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LightningDataModule -from lightning.pytorch.trainer.supporters import CombinedLoader +from pytorch_lightning import LightningDataModule +from pytorch_lightning.utilities import CombinedLoader from torch.utils.data import DataLoader -from torchvision.datasets import ImageFolder from torchvision.transforms import AugMix from transformers import DetrFeatureExtractor diff --git a/src/main.py b/src/main.py index 70a7bc9..fd71af4 100644 --- a/src/main.py +++ b/src/main.py @@ -1,12 +1,11 @@ -from lightning.pytorch.callbacks import ( +from datamodule import DETRDataModule +from module import DETR +from pytorch_lightning.callbacks import ( ModelCheckpoint, RichModelSummary, RichProgressBar, ) -from lightning.pytorch.cli import LightningCLI - -from datamodule import DETRDataModule -from module import DETR +from pytorch_lightning.cli import LightningCLI class MyLightningCLI(LightningCLI): diff --git a/src/module.py b/src/module.py index 6daf8fb..a745c39 100644 --- a/src/module.py +++ b/src/module.py @@ -1,6 +1,6 @@ import torch -from lightning.pytorch import LightningModule from PIL import ImageDraw +from pytorch_lightning import LightningModule from transformers import ( DetrForObjectDetection, get_cosine_with_hard_restarts_schedule_with_warmup, diff --git a/src/spheres_predict.py b/src/spheres_predict.py index 79e75b3..28a2235 100644 --- a/src/spheres_predict.py +++ b/src/spheres_predict.py @@ -74,8 +74,6 @@ class SpherePredict(datasets.GeneratorBasedBuilder): if __name__ == "__main__": - from PIL import ImageDraw - # load dataset dataset = datasets.load_dataset("src/spheres_predict.py", split="train")