diff --git a/poetry.lock b/poetry.lock index 60e716d21e0d9c35ad1e5138076e4c0adc6cc104..ca4e556be6ef15ace767759b6ed7064447a54b50 100644 --- a/poetry.lock +++ b/poetry.lock @@ -690,62 +690,57 @@ i18n = ["Babel (>=2.7)"] [[package]] name = "lightning" -version = "2.2.1" +version = "2.3.0" description = "The Deep Learning framework to train, deploy, and ship AI products Lightning fast." optional = false python-versions = ">=3.8" files = [ - {file = "lightning-2.2.1-py3-none-any.whl", hash = "sha256:fec9b49d29a6019e8fe49e825082bab8d5ea3fde8e4b36dcf5c8896c2bdb86c3"}, - {file = "lightning-2.2.1.tar.gz", hash = "sha256:b3e46d596b32cafd1fb9b21fdba1b1767df97b1af5cc702693d1c51df60b19aa"}, + {file = "lightning-2.3.0-py3-none-any.whl", hash = "sha256:ed66c2053be1295c8452b996b719badf5a26a0652607c121103dfdd5d2dccfae"}, + {file = "lightning-2.3.0.tar.gz", hash = "sha256:4bb4d6e3650d2d5f544ad60853a22efc4e164aa71b9596d13f0454b29df05130"}, ] [package.dependencies] -fsspec = {version = 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= "pytorch-gpu-src" } +# torchvision = { version = "0.17.2+cu118", source = "pytorch-gpu-src" } +# lightning = "2.2.1" -[[tool.poetry.source]] -name = "pytorch-gpu-src" -url = "https://download.pytorch.org/whl/cu118" -priority = "explicit" +# [[tool.poetry.source]] +# name = "pytorch-gpu-src" +# url = "https://download.pytorch.org/whl/cu118" +# priority = "explicit" [build-system] requires = ["poetry-core"] diff --git a/scripts/train_BENv2.py b/scripts/train_BENv2.py index 9a0d47781ed4eb6f8bfb6c89866fac93eee90570..ea3022fdc9d97d1f9399d5bd98f29f5ea73d6234 100644 --- a/scripts/train_BENv2.py +++ b/scripts/train_BENv2.py @@ -12,6 +12,7 @@ from configilm.ConfigILM import ILMType from configilm.extra.BENv2_utils import STANDARD_BANDS, resolve_data_dir from configilm.extra.DataModules.BENv2_DataModule import BENv2DataModule from lightning.pytorch.loggers import WandbLogger +from torchvision import transforms from ben_publication.BENv2ImageClassifier import BENv2ImageEncoder @@ -134,6 +135,17 @@ def main( num_workers_dataloader=workers, img_size=(channels, img_size, img_size), ) + mean = dm.train_transform.transforms[5].mean + std = dm.train_transform.transforms[5].std + train_transforms = transforms.Compose( + [ + transforms.RandomHorizontalFlip(), + transforms.RandomVerticalFlip(), + # torchvision.transforms.RandomRotation(180), + transforms.Normalize(mean, std), + ] + ) + dm.train_transform = train_transforms # fixed model parameters based on the BigEarthNet v2.0 dataset config = ILMConfiguration(