mirror of
https://github.com/Laurent2916/REVA-QCAV.git
synced 2024-11-08 14:39:00 +00:00
feat: checkpointing + torchmetrics + ...
Former-commit-id: 781a11f646964567b3bc6831caa5e380748b84e1 [formerly 7ea5f37ecb7fd5c95e5d1b7b1aa899d986ecff2a] Former-commit-id: 06974fa163a6a8eb881d5981f9c6debe63f1b4bb
This commit is contained in:
Laurent Fainsin
parent
c0a52196fd
commit
c312513eff
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@ -11,7 +11,7 @@ repos:
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- id: check-executables-have-shebangs
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- id: check-merge-conflict
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- id: check-symlinks
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- id: check-json
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# - id: check-json
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- id: check-toml
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- id: check-yaml
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- id: debug-statements
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22
.vscode/launch.json
vendored
22
.vscode/launch.json
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@ -2,20 +2,28 @@
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"version": "0.2.0",
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"configurations": [
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{
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"name": "Python: Current File",
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"name": "Train",
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"type": "python",
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"request": "launch",
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"program": "${file}",
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"program": "${workspaceFolder}/src/train.py",
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"console": "integratedTerminal",
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"justMyCode": false,
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},
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{
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"name": "Predict",
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"type": "python",
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"request": "launch",
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"program": "${workspaceFolder}/src/predict.py",
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"console": "integratedTerminal",
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"justMyCode": false,
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"args": [
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"--input",
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"images/test.png",
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"images/input.png",
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"--output",
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"output_onnx.png",
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"images/output.png",
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"--model",
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"good.onnx"
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],
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"justMyCode": false
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"checkpoints/model.onnx"
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]
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}
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]
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}
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1
.vscode/settings.json
vendored
1
.vscode/settings.json
vendored
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@ -20,5 +20,6 @@
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"**/.DS_Store": true,
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"**/Thumbs.db": true,
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"**/__pycache__": true,
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"**/.mypy_cache": true,
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}
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}
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106
poetry.lock
generated
106
poetry.lock
generated
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@ -203,7 +203,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
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name = "coloredlogs"
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version = "15.0.1"
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description = "Colored terminal output for Python's logging module"
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category = "dev"
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category = "main"
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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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@ -213,11 +213,22 @@ humanfriendly = ">=9.1"
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[package.extras]
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cron = ["capturer (>=2.4)"]
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[[package]]
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name = "commonmark"
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version = "0.9.1"
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description = "Python parser for the CommonMark Markdown spec"
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category = "main"
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optional = false
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python-versions = "*"
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[package.extras]
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test = ["flake8 (==3.7.8)", "hypothesis (==3.55.3)"]
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[[package]]
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name = "cycler"
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version = "0.11.0"
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description = "Composable style cycles"
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category = "dev"
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category = "main"
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optional = false
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python-versions = ">=3.6"
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@ -288,7 +299,7 @@ testing = ["covdefaults (>=2.2)", "coverage (>=6.4.2)", "pytest (>=7.1.2)", "pyt
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name = "flatbuffers"
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version = "2.0.7"
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description = "The FlatBuffers serialization format for Python"
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category = "dev"
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category = "main"
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optional = false
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python-versions = "*"
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@ -296,7 +307,7 @@ python-versions = "*"
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name = "fonttools"
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version = "4.37.1"
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description = "Tools to manipulate font files"
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category = "dev"
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category = "main"
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optional = false
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python-versions = ">=3.7"
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@ -432,7 +443,7 @@ protobuf = ["grpcio-tools (>=1.48.1)"]
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name = "humanfriendly"
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version = "10.0"
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description = "Human friendly output for text interfaces using Python"
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category = "dev"
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category = "main"
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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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@ -639,7 +650,7 @@ test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
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name = "kiwisolver"
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version = "1.4.4"
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description = "A fast implementation of the Cassowary constraint solver"
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category = "dev"
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category = "main"
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optional = false
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python-versions = ">=3.7"
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@ -669,7 +680,7 @@ python-versions = ">=3.7"
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name = "matplotlib"
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version = "3.5.3"
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description = "Python plotting package"
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category = "dev"
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category = "main"
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optional = false
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python-versions = ">=3.7"
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@ -699,7 +710,7 @@ traitlets = "*"
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name = "mpmath"
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version = "1.2.1"
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description = "Python library for arbitrary-precision floating-point arithmetic"
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category = "dev"
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category = "main"
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optional = false
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python-versions = "*"
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@ -825,6 +836,22 @@ packaging = "*"
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protobuf = "*"
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sympy = "*"
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[[package]]
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name = "onnxruntime-gpu"
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version = "1.12.1"
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description = "ONNX Runtime is a runtime accelerator for Machine Learning models"
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category = "main"
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optional = false
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python-versions = "*"
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[package.dependencies]
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coloredlogs = "*"
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flatbuffers = "*"
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numpy = ">=1.21.0"
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packaging = "*"
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protobuf = "*"
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sympy = "*"
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[[package]]
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name = "opencv-python-headless"
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version = "4.6.0.66"
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@ -1029,6 +1056,18 @@ python-versions = "*"
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[package.dependencies]
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pyasn1 = ">=0.4.6,<0.5.0"
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[[package]]
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name = "pycocotools"
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version = "2.0.4"
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description = "Official APIs for the MS-COCO dataset"
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category = "main"
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optional = false
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python-versions = ">=3.5"
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[package.dependencies]
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matplotlib = ">=2.1.0"
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numpy = "*"
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[[package]]
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name = "pycparser"
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version = "2.21"
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@ -1049,7 +1088,7 @@ python-versions = ">=3.6"
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name = "pygments"
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version = "2.13.0"
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description = "Pygments is a syntax highlighting package written in Python."
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category = "dev"
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category = "main"
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optional = false
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python-versions = ">=3.6"
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@ -1071,7 +1110,7 @@ diagrams = ["jinja2", "railroad-diagrams"]
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name = "pyreadline3"
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version = "3.4.1"
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description = "A python implementation of GNU readline."
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category = "dev"
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category = "main"
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optional = false
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python-versions = "*"
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@ -1079,7 +1118,7 @@ python-versions = "*"
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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 = "dev"
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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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@ -1205,6 +1244,22 @@ requests = ">=2.0.0"
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[package.extras]
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rsa = ["oauthlib[signedtoken] (>=3.0.0)"]
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[[package]]
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name = "rich"
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version = "12.5.1"
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description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
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category = "main"
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optional = false
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python-versions = ">=3.6.3,<4.0.0"
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[package.dependencies]
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commonmark = ">=0.9.0,<0.10.0"
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pygments = ">=2.6.0,<3.0.0"
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typing-extensions = {version = ">=4.0.0,<5.0", markers = "python_version < \"3.9\""}
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[package.extras]
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jupyter = ["ipywidgets (>=7.5.1,<8.0.0)"]
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[[package]]
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name = "rsa"
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version = "4.9"
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@ -1318,7 +1373,7 @@ test = ["pytest"]
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name = "setuptools-scm"
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version = "6.4.2"
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description = "the blessed package to manage your versions by scm tags"
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category = "dev"
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category = "main"
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optional = false
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python-versions = ">=3.6"
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@ -1374,7 +1429,7 @@ tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
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name = "sympy"
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version = "1.11.1"
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description = "Computer algebra system (CAS) in Python"
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category = "dev"
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category = "main"
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optional = false
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python-versions = ">=3.8"
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@ -1460,7 +1515,7 @@ python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
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name = "tomli"
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version = "2.0.1"
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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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python-versions = ">=3.7"
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@ -1673,7 +1728,7 @@ testing = ["func-timeout", "jaraco.itertools", "pytest (>=6)", "pytest-black (>=
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[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 = "41f098cb3b123362c4c88942260ee0fd07c8b175d0f7f4481ab9a6e4dc401d7f"
|
||||
content-hash = "a937d2dadd3250c71c1d8a43a3a4a72ac53e6f3793d856c17a4f4f199de71d0b"
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[metadata.files]
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absl-py = [
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@ -1901,6 +1956,10 @@ coloredlogs = [
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|||
{file = "coloredlogs-15.0.1-py2.py3-none-any.whl", hash = "sha256:612ee75c546f53e92e70049c9dbfcc18c935a2b9a53b66085ce9ef6a6e5c0934"},
|
||||
{file = "coloredlogs-15.0.1.tar.gz", hash = "sha256:7c991aa71a4577af2f82600d8f8f3a89f936baeaf9b50a9c197da014e5bf16b0"},
|
||||
]
|
||||
commonmark = [
|
||||
{file = "commonmark-0.9.1-py2.py3-none-any.whl", hash = "sha256:da2f38c92590f83de410ba1a3cbceafbc74fee9def35f9251ba9a971d6d66fd9"},
|
||||
{file = "commonmark-0.9.1.tar.gz", hash = "sha256:452f9dc859be7f06631ddcb328b6919c67984aca654e5fefb3914d54691aed60"},
|
||||
]
|
||||
cycler = [
|
||||
{file = "cycler-0.11.0-py3-none-any.whl", hash = "sha256:3a27e95f763a428a739d2add979fa7494c912a32c17c4c38c4d5f082cad165a3"},
|
||||
{file = "cycler-0.11.0.tar.gz", hash = "sha256:9c87405839a19696e837b3b818fed3f5f69f16f1eec1a1ad77e043dcea9c772f"},
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@ -2483,6 +2542,16 @@ onnxruntime = [
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{file = "onnxruntime-1.12.1-cp39-cp39-win32.whl", hash = "sha256:a9954f6ffab4a0a3877a4800d817950a236a6db4901399eec1ea52033f52da94"},
|
||||
{file = "onnxruntime-1.12.1-cp39-cp39-win_amd64.whl", hash = "sha256:76bbd92cbcc5b6b0f893565f072e33f921ae3350a77b74fb7c65757e683516c7"},
|
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]
|
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onnxruntime-gpu = [
|
||||
{file = "onnxruntime_gpu-1.12.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:42b0393c5122ed90fa2eb76192a486261d86e9526ccb78b2a98923c22791d2d1"},
|
||||
{file = "onnxruntime_gpu-1.12.1-cp310-cp310-win_amd64.whl", hash = "sha256:ecfe97335027e569d4f46725ba89316041e562b8c499690e25e11cfee4601cd1"},
|
||||
{file = "onnxruntime_gpu-1.12.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e2be6f7f5a1ce0bc8471ce42e10eab92cfb19d0748b857edcb5320b5e98311b7"},
|
||||
{file = "onnxruntime_gpu-1.12.1-cp37-cp37m-win_amd64.whl", hash = "sha256:d73204323aefebe4eecab9fcf76e22b1a00394e3d838c2962a28a27301186b73"},
|
||||
{file = "onnxruntime_gpu-1.12.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b37872527d03d3df10756408ca44014bd6ac354a044ab1c4286cd42dc138e518"},
|
||||
{file = "onnxruntime_gpu-1.12.1-cp38-cp38-win_amd64.whl", hash = "sha256:296bd9733986cb7517d15bef5535c555d3f863963a71e6575e92d2a854aee61d"},
|
||||
{file = "onnxruntime_gpu-1.12.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8e46d0724ce54c5908c5760037b78de741fbd48962b370c29ebc20e608b30eda"},
|
||||
{file = "onnxruntime_gpu-1.12.1-cp39-cp39-win_amd64.whl", hash = "sha256:fd919373be35b9ba54210688265df38ad5e19a530449385c40dab51da407345d"},
|
||||
]
|
||||
opencv-python-headless = [
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||||
{file = "opencv-python-headless-4.6.0.66.tar.gz", hash = "sha256:d5291d7e10aa2c19cab6fd86f0d61af8617290ecd2d7ffcb051e446868d04cc5"},
|
||||
{file = "opencv_python_headless-4.6.0.66-cp36-abi3-macosx_10_15_x86_64.whl", hash = "sha256:21e70f8b0c04098cdf466d27184fe6c3820aaef944a22548db95099959c95889"},
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@ -2694,6 +2763,9 @@ pyasn1-modules = [
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{file = "pyasn1_modules-0.2.8-py3.6.egg", hash = "sha256:cbac4bc38d117f2a49aeedec4407d23e8866ea4ac27ff2cf7fb3e5b570df19e0"},
|
||||
{file = "pyasn1_modules-0.2.8-py3.7.egg", hash = "sha256:c29a5e5cc7a3f05926aff34e097e84f8589cd790ce0ed41b67aed6857b26aafd"},
|
||||
]
|
||||
pycocotools = [
|
||||
{file = "pycocotools-2.0.4.tar.gz", hash = "sha256:2ab586aa389b9657b6d73c2b9a827a3681f8d00f36490c2e8ab05902e3fd9e93"},
|
||||
]
|
||||
pycparser = [
|
||||
{file = "pycparser-2.21-py2.py3-none-any.whl", hash = "sha256:8ee45429555515e1f6b185e78100aea234072576aa43ab53aefcae078162fca9"},
|
||||
{file = "pycparser-2.21.tar.gz", hash = "sha256:e644fdec12f7872f86c58ff790da456218b10f863970249516d60a5eaca77206"},
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||||
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@ -2892,6 +2964,10 @@ requests-oauthlib = [
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|||
{file = "requests-oauthlib-1.3.1.tar.gz", hash = "sha256:75beac4a47881eeb94d5ea5d6ad31ef88856affe2332b9aafb52c6452ccf0d7a"},
|
||||
{file = "requests_oauthlib-1.3.1-py2.py3-none-any.whl", hash = "sha256:2577c501a2fb8d05a304c09d090d6e47c306fef15809d102b327cf8364bddab5"},
|
||||
]
|
||||
rich = [
|
||||
{file = "rich-12.5.1-py3-none-any.whl", hash = "sha256:2eb4e6894cde1e017976d2975ac210ef515d7548bc595ba20e195fb9628acdeb"},
|
||||
{file = "rich-12.5.1.tar.gz", hash = "sha256:63a5c5ce3673d3d5fbbf23cd87e11ab84b6b451436f1b7f19ec54b6bc36ed7ca"},
|
||||
]
|
||||
rsa = [
|
||||
{file = "rsa-4.9-py3-none-any.whl", hash = "sha256:90260d9058e514786967344d0ef75fa8727eed8a7d2e43ce9f4bcf1b536174f7"},
|
||||
{file = "rsa-4.9.tar.gz", hash = "sha256:e38464a49c6c85d7f1351b0126661487a7e0a14a50f1675ec50eb34d4f20ef21"},
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@ -14,14 +14,17 @@ torch = "^1.12.1"
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torchmetrics = "^0.9.3"
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torchvision = "^0.13.1"
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||||
wandb = "^0.13.2"
|
||||
rich = "^12.5.1"
|
||||
pycocotools = "^2.0.4"
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[tool.poetry.dev-dependencies]
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black = {extras = ["jupyter"], version = "^22.8.0"}
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||||
black = { extras = ["jupyter"], version = "^22.8.0" }
|
||||
ipykernel = "^6.15.2"
|
||||
isort = "^5.10.1"
|
||||
matplotlib = "^3.5.3"
|
||||
mypy = "^0.971"
|
||||
onnxruntime = "^1.12.1"
|
||||
onnxruntime-gpu = "^1.12.1"
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pre-commit = "^2.20.0"
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[build-system]
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@ -1,9 +1,8 @@
|
|||
import albumentations as A
|
||||
import pytorch_lightning as pl
|
||||
from albumentations.pytorch import ToTensorV2
|
||||
from torch.utils.data import DataLoader, Subset
|
||||
|
||||
import wandb
|
||||
from albumentations.pytorch import ToTensorV2
|
||||
from torch.utils.data import DataLoader
|
||||
|
||||
from .dataset import RealDataset
|
||||
|
||||
|
|
@ -20,17 +19,18 @@ class Spheres(pl.LightningDataModule):
|
|||
transforms = A.Compose(
|
||||
[
|
||||
A.Flip(),
|
||||
A.ColorJitter(),
|
||||
A.ToGray(p=0.01),
|
||||
A.GaussianBlur(),
|
||||
A.MotionBlur(),
|
||||
A.ISONoise(),
|
||||
A.ImageCompression(),
|
||||
A.Normalize(
|
||||
mean=[0.485, 0.456, 0.406],
|
||||
std=[0.229, 0.224, 0.225],
|
||||
max_pixel_value=255,
|
||||
), # [0, 255] -> coco (?) normalized
|
||||
# A.ColorJitter(),
|
||||
# A.ToGray(p=0.01),
|
||||
# A.GaussianBlur(),
|
||||
# A.MotionBlur(),
|
||||
# A.ISONoise(),
|
||||
# A.ImageCompression(),
|
||||
# A.Normalize(
|
||||
# mean=[0.485, 0.456, 0.406],
|
||||
# std=[0.229, 0.224, 0.225],
|
||||
# max_pixel_value=255,
|
||||
# ), # [0, 255] -> coco (?) normalized
|
||||
A.ToFloat(max_value=255),
|
||||
ToTensorV2(), # HWC -> CHW
|
||||
],
|
||||
bbox_params=A.BboxParams(
|
||||
|
|
@ -57,11 +57,12 @@ class Spheres(pl.LightningDataModule):
|
|||
def val_dataloader(self):
|
||||
transforms = A.Compose(
|
||||
[
|
||||
A.Normalize(
|
||||
mean=[0.485, 0.456, 0.406],
|
||||
std=[0.229, 0.224, 0.225],
|
||||
max_pixel_value=255,
|
||||
), # [0, 255] -> [0.0, 1.0] normalized
|
||||
# A.Normalize(
|
||||
# mean=[0.485, 0.456, 0.406],
|
||||
# std=[0.229, 0.224, 0.225],
|
||||
# max_pixel_value=255,
|
||||
# ), # [0, 255] -> [0.0, 1.0] normalized
|
||||
A.ToFloat(max_value=255),
|
||||
ToTensorV2(), # HWC -> CHW
|
||||
],
|
||||
bbox_params=A.BboxParams(
|
||||
|
|
|
|||
|
|
@ -3,16 +3,14 @@
|
|||
import pytorch_lightning as pl
|
||||
import torch
|
||||
import torchvision
|
||||
import wandb
|
||||
from torchmetrics.detection.mean_ap import MeanAveragePrecision
|
||||
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
|
||||
from torchvision.models.detection.mask_rcnn import (
|
||||
MaskRCNN_ResNet50_FPN_Weights,
|
||||
MaskRCNNPredictor,
|
||||
)
|
||||
|
||||
import wandb
|
||||
from utils.coco_eval import CocoEvaluator
|
||||
from utils.coco_utils import get_coco_api_from_dataset, get_iou_types
|
||||
|
||||
|
||||
def get_model_instance_segmentation(num_classes):
|
||||
# load an instance segmentation model pre-trained on COCO
|
||||
|
|
@ -33,11 +31,10 @@ def get_model_instance_segmentation(num_classes):
|
|||
|
||||
|
||||
class MRCNNModule(pl.LightningModule):
|
||||
def __init__(self, hidden_layer_size, n_classes):
|
||||
def __init__(self, n_classes):
|
||||
super().__init__()
|
||||
|
||||
# Hyperparameters
|
||||
self.hidden_layers_size = hidden_layer_size
|
||||
self.n_classes = n_classes
|
||||
|
||||
# log hyperparameters
|
||||
|
|
@ -46,10 +43,12 @@ class MRCNNModule(pl.LightningModule):
|
|||
# Network
|
||||
self.model = get_model_instance_segmentation(n_classes)
|
||||
|
||||
# pycoco evaluator
|
||||
self.coco = None
|
||||
self.iou_types = get_iou_types(self.model)
|
||||
self.coco_evaluator = None
|
||||
# onnx
|
||||
self.example_input_array = torch.randn(1, 3, 1024, 1024, requires_grad=True)
|
||||
|
||||
def forward(self, imgs):
|
||||
self.model.eval()
|
||||
return self.model(imgs)
|
||||
|
||||
def training_step(self, batch, batch_idx):
|
||||
# unpack batch
|
||||
|
|
@ -67,20 +66,17 @@ class MRCNNModule(pl.LightningModule):
|
|||
return loss
|
||||
|
||||
def on_validation_epoch_start(self):
|
||||
if self.coco is None:
|
||||
self.coco = get_coco_api_from_dataset(self.trainer.val_dataloaders[0].dataset)
|
||||
|
||||
# init coco evaluator
|
||||
self.coco_evaluator = CocoEvaluator(self.coco, self.iou_types)
|
||||
self.metric = MeanAveragePrecision(iou_type="bbox")
|
||||
|
||||
def validation_step(self, batch, batch_idx):
|
||||
# unpack batch
|
||||
images, targets = batch
|
||||
|
||||
# compute metrics using pycocotools
|
||||
outputs = self.model(images)
|
||||
res = {target["image_id"].item(): output for target, output in zip(targets, outputs)}
|
||||
self.coco_evaluator.update(res)
|
||||
preds = self.model(images)
|
||||
for pred, target in zip(preds, targets):
|
||||
pred["masks"] = pred["masks"].squeeze(1).bool()
|
||||
target["masks"] = target["masks"].squeeze(1).bool()
|
||||
self.metric.update(preds, targets)
|
||||
|
||||
# compute validation loss
|
||||
self.model.train()
|
||||
|
|
@ -93,48 +89,22 @@ class MRCNNModule(pl.LightningModule):
|
|||
|
||||
def validation_epoch_end(self, outputs):
|
||||
# log validation loss
|
||||
loss_dict = {k: torch.stack([d[k] for d in outputs]).mean() for k in outputs[0].keys()}
|
||||
loss_dict = {
|
||||
k: torch.stack([d[k] for d in outputs]).mean() for k in outputs[0].keys()
|
||||
} # TODO: update un dict object
|
||||
self.log_dict(loss_dict)
|
||||
|
||||
# accumulate all predictions
|
||||
self.coco_evaluator.accumulate()
|
||||
self.coco_evaluator.summarize()
|
||||
|
||||
YEET = {
|
||||
"valid,bbox,AP,IoU=0.50:0.,area=all,maxDets=100": self.coco_evaluator.coco_eval["bbox"].stats[0],
|
||||
"valid,bbox,AP,IoU=0.50,area=all,maxDets=100": self.coco_evaluator.coco_eval["bbox"].stats[1],
|
||||
"valid,bbox,AP,IoU=0.75,area=all,maxDets=100": self.coco_evaluator.coco_eval["bbox"].stats[2],
|
||||
"valid,bbox,AP,IoU=0.50:0.,area=small,maxDets=100": self.coco_evaluator.coco_eval["bbox"].stats[3],
|
||||
"valid,bbox,AP,IoU=0.50:0.,area=medium,maxDets=100": self.coco_evaluator.coco_eval["bbox"].stats[4],
|
||||
"valid,bbox,AP,IoU=0.50:0.,area=large,maxDets=100": self.coco_evaluator.coco_eval["bbox"].stats[5],
|
||||
"valid,bbox,AR,IoU=0.50:0.,area=all,maxDets=1": self.coco_evaluator.coco_eval["bbox"].stats[6],
|
||||
"valid,bbox,AR,IoU=0.50:0.,area=all,maxDets=10": self.coco_evaluator.coco_eval["bbox"].stats[7],
|
||||
"valid,bbox,AR,IoU=0.50:0.,area=all,maxDets=100": self.coco_evaluator.coco_eval["bbox"].stats[8],
|
||||
"valid,bbox,AR,IoU=0.50:0.,area=small,maxDets=100": self.coco_evaluator.coco_eval["bbox"].stats[9],
|
||||
"valid,bbox,AR,IoU=0.50:0.,area=medium,maxDets=100": self.coco_evaluator.coco_eval["bbox"].stats[10],
|
||||
"valid,bbox,AR,IoU=0.50:0.,area=large,maxDets=100": self.coco_evaluator.coco_eval["bbox"].stats[11],
|
||||
"valid,segm,AP,IoU=0.50:0.,area=all,maxDets=100": self.coco_evaluator.coco_eval["segm"].stats[0],
|
||||
"valid,segm,AP,IoU=0.50,area=all,maxDets=100": self.coco_evaluator.coco_eval["segm"].stats[1],
|
||||
"valid,segm,AP,IoU=0.75,area=all,maxDets=100": self.coco_evaluator.coco_eval["segm"].stats[2],
|
||||
"valid,segm,AP,IoU=0.50:0.,area=small,maxDets=100": self.coco_evaluator.coco_eval["segm"].stats[3],
|
||||
"valid,segm,AP,IoU=0.50:0.,area=medium,maxDets=100": self.coco_evaluator.coco_eval["segm"].stats[4],
|
||||
"valid,segm,AP,IoU=0.50:0.,area=large,maxDets=100": self.coco_evaluator.coco_eval["segm"].stats[5],
|
||||
"valid,segm,AR,IoU=0.50:0.,area=all,maxDets=1": self.coco_evaluator.coco_eval["segm"].stats[6],
|
||||
"valid,segm,AR,IoU=0.50:0.,area=all,maxDets=10": self.coco_evaluator.coco_eval["segm"].stats[7],
|
||||
"valid,segm,AR,IoU=0.50:0.,area=all,maxDets=100": self.coco_evaluator.coco_eval["segm"].stats[8],
|
||||
"valid,segm,AR,IoU=0.50:0.,area=small,maxDets=100": self.coco_evaluator.coco_eval["segm"].stats[9],
|
||||
"valid,segm,AR,IoU=0.50:0.,area=medium,maxDets=100": self.coco_evaluator.coco_eval["segm"].stats[10],
|
||||
"valid,segm,AR,IoU=0.50:0.,area=large,maxDets=100": self.coco_evaluator.coco_eval["segm"].stats[11],
|
||||
}
|
||||
|
||||
self.log_dict(YEET)
|
||||
# log metrics
|
||||
metric_dict = self.metric.compute()
|
||||
metric_dict = {f"valid/{key}": val for key, val in metric_dict.items()}
|
||||
self.log_dict(metric_dict)
|
||||
|
||||
def configure_optimizers(self):
|
||||
optimizer = torch.optim.SGD(
|
||||
optimizer = torch.optim.Adam(
|
||||
self.parameters(),
|
||||
lr=wandb.config.LEARNING_RATE,
|
||||
momentum=wandb.config.MOMENTUM,
|
||||
weight_decay=wandb.config.WEIGHT_DECAY,
|
||||
# momentum=wandb.config.MOMENTUM,
|
||||
# weight_decay=wandb.config.WEIGHT_DECAY,
|
||||
)
|
||||
|
||||
# scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
dbe91cbc0788d93595dac272825aa57412f04d70
|
||||
1163aa4f284163ecb7d4289c4c5217ea28f63770
|
||||
|
|
@ -1,152 +0,0 @@
|
|||
"""Pytorch lightning wrapper for model."""
|
||||
|
||||
import pytorch_lightning as pl
|
||||
import torch
|
||||
import torchvision
|
||||
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
|
||||
from torchvision.models.detection.mask_rcnn import MaskRCNNPredictor
|
||||
|
||||
import wandb
|
||||
|
||||
|
||||
def get_model_instance_segmentation(num_classes):
|
||||
# load an instance segmentation model pre-trained on COCO
|
||||
model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True)
|
||||
|
||||
# get number of input features for the classifier
|
||||
in_features = model.roi_heads.box_predictor.cls_score.in_features
|
||||
# replace the pre-trained head with a new one
|
||||
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
|
||||
|
||||
# now get the number of input features for the mask classifier
|
||||
in_features_mask = model.roi_heads.mask_predictor.conv5_mask.in_channels
|
||||
hidden_layer = 256
|
||||
# and replace the mask predictor with a new one
|
||||
model.roi_heads.mask_predictor = MaskRCNNPredictor(in_features_mask, hidden_layer, num_classes)
|
||||
|
||||
return model
|
||||
|
||||
|
||||
class MRCNNModule(pl.LightningModule):
|
||||
def __init__(self, hidden_layer_size, n_classes):
|
||||
super().__init__()
|
||||
|
||||
# Hyperparameters
|
||||
self.hidden_layers_size = hidden_layer_size
|
||||
self.n_classes = n_classes
|
||||
|
||||
# log hyperparameters
|
||||
self.save_hyperparameters()
|
||||
|
||||
# Network
|
||||
self.model = get_model_instance_segmentation(n_classes)
|
||||
|
||||
# onnx
|
||||
self.example_input_array = torch.randn(1, 3, 512, 512, requires_grad=True)
|
||||
|
||||
def forward(self, imgs):
|
||||
self.model.eval()
|
||||
return self.model(imgs)
|
||||
|
||||
def training_step(self, batch, batch_idx):
|
||||
# unpack batch
|
||||
images, targets = batch
|
||||
|
||||
# enable train mode
|
||||
# self.model.train()
|
||||
|
||||
# fasterrcnn takes both images and targets for training
|
||||
loss_dict = self.model(images, targets)
|
||||
loss_dict = {f"train/{key}": val for key, val in loss_dict.items()}
|
||||
loss = sum(loss_dict.values())
|
||||
|
||||
# log everything
|
||||
self.log_dict(loss_dict)
|
||||
self.log("train/loss", loss)
|
||||
|
||||
return {"loss": loss, "log": loss_dict}
|
||||
|
||||
# def validation_step(self, batch, batch_idx):
|
||||
# # unpack batch
|
||||
# images, targets = batch
|
||||
|
||||
# # enable eval mode
|
||||
# # self.detector.eval()
|
||||
|
||||
# # make a prediction
|
||||
# preds = self.model(images)
|
||||
|
||||
# # compute validation loss
|
||||
# self.val_loss = torch.mean(
|
||||
# torch.stack(
|
||||
# [
|
||||
# self.accuracy(
|
||||
# target,
|
||||
# pred["boxes"],
|
||||
# iou_threshold=0.5,
|
||||
# )
|
||||
# for target, pred in zip(targets, preds)
|
||||
# ],
|
||||
# )
|
||||
# )
|
||||
|
||||
# return self.val_loss
|
||||
|
||||
# def accuracy(self, src_boxes, pred_boxes, iou_threshold=1.0):
|
||||
# """
|
||||
# The accuracy method is not the one used in the evaluator but very similar
|
||||
# """
|
||||
# total_gt = len(src_boxes)
|
||||
# total_pred = len(pred_boxes)
|
||||
# if total_gt > 0 and total_pred > 0:
|
||||
|
||||
# # Define the matcher and distance matrix based on iou
|
||||
# matcher = Matcher(iou_threshold, iou_threshold, allow_low_quality_matches=False)
|
||||
# match_quality_matrix = box_iou(src_boxes, pred_boxes)
|
||||
|
||||
# results = matcher(match_quality_matrix)
|
||||
|
||||
# true_positive = torch.count_nonzero(results.unique() != -1)
|
||||
# matched_elements = results[results > -1]
|
||||
|
||||
# # in Matcher, a pred element can be matched only twice
|
||||
# false_positive = torch.count_nonzero(results == -1) + (
|
||||
# len(matched_elements) - len(matched_elements.unique())
|
||||
# )
|
||||
# false_negative = total_gt - true_positive
|
||||
|
||||
# return true_positive / (true_positive + false_positive + false_negative)
|
||||
|
||||
# elif total_gt == 0:
|
||||
# if total_pred > 0:
|
||||
# return torch.tensor(0.0).cuda()
|
||||
# else:
|
||||
# return torch.tensor(1.0).cuda()
|
||||
# elif total_gt > 0 and total_pred == 0:
|
||||
# return torch.tensor(0.0).cuda()
|
||||
|
||||
def configure_optimizers(self):
|
||||
optimizer = torch.optim.SGD(
|
||||
self.parameters(),
|
||||
lr=wandb.config.LEARNING_RATE,
|
||||
momentum=wandb.config.MOMENTUM,
|
||||
weight_decay=wandb.config.WEIGHT_DECAY,
|
||||
)
|
||||
|
||||
# scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(
|
||||
# optimizer,
|
||||
# T_0=3,
|
||||
# T_mult=1,
|
||||
# lr=wandb.config.LEARNING_RATE_MIN,
|
||||
# verbose=True,
|
||||
# )
|
||||
|
||||
# return {
|
||||
# "optimizer": optimizer,
|
||||
# "lr_scheduler": {
|
||||
# "scheduler": scheduler,
|
||||
# "monitor": "val_accuracy",
|
||||
# },
|
||||
# }
|
||||
|
||||
return optimizer
|
||||
|
|
@ -1 +1 @@
|
|||
df787682f8ee4387834b713d1d48437b94d45f61
|
||||
ecf0b9ce39e210bc605fd3eab9db8b1215c35fda
|
||||
70
src/notebooks/test.py
Normal file
70
src/notebooks/test.py
Normal file
|
|
@ -0,0 +1,70 @@
|
|||
import torch
|
||||
from torchmetrics.detection.mean_ap import MeanAveragePrecision
|
||||
|
||||
preds = [
|
||||
dict(
|
||||
boxes=torch.tensor(
|
||||
[
|
||||
[880.0560, 41.7845, 966.9839, 131.3355],
|
||||
[1421.0029, 682.4420, 1512.7570, 765.2380],
|
||||
[132.0775, 818.5026, 216.0825, 1020.8573],
|
||||
]
|
||||
),
|
||||
scores=torch.tensor(
|
||||
[0.9989, 0.9936, 0.0932],
|
||||
),
|
||||
labels=torch.tensor(
|
||||
[1, 1, 1],
|
||||
),
|
||||
)
|
||||
]
|
||||
target = [
|
||||
dict(
|
||||
boxes=torch.tensor(
|
||||
[[879, 39, 1513, 766]],
|
||||
),
|
||||
labels=torch.tensor(
|
||||
[1],
|
||||
),
|
||||
)
|
||||
]
|
||||
metric = MeanAveragePrecision()
|
||||
metric.update(preds, target)
|
||||
|
||||
from pprint import pprint
|
||||
|
||||
pprint(metric.compute())
|
||||
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
preds = [
|
||||
dict(
|
||||
boxes=torch.tensor(
|
||||
[
|
||||
[880.0560, 41.7845, 1500, 700.3355],
|
||||
]
|
||||
),
|
||||
scores=torch.tensor(
|
||||
[0.9989],
|
||||
),
|
||||
labels=torch.tensor(
|
||||
[1],
|
||||
),
|
||||
)
|
||||
]
|
||||
target = [
|
||||
dict(
|
||||
boxes=torch.tensor(
|
||||
[[879, 39, 1513, 766]],
|
||||
),
|
||||
labels=torch.tensor(
|
||||
[1],
|
||||
),
|
||||
)
|
||||
]
|
||||
metric = MeanAveragePrecision()
|
||||
metric.update(preds, target)
|
||||
|
||||
from pprint import pprint
|
||||
|
||||
pprint(metric.compute())
|
||||
39
src/train.py
39
src/train.py
|
|
@ -1,43 +1,52 @@
|
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import logging
|
||||
|
||||
import pytorch_lightning as pl
|
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from pytorch_lightning.callbacks import RichProgressBar
|
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import torch
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import wandb
|
||||
from pytorch_lightning.callbacks import ModelCheckpoint, RichProgressBar
|
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from pytorch_lightning.loggers import WandbLogger
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|
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import wandb
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from data import Spheres
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from mrcnn import MRCNNModule
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from utils import ArtifactLog, TableLog
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if __name__ == "__main__":
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# setup logging
|
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logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
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logging.basicConfig(
|
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level=logging.INFO,
|
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format="%(levelname)s: %(message)s",
|
||||
)
|
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|
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# setup wandb
|
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logger = WandbLogger(
|
||||
project="Mask R-CNN",
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||||
config="wandb.yaml",
|
||||
save_dir="/tmp/",
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log_model="all",
|
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settings=wandb.Settings(
|
||||
code_dir="./src/",
|
||||
),
|
||||
)
|
||||
|
||||
# seed random generators
|
||||
pl.seed_everything(wandb.config.SEED, workers=True)
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pl.seed_everything(
|
||||
seed=wandb.config.SEED,
|
||||
workers=True,
|
||||
)
|
||||
|
||||
# Create Network
|
||||
module = MRCNNModule(
|
||||
hidden_layer_size=-1,
|
||||
n_classes=2,
|
||||
)
|
||||
|
||||
# load checkpoint
|
||||
# state_dict = torch.load("checkpoints/synth.pth")
|
||||
# state_dict = dict([(f"model.{key}", value) for key, value in state_dict.items()])
|
||||
# model.load_state_dict(state_dict)
|
||||
# module.load_state_dict(torch.load()["state_dict"])
|
||||
# module.load_from_checkpoint("/tmp/model.ckpt")
|
||||
|
||||
# log gradients and weights regularly
|
||||
logger.watch(module.model, log="all")
|
||||
logger.watch(
|
||||
model=module.model,
|
||||
log="all",
|
||||
)
|
||||
|
||||
# Create the dataloaders
|
||||
datamodule = Spheres()
|
||||
|
|
@ -52,15 +61,17 @@ if __name__ == "__main__":
|
|||
logger=logger,
|
||||
log_every_n_steps=5,
|
||||
val_check_interval=50,
|
||||
callbacks=[RichProgressBar(), ArtifactLog()],
|
||||
# callbacks=[RichProgressBar(), ArtifactLog(), TableLog()],
|
||||
callbacks=[
|
||||
ModelCheckpoint(monitor="valid/loss", mode="min"),
|
||||
RichProgressBar(),
|
||||
],
|
||||
# profiler="advanced",
|
||||
num_sanity_val_steps=3,
|
||||
devices=[0],
|
||||
devices=[1],
|
||||
)
|
||||
|
||||
# actually train the model
|
||||
trainer.fit(model=module, datamodule=datamodule)
|
||||
|
||||
# stop wandb
|
||||
wandb.run.finish()
|
||||
wandb.run.finish() # type: ignore
|
||||
|
|
|
|||
|
|
@ -1,2 +1,2 @@
|
|||
from .callback import ArtifactLog, TableLog
|
||||
from .callback import TableLog
|
||||
from .paste import RandomPaste
|
||||
|
|
|
|||
|
|
@ -1,8 +1,5 @@
|
|||
import numpy as np
|
||||
import torch
|
||||
from pytorch_lightning.callbacks import Callback
|
||||
|
||||
import wandb
|
||||
from pytorch_lightning.callbacks import Callback
|
||||
|
||||
columns = [
|
||||
"ID",
|
||||
|
|
@ -64,34 +61,3 @@ class TableLog(Callback):
|
|||
)
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
class ArtifactLog(Callback):
|
||||
# def on_fit_start(self, trainer, pl_module):
|
||||
# self.best = 1
|
||||
|
||||
def on_train_epoch_end(self, trainer, pl_module):
|
||||
# create checkpoint
|
||||
torch.save(pl_module.state_dict(), "checkpoints/model.pth")
|
||||
|
||||
# def on_validation_epoch_start(self, trainer, pl_module):
|
||||
# self.dices = []
|
||||
|
||||
# def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
|
||||
# # unpacking
|
||||
# metrics, _ = outputs
|
||||
# self.dices.append(metrics["dice"].cpu())
|
||||
|
||||
# def on_validation_epoch_end(self, trainer, pl_module):
|
||||
# dice = np.mean(self.dices)
|
||||
|
||||
# if dice < self.best:
|
||||
# self.best = dice
|
||||
|
||||
# # create checkpoint
|
||||
# trainer.save_checkpoint("checkpoints/model.ckpt")
|
||||
|
||||
# # log artifact
|
||||
# artifact = wandb.Artifact("ckpt", type="model")
|
||||
# artifact.add_file("checkpoints/model.ckpt")
|
||||
# wandb.run.log_artifact(artifact)
|
||||
|
|
|
|||
16
wandb.yaml
16
wandb.yaml
|
|
@ -1,9 +1,9 @@
|
|||
DIR_TRAIN_IMG:
|
||||
value: "/media/disk1/lfainsin/BACKGROUND/"
|
||||
DIR_VALID_IMG:
|
||||
value: "/media/disk1/lfainsin/TEST_batched/"
|
||||
DIR_SPHERE:
|
||||
value: "/media/disk1/lfainsin/SPHERES/"
|
||||
# DIR_TRAIN_IMG:
|
||||
# value: "/media/disk1/lfainsin/BACKGROUND/"
|
||||
# DIR_VALID_IMG:
|
||||
# value: "/media/disk1/lfainsin/TEST_batched/"
|
||||
# DIR_SPHERE:
|
||||
# value: "/media/disk1/lfainsin/SPHERES/"
|
||||
|
||||
N_CHANNELS:
|
||||
value: 3
|
||||
|
|
@ -28,7 +28,7 @@ WORKERS:
|
|||
value: 16
|
||||
|
||||
EPOCHS:
|
||||
value: 10
|
||||
value: 50
|
||||
TRAIN_BATCH_SIZE:
|
||||
value: 10
|
||||
VALID_BATCH_SIZE:
|
||||
|
|
@ -37,7 +37,7 @@ PREFETCH_FACTOR:
|
|||
value: 2
|
||||
|
||||
LEARNING_RATE:
|
||||
value: 0.005
|
||||
value: 0.0005
|
||||
WEIGHT_DECAY:
|
||||
value: 0.0005
|
||||
MOMENTUM:
|
||||
|
|
|
|||
Loading…
Reference in a new issue