fix: broken Ups
Former-commit-id: 9c33326beb0d44e8491b040e25fad57ffa820076
This commit is contained in:
Your Name
parent
d1083513f9
commit
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1
.gitignore
vendored
1
.gitignore
vendored
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@ -3,3 +3,4 @@
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__pycache__/
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checkpoints/
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wandb/
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644
poetry.lock
generated
644
poetry.lock
generated
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@ -19,6 +19,36 @@ develop = ["pytest", "imgaug (>=0.4.0)"]
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imgaug = ["imgaug (>=0.4.0)"]
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tests = ["pytest"]
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[[package]]
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name = "appnope"
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version = "0.1.3"
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description = "Disable App Nap on macOS >= 10.9"
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category = "dev"
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optional = false
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python-versions = "*"
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[[package]]
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name = "asttokens"
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version = "2.0.5"
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description = "Annotate AST trees with source code positions"
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category = "dev"
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optional = false
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python-versions = "*"
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[package.dependencies]
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six = "*"
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[package.extras]
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test = ["astroid", "pytest"]
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[[package]]
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name = "backcall"
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version = "0.2.0"
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description = "Specifications for callback functions passed in to an API"
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category = "dev"
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optional = false
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python-versions = "*"
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[[package]]
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name = "black"
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version = "22.3.0"
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@ -49,6 +79,17 @@ category = "main"
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optional = false
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python-versions = ">=3.6"
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[[package]]
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name = "cffi"
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version = "1.15.0"
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description = "Foreign Function Interface for Python calling C code."
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category = "dev"
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optional = false
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python-versions = "*"
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[package.dependencies]
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pycparser = "*"
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[[package]]
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name = "charset-normalizer"
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version = "2.0.12"
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@ -87,6 +128,22 @@ category = "main"
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optional = false
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python-versions = ">=3.6"
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[[package]]
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name = "debugpy"
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version = "1.6.0"
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description = "An implementation of the Debug Adapter Protocol for Python"
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category = "dev"
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optional = false
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python-versions = ">=3.7"
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[[package]]
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name = "decorator"
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version = "5.1.1"
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description = "Decorators for Humans"
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category = "dev"
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optional = false
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python-versions = ">=3.5"
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[[package]]
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name = "docker-pycreds"
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version = "0.4.0"
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@ -98,6 +155,22 @@ python-versions = "*"
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[package.dependencies]
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six = ">=1.4.0"
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[[package]]
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name = "entrypoints"
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version = "0.4"
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description = "Discover and load entry points from installed packages."
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category = "dev"
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optional = false
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python-versions = ">=3.6"
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[[package]]
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name = "executing"
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version = "0.8.3"
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description = "Get the currently executing AST node of a frame, and other information"
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category = "dev"
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optional = false
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python-versions = "*"
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[[package]]
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name = "fonttools"
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version = "4.33.3"
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@ -179,11 +252,70 @@ pyav = ["av"]
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test = ["invoke", "pytest", "pytest-cov", "fsspec"]
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tifffile = ["tifffile"]
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[[package]]
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name = "ipykernel"
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version = "6.15.0"
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description = "IPython Kernel for Jupyter"
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category = "dev"
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optional = false
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python-versions = ">=3.7"
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[package.dependencies]
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appnope = {version = "*", markers = "platform_system == \"Darwin\""}
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debugpy = ">=1.0"
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ipython = ">=7.23.1"
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jupyter-client = ">=6.1.12"
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matplotlib-inline = ">=0.1"
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nest-asyncio = "*"
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packaging = "*"
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psutil = "*"
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pyzmq = ">=17"
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tornado = ">=6.1"
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traitlets = ">=5.1.0"
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[package.extras]
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test = ["flaky", "ipyparallel", "pre-commit", "pytest-cov", "pytest-timeout", "pytest (>=6.0)"]
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[[package]]
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name = "ipython"
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version = "8.4.0"
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description = "IPython: Productive Interactive Computing"
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category = "dev"
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optional = false
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python-versions = ">=3.8"
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[package.dependencies]
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appnope = {version = "*", markers = "sys_platform == \"darwin\""}
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backcall = "*"
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colorama = {version = "*", markers = "sys_platform == \"win32\""}
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decorator = "*"
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jedi = ">=0.16"
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matplotlib-inline = "*"
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pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
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pickleshare = "*"
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prompt-toolkit = ">=2.0.0,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.1.0"
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pygments = ">=2.4.0"
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stack-data = "*"
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traitlets = ">=5"
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[package.extras]
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all = ["black", "Sphinx (>=1.3)", "ipykernel", "nbconvert", "nbformat", "ipywidgets", "notebook", "ipyparallel", "qtconsole", "pytest (<7.1)", "pytest-asyncio", "testpath", "curio", "matplotlib (!=3.2.0)", "numpy (>=1.19)", "pandas", "trio"]
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black = ["black"]
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doc = ["Sphinx (>=1.3)"]
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kernel = ["ipykernel"]
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nbconvert = ["nbconvert"]
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nbformat = ["nbformat"]
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notebook = ["ipywidgets", "notebook"]
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parallel = ["ipyparallel"]
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qtconsole = ["qtconsole"]
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test = ["pytest (<7.1)", "pytest-asyncio", "testpath"]
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test_extra = ["pytest (<7.1)", "pytest-asyncio", "testpath", "curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.19)", "pandas", "trio"]
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[[package]]
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name = "isort"
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version = "5.10.1"
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description = "A Python utility / library to sort Python imports."
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category = "main"
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category = "dev"
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optional = false
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python-versions = ">=3.6.1,<4.0"
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@ -193,6 +325,21 @@ requirements_deprecated_finder = ["pipreqs", "pip-api"]
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colors = ["colorama (>=0.4.3,<0.5.0)"]
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plugins = ["setuptools"]
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[[package]]
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name = "jedi"
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version = "0.18.1"
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description = "An autocompletion tool for Python that can be used for text editors."
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category = "dev"
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optional = false
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python-versions = ">=3.6"
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[package.dependencies]
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parso = ">=0.8.0,<0.9.0"
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[package.extras]
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qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
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testing = ["Django (<3.1)", "colorama", "docopt", "pytest (<7.0.0)"]
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[[package]]
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name = "joblib"
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version = "1.1.0"
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@ -201,6 +348,42 @@ category = "main"
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optional = false
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python-versions = ">=3.6"
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[[package]]
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name = "jupyter-client"
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version = "7.3.4"
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description = "Jupyter protocol implementation and client libraries"
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category = "dev"
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optional = false
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python-versions = ">=3.7"
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[package.dependencies]
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entrypoints = "*"
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jupyter-core = ">=4.9.2"
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nest-asyncio = ">=1.5.4"
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python-dateutil = ">=2.8.2"
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pyzmq = ">=23.0"
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tornado = ">=6.0"
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traitlets = "*"
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[package.extras]
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doc = ["ipykernel", "myst-parser", "sphinx-rtd-theme", "sphinx (>=1.3.6)", "sphinxcontrib-github-alt"]
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test = ["codecov", "coverage", "ipykernel (>=6.5)", "ipython", "mypy", "pre-commit", "pytest", "pytest-asyncio (>=0.18)", "pytest-cov", "pytest-timeout"]
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[[package]]
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name = "jupyter-core"
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version = "4.10.0"
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description = "Jupyter core package. A base package on which Jupyter projects rely."
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category = "dev"
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optional = false
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python-versions = ">=3.7"
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[package.dependencies]
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pywin32 = {version = ">=1.0", markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""}
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traitlets = "*"
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[package.extras]
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test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
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[[package]]
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name = "kiwisolver"
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version = "1.4.3"
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@ -228,6 +411,17 @@ pyparsing = ">=2.2.1"
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python-dateutil = ">=2.7"
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setuptools_scm = ">=4"
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[[package]]
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name = "matplotlib-inline"
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version = "0.1.3"
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description = "Inline Matplotlib backend for Jupyter"
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category = "dev"
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optional = false
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python-versions = ">=3.5"
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[package.dependencies]
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traitlets = "*"
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[[package]]
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name = "mypy-extensions"
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version = "0.4.3"
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@ -236,6 +430,14 @@ category = "dev"
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optional = false
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python-versions = "*"
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[[package]]
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name = "nest-asyncio"
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version = "1.5.5"
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description = "Patch asyncio to allow nested event loops"
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category = "dev"
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optional = false
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python-versions = ">=3.5"
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[[package]]
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name = "networkx"
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version = "2.8.4"
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@ -286,6 +488,18 @@ python-versions = ">=3.6"
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[package.dependencies]
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pyparsing = ">=2.0.2,<3.0.5 || >3.0.5"
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[[package]]
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name = "parso"
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version = "0.8.3"
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description = "A Python Parser"
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category = "dev"
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optional = false
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python-versions = ">=3.6"
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[package.extras]
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qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
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testing = ["docopt", "pytest (<6.0.0)"]
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[[package]]
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name = "pathspec"
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version = "0.9.0"
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@ -302,6 +516,25 @@ category = "main"
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optional = false
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python-versions = "*"
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[[package]]
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name = "pexpect"
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version = "4.8.0"
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description = "Pexpect allows easy control of interactive console applications."
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category = "dev"
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optional = false
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python-versions = "*"
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[package.dependencies]
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ptyprocess = ">=0.5"
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[[package]]
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name = "pickleshare"
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version = "0.7.5"
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description = "Tiny 'shelve'-like database with concurrency support"
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category = "dev"
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optional = false
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python-versions = "*"
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[[package]]
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name = "pillow"
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version = "9.1.1"
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@ -340,6 +573,17 @@ six = "*"
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[package.extras]
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test = ["pytest (>=2.7.3)", "pytest-cov", "coveralls", "futures", "pytest-benchmark", "mock"]
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[[package]]
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name = "prompt-toolkit"
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version = "3.0.30"
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description = "Library for building powerful interactive command lines in Python"
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category = "dev"
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optional = false
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python-versions = ">=3.6.2"
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[package.dependencies]
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wcwidth = "*"
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[[package]]
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name = "protobuf"
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version = "3.20.1"
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@ -359,6 +603,49 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
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[package.extras]
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test = ["ipaddress", "mock", "enum34", "pywin32", "wmi"]
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[[package]]
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name = "ptyprocess"
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version = "0.7.0"
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description = "Run a subprocess in a pseudo terminal"
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category = "dev"
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optional = false
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python-versions = "*"
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[[package]]
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name = "pure-eval"
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version = "0.2.2"
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description = "Safely evaluate AST nodes without side effects"
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category = "dev"
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optional = false
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python-versions = "*"
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[package.extras]
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tests = ["pytest"]
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[[package]]
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name = "py"
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version = "1.11.0"
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description = "library with cross-python path, ini-parsing, io, code, log facilities"
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category = "dev"
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optional = false
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python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
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[[package]]
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name = "pycparser"
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version = "2.21"
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description = "C parser in Python"
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category = "dev"
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optional = false
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python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
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[[package]]
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name = "pygments"
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version = "2.12.0"
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description = "Pygments is a syntax highlighting package written in Python."
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category = "dev"
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optional = false
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python-versions = ">=3.6"
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[[package]]
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name = "pyparsing"
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version = "3.0.9"
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@ -392,6 +679,14 @@ python-versions = ">=3.7"
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[package.dependencies]
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numpy = ">=1.17.3"
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[[package]]
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name = "pywin32"
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version = "304"
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description = "Python for Window Extensions"
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category = "dev"
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optional = false
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python-versions = "*"
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[[package]]
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name = "pyyaml"
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version = "6.0"
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@ -400,6 +695,18 @@ category = "main"
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optional = false
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python-versions = ">=3.6"
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[[package]]
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name = "pyzmq"
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version = "23.2.0"
|
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description = "Python bindings for 0MQ"
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category = "dev"
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optional = false
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python-versions = ">=3.6"
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[package.dependencies]
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cffi = {version = "*", markers = "implementation_name == \"pypy\""}
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py = {version = "*", markers = "implementation_name == \"pypy\""}
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[[package]]
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name = "qudida"
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version = "0.0.4"
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@ -569,6 +876,22 @@ category = "main"
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optional = false
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python-versions = ">=3.6"
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[[package]]
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name = "stack-data"
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version = "0.3.0"
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description = "Extract data from python stack frames and tracebacks for informative displays"
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category = "dev"
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optional = false
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python-versions = "*"
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[package.dependencies]
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asttokens = "*"
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executing = "*"
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pure-eval = "*"
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[package.extras]
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tests = ["pytest", "typeguard", "pygments", "littleutils", "cython"]
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[[package]]
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name = "threadpoolctl"
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version = "3.1.0"
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@ -628,6 +951,14 @@ typing-extensions = "*"
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[package.extras]
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scipy = ["scipy"]
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[[package]]
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name = "tornado"
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version = "6.1"
|
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description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
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category = "dev"
|
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optional = false
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python-versions = ">= 3.5"
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|
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[[package]]
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name = "tqdm"
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version = "4.64.0"
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@ -645,6 +976,17 @@ notebook = ["ipywidgets (>=6)"]
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slack = ["slack-sdk"]
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telegram = ["requests"]
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|
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[[package]]
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name = "traitlets"
|
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version = "5.3.0"
|
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description = ""
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category = "dev"
|
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optional = false
|
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python-versions = ">=3.7"
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|
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[package.extras]
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test = ["pre-commit", "pytest"]
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[[package]]
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name = "typing-extensions"
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version = "4.2.0"
|
||||
|
|
@ -699,16 +1041,36 @@ launch = ["nbconvert", "nbformat", "chardet", "iso8601", "typing-extensions", "b
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|||
media = ["numpy", "moviepy", "pillow", "bokeh", "soundfile", "plotly", "rdkit-pypi"]
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sweeps = ["sweeps (>=0.1.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "wcwidth"
|
||||
version = "0.2.5"
|
||||
description = "Measures the displayed width of unicode strings in a terminal"
|
||||
category = "dev"
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||||
optional = false
|
||||
python-versions = "*"
|
||||
|
||||
[metadata]
|
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lock-version = "1.1"
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python-versions = ">=3.8,<3.11"
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content-hash = "c176396881b9758ed2f1e14e68ca042ea676d349ff4e8100cae92ca8220046be"
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content-hash = "9f0407f3a873baf75816add27d7eeb538a83f688e470743c0d4a5660dc4470c0"
|
||||
|
||||
[metadata.files]
|
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albumentations = [
|
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{file = "albumentations-1.2.0-py3-none-any.whl", hash = "sha256:19d42158529a5382d9049258b52c35fa7d84b97b248c50d5b9bfa0948005ba4a"},
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appnope = [
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black = [
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@ -738,6 +1100,58 @@ certifi = [
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cffi = [
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{file = "cffi-1.15.0.tar.gz", hash = "sha256:920f0d66a896c2d99f0adbb391f990a84091179542c205fa53ce5787aff87954"},
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]
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charset-normalizer = [
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{file = "charset-normalizer-2.0.12.tar.gz", hash = "sha256:2857e29ff0d34db842cd7ca3230549d1a697f96ee6d3fb071cfa6c7393832597"},
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@ -754,10 +1168,42 @@ cycler = [
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decorator = [
|
||||
{file = "decorator-5.1.1-py3-none-any.whl", hash = "sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186"},
|
||||
{file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"},
|
||||
]
|
||||
docker-pycreds = [
|
||||
{file = "docker-pycreds-0.4.0.tar.gz", hash = "sha256:6ce3270bcaf404cc4c3e27e4b6c70d3521deae82fb508767870fdbf772d584d4"},
|
||||
{file = "docker_pycreds-0.4.0-py2.py3-none-any.whl", hash = "sha256:7266112468627868005106ec19cd0d722702d2b7d5912a28e19b826c3d37af49"},
|
||||
]
|
||||
entrypoints = [
|
||||
{file = "entrypoints-0.4-py3-none-any.whl", hash = "sha256:f174b5ff827504fd3cd97cc3f8649f3693f51538c7e4bdf3ef002c8429d42f9f"},
|
||||
{file = "entrypoints-0.4.tar.gz", hash = "sha256:b706eddaa9218a19ebcd67b56818f05bb27589b1ca9e8d797b74affad4ccacd4"},
|
||||
]
|
||||
executing = [
|
||||
{file = "executing-0.8.3-py2.py3-none-any.whl", hash = "sha256:d1eef132db1b83649a3905ca6dd8897f71ac6f8cac79a7e58a1a09cf137546c9"},
|
||||
{file = "executing-0.8.3.tar.gz", hash = "sha256:c6554e21c6b060590a6d3be4b82fb78f8f0194d809de5ea7df1c093763311501"},
|
||||
]
|
||||
fonttools = [
|
||||
{file = "fonttools-4.33.3-py3-none-any.whl", hash = "sha256:f829c579a8678fa939a1d9e9894d01941db869de44390adb49ce67055a06cc2a"},
|
||||
{file = "fonttools-4.33.3.zip", hash = "sha256:c0fdcfa8ceebd7c1b2021240bd46ef77aa8e7408cf10434be55df52384865f8e"},
|
||||
|
|
@ -778,14 +1224,34 @@ imageio = [
|
|||
{file = "imageio-2.19.3-py3-none-any.whl", hash = "sha256:d36ab8616175a093676693a4dbc85c6cc767f981c9ce93041422569c76d06347"},
|
||||
{file = "imageio-2.19.3.tar.gz", hash = "sha256:0c9df80e42f2ee68bea92001e7fcf612aa149910efe040eb757f5ce323250ae1"},
|
||||
]
|
||||
ipykernel = [
|
||||
{file = "ipykernel-6.15.0-py3-none-any.whl", hash = "sha256:b9ed519a29eb819eb82e87e0d3754088237b233e5c647b8bb0ff23c8c70ed16f"},
|
||||
{file = "ipykernel-6.15.0.tar.gz", hash = "sha256:b59f9d9672c3a483494bb75915a2b315e78b833a38b039b1ee36dc28683f0d89"},
|
||||
]
|
||||
ipython = [
|
||||
{file = "ipython-8.4.0-py3-none-any.whl", hash = "sha256:7ca74052a38fa25fe9bedf52da0be7d3fdd2fb027c3b778ea78dfe8c212937d1"},
|
||||
{file = "ipython-8.4.0.tar.gz", hash = "sha256:f2db3a10254241d9b447232cec8b424847f338d9d36f9a577a6192c332a46abd"},
|
||||
]
|
||||
isort = [
|
||||
{file = "isort-5.10.1-py3-none-any.whl", hash = "sha256:6f62d78e2f89b4500b080fe3a81690850cd254227f27f75c3a0c491a1f351ba7"},
|
||||
{file = "isort-5.10.1.tar.gz", hash = "sha256:e8443a5e7a020e9d7f97f1d7d9cd17c88bcb3bc7e218bf9cf5095fe550be2951"},
|
||||
]
|
||||
jedi = [
|
||||
{file = "jedi-0.18.1-py2.py3-none-any.whl", hash = "sha256:637c9635fcf47945ceb91cd7f320234a7be540ded6f3e99a50cb6febdfd1ba8d"},
|
||||
{file = "jedi-0.18.1.tar.gz", hash = "sha256:74137626a64a99c8eb6ae5832d99b3bdd7d29a3850fe2aa80a4126b2a7d949ab"},
|
||||
]
|
||||
joblib = [
|
||||
{file = "joblib-1.1.0-py2.py3-none-any.whl", hash = "sha256:f21f109b3c7ff9d95f8387f752d0d9c34a02aa2f7060c2135f465da0e5160ff6"},
|
||||
{file = "joblib-1.1.0.tar.gz", hash = "sha256:4158fcecd13733f8be669be0683b96ebdbbd38d23559f54dca7205aea1bf1e35"},
|
||||
]
|
||||
jupyter-client = [
|
||||
{file = "jupyter_client-7.3.4-py3-none-any.whl", hash = "sha256:17d74b0d0a7b24f1c8c527b24fcf4607c56bee542ffe8e3418e50b21e514b621"},
|
||||
{file = "jupyter_client-7.3.4.tar.gz", hash = "sha256:aa9a6c32054b290374f95f73bb0cae91455c58dfb84f65c8591912b8f65e6d56"},
|
||||
]
|
||||
jupyter-core = [
|
||||
{file = "jupyter_core-4.10.0-py3-none-any.whl", hash = "sha256:e7f5212177af7ab34179690140f188aa9bf3d322d8155ed972cbded19f55b6f3"},
|
||||
{file = "jupyter_core-4.10.0.tar.gz", hash = "sha256:a6de44b16b7b31d7271130c71a6792c4040f077011961138afed5e5e73181aec"},
|
||||
]
|
||||
kiwisolver = [
|
||||
{file = "kiwisolver-1.4.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:fd2842a0faed9ab9aba0922c951906132d9384be89690570f0ed18cd4f20e658"},
|
||||
{file = "kiwisolver-1.4.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:caa59e2cae0e23b1e225447d7a9ddb0f982f42a6a22d497a484dfe62a06f7c0e"},
|
||||
|
|
@ -868,10 +1334,18 @@ matplotlib = [
|
|||
{file = "matplotlib-3.5.2-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:24173c23d1bcbaed5bf47b8785d27933a1ac26a5d772200a0f3e0e38f471b001"},
|
||||
{file = "matplotlib-3.5.2.tar.gz", hash = "sha256:48cf850ce14fa18067f2d9e0d646763681948487a8080ec0af2686468b4607a2"},
|
||||
]
|
||||
matplotlib-inline = [
|
||||
{file = "matplotlib-inline-0.1.3.tar.gz", hash = "sha256:a04bfba22e0d1395479f866853ec1ee28eea1485c1d69a6faf00dc3e24ff34ee"},
|
||||
{file = "matplotlib_inline-0.1.3-py3-none-any.whl", hash = "sha256:aed605ba3b72462d64d475a21a9296f400a19c4f74a31b59103d2a99ffd5aa5c"},
|
||||
]
|
||||
mypy-extensions = [
|
||||
{file = "mypy_extensions-0.4.3-py2.py3-none-any.whl", hash = "sha256:090fedd75945a69ae91ce1303b5824f428daf5a028d2f6ab8a299250a846f15d"},
|
||||
{file = "mypy_extensions-0.4.3.tar.gz", hash = "sha256:2d82818f5bb3e369420cb3c4060a7970edba416647068eb4c5343488a6c604a8"},
|
||||
]
|
||||
nest-asyncio = [
|
||||
{file = "nest_asyncio-1.5.5-py3-none-any.whl", hash = "sha256:b98e3ec1b246135e4642eceffa5a6c23a3ab12c82ff816a92c612d68205813b2"},
|
||||
{file = "nest_asyncio-1.5.5.tar.gz", hash = "sha256:e442291cd942698be619823a17a86a5759eabe1f8613084790de189fe9e16d65"},
|
||||
]
|
||||
networkx = [
|
||||
{file = "networkx-2.8.4-py3-none-any.whl", hash = "sha256:6933b9b3174a0bdf03c911bb4a1ee43a86ce3edeb813e37e1d4c553b3f4a2c4f"},
|
||||
{file = "networkx-2.8.4.tar.gz", hash = "sha256:5e53f027c0d567cf1f884dbb283224df525644e43afd1145d64c9d88a3584762"},
|
||||
|
|
@ -913,6 +1387,10 @@ packaging = [
|
|||
{file = "packaging-21.3-py3-none-any.whl", hash = "sha256:ef103e05f519cdc783ae24ea4e2e0f508a9c99b2d4969652eed6a2e1ea5bd522"},
|
||||
{file = "packaging-21.3.tar.gz", hash = "sha256:dd47c42927d89ab911e606518907cc2d3a1f38bbd026385970643f9c5b8ecfeb"},
|
||||
]
|
||||
parso = [
|
||||
{file = "parso-0.8.3-py2.py3-none-any.whl", hash = "sha256:c001d4636cd3aecdaf33cbb40aebb59b094be2a74c556778ef5576c175e19e75"},
|
||||
{file = "parso-0.8.3.tar.gz", hash = "sha256:8c07be290bb59f03588915921e29e8a50002acaf2cdc5fa0e0114f91709fafa0"},
|
||||
]
|
||||
pathspec = [
|
||||
{file = "pathspec-0.9.0-py2.py3-none-any.whl", hash = "sha256:7d15c4ddb0b5c802d161efc417ec1a2558ea2653c2e8ad9c19098201dc1c993a"},
|
||||
{file = "pathspec-0.9.0.tar.gz", hash = "sha256:e564499435a2673d586f6b2130bb5b95f04a3ba06f81b8f895b651a3c76aabb1"},
|
||||
|
|
@ -920,6 +1398,14 @@ pathspec = [
|
|||
pathtools = [
|
||||
{file = "pathtools-0.1.2.tar.gz", hash = "sha256:7c35c5421a39bb82e58018febd90e3b6e5db34c5443aaaf742b3f33d4655f1c0"},
|
||||
]
|
||||
pexpect = [
|
||||
{file = "pexpect-4.8.0-py2.py3-none-any.whl", hash = "sha256:0b48a55dcb3c05f3329815901ea4fc1537514d6ba867a152b581d69ae3710937"},
|
||||
{file = "pexpect-4.8.0.tar.gz", hash = "sha256:fc65a43959d153d0114afe13997d439c22823a27cefceb5ff35c2178c6784c0c"},
|
||||
]
|
||||
pickleshare = [
|
||||
{file = "pickleshare-0.7.5-py2.py3-none-any.whl", hash = "sha256:9649af414d74d4df115d5d718f82acb59c9d418196b7b4290ed47a12ce62df56"},
|
||||
{file = "pickleshare-0.7.5.tar.gz", hash = "sha256:87683d47965c1da65cdacaf31c8441d12b8044cdec9aca500cd78fc2c683afca"},
|
||||
]
|
||||
pillow = [
|
||||
{file = "Pillow-9.1.1-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:42dfefbef90eb67c10c45a73a9bc1599d4dac920f7dfcbf4ec6b80cb620757fe"},
|
||||
{file = "Pillow-9.1.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ffde4c6fabb52891d81606411cbfaf77756e3b561b566efd270b3ed3791fde4e"},
|
||||
|
|
@ -967,6 +1453,10 @@ platformdirs = [
|
|||
promise = [
|
||||
{file = "promise-2.3.tar.gz", hash = "sha256:dfd18337c523ba4b6a58801c164c1904a9d4d1b1747c7d5dbf45b693a49d93d0"},
|
||||
]
|
||||
prompt-toolkit = [
|
||||
{file = "prompt_toolkit-3.0.30-py3-none-any.whl", hash = "sha256:d8916d3f62a7b67ab353a952ce4ced6a1d2587dfe9ef8ebc30dd7c386751f289"},
|
||||
{file = "prompt_toolkit-3.0.30.tar.gz", hash = "sha256:859b283c50bde45f5f97829f77a4674d1c1fcd88539364f1b28a37805cfd89c0"},
|
||||
]
|
||||
protobuf = [
|
||||
{file = "protobuf-3.20.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:3cc797c9d15d7689ed507b165cd05913acb992d78b379f6014e013f9ecb20996"},
|
||||
{file = "protobuf-3.20.1-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:ff8d8fa42675249bb456f5db06c00de6c2f4c27a065955917b28c4f15978b9c3"},
|
||||
|
|
@ -1027,6 +1517,26 @@ psutil = [
|
|||
{file = "psutil-5.9.1-cp39-cp39-win_amd64.whl", hash = "sha256:f65f9a46d984b8cd9b3750c2bdb419b2996895b005aefa6cbaba9a143b1ce2c5"},
|
||||
{file = "psutil-5.9.1.tar.gz", hash = "sha256:57f1819b5d9e95cdfb0c881a8a5b7d542ed0b7c522d575706a80bedc848c8954"},
|
||||
]
|
||||
ptyprocess = [
|
||||
{file = "ptyprocess-0.7.0-py2.py3-none-any.whl", hash = "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35"},
|
||||
{file = "ptyprocess-0.7.0.tar.gz", hash = "sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220"},
|
||||
]
|
||||
pure-eval = [
|
||||
{file = "pure_eval-0.2.2-py3-none-any.whl", hash = "sha256:01eaab343580944bc56080ebe0a674b39ec44a945e6d09ba7db3cb8cec289350"},
|
||||
{file = "pure_eval-0.2.2.tar.gz", hash = "sha256:2b45320af6dfaa1750f543d714b6d1c520a1688dec6fd24d339063ce0aaa9ac3"},
|
||||
]
|
||||
py = [
|
||||
{file = "py-1.11.0-py2.py3-none-any.whl", hash = "sha256:607c53218732647dff4acdfcd50cb62615cedf612e72d1724fb1a0cc6405b378"},
|
||||
{file = "py-1.11.0.tar.gz", hash = "sha256:51c75c4126074b472f746a24399ad32f6053d1b34b68d2fa41e558e6f4a98719"},
|
||||
]
|
||||
pycparser = [
|
||||
{file = "pycparser-2.21-py2.py3-none-any.whl", hash = "sha256:8ee45429555515e1f6b185e78100aea234072576aa43ab53aefcae078162fca9"},
|
||||
{file = "pycparser-2.21.tar.gz", hash = "sha256:e644fdec12f7872f86c58ff790da456218b10f863970249516d60a5eaca77206"},
|
||||
]
|
||||
pygments = [
|
||||
{file = "Pygments-2.12.0-py3-none-any.whl", hash = "sha256:dc9c10fb40944260f6ed4c688ece0cd2048414940f1cea51b8b226318411c519"},
|
||||
{file = "Pygments-2.12.0.tar.gz", hash = "sha256:5eb116118f9612ff1ee89ac96437bb6b49e8f04d8a13b514ba26f620208e26eb"},
|
||||
]
|
||||
pyparsing = [
|
||||
{file = "pyparsing-3.0.9-py3-none-any.whl", hash = "sha256:5026bae9a10eeaefb61dab2f09052b9f4307d44aee4eda64b309723d8d206bbc"},
|
||||
{file = "pyparsing-3.0.9.tar.gz", hash = "sha256:2b020ecf7d21b687f219b71ecad3631f644a47f01403fa1d1036b0c6416d70fb"},
|
||||
|
|
@ -1067,6 +1577,22 @@ pywavelets = [
|
|||
{file = "PyWavelets-1.3.0-cp39-cp39-win_amd64.whl", hash = "sha256:a354979e2ee8cd71a8952ded381f3d9f981692b73c6842bcc6c9f64047e0a5be"},
|
||||
{file = "PyWavelets-1.3.0.tar.gz", hash = "sha256:cbaa9d62052d9daf8da765fc8e7c30c38ea2b8e9e1c18841913dfb4aec671ee5"},
|
||||
]
|
||||
pywin32 = [
|
||||
{file = "pywin32-304-cp310-cp310-win32.whl", hash = "sha256:3c7bacf5e24298c86314f03fa20e16558a4e4138fc34615d7de4070c23e65af3"},
|
||||
{file = "pywin32-304-cp310-cp310-win_amd64.whl", hash = "sha256:4f32145913a2447736dad62495199a8e280a77a0ca662daa2332acf849f0be48"},
|
||||
{file = "pywin32-304-cp310-cp310-win_arm64.whl", hash = "sha256:d3ee45adff48e0551d1aa60d2ec066fec006083b791f5c3527c40cd8aefac71f"},
|
||||
{file = "pywin32-304-cp311-cp311-win32.whl", hash = "sha256:30c53d6ce44c12a316a06c153ea74152d3b1342610f1b99d40ba2795e5af0269"},
|
||||
{file = "pywin32-304-cp311-cp311-win_amd64.whl", hash = "sha256:7ffa0c0fa4ae4077e8b8aa73800540ef8c24530057768c3ac57c609f99a14fd4"},
|
||||
{file = "pywin32-304-cp311-cp311-win_arm64.whl", hash = "sha256:cbbe34dad39bdbaa2889a424d28752f1b4971939b14b1bb48cbf0182a3bcfc43"},
|
||||
{file = "pywin32-304-cp36-cp36m-win32.whl", hash = "sha256:be253e7b14bc601718f014d2832e4c18a5b023cbe72db826da63df76b77507a1"},
|
||||
{file = "pywin32-304-cp36-cp36m-win_amd64.whl", hash = "sha256:de9827c23321dcf43d2f288f09f3b6d772fee11e809015bdae9e69fe13213988"},
|
||||
{file = "pywin32-304-cp37-cp37m-win32.whl", hash = "sha256:f64c0377cf01b61bd5e76c25e1480ca8ab3b73f0c4add50538d332afdf8f69c5"},
|
||||
{file = "pywin32-304-cp37-cp37m-win_amd64.whl", hash = "sha256:bb2ea2aa81e96eee6a6b79d87e1d1648d3f8b87f9a64499e0b92b30d141e76df"},
|
||||
{file = "pywin32-304-cp38-cp38-win32.whl", hash = "sha256:94037b5259701988954931333aafd39cf897e990852115656b014ce72e052e96"},
|
||||
{file = "pywin32-304-cp38-cp38-win_amd64.whl", hash = "sha256:ead865a2e179b30fb717831f73cf4373401fc62fbc3455a0889a7ddac848f83e"},
|
||||
{file = "pywin32-304-cp39-cp39-win32.whl", hash = "sha256:25746d841201fd9f96b648a248f731c1dec851c9a08b8e33da8b56148e4c65cc"},
|
||||
{file = "pywin32-304-cp39-cp39-win_amd64.whl", hash = "sha256:d24a3382f013b21aa24a5cfbfad5a2cd9926610c0affde3e8ab5b3d7dbcf4ac9"},
|
||||
]
|
||||
pyyaml = [
|
||||
{file = "PyYAML-6.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d4db7c7aef085872ef65a8fd7d6d09a14ae91f691dec3e87ee5ee0539d516f53"},
|
||||
{file = "PyYAML-6.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9df7ed3b3d2e0ecfe09e14741b857df43adb5a3ddadc919a2d94fbdf78fea53c"},
|
||||
|
|
@ -1102,6 +1628,65 @@ pyyaml = [
|
|||
{file = "PyYAML-6.0-cp39-cp39-win_amd64.whl", hash = "sha256:b3d267842bf12586ba6c734f89d1f5b871df0273157918b0ccefa29deb05c21c"},
|
||||
{file = "PyYAML-6.0.tar.gz", hash = "sha256:68fb519c14306fec9720a2a5b45bc9f0c8d1b9c72adf45c37baedfcd949c35a2"},
|
||||
]
|
||||
pyzmq = [
|
||||
{file = "pyzmq-23.2.0-cp310-cp310-macosx_10_15_universal2.whl", hash = "sha256:22ac0243a41798e3eb5d5714b28c2f28e3d10792dffbc8a5fca092f975fdeceb"},
|
||||
{file = "pyzmq-23.2.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:f685003d836ad0e5d4f08d1e024ee3ac7816eb2f873b2266306eef858f058133"},
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||||
{file = "tornado-6.1-cp38-cp38-manylinux1_i686.whl", hash = "sha256:c77da1263aa361938476f04c4b6c8916001b90b2c2fdd92d8d535e1af48fba5a"},
|
||||
{file = "tornado-6.1-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:fba85b6cd9c39be262fcd23865652920832b61583de2a2ca907dbd8e8a8c81e5"},
|
||||
{file = "tornado-6.1-cp38-cp38-manylinux2010_i686.whl", hash = "sha256:1e8225a1070cd8eec59a996c43229fe8f95689cb16e552d130b9793cb570a288"},
|
||||
{file = "tornado-6.1-cp38-cp38-manylinux2010_x86_64.whl", hash = "sha256:d14d30e7f46a0476efb0deb5b61343b1526f73ebb5ed84f23dc794bdb88f9d9f"},
|
||||
{file = "tornado-6.1-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:8f959b26f2634a091bb42241c3ed8d3cedb506e7c27b8dd5c7b9f745318ddbb6"},
|
||||
{file = "tornado-6.1-cp38-cp38-win32.whl", hash = "sha256:34ca2dac9e4d7afb0bed4677512e36a52f09caa6fded70b4e3e1c89dbd92c326"},
|
||||
{file = "tornado-6.1-cp38-cp38-win_amd64.whl", hash = "sha256:6196a5c39286cc37c024cd78834fb9345e464525d8991c21e908cc046d1cc02c"},
|
||||
{file = "tornado-6.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:f0ba29bafd8e7e22920567ce0d232c26d4d47c8b5cf4ed7b562b5db39fa199c5"},
|
||||
{file = "tornado-6.1-cp39-cp39-manylinux1_i686.whl", hash = "sha256:33892118b165401f291070100d6d09359ca74addda679b60390b09f8ef325ffe"},
|
||||
{file = "tornado-6.1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:7da13da6f985aab7f6f28debab00c67ff9cbacd588e8477034c0652ac141feea"},
|
||||
{file = "tornado-6.1-cp39-cp39-manylinux2010_i686.whl", hash = "sha256:e0791ac58d91ac58f694d8d2957884df8e4e2f6687cdf367ef7eb7497f79eaa2"},
|
||||
{file = "tornado-6.1-cp39-cp39-manylinux2010_x86_64.whl", hash = "sha256:66324e4e1beede9ac79e60f88de548da58b1f8ab4b2f1354d8375774f997e6c0"},
|
||||
{file = "tornado-6.1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:a48900ecea1cbb71b8c71c620dee15b62f85f7c14189bdeee54966fbd9a0c5bd"},
|
||||
{file = "tornado-6.1-cp39-cp39-win32.whl", hash = "sha256:d3d20ea5782ba63ed13bc2b8c291a053c8d807a8fa927d941bd718468f7b950c"},
|
||||
{file = "tornado-6.1-cp39-cp39-win_amd64.whl", hash = "sha256:548430be2740e327b3fe0201abe471f314741efcb0067ec4f2d7dcfb4825f3e4"},
|
||||
{file = "tornado-6.1.tar.gz", hash = "sha256:33c6e81d7bd55b468d2e793517c909b139960b6c790a60b7991b9b6b76fb9791"},
|
||||
]
|
||||
tqdm = [
|
||||
{file = "tqdm-4.64.0-py2.py3-none-any.whl", hash = "sha256:74a2cdefe14d11442cedf3ba4e21a3b84ff9a2dbdc6cfae2c34addb2a14a5ea6"},
|
||||
{file = "tqdm-4.64.0.tar.gz", hash = "sha256:40be55d30e200777a307a7585aee69e4eabb46b4ec6a4b4a5f2d9f11e7d5408d"},
|
||||
]
|
||||
traitlets = [
|
||||
{file = "traitlets-5.3.0-py3-none-any.whl", hash = "sha256:65fa18961659635933100db8ca120ef6220555286949774b9cfc106f941d1c7a"},
|
||||
{file = "traitlets-5.3.0.tar.gz", hash = "sha256:0bb9f1f9f017aa8ec187d8b1b2a7a6626a2a1d877116baba52a129bfa124f8e2"},
|
||||
]
|
||||
typing-extensions = [
|
||||
{file = "typing_extensions-4.2.0-py3-none-any.whl", hash = "sha256:6657594ee297170d19f67d55c05852a874e7eb634f4f753dbd667855e07c1708"},
|
||||
{file = "typing_extensions-4.2.0.tar.gz", hash = "sha256:f1c24655a0da0d1b67f07e17a5e6b2a105894e6824b92096378bb3668ef02376"},
|
||||
|
|
@ -1339,3 +1975,7 @@ wandb = [
|
|||
{file = "wandb-0.12.19-py2.py3-none-any.whl", hash = "sha256:ff344e35a31a0d6ccfccaf0390895e4267392f994e395d16b348d7d2b2f67a18"},
|
||||
{file = "wandb-0.12.19.tar.gz", hash = "sha256:72c54a918e2453ffa661934faf6c3c8b39269423daba0999b9919bcaef3ae320"},
|
||||
]
|
||||
wcwidth = [
|
||||
{file = "wcwidth-0.2.5-py2.py3-none-any.whl", hash = "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784"},
|
||||
{file = "wcwidth-0.2.5.tar.gz", hash = "sha256:c4d647b99872929fdb7bdcaa4fbe7f01413ed3d98077df798530e5b04f116c83"},
|
||||
]
|
||||
|
|
|
|||
|
|
@ -18,6 +18,7 @@ wandb = "^0.12.19"
|
|||
[tool.poetry.dev-dependencies]
|
||||
black = "^22.3.0"
|
||||
isort = "^5.10.1"
|
||||
ipykernel = "^6.15.0"
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core>=1.0.0"]
|
||||
|
|
|
|||
103
src/train.py
103
src/train.py
|
|
@ -2,22 +2,28 @@ import argparse
|
|||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
import albumentations as A
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import wandb
|
||||
from albumentations.pytorch import ToTensorV2
|
||||
from torch import optim
|
||||
from torch.utils.data import DataLoader, random_split
|
||||
from torch.utils.data import DataLoader
|
||||
from tqdm import tqdm
|
||||
|
||||
import wandb
|
||||
from evaluate import evaluate
|
||||
from src.utils.dataset import BasicDataset, CarvanaDataset
|
||||
from unet import UNet
|
||||
from src.utils.dataset import SphereDataset
|
||||
from src.utils.dice import dice_loss
|
||||
from unet import UNet
|
||||
from utils.paste import RandomPaste
|
||||
|
||||
dir_img = Path("./data/imgs/")
|
||||
dir_mask = Path("./data/masks/")
|
||||
dir_checkpoint = Path("./checkpoints/")
|
||||
CHECKPOINT_DIR = Path("./checkpoints/")
|
||||
DIR_TRAIN_IMG = Path("/home/lilian/data_disk/lfainsin/train2017")
|
||||
DIR_VALID_IMG = Path("/home/lilian/data_disk/lfainsin/val2017/")
|
||||
# DIR_VALID_MASK = Path("/home/lilian/data_disk/lfainsin/val2017mask/")
|
||||
DIR_SPHERE_IMG = Path("/home/lilian/data_disk/lfainsin/spheres/Images/")
|
||||
DIR_SPHERE_MASK = Path("/home/lilian/data_disk/lfainsin/spheres/Masks/")
|
||||
|
||||
|
||||
def train_net(
|
||||
|
|
@ -27,37 +33,48 @@ def train_net(
|
|||
batch_size: int = 1,
|
||||
learning_rate: float = 1e-5,
|
||||
save_checkpoint: bool = True,
|
||||
img_scale: float = 0.5,
|
||||
amp: bool = False,
|
||||
):
|
||||
# 1. Create dataset
|
||||
try:
|
||||
dataset = CarvanaDataset(dir_img, dir_mask, img_scale)
|
||||
except (AssertionError, RuntimeError):
|
||||
dataset = BasicDataset(dir_img, dir_mask, img_scale)
|
||||
# 1. Create transforms
|
||||
tf_train = A.Compose(
|
||||
[
|
||||
A.Flip(),
|
||||
A.ColorJitter(),
|
||||
RandomPaste(5, 0.2, DIR_SPHERE_IMG, DIR_SPHERE_MASK),
|
||||
A.ISONoise(),
|
||||
A.ToFloat(max_value=255),
|
||||
A.pytorch.ToTensorV2(),
|
||||
],
|
||||
)
|
||||
|
||||
# 2. Split into train / validation partitions
|
||||
n_val = int(len(dataset) * val_percent)
|
||||
n_train = len(dataset) - n_val
|
||||
train_set, val_set = random_split(dataset, [n_train, n_val], generator=torch.Generator().manual_seed(0))
|
||||
tf_valid = A.Compose(
|
||||
[
|
||||
RandomPaste(5, 0.2, DIR_SPHERE_IMG, DIR_SPHERE_MASK),
|
||||
A.ToFloat(max_value=255),
|
||||
ToTensorV2(),
|
||||
],
|
||||
)
|
||||
|
||||
# 2. Create datasets
|
||||
ds_train = SphereDataset(images_dir=DIR_TRAIN_IMG, transform=tf_train)
|
||||
# ds_valid = SphereDataset(images_dir=DIR_VALID_IMG, masks_dir=DIR_VALID_MASK, transform=tf_valid)
|
||||
ds_valid = SphereDataset(images_dir=DIR_VALID_IMG, transform=tf_valid)
|
||||
|
||||
# 3. Create data loaders
|
||||
loader_args = dict(batch_size=batch_size, num_workers=4, pin_memory=True)
|
||||
train_loader = DataLoader(train_set, shuffle=True, **loader_args)
|
||||
val_loader = DataLoader(val_set, shuffle=False, drop_last=True, **loader_args)
|
||||
train_loader = DataLoader(ds_train, shuffle=True, **loader_args)
|
||||
val_loader = DataLoader(ds_valid, shuffle=False, drop_last=True, **loader_args)
|
||||
|
||||
# (Initialize logging)
|
||||
experiment = wandb.init(project="U-Net", resume="allow", anonymous="must")
|
||||
experiment.config.update(
|
||||
dict(
|
||||
experiment = wandb.init(
|
||||
project="U-Net",
|
||||
config=dict(
|
||||
epochs=epochs,
|
||||
batch_size=batch_size,
|
||||
learning_rate=learning_rate,
|
||||
val_percent=val_percent,
|
||||
save_checkpoint=save_checkpoint,
|
||||
img_scale=img_scale,
|
||||
amp=amp,
|
||||
)
|
||||
),
|
||||
)
|
||||
|
||||
logging.info(
|
||||
|
|
@ -65,13 +82,12 @@ def train_net(
|
|||
Epochs: {epochs}
|
||||
Batch size: {batch_size}
|
||||
Learning rate: {learning_rate}
|
||||
Training size: {n_train}
|
||||
Validation size: {n_val}
|
||||
Training size: {len(ds_train)}
|
||||
Validation size: {len(ds_valid)}
|
||||
Checkpoints: {save_checkpoint}
|
||||
Device: {device.type}
|
||||
Images scaling: {img_scale}
|
||||
Mixed Precision: {amp}
|
||||
"""
|
||||
"""
|
||||
)
|
||||
|
||||
# 4. Set up the optimizer, the loss, the learning rate scheduler and the loss scaling for AMP
|
||||
|
|
@ -85,7 +101,8 @@ def train_net(
|
|||
for epoch in range(1, epochs + 1):
|
||||
net.train()
|
||||
epoch_loss = 0
|
||||
with tqdm(total=n_train, desc=f"Epoch {epoch}/{epochs}", unit="img") as pbar:
|
||||
|
||||
with tqdm(total=len(ds_train), desc=f"Epoch {epoch}/{epochs}", unit="img") as pbar:
|
||||
for batch in train_loader:
|
||||
images = batch["image"]
|
||||
true_masks = batch["mask"]
|
||||
|
|
@ -119,7 +136,7 @@ def train_net(
|
|||
pbar.set_postfix(**{"loss (batch)": loss.item()})
|
||||
|
||||
# Evaluation round
|
||||
division_step = n_train // (10 * batch_size)
|
||||
division_step = len(ds_train) // (10 * batch_size)
|
||||
if division_step > 0:
|
||||
if global_step % division_step == 0:
|
||||
histograms = {}
|
||||
|
|
@ -150,8 +167,8 @@ def train_net(
|
|||
)
|
||||
|
||||
if save_checkpoint:
|
||||
Path(dir_checkpoint).mkdir(parents=True, exist_ok=True)
|
||||
torch.save(net.state_dict(), str(dir_checkpoint / "checkpoint_epoch{}.pth".format(epoch)))
|
||||
Path(CHECKPOINT_DIR).mkdir(parents=True, exist_ok=True)
|
||||
torch.save(net.state_dict(), str(CHECKPOINT_DIR / "checkpoint_epoch{}.pth".format(epoch)))
|
||||
logging.info(f"Checkpoint {epoch} saved!")
|
||||
|
||||
|
||||
|
|
@ -173,7 +190,7 @@ def get_args():
|
|||
dest="batch_size",
|
||||
metavar="B",
|
||||
type=int,
|
||||
default=1,
|
||||
default=32,
|
||||
help="Batch size",
|
||||
)
|
||||
parser.add_argument(
|
||||
|
|
@ -192,13 +209,6 @@ def get_args():
|
|||
default=False,
|
||||
help="Load model from a .pth file",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--scale",
|
||||
"-s",
|
||||
type=float,
|
||||
default=0.5,
|
||||
help="Downscaling factor of the images",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--amp",
|
||||
action="store_true",
|
||||
|
|
@ -220,16 +230,16 @@ if __name__ == "__main__":
|
|||
args = get_args()
|
||||
|
||||
logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
logging.info(f"Using device {device}")
|
||||
|
||||
net = UNet(n_channels=3, n_classes=args.classes)
|
||||
|
||||
logging.info(
|
||||
f"""
|
||||
Network:\n
|
||||
\t{net.n_channels} input channels\n
|
||||
\t{net.n_classes} output channels (classes)\n
|
||||
f"""Network:
|
||||
\t{net.n_channels} input channels
|
||||
\t{net.n_classes} output channels (classes)
|
||||
"""
|
||||
)
|
||||
|
||||
|
|
@ -238,6 +248,7 @@ if __name__ == "__main__":
|
|||
logging.info(f"Model loaded from {args.load}")
|
||||
|
||||
net.to(device=device)
|
||||
|
||||
try:
|
||||
train_net(
|
||||
net=net,
|
||||
|
|
@ -245,8 +256,6 @@ if __name__ == "__main__":
|
|||
batch_size=args.batch_size,
|
||||
learning_rate=args.lr,
|
||||
device=device,
|
||||
img_scale=args.scale,
|
||||
val_percent=args.val / 100,
|
||||
amp=args.amp,
|
||||
)
|
||||
except KeyboardInterrupt:
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@ class UNet(nn.Module):
|
|||
self.ups = nn.ModuleList()
|
||||
for i in range(len(features) - 1):
|
||||
self.ups.append(
|
||||
Up(*features[-1 - i : -1 - i + 3 : -1]),
|
||||
Up(*features[-1 - i : -3 - i : -1]),
|
||||
)
|
||||
|
||||
self.outc = OutConv(features[0], n_classes)
|
||||
|
|
|
|||
|
|
@ -3,6 +3,7 @@ from os import listdir
|
|||
from os.path import splitext
|
||||
from pathlib import Path
|
||||
|
||||
import albumentations as A
|
||||
import numpy as np
|
||||
import torch
|
||||
from PIL import Image
|
||||
|
|
@ -10,16 +11,15 @@ from torch.utils.data import Dataset
|
|||
|
||||
|
||||
class SphereDataset(Dataset):
|
||||
def __init__(self, images_dir: str, masks_dir: str, scale: float = 1.0, mask_suffix: str = ""):
|
||||
def __init__(self, images_dir: str, transform: A.Compose, masks_dir: str = None):
|
||||
self.images_dir = Path(images_dir)
|
||||
self.masks_dir = Path(masks_dir)
|
||||
assert 0 < scale <= 1, "Scale must be between 0 and 1"
|
||||
self.scale = scale
|
||||
self.mask_suffix = mask_suffix
|
||||
self.masks_dir = Path(masks_dir) if masks_dir else None
|
||||
|
||||
self.ids = [splitext(file)[0] for file in listdir(images_dir) if not file.startswith(".")]
|
||||
|
||||
if not self.ids:
|
||||
raise RuntimeError(f"No input file found in {images_dir}, make sure you put your images there")
|
||||
|
||||
logging.info(f"Creating dataset with {len(self.ids)} examples")
|
||||
|
||||
def __len__(self):
|
||||
|
|
@ -30,10 +30,7 @@ class SphereDataset(Dataset):
|
|||
w, h = pil_img.size
|
||||
newW, newH = int(scale * w), int(scale * h)
|
||||
|
||||
assert (
|
||||
newW > 0 and newH > 0,
|
||||
"Scale is too small, resized images would have no pixel",
|
||||
)
|
||||
assert newW > 0 and newH > 0, "Scale is too small, resized images would have no pixel"
|
||||
|
||||
pil_img = pil_img.resize((newW, newH), resample=Image.NEAREST if is_mask else Image.BICUBIC)
|
||||
img_ndarray = np.asarray(pil_img)
|
||||
|
|
@ -61,25 +58,19 @@ class SphereDataset(Dataset):
|
|||
|
||||
def __getitem__(self, idx):
|
||||
name = self.ids[idx]
|
||||
|
||||
mask_file = list(self.masks_dir.glob(name + self.mask_suffix + ".*"))
|
||||
img_file = list(self.images_dir.glob(name + ".*"))
|
||||
|
||||
assert (
|
||||
len(img_file) == 1,
|
||||
f"Either no image or multiple images found for the ID {name}: {img_file}",
|
||||
)
|
||||
assert (
|
||||
len(mask_file) == 1,
|
||||
f"Either no mask or multiple masks found for the ID {name}: {mask_file}",
|
||||
)
|
||||
assert len(img_file) == 1, f"Either no image or multiple images found for the ID {name}: {img_file}"
|
||||
assert len(mask_file) == 1, f"Either no mask or multiple masks found for the ID {name}: {mask_file}"
|
||||
|
||||
mask = self.load(mask_file[0])
|
||||
img = self.load(img_file[0])
|
||||
|
||||
assert (
|
||||
img.size == mask.size,
|
||||
f"Image and mask {name} should be the same size, but are {img.size} and {mask.size}",
|
||||
)
|
||||
img.size == mask.size
|
||||
), f"Image and mask {name} should be the same size, but are {img.size} and {mask.size}"
|
||||
|
||||
img = self.preprocess(img, self.scale, is_mask=False)
|
||||
mask = self.preprocess(mask, self.scale, is_mask=True)
|
||||
|
|
|
|||
126
src/utils/paste.py
Normal file
126
src/utils/paste.py
Normal file
|
|
@ -0,0 +1,126 @@
|
|||
import os
|
||||
import random as rd
|
||||
|
||||
import albumentations as A
|
||||
import cv2
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
|
||||
|
||||
class RandomPaste(A.DualTransform):
|
||||
"""Paste an object on a background.
|
||||
|
||||
Args:
|
||||
TODO
|
||||
|
||||
Targets:
|
||||
image, mask
|
||||
|
||||
Image types:
|
||||
uint8
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
nb,
|
||||
scale_limit,
|
||||
path_paste_img_dir,
|
||||
path_paste_mask_dir,
|
||||
always_apply=True,
|
||||
p=1.0,
|
||||
):
|
||||
super().__init__(always_apply, p)
|
||||
self.path_paste_img_dir = path_paste_img_dir
|
||||
self.path_paste_mask_dir = path_paste_mask_dir
|
||||
self.scale_limit = scale_limit
|
||||
self.nb = nb
|
||||
|
||||
@property
|
||||
def targets_as_params(self):
|
||||
return ["image"]
|
||||
|
||||
def apply(self, img, positions, paste_img, paste_mask, **params):
|
||||
img = img.copy()
|
||||
|
||||
w, h = paste_mask.shape
|
||||
mask_b = paste_mask > 0
|
||||
mask_rgb_b = np.stack([mask_b, mask_b, mask_b], axis=2)
|
||||
|
||||
for (x, y) in positions:
|
||||
img[x : x + w, y : y + h] = img[x : x + w, y : y + h] * ~mask_rgb_b + paste_img * mask_rgb_b
|
||||
|
||||
return img
|
||||
|
||||
def apply_to_mask(self, mask, positions, paste_mask, **params):
|
||||
mask = mask.copy()
|
||||
|
||||
w, h = paste_mask.shape
|
||||
mask_b = paste_mask > 0
|
||||
|
||||
for (x, y) in positions:
|
||||
mask[x : x + w, y : y + h] = mask[x : x + w, y : y + h] * ~mask_b + mask_b
|
||||
|
||||
return mask
|
||||
|
||||
def get_params_dependent_on_targets(self, params):
|
||||
filename = rd.choice(os.listdir(self.path_paste_img_dir))
|
||||
|
||||
paste_img = np.array(
|
||||
Image.open(
|
||||
os.path.join(
|
||||
self.path_paste_img_dir,
|
||||
filename,
|
||||
)
|
||||
).convert("RGB"),
|
||||
dtype=np.uint8,
|
||||
)
|
||||
|
||||
paste_mask = (
|
||||
np.array(
|
||||
Image.open(
|
||||
os.path.join(
|
||||
self.path_paste_mask_dir,
|
||||
filename,
|
||||
)
|
||||
).convert("L"),
|
||||
dtype=np.float32,
|
||||
)
|
||||
/ 255
|
||||
)
|
||||
|
||||
target_img = params["image"]
|
||||
|
||||
min_scale = min(
|
||||
target_img.shape[0] / paste_img.shape[0],
|
||||
target_img.shape[1] / paste_img.shape[1],
|
||||
)
|
||||
|
||||
rescale_rotate = A.Compose(
|
||||
[
|
||||
A.Rotate(limit=360, always_apply=True, border_mode=cv2.BORDER_CONSTANT),
|
||||
A.RandomScale(scale_limit=(min_scale * self.scale_limit - 1, -0.99), always_apply=True),
|
||||
],
|
||||
)
|
||||
|
||||
augmentations = rescale_rotate(image=paste_img, mask=paste_mask)
|
||||
paste_img = augmentations["image"]
|
||||
paste_mask = augmentations["mask"]
|
||||
|
||||
positions = []
|
||||
for _ in range(rd.randint(1, self.nb)):
|
||||
x = rd.randint(0, target_img.shape[0] - paste_img.shape[0])
|
||||
y = rd.randint(0, target_img.shape[1] - paste_img.shape[1])
|
||||
positions.append((x, y))
|
||||
|
||||
params.update(
|
||||
{
|
||||
"positions": positions,
|
||||
"paste_img": paste_img,
|
||||
"paste_mask": paste_mask,
|
||||
}
|
||||
)
|
||||
|
||||
return params
|
||||
|
||||
def get_transform_init_args_names(self):
|
||||
return "scale_limit", "path_paste_img_dir", "path_paste_mask_dir"
|
||||
Loading…
Reference in a new issue