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Setting the transform of the data module, should change the transform of the underlying dataset.
import pl_bolts from pl_bolts.datamodules import MNISTDataModule from torchvision import transforms as transform_lib mnist = MNISTDataModule(data_dir = "/tmp/mnist") mnist.prepare_data() mnist.setup(stage="fit") print("before set_transform") print(mnist.dataset_train.dataset.transforms) # # # Expect this to change the train dataset transform? mnist.train_transforms = transform_lib.Compose( [transform_lib.ToTensor(), transform_lib.Normalize(mean=(0.6,), std=(0.5,))] ) # # expect to print the new transform print("after transform") print(mnist.dataset_train.dataset.transforms)
before set_transform StandardTransform Transform: Compose( ToTensor() ) after transform StandardTransform Transform: Compose( ToTensor() )
I expected the datset transform to differ after I set the transform
conda
pip
The text was updated successfully, but these errors were encountered:
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🐛 Bug
Setting the transform of the data module, should change the transform of the underlying dataset.
Results
Expected
I expected the datset transform to differ after I set the transform
Environment
conda
,pip
, source): pipThe text was updated successfully, but these errors were encountered: