3]](https://drive.google.com/file/d/1VSL0SFoQxPXnIdBamOZJzHrHJ1N2gsTW/view?usp=sharing)
|
[[3]](https://drive.google.com/file/d/1VSL0SFoQxPXnIdBamOZJzHrHJ1N2gsTW/view?usp=sharing)
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Original dataset webpage: [Citypersons pedestrian detection dataset](https://bitbucket.org/shanshanzhang/citypersons)
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Original dataset webpage: [Citypersons pedestrian detection dataset](https://bitbucket.org/shanshanzhang/citypersons)
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### CUHK-SYSU
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### CUHK-SYSU
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Baidu NetDisk:
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Baidu NetDisk:
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[[0]](https://pan.baidu.com/s/1YFrlyB1WjcQmFW3Vt_sEaQ)
|
[[0]](https://pan.baidu.com/s/1YFrlyB1WjcQmFW3Vt_sEaQ)
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|
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Google Drive:
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Google Drive:
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[[0]](https://drive.google.com/file/d/1D7VL43kIV9uJrdSCYl53j89RE2K-IoQA/view?usp=sharing)
|
[[0]](https://drive.google.com/file/d/1D7VL43kIV9uJrdSCYl53j89RE2K-IoQA/view?usp=sharing)
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Original dataset webpage: [CUHK-SYSU Person Search Dataset](http://www.ee.cuhk.edu.hk/~xgwang/PS/dataset.html)
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Original dataset webpage: [CUHK-SYSU Person Search Dataset](http://www.ee.cuhk.edu.hk/~xgwang/PS/dataset.html)
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### PRW
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### PRW
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Baidu NetDisk:
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Baidu NetDisk:
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[[0]](https://pan.baidu.com/s/1iqOVKO57dL53OI1KOmWeGQ)
|
[[0]](https://pan.baidu.com/s/1iqOVKO57dL53OI1KOmWeGQ)
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|
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Google Drive:
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Google Drive:
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[[0]](https://drive.google.com/file/d/116_mIdjgB-WJXGe8RYJDWxlFnc_4sqS8/view?usp=sharing)
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[[0]](https://drive.google.com/file/d/116_mIdjgB-WJXGe8RYJDWxlFnc_4sqS8/view?usp=sharing)
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Original dataset webpage: [Person Search in the Wild datset](http://www.liangzheng.com.cn/Project/project_prw.html)
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Original dataset webpage: [Person Search in the Wild datset](http://www.liangzheng.com.cn/Project/project_prw.html)
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### ETHZ (overlapping videos with MOT-16 removed):
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### ETHZ (overlapping videos with MOT-16 removed):
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Baidu NetDisk:
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Baidu NetDisk:
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[[0]](https://pan.baidu.com/s/14EauGb2nLrcB3GRSlQ4K9Q)
|
[[0]](https://pan.baidu.com/s/14EauGb2nLrcB3GRSlQ4K9Q)
|
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|
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Google Drive:
|
Google Drive:
|
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[[0]](https://drive.google.com/file/d/19QyGOCqn8K_rc9TXJ8UwLSxCx17e0GoY/view?usp=sharing)
|
[[0]](https://drive.google.com/file/d/19QyGOCqn8K_rc9TXJ8UwLSxCx17e0GoY/view?usp=sharing)
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|
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Original dataset webpage: [ETHZ pedestrian datset](https://data.vision.ee.ethz.ch/cvl/aess/dataset/)
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Original dataset webpage: [ETHZ pedestrian datset](https://data.vision.ee.ethz.ch/cvl/aess/dataset/)
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### MOT-17
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### MOT-17
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Baidu NetDisk:
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Baidu NetDisk:
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[[0]](https://pan.baidu.com/s/1lHa6UagcosRBz-_Y308GvQ)
|
[[0]](https://pan.baidu.com/s/1lHa6UagcosRBz-_Y308GvQ)
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|
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Google Drive:
|
Google Drive:
|
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[[0]](https://drive.google.com/file/d/1ET-6w12yHNo8DKevOVgK1dBlYs739e_3/view?usp=sharing)
|
[[0]](https://drive.google.com/file/d/1ET-6w12yHNo8DKevOVgK1dBlYs739e_3/view?usp=sharing)
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Original dataset webpage: [MOT-17](https://motchallenge.net/data/MOT17/)
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Original dataset webpage: [MOT-17](https://motchallenge.net/data/MOT17/)
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### MOT-16 (for evaluation )
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### MOT-16 (for evaluation )
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Baidu NetDisk:
|
Baidu NetDisk:
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[[0]](https://pan.baidu.com/s/10pUuB32Hro-h-KUZv8duiw)
|
[[0]](https://pan.baidu.com/s/10pUuB32Hro-h-KUZv8duiw)
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|
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Google Drive:
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Google Drive:
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[[0]](https://drive.google.com/file/d/1254q3ruzBzgn4LUejDVsCtT05SIEieQg/view?usp=sharing)
|
[[0]](https://drive.google.com/file/d/1254q3ruzBzgn4LUejDVsCtT05SIEieQg/view?usp=sharing)
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Original dataset webpage: [MOT-16](https://motchallenge.net/data/MOT16/)
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Original dataset webpage: [MOT-16](https://motchallenge.net/data/MOT16/)
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# Citation
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# Citation
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Caltech:
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Caltech:
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```
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```
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@inproceedings{ dollarCVPR09peds,
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@inproceedings{ dollarCVPR09peds,
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author = "P. Doll\'ar and C. Wojek and B. Schiele and P. Perona",
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author = "P. Doll\'ar and C. Wojek and B. Schiele and P. Perona",
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title = "Pedestrian Detection: A Benchmark",
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title = "Pedestrian Detection: A Benchmark",
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booktitle = "CVPR",
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booktitle = "CVPR",
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month = "June",
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month = "June",
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year = "2009",
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year = "2009",
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city = "Miami",
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city = "Miami",
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}
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}
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```
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```
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Citypersons:
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Citypersons:
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```
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```
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@INPROCEEDINGS{Shanshan2017CVPR,
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@INPROCEEDINGS{Shanshan2017CVPR,
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Author = {Shanshan Zhang and Rodrigo Benenson and Bernt Schiele},
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Author = {Shanshan Zhang and Rodrigo Benenson and Bernt Schiele},
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Title = {CityPersons: A Diverse Dataset for Pedestrian Detection},
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Title = {CityPersons: A Diverse Dataset for Pedestrian Detection},
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Booktitle = {CVPR},
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Booktitle = {CVPR},
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Year = {2017}
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Year = {2017}
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}
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}
|
||||||
|
|
||||||
@INPROCEEDINGS{Cordts2016Cityscapes,
|
@INPROCEEDINGS{Cordts2016Cityscapes,
|
||||||
title={The Cityscapes Dataset for Semantic Urban Scene Understanding},
|
title={The Cityscapes Dataset for Semantic Urban Scene Understanding},
|
||||||
author={Cordts, Marius and Omran, Mohamed and Ramos, Sebastian and Rehfeld, Timo and Enzweiler, Markus and Benenson, Rodrigo and Franke, Uwe and Roth, Stefan and Schiele, Bernt},
|
author={Cordts, Marius and Omran, Mohamed and Ramos, Sebastian and Rehfeld, Timo and Enzweiler, Markus and Benenson, Rodrigo and Franke, Uwe and Roth, Stefan and Schiele, Bernt},
|
||||||
booktitle={Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
|
booktitle={Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
|
||||||
year={2016}
|
year={2016}
|
||||||
}
|
}
|
||||||
```
|
```
|
||||||
CUHK-SYSU:
|
CUHK-SYSU:
|
||||||
```
|
```
|
||||||
@inproceedings{xiaoli2017joint,
|
@inproceedings{xiaoli2017joint,
|
||||||
title={Joint Detection and Identification Feature Learning for Person Search},
|
title={Joint Detection and Identification Feature Learning for Person Search},
|
||||||
author={Xiao, Tong and Li, Shuang and Wang, Bochao and Lin, Liang and Wang, Xiaogang},
|
author={Xiao, Tong and Li, Shuang and Wang, Bochao and Lin, Liang and Wang, Xiaogang},
|
||||||
booktitle={CVPR},
|
booktitle={CVPR},
|
||||||
year={2017}
|
year={2017}
|
||||||
}
|
}
|
||||||
```
|
```
|
||||||
PRW:
|
PRW:
|
||||||
```
|
```
|
||||||
@inproceedings{zheng2017person,
|
@inproceedings{zheng2017person,
|
||||||
title={Person re-identification in the wild},
|
title={Person re-identification in the wild},
|
||||||
author={Zheng, Liang and Zhang, Hengheng and Sun, Shaoyan and Chandraker, Manmohan and Yang, Yi and Tian, Qi},
|
author={Zheng, Liang and Zhang, Hengheng and Sun, Shaoyan and Chandraker, Manmohan and Yang, Yi and Tian, Qi},
|
||||||
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
|
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
|
||||||
pages={1367--1376},
|
pages={1367--1376},
|
||||||
year={2017}
|
year={2017}
|
||||||
}
|
}
|
||||||
```
|
```
|
||||||
ETHZ:
|
ETHZ:
|
||||||
```
|
```
|
||||||
@InProceedings{eth_biwi_00534,
|
@InProceedings{eth_biwi_00534,
|
||||||
author = {A. Ess and B. Leibe and K. Schindler and and L. van Gool},
|
author = {A. Ess and B. Leibe and K. Schindler and and L. van Gool},
|
||||||
title = {A Mobile Vision System for Robust Multi-Person Tracking},
|
title = {A Mobile Vision System for Robust Multi-Person Tracking},
|
||||||
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR'08)},
|
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR'08)},
|
||||||
year = {2008},
|
year = {2008},
|
||||||
month = {June},
|
month = {June},
|
||||||
publisher = {IEEE Press},
|
publisher = {IEEE Press},
|
||||||
keywords = {}
|
keywords = {}
|
||||||
}
|
}
|
||||||
```
|
```
|
||||||
MOT-16&17:
|
MOT-16&17:
|
||||||
```
|
```
|
||||||
@article{milan2016mot16,
|
@article{milan2016mot16,
|
||||||
title={MOT16: A benchmark for multi-object tracking},
|
title={MOT16: A benchmark for multi-object tracking},
|
||||||
author={Milan, Anton and Leal-Taix{\'e}, Laura and Reid, Ian and Roth, Stefan and Schindler, Konrad},
|
author={Milan, Anton and Leal-Taix{\'e}, Laura and Reid, Ian and Roth, Stefan and Schindler, Konrad},
|
||||||
journal={arXiv preprint arXiv:1603.00831},
|
journal={arXiv preprint arXiv:1603.00831},
|
||||||
year={2016}
|
year={2016}
|
||||||
}
|
}
|
||||||
```
|
```
|
||||||
|
|
|
||||||
42
LICENSE
42
LICENSE
|
|
@ -1,21 +1,21 @@
|
||||||
MIT License
|
MIT License
|
||||||
|
|
||||||
Copyright (c) 2019 ZhongdaoWang
|
Copyright (c) 2019 ZhongdaoWang
|
||||||
|
|
||||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||||
of this software and associated documentation files (the "Software"), to deal
|
of this software and associated documentation files (the "Software"), to deal
|
||||||
in the Software without restriction, including without limitation the rights
|
in the Software without restriction, including without limitation the rights
|
||||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||||
copies of the Software, and to permit persons to whom the Software is
|
copies of the Software, and to permit persons to whom the Software is
|
||||||
furnished to do so, subject to the following conditions:
|
furnished to do so, subject to the following conditions:
|
||||||
|
|
||||||
The above copyright notice and this permission notice shall be included in all
|
The above copyright notice and this permission notice shall be included in all
|
||||||
copies or substantial portions of the Software.
|
copies or substantial portions of the Software.
|
||||||
|
|
||||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||||
SOFTWARE.
|
SOFTWARE.
|
||||||
|
|
|
||||||
104
README.md
104
README.md
|
|
@ -1,52 +1,52 @@
|
||||||
# Towards-Realtime-MOT
|
# Towards-Realtime-MOT
|
||||||
**NEWS:**
|
**NEWS:**
|
||||||
- **[2019.10.11]** Training and evaluation data uploaded! Please see [DATASET_ZOO.md](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md) for details.
|
- **[2019.10.11]** Training and evaluation data uploaded! Please see [DATASET_ZOO.md](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md) for details.
|
||||||
- **[2019.10.01]** Demo code and pre-trained model released!
|
- **[2019.10.01]** Demo code and pre-trained model released!
|
||||||
|
|
||||||
## Introduction
|
## Introduction
|
||||||
This repo is the a codebase of the Joint Detection and Embedding (JDE) model. JDE is a fast and high-performance multiple-object tracker that learns the object detection task and appearance embedding task simutaneously in a shared neural network. Techical details are described in our [arXiv preprint paper](https://arxiv.org/pdf/1909.12605v1.pdf). By using this repo, you can simply achieve **MOTA 64%+** on the "private" protocol of [MOT-16 challenge](https://motchallenge.net/tracker/JDE), and with a near real-time speed at **18~24 FPS** (Note this speed is for the entire system, including the detection step! ) .
|
This repo is the a codebase of the Joint Detection and Embedding (JDE) model. JDE is a fast and high-performance multiple-object tracker that learns the object detection task and appearance embedding task simutaneously in a shared neural network. Techical details are described in our [arXiv preprint paper](https://arxiv.org/pdf/1909.12605v1.pdf). By using this repo, you can simply achieve **MOTA 64%+** on the "private" protocol of [MOT-16 challenge](https://motchallenge.net/tracker/JDE), and with a near real-time speed at **18~24 FPS** (Note this speed is for the entire system, including the detection step! ) .
|
||||||
|
|
||||||
We hope this repo will help researches/engineers to develop more practical MOT systems. For algorithm development, we provide training data, baseline models and evaluation methods to make a level playground. For application usage, we also provide a small video demo that takes raw videos as input without any bells and whistles.
|
We hope this repo will help researches/engineers to develop more practical MOT systems. For algorithm development, we provide training data, baseline models and evaluation methods to make a level playground. For application usage, we also provide a small video demo that takes raw videos as input without any bells and whistles.
|
||||||
|
|
||||||
## Requirements
|
## Requirements
|
||||||
* Python 3.6
|
* Python 3.6
|
||||||
* [Pytorch](https://pytorch.org) >= 1.0.1
|
* [Pytorch](https://pytorch.org) >= 1.0.1
|
||||||
* [syncbn](https://github.com/ytoon/Synchronized-BatchNorm-PyTorch) (Optional, compile and place it under utils/syncbn, or simply replace with nn.BatchNorm [here](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/models.py#L12))
|
* [syncbn](https://github.com/ytoon/Synchronized-BatchNorm-PyTorch) (Optional, compile and place it under utils/syncbn, or simply replace with nn.BatchNorm [here](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/models.py#L12))
|
||||||
* [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) (Their GPU NMS is used in this project)
|
* [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) (Their GPU NMS is used in this project)
|
||||||
* python-opencv
|
* python-opencv
|
||||||
* ffmpeg (Optional, used in the video demo)
|
* ffmpeg (Optional, used in the video demo)
|
||||||
* [py-motmetrics](https://github.com/cheind/py-motmetrics) (Simply `pip install motmetrics`)
|
* [py-motmetrics](https://github.com/cheind/py-motmetrics) (Simply `pip install motmetrics`)
|
||||||
|
|
||||||
## Video Demo
|
## Video Demo
|
||||||
<img src="assets/MOT16-03.gif" width="400"/> <img src="assets/MOT16-14.gif" width="400"/>
|
<img src="assets/MOT16-03.gif" width="400"/> <img src="assets/MOT16-14.gif" width="400"/>
|
||||||
<img src="assets/IMG_0055.gif" width="400"/> <img src="assets/000011-00001.gif" width="400"/>
|
<img src="assets/IMG_0055.gif" width="400"/> <img src="assets/000011-00001.gif" width="400"/>
|
||||||
|
|
||||||
Usage:
|
Usage:
|
||||||
```
|
```
|
||||||
python demo.py --input-video path/to/your/input/video --weights path/to/model/weights
|
python demo.py --input-video path/to/your/input/video --weights path/to/model/weights
|
||||||
--output-format video --output-root path/to/output/root
|
--output-format video --output-root path/to/output/root
|
||||||
```
|
```
|
||||||
## Dataset zoo
|
## Dataset zoo
|
||||||
Please see [DATASET_ZOO.md](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md) for detailed description of the training/evaluation datasets.
|
Please see [DATASET_ZOO.md](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md) for detailed description of the training/evaluation datasets.
|
||||||
## Pretrained model and baseline models
|
## Pretrained model and baseline models
|
||||||
Darknet-53 ImageNet pretrained: [[DarkNet Official]](https://pjreddie.com/media/files/darknet53.conv.74)
|
Darknet-53 ImageNet pretrained: [[DarkNet Official]](https://pjreddie.com/media/files/darknet53.conv.74)
|
||||||
|
|
||||||
JDE-1088x608-uncertainty: [[Google Drive]](https://drive.google.com/open?id=1nlnuYfGNuHWZztQHXwVZSL_FvfE551pA) [[Baidu NetDisk]](https://pan.baidu.com/s/1Ifgn0Y_JZE65_qSrQM2l-Q)
|
JDE-1088x608-uncertainty: [[Google Drive]](https://drive.google.com/open?id=1nlnuYfGNuHWZztQHXwVZSL_FvfE551pA) [[Baidu NetDisk]](https://pan.baidu.com/s/1Ifgn0Y_JZE65_qSrQM2l-Q)
|
||||||
## Test on MOT-16 Challenge
|
## Test on MOT-16 Challenge
|
||||||
|
|
||||||
## Training instruction
|
## Training instruction
|
||||||
- Download the training datasets.
|
- Download the training datasets.
|
||||||
- Edit `cfg/ccmcpe.json`, config the training/validation combinations. A dataset is represented by an image list, please see `data/*.train` for example.
|
- Edit `cfg/ccmcpe.json`, config the training/validation combinations. A dataset is represented by an image list, please see `data/*.train` for example.
|
||||||
- Run the training script:
|
- Run the training script:
|
||||||
```
|
```
|
||||||
CUDA_VISIBLE_DEIVCES=0,1,2,3,4,5,6,7 python train.py
|
CUDA_VISIBLE_DEIVCES=0,1,2,3,4,5,6,7 python train.py
|
||||||
```
|
```
|
||||||
|
|
||||||
We use 8x Nvidia Titan Xp to train the model, with a batch size of 32. You can adjust the batch size (and the learning rate together) according to how many GPUs your have. You can also train with smaller image size, which will bring faster inference time. But note the image size had better to be multiples of 32 (the down-sampling rate).
|
We use 8x Nvidia Titan Xp to train the model, with a batch size of 32. You can adjust the batch size (and the learning rate together) according to how many GPUs your have. You can also train with smaller image size, which will bring faster inference time. But note the image size had better to be multiples of 32 (the down-sampling rate).
|
||||||
|
|
||||||
### Train with custom datasets
|
### Train with custom datasets
|
||||||
Adding custom datsets is quite simple, all you need to do is to organize your annotation files in the same format as in our training sets. Please refer to [DATASET_ZOO.md](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md) for the dataset format.
|
Adding custom datsets is quite simple, all you need to do is to organize your annotation files in the same format as in our training sets. Please refer to [DATASET_ZOO.md](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md) for the dataset format.
|
||||||
|
|
||||||
|
|
||||||
## Acknowledgement
|
## Acknowledgement
|
||||||
A large portion of code is borrowed from [ultralytics/yolov3](https://github.com/ultralytics/yolov3) and [longcw/MOTDT](https://github.com/longcw/MOTDT), many thanks to their wonderful work!
|
A large portion of code is borrowed from [ultralytics/yolov3](https://github.com/ultralytics/yolov3) and [longcw/MOTDT](https://github.com/longcw/MOTDT), many thanks to their wonderful work!
|
||||||
|
|
|
||||||
|
|
@ -1,24 +1,26 @@
|
||||||
{
|
{
|
||||||
"root":"/home/wangzd/datasets/MOT",
|
"root":"/home/wangzd/datasets/MOT",
|
||||||
"train":
|
"train":
|
||||||
{
|
{
|
||||||
"mot17":"./data/mot17.train",
|
"mot17":"./data/mot17.train",
|
||||||
"caltech":"./data/caltech.train",
|
"caltech":"./data/caltech.train",
|
||||||
"citypersons":"./data/citypersons.train",
|
"citypersons":"./data/citypersons.train",
|
||||||
"cuhksysu":"./data/cuhksysu.train",
|
"cuhksysu":"./data/cuhksysu.train",
|
||||||
"prw":"./data/prw.train",
|
"prw":"./data/prw.train",
|
||||||
"eth":"./data/eth.train"
|
"eth":"./data/eth.train",
|
||||||
},
|
"03":"./data/mot16-03.test",
|
||||||
"test_emb":
|
"01":"./data/mot16-01.test",
|
||||||
{
|
"14":"./data/mot16-14.test"
|
||||||
"caltech":"./data/caltech.10k.val",
|
},
|
||||||
"cuhksysu":"./data/cuhksysu.val",
|
"test_emb":
|
||||||
"prw":"./data/prw.val"
|
{
|
||||||
},
|
"caltech":"./data/caltech.10k.val",
|
||||||
"test":
|
"cuhksysu":"./data/cuhksysu.val",
|
||||||
{
|
"prw":"./data/prw.val"
|
||||||
"mot19":"./data/mot19.train",
|
},
|
||||||
"caltech":"./data/caltech.val",
|
"test":
|
||||||
"citypersons":"./data/citypersons.val"
|
{
|
||||||
}
|
"caltech":"./data/caltech.val",
|
||||||
}
|
"citypersons":"./data/citypersons.val"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
|
||||||
1666
cfg/yolov3.cfg
1666
cfg/yolov3.cfg
File diff suppressed because it is too large
Load diff
833
cfg/yolov3_864x480.cfg
Normal file
833
cfg/yolov3_864x480.cfg
Normal file
|
|
@ -0,0 +1,833 @@
|
||||||
|
[net]
|
||||||
|
# Testing
|
||||||
|
#batch=1
|
||||||
|
#subdivisions=1
|
||||||
|
# Training
|
||||||
|
batch=16
|
||||||
|
subdivisions=1
|
||||||
|
width=480
|
||||||
|
height=864
|
||||||
|
channels=3
|
||||||
|
momentum=0.9
|
||||||
|
decay=0.0005
|
||||||
|
angle=0
|
||||||
|
saturation = 1.5
|
||||||
|
exposure = 1.5
|
||||||
|
hue=.1
|
||||||
|
|
||||||
|
learning_rate=0.001
|
||||||
|
burn_in=1000
|
||||||
|
max_batches = 500200
|
||||||
|
policy=steps
|
||||||
|
steps=400000,450000
|
||||||
|
scales=.1,.1
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=32
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
# Downsample
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=64
|
||||||
|
size=3
|
||||||
|
stride=2
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=32
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=64
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[shortcut]
|
||||||
|
from=-3
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
# Downsample
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=128
|
||||||
|
size=3
|
||||||
|
stride=2
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=64
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=128
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[shortcut]
|
||||||
|
from=-3
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=64
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=128
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[shortcut]
|
||||||
|
from=-3
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
# Downsample
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=256
|
||||||
|
size=3
|
||||||
|
stride=2
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=128
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=256
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[shortcut]
|
||||||
|
from=-3
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=128
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=256
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[shortcut]
|
||||||
|
from=-3
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=128
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=256
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[shortcut]
|
||||||
|
from=-3
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=128
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=256
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[shortcut]
|
||||||
|
from=-3
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=128
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=256
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[shortcut]
|
||||||
|
from=-3
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=128
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=256
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[shortcut]
|
||||||
|
from=-3
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=128
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=256
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[shortcut]
|
||||||
|
from=-3
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=128
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=256
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[shortcut]
|
||||||
|
from=-3
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
# Downsample
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=512
|
||||||
|
size=3
|
||||||
|
stride=2
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=256
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=512
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[shortcut]
|
||||||
|
from=-3
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=256
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=512
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[shortcut]
|
||||||
|
from=-3
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=256
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=512
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[shortcut]
|
||||||
|
from=-3
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=256
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=512
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[shortcut]
|
||||||
|
from=-3
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=256
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=512
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[shortcut]
|
||||||
|
from=-3
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=256
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=512
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[shortcut]
|
||||||
|
from=-3
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=256
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=512
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[shortcut]
|
||||||
|
from=-3
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=256
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=512
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[shortcut]
|
||||||
|
from=-3
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
# Downsample
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=1024
|
||||||
|
size=3
|
||||||
|
stride=2
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=512
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=1024
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[shortcut]
|
||||||
|
from=-3
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=512
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=1024
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[shortcut]
|
||||||
|
from=-3
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=512
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=1024
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[shortcut]
|
||||||
|
from=-3
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=512
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=1024
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[shortcut]
|
||||||
|
from=-3
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
######################
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=512
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
filters=1024
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=512
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
filters=1024
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=512
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
filters=1024
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
filters=24
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
######### embedding ###########
|
||||||
|
[route]
|
||||||
|
layers = -3
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
filters=512
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
[route]
|
||||||
|
layers = -3, -1
|
||||||
|
###############################
|
||||||
|
|
||||||
|
|
||||||
|
[yolo]
|
||||||
|
mask = 8,9,10,11
|
||||||
|
anchors = 6,19, 9,27, 13,38, 18,54, 25,76, 36,107, 51,152, 71,215, 102,305, 143, 429, 203,508, 407,508
|
||||||
|
classes=1
|
||||||
|
num=12
|
||||||
|
jitter=.3
|
||||||
|
ignore_thresh = .7
|
||||||
|
truth_thresh = 1
|
||||||
|
random=1
|
||||||
|
|
||||||
|
|
||||||
|
[route]
|
||||||
|
layers = -7
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=256
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[upsample]
|
||||||
|
stride=2
|
||||||
|
|
||||||
|
[route]
|
||||||
|
layers = -1, 61
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=256
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
filters=512
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=256
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
filters=512
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=256
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
filters=512
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
filters=24
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
######### embedding ###########
|
||||||
|
[route]
|
||||||
|
layers = -3
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
filters=512
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
[route]
|
||||||
|
layers = -3, -1
|
||||||
|
###############################
|
||||||
|
|
||||||
|
[yolo]
|
||||||
|
mask = 4,5,6,7
|
||||||
|
anchors = 6,19, 9,27, 13,38, 18,54, 25,76, 36,107, 51,152, 71,215, 102,305, 143, 429, 203,508, 407,508
|
||||||
|
classes=1
|
||||||
|
num=12
|
||||||
|
jitter=.3
|
||||||
|
ignore_thresh = .7
|
||||||
|
truth_thresh = 1
|
||||||
|
random=1
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
[route]
|
||||||
|
layers = -7
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=128
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[upsample]
|
||||||
|
stride=2
|
||||||
|
|
||||||
|
[route]
|
||||||
|
layers = -1, 36
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=128
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
filters=256
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=128
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
filters=256
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
filters=128
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
batch_normalize=1
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
filters=256
|
||||||
|
activation=leaky
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
size=1
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
filters=24
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
######### embedding ###########
|
||||||
|
[route]
|
||||||
|
layers = -3
|
||||||
|
|
||||||
|
[convolutional]
|
||||||
|
size=3
|
||||||
|
stride=1
|
||||||
|
pad=1
|
||||||
|
filters=512
|
||||||
|
activation=linear
|
||||||
|
|
||||||
|
[route]
|
||||||
|
layers = -3, -1
|
||||||
|
###############################
|
||||||
|
|
||||||
|
[yolo]
|
||||||
|
mask = 0,1,2,3
|
||||||
|
anchors = 6,19, 9,27, 13,38, 18,54, 25,76, 36,107, 51,152, 71,215, 102,305, 143, 429, 203,508, 407,508
|
||||||
|
classes=1
|
||||||
|
num=12
|
||||||
|
jitter=.3
|
||||||
|
ignore_thresh = .7
|
||||||
|
truth_thresh = 1
|
||||||
|
random=1
|
||||||
20000
data/caltech.10k.val
20000
data/caltech.10k.val
File diff suppressed because it is too large
Load diff
53476
data/caltech.train
53476
data/caltech.train
File diff suppressed because it is too large
Load diff
59824
data/caltech.val
59824
data/caltech.val
File diff suppressed because it is too large
Load diff
File diff suppressed because it is too large
Load diff
|
|
@ -1,441 +1,441 @@
|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_066574_leftImg8bit.png
|
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|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_067474_leftImg8bit.png
|
Cityscapes/images/val/frankfurt/frankfurt_000001_067474_leftImg8bit.png
|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_077092_leftImg8bit.png
|
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|
||||||
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|
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|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_028335_leftImg8bit.png
|
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|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_050149_leftImg8bit.png
|
Cityscapes/images/val/frankfurt/frankfurt_000001_050149_leftImg8bit.png
|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_043395_leftImg8bit.png
|
Cityscapes/images/val/frankfurt/frankfurt_000001_043395_leftImg8bit.png
|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_059119_leftImg8bit.png
|
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|
||||||
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|
Cityscapes/images/val/frankfurt/frankfurt_000000_005543_leftImg8bit.png
|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_010156_leftImg8bit.png
|
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|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_064130_leftImg8bit.png
|
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|
||||||
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|
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|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000000_003025_leftImg8bit.png
|
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|
||||||
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|
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|
||||||
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|
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|
||||||
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|
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|
||||||
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|
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|
||||||
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|
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|
||||||
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|
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|
||||||
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|
Cityscapes/images/val/frankfurt/frankfurt_000001_080091_leftImg8bit.png
|
||||||
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|
Cityscapes/images/val/frankfurt/frankfurt_000000_007365_leftImg8bit.png
|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_013710_leftImg8bit.png
|
Cityscapes/images/val/frankfurt/frankfurt_000001_013710_leftImg8bit.png
|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000000_013942_leftImg8bit.png
|
Cityscapes/images/val/frankfurt/frankfurt_000000_013942_leftImg8bit.png
|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_007973_leftImg8bit.png
|
Cityscapes/images/val/frankfurt/frankfurt_000001_007973_leftImg8bit.png
|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_020693_leftImg8bit.png
|
Cityscapes/images/val/frankfurt/frankfurt_000001_020693_leftImg8bit.png
|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000000_016286_leftImg8bit.png
|
Cityscapes/images/val/frankfurt/frankfurt_000000_016286_leftImg8bit.png
|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_073088_leftImg8bit.png
|
Cityscapes/images/val/frankfurt/frankfurt_000001_073088_leftImg8bit.png
|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_066438_leftImg8bit.png
|
Cityscapes/images/val/frankfurt/frankfurt_000001_066438_leftImg8bit.png
|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_030067_leftImg8bit.png
|
Cityscapes/images/val/frankfurt/frankfurt_000001_030067_leftImg8bit.png
|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_067178_leftImg8bit.png
|
Cityscapes/images/val/frankfurt/frankfurt_000001_067178_leftImg8bit.png
|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000000_014480_leftImg8bit.png
|
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|
||||||
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|
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|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000000_011810_leftImg8bit.png
|
Cityscapes/images/val/frankfurt/frankfurt_000000_011810_leftImg8bit.png
|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_005898_leftImg8bit.png
|
Cityscapes/images/val/frankfurt/frankfurt_000001_005898_leftImg8bit.png
|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_019854_leftImg8bit.png
|
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|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_055709_leftImg8bit.png
|
Cityscapes/images/val/frankfurt/frankfurt_000001_055709_leftImg8bit.png
|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_002512_leftImg8bit.png
|
Cityscapes/images/val/frankfurt/frankfurt_000001_002512_leftImg8bit.png
|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_007622_leftImg8bit.png
|
Cityscapes/images/val/frankfurt/frankfurt_000001_007622_leftImg8bit.png
|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_054077_leftImg8bit.png
|
Cityscapes/images/val/frankfurt/frankfurt_000001_054077_leftImg8bit.png
|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_060545_leftImg8bit.png
|
Cityscapes/images/val/frankfurt/frankfurt_000001_060545_leftImg8bit.png
|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_063045_leftImg8bit.png
|
Cityscapes/images/val/frankfurt/frankfurt_000001_063045_leftImg8bit.png
|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_032556_leftImg8bit.png
|
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|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_064305_leftImg8bit.png
|
Cityscapes/images/val/frankfurt/frankfurt_000001_064305_leftImg8bit.png
|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_049698_leftImg8bit.png
|
Cityscapes/images/val/frankfurt/frankfurt_000001_049698_leftImg8bit.png
|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_007857_leftImg8bit.png
|
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|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_012519_leftImg8bit.png
|
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|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_034816_leftImg8bit.png
|
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|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_032018_leftImg8bit.png
|
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|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_019969_leftImg8bit.png
|
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|
||||||
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|
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|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_065617_leftImg8bit.png
|
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|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000000_017228_leftImg8bit.png
|
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|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_062016_leftImg8bit.png
|
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|
||||||
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|
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|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000000_010763_leftImg8bit.png
|
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|
||||||
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|
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|
||||||
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|
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|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_001464_leftImg8bit.png
|
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|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_062396_leftImg8bit.png
|
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|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000000_008206_leftImg8bit.png
|
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|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_004327_leftImg8bit.png
|
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|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_075984_leftImg8bit.png
|
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|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_052120_leftImg8bit.png
|
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|
||||||
Cityscapes/images/val/frankfurt/frankfurt_000001_020046_leftImg8bit.png
|
Cityscapes/images/val/frankfurt/frankfurt_000001_020046_leftImg8bit.png
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|
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||||||
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|
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|
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||||||
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38
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|
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|
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|
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|
||||||
10632
data/mot17.train
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data/mot17.train
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17862
data/mot19.train
17862
data/mot19.train
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11402
data/prw.train
11402
data/prw.train
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data/prw.val
4000
data/prw.val
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128
demo.py
128
demo.py
|
|
@ -1,64 +1,64 @@
|
||||||
import os
|
import os
|
||||||
import os.path as osp
|
import os.path as osp
|
||||||
import cv2
|
import cv2
|
||||||
import logging
|
import logging
|
||||||
import argparse
|
import argparse
|
||||||
import motmetrics as mm
|
import motmetrics as mm
|
||||||
|
|
||||||
from tracker.multitracker import JDETracker
|
from tracker.multitracker import JDETracker
|
||||||
from utils import visualization as vis
|
from utils import visualization as vis
|
||||||
from utils.utils import *
|
from utils.utils import *
|
||||||
from utils.io import read_results
|
from utils.io import read_results
|
||||||
from utils.log import logger
|
from utils.log import logger
|
||||||
from utils.timer import Timer
|
from utils.timer import Timer
|
||||||
from utils.evaluation import Evaluator
|
from utils.evaluation import Evaluator
|
||||||
import utils.datasets as datasets
|
import utils.datasets as datasets
|
||||||
import torch
|
import torch
|
||||||
from track import eval_seq
|
from track import eval_seq
|
||||||
|
|
||||||
|
|
||||||
def track(opt):
|
def track(opt):
|
||||||
logger.setLevel(logging.INFO)
|
logger.setLevel(logging.INFO)
|
||||||
result_root = opt.output_root if opt.output_root!='' else '.'
|
result_root = opt.output_root if opt.output_root!='' else '.'
|
||||||
mkdir_if_missing(result_root)
|
mkdir_if_missing(result_root)
|
||||||
|
|
||||||
# run tracking
|
# run tracking
|
||||||
timer = Timer()
|
timer = Timer()
|
||||||
accs = []
|
accs = []
|
||||||
n_frame = 0
|
n_frame = 0
|
||||||
|
|
||||||
logger.info('start tracking...')
|
logger.info('start tracking...')
|
||||||
dataloader = datasets.LoadVideo(opt.input_video, opt.img_size)
|
dataloader = datasets.LoadVideo(opt.input_video, opt.img_size)
|
||||||
result_filename = os.path.join(result_root, 'results.txt')
|
result_filename = os.path.join(result_root, 'results.txt')
|
||||||
frame_rate = dataloader.frame_rate
|
frame_rate = dataloader.frame_rate
|
||||||
|
|
||||||
frame_dir = None if opt.output_format=='text' else osp.join(result_root, 'frame')
|
frame_dir = None if opt.output_format=='text' else osp.join(result_root, 'frame')
|
||||||
try:
|
try:
|
||||||
eval_seq(opt, dataloader, 'mot', result_filename,
|
eval_seq(opt, dataloader, 'mot', result_filename,
|
||||||
save_dir=frame_dir, show_image=False, frame_rate=frame_rate)
|
save_dir=frame_dir, show_image=False, frame_rate=frame_rate)
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
logger.info(e)
|
logger.info(e)
|
||||||
|
|
||||||
if opt.output_format == 'video':
|
if opt.output_format == 'video':
|
||||||
output_video_path = osp.join(result_root, 'result.mp4')
|
output_video_path = osp.join(result_root, 'result.mp4')
|
||||||
cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg -c:v copy {}'.format(osp.join(result_root, 'frame'), output_video_path)
|
cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg -c:v copy {}'.format(osp.join(result_root, 'frame'), output_video_path)
|
||||||
os.system(cmd_str)
|
os.system(cmd_str)
|
||||||
|
|
||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
parser = argparse.ArgumentParser(prog='demo.py')
|
parser = argparse.ArgumentParser(prog='demo.py')
|
||||||
parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
|
parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
|
||||||
parser.add_argument('--weights', type=str, default='weights/latest.pt', help='path to weights file')
|
parser.add_argument('--weights', type=str, default='weights/latest.pt', help='path to weights file')
|
||||||
parser.add_argument('--img-size', type=int, default=(1088, 608), help='size of each image dimension')
|
parser.add_argument('--img-size', type=int, default=(1088, 608), help='size of each image dimension')
|
||||||
parser.add_argument('--iou-thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
|
parser.add_argument('--iou-thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
|
||||||
parser.add_argument('--conf-thres', type=float, default=0.5, help='object confidence threshold')
|
parser.add_argument('--conf-thres', type=float, default=0.5, help='object confidence threshold')
|
||||||
parser.add_argument('--nms-thres', type=float, default=0.4, help='iou threshold for non-maximum suppression')
|
parser.add_argument('--nms-thres', type=float, default=0.4, help='iou threshold for non-maximum suppression')
|
||||||
parser.add_argument('--min-box-area', type=float, default=200, help='filter out tiny boxes')
|
parser.add_argument('--min-box-area', type=float, default=200, help='filter out tiny boxes')
|
||||||
parser.add_argument('--track-buffer', type=int, default=30, help='tracking buffer')
|
parser.add_argument('--track-buffer', type=int, default=30, help='tracking buffer')
|
||||||
parser.add_argument('--input-video', type=str, help='path to the input video')
|
parser.add_argument('--input-video', type=str, help='path to the input video')
|
||||||
parser.add_argument('--output-format', type=str, default='video', help='expected output format, can be video, or text')
|
parser.add_argument('--output-format', type=str, default='video', help='expected output format, can be video, or text')
|
||||||
parser.add_argument('--output-root', type=str, default='results', help='expected output root path')
|
parser.add_argument('--output-root', type=str, default='results', help='expected output root path')
|
||||||
opt = parser.parse_args()
|
opt = parser.parse_args()
|
||||||
print(opt, end='\n\n')
|
print(opt, end='\n\n')
|
||||||
|
|
||||||
track(opt)
|
track(opt)
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -1,98 +1,98 @@
|
||||||
import argparse
|
import argparse
|
||||||
import json
|
import json
|
||||||
import time
|
import time
|
||||||
from pathlib import Path
|
from pathlib import Path
|
||||||
|
|
||||||
from sklearn import metrics
|
from sklearn import metrics
|
||||||
from scipy import interpolate
|
from scipy import interpolate
|
||||||
import torch.nn.functional as F
|
import torch.nn.functional as F
|
||||||
from models import *
|
from models import *
|
||||||
from utils.utils import *
|
from utils.utils import *
|
||||||
from torchvision.transforms import transforms as T
|
from torchvision.transforms import transforms as T
|
||||||
from utils.datasets import LoadImages, JointDataset, collate_fn
|
from utils.datasets import LoadImages, JointDataset, collate_fn
|
||||||
|
|
||||||
def extract_ped_per_frame(
|
def extract_ped_per_frame(
|
||||||
cfg,
|
cfg,
|
||||||
input_root,
|
input_root,
|
||||||
output_root,
|
output_root,
|
||||||
weights,
|
weights,
|
||||||
batch_size=16,
|
batch_size=16,
|
||||||
img_size=416,
|
img_size=416,
|
||||||
iou_thres=0.5,
|
iou_thres=0.5,
|
||||||
conf_thres=0.3,
|
conf_thres=0.3,
|
||||||
nms_thres=0.45,
|
nms_thres=0.45,
|
||||||
print_interval=40,
|
print_interval=40,
|
||||||
nID=14455,
|
nID=14455,
|
||||||
):
|
):
|
||||||
mkdir_if_missing(output_root)
|
mkdir_if_missing(output_root)
|
||||||
|
|
||||||
# Initialize model
|
# Initialize model
|
||||||
model = Darknet(cfg, img_size, nID)
|
model = Darknet(cfg, img_size, nID)
|
||||||
|
|
||||||
# Load weights
|
# Load weights
|
||||||
if weights.endswith('.pt'): # pytorch format
|
if weights.endswith('.pt'): # pytorch format
|
||||||
model.load_state_dict(torch.load(weights, map_location='cpu')['model'], strict=False)
|
model.load_state_dict(torch.load(weights, map_location='cpu')['model'], strict=False)
|
||||||
else: # darknet format
|
else: # darknet format
|
||||||
load_darknet_weights(model, weights)
|
load_darknet_weights(model, weights)
|
||||||
|
|
||||||
model = torch.nn.DataParallel(model)
|
model = torch.nn.DataParallel(model)
|
||||||
model.cuda().eval()
|
model.cuda().eval()
|
||||||
|
|
||||||
vlist = os.listdir(input_root)
|
vlist = os.listdir(input_root)
|
||||||
vlist = [osp.join(input_root, v, 'img1') for v in vlist]
|
vlist = [osp.join(input_root, v, 'img1') for v in vlist]
|
||||||
|
|
||||||
for vpath in vlist:
|
for vpath in vlist:
|
||||||
vroot = osp.join('/',*vpath.split('/')[:-1])
|
vroot = osp.join('/',*vpath.split('/')[:-1])
|
||||||
out_vroot = vroot.replace(input_root, output_root)
|
out_vroot = vroot.replace(input_root, output_root)
|
||||||
mkdir_if_missing(out_vroot)
|
mkdir_if_missing(out_vroot)
|
||||||
dataloader = LoadImages(vpath, img_size)
|
dataloader = LoadImages(vpath, img_size)
|
||||||
for frame_id, (frame_path, frame, frame_ori) in enumerate(dataloader):
|
for frame_id, (frame_path, frame, frame_ori) in enumerate(dataloader):
|
||||||
frame_ground_id = frame_path.split('/')[-1].split('.')[0]
|
frame_ground_id = frame_path.split('/')[-1].split('.')[0]
|
||||||
if frame_id % 20 == 0:
|
if frame_id % 20 == 0:
|
||||||
print('Processing frame {} of video {}'.format(frame_id, frame_path))
|
print('Processing frame {} of video {}'.format(frame_id, frame_path))
|
||||||
blob = torch.from_numpy(frame).cuda().unsqueeze(0)
|
blob = torch.from_numpy(frame).cuda().unsqueeze(0)
|
||||||
pred = model(blob)
|
pred = model(blob)
|
||||||
pred = pred[pred[:,:,4] > conf_thres]
|
pred = pred[pred[:,:,4] > conf_thres]
|
||||||
if len(pred) > 0:
|
if len(pred) > 0:
|
||||||
dets = non_max_suppression(pred.unsqueeze(0), conf_thres, nms_thres)[0].cpu()
|
dets = non_max_suppression(pred.unsqueeze(0), conf_thres, nms_thres)[0].cpu()
|
||||||
scale_coords(img_size, dets[:, :4], frame_ori.shape).round()
|
scale_coords(img_size, dets[:, :4], frame_ori.shape).round()
|
||||||
frame_dir = osp.join(out_vroot, frame_ground_id)
|
frame_dir = osp.join(out_vroot, frame_ground_id)
|
||||||
mkdir_if_missing(frame_dir)
|
mkdir_if_missing(frame_dir)
|
||||||
dets = dets[:, :5]
|
dets = dets[:, :5]
|
||||||
|
|
||||||
for ped_id, det in enumerate(dets):
|
for ped_id, det in enumerate(dets):
|
||||||
box = det[:4].int()
|
box = det[:4].int()
|
||||||
conf = det[4]
|
conf = det[4]
|
||||||
ped = frame_ori[box[1]:box[3], box[0]:box[2]]
|
ped = frame_ori[box[1]:box[3], box[0]:box[2]]
|
||||||
ped_path = osp.join(frame_dir, ('{:04d}_'+ '{:d}_'*4 + '{:.2f}.jpg').format(ped_id, *box, conf))
|
ped_path = osp.join(frame_dir, ('{:04d}_'+ '{:d}_'*4 + '{:.2f}.jpg').format(ped_id, *box, conf))
|
||||||
cv2.imwrite(ped_path, ped)
|
cv2.imwrite(ped_path, ped)
|
||||||
|
|
||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
parser = argparse.ArgumentParser(prog='test.py')
|
parser = argparse.ArgumentParser(prog='test.py')
|
||||||
parser.add_argument('--batch-size', type=int, default=40, help='size of each image batch')
|
parser.add_argument('--batch-size', type=int, default=40, help='size of each image batch')
|
||||||
parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
|
parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
|
||||||
parser.add_argument('--weights', type=str, default='weights/mot_64/latest.pt', help='path to weights file')
|
parser.add_argument('--weights', type=str, default='weights/mot_64/latest.pt', help='path to weights file')
|
||||||
parser.add_argument('--iou-thres', type=float, default=0.3, help='iou threshold required to qualify as detected')
|
parser.add_argument('--iou-thres', type=float, default=0.3, help='iou threshold required to qualify as detected')
|
||||||
parser.add_argument('--conf-thres', type=float, default=0.3, help='object confidence threshold')
|
parser.add_argument('--conf-thres', type=float, default=0.3, help='object confidence threshold')
|
||||||
parser.add_argument('--nms-thres', type=float, default=0.3, help='iou threshold for non-maximum suppression')
|
parser.add_argument('--nms-thres', type=float, default=0.3, help='iou threshold for non-maximum suppression')
|
||||||
parser.add_argument('--img-size', type=int, default=(1088, 608), help='size of each image dimension')
|
parser.add_argument('--img-size', type=int, default=(1088, 608), help='size of each image dimension')
|
||||||
parser.add_argument('--print-interval', type=int, default=10, help='size of each image dimension')
|
parser.add_argument('--print-interval', type=int, default=10, help='size of each image dimension')
|
||||||
parser.add_argument('--input-root', type=str, default='/home/wangzd/datasets/youtube/data/0004/frame', help='path to input frames')
|
parser.add_argument('--input-root', type=str, default='/home/wangzd/datasets/youtube/data/0004/frame', help='path to input frames')
|
||||||
parser.add_argument('--output-root', type=str, default='/home/wangzd/datasets/youtube/data/0004/ped_per_frame', help='path to output frames')
|
parser.add_argument('--output-root', type=str, default='/home/wangzd/datasets/youtube/data/0004/ped_per_frame', help='path to output frames')
|
||||||
opt = parser.parse_args()
|
opt = parser.parse_args()
|
||||||
print(opt, end='\n\n')
|
print(opt, end='\n\n')
|
||||||
|
|
||||||
with torch.no_grad():
|
with torch.no_grad():
|
||||||
extract_ped_per_frame(
|
extract_ped_per_frame(
|
||||||
opt.cfg,
|
opt.cfg,
|
||||||
opt.input_root,
|
opt.input_root,
|
||||||
opt.output_root,
|
opt.output_root,
|
||||||
opt.weights,
|
opt.weights,
|
||||||
opt.batch_size,
|
opt.batch_size,
|
||||||
opt.img_size,
|
opt.img_size,
|
||||||
opt.iou_thres,
|
opt.iou_thres,
|
||||||
opt.conf_thres,
|
opt.conf_thres,
|
||||||
opt.nms_thres,
|
opt.nms_thres,
|
||||||
opt.print_interval,
|
opt.print_interval,
|
||||||
)
|
)
|
||||||
|
|
||||||
|
|
|
||||||
754
models.py
754
models.py
|
|
@ -1,374 +1,380 @@
|
||||||
import os
|
import os
|
||||||
from collections import defaultdict,OrderedDict
|
from collections import defaultdict,OrderedDict
|
||||||
|
|
||||||
import torch.nn as nn
|
import torch.nn as nn
|
||||||
|
|
||||||
from utils.parse_config import *
|
from utils.parse_config import *
|
||||||
from utils.utils import *
|
from utils.utils import *
|
||||||
from utils.syncbn import SyncBN
|
from utils.syncbn import SyncBN
|
||||||
import time
|
import time
|
||||||
import math
|
import math
|
||||||
|
|
||||||
batch_norm=SyncBN #nn.BatchNorm2d
|
batch_norm=SyncBN #nn.BatchNorm2d
|
||||||
|
#batch_norm=nn.BatchNorm2d
|
||||||
def create_modules(module_defs):
|
def create_modules(module_defs):
|
||||||
"""
|
"""
|
||||||
Constructs module list of layer blocks from module configuration in module_defs
|
Constructs module list of layer blocks from module configuration in module_defs
|
||||||
"""
|
"""
|
||||||
hyperparams = module_defs.pop(0)
|
hyperparams = module_defs.pop(0)
|
||||||
output_filters = [int(hyperparams['channels'])]
|
output_filters = [int(hyperparams['channels'])]
|
||||||
module_list = nn.ModuleList()
|
module_list = nn.ModuleList()
|
||||||
yolo_layer_count = 0
|
yolo_layer_count = 0
|
||||||
for i, module_def in enumerate(module_defs):
|
for i, module_def in enumerate(module_defs):
|
||||||
modules = nn.Sequential()
|
modules = nn.Sequential()
|
||||||
|
|
||||||
if module_def['type'] == 'convolutional':
|
if module_def['type'] == 'convolutional':
|
||||||
bn = int(module_def['batch_normalize'])
|
bn = int(module_def['batch_normalize'])
|
||||||
filters = int(module_def['filters'])
|
filters = int(module_def['filters'])
|
||||||
kernel_size = int(module_def['size'])
|
kernel_size = int(module_def['size'])
|
||||||
pad = (kernel_size - 1) // 2 if int(module_def['pad']) else 0
|
pad = (kernel_size - 1) // 2 if int(module_def['pad']) else 0
|
||||||
modules.add_module('conv_%d' % i, nn.Conv2d(in_channels=output_filters[-1],
|
modules.add_module('conv_%d' % i, nn.Conv2d(in_channels=output_filters[-1],
|
||||||
out_channels=filters,
|
out_channels=filters,
|
||||||
kernel_size=kernel_size,
|
kernel_size=kernel_size,
|
||||||
stride=int(module_def['stride']),
|
stride=int(module_def['stride']),
|
||||||
padding=pad,
|
padding=pad,
|
||||||
bias=not bn))
|
bias=not bn))
|
||||||
if bn:
|
if bn:
|
||||||
modules.add_module('batch_norm_%d' % i, batch_norm(filters))
|
after_bn = batch_norm(filters)
|
||||||
if module_def['activation'] == 'leaky':
|
modules.add_module('batch_norm_%d' % i, after_bn)
|
||||||
modules.add_module('leaky_%d' % i, nn.LeakyReLU(0.1))
|
# BN is uniformly initialized by default in pytorch 1.0.1.
|
||||||
|
# In pytorch>1.2.0, BN weights are initialized with constant 1,
|
||||||
elif module_def['type'] == 'maxpool':
|
# but we find with the uniform initialization the model converges faster.
|
||||||
kernel_size = int(module_def['size'])
|
nn.init.uniform_(after_bn.weight)
|
||||||
stride = int(module_def['stride'])
|
nn.init.zeros_(after_bn.bias)
|
||||||
if kernel_size == 2 and stride == 1:
|
if module_def['activation'] == 'leaky':
|
||||||
modules.add_module('_debug_padding_%d' % i, nn.ZeroPad2d((0, 1, 0, 1)))
|
modules.add_module('leaky_%d' % i, nn.LeakyReLU(0.1))
|
||||||
maxpool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride, padding=int((kernel_size - 1) // 2))
|
|
||||||
modules.add_module('maxpool_%d' % i, maxpool)
|
elif module_def['type'] == 'maxpool':
|
||||||
|
kernel_size = int(module_def['size'])
|
||||||
elif module_def['type'] == 'upsample':
|
stride = int(module_def['stride'])
|
||||||
upsample = Upsample(scale_factor=int(module_def['stride']))
|
if kernel_size == 2 and stride == 1:
|
||||||
modules.add_module('upsample_%d' % i, upsample)
|
modules.add_module('_debug_padding_%d' % i, nn.ZeroPad2d((0, 1, 0, 1)))
|
||||||
|
maxpool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride, padding=int((kernel_size - 1) // 2))
|
||||||
elif module_def['type'] == 'route':
|
modules.add_module('maxpool_%d' % i, maxpool)
|
||||||
layers = [int(x) for x in module_def['layers'].split(',')]
|
|
||||||
filters = sum([output_filters[i + 1 if i > 0 else i] for i in layers])
|
elif module_def['type'] == 'upsample':
|
||||||
modules.add_module('route_%d' % i, EmptyLayer())
|
upsample = Upsample(scale_factor=int(module_def['stride']))
|
||||||
|
modules.add_module('upsample_%d' % i, upsample)
|
||||||
elif module_def['type'] == 'shortcut':
|
|
||||||
filters = output_filters[int(module_def['from'])]
|
elif module_def['type'] == 'route':
|
||||||
modules.add_module('shortcut_%d' % i, EmptyLayer())
|
layers = [int(x) for x in module_def['layers'].split(',')]
|
||||||
|
filters = sum([output_filters[i + 1 if i > 0 else i] for i in layers])
|
||||||
elif module_def['type'] == 'yolo':
|
modules.add_module('route_%d' % i, EmptyLayer())
|
||||||
anchor_idxs = [int(x) for x in module_def['mask'].split(',')]
|
|
||||||
# Extract anchors
|
elif module_def['type'] == 'shortcut':
|
||||||
anchors = [float(x) for x in module_def['anchors'].split(',')]
|
filters = output_filters[int(module_def['from'])]
|
||||||
anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]
|
modules.add_module('shortcut_%d' % i, EmptyLayer())
|
||||||
anchors = [anchors[i] for i in anchor_idxs]
|
|
||||||
nC = int(module_def['classes']) # number of classes
|
elif module_def['type'] == 'yolo':
|
||||||
img_size = (int(hyperparams['width']),int(hyperparams['height']))
|
anchor_idxs = [int(x) for x in module_def['mask'].split(',')]
|
||||||
# Define detection layer
|
# Extract anchors
|
||||||
yolo_layer = YOLOLayer(anchors, nC, hyperparams['nID'], img_size, yolo_layer_count, cfg=hyperparams['cfg'])
|
anchors = [float(x) for x in module_def['anchors'].split(',')]
|
||||||
modules.add_module('yolo_%d' % i, yolo_layer)
|
anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]
|
||||||
yolo_layer_count += 1
|
anchors = [anchors[i] for i in anchor_idxs]
|
||||||
|
nC = int(module_def['classes']) # number of classes
|
||||||
# Register module list and number of output filters
|
img_size = (int(hyperparams['width']),int(hyperparams['height']))
|
||||||
module_list.append(modules)
|
# Define detection layer
|
||||||
output_filters.append(filters)
|
yolo_layer = YOLOLayer(anchors, nC, hyperparams['nID'], img_size, yolo_layer_count, cfg=hyperparams['cfg'])
|
||||||
|
modules.add_module('yolo_%d' % i, yolo_layer)
|
||||||
return hyperparams, module_list
|
yolo_layer_count += 1
|
||||||
|
|
||||||
|
# Register module list and number of output filters
|
||||||
class EmptyLayer(nn.Module):
|
module_list.append(modules)
|
||||||
"""Placeholder for 'route' and 'shortcut' layers"""
|
output_filters.append(filters)
|
||||||
|
|
||||||
def __init__(self):
|
return hyperparams, module_list
|
||||||
super(EmptyLayer, self).__init__()
|
|
||||||
|
|
||||||
def forward(self, x):
|
class EmptyLayer(nn.Module):
|
||||||
return x
|
"""Placeholder for 'route' and 'shortcut' layers"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
class Upsample(nn.Module):
|
super(EmptyLayer, self).__init__()
|
||||||
# Custom Upsample layer (nn.Upsample gives deprecated warning message)
|
|
||||||
|
def forward(self, x):
|
||||||
def __init__(self, scale_factor=1, mode='nearest'):
|
return x
|
||||||
super(Upsample, self).__init__()
|
|
||||||
self.scale_factor = scale_factor
|
|
||||||
self.mode = mode
|
class Upsample(nn.Module):
|
||||||
|
# Custom Upsample layer (nn.Upsample gives deprecated warning message)
|
||||||
def forward(self, x):
|
|
||||||
return F.interpolate(x, scale_factor=self.scale_factor, mode=self.mode)
|
def __init__(self, scale_factor=1, mode='nearest'):
|
||||||
|
super(Upsample, self).__init__()
|
||||||
|
self.scale_factor = scale_factor
|
||||||
class YOLOLayer(nn.Module):
|
self.mode = mode
|
||||||
def __init__(self, anchors, nC, nID, img_size, yolo_layer, cfg):
|
|
||||||
super(YOLOLayer, self).__init__()
|
def forward(self, x):
|
||||||
self.layer = yolo_layer
|
return F.interpolate(x, scale_factor=self.scale_factor, mode=self.mode)
|
||||||
nA = len(anchors)
|
|
||||||
self.anchors = torch.FloatTensor(anchors)
|
|
||||||
self.nA = nA # number of anchors (3)
|
class YOLOLayer(nn.Module):
|
||||||
self.nC = nC # number of classes (80)
|
def __init__(self, anchors, nC, nID, img_size, yolo_layer, cfg):
|
||||||
self.nID = nID # number of identities
|
super(YOLOLayer, self).__init__()
|
||||||
self.img_size = 0
|
self.layer = yolo_layer
|
||||||
self.emb_dim = 512
|
nA = len(anchors)
|
||||||
|
self.anchors = torch.FloatTensor(anchors)
|
||||||
self.SmoothL1Loss = nn.SmoothL1Loss()
|
self.nA = nA # number of anchors (3)
|
||||||
self.SoftmaxLoss = nn.CrossEntropyLoss(ignore_index=-1)
|
self.nC = nC # number of classes (80)
|
||||||
self.CrossEntropyLoss = nn.CrossEntropyLoss()
|
self.nID = nID # number of identities
|
||||||
self.IDLoss = nn.CrossEntropyLoss(ignore_index=-1)
|
self.img_size = 0
|
||||||
self.s_c = nn.Parameter(-4.15*torch.ones(1)) # -4.15
|
self.emb_dim = 512
|
||||||
self.s_r = nn.Parameter(-4.85*torch.ones(1)) # -4.85
|
|
||||||
self.s_id = nn.Parameter(-2.3*torch.ones(1)) # -2.3
|
self.SmoothL1Loss = nn.SmoothL1Loss()
|
||||||
self.emb_scale = math.sqrt(2) * math.log(self.nID-1)
|
self.SoftmaxLoss = nn.CrossEntropyLoss(ignore_index=-1)
|
||||||
|
self.CrossEntropyLoss = nn.CrossEntropyLoss()
|
||||||
|
self.IDLoss = nn.CrossEntropyLoss(ignore_index=-1)
|
||||||
def forward(self, p_cat, img_size, targets=None, classifier=None, test_emb=False):
|
self.s_c = nn.Parameter(-4.15*torch.ones(1)) # -4.15
|
||||||
p, p_emb = p_cat[:, :24, ...], p_cat[:, 24:, ...]
|
self.s_r = nn.Parameter(-4.85*torch.ones(1)) # -4.85
|
||||||
nB, nGh, nGw = p.shape[0], p.shape[-2], p.shape[-1]
|
self.s_id = nn.Parameter(-2.3*torch.ones(1)) # -2.3
|
||||||
|
self.emb_scale = math.sqrt(2) * math.log(self.nID-1)
|
||||||
if self.img_size != img_size:
|
|
||||||
create_grids(self, img_size, nGh, nGw)
|
|
||||||
|
def forward(self, p_cat, img_size, targets=None, classifier=None, test_emb=False):
|
||||||
if p.is_cuda:
|
p, p_emb = p_cat[:, :24, ...], p_cat[:, 24:, ...]
|
||||||
self.grid_xy = self.grid_xy.cuda()
|
nB, nGh, nGw = p.shape[0], p.shape[-2], p.shape[-1]
|
||||||
self.anchor_wh = self.anchor_wh.cuda()
|
|
||||||
|
if self.img_size != img_size:
|
||||||
p = p.view(nB, self.nA, self.nC + 5, nGh, nGw).permute(0, 1, 3, 4, 2).contiguous() # prediction
|
create_grids(self, img_size, nGh, nGw)
|
||||||
|
|
||||||
p_emb = p_emb.permute(0,2,3,1).contiguous()
|
if p.is_cuda:
|
||||||
p_box = p[..., :4]
|
self.grid_xy = self.grid_xy.cuda()
|
||||||
p_conf = p[..., 4:6].permute(0, 4, 1, 2, 3) # Conf
|
self.anchor_wh = self.anchor_wh.cuda()
|
||||||
|
|
||||||
# Training
|
p = p.view(nB, self.nA, self.nC + 5, nGh, nGw).permute(0, 1, 3, 4, 2).contiguous() # prediction
|
||||||
if targets is not None:
|
|
||||||
if test_emb:
|
p_emb = p_emb.permute(0,2,3,1).contiguous()
|
||||||
tconf, tbox, tids = build_targets_max(targets, self.anchor_vec.cuda(), self.nA, self.nC, nGh, nGw)
|
p_box = p[..., :4]
|
||||||
else:
|
p_conf = p[..., 4:6].permute(0, 4, 1, 2, 3) # Conf
|
||||||
tconf, tbox, tids = build_targets_thres(targets, self.anchor_vec.cuda(), self.nA, self.nC, nGh, nGw)
|
|
||||||
tconf, tbox, tids = tconf.cuda(), tbox.cuda(), tids.cuda()
|
# Training
|
||||||
mask = tconf > 0
|
if targets is not None:
|
||||||
|
if test_emb:
|
||||||
# Compute losses
|
tconf, tbox, tids = build_targets_max(targets, self.anchor_vec.cuda(), self.nA, self.nC, nGh, nGw)
|
||||||
nT = sum([len(x) for x in targets]) # number of targets
|
else:
|
||||||
nM = mask.sum().float() # number of anchors (assigned to targets)
|
tconf, tbox, tids = build_targets_thres(targets, self.anchor_vec.cuda(), self.nA, self.nC, nGh, nGw)
|
||||||
nP = torch.ones_like(mask).sum().float()
|
tconf, tbox, tids = tconf.cuda(), tbox.cuda(), tids.cuda()
|
||||||
if nM > 0:
|
mask = tconf > 0
|
||||||
lbox = self.SmoothL1Loss(p_box[mask], tbox[mask])
|
|
||||||
else:
|
# Compute losses
|
||||||
FT = torch.cuda.FloatTensor if p_conf.is_cuda else torch.FloatTensor
|
nT = sum([len(x) for x in targets]) # number of targets
|
||||||
lbox, lconf = FT([0]), FT([0])
|
nM = mask.sum().float() # number of anchors (assigned to targets)
|
||||||
lconf = self.SoftmaxLoss(p_conf, tconf)
|
nP = torch.ones_like(mask).sum().float()
|
||||||
lid = torch.Tensor(1).fill_(0).squeeze().cuda()
|
if nM > 0:
|
||||||
emb_mask,_ = mask.max(1)
|
lbox = self.SmoothL1Loss(p_box[mask], tbox[mask])
|
||||||
|
else:
|
||||||
# For convenience we use max(1) to decide the id, TODO: more reseanable strategy
|
FT = torch.cuda.FloatTensor if p_conf.is_cuda else torch.FloatTensor
|
||||||
tids,_ = tids.max(1)
|
lbox, lconf = FT([0]), FT([0])
|
||||||
tids = tids[emb_mask]
|
lconf = self.SoftmaxLoss(p_conf, tconf)
|
||||||
embedding = p_emb[emb_mask].contiguous()
|
lid = torch.Tensor(1).fill_(0).squeeze().cuda()
|
||||||
embedding = self.emb_scale * F.normalize(embedding)
|
emb_mask,_ = mask.max(1)
|
||||||
nI = emb_mask.sum().float()
|
|
||||||
|
# For convenience we use max(1) to decide the id, TODO: more reseanable strategy
|
||||||
if test_emb:
|
tids,_ = tids.max(1)
|
||||||
if np.prod(embedding.shape)==0 or np.prod(tids.shape) == 0:
|
tids = tids[emb_mask]
|
||||||
return torch.zeros(0, self. emb_dim+1).cuda()
|
embedding = p_emb[emb_mask].contiguous()
|
||||||
emb_and_gt = torch.cat([embedding, tids.float()], dim=1)
|
embedding = self.emb_scale * F.normalize(embedding)
|
||||||
return emb_and_gt
|
nI = emb_mask.sum().float()
|
||||||
|
|
||||||
if len(embedding) > 1:
|
if test_emb:
|
||||||
logits = classifier(embedding).contiguous()
|
if np.prod(embedding.shape)==0 or np.prod(tids.shape) == 0:
|
||||||
lid = self.IDLoss(logits, tids.squeeze())
|
return torch.zeros(0, self. emb_dim+1).cuda()
|
||||||
|
emb_and_gt = torch.cat([embedding, tids.float()], dim=1)
|
||||||
# Sum loss components
|
return emb_and_gt
|
||||||
loss = torch.exp(-self.s_r)*lbox + torch.exp(-self.s_c)*lconf + torch.exp(-self.s_id)*lid + \
|
|
||||||
(self.s_r + self.s_c + self.s_id)
|
if len(embedding) > 1:
|
||||||
loss *= 0.5
|
logits = classifier(embedding).contiguous()
|
||||||
|
lid = self.IDLoss(logits, tids.squeeze())
|
||||||
return loss, loss.item(), lbox.item(), lconf.item(), lid.item(), nT
|
|
||||||
|
# Sum loss components
|
||||||
else:
|
loss = torch.exp(-self.s_r)*lbox + torch.exp(-self.s_c)*lconf + torch.exp(-self.s_id)*lid + \
|
||||||
p_conf = torch.softmax(p_conf, dim=1)[:,1,...].unsqueeze(-1)
|
(self.s_r + self.s_c + self.s_id)
|
||||||
p_emb = p_emb.unsqueeze(1).repeat(1,self.nA,1,1,1).contiguous()
|
loss *= 0.5
|
||||||
p_cls = torch.zeros(nB,self.nA,nGh,nGw,1).cuda() # Temp
|
|
||||||
p = torch.cat([p_box, p_conf, p_cls, p_emb], dim=-1)
|
return loss, loss.item(), lbox.item(), lconf.item(), lid.item(), nT
|
||||||
p[..., :4] = decode_delta_map(p[..., :4], self.anchor_vec.to(p))
|
|
||||||
p[..., :4] *= self.stride
|
else:
|
||||||
|
p_conf = torch.softmax(p_conf, dim=1)[:,1,...].unsqueeze(-1)
|
||||||
return p.view(nB, -1, p.shape[-1])
|
p_emb = p_emb.unsqueeze(1).repeat(1,self.nA,1,1,1).contiguous()
|
||||||
|
p_cls = torch.zeros(nB,self.nA,nGh,nGw,1).cuda() # Temp
|
||||||
|
p = torch.cat([p_box, p_conf, p_cls, p_emb], dim=-1)
|
||||||
class Darknet(nn.Module):
|
p[..., :4] = decode_delta_map(p[..., :4], self.anchor_vec.to(p))
|
||||||
"""YOLOv3 object detection model"""
|
p[..., :4] *= self.stride
|
||||||
|
|
||||||
def __init__(self, cfg_path, img_size=(1088, 608), nID=1591, test_emb=False):
|
return p.view(nB, -1, p.shape[-1])
|
||||||
super(Darknet, self).__init__()
|
|
||||||
|
|
||||||
self.module_defs = parse_model_cfg(cfg_path)
|
class Darknet(nn.Module):
|
||||||
self.module_defs[0]['cfg'] = cfg_path
|
"""YOLOv3 object detection model"""
|
||||||
self.module_defs[0]['nID'] = nID
|
|
||||||
self.hyperparams, self.module_list = create_modules(self.module_defs)
|
def __init__(self, cfg_path, img_size=(1088, 608), nID=1591, test_emb=False):
|
||||||
self.img_size = img_size
|
super(Darknet, self).__init__()
|
||||||
self.loss_names = ['loss', 'box', 'conf', 'id', 'nT']
|
|
||||||
self.losses = OrderedDict()
|
self.module_defs = parse_model_cfg(cfg_path)
|
||||||
for ln in self.loss_names:
|
self.module_defs[0]['cfg'] = cfg_path
|
||||||
self.losses[ln] = 0
|
self.module_defs[0]['nID'] = nID
|
||||||
self.emb_dim = 512
|
self.hyperparams, self.module_list = create_modules(self.module_defs)
|
||||||
self.classifier = nn.Linear(self.emb_dim, nID)
|
self.img_size = img_size
|
||||||
self.test_emb=test_emb
|
self.loss_names = ['loss', 'box', 'conf', 'id', 'nT']
|
||||||
|
self.losses = OrderedDict()
|
||||||
|
for ln in self.loss_names:
|
||||||
def forward(self, x, targets=None, targets_len=None):
|
self.losses[ln] = 0
|
||||||
self.losses = OrderedDict()
|
self.emb_dim = 512
|
||||||
for ln in self.loss_names:
|
self.classifier = nn.Linear(self.emb_dim, nID)
|
||||||
self.losses[ln] = 0
|
self.test_emb=test_emb
|
||||||
is_training = (targets is not None) and (not self.test_emb)
|
|
||||||
#img_size = x.shape[-1]
|
|
||||||
layer_outputs = []
|
def forward(self, x, targets=None, targets_len=None):
|
||||||
output = []
|
self.losses = OrderedDict()
|
||||||
|
for ln in self.loss_names:
|
||||||
for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
|
self.losses[ln] = 0
|
||||||
mtype = module_def['type']
|
is_training = (targets is not None) and (not self.test_emb)
|
||||||
if mtype in ['convolutional', 'upsample', 'maxpool']:
|
#img_size = x.shape[-1]
|
||||||
x = module(x)
|
layer_outputs = []
|
||||||
elif mtype == 'route':
|
output = []
|
||||||
layer_i = [int(x) for x in module_def['layers'].split(',')]
|
|
||||||
if len(layer_i) == 1:
|
for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
|
||||||
x = layer_outputs[layer_i[0]]
|
mtype = module_def['type']
|
||||||
else:
|
if mtype in ['convolutional', 'upsample', 'maxpool']:
|
||||||
x = torch.cat([layer_outputs[i] for i in layer_i], 1)
|
x = module(x)
|
||||||
elif mtype == 'shortcut':
|
elif mtype == 'route':
|
||||||
layer_i = int(module_def['from'])
|
layer_i = [int(x) for x in module_def['layers'].split(',')]
|
||||||
x = layer_outputs[-1] + layer_outputs[layer_i]
|
if len(layer_i) == 1:
|
||||||
elif mtype == 'yolo':
|
x = layer_outputs[layer_i[0]]
|
||||||
if is_training: # get loss
|
else:
|
||||||
targets = [targets[i][:int(l)] for i,l in enumerate(targets_len)]
|
x = torch.cat([layer_outputs[i] for i in layer_i], 1)
|
||||||
x, *losses = module[0](x, self.img_size, targets, self.classifier)
|
elif mtype == 'shortcut':
|
||||||
for name, loss in zip(self.loss_names, losses):
|
layer_i = int(module_def['from'])
|
||||||
self.losses[name] += loss
|
x = layer_outputs[-1] + layer_outputs[layer_i]
|
||||||
elif self.test_emb:
|
elif mtype == 'yolo':
|
||||||
targets = [targets[i][:int(l)] for i,l in enumerate(targets_len)]
|
if is_training: # get loss
|
||||||
x = module[0](x, self.img_size, targets, self.classifier, self.test_emb)
|
targets = [targets[i][:int(l)] for i,l in enumerate(targets_len)]
|
||||||
else: # get detections
|
x, *losses = module[0](x, self.img_size, targets, self.classifier)
|
||||||
x = module[0](x, self.img_size)
|
for name, loss in zip(self.loss_names, losses):
|
||||||
output.append(x)
|
self.losses[name] += loss
|
||||||
layer_outputs.append(x)
|
elif self.test_emb:
|
||||||
|
targets = [targets[i][:int(l)] for i,l in enumerate(targets_len)]
|
||||||
if is_training:
|
x = module[0](x, self.img_size, targets, self.classifier, self.test_emb)
|
||||||
self.losses['nT'] /= 3
|
else: # get detections
|
||||||
output = [o.squeeze() for o in output]
|
x = module[0](x, self.img_size)
|
||||||
return sum(output), torch.Tensor(list(self.losses.values())).cuda()
|
output.append(x)
|
||||||
elif self.test_emb:
|
layer_outputs.append(x)
|
||||||
return torch.cat(output, 0)
|
|
||||||
return torch.cat(output, 1)
|
if is_training:
|
||||||
|
self.losses['nT'] /= 3
|
||||||
|
output = [o.squeeze() for o in output]
|
||||||
def create_grids(self, img_size, nGh, nGw):
|
return sum(output), torch.Tensor(list(self.losses.values())).cuda()
|
||||||
self.stride = img_size[0]/nGw
|
elif self.test_emb:
|
||||||
assert self.stride == img_size[1] / nGh
|
return torch.cat(output, 0)
|
||||||
|
return torch.cat(output, 1)
|
||||||
# build xy offsets
|
|
||||||
grid_x = torch.arange(nGw).repeat((nGh, 1)).view((1, 1, nGh, nGw)).float()
|
|
||||||
grid_y = torch.arange(nGh).repeat((nGw, 1)).transpose(0,1).view((1, 1, nGh, nGw)).float()
|
def create_grids(self, img_size, nGh, nGw):
|
||||||
#grid_y = grid_x.permute(0, 1, 3, 2)
|
self.stride = img_size[0]/nGw
|
||||||
self.grid_xy = torch.stack((grid_x, grid_y), 4)
|
assert self.stride == img_size[1] / nGh
|
||||||
|
|
||||||
# build wh gains
|
# build xy offsets
|
||||||
self.anchor_vec = self.anchors / self.stride
|
grid_x = torch.arange(nGw).repeat((nGh, 1)).view((1, 1, nGh, nGw)).float()
|
||||||
self.anchor_wh = self.anchor_vec.view(1, self.nA, 1, 1, 2)
|
grid_y = torch.arange(nGh).repeat((nGw, 1)).transpose(0,1).view((1, 1, nGh, nGw)).float()
|
||||||
|
#grid_y = grid_x.permute(0, 1, 3, 2)
|
||||||
|
self.grid_xy = torch.stack((grid_x, grid_y), 4)
|
||||||
def load_darknet_weights(self, weights, cutoff=-1):
|
|
||||||
# Parses and loads the weights stored in 'weights'
|
# build wh gains
|
||||||
# cutoff: save layers between 0 and cutoff (if cutoff = -1 all are saved)
|
self.anchor_vec = self.anchors / self.stride
|
||||||
weights_file = weights.split(os.sep)[-1]
|
self.anchor_wh = self.anchor_vec.view(1, self.nA, 1, 1, 2)
|
||||||
|
|
||||||
# Try to download weights if not available locally
|
|
||||||
if not os.path.isfile(weights):
|
def load_darknet_weights(self, weights, cutoff=-1):
|
||||||
try:
|
# Parses and loads the weights stored in 'weights'
|
||||||
os.system('wget https://pjreddie.com/media/files/' + weights_file + ' -O ' + weights)
|
# cutoff: save layers between 0 and cutoff (if cutoff = -1 all are saved)
|
||||||
except IOError:
|
weights_file = weights.split(os.sep)[-1]
|
||||||
print(weights + ' not found')
|
|
||||||
|
# Try to download weights if not available locally
|
||||||
# Establish cutoffs
|
if not os.path.isfile(weights):
|
||||||
if weights_file == 'darknet53.conv.74':
|
try:
|
||||||
cutoff = 75
|
os.system('wget https://pjreddie.com/media/files/' + weights_file + ' -O ' + weights)
|
||||||
elif weights_file == 'yolov3-tiny.conv.15':
|
except IOError:
|
||||||
cutoff = 15
|
print(weights + ' not found')
|
||||||
|
|
||||||
# Open the weights file
|
# Establish cutoffs
|
||||||
fp = open(weights, 'rb')
|
if weights_file == 'darknet53.conv.74':
|
||||||
header = np.fromfile(fp, dtype=np.int32, count=5) # First five are header values
|
cutoff = 75
|
||||||
|
elif weights_file == 'yolov3-tiny.conv.15':
|
||||||
# Needed to write header when saving weights
|
cutoff = 15
|
||||||
self.header_info = header
|
|
||||||
|
# Open the weights file
|
||||||
self.seen = header[3] # number of images seen during training
|
fp = open(weights, 'rb')
|
||||||
weights = np.fromfile(fp, dtype=np.float32) # The rest are weights
|
header = np.fromfile(fp, dtype=np.int32, count=5) # First five are header values
|
||||||
fp.close()
|
|
||||||
|
# Needed to write header when saving weights
|
||||||
ptr = 0
|
self.header_info = header
|
||||||
for i, (module_def, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
|
|
||||||
if module_def['type'] == 'convolutional':
|
self.seen = header[3] # number of images seen during training
|
||||||
conv_layer = module[0]
|
weights = np.fromfile(fp, dtype=np.float32) # The rest are weights
|
||||||
if module_def['batch_normalize']:
|
fp.close()
|
||||||
# Load BN bias, weights, running mean and running variance
|
|
||||||
bn_layer = module[1]
|
ptr = 0
|
||||||
num_b = bn_layer.bias.numel() # Number of biases
|
for i, (module_def, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
|
||||||
# Bias
|
if module_def['type'] == 'convolutional':
|
||||||
bn_b = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.bias)
|
conv_layer = module[0]
|
||||||
bn_layer.bias.data.copy_(bn_b)
|
if module_def['batch_normalize']:
|
||||||
ptr += num_b
|
# Load BN bias, weights, running mean and running variance
|
||||||
# Weight
|
bn_layer = module[1]
|
||||||
bn_w = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.weight)
|
num_b = bn_layer.bias.numel() # Number of biases
|
||||||
bn_layer.weight.data.copy_(bn_w)
|
# Bias
|
||||||
ptr += num_b
|
bn_b = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.bias)
|
||||||
# Running Mean
|
bn_layer.bias.data.copy_(bn_b)
|
||||||
bn_rm = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.running_mean)
|
ptr += num_b
|
||||||
bn_layer.running_mean.data.copy_(bn_rm)
|
# Weight
|
||||||
ptr += num_b
|
bn_w = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.weight)
|
||||||
# Running Var
|
bn_layer.weight.data.copy_(bn_w)
|
||||||
bn_rv = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.running_var)
|
ptr += num_b
|
||||||
bn_layer.running_var.data.copy_(bn_rv)
|
# Running Mean
|
||||||
ptr += num_b
|
bn_rm = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.running_mean)
|
||||||
else:
|
bn_layer.running_mean.data.copy_(bn_rm)
|
||||||
# Load conv. bias
|
ptr += num_b
|
||||||
num_b = conv_layer.bias.numel()
|
# Running Var
|
||||||
conv_b = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(conv_layer.bias)
|
bn_rv = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.running_var)
|
||||||
conv_layer.bias.data.copy_(conv_b)
|
bn_layer.running_var.data.copy_(bn_rv)
|
||||||
ptr += num_b
|
ptr += num_b
|
||||||
# Load conv. weights
|
else:
|
||||||
num_w = conv_layer.weight.numel()
|
# Load conv. bias
|
||||||
conv_w = torch.from_numpy(weights[ptr:ptr + num_w]).view_as(conv_layer.weight)
|
num_b = conv_layer.bias.numel()
|
||||||
conv_layer.weight.data.copy_(conv_w)
|
conv_b = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(conv_layer.bias)
|
||||||
ptr += num_w
|
conv_layer.bias.data.copy_(conv_b)
|
||||||
|
ptr += num_b
|
||||||
|
# Load conv. weights
|
||||||
"""
|
num_w = conv_layer.weight.numel()
|
||||||
@:param path - path of the new weights file
|
conv_w = torch.from_numpy(weights[ptr:ptr + num_w]).view_as(conv_layer.weight)
|
||||||
@:param cutoff - save layers between 0 and cutoff (cutoff = -1 -> all are saved)
|
conv_layer.weight.data.copy_(conv_w)
|
||||||
"""
|
ptr += num_w
|
||||||
|
|
||||||
|
|
||||||
def save_weights(self, path, cutoff=-1):
|
"""
|
||||||
fp = open(path, 'wb')
|
@:param path - path of the new weights file
|
||||||
self.header_info[3] = self.seen # number of images seen during training
|
@:param cutoff - save layers between 0 and cutoff (cutoff = -1 -> all are saved)
|
||||||
self.header_info.tofile(fp)
|
"""
|
||||||
|
|
||||||
# Iterate through layers
|
|
||||||
for i, (module_def, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
|
def save_weights(self, path, cutoff=-1):
|
||||||
if module_def['type'] == 'convolutional':
|
fp = open(path, 'wb')
|
||||||
conv_layer = module[0]
|
self.header_info[3] = self.seen # number of images seen during training
|
||||||
# If batch norm, load bn first
|
self.header_info.tofile(fp)
|
||||||
if module_def['batch_normalize']:
|
|
||||||
bn_layer = module[1]
|
# Iterate through layers
|
||||||
bn_layer.bias.data.cpu().numpy().tofile(fp)
|
for i, (module_def, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
|
||||||
bn_layer.weight.data.cpu().numpy().tofile(fp)
|
if module_def['type'] == 'convolutional':
|
||||||
bn_layer.running_mean.data.cpu().numpy().tofile(fp)
|
conv_layer = module[0]
|
||||||
bn_layer.running_var.data.cpu().numpy().tofile(fp)
|
# If batch norm, load bn first
|
||||||
# Load conv bias
|
if module_def['batch_normalize']:
|
||||||
else:
|
bn_layer = module[1]
|
||||||
conv_layer.bias.data.cpu().numpy().tofile(fp)
|
bn_layer.bias.data.cpu().numpy().tofile(fp)
|
||||||
# Load conv weights
|
bn_layer.weight.data.cpu().numpy().tofile(fp)
|
||||||
conv_layer.weight.data.cpu().numpy().tofile(fp)
|
bn_layer.running_mean.data.cpu().numpy().tofile(fp)
|
||||||
|
bn_layer.running_var.data.cpu().numpy().tofile(fp)
|
||||||
fp.close()
|
# Load conv bias
|
||||||
|
else:
|
||||||
|
conv_layer.bias.data.cpu().numpy().tofile(fp)
|
||||||
|
# Load conv weights
|
||||||
|
conv_layer.weight.data.cpu().numpy().tofile(fp)
|
||||||
|
|
||||||
|
fp.close()
|
||||||
|
|
|
||||||
75
setup.py
Normal file
75
setup.py
Normal file
|
|
@ -0,0 +1,75 @@
|
||||||
|
###################################################################
|
||||||
|
# File Name: setup.py
|
||||||
|
# Author: Zhongdao Wang
|
||||||
|
# mail: wcd17@mails.tsinghua.edu.cn
|
||||||
|
# Created Time: Thu 19 Dec 2019 07:29:02 PM CST
|
||||||
|
###################################################################
|
||||||
|
|
||||||
|
from __future__ import print_function
|
||||||
|
from __future__ import division
|
||||||
|
from __future__ import absolute_import
|
||||||
|
|
||||||
|
import os
|
||||||
|
import glob
|
||||||
|
|
||||||
|
import torch
|
||||||
|
from setuptools import find_packages
|
||||||
|
from setuptools import setup
|
||||||
|
from torch.utils.cpp_extension import CUDA_HOME
|
||||||
|
from torch.utils.cpp_extension import CppExtension
|
||||||
|
from torch.utils.cpp_extension import CUDAExtension
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
def get_extensions():
|
||||||
|
this_dir = os.path.dirname(os.path.abspath(__file__))
|
||||||
|
extensions_dir = os.path.join(this_dir, "utils", "nms")
|
||||||
|
|
||||||
|
main_file = glob.glob(os.path.join(extensions_dir, "*.cpp"))
|
||||||
|
source_cpu = glob.glob(os.path.join(extensions_dir, "*.cpp"))
|
||||||
|
source_cuda = glob.glob(os.path.join(extensions_dir, "*.cu"))
|
||||||
|
|
||||||
|
sources = main_file
|
||||||
|
extension = CppExtension
|
||||||
|
|
||||||
|
extra_compile_args = {"cxx": []}
|
||||||
|
define_macros = []
|
||||||
|
|
||||||
|
#if (torch.cuda.is_available() and CUDA_HOME is not None) or os.getenv("FORCE_CUDA", "0") == "1":
|
||||||
|
if False:
|
||||||
|
extension = CUDAExtension
|
||||||
|
sources += source_cuda
|
||||||
|
define_macros += [("WITH_CUDA", None)]
|
||||||
|
extra_compile_args["nvcc"] = [
|
||||||
|
"-DCUDA_HAS_FP16=1",
|
||||||
|
"-D__CUDA_NO_HALF_OPERATORS__",
|
||||||
|
"-D__CUDA_NO_HALF_CONVERSIONS__",
|
||||||
|
"-D__CUDA_NO_HALF2_OPERATORS__",
|
||||||
|
]
|
||||||
|
|
||||||
|
sources = [os.path.join(extensions_dir, s) for s in sources]
|
||||||
|
|
||||||
|
include_dirs = [extensions_dir]
|
||||||
|
|
||||||
|
ext_modules = [
|
||||||
|
extension(
|
||||||
|
"nms",
|
||||||
|
sources,
|
||||||
|
include_dirs=include_dirs,
|
||||||
|
define_macros=define_macros,
|
||||||
|
extra_compile_args=extra_compile_args,
|
||||||
|
)
|
||||||
|
]
|
||||||
|
|
||||||
|
return ext_modules
|
||||||
|
|
||||||
|
print(get_extensions())
|
||||||
|
setup(
|
||||||
|
name="nms",
|
||||||
|
version="0.1",
|
||||||
|
author="fmassa",
|
||||||
|
url="https://github.com/facebookresearch/maskrcnn-benchmark",
|
||||||
|
description="GPU supported NMS",
|
||||||
|
ext_modules=get_extensions(),
|
||||||
|
cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension},
|
||||||
|
)
|
||||||
530
test.py
530
test.py
|
|
@ -1,265 +1,265 @@
|
||||||
import argparse
|
import argparse
|
||||||
import json
|
import json
|
||||||
import time
|
import time
|
||||||
from pathlib import Path
|
from pathlib import Path
|
||||||
|
|
||||||
from sklearn import metrics
|
from sklearn import metrics
|
||||||
from scipy import interpolate
|
from scipy import interpolate
|
||||||
import torch.nn.functional as F
|
import torch.nn.functional as F
|
||||||
from models import *
|
from models import *
|
||||||
from utils.utils import *
|
from utils.utils import *
|
||||||
from torchvision.transforms import transforms as T
|
from torchvision.transforms import transforms as T
|
||||||
from utils.datasets import LoadImagesAndLabels, JointDataset, collate_fn
|
from utils.datasets import LoadImagesAndLabels, JointDataset, collate_fn
|
||||||
|
|
||||||
def test(
|
def test(
|
||||||
cfg,
|
cfg,
|
||||||
data_cfg,
|
data_cfg,
|
||||||
weights,
|
weights,
|
||||||
batch_size=16,
|
batch_size=16,
|
||||||
img_size=416,
|
img_size=416,
|
||||||
iou_thres=0.5,
|
iou_thres=0.5,
|
||||||
conf_thres=0.3,
|
conf_thres=0.3,
|
||||||
nms_thres=0.45,
|
nms_thres=0.45,
|
||||||
print_interval=40,
|
print_interval=40,
|
||||||
nID=14455,
|
nID=14455,
|
||||||
):
|
):
|
||||||
|
|
||||||
# Configure run
|
# Configure run
|
||||||
f = open(data_cfg)
|
f = open(data_cfg)
|
||||||
data_cfg_dict = json.load(f)
|
data_cfg_dict = json.load(f)
|
||||||
f.close()
|
f.close()
|
||||||
#nC = int(data_cfg_dict['classes']) # number of classes (80 for COCO)
|
#nC = int(data_cfg_dict['classes']) # number of classes (80 for COCO)
|
||||||
nC = 1
|
nC = 1
|
||||||
test_path = data_cfg_dict['test']
|
test_path = data_cfg_dict['test']
|
||||||
dataset_root = data_cfg_dict['root']
|
dataset_root = data_cfg_dict['root']
|
||||||
|
|
||||||
# Initialize model
|
# Initialize model
|
||||||
model = Darknet(cfg, img_size, nID)
|
model = Darknet(cfg, img_size, nID)
|
||||||
|
|
||||||
# Load weights
|
# Load weights
|
||||||
if weights.endswith('.pt'): # pytorch format
|
if weights.endswith('.pt'): # pytorch format
|
||||||
model.load_state_dict(torch.load(weights, map_location='cpu')['model'], strict=False)
|
model.load_state_dict(torch.load(weights, map_location='cpu')['model'], strict=False)
|
||||||
else: # darknet format
|
else: # darknet format
|
||||||
load_darknet_weights(model, weights)
|
load_darknet_weights(model, weights)
|
||||||
|
|
||||||
model = torch.nn.DataParallel(model)
|
model = torch.nn.DataParallel(model)
|
||||||
model.cuda().eval()
|
model.cuda().eval()
|
||||||
|
|
||||||
# Get dataloader
|
# Get dataloader
|
||||||
transforms = T.Compose([T.ToTensor()])
|
transforms = T.Compose([T.ToTensor()])
|
||||||
dataset = JointDataset(dataset_root, test_path, img_size, augment=False, transforms=transforms)
|
dataset = JointDataset(dataset_root, test_path, img_size, augment=False, transforms=transforms)
|
||||||
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False,
|
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False,
|
||||||
num_workers=8, drop_last=False, collate_fn=collate_fn)
|
num_workers=8, drop_last=False, collate_fn=collate_fn)
|
||||||
|
|
||||||
mean_mAP, mean_R, mean_P, seen = 0.0, 0.0, 0.0, 0
|
mean_mAP, mean_R, mean_P, seen = 0.0, 0.0, 0.0, 0
|
||||||
print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP'))
|
print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP'))
|
||||||
outputs, mAPs, mR, mP, TP, confidence, pred_class, target_class, jdict = \
|
outputs, mAPs, mR, mP, TP, confidence, pred_class, target_class, jdict = \
|
||||||
[], [], [], [], [], [], [], [], []
|
[], [], [], [], [], [], [], [], []
|
||||||
AP_accum, AP_accum_count = np.zeros(nC), np.zeros(nC)
|
AP_accum, AP_accum_count = np.zeros(nC), np.zeros(nC)
|
||||||
for batch_i, (imgs, targets, paths, shapes, targets_len) in enumerate(dataloader):
|
for batch_i, (imgs, targets, paths, shapes, targets_len) in enumerate(dataloader):
|
||||||
t = time.time()
|
t = time.time()
|
||||||
output = model(imgs.cuda())
|
output = model(imgs.cuda())
|
||||||
output = non_max_suppression(output, conf_thres=conf_thres, nms_thres=nms_thres)
|
output = non_max_suppression(output, conf_thres=conf_thres, nms_thres=nms_thres)
|
||||||
for i, o in enumerate(output):
|
for i, o in enumerate(output):
|
||||||
if o is not None:
|
if o is not None:
|
||||||
output[i] = o[:, :6]
|
output[i] = o[:, :6]
|
||||||
|
|
||||||
# Compute average precision for each sample
|
# Compute average precision for each sample
|
||||||
targets = [targets[i][:int(l)] for i,l in enumerate(targets_len)]
|
targets = [targets[i][:int(l)] for i,l in enumerate(targets_len)]
|
||||||
for si, (labels, detections) in enumerate(zip(targets, output)):
|
for si, (labels, detections) in enumerate(zip(targets, output)):
|
||||||
seen += 1
|
seen += 1
|
||||||
|
|
||||||
if detections is None:
|
if detections is None:
|
||||||
# If there are labels but no detections mark as zero AP
|
# If there are labels but no detections mark as zero AP
|
||||||
if labels.size(0) != 0:
|
if labels.size(0) != 0:
|
||||||
mAPs.append(0), mR.append(0), mP.append(0)
|
mAPs.append(0), mR.append(0), mP.append(0)
|
||||||
continue
|
continue
|
||||||
|
|
||||||
# Get detections sorted by decreasing confidence scores
|
# Get detections sorted by decreasing confidence scores
|
||||||
detections = detections.cpu().numpy()
|
detections = detections.cpu().numpy()
|
||||||
detections = detections[np.argsort(-detections[:, 4])]
|
detections = detections[np.argsort(-detections[:, 4])]
|
||||||
|
|
||||||
|
|
||||||
# If no labels add number of detections as incorrect
|
# If no labels add number of detections as incorrect
|
||||||
correct = []
|
correct = []
|
||||||
if labels.size(0) == 0:
|
if labels.size(0) == 0:
|
||||||
# correct.extend([0 for _ in range(len(detections))])
|
# correct.extend([0 for _ in range(len(detections))])
|
||||||
mAPs.append(0), mR.append(0), mP.append(0)
|
mAPs.append(0), mR.append(0), mP.append(0)
|
||||||
continue
|
continue
|
||||||
else:
|
else:
|
||||||
target_cls = labels[:, 0]
|
target_cls = labels[:, 0]
|
||||||
|
|
||||||
# Extract target boxes as (x1, y1, x2, y2)
|
# Extract target boxes as (x1, y1, x2, y2)
|
||||||
target_boxes = xywh2xyxy(labels[:, 2:6])
|
target_boxes = xywh2xyxy(labels[:, 2:6])
|
||||||
target_boxes[:, 0] *= img_size[0]
|
target_boxes[:, 0] *= img_size[0]
|
||||||
target_boxes[:, 2] *= img_size[0]
|
target_boxes[:, 2] *= img_size[0]
|
||||||
target_boxes[:, 1] *= img_size[1]
|
target_boxes[:, 1] *= img_size[1]
|
||||||
target_boxes[:, 3] *= img_size[1]
|
target_boxes[:, 3] *= img_size[1]
|
||||||
|
|
||||||
detected = []
|
detected = []
|
||||||
for *pred_bbox, conf, obj_conf in detections:
|
for *pred_bbox, conf, obj_conf in detections:
|
||||||
obj_pred = 0
|
obj_pred = 0
|
||||||
pred_bbox = torch.FloatTensor(pred_bbox).view(1, -1)
|
pred_bbox = torch.FloatTensor(pred_bbox).view(1, -1)
|
||||||
# Compute iou with target boxes
|
# Compute iou with target boxes
|
||||||
iou = bbox_iou(pred_bbox, target_boxes, x1y1x2y2=True)[0]
|
iou = bbox_iou(pred_bbox, target_boxes, x1y1x2y2=True)[0]
|
||||||
# Extract index of largest overlap
|
# Extract index of largest overlap
|
||||||
best_i = np.argmax(iou)
|
best_i = np.argmax(iou)
|
||||||
# If overlap exceeds threshold and classification is correct mark as correct
|
# If overlap exceeds threshold and classification is correct mark as correct
|
||||||
if iou[best_i] > iou_thres and obj_pred == labels[best_i, 0] and best_i not in detected:
|
if iou[best_i] > iou_thres and obj_pred == labels[best_i, 0] and best_i not in detected:
|
||||||
correct.append(1)
|
correct.append(1)
|
||||||
detected.append(best_i)
|
detected.append(best_i)
|
||||||
else:
|
else:
|
||||||
correct.append(0)
|
correct.append(0)
|
||||||
|
|
||||||
# Compute Average Precision (AP) per class
|
# Compute Average Precision (AP) per class
|
||||||
AP, AP_class, R, P = ap_per_class(tp=correct,
|
AP, AP_class, R, P = ap_per_class(tp=correct,
|
||||||
conf=detections[:, 4],
|
conf=detections[:, 4],
|
||||||
pred_cls=np.zeros_like(detections[:, 5]), # detections[:, 6]
|
pred_cls=np.zeros_like(detections[:, 5]), # detections[:, 6]
|
||||||
target_cls=target_cls)
|
target_cls=target_cls)
|
||||||
|
|
||||||
# Accumulate AP per class
|
# Accumulate AP per class
|
||||||
AP_accum_count += np.bincount(AP_class, minlength=nC)
|
AP_accum_count += np.bincount(AP_class, minlength=nC)
|
||||||
AP_accum += np.bincount(AP_class, minlength=nC, weights=AP)
|
AP_accum += np.bincount(AP_class, minlength=nC, weights=AP)
|
||||||
|
|
||||||
# Compute mean AP across all classes in this image, and append to image list
|
# Compute mean AP across all classes in this image, and append to image list
|
||||||
mAPs.append(AP.mean())
|
mAPs.append(AP.mean())
|
||||||
mR.append(R.mean())
|
mR.append(R.mean())
|
||||||
mP.append(P.mean())
|
mP.append(P.mean())
|
||||||
|
|
||||||
# Means of all images
|
# Means of all images
|
||||||
mean_mAP = np.sum(mAPs) / ( AP_accum_count + 1E-16)
|
mean_mAP = np.sum(mAPs) / ( AP_accum_count + 1E-16)
|
||||||
mean_R = np.sum(mR) / ( AP_accum_count + 1E-16)
|
mean_R = np.sum(mR) / ( AP_accum_count + 1E-16)
|
||||||
mean_P = np.sum(mP) / (AP_accum_count + 1E-16)
|
mean_P = np.sum(mP) / (AP_accum_count + 1E-16)
|
||||||
|
|
||||||
if batch_i % print_interval==0:
|
if batch_i % print_interval==0:
|
||||||
# Print image mAP and running mean mAP
|
# Print image mAP and running mean mAP
|
||||||
print(('%11s%11s' + '%11.3g' * 4 + 's') %
|
print(('%11s%11s' + '%11.3g' * 4 + 's') %
|
||||||
(seen, dataloader.dataset.nF, mean_P, mean_R, mean_mAP, time.time() - t))
|
(seen, dataloader.dataset.nF, mean_P, mean_R, mean_mAP, time.time() - t))
|
||||||
# Print mAP per class
|
# Print mAP per class
|
||||||
print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP'))
|
print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP'))
|
||||||
|
|
||||||
print('AP: %-.4f\n\n' % (AP_accum[0] / (AP_accum_count[0] + 1E-16)))
|
print('AP: %-.4f\n\n' % (AP_accum[0] / (AP_accum_count[0] + 1E-16)))
|
||||||
|
|
||||||
# Return mAP
|
# Return mAP
|
||||||
return mean_mAP, mean_R, mean_P
|
return mean_mAP, mean_R, mean_P
|
||||||
|
|
||||||
|
|
||||||
def test_emb(
|
def test_emb(
|
||||||
cfg,
|
cfg,
|
||||||
data_cfg,
|
data_cfg,
|
||||||
weights,
|
weights,
|
||||||
batch_size=16,
|
batch_size=16,
|
||||||
img_size=416,
|
img_size=416,
|
||||||
iou_thres=0.5,
|
iou_thres=0.5,
|
||||||
conf_thres=0.3,
|
conf_thres=0.3,
|
||||||
nms_thres=0.45,
|
nms_thres=0.45,
|
||||||
print_interval=40,
|
print_interval=40,
|
||||||
nID=14455,
|
nID=14455,
|
||||||
):
|
):
|
||||||
|
|
||||||
# Configure run
|
# Configure run
|
||||||
f = open(data_cfg)
|
f = open(data_cfg)
|
||||||
data_cfg_dict = json.load(f)
|
data_cfg_dict = json.load(f)
|
||||||
f.close()
|
f.close()
|
||||||
test_paths = data_cfg_dict['test_emb']
|
test_paths = data_cfg_dict['test_emb']
|
||||||
dataset_root = data_cfg_dict['root']
|
dataset_root = data_cfg_dict['root']
|
||||||
|
|
||||||
# Initialize model
|
# Initialize model
|
||||||
model = Darknet(cfg, img_size, nID, test_emb=True)
|
model = Darknet(cfg, img_size, nID, test_emb=True)
|
||||||
|
|
||||||
# Load weights
|
# Load weights
|
||||||
if weights.endswith('.pt'): # pytorch format
|
if weights.endswith('.pt'): # pytorch format
|
||||||
model.load_state_dict(torch.load(weights, map_location='cpu')['model'], strict=False)
|
model.load_state_dict(torch.load(weights, map_location='cpu')['model'], strict=False)
|
||||||
else: # darknet format
|
else: # darknet format
|
||||||
load_darknet_weights(model, weights)
|
load_darknet_weights(model, weights)
|
||||||
|
|
||||||
model = torch.nn.DataParallel(model)
|
model = torch.nn.DataParallel(model)
|
||||||
model.cuda().eval()
|
model.cuda().eval()
|
||||||
|
|
||||||
# Get dataloader
|
# Get dataloader
|
||||||
transforms = T.Compose([T.ToTensor()])
|
transforms = T.Compose([T.ToTensor()])
|
||||||
dataset = JointDataset(dataset_root, test_paths, img_size, augment=False, transforms=transforms)
|
dataset = JointDataset(dataset_root, test_paths, img_size, augment=False, transforms=transforms)
|
||||||
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False,
|
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False,
|
||||||
num_workers=8, drop_last=False, collate_fn=collate_fn)
|
num_workers=8, drop_last=False, collate_fn=collate_fn)
|
||||||
embedding, id_labels = [], []
|
embedding, id_labels = [], []
|
||||||
print('Extracting pedestrain features...')
|
print('Extracting pedestrain features...')
|
||||||
for batch_i, (imgs, targets, paths, shapes, targets_len) in enumerate(dataloader):
|
for batch_i, (imgs, targets, paths, shapes, targets_len) in enumerate(dataloader):
|
||||||
t = time.time()
|
t = time.time()
|
||||||
output = model(imgs.cuda(), targets.cuda(), targets_len.cuda()).squeeze()
|
output = model(imgs.cuda(), targets.cuda(), targets_len.cuda()).squeeze()
|
||||||
|
|
||||||
for out in output:
|
for out in output:
|
||||||
feat, label = out[:-1], out[-1].long()
|
feat, label = out[:-1], out[-1].long()
|
||||||
if label != -1:
|
if label != -1:
|
||||||
embedding.append(feat)
|
embedding.append(feat)
|
||||||
id_labels.append(label)
|
id_labels.append(label)
|
||||||
|
|
||||||
if batch_i % print_interval==0:
|
if batch_i % print_interval==0:
|
||||||
print('Extracting {}/{}, # of instances {}, time {:.2f} sec.'.format(batch_i, len(dataloader), len(id_labels), time.time() - t))
|
print('Extracting {}/{}, # of instances {}, time {:.2f} sec.'.format(batch_i, len(dataloader), len(id_labels), time.time() - t))
|
||||||
|
|
||||||
print('Computing pairwise similairity...')
|
print('Computing pairwise similairity...')
|
||||||
if len(embedding) <1 :
|
if len(embedding) <1 :
|
||||||
return None
|
return None
|
||||||
embedding = torch.stack(embedding, dim=0).cuda()
|
embedding = torch.stack(embedding, dim=0).cuda()
|
||||||
id_labels = torch.LongTensor(id_labels)
|
id_labels = torch.LongTensor(id_labels)
|
||||||
n = len(id_labels)
|
n = len(id_labels)
|
||||||
print(n, len(embedding))
|
print(n, len(embedding))
|
||||||
assert len(embedding) == n
|
assert len(embedding) == n
|
||||||
|
|
||||||
embedding = F.normalize(embedding, dim=1)
|
embedding = F.normalize(embedding, dim=1)
|
||||||
pdist = torch.mm(embedding, embedding.t()).cpu().numpy()
|
pdist = torch.mm(embedding, embedding.t()).cpu().numpy()
|
||||||
gt = id_labels.expand(n,n).eq(id_labels.expand(n,n).t()).numpy()
|
gt = id_labels.expand(n,n).eq(id_labels.expand(n,n).t()).numpy()
|
||||||
|
|
||||||
up_triangle = np.where(np.triu(pdist)- np.eye(n)*pdist !=0)
|
up_triangle = np.where(np.triu(pdist)- np.eye(n)*pdist !=0)
|
||||||
pdist = pdist[up_triangle]
|
pdist = pdist[up_triangle]
|
||||||
gt = gt[up_triangle]
|
gt = gt[up_triangle]
|
||||||
|
|
||||||
far_levels = [ 1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1]
|
far_levels = [ 1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1]
|
||||||
far,tar,threshold = metrics.roc_curve(gt, pdist)
|
far,tar,threshold = metrics.roc_curve(gt, pdist)
|
||||||
interp = interpolate.interp1d(far, tar)
|
interp = interpolate.interp1d(far, tar)
|
||||||
tar_at_far = [interp(x) for x in far_levels]
|
tar_at_far = [interp(x) for x in far_levels]
|
||||||
for f,fa in enumerate(far_levels):
|
for f,fa in enumerate(far_levels):
|
||||||
print('TPR@FAR={:.7f}: {:.4f}'.format(fa, tar_at_far[f]))
|
print('TPR@FAR={:.7f}: {:.4f}'.format(fa, tar_at_far[f]))
|
||||||
return tar_at_far
|
return tar_at_far
|
||||||
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
parser = argparse.ArgumentParser(prog='test.py')
|
parser = argparse.ArgumentParser(prog='test.py')
|
||||||
parser.add_argument('--batch-size', type=int, default=40, help='size of each image batch')
|
parser.add_argument('--batch-size', type=int, default=40, help='size of each image batch')
|
||||||
parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
|
parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
|
||||||
parser.add_argument('--data-cfg', type=str, default='cfg/ccmcpe.json', help='data config')
|
parser.add_argument('--data-cfg', type=str, default='cfg/ccmcpe.json', help='data config')
|
||||||
parser.add_argument('--weights', type=str, default='weights/latest.pt', help='path to weights file')
|
parser.add_argument('--weights', type=str, default='weights/latest.pt', help='path to weights file')
|
||||||
parser.add_argument('--iou-thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
|
parser.add_argument('--iou-thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
|
||||||
parser.add_argument('--conf-thres', type=float, default=0.3, help='object confidence threshold')
|
parser.add_argument('--conf-thres', type=float, default=0.3, help='object confidence threshold')
|
||||||
parser.add_argument('--nms-thres', type=float, default=0.5, help='iou threshold for non-maximum suppression')
|
parser.add_argument('--nms-thres', type=float, default=0.5, help='iou threshold for non-maximum suppression')
|
||||||
parser.add_argument('--img-size', type=int, default=(1088, 608), help='size of each image dimension')
|
parser.add_argument('--img-size', type=int, default=(1088, 608), help='size of each image dimension')
|
||||||
parser.add_argument('--print-interval', type=int, default=10, help='size of each image dimension')
|
parser.add_argument('--print-interval', type=int, default=10, help='size of each image dimension')
|
||||||
parser.add_argument('--test-emb', action='store_true', help='test embedding')
|
parser.add_argument('--test-emb', action='store_true', help='test embedding')
|
||||||
opt = parser.parse_args()
|
opt = parser.parse_args()
|
||||||
print(opt, end='\n\n')
|
print(opt, end='\n\n')
|
||||||
|
|
||||||
with torch.no_grad():
|
with torch.no_grad():
|
||||||
if opt.test_emb:
|
if opt.test_emb:
|
||||||
res = test_emb(
|
res = test_emb(
|
||||||
opt.cfg,
|
opt.cfg,
|
||||||
opt.data_cfg,
|
opt.data_cfg,
|
||||||
opt.weights,
|
opt.weights,
|
||||||
opt.batch_size,
|
opt.batch_size,
|
||||||
opt.img_size,
|
opt.img_size,
|
||||||
opt.iou_thres,
|
opt.iou_thres,
|
||||||
opt.conf_thres,
|
opt.conf_thres,
|
||||||
opt.nms_thres,
|
opt.nms_thres,
|
||||||
opt.print_interval,
|
opt.print_interval,
|
||||||
)
|
)
|
||||||
else:
|
else:
|
||||||
mAP = test(
|
mAP = test(
|
||||||
opt.cfg,
|
opt.cfg,
|
||||||
opt.data_cfg,
|
opt.data_cfg,
|
||||||
opt.weights,
|
opt.weights,
|
||||||
opt.batch_size,
|
opt.batch_size,
|
||||||
opt.img_size,
|
opt.img_size,
|
||||||
opt.iou_thres,
|
opt.iou_thres,
|
||||||
opt.conf_thres,
|
opt.conf_thres,
|
||||||
opt.nms_thres,
|
opt.nms_thres,
|
||||||
opt.print_interval,
|
opt.print_interval,
|
||||||
)
|
)
|
||||||
|
|
||||||
|
|
|
||||||
359
track.py
359
track.py
|
|
@ -1,175 +1,184 @@
|
||||||
import os
|
import os
|
||||||
import os.path as osp
|
import os.path as osp
|
||||||
import cv2
|
import cv2
|
||||||
import logging
|
import logging
|
||||||
import argparse
|
import argparse
|
||||||
import motmetrics as mm
|
import motmetrics as mm
|
||||||
|
|
||||||
from tracker.multitracker import JDETracker
|
from tracker.multitracker import JDETracker
|
||||||
from utils import visualization as vis
|
from utils import visualization as vis
|
||||||
from utils.log import logger
|
from utils.log import logger
|
||||||
from utils.timer import Timer
|
from utils.timer import Timer
|
||||||
from utils.evaluation import Evaluator
|
from utils.evaluation import Evaluator
|
||||||
import utils.datasets as datasets
|
import utils.datasets as datasets
|
||||||
import torch
|
import torch
|
||||||
from utils.utils import *
|
from utils.utils import *
|
||||||
|
|
||||||
|
|
||||||
def write_results(filename, results, data_type):
|
def write_results(filename, results, data_type):
|
||||||
if data_type == 'mot':
|
if data_type == 'mot':
|
||||||
save_format = '{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\n'
|
save_format = '{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\n'
|
||||||
elif data_type == 'kitti':
|
elif data_type == 'kitti':
|
||||||
save_format = '{frame} {id} pedestrian 0 0 -10 {x1} {y1} {x2} {y2} -10 -10 -10 -1000 -1000 -1000 -10\n'
|
save_format = '{frame} {id} pedestrian 0 0 -10 {x1} {y1} {x2} {y2} -10 -10 -10 -1000 -1000 -1000 -10\n'
|
||||||
else:
|
else:
|
||||||
raise ValueError(data_type)
|
raise ValueError(data_type)
|
||||||
|
|
||||||
with open(filename, 'w') as f:
|
with open(filename, 'w') as f:
|
||||||
for frame_id, tlwhs, track_ids in results:
|
for frame_id, tlwhs, track_ids in results:
|
||||||
if data_type == 'kitti':
|
if data_type == 'kitti':
|
||||||
frame_id -= 1
|
frame_id -= 1
|
||||||
for tlwh, track_id in zip(tlwhs, track_ids):
|
for tlwh, track_id in zip(tlwhs, track_ids):
|
||||||
if track_id < 0:
|
if track_id < 0:
|
||||||
continue
|
continue
|
||||||
x1, y1, w, h = tlwh
|
x1, y1, w, h = tlwh
|
||||||
x2, y2 = x1 + w, y1 + h
|
x2, y2 = x1 + w, y1 + h
|
||||||
line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h)
|
line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h)
|
||||||
f.write(line)
|
f.write(line)
|
||||||
logger.info('save results to {}'.format(filename))
|
logger.info('save results to {}'.format(filename))
|
||||||
|
|
||||||
|
|
||||||
def eval_seq(opt, dataloader, data_type, result_filename, save_dir=None, show_image=True, frame_rate=30):
|
def eval_seq(opt, dataloader, data_type, result_filename, save_dir=None, show_image=True, frame_rate=30):
|
||||||
if save_dir:
|
if save_dir:
|
||||||
mkdir_if_missing(save_dir)
|
mkdir_if_missing(save_dir)
|
||||||
tracker = JDETracker(opt, frame_rate=frame_rate)
|
tracker = JDETracker(opt, frame_rate=frame_rate)
|
||||||
timer = Timer()
|
timer = Timer()
|
||||||
results = []
|
results = []
|
||||||
frame_id = 0
|
frame_id = 0
|
||||||
for path, img, img0 in dataloader:
|
for path, img, img0 in dataloader:
|
||||||
if frame_id % 20 == 0:
|
if frame_id % 20 == 0:
|
||||||
logger.info('Processing frame {} ({:.2f} fps)'.format(frame_id, 1./max(1e-5, timer.average_time)))
|
logger.info('Processing frame {} ({:.2f} fps)'.format(frame_id, 1./max(1e-5, timer.average_time)))
|
||||||
|
|
||||||
# run tracking
|
# run tracking
|
||||||
timer.tic()
|
timer.tic()
|
||||||
blob = torch.from_numpy(img).cuda().unsqueeze(0)
|
blob = torch.from_numpy(img).cuda().unsqueeze(0)
|
||||||
online_targets = tracker.update(blob, img0)
|
online_targets = tracker.update(blob, img0)
|
||||||
online_tlwhs = []
|
online_tlwhs = []
|
||||||
online_ids = []
|
online_ids = []
|
||||||
for t in online_targets:
|
for t in online_targets:
|
||||||
tlwh = t.tlwh
|
tlwh = t.tlwh
|
||||||
tid = t.track_id
|
tid = t.track_id
|
||||||
vertical = tlwh[2] / tlwh[3] > 1.6
|
vertical = tlwh[2] / tlwh[3] > 1.6
|
||||||
if tlwh[2] * tlwh[3] > opt.min_box_area and not vertical:
|
if tlwh[2] * tlwh[3] > opt.min_box_area and not vertical:
|
||||||
online_tlwhs.append(tlwh)
|
online_tlwhs.append(tlwh)
|
||||||
online_ids.append(tid)
|
online_ids.append(tid)
|
||||||
timer.toc()
|
timer.toc()
|
||||||
# save results
|
# save results
|
||||||
results.append((frame_id + 1, online_tlwhs, online_ids))
|
results.append((frame_id + 1, online_tlwhs, online_ids))
|
||||||
if show_image or save_dir is not None:
|
if show_image or save_dir is not None:
|
||||||
online_im = vis.plot_tracking(img0, online_tlwhs, online_ids, frame_id=frame_id,
|
online_im = vis.plot_tracking(img0, online_tlwhs, online_ids, frame_id=frame_id,
|
||||||
fps=1. / timer.average_time)
|
fps=1. / timer.average_time)
|
||||||
if show_image:
|
if show_image:
|
||||||
cv2.imshow('online_im', online_im)
|
cv2.imshow('online_im', online_im)
|
||||||
if save_dir is not None:
|
if save_dir is not None:
|
||||||
cv2.imwrite(os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)), online_im)
|
cv2.imwrite(os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)), online_im)
|
||||||
frame_id += 1
|
frame_id += 1
|
||||||
# save results
|
# save results
|
||||||
write_results(result_filename, results, data_type)
|
write_results(result_filename, results, data_type)
|
||||||
return frame_id, timer.average_time, timer.calls
|
return frame_id, timer.average_time, timer.calls
|
||||||
|
|
||||||
|
|
||||||
def main(opt, data_root='/data/MOT16/train', det_root=None, seqs=('MOT16-05',), exp_name='demo',
|
def main(opt, data_root='/data/MOT16/train', det_root=None, seqs=('MOT16-05',), exp_name='demo',
|
||||||
save_images=False, save_videos=False, show_image=True):
|
save_images=False, save_videos=False, show_image=True):
|
||||||
logger.setLevel(logging.INFO)
|
logger.setLevel(logging.INFO)
|
||||||
result_root = os.path.join(data_root, '..', 'results', exp_name)
|
result_root = os.path.join(data_root, '..', 'results', exp_name)
|
||||||
mkdir_if_missing(result_root)
|
mkdir_if_missing(result_root)
|
||||||
data_type = 'mot'
|
data_type = 'mot'
|
||||||
|
|
||||||
# run tracking
|
# run tracking
|
||||||
accs = []
|
accs = []
|
||||||
n_frame = 0
|
n_frame = 0
|
||||||
timer_avgs, timer_calls = [], []
|
timer_avgs, timer_calls = [], []
|
||||||
for seq in seqs:
|
for seq in seqs:
|
||||||
output_dir = os.path.join(data_root, '..','outputs', exp_name, seq) if save_images or save_videos else None
|
output_dir = os.path.join(data_root, '..','outputs', exp_name, seq) if save_images or save_videos else None
|
||||||
|
|
||||||
logger.info('start seq: {}'.format(seq))
|
logger.info('start seq: {}'.format(seq))
|
||||||
dataloader = datasets.LoadImages(osp.join(data_root, seq, 'img1'), opt.img_size)
|
dataloader = datasets.LoadImages(osp.join(data_root, seq, 'img1'), opt.img_size)
|
||||||
result_filename = os.path.join(result_root, '{}.txt'.format(seq))
|
result_filename = os.path.join(result_root, '{}.txt'.format(seq))
|
||||||
meta_info = open(os.path.join(data_root, seq, 'seqinfo.ini')).read()
|
meta_info = open(os.path.join(data_root, seq, 'seqinfo.ini')).read()
|
||||||
frame_rate = int(meta_info[meta_info.find('frameRate')+10:meta_info.find('\nseqLength')])
|
frame_rate = int(meta_info[meta_info.find('frameRate')+10:meta_info.find('\nseqLength')])
|
||||||
nf, ta, tc = eval_seq(opt, dataloader, data_type, result_filename,
|
nf, ta, tc = eval_seq(opt, dataloader, data_type, result_filename,
|
||||||
save_dir=output_dir, show_image=show_image, frame_rate=frame_rate)
|
save_dir=output_dir, show_image=show_image, frame_rate=frame_rate)
|
||||||
n_frame += nf
|
n_frame += nf
|
||||||
timer_avgs.append(ta)
|
timer_avgs.append(ta)
|
||||||
timer_calls.append(tc)
|
timer_calls.append(tc)
|
||||||
|
|
||||||
# eval
|
# eval
|
||||||
logger.info('Evaluate seq: {}'.format(seq))
|
logger.info('Evaluate seq: {}'.format(seq))
|
||||||
evaluator = Evaluator(data_root, seq, data_type)
|
evaluator = Evaluator(data_root, seq, data_type)
|
||||||
accs.append(evaluator.eval_file(result_filename))
|
accs.append(evaluator.eval_file(result_filename))
|
||||||
if save_videos:
|
if save_videos:
|
||||||
output_video_path = osp.join(output_dir, '{}.mp4'.format(seq))
|
output_video_path = osp.join(output_dir, '{}.mp4'.format(seq))
|
||||||
cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg -c:v copy {}'.format(output_dir, output_video_path)
|
cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg -c:v copy {}'.format(output_dir, output_video_path)
|
||||||
os.system(cmd_str)
|
os.system(cmd_str)
|
||||||
timer_avgs = np.asarray(timer_avgs)
|
timer_avgs = np.asarray(timer_avgs)
|
||||||
timer_calls = np.asarray(timer_calls)
|
timer_calls = np.asarray(timer_calls)
|
||||||
all_time = np.dot(timer_avgs, timer_calls)
|
all_time = np.dot(timer_avgs, timer_calls)
|
||||||
avg_time = all_time / np.sum(timer_calls)
|
avg_time = all_time / np.sum(timer_calls)
|
||||||
logger.info('Time elapsed: {:.2f} seconds, FPS: {:.2f}'.format(all_time, 1.0 / avg_time))
|
logger.info('Time elapsed: {:.2f} seconds, FPS: {:.2f}'.format(all_time, 1.0 / avg_time))
|
||||||
|
|
||||||
# get summary
|
# get summary
|
||||||
metrics = mm.metrics.motchallenge_metrics
|
metrics = mm.metrics.motchallenge_metrics
|
||||||
mh = mm.metrics.create()
|
mh = mm.metrics.create()
|
||||||
summary = Evaluator.get_summary(accs, seqs, metrics)
|
summary = Evaluator.get_summary(accs, seqs, metrics)
|
||||||
strsummary = mm.io.render_summary(
|
strsummary = mm.io.render_summary(
|
||||||
summary,
|
summary,
|
||||||
formatters=mh.formatters,
|
formatters=mh.formatters,
|
||||||
namemap=mm.io.motchallenge_metric_names
|
namemap=mm.io.motchallenge_metric_names
|
||||||
)
|
)
|
||||||
print(strsummary)
|
print(strsummary)
|
||||||
Evaluator.save_summary(summary, os.path.join(result_root, 'summary_{}.xlsx'.format(exp_name)))
|
Evaluator.save_summary(summary, os.path.join(result_root, 'summary_{}.xlsx'.format(exp_name)))
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
parser = argparse.ArgumentParser(prog='track.py')
|
parser = argparse.ArgumentParser(prog='track.py')
|
||||||
parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
|
parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
|
||||||
parser.add_argument('--weights', type=str, default='weights/latest.pt', help='path to weights file')
|
parser.add_argument('--weights', type=str, default='weights/latest.pt', help='path to weights file')
|
||||||
parser.add_argument('--img-size', type=int, default=(1088, 608), help='size of each image dimension')
|
parser.add_argument('--img-size', type=int, default=(1088, 608), help='size of each image dimension')
|
||||||
parser.add_argument('--iou-thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
|
parser.add_argument('--iou-thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
|
||||||
parser.add_argument('--conf-thres', type=float, default=0.5, help='object confidence threshold')
|
parser.add_argument('--conf-thres', type=float, default=0.5, help='object confidence threshold')
|
||||||
parser.add_argument('--nms-thres', type=float, default=0.4, help='iou threshold for non-maximum suppression')
|
parser.add_argument('--nms-thres', type=float, default=0.4, help='iou threshold for non-maximum suppression')
|
||||||
parser.add_argument('--min-box-area', type=float, default=200, help='filter out tiny boxes')
|
parser.add_argument('--min-box-area', type=float, default=200, help='filter out tiny boxes')
|
||||||
parser.add_argument('--track-buffer', type=int, default=30, help='tracking buffer')
|
parser.add_argument('--track-buffer', type=int, default=30, help='tracking buffer')
|
||||||
parser.add_argument('--test-mot16', action='store_true', help='tracking buffer')
|
parser.add_argument('--test-mot16', action='store_true', help='tracking buffer')
|
||||||
parser.add_argument('--save-images', action='store_true', help='save tracking results (image)')
|
parser.add_argument('--save-images', action='store_true', help='save tracking results (image)')
|
||||||
parser.add_argument('--save-videos', action='store_true', help='save tracking results (video)')
|
parser.add_argument('--save-videos', action='store_true', help='save tracking results (video)')
|
||||||
opt = parser.parse_args()
|
opt = parser.parse_args()
|
||||||
print(opt, end='\n\n')
|
print(opt, end='\n\n')
|
||||||
|
|
||||||
if not opt.test_mot16:
|
if not opt.test_mot16:
|
||||||
seqs_str = '''KITTI-13
|
#seqs_str = '''KITTI-13
|
||||||
KITTI-17
|
# KITTI-17
|
||||||
ADL-Rundle-6
|
# ADL-Rundle-6
|
||||||
PETS09-S2L1
|
# PETS09-S2L1
|
||||||
TUD-Campus
|
# TUD-Campus
|
||||||
TUD-Stadtmitte'''
|
# TUD-Stadtmitte'''
|
||||||
data_root = '/home/wangzd/datasets/MOT/MOT15/train'
|
#data_root = '/home/wangzd/datasets/MOT/MOT15/train'
|
||||||
else:
|
seqs_str = '''MOT17-02-SDP
|
||||||
seqs_str = '''MOT16-01
|
MOT17-04-SDP
|
||||||
MOT16-03
|
MOT17-05-SDP
|
||||||
MOT16-06
|
MOT17-09-SDP
|
||||||
MOT16-07
|
MOT17-10-SDP
|
||||||
MOT16-08
|
MOT17-11-SDP
|
||||||
MOT16-12
|
MOT17-13-SDP
|
||||||
MOT16-14'''
|
'''
|
||||||
data_root = '/home/wangzd/datasets/MOT/MOT16/images/test'
|
data_root = '/home/wangzd/datasets/MOT/MOT17/images/train'
|
||||||
seqs = [seq.strip() for seq in seqs_str.split()]
|
else:
|
||||||
|
seqs_str = '''MOT16-01
|
||||||
main(opt,
|
MOT16-03
|
||||||
data_root=data_root,
|
MOT16-06
|
||||||
seqs=seqs,
|
MOT16-07
|
||||||
exp_name=opt.weights.split('/')[-2],
|
MOT16-08
|
||||||
show_image=False,
|
MOT16-12
|
||||||
save_images=opt.save_images,
|
MOT16-14'''
|
||||||
save_videos=opt.save_videos)
|
data_root = '/home/wangzd/datasets/MOT/MOT16/images/test'
|
||||||
|
seqs = [seq.strip() for seq in seqs_str.split()]
|
||||||
|
|
||||||
|
main(opt,
|
||||||
|
data_root=data_root,
|
||||||
|
seqs=seqs,
|
||||||
|
exp_name=opt.weights.split('/')[-2],
|
||||||
|
show_image=False,
|
||||||
|
save_images=opt.save_images,
|
||||||
|
save_videos=opt.save_videos)
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -1,53 +1,53 @@
|
||||||
import numpy as np
|
import numpy as np
|
||||||
from collections import OrderedDict
|
from collections import OrderedDict
|
||||||
|
|
||||||
|
|
||||||
class TrackState(object):
|
class TrackState(object):
|
||||||
New = 0
|
New = 0
|
||||||
Tracked = 1
|
Tracked = 1
|
||||||
Lost = 2
|
Lost = 2
|
||||||
Removed = 3
|
Removed = 3
|
||||||
|
|
||||||
|
|
||||||
class BaseTrack(object):
|
class BaseTrack(object):
|
||||||
_count = 0
|
_count = 0
|
||||||
|
|
||||||
track_id = 0
|
track_id = 0
|
||||||
is_activated = False
|
is_activated = False
|
||||||
state = TrackState.New
|
state = TrackState.New
|
||||||
|
|
||||||
history = OrderedDict()
|
history = OrderedDict()
|
||||||
features = []
|
features = []
|
||||||
curr_feature = None
|
curr_feature = None
|
||||||
score = 0
|
score = 0
|
||||||
start_frame = 0
|
start_frame = 0
|
||||||
frame_id = 0
|
frame_id = 0
|
||||||
time_since_update = 0
|
time_since_update = 0
|
||||||
|
|
||||||
# multi-camera
|
# multi-camera
|
||||||
location = (np.inf, np.inf)
|
location = (np.inf, np.inf)
|
||||||
|
|
||||||
@property
|
@property
|
||||||
def end_frame(self):
|
def end_frame(self):
|
||||||
return self.frame_id
|
return self.frame_id
|
||||||
|
|
||||||
@staticmethod
|
@staticmethod
|
||||||
def next_id():
|
def next_id():
|
||||||
BaseTrack._count += 1
|
BaseTrack._count += 1
|
||||||
return BaseTrack._count
|
return BaseTrack._count
|
||||||
|
|
||||||
def activate(self, *args):
|
def activate(self, *args):
|
||||||
raise NotImplementedError
|
raise NotImplementedError
|
||||||
|
|
||||||
def predict(self):
|
def predict(self):
|
||||||
raise NotImplementedError
|
raise NotImplementedError
|
||||||
|
|
||||||
def update(self, *args, **kwargs):
|
def update(self, *args, **kwargs):
|
||||||
raise NotImplementedError
|
raise NotImplementedError
|
||||||
|
|
||||||
def mark_lost(self):
|
def mark_lost(self):
|
||||||
self.state = TrackState.Lost
|
self.state = TrackState.Lost
|
||||||
|
|
||||||
def mark_removed(self):
|
def mark_removed(self):
|
||||||
self.state = TrackState.Removed
|
self.state = TrackState.Removed
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -1,122 +1,122 @@
|
||||||
import cv2
|
import cv2
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import scipy
|
import scipy
|
||||||
from scipy.spatial.distance import cdist
|
from scipy.spatial.distance import cdist
|
||||||
from sklearn.utils import linear_assignment_
|
from sklearn.utils import linear_assignment_
|
||||||
|
|
||||||
from utils.cython_bbox import bbox_ious
|
from cython_bbox import bbox_overlaps as bbox_ious
|
||||||
from utils import kalman_filter
|
from utils import kalman_filter
|
||||||
import time
|
import time
|
||||||
|
|
||||||
def merge_matches(m1, m2, shape):
|
def merge_matches(m1, m2, shape):
|
||||||
O,P,Q = shape
|
O,P,Q = shape
|
||||||
m1 = np.asarray(m1)
|
m1 = np.asarray(m1)
|
||||||
m2 = np.asarray(m2)
|
m2 = np.asarray(m2)
|
||||||
|
|
||||||
M1 = scipy.sparse.coo_matrix((np.ones(len(m1)), (m1[:, 0], m1[:, 1])), shape=(O, P))
|
M1 = scipy.sparse.coo_matrix((np.ones(len(m1)), (m1[:, 0], m1[:, 1])), shape=(O, P))
|
||||||
M2 = scipy.sparse.coo_matrix((np.ones(len(m2)), (m2[:, 0], m2[:, 1])), shape=(P, Q))
|
M2 = scipy.sparse.coo_matrix((np.ones(len(m2)), (m2[:, 0], m2[:, 1])), shape=(P, Q))
|
||||||
|
|
||||||
mask = M1*M2
|
mask = M1*M2
|
||||||
match = mask.nonzero()
|
match = mask.nonzero()
|
||||||
match = list(zip(match[0], match[1]))
|
match = list(zip(match[0], match[1]))
|
||||||
unmatched_O = tuple(set(range(O)) - set([i for i, j in match]))
|
unmatched_O = tuple(set(range(O)) - set([i for i, j in match]))
|
||||||
unmatched_Q = tuple(set(range(Q)) - set([j for i, j in match]))
|
unmatched_Q = tuple(set(range(Q)) - set([j for i, j in match]))
|
||||||
|
|
||||||
return match, unmatched_O, unmatched_Q
|
return match, unmatched_O, unmatched_Q
|
||||||
|
|
||||||
|
|
||||||
def _indices_to_matches(cost_matrix, indices, thresh):
|
def _indices_to_matches(cost_matrix, indices, thresh):
|
||||||
matched_cost = cost_matrix[tuple(zip(*indices))]
|
matched_cost = cost_matrix[tuple(zip(*indices))]
|
||||||
matched_mask = (matched_cost <= thresh)
|
matched_mask = (matched_cost <= thresh)
|
||||||
|
|
||||||
matches = indices[matched_mask]
|
matches = indices[matched_mask]
|
||||||
unmatched_a = tuple(set(range(cost_matrix.shape[0])) - set(matches[:, 0]))
|
unmatched_a = tuple(set(range(cost_matrix.shape[0])) - set(matches[:, 0]))
|
||||||
unmatched_b = tuple(set(range(cost_matrix.shape[1])) - set(matches[:, 1]))
|
unmatched_b = tuple(set(range(cost_matrix.shape[1])) - set(matches[:, 1]))
|
||||||
|
|
||||||
return matches, unmatched_a, unmatched_b
|
return matches, unmatched_a, unmatched_b
|
||||||
|
|
||||||
|
|
||||||
def linear_assignment(cost_matrix, thresh):
|
def linear_assignment(cost_matrix, thresh):
|
||||||
"""
|
"""
|
||||||
Simple linear assignment
|
Simple linear assignment
|
||||||
:type cost_matrix: np.ndarray
|
:type cost_matrix: np.ndarray
|
||||||
:type thresh: float
|
:type thresh: float
|
||||||
:return: matches, unmatched_a, unmatched_b
|
:return: matches, unmatched_a, unmatched_b
|
||||||
"""
|
"""
|
||||||
if cost_matrix.size == 0:
|
if cost_matrix.size == 0:
|
||||||
return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1]))
|
return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1]))
|
||||||
|
|
||||||
cost_matrix[cost_matrix > thresh] = thresh + 1e-4
|
cost_matrix[cost_matrix > thresh] = thresh + 1e-4
|
||||||
indices = linear_assignment_.linear_assignment(cost_matrix)
|
indices = linear_assignment_.linear_assignment(cost_matrix)
|
||||||
|
|
||||||
return _indices_to_matches(cost_matrix, indices, thresh)
|
return _indices_to_matches(cost_matrix, indices, thresh)
|
||||||
|
|
||||||
|
|
||||||
def ious(atlbrs, btlbrs):
|
def ious(atlbrs, btlbrs):
|
||||||
"""
|
"""
|
||||||
Compute cost based on IoU
|
Compute cost based on IoU
|
||||||
:type atlbrs: list[tlbr] | np.ndarray
|
:type atlbrs: list[tlbr] | np.ndarray
|
||||||
:type atlbrs: list[tlbr] | np.ndarray
|
:type atlbrs: list[tlbr] | np.ndarray
|
||||||
|
|
||||||
:rtype ious np.ndarray
|
:rtype ious np.ndarray
|
||||||
"""
|
"""
|
||||||
ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float)
|
ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float)
|
||||||
if ious.size == 0:
|
if ious.size == 0:
|
||||||
return ious
|
return ious
|
||||||
|
|
||||||
ious = bbox_ious(
|
ious = bbox_ious(
|
||||||
np.ascontiguousarray(atlbrs, dtype=np.float),
|
np.ascontiguousarray(atlbrs, dtype=np.float),
|
||||||
np.ascontiguousarray(btlbrs, dtype=np.float)
|
np.ascontiguousarray(btlbrs, dtype=np.float)
|
||||||
)
|
)
|
||||||
|
|
||||||
return ious
|
return ious
|
||||||
|
|
||||||
|
|
||||||
def iou_distance(atracks, btracks):
|
def iou_distance(atracks, btracks):
|
||||||
"""
|
"""
|
||||||
Compute cost based on IoU
|
Compute cost based on IoU
|
||||||
:type atracks: list[STrack]
|
:type atracks: list[STrack]
|
||||||
:type btracks: list[STrack]
|
:type btracks: list[STrack]
|
||||||
|
|
||||||
:rtype cost_matrix np.ndarray
|
:rtype cost_matrix np.ndarray
|
||||||
"""
|
"""
|
||||||
|
|
||||||
if (len(atracks)>0 and isinstance(atracks[0], np.ndarray)) or (len(btracks) > 0 and isinstance(btracks[0], np.ndarray)):
|
if (len(atracks)>0 and isinstance(atracks[0], np.ndarray)) or (len(btracks) > 0 and isinstance(btracks[0], np.ndarray)):
|
||||||
atlbrs = atracks
|
atlbrs = atracks
|
||||||
btlbrs = btracks
|
btlbrs = btracks
|
||||||
else:
|
else:
|
||||||
atlbrs = [track.tlbr for track in atracks]
|
atlbrs = [track.tlbr for track in atracks]
|
||||||
btlbrs = [track.tlbr for track in btracks]
|
btlbrs = [track.tlbr for track in btracks]
|
||||||
_ious = ious(atlbrs, btlbrs)
|
_ious = ious(atlbrs, btlbrs)
|
||||||
cost_matrix = 1 - _ious
|
cost_matrix = 1 - _ious
|
||||||
|
|
||||||
return cost_matrix
|
return cost_matrix
|
||||||
|
|
||||||
def embedding_distance(tracks, detections, metric='cosine'):
|
def embedding_distance(tracks, detections, metric='cosine'):
|
||||||
"""
|
"""
|
||||||
:param tracks: list[STrack]
|
:param tracks: list[STrack]
|
||||||
:param detections: list[BaseTrack]
|
:param detections: list[BaseTrack]
|
||||||
:param metric:
|
:param metric:
|
||||||
:return: cost_matrix np.ndarray
|
:return: cost_matrix np.ndarray
|
||||||
"""
|
"""
|
||||||
|
|
||||||
cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)
|
cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)
|
||||||
if cost_matrix.size == 0:
|
if cost_matrix.size == 0:
|
||||||
return cost_matrix
|
return cost_matrix
|
||||||
det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float)
|
det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float)
|
||||||
for i, track in enumerate(tracks):
|
for i, track in enumerate(tracks):
|
||||||
cost_matrix[i, :] = np.maximum(0.0, cdist(track.smooth_feat.reshape(1,-1), det_features, metric))
|
cost_matrix[i, :] = np.maximum(0.0, cdist(track.smooth_feat.reshape(1,-1), det_features, metric))
|
||||||
return cost_matrix
|
return cost_matrix
|
||||||
|
|
||||||
|
|
||||||
def gate_cost_matrix(kf, cost_matrix, tracks, detections, only_position=False):
|
def gate_cost_matrix(kf, cost_matrix, tracks, detections, only_position=False):
|
||||||
if cost_matrix.size == 0:
|
if cost_matrix.size == 0:
|
||||||
return cost_matrix
|
return cost_matrix
|
||||||
gating_dim = 2 if only_position else 4
|
gating_dim = 2 if only_position else 4
|
||||||
gating_threshold = kalman_filter.chi2inv95[gating_dim]
|
gating_threshold = kalman_filter.chi2inv95[gating_dim]
|
||||||
measurements = np.asarray([det.to_xyah() for det in detections])
|
measurements = np.asarray([det.to_xyah() for det in detections])
|
||||||
for row, track in enumerate(tracks):
|
for row, track in enumerate(tracks):
|
||||||
gating_distance = kf.gating_distance(
|
gating_distance = kf.gating_distance(
|
||||||
track.mean, track.covariance, measurements, only_position)
|
track.mean, track.covariance, measurements, only_position)
|
||||||
cost_matrix[row, gating_distance > gating_threshold] = np.inf
|
cost_matrix[row, gating_distance > gating_threshold] = np.inf
|
||||||
return cost_matrix
|
return cost_matrix
|
||||||
|
|
|
||||||
|
|
@ -1,335 +1,335 @@
|
||||||
import numpy as np
|
import numpy as np
|
||||||
from numba import jit
|
from numba import jit
|
||||||
from collections import deque
|
from collections import deque
|
||||||
import itertools
|
import itertools
|
||||||
import os
|
import os
|
||||||
import os.path as osp
|
import os.path as osp
|
||||||
import time
|
import time
|
||||||
import torch
|
import torch
|
||||||
|
|
||||||
from utils.utils import *
|
from utils.utils import *
|
||||||
from utils.log import logger
|
from utils.log import logger
|
||||||
from utils.kalman_filter import KalmanFilter
|
from utils.kalman_filter import KalmanFilter
|
||||||
from models import *
|
from models import *
|
||||||
from tracker import matching
|
from tracker import matching
|
||||||
from .basetrack import BaseTrack, TrackState
|
from .basetrack import BaseTrack, TrackState
|
||||||
|
|
||||||
|
|
||||||
class STrack(BaseTrack):
|
class STrack(BaseTrack):
|
||||||
|
|
||||||
def __init__(self, tlwh, score, temp_feat, buffer_size=30):
|
def __init__(self, tlwh, score, temp_feat, buffer_size=30):
|
||||||
|
|
||||||
# wait activate
|
# wait activate
|
||||||
self._tlwh = np.asarray(tlwh, dtype=np.float)
|
self._tlwh = np.asarray(tlwh, dtype=np.float)
|
||||||
self.kalman_filter = None
|
self.kalman_filter = None
|
||||||
self.mean, self.covariance = None, None
|
self.mean, self.covariance = None, None
|
||||||
self.is_activated = False
|
self.is_activated = False
|
||||||
|
|
||||||
self.score = score
|
self.score = score
|
||||||
self.tracklet_len = 0
|
self.tracklet_len = 0
|
||||||
|
|
||||||
self.smooth_feat = None
|
self.smooth_feat = None
|
||||||
self.update_features(temp_feat)
|
self.update_features(temp_feat)
|
||||||
self.features = deque([], maxlen=buffer_size)
|
self.features = deque([], maxlen=buffer_size)
|
||||||
self.alpha = 0.9
|
self.alpha = 0.9
|
||||||
|
|
||||||
def update_features(self, feat):
|
def update_features(self, feat):
|
||||||
self.curr_feat = feat
|
self.curr_feat = feat
|
||||||
if self.smooth_feat is None:
|
if self.smooth_feat is None:
|
||||||
self.smooth_feat = feat
|
self.smooth_feat = feat
|
||||||
else:
|
else:
|
||||||
self.smooth_feat = self.alpha *self.smooth_feat + (1-self.alpha) * feat
|
self.smooth_feat = self.alpha *self.smooth_feat + (1-self.alpha) * feat
|
||||||
self.features.append(feat)
|
self.features.append(feat)
|
||||||
self.smooth_feat /= np.linalg.norm(self.smooth_feat)
|
self.smooth_feat /= np.linalg.norm(self.smooth_feat)
|
||||||
|
|
||||||
def predict(self):
|
def predict(self):
|
||||||
mean_state = self.mean.copy()
|
mean_state = self.mean.copy()
|
||||||
if self.state != TrackState.Tracked:
|
if self.state != TrackState.Tracked:
|
||||||
mean_state[7] = 0
|
mean_state[7] = 0
|
||||||
self.mean, self.covariance = self.kalman_filter.predict(mean_state, self.covariance)
|
self.mean, self.covariance = self.kalman_filter.predict(mean_state, self.covariance)
|
||||||
|
|
||||||
|
|
||||||
def activate(self, kalman_filter, frame_id):
|
def activate(self, kalman_filter, frame_id):
|
||||||
"""Start a new tracklet"""
|
"""Start a new tracklet"""
|
||||||
self.kalman_filter = kalman_filter
|
self.kalman_filter = kalman_filter
|
||||||
self.track_id = self.next_id()
|
self.track_id = self.next_id()
|
||||||
self.mean, self.covariance = self.kalman_filter.initiate(self.tlwh_to_xyah(self._tlwh))
|
self.mean, self.covariance = self.kalman_filter.initiate(self.tlwh_to_xyah(self._tlwh))
|
||||||
|
|
||||||
self.tracklet_len = 0
|
self.tracklet_len = 0
|
||||||
self.state = TrackState.Tracked
|
self.state = TrackState.Tracked
|
||||||
#self.is_activated = True
|
#self.is_activated = True
|
||||||
self.frame_id = frame_id
|
self.frame_id = frame_id
|
||||||
self.start_frame = frame_id
|
self.start_frame = frame_id
|
||||||
|
|
||||||
def re_activate(self, new_track, frame_id, new_id=False):
|
def re_activate(self, new_track, frame_id, new_id=False):
|
||||||
self.mean, self.covariance = self.kalman_filter.update(
|
self.mean, self.covariance = self.kalman_filter.update(
|
||||||
self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh)
|
self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh)
|
||||||
)
|
)
|
||||||
|
|
||||||
self.update_features(new_track.curr_feat)
|
self.update_features(new_track.curr_feat)
|
||||||
self.tracklet_len = 0
|
self.tracklet_len = 0
|
||||||
self.state = TrackState.Tracked
|
self.state = TrackState.Tracked
|
||||||
self.is_activated = True
|
self.is_activated = True
|
||||||
self.frame_id = frame_id
|
self.frame_id = frame_id
|
||||||
if new_id:
|
if new_id:
|
||||||
self.track_id = self.next_id()
|
self.track_id = self.next_id()
|
||||||
|
|
||||||
def update(self, new_track, frame_id, update_feature=True):
|
def update(self, new_track, frame_id, update_feature=True):
|
||||||
"""
|
"""
|
||||||
Update a matched track
|
Update a matched track
|
||||||
:type new_track: STrack
|
:type new_track: STrack
|
||||||
:type frame_id: int
|
:type frame_id: int
|
||||||
:type update_feature: bool
|
:type update_feature: bool
|
||||||
:return:
|
:return:
|
||||||
"""
|
"""
|
||||||
self.frame_id = frame_id
|
self.frame_id = frame_id
|
||||||
self.tracklet_len += 1
|
self.tracklet_len += 1
|
||||||
|
|
||||||
new_tlwh = new_track.tlwh
|
new_tlwh = new_track.tlwh
|
||||||
self.mean, self.covariance = self.kalman_filter.update(
|
self.mean, self.covariance = self.kalman_filter.update(
|
||||||
self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh))
|
self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh))
|
||||||
self.state = TrackState.Tracked
|
self.state = TrackState.Tracked
|
||||||
self.is_activated = True
|
self.is_activated = True
|
||||||
|
|
||||||
self.score = new_track.score
|
self.score = new_track.score
|
||||||
if update_feature:
|
if update_feature:
|
||||||
self.update_features(new_track.curr_feat)
|
self.update_features(new_track.curr_feat)
|
||||||
|
|
||||||
@property
|
@property
|
||||||
@jit
|
@jit
|
||||||
def tlwh(self):
|
def tlwh(self):
|
||||||
"""Get current position in bounding box format `(top left x, top left y,
|
"""Get current position in bounding box format `(top left x, top left y,
|
||||||
width, height)`.
|
width, height)`.
|
||||||
"""
|
"""
|
||||||
if self.mean is None:
|
if self.mean is None:
|
||||||
return self._tlwh.copy()
|
return self._tlwh.copy()
|
||||||
ret = self.mean[:4].copy()
|
ret = self.mean[:4].copy()
|
||||||
ret[2] *= ret[3]
|
ret[2] *= ret[3]
|
||||||
ret[:2] -= ret[2:] / 2
|
ret[:2] -= ret[2:] / 2
|
||||||
return ret
|
return ret
|
||||||
|
|
||||||
@property
|
@property
|
||||||
@jit
|
@jit
|
||||||
def tlbr(self):
|
def tlbr(self):
|
||||||
"""Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
|
"""Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
|
||||||
`(top left, bottom right)`.
|
`(top left, bottom right)`.
|
||||||
"""
|
"""
|
||||||
ret = self.tlwh.copy()
|
ret = self.tlwh.copy()
|
||||||
ret[2:] += ret[:2]
|
ret[2:] += ret[:2]
|
||||||
return ret
|
return ret
|
||||||
|
|
||||||
@staticmethod
|
@staticmethod
|
||||||
@jit
|
@jit
|
||||||
def tlwh_to_xyah(tlwh):
|
def tlwh_to_xyah(tlwh):
|
||||||
"""Convert bounding box to format `(center x, center y, aspect ratio,
|
"""Convert bounding box to format `(center x, center y, aspect ratio,
|
||||||
height)`, where the aspect ratio is `width / height`.
|
height)`, where the aspect ratio is `width / height`.
|
||||||
"""
|
"""
|
||||||
ret = np.asarray(tlwh).copy()
|
ret = np.asarray(tlwh).copy()
|
||||||
ret[:2] += ret[2:] / 2
|
ret[:2] += ret[2:] / 2
|
||||||
ret[2] /= ret[3]
|
ret[2] /= ret[3]
|
||||||
return ret
|
return ret
|
||||||
|
|
||||||
def to_xyah(self):
|
def to_xyah(self):
|
||||||
return self.tlwh_to_xyah(self.tlwh)
|
return self.tlwh_to_xyah(self.tlwh)
|
||||||
|
|
||||||
@staticmethod
|
@staticmethod
|
||||||
@jit
|
@jit
|
||||||
def tlbr_to_tlwh(tlbr):
|
def tlbr_to_tlwh(tlbr):
|
||||||
ret = np.asarray(tlbr).copy()
|
ret = np.asarray(tlbr).copy()
|
||||||
ret[2:] -= ret[:2]
|
ret[2:] -= ret[:2]
|
||||||
return ret
|
return ret
|
||||||
|
|
||||||
@staticmethod
|
@staticmethod
|
||||||
@jit
|
@jit
|
||||||
def tlwh_to_tlbr(tlwh):
|
def tlwh_to_tlbr(tlwh):
|
||||||
ret = np.asarray(tlwh).copy()
|
ret = np.asarray(tlwh).copy()
|
||||||
ret[2:] += ret[:2]
|
ret[2:] += ret[:2]
|
||||||
return ret
|
return ret
|
||||||
|
|
||||||
def __repr__(self):
|
def __repr__(self):
|
||||||
return 'OT_{}_({}-{})'.format(self.track_id, self.start_frame, self.end_frame)
|
return 'OT_{}_({}-{})'.format(self.track_id, self.start_frame, self.end_frame)
|
||||||
|
|
||||||
|
|
||||||
class JDETracker(object):
|
class JDETracker(object):
|
||||||
def __init__(self, opt, frame_rate=30):
|
def __init__(self, opt, frame_rate=30):
|
||||||
self.opt = opt
|
self.opt = opt
|
||||||
self.model = Darknet(opt.cfg, opt.img_size, nID=14455)
|
self.model = Darknet(opt.cfg, opt.img_size, nID=14455)
|
||||||
# load_darknet_weights(self.model, opt.weights)
|
# load_darknet_weights(self.model, opt.weights)
|
||||||
self.model.load_state_dict(torch.load(opt.weights, map_location='cpu')['model'], strict=False)
|
self.model.load_state_dict(torch.load(opt.weights, map_location='cpu')['model'], strict=False)
|
||||||
self.model.cuda().eval()
|
self.model.cuda().eval()
|
||||||
|
|
||||||
self.tracked_stracks = [] # type: list[STrack]
|
self.tracked_stracks = [] # type: list[STrack]
|
||||||
self.lost_stracks = [] # type: list[STrack]
|
self.lost_stracks = [] # type: list[STrack]
|
||||||
self.removed_stracks = [] # type: list[STrack]
|
self.removed_stracks = [] # type: list[STrack]
|
||||||
|
|
||||||
self.frame_id = 0
|
self.frame_id = 0
|
||||||
self.det_thresh = opt.conf_thres
|
self.det_thresh = opt.conf_thres
|
||||||
self.buffer_size = int(frame_rate / 30.0 * opt.track_buffer)
|
self.buffer_size = int(frame_rate / 30.0 * opt.track_buffer)
|
||||||
self.max_time_lost = self.buffer_size
|
self.max_time_lost = self.buffer_size
|
||||||
|
|
||||||
self.kalman_filter = KalmanFilter()
|
self.kalman_filter = KalmanFilter()
|
||||||
|
|
||||||
def update(self, im_blob, img0):
|
def update(self, im_blob, img0):
|
||||||
self.frame_id += 1
|
self.frame_id += 1
|
||||||
activated_starcks = []
|
activated_starcks = []
|
||||||
refind_stracks = []
|
refind_stracks = []
|
||||||
lost_stracks = []
|
lost_stracks = []
|
||||||
removed_stracks = []
|
removed_stracks = []
|
||||||
|
|
||||||
t1 = time.time()
|
t1 = time.time()
|
||||||
''' Step 1: Network forward, get detections & embeddings'''
|
''' Step 1: Network forward, get detections & embeddings'''
|
||||||
with torch.no_grad():
|
with torch.no_grad():
|
||||||
pred = self.model(im_blob)
|
pred = self.model(im_blob)
|
||||||
pred = pred[pred[:, :, 4] > self.opt.conf_thres]
|
pred = pred[pred[:, :, 4] > self.opt.conf_thres]
|
||||||
if len(pred) > 0:
|
if len(pred) > 0:
|
||||||
dets = non_max_suppression(pred.unsqueeze(0), self.opt.conf_thres, self.opt.nms_thres)[0].cpu()
|
dets = non_max_suppression(pred.unsqueeze(0), self.opt.conf_thres, self.opt.nms_thres)[0].cpu()
|
||||||
scale_coords(self.opt.img_size, dets[:, :4], img0.shape).round()
|
scale_coords(self.opt.img_size, dets[:, :4], img0.shape).round()
|
||||||
'''Detections'''
|
'''Detections'''
|
||||||
detections = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f.numpy(), 30) for
|
detections = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f.numpy(), 30) for
|
||||||
(tlbrs, f) in zip(dets[:, :5], dets[:, -self.model.emb_dim:])]
|
(tlbrs, f) in zip(dets[:, :5], dets[:, -self.model.emb_dim:])]
|
||||||
else:
|
else:
|
||||||
detections = []
|
detections = []
|
||||||
|
|
||||||
t2 = time.time()
|
t2 = time.time()
|
||||||
# print('Forward: {} s'.format(t2-t1))
|
# print('Forward: {} s'.format(t2-t1))
|
||||||
|
|
||||||
''' Add newly detected tracklets to tracked_stracks'''
|
''' Add newly detected tracklets to tracked_stracks'''
|
||||||
unconfirmed = []
|
unconfirmed = []
|
||||||
tracked_stracks = [] # type: list[STrack]
|
tracked_stracks = [] # type: list[STrack]
|
||||||
for track in self.tracked_stracks:
|
for track in self.tracked_stracks:
|
||||||
if not track.is_activated:
|
if not track.is_activated:
|
||||||
unconfirmed.append(track)
|
unconfirmed.append(track)
|
||||||
else:
|
else:
|
||||||
tracked_stracks.append(track)
|
tracked_stracks.append(track)
|
||||||
|
|
||||||
''' Step 2: First association, with embedding'''
|
''' Step 2: First association, with embedding'''
|
||||||
strack_pool = joint_stracks(tracked_stracks, self.lost_stracks)
|
strack_pool = joint_stracks(tracked_stracks, self.lost_stracks)
|
||||||
# Predict the current location with KF
|
# Predict the current location with KF
|
||||||
for strack in strack_pool:
|
for strack in strack_pool:
|
||||||
strack.predict()
|
strack.predict()
|
||||||
|
|
||||||
dists = matching.embedding_distance(strack_pool, detections)
|
dists = matching.embedding_distance(strack_pool, detections)
|
||||||
dists = matching.gate_cost_matrix(self.kalman_filter, dists, strack_pool, detections)
|
dists = matching.gate_cost_matrix(self.kalman_filter, dists, strack_pool, detections)
|
||||||
matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.7)
|
matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.7)
|
||||||
|
|
||||||
for itracked, idet in matches:
|
for itracked, idet in matches:
|
||||||
track = strack_pool[itracked]
|
track = strack_pool[itracked]
|
||||||
det = detections[idet]
|
det = detections[idet]
|
||||||
if track.state == TrackState.Tracked:
|
if track.state == TrackState.Tracked:
|
||||||
track.update(detections[idet], self.frame_id)
|
track.update(detections[idet], self.frame_id)
|
||||||
activated_starcks.append(track)
|
activated_starcks.append(track)
|
||||||
else:
|
else:
|
||||||
track.re_activate(det, self.frame_id, new_id=False)
|
track.re_activate(det, self.frame_id, new_id=False)
|
||||||
refind_stracks.append(track)
|
refind_stracks.append(track)
|
||||||
|
|
||||||
''' Step 3: Second association, with IOU'''
|
''' Step 3: Second association, with IOU'''
|
||||||
detections = [detections[i] for i in u_detection]
|
detections = [detections[i] for i in u_detection]
|
||||||
r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state==TrackState.Tracked ]
|
r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state==TrackState.Tracked ]
|
||||||
dists = matching.iou_distance(r_tracked_stracks, detections)
|
dists = matching.iou_distance(r_tracked_stracks, detections)
|
||||||
matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.5)
|
matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.5)
|
||||||
|
|
||||||
for itracked, idet in matches:
|
for itracked, idet in matches:
|
||||||
track = r_tracked_stracks[itracked]
|
track = r_tracked_stracks[itracked]
|
||||||
det = detections[idet]
|
det = detections[idet]
|
||||||
if track.state == TrackState.Tracked:
|
if track.state == TrackState.Tracked:
|
||||||
track.update(det, self.frame_id)
|
track.update(det, self.frame_id)
|
||||||
activated_starcks.append(track)
|
activated_starcks.append(track)
|
||||||
else:
|
else:
|
||||||
track.re_activate(det, self.frame_id, new_id=False)
|
track.re_activate(det, self.frame_id, new_id=False)
|
||||||
refind_stracks.append(track)
|
refind_stracks.append(track)
|
||||||
|
|
||||||
for it in u_track:
|
for it in u_track:
|
||||||
track = r_tracked_stracks[it]
|
track = r_tracked_stracks[it]
|
||||||
if not track.state == TrackState.Lost:
|
if not track.state == TrackState.Lost:
|
||||||
track.mark_lost()
|
track.mark_lost()
|
||||||
lost_stracks.append(track)
|
lost_stracks.append(track)
|
||||||
|
|
||||||
'''Deal with unconfirmed tracks, usually tracks with only one beginning frame'''
|
'''Deal with unconfirmed tracks, usually tracks with only one beginning frame'''
|
||||||
detections = [detections[i] for i in u_detection]
|
detections = [detections[i] for i in u_detection]
|
||||||
dists = matching.iou_distance(unconfirmed, detections)
|
dists = matching.iou_distance(unconfirmed, detections)
|
||||||
matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7)
|
matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7)
|
||||||
for itracked, idet in matches:
|
for itracked, idet in matches:
|
||||||
unconfirmed[itracked].update(detections[idet], self.frame_id)
|
unconfirmed[itracked].update(detections[idet], self.frame_id)
|
||||||
activated_starcks.append(unconfirmed[itracked])
|
activated_starcks.append(unconfirmed[itracked])
|
||||||
for it in u_unconfirmed:
|
for it in u_unconfirmed:
|
||||||
track = unconfirmed[it]
|
track = unconfirmed[it]
|
||||||
track.mark_removed()
|
track.mark_removed()
|
||||||
removed_stracks.append(track)
|
removed_stracks.append(track)
|
||||||
|
|
||||||
""" Step 4: Init new stracks"""
|
""" Step 4: Init new stracks"""
|
||||||
for inew in u_detection:
|
for inew in u_detection:
|
||||||
track = detections[inew]
|
track = detections[inew]
|
||||||
if track.score < self.det_thresh:
|
if track.score < self.det_thresh:
|
||||||
continue
|
continue
|
||||||
track.activate(self.kalman_filter, self.frame_id)
|
track.activate(self.kalman_filter, self.frame_id)
|
||||||
activated_starcks.append(track)
|
activated_starcks.append(track)
|
||||||
|
|
||||||
""" Step 5: Update state"""
|
""" Step 5: Update state"""
|
||||||
for track in self.lost_stracks:
|
for track in self.lost_stracks:
|
||||||
if self.frame_id - track.end_frame > self.max_time_lost:
|
if self.frame_id - track.end_frame > self.max_time_lost:
|
||||||
track.mark_removed()
|
track.mark_removed()
|
||||||
removed_stracks.append(track)
|
removed_stracks.append(track)
|
||||||
t4 = time.time()
|
t4 = time.time()
|
||||||
# print('Ramained match {} s'.format(t4-t3))
|
# print('Ramained match {} s'.format(t4-t3))
|
||||||
|
|
||||||
self.tracked_stracks = [t for t in self.tracked_stracks if t.state == TrackState.Tracked]
|
self.tracked_stracks = [t for t in self.tracked_stracks if t.state == TrackState.Tracked]
|
||||||
self.tracked_stracks = joint_stracks(self.tracked_stracks, activated_starcks)
|
self.tracked_stracks = joint_stracks(self.tracked_stracks, activated_starcks)
|
||||||
self.tracked_stracks = joint_stracks(self.tracked_stracks, refind_stracks)
|
self.tracked_stracks = joint_stracks(self.tracked_stracks, refind_stracks)
|
||||||
# self.lost_stracks = [t for t in self.lost_stracks if t.state == TrackState.Lost] # type: list[STrack]
|
# self.lost_stracks = [t for t in self.lost_stracks if t.state == TrackState.Lost] # type: list[STrack]
|
||||||
self.lost_stracks = sub_stracks(self.lost_stracks, self.tracked_stracks)
|
self.lost_stracks = sub_stracks(self.lost_stracks, self.tracked_stracks)
|
||||||
self.lost_stracks.extend(lost_stracks)
|
self.lost_stracks.extend(lost_stracks)
|
||||||
self.lost_stracks = sub_stracks(self.lost_stracks, self.removed_stracks)
|
self.lost_stracks = sub_stracks(self.lost_stracks, self.removed_stracks)
|
||||||
self.removed_stracks.extend(removed_stracks)
|
self.removed_stracks.extend(removed_stracks)
|
||||||
self.tracked_stracks, self.lost_stracks = remove_duplicate_stracks(self.tracked_stracks, self.lost_stracks)
|
self.tracked_stracks, self.lost_stracks = remove_duplicate_stracks(self.tracked_stracks, self.lost_stracks)
|
||||||
|
|
||||||
# get scores of lost tracks
|
# get scores of lost tracks
|
||||||
output_stracks = [track for track in self.tracked_stracks if track.is_activated]
|
output_stracks = [track for track in self.tracked_stracks if track.is_activated]
|
||||||
|
|
||||||
logger.debug('===========Frame {}=========='.format(self.frame_id))
|
logger.debug('===========Frame {}=========='.format(self.frame_id))
|
||||||
logger.debug('Activated: {}'.format([track.track_id for track in activated_starcks]))
|
logger.debug('Activated: {}'.format([track.track_id for track in activated_starcks]))
|
||||||
logger.debug('Refind: {}'.format([track.track_id for track in refind_stracks]))
|
logger.debug('Refind: {}'.format([track.track_id for track in refind_stracks]))
|
||||||
logger.debug('Lost: {}'.format([track.track_id for track in lost_stracks]))
|
logger.debug('Lost: {}'.format([track.track_id for track in lost_stracks]))
|
||||||
logger.debug('Removed: {}'.format([track.track_id for track in removed_stracks]))
|
logger.debug('Removed: {}'.format([track.track_id for track in removed_stracks]))
|
||||||
t5 = time.time()
|
t5 = time.time()
|
||||||
# print('Final {} s'.format(t5-t4))
|
# print('Final {} s'.format(t5-t4))
|
||||||
return output_stracks
|
return output_stracks
|
||||||
|
|
||||||
def joint_stracks(tlista, tlistb):
|
def joint_stracks(tlista, tlistb):
|
||||||
exists = {}
|
exists = {}
|
||||||
res = []
|
res = []
|
||||||
for t in tlista:
|
for t in tlista:
|
||||||
exists[t.track_id] = 1
|
exists[t.track_id] = 1
|
||||||
res.append(t)
|
res.append(t)
|
||||||
for t in tlistb:
|
for t in tlistb:
|
||||||
tid = t.track_id
|
tid = t.track_id
|
||||||
if not exists.get(tid, 0):
|
if not exists.get(tid, 0):
|
||||||
exists[tid] = 1
|
exists[tid] = 1
|
||||||
res.append(t)
|
res.append(t)
|
||||||
return res
|
return res
|
||||||
|
|
||||||
def sub_stracks(tlista, tlistb):
|
def sub_stracks(tlista, tlistb):
|
||||||
stracks = {}
|
stracks = {}
|
||||||
for t in tlista:
|
for t in tlista:
|
||||||
stracks[t.track_id] = t
|
stracks[t.track_id] = t
|
||||||
for t in tlistb:
|
for t in tlistb:
|
||||||
tid = t.track_id
|
tid = t.track_id
|
||||||
if stracks.get(tid, 0):
|
if stracks.get(tid, 0):
|
||||||
del stracks[tid]
|
del stracks[tid]
|
||||||
return list(stracks.values())
|
return list(stracks.values())
|
||||||
|
|
||||||
def remove_duplicate_stracks(stracksa, stracksb):
|
def remove_duplicate_stracks(stracksa, stracksb):
|
||||||
pdist = matching.iou_distance(stracksa, stracksb)
|
pdist = matching.iou_distance(stracksa, stracksb)
|
||||||
pairs = np.where(pdist<0.15)
|
pairs = np.where(pdist<0.15)
|
||||||
dupa, dupb = list(), list()
|
dupa, dupb = list(), list()
|
||||||
for p,q in zip(*pairs):
|
for p,q in zip(*pairs):
|
||||||
timep = stracksa[p].frame_id - stracksa[p].start_frame
|
timep = stracksa[p].frame_id - stracksa[p].start_frame
|
||||||
timeq = stracksb[q].frame_id - stracksb[q].start_frame
|
timeq = stracksb[q].frame_id - stracksb[q].start_frame
|
||||||
if timep > timeq:
|
if timep > timeq:
|
||||||
dupb.append(q)
|
dupb.append(q)
|
||||||
else:
|
else:
|
||||||
dupa.append(p)
|
dupa.append(p)
|
||||||
resa = [t for i,t in enumerate(stracksa) if not i in dupa]
|
resa = [t for i,t in enumerate(stracksa) if not i in dupa]
|
||||||
resb = [t for i,t in enumerate(stracksb) if not i in dupb]
|
resb = [t for i,t in enumerate(stracksb) if not i in dupb]
|
||||||
return resa, resb
|
return resa, resb
|
||||||
|
|
||||||
|
|
||||||
|
|
|
||||||
381
train.py
381
train.py
|
|
@ -1,191 +1,190 @@
|
||||||
import argparse
|
import argparse
|
||||||
import json
|
import json
|
||||||
import time
|
import time
|
||||||
|
|
||||||
import test
|
import test
|
||||||
from models import *
|
from models import *
|
||||||
from utils.datasets import JointDataset, collate_fn
|
from utils.datasets import JointDataset, collate_fn
|
||||||
from utils.utils import *
|
from utils.utils import *
|
||||||
from utils.log import logger
|
from utils.log import logger
|
||||||
from torchvision.transforms import transforms as T
|
from torchvision.transforms import transforms as T
|
||||||
|
|
||||||
|
|
||||||
def train(
|
def train(
|
||||||
cfg,
|
cfg,
|
||||||
data_cfg,
|
data_cfg,
|
||||||
img_size=(1088,608),
|
img_size=(1088,608),
|
||||||
resume=False,
|
resume=False,
|
||||||
epochs=100,
|
epochs=100,
|
||||||
batch_size=16,
|
batch_size=16,
|
||||||
accumulated_batches=1,
|
accumulated_batches=1,
|
||||||
freeze_backbone=False,
|
freeze_backbone=False,
|
||||||
opt=None,
|
opt=None,
|
||||||
):
|
):
|
||||||
weights = 'weights'
|
weights = 'weights'
|
||||||
mkdir_if_missing(weights)
|
mkdir_if_missing(weights)
|
||||||
latest = osp.join(weights, 'latest.pt')
|
latest = osp.join(weights, 'latest.pt')
|
||||||
|
|
||||||
torch.backends.cudnn.benchmark = True # unsuitable for multiscale
|
torch.backends.cudnn.benchmark = True # unsuitable for multiscale
|
||||||
|
|
||||||
# Configure run
|
# Configure run
|
||||||
f = open(data_cfg)
|
f = open(data_cfg)
|
||||||
data_config = json.load(f)
|
data_config = json.load(f)
|
||||||
trainset_paths = data_config['train']
|
trainset_paths = data_config['train']
|
||||||
dataset_root = data_config['root']
|
dataset_root = data_config['root']
|
||||||
f.close()
|
f.close()
|
||||||
|
|
||||||
|
|
||||||
transforms = T.Compose([T.ToTensor()])
|
transforms = T.Compose([T.ToTensor()])
|
||||||
# Get dataloader
|
# Get dataloader
|
||||||
dataset = JointDataset(dataset_root, trainset_paths, img_size, augment=True, transforms=transforms)
|
dataset = JointDataset(dataset_root, trainset_paths, img_size, augment=True, transforms=transforms)
|
||||||
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True,
|
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True,
|
||||||
num_workers=8, pin_memory=True, drop_last=True, collate_fn=collate_fn)
|
num_workers=8, pin_memory=True, drop_last=True, collate_fn=collate_fn)
|
||||||
|
|
||||||
# Initialize model
|
# Initialize model
|
||||||
model = Darknet(cfg, img_size, dataset.nID)
|
model = Darknet(cfg, img_size, dataset.nID)
|
||||||
|
|
||||||
cutoff = -1 # backbone reaches to cutoff layer
|
cutoff = -1 # backbone reaches to cutoff layer
|
||||||
start_epoch = 0
|
start_epoch = 0
|
||||||
if resume:
|
if resume:
|
||||||
checkpoint = torch.load(latest, map_location='cpu')
|
checkpoint = torch.load(latest, map_location='cpu')
|
||||||
|
|
||||||
# Load weights to resume from
|
# Load weights to resume from
|
||||||
model.load_state_dict(checkpoint['model'])
|
model.load_state_dict(checkpoint['model'])
|
||||||
model.cuda().train()
|
model.cuda().train()
|
||||||
|
|
||||||
# Set optimizer
|
# Set optimizer
|
||||||
optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad, model.parameters()), lr=opt.lr, momentum=.9)
|
optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad, model.parameters()), lr=opt.lr, momentum=.9)
|
||||||
|
|
||||||
start_epoch = checkpoint['epoch'] + 1
|
start_epoch = checkpoint['epoch'] + 1
|
||||||
if checkpoint['optimizer'] is not None:
|
if checkpoint['optimizer'] is not None:
|
||||||
optimizer.load_state_dict(checkpoint['optimizer'])
|
optimizer.load_state_dict(checkpoint['optimizer'])
|
||||||
|
|
||||||
del checkpoint # current, saved
|
del checkpoint # current, saved
|
||||||
|
|
||||||
else:
|
else:
|
||||||
# Initialize model with backbone (optional)
|
# Initialize model with backbone (optional)
|
||||||
if cfg.endswith('yolov3.cfg'):
|
if cfg.endswith('yolov3.cfg'):
|
||||||
load_darknet_weights(model, osp.join(weights ,'darknet53.conv.74'))
|
load_darknet_weights(model, osp.join(weights ,'darknet53.conv.74'))
|
||||||
cutoff = 75
|
cutoff = 75
|
||||||
elif cfg.endswith('yolov3-tiny.cfg'):
|
elif cfg.endswith('yolov3-tiny.cfg'):
|
||||||
load_darknet_weights(model, osp.join(weights , 'yolov3-tiny.conv.15'))
|
load_darknet_weights(model, osp.join(weights , 'yolov3-tiny.conv.15'))
|
||||||
cutoff = 15
|
cutoff = 15
|
||||||
|
|
||||||
model.cuda().train()
|
model.cuda().train()
|
||||||
|
|
||||||
# Set optimizer
|
# Set optimizer
|
||||||
optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad, model.parameters()), lr=opt.lr, momentum=.9, weight_decay=1e-4)
|
optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad, model.parameters()), lr=opt.lr, momentum=.9, weight_decay=1e-4)
|
||||||
|
|
||||||
model = torch.nn.DataParallel(model)
|
model = torch.nn.DataParallel(model)
|
||||||
# Set scheduler
|
# Set scheduler
|
||||||
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
|
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
|
||||||
milestones=[int(0.5*opt.epochs), int(0.75*opt.epochs)], gamma=0.1)
|
milestones=[int(0.5*opt.epochs), int(0.75*opt.epochs)], gamma=0.1)
|
||||||
|
|
||||||
# An important trick for detection: freeze bn during fine-tuning
|
# An important trick for detection: freeze bn during fine-tuning
|
||||||
if not opt.unfreeze_bn:
|
if not opt.unfreeze_bn:
|
||||||
for i, (name, p) in enumerate(model.named_parameters()):
|
for i, (name, p) in enumerate(model.named_parameters()):
|
||||||
p.requires_grad = False if 'batch_norm' in name else True
|
p.requires_grad = False if 'batch_norm' in name else True
|
||||||
|
|
||||||
model_info(model)
|
model_info(model)
|
||||||
t0 = time.time()
|
t0 = time.time()
|
||||||
for epoch in range(epochs):
|
for epoch in range(epochs):
|
||||||
epoch += start_epoch
|
epoch += start_epoch
|
||||||
|
|
||||||
logger.info(('%8s%12s' + '%10s' * 6) % (
|
logger.info(('%8s%12s' + '%10s' * 6) % (
|
||||||
'Epoch', 'Batch', 'box', 'conf', 'id', 'total', 'nTargets', 'time'))
|
'Epoch', 'Batch', 'box', 'conf', 'id', 'total', 'nTargets', 'time'))
|
||||||
|
|
||||||
# Update scheduler (automatic)
|
|
||||||
scheduler.step()
|
|
||||||
|
# Freeze darknet53.conv.74 for first epoch
|
||||||
|
if freeze_backbone and (epoch < 2):
|
||||||
# Freeze darknet53.conv.74 for first epoch
|
for i, (name, p) in enumerate(model.named_parameters()):
|
||||||
if freeze_backbone and (epoch < 2):
|
if int(name.split('.')[2]) < cutoff: # if layer < 75
|
||||||
for i, (name, p) in enumerate(model.named_parameters()):
|
p.requires_grad = False if (epoch == 0) else True
|
||||||
if int(name.split('.')[2]) < cutoff: # if layer < 75
|
|
||||||
p.requires_grad = False if (epoch == 0) else True
|
ui = -1
|
||||||
|
rloss = defaultdict(float) # running loss
|
||||||
ui = -1
|
optimizer.zero_grad()
|
||||||
rloss = defaultdict(float) # running loss
|
for i, (imgs, targets, _, _, targets_len) in enumerate(dataloader):
|
||||||
optimizer.zero_grad()
|
if sum([len(x) for x in targets]) < 1: # if no targets continue
|
||||||
for i, (imgs, targets, _, _, targets_len) in enumerate(dataloader):
|
continue
|
||||||
if sum([len(x) for x in targets]) < 1: # if no targets continue
|
|
||||||
continue
|
# SGD burn-in
|
||||||
|
burnin = min(1000, len(dataloader))
|
||||||
# SGD burn-in
|
if (epoch == 0) & (i <= burnin):
|
||||||
burnin = min(1000, len(dataloader))
|
lr = opt.lr * (i / burnin) **4
|
||||||
if (epoch == 0) & (i <= burnin):
|
for g in optimizer.param_groups:
|
||||||
lr = opt.lr * (i / burnin) **4
|
g['lr'] = lr
|
||||||
for g in optimizer.param_groups:
|
|
||||||
g['lr'] = lr
|
# Compute loss, compute gradient, update parameters
|
||||||
|
loss, components = model(imgs.cuda(), targets.cuda(), targets_len.cuda())
|
||||||
# Compute loss, compute gradient, update parameters
|
components = torch.mean(components.view(-1, 5),dim=0)
|
||||||
loss, components = model(imgs.cuda(), targets.cuda(), targets_len.cuda())
|
|
||||||
components = torch.mean(components.view(-1, 5),dim=0)
|
loss = torch.mean(loss)
|
||||||
|
loss.backward()
|
||||||
loss = torch.mean(loss)
|
|
||||||
loss.backward()
|
# accumulate gradient for x batches before optimizing
|
||||||
|
if ((i + 1) % accumulated_batches == 0) or (i == len(dataloader) - 1):
|
||||||
# accumulate gradient for x batches before optimizing
|
optimizer.step()
|
||||||
if ((i + 1) % accumulated_batches == 0) or (i == len(dataloader) - 1):
|
optimizer.zero_grad()
|
||||||
optimizer.step()
|
|
||||||
optimizer.zero_grad()
|
# Running epoch-means of tracked metrics
|
||||||
|
ui += 1
|
||||||
# Running epoch-means of tracked metrics
|
|
||||||
ui += 1
|
for ii, key in enumerate(model.module.loss_names):
|
||||||
|
rloss[key] = (rloss[key] * ui + components[ii]) / (ui + 1)
|
||||||
for ii, key in enumerate(model.module.loss_names):
|
|
||||||
rloss[key] = (rloss[key] * ui + components[ii]) / (ui + 1)
|
s = ('%8s%12s' + '%10.3g' * 6) % (
|
||||||
|
'%g/%g' % (epoch, epochs - 1),
|
||||||
s = ('%8s%12s' + '%10.3g' * 6) % (
|
'%g/%g' % (i, len(dataloader) - 1),
|
||||||
'%g/%g' % (epoch, epochs - 1),
|
rloss['box'], rloss['conf'],
|
||||||
'%g/%g' % (i, len(dataloader) - 1),
|
rloss['id'],rloss['loss'],
|
||||||
rloss['box'], rloss['conf'],
|
rloss['nT'], time.time() - t0)
|
||||||
rloss['id'],rloss['loss'],
|
t0 = time.time()
|
||||||
rloss['nT'], time.time() - t0)
|
if i % opt.print_interval == 0:
|
||||||
t0 = time.time()
|
logger.info(s)
|
||||||
if i % opt.print_interval == 0:
|
|
||||||
logger.info(s)
|
# Save latest checkpoint
|
||||||
|
checkpoint = {'epoch': epoch,
|
||||||
|
'model': model.module.state_dict(),
|
||||||
# Save latest checkpoint
|
'optimizer': optimizer.state_dict()}
|
||||||
checkpoint = {'epoch': epoch,
|
torch.save(checkpoint, latest)
|
||||||
'model': model.module.state_dict(),
|
|
||||||
'optimizer': optimizer.state_dict()}
|
|
||||||
torch.save(checkpoint, latest)
|
# Calculate mAP
|
||||||
|
if epoch % opt.test_interval ==0:
|
||||||
|
with torch.no_grad():
|
||||||
# Calculate mAP
|
mAP, R, P = test.test(cfg, data_cfg, weights=latest, batch_size=batch_size, img_size=img_size, print_interval=40, nID=dataset.nID)
|
||||||
if epoch % opt.test_interval ==0:
|
test.test_emb(cfg, data_cfg, weights=latest, batch_size=batch_size, img_size=img_size, print_interval=40, nID=dataset.nID)
|
||||||
with torch.no_grad():
|
|
||||||
mAP, R, P = test.test(cfg, data_cfg, weights=latest, batch_size=batch_size, img_size=img_size, print_interval=40, nID=dataset.nID)
|
|
||||||
test.test_emb(cfg, data_cfg, weights=latest, batch_size=batch_size, img_size=img_size, print_interval=40, nID=dataset.nID)
|
# Call scheduler.step() after opimizer.step() with pytorch > 1.1.0
|
||||||
|
scheduler.step()
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
if __name__ == '__main__':
|
parser = argparse.ArgumentParser()
|
||||||
parser = argparse.ArgumentParser()
|
parser.add_argument('--epochs', type=int, default=30, help='number of epochs')
|
||||||
parser.add_argument('--epochs', type=int, default=30, help='number of epochs')
|
parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch')
|
||||||
parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch')
|
parser.add_argument('--accumulated-batches', type=int, default=1, help='number of batches before optimizer step')
|
||||||
parser.add_argument('--accumulated-batches', type=int, default=1, help='number of batches before optimizer step')
|
parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
|
||||||
parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
|
parser.add_argument('--data-cfg', type=str, default='cfg/ccmcpe.json', help='coco.data file path')
|
||||||
parser.add_argument('--data-cfg', type=str, default='cfg/ccmcpe.json', help='coco.data file path')
|
parser.add_argument('--img-size', type=int, default=(1088, 608), help='pixels')
|
||||||
parser.add_argument('--img-size', type=int, default=(1088, 608), help='pixels')
|
parser.add_argument('--resume', action='store_true', help='resume training flag')
|
||||||
parser.add_argument('--resume', action='store_true', help='resume training flag')
|
parser.add_argument('--print-interval', type=int, default=40, help='print interval')
|
||||||
parser.add_argument('--print-interval', type=int, default=40, help='print interval')
|
parser.add_argument('--test-interval', type=int, default=9, help='test interval')
|
||||||
parser.add_argument('--test-interval', type=int, default=9, help='test interval')
|
parser.add_argument('--lr', type=float, default=1e-2, help='init lr')
|
||||||
parser.add_argument('--lr', type=float, default=1e-2, help='init lr')
|
parser.add_argument('--unfreeze-bn', action='store_true', help='unfreeze bn')
|
||||||
parser.add_argument('--unfreeze-bn', action='store_true', help='unfreeze bn')
|
opt = parser.parse_args()
|
||||||
opt = parser.parse_args()
|
|
||||||
|
init_seeds()
|
||||||
init_seeds()
|
|
||||||
|
train(
|
||||||
train(
|
opt.cfg,
|
||||||
opt.cfg,
|
opt.data_cfg,
|
||||||
opt.data_cfg,
|
img_size=opt.img_size,
|
||||||
img_size=opt.img_size,
|
resume=opt.resume,
|
||||||
resume=opt.resume,
|
epochs=opt.epochs,
|
||||||
epochs=opt.epochs,
|
batch_size=opt.batch_size,
|
||||||
batch_size=opt.batch_size,
|
accumulated_batches=opt.accumulated_batches,
|
||||||
accumulated_batches=opt.accumulated_batches,
|
opt=opt,
|
||||||
opt=opt,
|
)
|
||||||
)
|
|
||||||
|
|
|
||||||
Binary file not shown.
Binary file not shown.
|
|
@ -1,411 +1,410 @@
|
||||||
import glob
|
import glob
|
||||||
import math
|
import math
|
||||||
import os
|
import os
|
||||||
import os.path as osp
|
import os.path as osp
|
||||||
import random
|
import random
|
||||||
import time
|
import time
|
||||||
from collections import OrderedDict
|
from collections import OrderedDict
|
||||||
|
|
||||||
import cv2
|
import cv2
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import torch
|
import torch
|
||||||
|
|
||||||
from torch.utils.data import Dataset
|
from torch.utils.data import Dataset
|
||||||
from utils.utils import xyxy2xywh
|
from utils.utils import xyxy2xywh
|
||||||
|
|
||||||
class LoadImages: # for inference
|
class LoadImages: # for inference
|
||||||
def __init__(self, path, img_size=(1088, 608)):
|
def __init__(self, path, img_size=(1088, 608)):
|
||||||
if os.path.isdir(path):
|
if os.path.isdir(path):
|
||||||
image_format = ['.jpg', '.jpeg', '.png', '.tif']
|
image_format = ['.jpg', '.jpeg', '.png', '.tif']
|
||||||
self.files = sorted(glob.glob('%s/*.*' % path))
|
self.files = sorted(glob.glob('%s/*.*' % path))
|
||||||
self.files = list(filter(lambda x: os.path.splitext(x)[1].lower() in image_format, self.files))
|
self.files = list(filter(lambda x: os.path.splitext(x)[1].lower() in image_format, self.files))
|
||||||
elif os.path.isfile(path):
|
elif os.path.isfile(path):
|
||||||
self.files = [path]
|
self.files = [path]
|
||||||
|
|
||||||
self.nF = len(self.files) # number of image files
|
self.nF = len(self.files) # number of image files
|
||||||
self.width = img_size[0]
|
self.width = img_size[0]
|
||||||
self.height = img_size[1]
|
self.height = img_size[1]
|
||||||
self.count = 0
|
self.count = 0
|
||||||
|
|
||||||
assert self.nF > 0, 'No images found in ' + path
|
assert self.nF > 0, 'No images found in ' + path
|
||||||
|
|
||||||
def __iter__(self):
|
def __iter__(self):
|
||||||
self.count = -1
|
self.count = -1
|
||||||
return self
|
return self
|
||||||
|
|
||||||
def __next__(self):
|
def __next__(self):
|
||||||
self.count += 1
|
self.count += 1
|
||||||
if self.count == self.nF:
|
if self.count == self.nF:
|
||||||
raise StopIteration
|
raise StopIteration
|
||||||
img_path = self.files[self.count]
|
img_path = self.files[self.count]
|
||||||
|
|
||||||
# Read image
|
# Read image
|
||||||
img0 = cv2.imread(img_path) # BGR
|
img0 = cv2.imread(img_path) # BGR
|
||||||
assert img0 is not None, 'Failed to load ' + img_path
|
assert img0 is not None, 'Failed to load ' + img_path
|
||||||
|
|
||||||
# Padded resize
|
# Padded resize
|
||||||
img, _, _, _ = letterbox(img0, height=self.height, width=self.width)
|
img, _, _, _ = letterbox(img0, height=self.height, width=self.width)
|
||||||
|
|
||||||
# Normalize RGB
|
# Normalize RGB
|
||||||
img = img[:, :, ::-1].transpose(2, 0, 1)
|
img = img[:, :, ::-1].transpose(2, 0, 1)
|
||||||
img = np.ascontiguousarray(img, dtype=np.float32)
|
img = np.ascontiguousarray(img, dtype=np.float32)
|
||||||
img /= 255.0
|
img /= 255.0
|
||||||
|
|
||||||
# cv2.imwrite(img_path + '.letterbox.jpg', 255 * img.transpose((1, 2, 0))[:, :, ::-1]) # save letterbox image
|
# cv2.imwrite(img_path + '.letterbox.jpg', 255 * img.transpose((1, 2, 0))[:, :, ::-1]) # save letterbox image
|
||||||
return img_path, img, img0
|
return img_path, img, img0
|
||||||
|
|
||||||
def __getitem__(self, idx):
|
def __getitem__(self, idx):
|
||||||
idx = idx % self.nF
|
idx = idx % self.nF
|
||||||
img_path = self.files[idx]
|
img_path = self.files[idx]
|
||||||
|
|
||||||
# Read image
|
# Read image
|
||||||
img0 = cv2.imread(img_path) # BGR
|
img0 = cv2.imread(img_path) # BGR
|
||||||
assert img0 is not None, 'Failed to load ' + img_path
|
assert img0 is not None, 'Failed to load ' + img_path
|
||||||
|
|
||||||
# Padded resize
|
# Padded resize
|
||||||
img, _, _, _ = letterbox(img0, height=self.height, width=self.width)
|
img, _, _, _ = letterbox(img0, height=self.height, width=self.width)
|
||||||
|
|
||||||
# Normalize RGB
|
# Normalize RGB
|
||||||
img = img[:, :, ::-1].transpose(2, 0, 1)
|
img = img[:, :, ::-1].transpose(2, 0, 1)
|
||||||
img = np.ascontiguousarray(img, dtype=np.float32)
|
img = np.ascontiguousarray(img, dtype=np.float32)
|
||||||
img /= 255.0
|
img /= 255.0
|
||||||
|
|
||||||
return img_path, img, img0
|
return img_path, img, img0
|
||||||
|
|
||||||
def __len__(self):
|
def __len__(self):
|
||||||
return self.nF # number of files
|
return self.nF # number of files
|
||||||
|
|
||||||
|
|
||||||
class LoadVideo: # for inference
|
class LoadVideo: # for inference
|
||||||
def __init__(self, path, img_size=(1088, 608)):
|
def __init__(self, path, img_size=(1088, 608)):
|
||||||
self.cap = cv2.VideoCapture(path)
|
self.cap = cv2.VideoCapture(path)
|
||||||
self.frame_rate = int(round(self.cap.get(cv2.CAP_PROP_FPS)))
|
self.frame_rate = int(round(self.cap.get(cv2.CAP_PROP_FPS)))
|
||||||
self.vw = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
self.vw = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||||
self.vh = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
self.vh = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||||
self.vn = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
self.vn = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
||||||
|
|
||||||
self.width = img_size[0]
|
self.width = img_size[0]
|
||||||
self.height = img_size[1]
|
self.height = img_size[1]
|
||||||
self.count = 0
|
self.count = 0
|
||||||
|
|
||||||
self.w, self.h = self.get_size(self.vw, self.vh, self.width, self.height)
|
self.w, self.h = self.get_size(self.vw, self.vh, self.width, self.height)
|
||||||
print('Lenth of the video: {:d} frames'.format(self.vn))
|
print('Lenth of the video: {:d} frames'.format(self.vn))
|
||||||
|
|
||||||
def get_size(self, vw, vh, dw, dh):
|
def get_size(self, vw, vh, dw, dh):
|
||||||
wa, ha = float(dw) / vw, float(dh) / vh
|
wa, ha = float(dw) / vw, float(dh) / vh
|
||||||
a = min(wa, ha)
|
a = min(wa, ha)
|
||||||
return int(vw *a), int(vh*a)
|
return int(vw *a), int(vh*a)
|
||||||
|
|
||||||
def __iter__(self):
|
def __iter__(self):
|
||||||
self.count = -1
|
self.count = -1
|
||||||
return self
|
return self
|
||||||
|
|
||||||
def __next__(self):
|
def __next__(self):
|
||||||
self.count += 1
|
self.count += 1
|
||||||
if self.count == len(self):
|
if self.count == len(self):
|
||||||
raise StopIteration
|
raise StopIteration
|
||||||
# Read image
|
# Read image
|
||||||
res, img0 = self.cap.read() # BGR
|
res, img0 = self.cap.read() # BGR
|
||||||
assert img0 is not None, 'Failed to load frame {:d}'.format(self.count)
|
assert img0 is not None, 'Failed to load frame {:d}'.format(self.count)
|
||||||
img0 = cv2.resize(img0, (self.w, self.h))
|
img0 = cv2.resize(img0, (self.w, self.h))
|
||||||
|
|
||||||
# Padded resize
|
# Padded resize
|
||||||
img, _, _, _ = letterbox(img0, height=self.height, width=self.width)
|
img, _, _, _ = letterbox(img0, height=self.height, width=self.width)
|
||||||
|
|
||||||
# Normalize RGB
|
# Normalize RGB
|
||||||
img = img[:, :, ::-1].transpose(2, 0, 1)
|
img = img[:, :, ::-1].transpose(2, 0, 1)
|
||||||
img = np.ascontiguousarray(img, dtype=np.float32)
|
img = np.ascontiguousarray(img, dtype=np.float32)
|
||||||
img /= 255.0
|
img /= 255.0
|
||||||
|
|
||||||
# cv2.imwrite(img_path + '.letterbox.jpg', 255 * img.transpose((1, 2, 0))[:, :, ::-1]) # save letterbox image
|
# cv2.imwrite(img_path + '.letterbox.jpg', 255 * img.transpose((1, 2, 0))[:, :, ::-1]) # save letterbox image
|
||||||
return self.count, img, img0
|
return self.count, img, img0
|
||||||
|
|
||||||
def __len__(self):
|
def __len__(self):
|
||||||
return self.vn # number of files
|
return self.vn # number of files
|
||||||
|
|
||||||
|
|
||||||
class LoadImagesAndLabels: # for training
|
class LoadImagesAndLabels: # for training
|
||||||
def __init__(self, path, img_size=(1088,608), augment=False, transforms=None):
|
def __init__(self, path, img_size=(1088,608), augment=False, transforms=None):
|
||||||
with open(path, 'r') as file:
|
with open(path, 'r') as file:
|
||||||
self.img_files = file.readlines()
|
self.img_files = file.readlines()
|
||||||
self.img_files = [x.replace('\n', '') for x in self.img_files]
|
self.img_files = [x.replace('\n', '') for x in self.img_files]
|
||||||
self.img_files = list(filter(lambda x: len(x) > 0, self.img_files))
|
self.img_files = list(filter(lambda x: len(x) > 0, self.img_files))
|
||||||
|
|
||||||
self.label_files = [x.replace('images', 'labels_with_ids').replace('.png', '.txt').replace('.jpg', '.txt')
|
self.label_files = [x.replace('images', 'labels_with_ids').replace('.png', '.txt').replace('.jpg', '.txt')
|
||||||
for x in self.img_files]
|
for x in self.img_files]
|
||||||
|
|
||||||
self.nF = len(self.img_files) # number of image files
|
self.nF = len(self.img_files) # number of image files
|
||||||
self.width = img_size[0]
|
self.width = img_size[0]
|
||||||
self.height = img_size[1]
|
self.height = img_size[1]
|
||||||
self.augment = augment
|
self.augment = augment
|
||||||
self.transforms = transforms
|
self.transforms = transforms
|
||||||
|
|
||||||
|
|
||||||
def __getitem__(self, files_index):
|
def __getitem__(self, files_index):
|
||||||
img_path = self.img_files[files_index]
|
img_path = self.img_files[files_index]
|
||||||
label_path = self.label_files[files_index]
|
label_path = self.label_files[files_index]
|
||||||
return self.get_data(img_path, label_path)
|
return self.get_data(img_path, label_path)
|
||||||
|
|
||||||
def get_data(self, img_path, label_path):
|
def get_data(self, img_path, label_path):
|
||||||
height = self.height
|
height = self.height
|
||||||
width = self.width
|
width = self.width
|
||||||
img = cv2.imread(img_path) # BGR
|
img = cv2.imread(img_path) # BGR
|
||||||
if img is None:
|
if img is None:
|
||||||
raise ValueError('File corrupt {}'.format(img_path))
|
raise ValueError('File corrupt {}'.format(img_path))
|
||||||
augment_hsv = True
|
augment_hsv = True
|
||||||
if self.augment and augment_hsv:
|
if self.augment and augment_hsv:
|
||||||
# SV augmentation by 50%
|
# SV augmentation by 50%
|
||||||
fraction = 0.50
|
fraction = 0.50
|
||||||
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
|
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
|
||||||
S = img_hsv[:, :, 1].astype(np.float32)
|
S = img_hsv[:, :, 1].astype(np.float32)
|
||||||
V = img_hsv[:, :, 2].astype(np.float32)
|
V = img_hsv[:, :, 2].astype(np.float32)
|
||||||
|
|
||||||
a = (random.random() * 2 - 1) * fraction + 1
|
a = (random.random() * 2 - 1) * fraction + 1
|
||||||
S *= a
|
S *= a
|
||||||
if a > 1:
|
if a > 1:
|
||||||
np.clip(S, a_min=0, a_max=255, out=S)
|
np.clip(S, a_min=0, a_max=255, out=S)
|
||||||
|
|
||||||
a = (random.random() * 2 - 1) * fraction + 1
|
a = (random.random() * 2 - 1) * fraction + 1
|
||||||
V *= a
|
V *= a
|
||||||
if a > 1:
|
if a > 1:
|
||||||
np.clip(V, a_min=0, a_max=255, out=V)
|
np.clip(V, a_min=0, a_max=255, out=V)
|
||||||
|
|
||||||
img_hsv[:, :, 1] = S.astype(np.uint8)
|
img_hsv[:, :, 1] = S.astype(np.uint8)
|
||||||
img_hsv[:, :, 2] = V.astype(np.uint8)
|
img_hsv[:, :, 2] = V.astype(np.uint8)
|
||||||
cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)
|
cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)
|
||||||
|
|
||||||
h, w, _ = img.shape
|
h, w, _ = img.shape
|
||||||
img, ratio, padw, padh = letterbox(img, height=height, width=width)
|
img, ratio, padw, padh = letterbox(img, height=height, width=width)
|
||||||
|
|
||||||
# Load labels
|
# Load labels
|
||||||
if os.path.isfile(label_path):
|
if os.path.isfile(label_path):
|
||||||
labels0 = np.loadtxt(label_path, dtype=np.float32).reshape(-1, 6)
|
labels0 = np.loadtxt(label_path, dtype=np.float32).reshape(-1, 6)
|
||||||
|
|
||||||
# Normalized xywh to pixel xyxy format
|
# Normalized xywh to pixel xyxy format
|
||||||
labels = labels0.copy()
|
labels = labels0.copy()
|
||||||
labels[:, 2] = ratio * w * (labels0[:, 2] - labels0[:, 4] / 2) + padw
|
labels[:, 2] = ratio * w * (labels0[:, 2] - labels0[:, 4] / 2) + padw
|
||||||
labels[:, 3] = ratio * h * (labels0[:, 3] - labels0[:, 5] / 2) + padh
|
labels[:, 3] = ratio * h * (labels0[:, 3] - labels0[:, 5] / 2) + padh
|
||||||
labels[:, 4] = ratio * w * (labels0[:, 2] + labels0[:, 4] / 2) + padw
|
labels[:, 4] = ratio * w * (labels0[:, 2] + labels0[:, 4] / 2) + padw
|
||||||
labels[:, 5] = ratio * h * (labels0[:, 3] + labels0[:, 5] / 2) + padh
|
labels[:, 5] = ratio * h * (labels0[:, 3] + labels0[:, 5] / 2) + padh
|
||||||
else:
|
else:
|
||||||
labels = np.array([])
|
labels = np.array([])
|
||||||
|
|
||||||
# Augment image and labels
|
# Augment image and labels
|
||||||
if self.augment:
|
if self.augment:
|
||||||
img, labels, M = random_affine(img, labels, degrees=(-5, 5), translate=(0.10, 0.10), scale=(0.50, 1.20))
|
img, labels, M = random_affine(img, labels, degrees=(-5, 5), translate=(0.10, 0.10), scale=(0.50, 1.20))
|
||||||
|
|
||||||
plotFlag = False
|
|
||||||
if plotFlag:
|
plotFlag = False
|
||||||
import matplotlib
|
if plotFlag:
|
||||||
matplotlib.use('Agg')
|
import matplotlib
|
||||||
import matplotlib.pyplot as plt
|
matplotlib.use('Agg')
|
||||||
plt.figure(figsize=(50, 50))
|
import matplotlib.pyplot as plt
|
||||||
plt.imshow(img[:, :, ::-1])
|
plt.figure(figsize=(50, 50))
|
||||||
plt.plot(labels[:, [1, 3, 3, 1, 1]].T, labels[:, [2, 2, 4, 4, 2]].T, '.-')
|
plt.imshow(img[:, :, ::-1])
|
||||||
plt.axis('off')
|
plt.plot(labels[:, [1, 3, 3, 1, 1]].T, labels[:, [2, 2, 4, 4, 2]].T, '.-')
|
||||||
plt.savefig('test.jpg')
|
plt.axis('off')
|
||||||
time.sleep(10)
|
plt.savefig('test.jpg')
|
||||||
|
time.sleep(10)
|
||||||
nL = len(labels)
|
|
||||||
if nL > 0:
|
nL = len(labels)
|
||||||
# convert xyxy to xywh
|
if nL > 0:
|
||||||
labels[:, 2:6] = xyxy2xywh(labels[:, 2:6].copy()) #/ height
|
# convert xyxy to xywh
|
||||||
labels[:, 2] /= width
|
labels[:, 2:6] = xyxy2xywh(labels[:, 2:6].copy()) #/ height
|
||||||
labels[:, 3] /= height
|
labels[:, 2] /= width
|
||||||
labels[:, 4] /= width
|
labels[:, 3] /= height
|
||||||
labels[:, 5] /= height
|
labels[:, 4] /= width
|
||||||
if self.augment:
|
labels[:, 5] /= height
|
||||||
# random left-right flip
|
if self.augment:
|
||||||
lr_flip = True
|
# random left-right flip
|
||||||
if lr_flip & (random.random() > 0.5):
|
lr_flip = True
|
||||||
img = np.fliplr(img)
|
if lr_flip & (random.random() > 0.5):
|
||||||
if nL > 0:
|
img = np.fliplr(img)
|
||||||
labels[:, 2] = 1 - labels[:, 2]
|
if nL > 0:
|
||||||
|
labels[:, 2] = 1 - labels[:, 2]
|
||||||
img = np.ascontiguousarray(img[ :, :, ::-1]) # BGR to RGB
|
|
||||||
if self.transforms is not None:
|
img = np.ascontiguousarray(img[ :, :, ::-1]) # BGR to RGB
|
||||||
img = self.transforms(img)
|
if self.transforms is not None:
|
||||||
|
img = self.transforms(img)
|
||||||
return img, labels, img_path, (h, w)
|
|
||||||
|
return img, labels, img_path, (h, w)
|
||||||
def __len__(self):
|
|
||||||
return self.nF # number of batches
|
def __len__(self):
|
||||||
|
return self.nF # number of batches
|
||||||
|
|
||||||
def letterbox(img, height=608, width=1088, color=(127.5, 127.5, 127.5)): # resize a rectangular image to a padded rectangular
|
|
||||||
shape = img.shape[:2] # shape = [height, width]
|
def letterbox(img, height=608, width=1088, color=(127.5, 127.5, 127.5)): # resize a rectangular image to a padded rectangular
|
||||||
ratio = min(float(height)/shape[0], float(width)/shape[1])
|
shape = img.shape[:2] # shape = [height, width]
|
||||||
new_shape = (round(shape[1] * ratio), round(shape[0] * ratio)) # new_shape = [width, height]
|
ratio = min(float(height)/shape[0], float(width)/shape[1])
|
||||||
dw = (width - new_shape[0]) / 2 # width padding
|
new_shape = (round(shape[1] * ratio), round(shape[0] * ratio)) # new_shape = [width, height]
|
||||||
dh = (height - new_shape[1]) / 2 # height padding
|
dw = (width - new_shape[0]) / 2 # width padding
|
||||||
top, bottom = round(dh - 0.1), round(dh + 0.1)
|
dh = (height - new_shape[1]) / 2 # height padding
|
||||||
left, right = round(dw - 0.1), round(dw + 0.1)
|
top, bottom = round(dh - 0.1), round(dh + 0.1)
|
||||||
img = cv2.resize(img, new_shape, interpolation=cv2.INTER_AREA) # resized, no border
|
left, right = round(dw - 0.1), round(dw + 0.1)
|
||||||
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # padded rectangular
|
img = cv2.resize(img, new_shape, interpolation=cv2.INTER_AREA) # resized, no border
|
||||||
return img, ratio, dw, dh
|
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # padded rectangular
|
||||||
|
return img, ratio, dw, dh
|
||||||
|
|
||||||
def random_affine(img, targets=None, degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-2, 2),
|
|
||||||
borderValue=(127.5, 127.5, 127.5)):
|
def random_affine(img, targets=None, degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-2, 2),
|
||||||
# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
|
borderValue=(127.5, 127.5, 127.5)):
|
||||||
# https://medium.com/uruvideo/dataset-augmentation-with-random-homographies-a8f4b44830d4
|
# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
|
||||||
|
# https://medium.com/uruvideo/dataset-augmentation-with-random-homographies-a8f4b44830d4
|
||||||
border = 0 # width of added border (optional)
|
|
||||||
height = img.shape[0]
|
border = 0 # width of added border (optional)
|
||||||
width = img.shape[1]
|
height = img.shape[0]
|
||||||
|
width = img.shape[1]
|
||||||
# Rotation and Scale
|
|
||||||
R = np.eye(3)
|
# Rotation and Scale
|
||||||
a = random.random() * (degrees[1] - degrees[0]) + degrees[0]
|
R = np.eye(3)
|
||||||
# a += random.choice([-180, -90, 0, 90]) # 90deg rotations added to small rotations
|
a = random.random() * (degrees[1] - degrees[0]) + degrees[0]
|
||||||
s = random.random() * (scale[1] - scale[0]) + scale[0]
|
# a += random.choice([-180, -90, 0, 90]) # 90deg rotations added to small rotations
|
||||||
R[:2] = cv2.getRotationMatrix2D(angle=a, center=(img.shape[1] / 2, img.shape[0] / 2), scale=s)
|
s = random.random() * (scale[1] - scale[0]) + scale[0]
|
||||||
|
R[:2] = cv2.getRotationMatrix2D(angle=a, center=(img.shape[1] / 2, img.shape[0] / 2), scale=s)
|
||||||
# Translation
|
|
||||||
T = np.eye(3)
|
# Translation
|
||||||
T[0, 2] = (random.random() * 2 - 1) * translate[0] * img.shape[0] + border # x translation (pixels)
|
T = np.eye(3)
|
||||||
T[1, 2] = (random.random() * 2 - 1) * translate[1] * img.shape[1] + border # y translation (pixels)
|
T[0, 2] = (random.random() * 2 - 1) * translate[0] * img.shape[0] + border # x translation (pixels)
|
||||||
|
T[1, 2] = (random.random() * 2 - 1) * translate[1] * img.shape[1] + border # y translation (pixels)
|
||||||
# Shear
|
|
||||||
S = np.eye(3)
|
# Shear
|
||||||
S[0, 1] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180) # x shear (deg)
|
S = np.eye(3)
|
||||||
S[1, 0] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180) # y shear (deg)
|
S[0, 1] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180) # x shear (deg)
|
||||||
|
S[1, 0] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180) # y shear (deg)
|
||||||
M = S @ T @ R # Combined rotation matrix. ORDER IS IMPORTANT HERE!!
|
|
||||||
imw = cv2.warpPerspective(img, M, dsize=(width, height), flags=cv2.INTER_LINEAR,
|
M = S @ T @ R # Combined rotation matrix. ORDER IS IMPORTANT HERE!!
|
||||||
borderValue=borderValue) # BGR order borderValue
|
imw = cv2.warpPerspective(img, M, dsize=(width, height), flags=cv2.INTER_LINEAR,
|
||||||
|
borderValue=borderValue) # BGR order borderValue
|
||||||
# Return warped points also
|
|
||||||
if targets is not None:
|
# Return warped points also
|
||||||
if len(targets) > 0:
|
if targets is not None:
|
||||||
n = targets.shape[0]
|
if len(targets) > 0:
|
||||||
points = targets[:, 2:6].copy()
|
n = targets.shape[0]
|
||||||
area0 = (points[:, 2] - points[:, 0]) * (points[:, 3] - points[:, 1])
|
points = targets[:, 2:6].copy()
|
||||||
|
area0 = (points[:, 2] - points[:, 0]) * (points[:, 3] - points[:, 1])
|
||||||
# warp points
|
|
||||||
xy = np.ones((n * 4, 3))
|
# warp points
|
||||||
xy[:, :2] = points[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
|
xy = np.ones((n * 4, 3))
|
||||||
xy = (xy @ M.T)[:, :2].reshape(n, 8)
|
xy[:, :2] = points[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
|
||||||
|
xy = (xy @ M.T)[:, :2].reshape(n, 8)
|
||||||
# create new boxes
|
|
||||||
x = xy[:, [0, 2, 4, 6]]
|
# create new boxes
|
||||||
y = xy[:, [1, 3, 5, 7]]
|
x = xy[:, [0, 2, 4, 6]]
|
||||||
xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
|
y = xy[:, [1, 3, 5, 7]]
|
||||||
|
xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
|
||||||
# apply angle-based reduction
|
|
||||||
radians = a * math.pi / 180
|
# apply angle-based reduction
|
||||||
reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5
|
radians = a * math.pi / 180
|
||||||
x = (xy[:, 2] + xy[:, 0]) / 2
|
reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5
|
||||||
y = (xy[:, 3] + xy[:, 1]) / 2
|
x = (xy[:, 2] + xy[:, 0]) / 2
|
||||||
w = (xy[:, 2] - xy[:, 0]) * reduction
|
y = (xy[:, 3] + xy[:, 1]) / 2
|
||||||
h = (xy[:, 3] - xy[:, 1]) * reduction
|
w = (xy[:, 2] - xy[:, 0]) * reduction
|
||||||
xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T
|
h = (xy[:, 3] - xy[:, 1]) * reduction
|
||||||
|
xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T
|
||||||
# reject warped points outside of image
|
|
||||||
np.clip(xy[:, 0], 0, width, out=xy[:, 0])
|
# reject warped points outside of image
|
||||||
np.clip(xy[:, 2], 0, width, out=xy[:, 2])
|
np.clip(xy[:, 0], 0, width, out=xy[:, 0])
|
||||||
np.clip(xy[:, 1], 0, height, out=xy[:, 1])
|
np.clip(xy[:, 2], 0, width, out=xy[:, 2])
|
||||||
np.clip(xy[:, 3], 0, height, out=xy[:, 3])
|
np.clip(xy[:, 1], 0, height, out=xy[:, 1])
|
||||||
w = xy[:, 2] - xy[:, 0]
|
np.clip(xy[:, 3], 0, height, out=xy[:, 3])
|
||||||
h = xy[:, 3] - xy[:, 1]
|
w = xy[:, 2] - xy[:, 0]
|
||||||
area = w * h
|
h = xy[:, 3] - xy[:, 1]
|
||||||
ar = np.maximum(w / (h + 1e-16), h / (w + 1e-16))
|
area = w * h
|
||||||
i = (w > 4) & (h > 4) & (area / (area0 + 1e-16) > 0.1) & (ar < 10)
|
ar = np.maximum(w / (h + 1e-16), h / (w + 1e-16))
|
||||||
|
i = (w > 4) & (h > 4) & (area / (area0 + 1e-16) > 0.1) & (ar < 10)
|
||||||
targets = targets[i]
|
|
||||||
targets[:, 2:6] = xy[i]
|
targets = targets[i]
|
||||||
|
targets[:, 2:6] = xy[i]
|
||||||
return imw, targets, M
|
|
||||||
else:
|
return imw, targets, M
|
||||||
return imw
|
else:
|
||||||
|
return imw
|
||||||
def collate_fn(batch):
|
|
||||||
imgs, labels, paths, sizes = zip(*batch)
|
def collate_fn(batch):
|
||||||
batch_size = len(labels)
|
imgs, labels, paths, sizes = zip(*batch)
|
||||||
imgs = torch.stack(imgs, 0)
|
batch_size = len(labels)
|
||||||
max_box_len = max([l.shape[0] for l in labels])
|
imgs = torch.stack(imgs, 0)
|
||||||
labels = [torch.from_numpy(l) for l in labels]
|
max_box_len = max([l.shape[0] for l in labels])
|
||||||
filled_labels = torch.zeros(batch_size, max_box_len, 6)
|
labels = [torch.from_numpy(l) for l in labels]
|
||||||
labels_len = torch.zeros(batch_size)
|
filled_labels = torch.zeros(batch_size, max_box_len, 6)
|
||||||
|
labels_len = torch.zeros(batch_size)
|
||||||
for i in range(batch_size):
|
|
||||||
isize = labels[i].shape[0]
|
for i in range(batch_size):
|
||||||
if len(labels[i])>0:
|
isize = labels[i].shape[0]
|
||||||
filled_labels[i, :isize, :] = labels[i]
|
if len(labels[i])>0:
|
||||||
labels_len[i] = isize
|
filled_labels[i, :isize, :] = labels[i]
|
||||||
|
labels_len[i] = isize
|
||||||
return imgs, filled_labels, paths, sizes, labels_len.unsqueeze(1)
|
|
||||||
|
return imgs, filled_labels, paths, sizes, labels_len.unsqueeze(1)
|
||||||
|
|
||||||
class JointDataset(LoadImagesAndLabels): # for training
|
|
||||||
def __init__(self, root, paths, img_size=(1088,608), augment=False, transforms=None):
|
class JointDataset(LoadImagesAndLabels): # for training
|
||||||
|
def __init__(self, root, paths, img_size=(1088,608), augment=False, transforms=None):
|
||||||
dataset_names = paths.keys()
|
|
||||||
self.img_files = OrderedDict()
|
dataset_names = paths.keys()
|
||||||
self.label_files = OrderedDict()
|
self.img_files = OrderedDict()
|
||||||
self.tid_num = OrderedDict()
|
self.label_files = OrderedDict()
|
||||||
self.tid_start_index = OrderedDict()
|
self.tid_num = OrderedDict()
|
||||||
for ds, path in paths.items():
|
self.tid_start_index = OrderedDict()
|
||||||
with open(path, 'r') as file:
|
for ds, path in paths.items():
|
||||||
self.img_files[ds] = file.readlines()
|
with open(path, 'r') as file:
|
||||||
self.img_files[ds] = [osp.join(root, x.strip()) for x in self.img_files[ds]]
|
self.img_files[ds] = file.readlines()
|
||||||
self.img_files[ds] = list(filter(lambda x: len(x) > 0, self.img_files[ds]))
|
self.img_files[ds] = [osp.join(root, x.strip()) for x in self.img_files[ds]]
|
||||||
|
self.img_files[ds] = list(filter(lambda x: len(x) > 0, self.img_files[ds]))
|
||||||
self.label_files[ds] = [x.replace('images', 'labels_with_ids').replace('.png', '.txt').replace('.jpg', '.txt')
|
|
||||||
for x in self.img_files[ds]]
|
self.label_files[ds] = [x.replace('images', 'labels_with_ids').replace('.png', '.txt').replace('.jpg', '.txt')
|
||||||
|
for x in self.img_files[ds]]
|
||||||
for ds, label_paths in self.label_files.items():
|
|
||||||
max_index = -1
|
for ds, label_paths in self.label_files.items():
|
||||||
for lp in label_paths:
|
max_index = -1
|
||||||
lb = np.loadtxt(lp)
|
for lp in label_paths:
|
||||||
if len(lb) < 1:
|
lb = np.loadtxt(lp)
|
||||||
continue
|
if len(lb) < 1:
|
||||||
if len(lb.shape) < 2:
|
continue
|
||||||
img_max = lb[1]
|
if len(lb.shape) < 2:
|
||||||
else:
|
img_max = lb[1]
|
||||||
img_max = np.max(lb[:,1])
|
else:
|
||||||
if img_max >max_index:
|
img_max = np.max(lb[:,1])
|
||||||
max_index = img_max
|
if img_max >max_index:
|
||||||
self.tid_num[ds] = max_index + 1
|
max_index = img_max
|
||||||
|
self.tid_num[ds] = max_index + 1
|
||||||
last_index = 0
|
|
||||||
for i, (k, v) in enumerate(self.tid_num.items()):
|
last_index = 0
|
||||||
self.tid_start_index[k] = last_index
|
for i, (k, v) in enumerate(self.tid_num.items()):
|
||||||
last_index += v
|
self.tid_start_index[k] = last_index
|
||||||
|
last_index += v
|
||||||
self.nID = int(last_index+1)
|
|
||||||
self.nds = [len(x) for x in self.img_files.values()]
|
self.nID = int(last_index+1)
|
||||||
self.cds = [sum(self.nds[:i]) for i in range(len(self.nds))]
|
self.nds = [len(x) for x in self.img_files.values()]
|
||||||
self.nF = sum(self.nds)
|
self.cds = [sum(self.nds[:i]) for i in range(len(self.nds))]
|
||||||
self.width = img_size[0]
|
self.nF = sum(self.nds)
|
||||||
self.height = img_size[1]
|
self.width = img_size[0]
|
||||||
self.augment = augment
|
self.height = img_size[1]
|
||||||
self.transforms = transforms
|
self.augment = augment
|
||||||
|
self.transforms = transforms
|
||||||
print('='*80)
|
|
||||||
print('dataset summary')
|
print('='*80)
|
||||||
print(self.tid_num)
|
print('dataset summary')
|
||||||
print('total # identities:', self.nID)
|
print(self.tid_num)
|
||||||
print('start index')
|
print('total # identities:', self.nID)
|
||||||
print(self.tid_start_index)
|
print('start index')
|
||||||
print('='*80)
|
print(self.tid_start_index)
|
||||||
|
print('='*80)
|
||||||
|
|
||||||
def __getitem__(self, files_index):
|
|
||||||
|
def __getitem__(self, files_index):
|
||||||
for i, c in enumerate(self.cds):
|
for i, c in enumerate(self.cds):
|
||||||
if files_index >= c:
|
if files_index >= c:
|
||||||
ds = list(self.label_files.keys())[i]
|
ds = list(self.label_files.keys())[i]
|
||||||
start_index = c
|
start_index = c
|
||||||
|
img_path = self.img_files[ds][files_index - start_index]
|
||||||
img_path = self.img_files[ds][files_index - start_index]
|
label_path = self.label_files[ds][files_index - start_index]
|
||||||
label_path = self.label_files[ds][files_index - start_index]
|
|
||||||
|
imgs, labels, img_path, (h, w) = self.get_data(img_path, label_path)
|
||||||
imgs, labels, img_path, (h, w) = self.get_data(img_path, label_path)
|
for i, _ in enumerate(labels):
|
||||||
for i, _ in enumerate(labels):
|
if labels[i,1] > -1:
|
||||||
if labels[i,1] > -1:
|
labels[i,1] += self.tid_start_index[ds]
|
||||||
labels[i,1] += self.tid_start_index[ds]
|
|
||||||
|
return imgs, labels, img_path, (h, w)
|
||||||
return imgs, labels, img_path, (h, w)
|
|
||||||
|
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -1,101 +1,101 @@
|
||||||
import os
|
import os
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import copy
|
import copy
|
||||||
import motmetrics as mm
|
import motmetrics as mm
|
||||||
|
|
||||||
from utils.io import read_results, unzip_objs
|
from utils.io import read_results, unzip_objs
|
||||||
|
|
||||||
|
|
||||||
class Evaluator(object):
|
class Evaluator(object):
|
||||||
|
|
||||||
def __init__(self, data_root, seq_name, data_type):
|
def __init__(self, data_root, seq_name, data_type):
|
||||||
self.data_root = data_root
|
self.data_root = data_root
|
||||||
self.seq_name = seq_name
|
self.seq_name = seq_name
|
||||||
self.data_type = data_type
|
self.data_type = data_type
|
||||||
|
|
||||||
self.load_annotations()
|
self.load_annotations()
|
||||||
self.reset_accumulator()
|
self.reset_accumulator()
|
||||||
|
|
||||||
def load_annotations(self):
|
def load_annotations(self):
|
||||||
assert self.data_type == 'mot'
|
assert self.data_type == 'mot'
|
||||||
|
|
||||||
gt_filename = os.path.join(self.data_root, self.seq_name, 'gt', 'gt.txt')
|
gt_filename = os.path.join(self.data_root, self.seq_name, 'gt', 'gt.txt')
|
||||||
self.gt_frame_dict = read_results(gt_filename, self.data_type, is_gt=True)
|
self.gt_frame_dict = read_results(gt_filename, self.data_type, is_gt=True)
|
||||||
self.gt_ignore_frame_dict = read_results(gt_filename, self.data_type, is_ignore=True)
|
self.gt_ignore_frame_dict = read_results(gt_filename, self.data_type, is_ignore=True)
|
||||||
|
|
||||||
def reset_accumulator(self):
|
def reset_accumulator(self):
|
||||||
self.acc = mm.MOTAccumulator(auto_id=True)
|
self.acc = mm.MOTAccumulator(auto_id=True)
|
||||||
|
|
||||||
def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False):
|
def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False):
|
||||||
# results
|
# results
|
||||||
trk_tlwhs = np.copy(trk_tlwhs)
|
trk_tlwhs = np.copy(trk_tlwhs)
|
||||||
trk_ids = np.copy(trk_ids)
|
trk_ids = np.copy(trk_ids)
|
||||||
|
|
||||||
# gts
|
# gts
|
||||||
gt_objs = self.gt_frame_dict.get(frame_id, [])
|
gt_objs = self.gt_frame_dict.get(frame_id, [])
|
||||||
gt_tlwhs, gt_ids = unzip_objs(gt_objs)[:2]
|
gt_tlwhs, gt_ids = unzip_objs(gt_objs)[:2]
|
||||||
|
|
||||||
# ignore boxes
|
# ignore boxes
|
||||||
ignore_objs = self.gt_ignore_frame_dict.get(frame_id, [])
|
ignore_objs = self.gt_ignore_frame_dict.get(frame_id, [])
|
||||||
ignore_tlwhs = unzip_objs(ignore_objs)[0]
|
ignore_tlwhs = unzip_objs(ignore_objs)[0]
|
||||||
|
|
||||||
# remove ignored results
|
# remove ignored results
|
||||||
keep = np.ones(len(trk_tlwhs), dtype=bool)
|
keep = np.ones(len(trk_tlwhs), dtype=bool)
|
||||||
iou_distance = mm.distances.iou_matrix(ignore_tlwhs, trk_tlwhs, max_iou=0.5)
|
iou_distance = mm.distances.iou_matrix(ignore_tlwhs, trk_tlwhs, max_iou=0.5)
|
||||||
match_is, match_js = mm.lap.linear_sum_assignment(iou_distance)
|
match_is, match_js = mm.lap.linear_sum_assignment(iou_distance)
|
||||||
match_is, match_js = map(lambda a: np.asarray(a, dtype=int), [match_is, match_js])
|
match_is, match_js = map(lambda a: np.asarray(a, dtype=int), [match_is, match_js])
|
||||||
match_ious = iou_distance[match_is, match_js]
|
match_ious = iou_distance[match_is, match_js]
|
||||||
|
|
||||||
match_js = np.asarray(match_js, dtype=int)
|
match_js = np.asarray(match_js, dtype=int)
|
||||||
match_js = match_js[np.logical_not(np.isnan(match_ious))]
|
match_js = match_js[np.logical_not(np.isnan(match_ious))]
|
||||||
keep[match_js] = False
|
keep[match_js] = False
|
||||||
trk_tlwhs = trk_tlwhs[keep]
|
trk_tlwhs = trk_tlwhs[keep]
|
||||||
trk_ids = trk_ids[keep]
|
trk_ids = trk_ids[keep]
|
||||||
|
|
||||||
# get distance matrix
|
# get distance matrix
|
||||||
iou_distance = mm.distances.iou_matrix(gt_tlwhs, trk_tlwhs, max_iou=0.5)
|
iou_distance = mm.distances.iou_matrix(gt_tlwhs, trk_tlwhs, max_iou=0.5)
|
||||||
|
|
||||||
# acc
|
# acc
|
||||||
self.acc.update(gt_ids, trk_ids, iou_distance)
|
self.acc.update(gt_ids, trk_ids, iou_distance)
|
||||||
|
|
||||||
if rtn_events and iou_distance.size > 0 and hasattr(self.acc, 'last_mot_events'):
|
if rtn_events and iou_distance.size > 0 and hasattr(self.acc, 'last_mot_events'):
|
||||||
events = self.acc.last_mot_events # only supported by https://github.com/longcw/py-motmetrics
|
events = self.acc.last_mot_events # only supported by https://github.com/longcw/py-motmetrics
|
||||||
else:
|
else:
|
||||||
events = None
|
events = None
|
||||||
return events
|
return events
|
||||||
|
|
||||||
def eval_file(self, filename):
|
def eval_file(self, filename):
|
||||||
self.reset_accumulator()
|
self.reset_accumulator()
|
||||||
|
|
||||||
result_frame_dict = read_results(filename, self.data_type, is_gt=False)
|
result_frame_dict = read_results(filename, self.data_type, is_gt=False)
|
||||||
frames = sorted(list(set(self.gt_frame_dict.keys()) | set(result_frame_dict.keys())))
|
frames = sorted(list(set(self.gt_frame_dict.keys()) | set(result_frame_dict.keys())))
|
||||||
for frame_id in frames:
|
for frame_id in frames:
|
||||||
trk_objs = result_frame_dict.get(frame_id, [])
|
trk_objs = result_frame_dict.get(frame_id, [])
|
||||||
trk_tlwhs, trk_ids = unzip_objs(trk_objs)[:2]
|
trk_tlwhs, trk_ids = unzip_objs(trk_objs)[:2]
|
||||||
self.eval_frame(frame_id, trk_tlwhs, trk_ids, rtn_events=False)
|
self.eval_frame(frame_id, trk_tlwhs, trk_ids, rtn_events=False)
|
||||||
|
|
||||||
return self.acc
|
return self.acc
|
||||||
|
|
||||||
@staticmethod
|
@staticmethod
|
||||||
def get_summary(accs, names, metrics=('mota', 'num_switches', 'idp', 'idr', 'idf1', 'precision', 'recall')):
|
def get_summary(accs, names, metrics=('mota', 'num_switches', 'idp', 'idr', 'idf1', 'precision', 'recall')):
|
||||||
names = copy.deepcopy(names)
|
names = copy.deepcopy(names)
|
||||||
if metrics is None:
|
if metrics is None:
|
||||||
metrics = mm.metrics.motchallenge_metrics
|
metrics = mm.metrics.motchallenge_metrics
|
||||||
metrics = copy.deepcopy(metrics)
|
metrics = copy.deepcopy(metrics)
|
||||||
|
|
||||||
mh = mm.metrics.create()
|
mh = mm.metrics.create()
|
||||||
summary = mh.compute_many(
|
summary = mh.compute_many(
|
||||||
accs,
|
accs,
|
||||||
metrics=metrics,
|
metrics=metrics,
|
||||||
names=names,
|
names=names,
|
||||||
generate_overall=True
|
generate_overall=True
|
||||||
)
|
)
|
||||||
|
|
||||||
return summary
|
return summary
|
||||||
|
|
||||||
@staticmethod
|
@staticmethod
|
||||||
def save_summary(summary, filename):
|
def save_summary(summary, filename):
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
writer = pd.ExcelWriter(filename)
|
writer = pd.ExcelWriter(filename)
|
||||||
summary.to_excel(writer)
|
summary.to_excel(writer)
|
||||||
writer.save()
|
writer.save()
|
||||||
|
|
|
||||||
222
utils/io.py
222
utils/io.py
|
|
@ -1,112 +1,112 @@
|
||||||
import os
|
import os
|
||||||
from typing import Dict
|
from typing import Dict
|
||||||
import numpy as np
|
import numpy as np
|
||||||
|
|
||||||
from utils.log import logger
|
from utils.log import logger
|
||||||
|
|
||||||
|
|
||||||
def write_results(filename, results_dict: Dict, data_type: str):
|
def write_results(filename, results_dict: Dict, data_type: str):
|
||||||
if not filename:
|
if not filename:
|
||||||
return
|
return
|
||||||
path = os.path.dirname(filename)
|
path = os.path.dirname(filename)
|
||||||
if not os.path.exists(path):
|
if not os.path.exists(path):
|
||||||
os.makedirs(path)
|
os.makedirs(path)
|
||||||
|
|
||||||
if data_type in ('mot', 'mcmot', 'lab'):
|
if data_type in ('mot', 'mcmot', 'lab'):
|
||||||
save_format = '{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\n'
|
save_format = '{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\n'
|
||||||
elif data_type == 'kitti':
|
elif data_type == 'kitti':
|
||||||
save_format = '{frame} {id} pedestrian -1 -1 -10 {x1} {y1} {x2} {y2} -1 -1 -1 -1000 -1000 -1000 -10 {score}\n'
|
save_format = '{frame} {id} pedestrian -1 -1 -10 {x1} {y1} {x2} {y2} -1 -1 -1 -1000 -1000 -1000 -10 {score}\n'
|
||||||
else:
|
else:
|
||||||
raise ValueError(data_type)
|
raise ValueError(data_type)
|
||||||
|
|
||||||
with open(filename, 'w') as f:
|
with open(filename, 'w') as f:
|
||||||
for frame_id, frame_data in results_dict.items():
|
for frame_id, frame_data in results_dict.items():
|
||||||
if data_type == 'kitti':
|
if data_type == 'kitti':
|
||||||
frame_id -= 1
|
frame_id -= 1
|
||||||
for tlwh, track_id in frame_data:
|
for tlwh, track_id in frame_data:
|
||||||
if track_id < 0:
|
if track_id < 0:
|
||||||
continue
|
continue
|
||||||
x1, y1, w, h = tlwh
|
x1, y1, w, h = tlwh
|
||||||
x2, y2 = x1 + w, y1 + h
|
x2, y2 = x1 + w, y1 + h
|
||||||
line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h, score=1.0)
|
line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h, score=1.0)
|
||||||
f.write(line)
|
f.write(line)
|
||||||
logger.info('Save results to {}'.format(filename))
|
logger.info('Save results to {}'.format(filename))
|
||||||
|
|
||||||
|
|
||||||
def read_results(filename, data_type: str, is_gt=False, is_ignore=False):
|
def read_results(filename, data_type: str, is_gt=False, is_ignore=False):
|
||||||
if data_type in ('mot', 'lab'):
|
if data_type in ('mot', 'lab'):
|
||||||
read_fun = read_mot_results
|
read_fun = read_mot_results
|
||||||
else:
|
else:
|
||||||
raise ValueError('Unknown data type: {}'.format(data_type))
|
raise ValueError('Unknown data type: {}'.format(data_type))
|
||||||
|
|
||||||
return read_fun(filename, is_gt, is_ignore)
|
return read_fun(filename, is_gt, is_ignore)
|
||||||
|
|
||||||
|
|
||||||
"""
|
"""
|
||||||
labels={'ped', ... % 1
|
labels={'ped', ... % 1
|
||||||
'person_on_vhcl', ... % 2
|
'person_on_vhcl', ... % 2
|
||||||
'car', ... % 3
|
'car', ... % 3
|
||||||
'bicycle', ... % 4
|
'bicycle', ... % 4
|
||||||
'mbike', ... % 5
|
'mbike', ... % 5
|
||||||
'non_mot_vhcl', ... % 6
|
'non_mot_vhcl', ... % 6
|
||||||
'static_person', ... % 7
|
'static_person', ... % 7
|
||||||
'distractor', ... % 8
|
'distractor', ... % 8
|
||||||
'occluder', ... % 9
|
'occluder', ... % 9
|
||||||
'occluder_on_grnd', ... %10
|
'occluder_on_grnd', ... %10
|
||||||
'occluder_full', ... % 11
|
'occluder_full', ... % 11
|
||||||
'reflection', ... % 12
|
'reflection', ... % 12
|
||||||
'crowd' ... % 13
|
'crowd' ... % 13
|
||||||
};
|
};
|
||||||
"""
|
"""
|
||||||
|
|
||||||
|
|
||||||
def read_mot_results(filename, is_gt, is_ignore):
|
def read_mot_results(filename, is_gt, is_ignore):
|
||||||
valid_labels = {1}
|
valid_labels = {1}
|
||||||
ignore_labels = {2, 7, 8, 12}
|
ignore_labels = {2, 7, 8, 12}
|
||||||
results_dict = dict()
|
results_dict = dict()
|
||||||
if os.path.isfile(filename):
|
if os.path.isfile(filename):
|
||||||
with open(filename, 'r') as f:
|
with open(filename, 'r') as f:
|
||||||
for line in f.readlines():
|
for line in f.readlines():
|
||||||
linelist = line.split(',')
|
linelist = line.split(',')
|
||||||
if len(linelist) < 7:
|
if len(linelist) < 7:
|
||||||
continue
|
continue
|
||||||
fid = int(linelist[0])
|
fid = int(linelist[0])
|
||||||
if fid < 1:
|
if fid < 1:
|
||||||
continue
|
continue
|
||||||
results_dict.setdefault(fid, list())
|
results_dict.setdefault(fid, list())
|
||||||
|
|
||||||
if is_gt:
|
if is_gt:
|
||||||
if 'MOT16-' in filename or 'MOT17-' in filename:
|
if 'MOT16-' in filename or 'MOT17-' in filename:
|
||||||
label = int(float(linelist[7]))
|
label = int(float(linelist[7]))
|
||||||
mark = int(float(linelist[6]))
|
mark = int(float(linelist[6]))
|
||||||
if mark == 0 or label not in valid_labels:
|
if mark == 0 or label not in valid_labels:
|
||||||
continue
|
continue
|
||||||
score = 1
|
score = 1
|
||||||
elif is_ignore:
|
elif is_ignore:
|
||||||
if 'MOT16-' in filename or 'MOT17-' in filename:
|
if 'MOT16-' in filename or 'MOT17-' in filename:
|
||||||
label = int(float(linelist[7]))
|
label = int(float(linelist[7]))
|
||||||
vis_ratio = float(linelist[8])
|
vis_ratio = float(linelist[8])
|
||||||
if label not in ignore_labels and vis_ratio >= 0:
|
if label not in ignore_labels and vis_ratio >= 0:
|
||||||
continue
|
continue
|
||||||
else:
|
else:
|
||||||
continue
|
continue
|
||||||
score = 1
|
score = 1
|
||||||
else:
|
else:
|
||||||
score = float(linelist[6])
|
score = float(linelist[6])
|
||||||
|
|
||||||
tlwh = tuple(map(float, linelist[2:6]))
|
tlwh = tuple(map(float, linelist[2:6]))
|
||||||
target_id = int(linelist[1])
|
target_id = int(linelist[1])
|
||||||
|
|
||||||
results_dict[fid].append((tlwh, target_id, score))
|
results_dict[fid].append((tlwh, target_id, score))
|
||||||
|
|
||||||
return results_dict
|
return results_dict
|
||||||
|
|
||||||
|
|
||||||
def unzip_objs(objs):
|
def unzip_objs(objs):
|
||||||
if len(objs) > 0:
|
if len(objs) > 0:
|
||||||
tlwhs, ids, scores = zip(*objs)
|
tlwhs, ids, scores = zip(*objs)
|
||||||
else:
|
else:
|
||||||
tlwhs, ids, scores = [], [], []
|
tlwhs, ids, scores = [], [], []
|
||||||
tlwhs = np.asarray(tlwhs, dtype=float).reshape(-1, 4)
|
tlwhs = np.asarray(tlwhs, dtype=float).reshape(-1, 4)
|
||||||
|
|
||||||
return tlwhs, ids, scores
|
return tlwhs, ids, scores
|
||||||
36
utils/log.py
36
utils/log.py
|
|
@ -1,18 +1,18 @@
|
||||||
import logging
|
import logging
|
||||||
|
|
||||||
|
|
||||||
def get_logger(name='root'):
|
def get_logger(name='root'):
|
||||||
formatter = logging.Formatter(
|
formatter = logging.Formatter(
|
||||||
# fmt='%(asctime)s [%(levelname)s]: %(filename)s(%(funcName)s:%(lineno)s) >> %(message)s')
|
# fmt='%(asctime)s [%(levelname)s]: %(filename)s(%(funcName)s:%(lineno)s) >> %(message)s')
|
||||||
fmt='%(asctime)s [%(levelname)s]: %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
|
fmt='%(asctime)s [%(levelname)s]: %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
|
||||||
|
|
||||||
handler = logging.StreamHandler()
|
handler = logging.StreamHandler()
|
||||||
handler.setFormatter(formatter)
|
handler.setFormatter(formatter)
|
||||||
|
|
||||||
logger = logging.getLogger(name)
|
logger = logging.getLogger(name)
|
||||||
logger.setLevel(logging.DEBUG)
|
logger.setLevel(logging.DEBUG)
|
||||||
logger.addHandler(handler)
|
logger.addHandler(handler)
|
||||||
return logger
|
return logger
|
||||||
|
|
||||||
|
|
||||||
logger = get_logger('root')
|
logger = get_logger('root')
|
||||||
|
|
|
||||||
|
|
@ -1,7 +0,0 @@
|
||||||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
|
|
||||||
# from ._utils import _C
|
|
||||||
from utils import _C
|
|
||||||
|
|
||||||
nms = _C.nms
|
|
||||||
# nms.__doc__ = """
|
|
||||||
# This function performs Non-maximum suppresion"""
|
|
||||||
32
utils/nms/nms.h
Normal file
32
utils/nms/nms.h
Normal file
|
|
@ -0,0 +1,32 @@
|
||||||
|
// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
|
||||||
|
#pragma once
|
||||||
|
#include <torch/extension.h>
|
||||||
|
at::Tensor nms_cpu(const at::Tensor& dets, const at::Tensor& scores, const float threshold);
|
||||||
|
#ifdef WITH_CUDA
|
||||||
|
at::Tensor nms_cuda(const at::Tensor boxes, float nms_overlap_thresh);
|
||||||
|
#endif
|
||||||
|
|
||||||
|
|
||||||
|
at::Tensor nms(const at::Tensor& dets,
|
||||||
|
const at::Tensor& scores,
|
||||||
|
const float threshold) {
|
||||||
|
|
||||||
|
if (dets.type().is_cuda()) {
|
||||||
|
#ifdef WITH_CUDA
|
||||||
|
// TODO raise error if not compiled with CUDA
|
||||||
|
if (dets.numel() == 0)
|
||||||
|
return at::empty({0}, dets.options().dtype(at::kLong).device(at::kCPU));
|
||||||
|
auto b = at::cat({dets, scores.unsqueeze(1)}, 1);
|
||||||
|
return nms_cuda(b, threshold);
|
||||||
|
#else
|
||||||
|
AT_ERROR("Not compiled with GPU support");
|
||||||
|
#endif
|
||||||
|
}
|
||||||
|
|
||||||
|
at::Tensor result = nms_cpu(dets, scores, threshold);
|
||||||
|
return result;
|
||||||
|
}
|
||||||
|
|
||||||
|
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m){
|
||||||
|
m.def("nms", &nms, "non-maximum suppression");
|
||||||
|
}
|
||||||
74
utils/nms/nms_cpu.cpp
Normal file
74
utils/nms/nms_cpu.cpp
Normal file
|
|
@ -0,0 +1,74 @@
|
||||||
|
// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
|
||||||
|
#include "nms.h"
|
||||||
|
template <typename scalar_t>
|
||||||
|
at::Tensor nms_cpu_kernel(const at::Tensor& dets,
|
||||||
|
const at::Tensor& scores,
|
||||||
|
const float threshold) {
|
||||||
|
AT_ASSERTM(!dets.type().is_cuda(), "dets must be a CPU tensor");
|
||||||
|
AT_ASSERTM(!scores.type().is_cuda(), "scores must be a CPU tensor");
|
||||||
|
AT_ASSERTM(dets.type() == scores.type(), "dets should have the same type as scores");
|
||||||
|
|
||||||
|
if (dets.numel() == 0) {
|
||||||
|
return at::empty({0}, dets.options().dtype(at::kLong).device(at::kCPU));
|
||||||
|
}
|
||||||
|
|
||||||
|
auto x1_t = dets.select(1, 0).contiguous();
|
||||||
|
auto y1_t = dets.select(1, 1).contiguous();
|
||||||
|
auto x2_t = dets.select(1, 2).contiguous();
|
||||||
|
auto y2_t = dets.select(1, 3).contiguous();
|
||||||
|
|
||||||
|
at::Tensor areas_t = (x2_t - x1_t + 1) * (y2_t - y1_t + 1);
|
||||||
|
|
||||||
|
auto order_t = std::get<1>(scores.sort(0, /* descending=*/true));
|
||||||
|
|
||||||
|
auto ndets = dets.size(0);
|
||||||
|
at::Tensor suppressed_t = at::zeros({ndets}, dets.options().dtype(at::kByte).device(at::kCPU));
|
||||||
|
|
||||||
|
auto suppressed = suppressed_t.data<uint8_t>();
|
||||||
|
auto order = order_t.data<int64_t>();
|
||||||
|
auto x1 = x1_t.data<scalar_t>();
|
||||||
|
auto y1 = y1_t.data<scalar_t>();
|
||||||
|
auto x2 = x2_t.data<scalar_t>();
|
||||||
|
auto y2 = y2_t.data<scalar_t>();
|
||||||
|
auto areas = areas_t.data<scalar_t>();
|
||||||
|
|
||||||
|
for (int64_t _i = 0; _i < ndets; _i++) {
|
||||||
|
auto i = order[_i];
|
||||||
|
if (suppressed[i] == 1)
|
||||||
|
continue;
|
||||||
|
auto ix1 = x1[i];
|
||||||
|
auto iy1 = y1[i];
|
||||||
|
auto ix2 = x2[i];
|
||||||
|
auto iy2 = y2[i];
|
||||||
|
auto iarea = areas[i];
|
||||||
|
|
||||||
|
for (int64_t _j = _i + 1; _j < ndets; _j++) {
|
||||||
|
auto j = order[_j];
|
||||||
|
if (suppressed[j] == 1)
|
||||||
|
continue;
|
||||||
|
auto xx1 = std::max(ix1, x1[j]);
|
||||||
|
auto yy1 = std::max(iy1, y1[j]);
|
||||||
|
auto xx2 = std::min(ix2, x2[j]);
|
||||||
|
auto yy2 = std::min(iy2, y2[j]);
|
||||||
|
|
||||||
|
auto w = std::max(static_cast<scalar_t>(0), xx2 - xx1 + 1);
|
||||||
|
auto h = std::max(static_cast<scalar_t>(0), yy2 - yy1 + 1);
|
||||||
|
auto inter = w * h;
|
||||||
|
auto ovr = inter / (iarea + areas[j] - inter);
|
||||||
|
if (ovr >= threshold)
|
||||||
|
suppressed[j] = 1;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
return at::nonzero(suppressed_t == 0).squeeze(1);
|
||||||
|
}
|
||||||
|
|
||||||
|
at::Tensor nms_cpu(const at::Tensor& dets,
|
||||||
|
const at::Tensor& scores,
|
||||||
|
const float threshold) {
|
||||||
|
at::Tensor result;
|
||||||
|
AT_DISPATCH_FLOATING_TYPES(dets.type(), "nms", [&] {
|
||||||
|
result = nms_cpu_kernel<scalar_t>(dets, scores, threshold);
|
||||||
|
});
|
||||||
|
return result;
|
||||||
|
}
|
||||||
|
|
||||||
131
utils/nms/nms_kernel.cu
Normal file
131
utils/nms/nms_kernel.cu
Normal file
|
|
@ -0,0 +1,131 @@
|
||||||
|
#include <ATen/ATen.h>
|
||||||
|
#include <ATen/cuda/CUDAContext.h>
|
||||||
|
|
||||||
|
#include <THC/THC.h>
|
||||||
|
#include <THC/THCDeviceUtils.cuh>
|
||||||
|
|
||||||
|
#include <vector>
|
||||||
|
#include <iostream>
|
||||||
|
|
||||||
|
|
||||||
|
int const threadsPerBlock = sizeof(unsigned long long) * 8;
|
||||||
|
|
||||||
|
__device__ inline float devIoU(float const * const a, float const * const b) {
|
||||||
|
float left = max(a[0], b[0]), right = min(a[2], b[2]);
|
||||||
|
float top = max(a[1], b[1]), bottom = min(a[3], b[3]);
|
||||||
|
float width = max(right - left + 1, 0.f), height = max(bottom - top + 1, 0.f);
|
||||||
|
float interS = width * height;
|
||||||
|
float Sa = (a[2] - a[0] + 1) * (a[3] - a[1] + 1);
|
||||||
|
float Sb = (b[2] - b[0] + 1) * (b[3] - b[1] + 1);
|
||||||
|
return interS / (Sa + Sb - interS);
|
||||||
|
}
|
||||||
|
|
||||||
|
__global__ void nms_kernel(const int n_boxes, const float nms_overlap_thresh,
|
||||||
|
const float *dev_boxes, unsigned long long *dev_mask) {
|
||||||
|
const int row_start = blockIdx.y;
|
||||||
|
const int col_start = blockIdx.x;
|
||||||
|
|
||||||
|
// if (row_start > col_start) return;
|
||||||
|
|
||||||
|
const int row_size =
|
||||||
|
min(n_boxes - row_start * threadsPerBlock, threadsPerBlock);
|
||||||
|
const int col_size =
|
||||||
|
min(n_boxes - col_start * threadsPerBlock, threadsPerBlock);
|
||||||
|
|
||||||
|
__shared__ float block_boxes[threadsPerBlock * 5];
|
||||||
|
if (threadIdx.x < col_size) {
|
||||||
|
block_boxes[threadIdx.x * 5 + 0] =
|
||||||
|
dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 0];
|
||||||
|
block_boxes[threadIdx.x * 5 + 1] =
|
||||||
|
dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 1];
|
||||||
|
block_boxes[threadIdx.x * 5 + 2] =
|
||||||
|
dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 2];
|
||||||
|
block_boxes[threadIdx.x * 5 + 3] =
|
||||||
|
dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 3];
|
||||||
|
block_boxes[threadIdx.x * 5 + 4] =
|
||||||
|
dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 4];
|
||||||
|
}
|
||||||
|
__syncthreads();
|
||||||
|
|
||||||
|
if (threadIdx.x < row_size) {
|
||||||
|
const int cur_box_idx = threadsPerBlock * row_start + threadIdx.x;
|
||||||
|
const float *cur_box = dev_boxes + cur_box_idx * 5;
|
||||||
|
int i = 0;
|
||||||
|
unsigned long long t = 0;
|
||||||
|
int start = 0;
|
||||||
|
if (row_start == col_start) {
|
||||||
|
start = threadIdx.x + 1;
|
||||||
|
}
|
||||||
|
for (i = start; i < col_size; i++) {
|
||||||
|
if (devIoU(cur_box, block_boxes + i * 5) > nms_overlap_thresh) {
|
||||||
|
t |= 1ULL << i;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
const int col_blocks = THCCeilDiv(n_boxes, threadsPerBlock);
|
||||||
|
dev_mask[cur_box_idx * col_blocks + col_start] = t;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// boxes is a N x 5 tensor
|
||||||
|
at::Tensor nms_cuda(const at::Tensor boxes, float nms_overlap_thresh) {
|
||||||
|
using scalar_t = float;
|
||||||
|
AT_ASSERTM(boxes.type().is_cuda(), "boxes must be a CUDA tensor");
|
||||||
|
auto scores = boxes.select(1, 4);
|
||||||
|
auto order_t = std::get<1>(scores.sort(0, /* descending=*/true));
|
||||||
|
auto boxes_sorted = boxes.index_select(0, order_t);
|
||||||
|
|
||||||
|
int boxes_num = boxes.size(0);
|
||||||
|
|
||||||
|
const int col_blocks = THCCeilDiv(boxes_num, threadsPerBlock);
|
||||||
|
|
||||||
|
scalar_t* boxes_dev = boxes_sorted.data<scalar_t>();
|
||||||
|
|
||||||
|
THCState *state = at::globalContext().lazyInitCUDA(); // TODO replace with getTHCState
|
||||||
|
|
||||||
|
unsigned long long* mask_dev = NULL;
|
||||||
|
//THCudaCheck(THCudaMalloc(state, (void**) &mask_dev,
|
||||||
|
// boxes_num * col_blocks * sizeof(unsigned long long)));
|
||||||
|
|
||||||
|
mask_dev = (unsigned long long*) THCudaMalloc(state, boxes_num * col_blocks * sizeof(unsigned long long));
|
||||||
|
|
||||||
|
dim3 blocks(THCCeilDiv(boxes_num, threadsPerBlock),
|
||||||
|
THCCeilDiv(boxes_num, threadsPerBlock));
|
||||||
|
dim3 threads(threadsPerBlock);
|
||||||
|
nms_kernel<<<blocks, threads>>>(boxes_num,
|
||||||
|
nms_overlap_thresh,
|
||||||
|
boxes_dev,
|
||||||
|
mask_dev);
|
||||||
|
|
||||||
|
std::vector<unsigned long long> mask_host(boxes_num * col_blocks);
|
||||||
|
THCudaCheck(cudaMemcpy(&mask_host[0],
|
||||||
|
mask_dev,
|
||||||
|
sizeof(unsigned long long) * boxes_num * col_blocks,
|
||||||
|
cudaMemcpyDeviceToHost));
|
||||||
|
|
||||||
|
std::vector<unsigned long long> remv(col_blocks);
|
||||||
|
memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks);
|
||||||
|
|
||||||
|
at::Tensor keep = at::empty({boxes_num}, boxes.options().dtype(at::kLong).device(at::kCPU));
|
||||||
|
int64_t* keep_out = keep.data<int64_t>();
|
||||||
|
|
||||||
|
int num_to_keep = 0;
|
||||||
|
for (int i = 0; i < boxes_num; i++) {
|
||||||
|
int nblock = i / threadsPerBlock;
|
||||||
|
int inblock = i % threadsPerBlock;
|
||||||
|
|
||||||
|
if (!(remv[nblock] & (1ULL << inblock))) {
|
||||||
|
keep_out[num_to_keep++] = i;
|
||||||
|
unsigned long long *p = &mask_host[0] + i * col_blocks;
|
||||||
|
for (int j = nblock; j < col_blocks; j++) {
|
||||||
|
remv[j] |= p[j];
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
THCudaFree(state, mask_dev);
|
||||||
|
// TODO improve this part
|
||||||
|
return std::get<0>(order_t.index({
|
||||||
|
keep.narrow(/*dim=*/0, /*start=*/0, /*length=*/num_to_keep).to(
|
||||||
|
order_t.device(), keep.scalar_type())
|
||||||
|
}).sort(0, false));
|
||||||
|
}
|
||||||
|
|
@ -1,35 +1,35 @@
|
||||||
def parse_model_cfg(path):
|
def parse_model_cfg(path):
|
||||||
"""Parses the yolo-v3 layer configuration file and returns module definitions"""
|
"""Parses the yolo-v3 layer configuration file and returns module definitions"""
|
||||||
file = open(path, 'r')
|
file = open(path, 'r')
|
||||||
lines = file.read().split('\n')
|
lines = file.read().split('\n')
|
||||||
lines = [x for x in lines if x and not x.startswith('#')]
|
lines = [x for x in lines if x and not x.startswith('#')]
|
||||||
lines = [x.rstrip().lstrip() for x in lines] # get rid of fringe whitespaces
|
lines = [x.rstrip().lstrip() for x in lines] # get rid of fringe whitespaces
|
||||||
module_defs = []
|
module_defs = []
|
||||||
for line in lines:
|
for line in lines:
|
||||||
if line.startswith('['): # This marks the start of a new block
|
if line.startswith('['): # This marks the start of a new block
|
||||||
module_defs.append({})
|
module_defs.append({})
|
||||||
module_defs[-1]['type'] = line[1:-1].rstrip()
|
module_defs[-1]['type'] = line[1:-1].rstrip()
|
||||||
if module_defs[-1]['type'] == 'convolutional':
|
if module_defs[-1]['type'] == 'convolutional':
|
||||||
module_defs[-1]['batch_normalize'] = 0
|
module_defs[-1]['batch_normalize'] = 0
|
||||||
else:
|
else:
|
||||||
key, value = line.split("=")
|
key, value = line.split("=")
|
||||||
value = value.strip()
|
value = value.strip()
|
||||||
module_defs[-1][key.rstrip()] = value.strip()
|
module_defs[-1][key.rstrip()] = value.strip()
|
||||||
|
|
||||||
return module_defs
|
return module_defs
|
||||||
|
|
||||||
|
|
||||||
def parse_data_cfg(path):
|
def parse_data_cfg(path):
|
||||||
"""Parses the data configuration file"""
|
"""Parses the data configuration file"""
|
||||||
options = dict()
|
options = dict()
|
||||||
options['gpus'] = '0'
|
options['gpus'] = '0'
|
||||||
options['num_workers'] = '10'
|
options['num_workers'] = '10'
|
||||||
with open(path, 'r') as fp:
|
with open(path, 'r') as fp:
|
||||||
lines = fp.readlines()
|
lines = fp.readlines()
|
||||||
for line in lines:
|
for line in lines:
|
||||||
line = line.strip()
|
line = line.strip()
|
||||||
if line == '' or line.startswith('#'):
|
if line == '' or line.startswith('#'):
|
||||||
continue
|
continue
|
||||||
key, value = line.split('=')
|
key, value = line.split('=')
|
||||||
options[key.strip()] = value.strip()
|
options[key.strip()] = value.strip()
|
||||||
return options
|
return options
|
||||||
|
|
|
||||||
|
|
@ -1,45 +1,45 @@
|
||||||
# --------------------------------------------------------
|
# --------------------------------------------------------
|
||||||
# Fast R-CNN
|
# Fast R-CNN
|
||||||
# Copyright (c) 2015 Microsoft
|
# Copyright (c) 2015 Microsoft
|
||||||
# Licensed under The MIT License [see LICENSE for details]
|
# Licensed under The MIT License [see LICENSE for details]
|
||||||
# Written by Ross Girshick
|
# Written by Ross Girshick
|
||||||
# --------------------------------------------------------
|
# --------------------------------------------------------
|
||||||
|
|
||||||
import time
|
import time
|
||||||
|
|
||||||
|
|
||||||
class Timer(object):
|
class Timer(object):
|
||||||
"""A simple timer."""
|
"""A simple timer."""
|
||||||
def __init__(self):
|
def __init__(self):
|
||||||
self.total_time = 0.
|
self.total_time = 0.
|
||||||
self.calls = 0
|
self.calls = 0
|
||||||
self.start_time = 0.
|
self.start_time = 0.
|
||||||
self.diff = 0.
|
self.diff = 0.
|
||||||
self.average_time = 0.
|
self.average_time = 0.
|
||||||
|
|
||||||
self.duration = 0.
|
self.duration = 0.
|
||||||
|
|
||||||
def tic(self):
|
def tic(self):
|
||||||
# using time.time instead of time.clock because time time.clock
|
# using time.time instead of time.clock because time time.clock
|
||||||
# does not normalize for multithreading
|
# does not normalize for multithreading
|
||||||
self.start_time = time.time()
|
self.start_time = time.time()
|
||||||
|
|
||||||
def toc(self, average=True):
|
def toc(self, average=True):
|
||||||
self.diff = time.time() - self.start_time
|
self.diff = time.time() - self.start_time
|
||||||
self.total_time += self.diff
|
self.total_time += self.diff
|
||||||
self.calls += 1
|
self.calls += 1
|
||||||
self.average_time = self.total_time / self.calls
|
self.average_time = self.total_time / self.calls
|
||||||
if average:
|
if average:
|
||||||
self.duration = self.average_time
|
self.duration = self.average_time
|
||||||
else:
|
else:
|
||||||
self.duration = self.diff
|
self.duration = self.diff
|
||||||
return self.duration
|
return self.duration
|
||||||
|
|
||||||
def clear(self):
|
def clear(self):
|
||||||
self.total_time = 0.
|
self.total_time = 0.
|
||||||
self.calls = 0
|
self.calls = 0
|
||||||
self.start_time = 0.
|
self.start_time = 0.
|
||||||
self.diff = 0.
|
self.diff = 0.
|
||||||
self.average_time = 0.
|
self.average_time = 0.
|
||||||
self.duration = 0.
|
self.duration = 0.
|
||||||
|
|
||||||
|
|
|
||||||
1094
utils/utils.py
1094
utils/utils.py
File diff suppressed because it is too large
Load diff
|
|
@ -1,90 +1,90 @@
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import cv2
|
import cv2
|
||||||
|
|
||||||
|
|
||||||
def tlwhs_to_tlbrs(tlwhs):
|
def tlwhs_to_tlbrs(tlwhs):
|
||||||
tlbrs = np.copy(tlwhs)
|
tlbrs = np.copy(tlwhs)
|
||||||
if len(tlbrs) == 0:
|
if len(tlbrs) == 0:
|
||||||
return tlbrs
|
return tlbrs
|
||||||
tlbrs[:, 2] += tlwhs[:, 0]
|
tlbrs[:, 2] += tlwhs[:, 0]
|
||||||
tlbrs[:, 3] += tlwhs[:, 1]
|
tlbrs[:, 3] += tlwhs[:, 1]
|
||||||
return tlbrs
|
return tlbrs
|
||||||
|
|
||||||
|
|
||||||
def get_color(idx):
|
def get_color(idx):
|
||||||
idx = idx * 3
|
idx = idx * 3
|
||||||
color = ((37 * idx) % 255, (17 * idx) % 255, (29 * idx) % 255)
|
color = ((37 * idx) % 255, (17 * idx) % 255, (29 * idx) % 255)
|
||||||
|
|
||||||
return color
|
return color
|
||||||
|
|
||||||
|
|
||||||
def resize_image(image, max_size=800):
|
def resize_image(image, max_size=800):
|
||||||
if max(image.shape[:2]) > max_size:
|
if max(image.shape[:2]) > max_size:
|
||||||
scale = float(max_size) / max(image.shape[:2])
|
scale = float(max_size) / max(image.shape[:2])
|
||||||
image = cv2.resize(image, None, fx=scale, fy=scale)
|
image = cv2.resize(image, None, fx=scale, fy=scale)
|
||||||
return image
|
return image
|
||||||
|
|
||||||
|
|
||||||
def plot_tracking(image, tlwhs, obj_ids, scores=None, frame_id=0, fps=0., ids2=None):
|
def plot_tracking(image, tlwhs, obj_ids, scores=None, frame_id=0, fps=0., ids2=None):
|
||||||
im = np.ascontiguousarray(np.copy(image))
|
im = np.ascontiguousarray(np.copy(image))
|
||||||
im_h, im_w = im.shape[:2]
|
im_h, im_w = im.shape[:2]
|
||||||
|
|
||||||
top_view = np.zeros([im_w, im_w, 3], dtype=np.uint8) + 255
|
top_view = np.zeros([im_w, im_w, 3], dtype=np.uint8) + 255
|
||||||
|
|
||||||
text_scale = max(1, image.shape[1] / 1600.)
|
text_scale = max(1, image.shape[1] / 1600.)
|
||||||
text_thickness = 1 if text_scale > 1.1 else 1
|
text_thickness = 1 if text_scale > 1.1 else 1
|
||||||
line_thickness = max(1, int(image.shape[1] / 500.))
|
line_thickness = max(1, int(image.shape[1] / 500.))
|
||||||
|
|
||||||
radius = max(5, int(im_w/140.))
|
radius = max(5, int(im_w/140.))
|
||||||
cv2.putText(im, 'frame: %d fps: %.2f num: %d' % (frame_id, fps, len(tlwhs)),
|
cv2.putText(im, 'frame: %d fps: %.2f num: %d' % (frame_id, fps, len(tlwhs)),
|
||||||
(0, int(15 * text_scale)), cv2.FONT_HERSHEY_PLAIN, text_scale, (0, 0, 255), thickness=2)
|
(0, int(15 * text_scale)), cv2.FONT_HERSHEY_PLAIN, text_scale, (0, 0, 255), thickness=2)
|
||||||
|
|
||||||
for i, tlwh in enumerate(tlwhs):
|
for i, tlwh in enumerate(tlwhs):
|
||||||
x1, y1, w, h = tlwh
|
x1, y1, w, h = tlwh
|
||||||
intbox = tuple(map(int, (x1, y1, x1 + w, y1 + h)))
|
intbox = tuple(map(int, (x1, y1, x1 + w, y1 + h)))
|
||||||
obj_id = int(obj_ids[i])
|
obj_id = int(obj_ids[i])
|
||||||
id_text = '{}'.format(int(obj_id))
|
id_text = '{}'.format(int(obj_id))
|
||||||
if ids2 is not None:
|
if ids2 is not None:
|
||||||
id_text = id_text + ', {}'.format(int(ids2[i]))
|
id_text = id_text + ', {}'.format(int(ids2[i]))
|
||||||
_line_thickness = 1 if obj_id <= 0 else line_thickness
|
_line_thickness = 1 if obj_id <= 0 else line_thickness
|
||||||
color = get_color(abs(obj_id))
|
color = get_color(abs(obj_id))
|
||||||
cv2.rectangle(im, intbox[0:2], intbox[2:4], color=color, thickness=line_thickness)
|
cv2.rectangle(im, intbox[0:2], intbox[2:4], color=color, thickness=line_thickness)
|
||||||
cv2.putText(im, id_text, (intbox[0], intbox[1] + 30), cv2.FONT_HERSHEY_PLAIN, text_scale, (0, 0, 255),
|
cv2.putText(im, id_text, (intbox[0], intbox[1] + 30), cv2.FONT_HERSHEY_PLAIN, text_scale, (0, 0, 255),
|
||||||
thickness=text_thickness)
|
thickness=text_thickness)
|
||||||
return im
|
return im
|
||||||
|
|
||||||
|
|
||||||
def plot_trajectory(image, tlwhs, track_ids):
|
def plot_trajectory(image, tlwhs, track_ids):
|
||||||
image = image.copy()
|
image = image.copy()
|
||||||
for one_tlwhs, track_id in zip(tlwhs, track_ids):
|
for one_tlwhs, track_id in zip(tlwhs, track_ids):
|
||||||
color = get_color(int(track_id))
|
color = get_color(int(track_id))
|
||||||
for tlwh in one_tlwhs:
|
for tlwh in one_tlwhs:
|
||||||
x1, y1, w, h = tuple(map(int, tlwh))
|
x1, y1, w, h = tuple(map(int, tlwh))
|
||||||
cv2.circle(image, (int(x1 + 0.5 * w), int(y1 + h)), 2, color, thickness=2)
|
cv2.circle(image, (int(x1 + 0.5 * w), int(y1 + h)), 2, color, thickness=2)
|
||||||
|
|
||||||
return image
|
return image
|
||||||
|
|
||||||
|
|
||||||
def plot_detections(image, tlbrs, scores=None, color=(255, 0, 0), ids=None):
|
def plot_detections(image, tlbrs, scores=None, color=(255, 0, 0), ids=None):
|
||||||
im = np.copy(image)
|
im = np.copy(image)
|
||||||
text_scale = max(1, image.shape[1] / 800.)
|
text_scale = max(1, image.shape[1] / 800.)
|
||||||
thickness = 2 if text_scale > 1.3 else 1
|
thickness = 2 if text_scale > 1.3 else 1
|
||||||
for i, det in enumerate(tlbrs):
|
for i, det in enumerate(tlbrs):
|
||||||
x1, y1, x2, y2 = np.asarray(det[:4], dtype=np.int)
|
x1, y1, x2, y2 = np.asarray(det[:4], dtype=np.int)
|
||||||
if len(det) >= 7:
|
if len(det) >= 7:
|
||||||
label = 'det' if det[5] > 0 else 'trk'
|
label = 'det' if det[5] > 0 else 'trk'
|
||||||
if ids is not None:
|
if ids is not None:
|
||||||
text = '{}# {:.2f}: {:d}'.format(label, det[6], ids[i])
|
text = '{}# {:.2f}: {:d}'.format(label, det[6], ids[i])
|
||||||
cv2.putText(im, text, (x1, y1 + 30), cv2.FONT_HERSHEY_PLAIN, text_scale, (0, 255, 255),
|
cv2.putText(im, text, (x1, y1 + 30), cv2.FONT_HERSHEY_PLAIN, text_scale, (0, 255, 255),
|
||||||
thickness=thickness)
|
thickness=thickness)
|
||||||
else:
|
else:
|
||||||
text = '{}# {:.2f}'.format(label, det[6])
|
text = '{}# {:.2f}'.format(label, det[6])
|
||||||
|
|
||||||
if scores is not None:
|
if scores is not None:
|
||||||
text = '{:.2f}'.format(scores[i])
|
text = '{:.2f}'.format(scores[i])
|
||||||
cv2.putText(im, text, (x1, y1 + 30), cv2.FONT_HERSHEY_PLAIN, text_scale, (0, 255, 255),
|
cv2.putText(im, text, (x1, y1 + 30), cv2.FONT_HERSHEY_PLAIN, text_scale, (0, 255, 255),
|
||||||
thickness=thickness)
|
thickness=thickness)
|
||||||
|
|
||||||
cv2.rectangle(im, (x1, y1), (x2, y2), color, 2)
|
cv2.rectangle(im, (x1, y1), (x2, y2), color, 2)
|
||||||
|
|
||||||
return im
|
return im
|
||||||
|
|
|
||||||
Loading…
Reference in a new issue