Merge branch 'master' of github.com:Tocard-Inc/Deep-Learning
This commit is contained in:
Laureηt
commit
25ff39f3bc
216
poetry.lock
generated
216
poetry.lock
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@ -87,6 +87,14 @@ category = "dev"
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||||||
optional = false
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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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python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
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||||||
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||||||
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[[package]]
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||||||
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name = "cycler"
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||||||
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version = "0.11.0"
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||||||
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description = "Composable style cycles"
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category = "main"
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||||||
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optional = false
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||||||
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python-versions = ">=3.6"
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||||||
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[[package]]
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[[package]]
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||||||
name = "flatbuffers"
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name = "flatbuffers"
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||||||
version = "2.0"
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version = "2.0"
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||||||
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@ -95,6 +103,28 @@ category = "main"
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||||||
optional = false
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optional = false
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||||||
python-versions = "*"
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python-versions = "*"
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||||||
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||||||
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[[package]]
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||||||
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name = "fonttools"
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||||||
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version = "4.33.3"
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||||||
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description = "Tools to manipulate font files"
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||||||
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category = "main"
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||||||
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optional = false
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||||||
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python-versions = ">=3.7"
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||||||
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[package.extras]
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||||||
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all = ["fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "zopfli (>=0.1.4)", "lz4 (>=1.7.4.2)", "matplotlib", "sympy", "skia-pathops (>=0.5.0)", "uharfbuzz (>=0.23.0)", "brotlicffi (>=0.8.0)", "scipy", "brotli (>=1.0.1)", "munkres", "unicodedata2 (>=14.0.0)", "xattr"]
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||||||
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graphite = ["lz4 (>=1.7.4.2)"]
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||||||
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interpolatable = ["scipy", "munkres"]
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||||||
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lxml = ["lxml (>=4.0,<5)"]
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||||||
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pathops = ["skia-pathops (>=0.5.0)"]
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||||||
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plot = ["matplotlib"]
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||||||
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repacker = ["uharfbuzz (>=0.23.0)"]
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||||||
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symfont = ["sympy"]
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||||||
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type1 = ["xattr"]
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||||||
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ufo = ["fs (>=2.2.0,<3)"]
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||||||
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unicode = ["unicodedata2 (>=14.0.0)"]
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||||||
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woff = ["zopfli (>=0.1.4)", "brotlicffi (>=0.8.0)", "brotli (>=1.0.1)"]
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[[package]]
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[[package]]
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name = "gast"
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name = "gast"
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version = "0.5.3"
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version = "0.5.3"
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@ -220,6 +250,14 @@ image = ["scipy (>=0.14)", "Pillow (>=5.2.0)"]
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pep8 = ["flake8"]
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pep8 = ["flake8"]
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tests = ["pandas", "pillow", "tensorflow", "keras", "pytest", "pytest-xdist", "pytest-cov"]
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tests = ["pandas", "pillow", "tensorflow", "keras", "pytest", "pytest-xdist", "pytest-cov"]
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[[package]]
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name = "kiwisolver"
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version = "1.4.2"
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description = "A fast implementation of the Cassowary constraint solver"
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category = "main"
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optional = false
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python-versions = ">=3.7"
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[[package]]
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[[package]]
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name = "libclang"
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name = "libclang"
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version = "13.0.0"
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version = "13.0.0"
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@ -239,6 +277,25 @@ python-versions = ">=3.6"
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[package.extras]
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[package.extras]
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testing = ["coverage", "pyyaml"]
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testing = ["coverage", "pyyaml"]
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[[package]]
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name = "matplotlib"
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version = "3.5.2"
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description = "Python plotting package"
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category = "main"
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optional = false
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python-versions = ">=3.7"
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[package.dependencies]
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cycler = ">=0.10"
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fonttools = ">=4.22.0"
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kiwisolver = ">=1.0.1"
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numpy = ">=1.17"
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packaging = ">=20.0"
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pillow = ">=6.2.0"
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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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[[package]]
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name = "mypy"
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name = "mypy"
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version = "0.931"
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version = "0.931"
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@ -300,6 +357,17 @@ numpy = ">=1.7"
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docs = ["sphinx (==1.2.3)", "sphinxcontrib-napoleon", "sphinx-rtd-theme", "numpydoc"]
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docs = ["sphinx (==1.2.3)", "sphinxcontrib-napoleon", "sphinx-rtd-theme", "numpydoc"]
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||||||
tests = ["pytest", "pytest-cov", "pytest-pep8"]
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tests = ["pytest", "pytest-cov", "pytest-pep8"]
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[[package]]
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name = "packaging"
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version = "21.3"
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||||||
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description = "Core utilities for Python packages"
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category = "main"
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optional = false
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||||||
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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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[[package]]
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name = "pathspec"
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name = "pathspec"
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version = "0.9.0"
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version = "0.9.0"
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@ -366,6 +434,28 @@ python-versions = "*"
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[package.dependencies]
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[package.dependencies]
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pyasn1 = ">=0.4.6,<0.5.0"
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pyasn1 = ">=0.4.6,<0.5.0"
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[[package]]
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name = "pyparsing"
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version = "3.0.9"
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description = "pyparsing module - Classes and methods to define and execute parsing grammars"
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category = "main"
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optional = false
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python-versions = ">=3.6.8"
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[package.extras]
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diagrams = ["railroad-diagrams", "jinja2"]
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[[package]]
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name = "python-dateutil"
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version = "2.8.2"
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description = "Extensions to the standard Python datetime module"
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category = "main"
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optional = false
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python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
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[package.dependencies]
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six = ">=1.5"
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[[package]]
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[[package]]
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name = "requests"
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name = "requests"
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version = "2.27.1"
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version = "2.27.1"
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@ -410,6 +500,22 @@ python-versions = ">=3.6,<4"
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[package.dependencies]
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[package.dependencies]
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pyasn1 = ">=0.1.3"
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pyasn1 = ">=0.1.3"
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[[package]]
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name = "setuptools-scm"
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version = "6.4.2"
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||||||
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description = "the blessed package to manage your versions by scm tags"
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category = "main"
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optional = false
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python-versions = ">=3.6"
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[package.dependencies]
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packaging = ">=20.0"
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tomli = ">=1.0.0"
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[package.extras]
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test = ["pytest (>=6.2)", "virtualenv (>20)"]
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toml = ["setuptools (>=42)"]
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[[package]]
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[[package]]
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name = "six"
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name = "six"
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version = "1.16.0"
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version = "1.16.0"
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@ -520,7 +626,7 @@ python-versions = "*"
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name = "tomli"
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name = "tomli"
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version = "2.0.1"
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version = "2.0.1"
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||||||
description = "A lil' TOML parser"
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description = "A lil' TOML parser"
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||||||
category = "dev"
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category = "main"
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||||||
optional = false
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optional = false
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python-versions = ">=3.7"
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python-versions = ">=3.7"
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@ -567,7 +673,7 @@ python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
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[metadata]
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[metadata]
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||||||
lock-version = "1.1"
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lock-version = "1.1"
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||||||
python-versions = ">=3.10,<3.11"
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python-versions = ">=3.10,<3.11"
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||||||
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[metadata.files]
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[metadata.files]
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absl-py = [
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absl-py = [
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@ -623,10 +729,18 @@ colorama = [
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||||||
{file = "colorama-0.4.4-py2.py3-none-any.whl", hash = "sha256:9f47eda37229f68eee03b24b9748937c7dc3868f906e8ba69fbcbdd3bc5dc3e2"},
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{file = "colorama-0.4.4.tar.gz", hash = "sha256:5941b2b48a20143d2267e95b1c2a7603ce057ee39fd88e7329b0c292aa16869b"},
|
||||||
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]
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||||||
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cycler = [
|
||||||
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{file = "cycler-0.11.0-py3-none-any.whl", hash = "sha256:3a27e95f763a428a739d2add979fa7494c912a32c17c4c38c4d5f082cad165a3"},
|
||||||
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{file = "cycler-0.11.0.tar.gz", hash = "sha256:9c87405839a19696e837b3b818fed3f5f69f16f1eec1a1ad77e043dcea9c772f"},
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{file = "flatbuffers-2.0-py2.py3-none-any.whl", hash = "sha256:3751954f0604580d3219ae49a85fafec9d85eec599c0b96226e1bc0b48e57474"},
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|
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|
||||||
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fonttools = [
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||||||
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{file = "fonttools-4.33.3-py3-none-any.whl", hash = "sha256:f829c579a8678fa939a1d9e9894d01941db869de44390adb49ce67055a06cc2a"},
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|
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|
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|
||||||
gast = [
|
gast = [
|
||||||
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|
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|
||||||
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@ -733,6 +847,51 @@ keras-preprocessing = [
|
||||||
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|
||||||
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|
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|
||||||
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kiwisolver = [
|
||||||
|
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||||||
|
{file = "kiwisolver-1.4.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:cbb5eb4a2ea1ffec26268d49766cafa8f957fe5c1b41ad00733763fae77f9436"},
|
||||||
|
{file = "kiwisolver-1.4.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2e6cda72db409eefad6b021e8a4f964965a629f577812afc7860c69df7bdb84a"},
|
||||||
|
{file = "kiwisolver-1.4.2-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b1605c7c38cc6a85212dfd6a641f3905a33412e49f7c003f35f9ac6d71f67720"},
|
||||||
|
{file = "kiwisolver-1.4.2-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:81237957b15469ea9151ec8ca08ce05656090ffabc476a752ef5ad7e2644c526"},
|
||||||
|
{file = "kiwisolver-1.4.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:240009fdf4fa87844f805e23f48995537a8cb8f8c361e35fda6b5ac97fcb906f"},
|
||||||
|
{file = "kiwisolver-1.4.2-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:240c2d51d098395c012ddbcb9bd7b3ba5de412a1d11840698859f51d0e643c4f"},
|
||||||
|
{file = "kiwisolver-1.4.2-cp38-cp38-win32.whl", hash = "sha256:8b6086aa6936865962b2cee0e7aaecf01ab6778ce099288354a7229b4d9f1408"},
|
||||||
|
{file = "kiwisolver-1.4.2-cp38-cp38-win_amd64.whl", hash = "sha256:0d98dca86f77b851350c250f0149aa5852b36572514d20feeadd3c6b1efe38d0"},
|
||||||
|
{file = "kiwisolver-1.4.2-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:91eb4916271655dfe3a952249cb37a5c00b6ba68b4417ee15af9ba549b5ba61d"},
|
||||||
|
{file = "kiwisolver-1.4.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:fa4d97d7d2b2c082e67907c0b8d9f31b85aa5d3ba0d33096b7116f03f8061261"},
|
||||||
|
{file = "kiwisolver-1.4.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:71469b5845b9876b8d3d252e201bef6f47bf7456804d2fbe9a1d6e19e78a1e65"},
|
||||||
|
{file = "kiwisolver-1.4.2-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:8ff3033e43e7ca1389ee59fb7ecb8303abb8713c008a1da49b00869e92e3dd7c"},
|
||||||
|
{file = "kiwisolver-1.4.2-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:89b57c2984f4464840e4b768affeff6b6809c6150d1166938ade3e22fbe22db8"},
|
||||||
|
{file = "kiwisolver-1.4.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ffbdb9a96c536f0405895b5e21ee39ec579cb0ed97bdbd169ae2b55f41d73219"},
|
||||||
|
{file = "kiwisolver-1.4.2-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8a830a03970c462d1a2311c90e05679da56d3bd8e78a4ba9985cb78ef7836c9f"},
|
||||||
|
{file = "kiwisolver-1.4.2-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f74f2a13af201559e3d32b9ddfc303c94ae63d63d7f4326d06ce6fe67e7a8255"},
|
||||||
|
{file = "kiwisolver-1.4.2-cp39-cp39-win32.whl", hash = "sha256:e677cc3626287f343de751e11b1e8a5b915a6ac897e8aecdbc996cd34de753a0"},
|
||||||
|
{file = "kiwisolver-1.4.2-cp39-cp39-win_amd64.whl", hash = "sha256:b3e251e5c38ac623c5d786adb21477f018712f8c6fa54781bd38aa1c60b60fc2"},
|
||||||
|
{file = "kiwisolver-1.4.2-pp37-pypy37_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:0c380bb5ae20d829c1a5473cfcae64267b73aaa4060adc091f6df1743784aae0"},
|
||||||
|
{file = "kiwisolver-1.4.2-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:484f2a5f0307bc944bc79db235f41048bae4106ffa764168a068d88b644b305d"},
|
||||||
|
{file = "kiwisolver-1.4.2-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0e8afdf533b613122e4bbaf3c1e42c2a5e9e2d1dd3a0a017749a7658757cb377"},
|
||||||
|
{file = "kiwisolver-1.4.2-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:42f6ef9b640deb6f7d438e0a371aedd8bef6ddfde30683491b2e6f568b4e884e"},
|
||||||
|
{file = "kiwisolver-1.4.2.tar.gz", hash = "sha256:7f606d91b8a8816be476513a77fd30abe66227039bd6f8b406c348cb0247dcc9"},
|
||||||
|
]
|
||||||
libclang = [
|
libclang = [
|
||||||
{file = "libclang-13.0.0-py2.py3-none-macosx_10_9_x86_64.whl", hash = "sha256:bcaffec6b1ab9486811670db7af29d4a361830d6cb75da4f5672e884aa973bda"},
|
{file = "libclang-13.0.0-py2.py3-none-macosx_10_9_x86_64.whl", hash = "sha256:bcaffec6b1ab9486811670db7af29d4a361830d6cb75da4f5672e884aa973bda"},
|
||||||
{file = "libclang-13.0.0-py2.py3-none-macosx_11_0_arm64.whl", hash = "sha256:069407eac2e20ea8f18212d28c6598db31014e7b8a77febc92e762ec133c3226"},
|
{file = "libclang-13.0.0-py2.py3-none-macosx_11_0_arm64.whl", hash = "sha256:069407eac2e20ea8f18212d28c6598db31014e7b8a77febc92e762ec133c3226"},
|
||||||
|
|
@ -747,6 +906,43 @@ markdown = [
|
||||||
{file = "Markdown-3.3.6-py3-none-any.whl", hash = "sha256:9923332318f843411e9932237530df53162e29dc7a4e2b91e35764583c46c9a3"},
|
{file = "Markdown-3.3.6-py3-none-any.whl", hash = "sha256:9923332318f843411e9932237530df53162e29dc7a4e2b91e35764583c46c9a3"},
|
||||||
{file = "Markdown-3.3.6.tar.gz", hash = "sha256:76df8ae32294ec39dcf89340382882dfa12975f87f45c3ed1ecdb1e8cefc7006"},
|
{file = "Markdown-3.3.6.tar.gz", hash = "sha256:76df8ae32294ec39dcf89340382882dfa12975f87f45c3ed1ecdb1e8cefc7006"},
|
||||||
]
|
]
|
||||||
|
matplotlib = [
|
||||||
|
{file = "matplotlib-3.5.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:03bbb3f5f78836855e127b5dab228d99551ad0642918ccbf3067fcd52ac7ac5e"},
|
||||||
|
{file = "matplotlib-3.5.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:49a5938ed6ef9dda560f26ea930a2baae11ea99e1c2080c8714341ecfda72a89"},
|
||||||
|
{file = "matplotlib-3.5.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:77157be0fc4469cbfb901270c205e7d8adb3607af23cef8bd11419600647ceed"},
|
||||||
|
{file = "matplotlib-3.5.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5844cea45d804174bf0fac219b4ab50774e504bef477fc10f8f730ce2d623441"},
|
||||||
|
{file = "matplotlib-3.5.2-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c87973ddec10812bddc6c286b88fdd654a666080fbe846a1f7a3b4ba7b11ab78"},
|
||||||
|
{file = "matplotlib-3.5.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4a05f2b37222319753a5d43c0a4fd97ed4ff15ab502113e3f2625c26728040cf"},
|
||||||
|
{file = "matplotlib-3.5.2-cp310-cp310-win32.whl", hash = "sha256:9776e1a10636ee5f06ca8efe0122c6de57ffe7e8c843e0fb6e001e9d9256ec95"},
|
||||||
|
{file = "matplotlib-3.5.2-cp310-cp310-win_amd64.whl", hash = "sha256:b4fedaa5a9aa9ce14001541812849ed1713112651295fdddd640ea6620e6cf98"},
|
||||||
|
{file = "matplotlib-3.5.2-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:ee175a571e692fc8ae8e41ac353c0e07259113f4cb063b0ec769eff9717e84bb"},
|
||||||
|
{file = "matplotlib-3.5.2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2e8bda1088b941ead50caabd682601bece983cadb2283cafff56e8fcddbf7d7f"},
|
||||||
|
{file = "matplotlib-3.5.2-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:9480842d5aadb6e754f0b8f4ebeb73065ac8be1855baa93cd082e46e770591e9"},
|
||||||
|
{file = "matplotlib-3.5.2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:6c623b355d605a81c661546af7f24414165a8a2022cddbe7380a31a4170fa2e9"},
|
||||||
|
{file = "matplotlib-3.5.2-cp37-cp37m-win32.whl", hash = "sha256:a91426ae910819383d337ba0dc7971c7cefdaa38599868476d94389a329e599b"},
|
||||||
|
{file = "matplotlib-3.5.2-cp37-cp37m-win_amd64.whl", hash = "sha256:c4b82c2ae6d305fcbeb0eb9c93df2602ebd2f174f6e8c8a5d92f9445baa0c1d3"},
|
||||||
|
{file = "matplotlib-3.5.2-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:ebc27ad11df3c1661f4677a7762e57a8a91dd41b466c3605e90717c9a5f90c82"},
|
||||||
|
{file = "matplotlib-3.5.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:5a32ea6e12e80dedaca2d4795d9ed40f97bfa56e6011e14f31502fdd528b9c89"},
|
||||||
|
{file = "matplotlib-3.5.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:2a0967d4156adbd0d46db06bc1a877f0370bce28d10206a5071f9ecd6dc60b79"},
|
||||||
|
{file = "matplotlib-3.5.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e2b696699386766ef171a259d72b203a3c75d99d03ec383b97fc2054f52e15cf"},
|
||||||
|
{file = "matplotlib-3.5.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:7f409716119fa39b03da3d9602bd9b41142fab7a0568758cd136cd80b1bf36c8"},
|
||||||
|
{file = "matplotlib-3.5.2-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:b8d3f4e71e26307e8c120b72c16671d70c5cd08ae412355c11254aa8254fb87f"},
|
||||||
|
{file = "matplotlib-3.5.2-cp38-cp38-win32.whl", hash = "sha256:b6c63cd01cad0ea8704f1fd586e9dc5777ccedcd42f63cbbaa3eae8dd41172a1"},
|
||||||
|
{file = "matplotlib-3.5.2-cp38-cp38-win_amd64.whl", hash = "sha256:75c406c527a3aa07638689586343f4b344fcc7ab1f79c396699eb550cd2b91f7"},
|
||||||
|
{file = "matplotlib-3.5.2-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:4a44cdfdb9d1b2f18b1e7d315eb3843abb097869cd1ef89cfce6a488cd1b5182"},
|
||||||
|
{file = "matplotlib-3.5.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:3d8e129af95b156b41cb3be0d9a7512cc6d73e2b2109f82108f566dbabdbf377"},
|
||||||
|
{file = "matplotlib-3.5.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:364e6bca34edc10a96aa3b1d7cd76eb2eea19a4097198c1b19e89bee47ed5781"},
|
||||||
|
{file = "matplotlib-3.5.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ea75df8e567743207e2b479ba3d8843537be1c146d4b1e3e395319a4e1a77fe9"},
|
||||||
|
{file = "matplotlib-3.5.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:44c6436868186564450df8fd2fc20ed9daaef5caad699aa04069e87099f9b5a8"},
|
||||||
|
{file = "matplotlib-3.5.2-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:7d7705022df2c42bb02937a2a824f4ec3cca915700dd80dc23916af47ff05f1a"},
|
||||||
|
{file = "matplotlib-3.5.2-cp39-cp39-win32.whl", hash = "sha256:ee0b8e586ac07f83bb2950717e66cb305e2859baf6f00a9c39cc576e0ce9629c"},
|
||||||
|
{file = "matplotlib-3.5.2-cp39-cp39-win_amd64.whl", hash = "sha256:c772264631e5ae61f0bd41313bbe48e1b9bcc95b974033e1118c9caa1a84d5c6"},
|
||||||
|
{file = "matplotlib-3.5.2-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:751d3815b555dcd6187ad35b21736dc12ce6925fc3fa363bbc6dc0f86f16484f"},
|
||||||
|
{file = "matplotlib-3.5.2-pp37-pypy37_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:31fbc2af27ebb820763f077ec7adc79b5a031c2f3f7af446bd7909674cd59460"},
|
||||||
|
{file = "matplotlib-3.5.2-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:4fa28ca76ac5c2b2d54bc058b3dad8e22ee85d26d1ee1b116a6fd4d2277b6a04"},
|
||||||
|
{file = "matplotlib-3.5.2-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:24173c23d1bcbaed5bf47b8785d27933a1ac26a5d772200a0f3e0e38f471b001"},
|
||||||
|
{file = "matplotlib-3.5.2.tar.gz", hash = "sha256:48cf850ce14fa18067f2d9e0d646763681948487a8080ec0af2686468b4607a2"},
|
||||||
|
]
|
||||||
mypy = [
|
mypy = [
|
||||||
{file = "mypy-0.931-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:3c5b42d0815e15518b1f0990cff7a705805961613e701db60387e6fb663fe78a"},
|
{file = "mypy-0.931-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:3c5b42d0815e15518b1f0990cff7a705805961613e701db60387e6fb663fe78a"},
|
||||||
{file = "mypy-0.931-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c89702cac5b302f0c5d33b172d2b55b5df2bede3344a2fbed99ff96bddb2cf00"},
|
{file = "mypy-0.931-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c89702cac5b302f0c5d33b172d2b55b5df2bede3344a2fbed99ff96bddb2cf00"},
|
||||||
|
|
@ -803,6 +999,10 @@ opt-einsum = [
|
||||||
{file = "opt_einsum-3.3.0-py3-none-any.whl", hash = "sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147"},
|
{file = "opt_einsum-3.3.0-py3-none-any.whl", hash = "sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147"},
|
||||||
{file = "opt_einsum-3.3.0.tar.gz", hash = "sha256:59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549"},
|
{file = "opt_einsum-3.3.0.tar.gz", hash = "sha256:59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549"},
|
||||||
]
|
]
|
||||||
|
packaging = [
|
||||||
|
{file = "packaging-21.3-py3-none-any.whl", hash = "sha256:ef103e05f519cdc783ae24ea4e2e0f508a9c99b2d4969652eed6a2e1ea5bd522"},
|
||||||
|
{file = "packaging-21.3.tar.gz", hash = "sha256:dd47c42927d89ab911e606518907cc2d3a1f38bbd026385970643f9c5b8ecfeb"},
|
||||||
|
]
|
||||||
pathspec = [
|
pathspec = [
|
||||||
{file = "pathspec-0.9.0-py2.py3-none-any.whl", hash = "sha256:7d15c4ddb0b5c802d161efc417ec1a2558ea2653c2e8ad9c19098201dc1c993a"},
|
{file = "pathspec-0.9.0-py2.py3-none-any.whl", hash = "sha256:7d15c4ddb0b5c802d161efc417ec1a2558ea2653c2e8ad9c19098201dc1c993a"},
|
||||||
{file = "pathspec-0.9.0.tar.gz", hash = "sha256:e564499435a2673d586f6b2130bb5b95f04a3ba06f81b8f895b651a3c76aabb1"},
|
{file = "pathspec-0.9.0.tar.gz", hash = "sha256:e564499435a2673d586f6b2130bb5b95f04a3ba06f81b8f895b651a3c76aabb1"},
|
||||||
|
|
@ -907,6 +1107,14 @@ pyasn1-modules = [
|
||||||
{file = "pyasn1_modules-0.2.8-py3.6.egg", hash = "sha256:cbac4bc38d117f2a49aeedec4407d23e8866ea4ac27ff2cf7fb3e5b570df19e0"},
|
{file = "pyasn1_modules-0.2.8-py3.6.egg", hash = "sha256:cbac4bc38d117f2a49aeedec4407d23e8866ea4ac27ff2cf7fb3e5b570df19e0"},
|
||||||
{file = "pyasn1_modules-0.2.8-py3.7.egg", hash = "sha256:c29a5e5cc7a3f05926aff34e097e84f8589cd790ce0ed41b67aed6857b26aafd"},
|
{file = "pyasn1_modules-0.2.8-py3.7.egg", hash = "sha256:c29a5e5cc7a3f05926aff34e097e84f8589cd790ce0ed41b67aed6857b26aafd"},
|
||||||
]
|
]
|
||||||
|
pyparsing = [
|
||||||
|
{file = "pyparsing-3.0.9-py3-none-any.whl", hash = "sha256:5026bae9a10eeaefb61dab2f09052b9f4307d44aee4eda64b309723d8d206bbc"},
|
||||||
|
{file = "pyparsing-3.0.9.tar.gz", hash = "sha256:2b020ecf7d21b687f219b71ecad3631f644a47f01403fa1d1036b0c6416d70fb"},
|
||||||
|
]
|
||||||
|
python-dateutil = [
|
||||||
|
{file = "python-dateutil-2.8.2.tar.gz", hash = "sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86"},
|
||||||
|
{file = "python_dateutil-2.8.2-py2.py3-none-any.whl", hash = "sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9"},
|
||||||
|
]
|
||||||
requests = [
|
requests = [
|
||||||
{file = "requests-2.27.1-py2.py3-none-any.whl", hash = "sha256:f22fa1e554c9ddfd16e6e41ac79759e17be9e492b3587efa038054674760e72d"},
|
{file = "requests-2.27.1-py2.py3-none-any.whl", hash = "sha256:f22fa1e554c9ddfd16e6e41ac79759e17be9e492b3587efa038054674760e72d"},
|
||||||
{file = "requests-2.27.1.tar.gz", hash = "sha256:68d7c56fd5a8999887728ef304a6d12edc7be74f1cfa47714fc8b414525c9a61"},
|
{file = "requests-2.27.1.tar.gz", hash = "sha256:68d7c56fd5a8999887728ef304a6d12edc7be74f1cfa47714fc8b414525c9a61"},
|
||||||
|
|
@ -919,6 +1127,10 @@ rsa = [
|
||||||
{file = "rsa-4.8-py3-none-any.whl", hash = "sha256:95c5d300c4e879ee69708c428ba566c59478fd653cc3a22243eeb8ed846950bb"},
|
{file = "rsa-4.8-py3-none-any.whl", hash = "sha256:95c5d300c4e879ee69708c428ba566c59478fd653cc3a22243eeb8ed846950bb"},
|
||||||
{file = "rsa-4.8.tar.gz", hash = "sha256:5c6bd9dc7a543b7fe4304a631f8a8a3b674e2bbfc49c2ae96200cdbe55df6b17"},
|
{file = "rsa-4.8.tar.gz", hash = "sha256:5c6bd9dc7a543b7fe4304a631f8a8a3b674e2bbfc49c2ae96200cdbe55df6b17"},
|
||||||
]
|
]
|
||||||
|
setuptools-scm = [
|
||||||
|
{file = "setuptools_scm-6.4.2-py3-none-any.whl", hash = "sha256:acea13255093849de7ccb11af9e1fb8bde7067783450cee9ef7a93139bddf6d4"},
|
||||||
|
{file = "setuptools_scm-6.4.2.tar.gz", hash = "sha256:6833ac65c6ed9711a4d5d2266f8024cfa07c533a0e55f4c12f6eff280a5a9e30"},
|
||||||
|
]
|
||||||
six = [
|
six = [
|
||||||
{file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
|
{file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
|
||||||
{file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
|
{file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
|
||||||
|
|
|
||||||
|
|
@ -15,6 +15,7 @@ numpy = "^1.22.3"
|
||||||
pure-python-adb = "^0.3.0-alpha.0"
|
pure-python-adb = "^0.3.0-alpha.0"
|
||||||
python = ">=3.10,<3.11"
|
python = ">=3.10,<3.11"
|
||||||
tensorflow = "^2.8.0"
|
tensorflow = "^2.8.0"
|
||||||
|
matplotlib = "^3.5.2"
|
||||||
|
|
||||||
[tool.poetry.dev-dependencies]
|
[tool.poetry.dev-dependencies]
|
||||||
black = "^22.1"
|
black = "^22.1"
|
||||||
|
|
|
||||||
109
src/live.py
109
src/live.py
|
|
@ -1,36 +1,106 @@
|
||||||
import os
|
import os
|
||||||
import subprocess
|
|
||||||
import time
|
import time
|
||||||
|
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import PIL
|
import PIL
|
||||||
import ppadb.client
|
|
||||||
import ppadb.command.serial
|
import ppadb.command.serial
|
||||||
import ppadb.device
|
import ppadb.device
|
||||||
import tensorflow
|
import tensorflow as tf
|
||||||
from keras import models
|
from keras import models
|
||||||
|
|
||||||
import utils
|
import utils
|
||||||
|
|
||||||
|
|
||||||
|
def make_heatmap(img_array, model, last_conv_layer_name, pred_index=None):
|
||||||
|
# First, we create a model that maps the input image to the activations
|
||||||
|
# of the last conv layer as well as the output predictions
|
||||||
|
grad_model = tf.keras.models.Model([model.inputs], [model.get_layer(last_conv_layer_name).output, model.output])
|
||||||
|
|
||||||
|
# Then, we compute the gradient of the top predicted class for our input image
|
||||||
|
# with respect to the activations of the last conv layer
|
||||||
|
with tf.GradientTape() as tape:
|
||||||
|
last_conv_layer_output, preds = grad_model(img_array)
|
||||||
|
if pred_index is None:
|
||||||
|
pred_index = tf.argmax(preds[0])
|
||||||
|
class_channel = preds[:, pred_index]
|
||||||
|
|
||||||
|
# This is the gradient of the output neuron (top predicted or chosen)
|
||||||
|
# with regard to the output feature map of the last conv layer
|
||||||
|
grads = tape.gradient(class_channel, last_conv_layer_output)
|
||||||
|
|
||||||
|
# This is a vector where each entry is the mean intensity of the gradient
|
||||||
|
# over a specific feature map channel
|
||||||
|
pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
|
||||||
|
|
||||||
|
# We multiply each channel in the feature map array
|
||||||
|
# by "how important this channel is" with regard to the top predicted class
|
||||||
|
# then sum all the channels to obtain the heatmap class activation
|
||||||
|
last_conv_layer_output = last_conv_layer_output[0]
|
||||||
|
heatmap = last_conv_layer_output @ pooled_grads[..., tf.newaxis]
|
||||||
|
heatmap = tf.squeeze(heatmap)
|
||||||
|
|
||||||
|
# For visualization purpose, we will also normalize the heatmap between 0 & 1
|
||||||
|
heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)
|
||||||
|
|
||||||
|
return heatmap.numpy()
|
||||||
|
|
||||||
|
|
||||||
|
def make_gradcam(img, heatmap, alpha=0.5):
|
||||||
|
# convert img to float32 to support alpha blending
|
||||||
|
img = tf.image.convert_image_dtype(img, dtype=tf.float32)
|
||||||
|
|
||||||
|
# Rescale heatmap to a range 0-255
|
||||||
|
heatmap = np.uint8(255 * heatmap)
|
||||||
|
|
||||||
|
# Use jet colormap to colorize heatmap
|
||||||
|
jet = plt.get_cmap("jet")
|
||||||
|
|
||||||
|
# Use RGB values of the colormap
|
||||||
|
jet_colors = jet(np.arange(256))[:, :3]
|
||||||
|
jet_heatmap = jet_colors[heatmap]
|
||||||
|
|
||||||
|
# Create an image with RGB colorized heatmap
|
||||||
|
jet_heatmap = tf.keras.preprocessing.image.array_to_img(jet_heatmap)
|
||||||
|
jet_heatmap = jet_heatmap.resize((img.shape[1], img.shape[0]))
|
||||||
|
jet_heatmap = tf.keras.preprocessing.image.img_to_array(jet_heatmap)
|
||||||
|
jet_heatmap = jet_heatmap / 255
|
||||||
|
|
||||||
|
# Superimpose the heatmap on original image
|
||||||
|
superimposed_img = jet_heatmap * alpha + img * (1 - alpha)
|
||||||
|
superimposed_img = tf.keras.preprocessing.image.array_to_img(superimposed_img)
|
||||||
|
|
||||||
|
# Display Grad CAM
|
||||||
|
return superimposed_img
|
||||||
|
|
||||||
|
|
||||||
RESIZED_SIZE = (100, 50, 3)
|
RESIZED_SIZE = (100, 50, 3)
|
||||||
LABELS = ["octane", "werewolf", "breakout", "aftershock"]
|
LABELS = ["octane", "werewolf", "breakout", "aftershock"]
|
||||||
|
|
||||||
MODELS_PATH = "models"
|
MODELS_PATH = "models"
|
||||||
MODEL_FILENAME = "rot_25e"
|
MODEL_FILENAME = "full_aug_5e"
|
||||||
|
|
||||||
|
last_conv_layer_name = "C2D_last"
|
||||||
|
|
||||||
# Load model
|
# Load model
|
||||||
model = models.load_model(MODELS_PATH + "/" + MODEL_FILENAME)
|
model = models.load_model(MODELS_PATH + "/" + MODEL_FILENAME)
|
||||||
|
|
||||||
utils.startup(need_focus=False)
|
utils.startup(need_focus=False)
|
||||||
|
utils.screenshot(filename="live", folder=MODELS_PATH)
|
||||||
|
|
||||||
|
# Attendre que la première image soit créée
|
||||||
|
time.sleep(10)
|
||||||
|
|
||||||
running = True
|
running = True
|
||||||
|
|
||||||
X = np.zeros((1, RESIZED_SIZE[1], RESIZED_SIZE[0], RESIZED_SIZE[2]))
|
X = np.zeros((1, RESIZED_SIZE[1], RESIZED_SIZE[0], RESIZED_SIZE[2]))
|
||||||
|
|
||||||
while running:
|
plt.ion()
|
||||||
utils.screenshot(filename="live", folder=MODELS_PATH)
|
plt.show()
|
||||||
|
|
||||||
# time.sleep(1)
|
while running:
|
||||||
|
|
||||||
|
utils.screenshot(filename="live", folder=MODELS_PATH)
|
||||||
|
|
||||||
# Lecture de l'image
|
# Lecture de l'image
|
||||||
img = PIL.Image.open(MODELS_PATH + "/live.jpg")
|
img = PIL.Image.open(MODELS_PATH + "/live.jpg")
|
||||||
|
|
@ -40,11 +110,28 @@ while running:
|
||||||
|
|
||||||
X[0] = np.asarray(img)
|
X[0] = np.asarray(img)
|
||||||
|
|
||||||
Y = model.predict(X)
|
preds = model.predict(X)
|
||||||
|
index = np.argmax(preds)
|
||||||
index = int(np.dot(Y, np.array([0, 1, 2, 3]).T))
|
|
||||||
|
|
||||||
os.system("clear")
|
os.system("clear")
|
||||||
print(f"Model detected : {LABELS[index]}")
|
print(f"Model detected : {LABELS[index]}")
|
||||||
for i in range(len(LABELS)):
|
for i in range(len(LABELS)):
|
||||||
print(f"\t- {LABELS[i]} {Y[0,i]:.03f}")
|
print(f"\t- {LABELS[i]} {preds[0,i]:.03f}")
|
||||||
|
|
||||||
|
plt.subplot(1, 5, 1)
|
||||||
|
plt.imshow(img)
|
||||||
|
plt.title(f"prediction: {LABELS[index]}")
|
||||||
|
|
||||||
|
for i in range(4):
|
||||||
|
# generate class activation heatmap
|
||||||
|
heatmap = make_heatmap(X, model, last_conv_layer_name, pred_index=i)
|
||||||
|
# generate gradmap
|
||||||
|
gradcam = make_gradcam(img, heatmap)
|
||||||
|
|
||||||
|
plt.subplot(1, 5, i + 2)
|
||||||
|
plt.imshow(gradcam)
|
||||||
|
plt.title(f"{LABELS[i]} ({preds[0][i]:.4f})")
|
||||||
|
|
||||||
|
plt.tight_layout()
|
||||||
|
plt.draw()
|
||||||
|
plt.pause(0.001)
|
||||||
|
|
|
||||||
Loading…
Reference in a new issue