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CachedDatasetfor the case of all parts being cached. It supports random access then, but previously it was broken due to still callingself.dataset.get_sample[s]with the out parameter being allFalse.Dataset.create_torch_datasettransformparameterdival.util.torch_utilitytorch.utils.data.IterableDataset(instead of usingtorch.utils.data.Datasetand ignoring the index) for generator-based datasets'ellipses'standard dataset pickleable and addfixed_noise_seedsparameter (stillTrueby default, in contrast tonoise_seeds(used for the ground truth image generation), which isFalseby default. Both (different) defaults are kept like they were).evaluation.runimplementation by avoiding to save reconstructions when run withsave_reconstructions=False, reducing RAM usageFBPUNetReconstructor, remove warning about shuffling not working for generator-based datasets (forshuffle=True, it will raise an error due toDataset.create_torch_datasetnow returning atorch.utils.data.IterableDataset, and forshuffle='auto'the docs forStandardLearnedReconstructordescribe that shuffling is not used in this case)StandardLearnedReconstructorinit_transformin a subclassshuffleoption, defaulting to'auto'which depends on whether the dataset supports random access. Previouslyshuffle=Truewas always used, but for generator-based datasets the index was ignored, effectively disabling shuffling.dival.util.odl_utility.patch_ray_trafo_for_pickling