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feat: switch from poetry to micromamba
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.gitignore
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.gitignore
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data/
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dataset*
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dataset*/
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lightning_logs
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*.parquet
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__pycache__
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.venv/
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lightning_logs/
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__pycache__/
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*.jpg
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*.jpg
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*.png
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34
env.yml
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env.yml
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name: qcav
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channels:
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- nodefaults
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- pytorch
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- nvidia
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- conda-forge
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dependencies:
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# basic python
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- rich
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# science
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- numpy
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- scipy
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- opencv
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# pytorch
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- pytorch
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- torchvision
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- torchaudio
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- pytorch-cuda=11.8
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- pytorch-lightning
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# deep learning libraries
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- transformers
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- datasets
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- timm
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# dev tools
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- ruff
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- isort
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- mypy
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- pre-commit
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# logging
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- tensorboard
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# visualization
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- matplotlib
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5596
poetry.lock
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5596
poetry.lock
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[virtualenvs]
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create = true
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in-project = true
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[tool.poetry]
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[tool.ruff]
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authors = ["Laurent Fainsin <laurentfainsin@protonmail.com>"]
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line-length = 120
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description = ""
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select = ["E", "F", "I"]
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name = "label-studio"
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version = "1.0.0"
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[tool.poetry.dependencies]
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datasets = "^2.9.0"
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fastapi = "0.86.0"
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jsonargparse = {extras = ["signatures"], version = "^4.20.0"}
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lightning = "1.9.1"
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matplotlib = "^3.7.0"
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numpy = "^1.24.2"
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opencv-python = "^4.7.0.72"
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opencv-python-headless = "^4.7.0.72"
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python = ">=3.8,<3.12"
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rich = "^13.3.1"
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scipy = "^1.10.0"
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timm = "^0.6.12"
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torch = "^1.13.1"
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transformers = "^4.26.1"
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[tool.poetry.group.notebooks]
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optional = true
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[tool.poetry.group.notebooks.dependencies]
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ipykernel = "^6.20.2"
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ipywidgets = "^8.0.4"
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jupyter = "^1.0.0"
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matplotlib = "^3.6.3"
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[tool.poetry.group.dev.dependencies]
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Flake8-pyproject = "^1.1.0"
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bandit = "^1.7.4"
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black = "^22.8.0"
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flake8 = "^5.0.4"
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flake8-docstrings = "^1.6.0"
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isort = "^5.10.1"
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mypy = "^0.971"
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pre-commit = "^2.20.0"
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tensorboard = "^2.12.0"
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torchtyping = "^0.1.4"
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torch-tb-profiler = "^0.4.1"
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[build-system]
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build-backend = "poetry.core.masonry.api"
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requires = ["poetry-core"]
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[tool.flake8]
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# rules ignored
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extend-ignore = ["W503", "D401", "D100", "D104"]
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per-file-ignores = ["__init__.py:F401"]
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# black
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ignore = "E203"
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max-line-length = 120
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[tool.black]
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[tool.black]
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exclude = '''
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exclude = '''
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import datasets
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import datasets
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import torch
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import torch
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from lightning.pytorch import LightningDataModule
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from pytorch_lightning import LightningDataModule
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from lightning.pytorch.trainer.supporters import CombinedLoader
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from pytorch_lightning.utilities import CombinedLoader
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from torch.utils.data import DataLoader
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from torch.utils.data import DataLoader
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from torchvision.datasets import ImageFolder
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from torchvision.transforms import AugMix
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from torchvision.transforms import AugMix
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from transformers import DetrFeatureExtractor
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from transformers import DetrFeatureExtractor
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from lightning.pytorch.callbacks import (
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from datamodule import DETRDataModule
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from module import DETR
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from pytorch_lightning.callbacks import (
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ModelCheckpoint,
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ModelCheckpoint,
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RichModelSummary,
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RichModelSummary,
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RichProgressBar,
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RichProgressBar,
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)
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)
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from lightning.pytorch.cli import LightningCLI
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from pytorch_lightning.cli import LightningCLI
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from datamodule import DETRDataModule
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from module import DETR
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class MyLightningCLI(LightningCLI):
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class MyLightningCLI(LightningCLI):
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import torch
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import torch
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from lightning.pytorch import LightningModule
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from PIL import ImageDraw
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from PIL import ImageDraw
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from pytorch_lightning import LightningModule
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from transformers import (
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from transformers import (
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DetrForObjectDetection,
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DetrForObjectDetection,
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get_cosine_with_hard_restarts_schedule_with_warmup,
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get_cosine_with_hard_restarts_schedule_with_warmup,
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@ -74,8 +74,6 @@ class SpherePredict(datasets.GeneratorBasedBuilder):
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if __name__ == "__main__":
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if __name__ == "__main__":
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from PIL import ImageDraw
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# load dataset
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# load dataset
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dataset = datasets.load_dataset("src/spheres_predict.py", split="train")
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dataset = datasets.load_dataset("src/spheres_predict.py", split="train")
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