From 304efee0ecca24e96df9a8e0d29845c691457c34 Mon Sep 17 00:00:00 2001 From: KumoLiu Date: Thu, 27 Jul 2023 11:38:42 +0800 Subject: [PATCH] update docstring Signed-off-by: KumoLiu --- monai/data/dataset.py | 9 --------- 1 file changed, 9 deletions(-) diff --git a/monai/data/dataset.py b/monai/data/dataset.py index 6d9f4b7751..acf9f476ab 100644 --- a/monai/data/dataset.py +++ b/monai/data/dataset.py @@ -1550,15 +1550,6 @@ def __init__( specify the gpu to be used. hash_func: a callable to compute hash from data items to be cached. defaults to `monai.data.utils.pickle_hashing`. - pickle_module: string representing the module used for pickling metadata and objects, - default to `"pickle"`. due to the pickle limitation in multi-processing of Dataloader, - we can't use `pickle` as arg directly, so here we use a string name instead. - if want to use other pickle module at runtime, just register like: - >>> from monai.data import utils - >>> utils.SUPPORTED_PICKLE_MOD["test"] = other_pickle - this arg is used by `torch.save`, for more details, please check: - https://pytorch.org/docs/stable/generated/torch.save.html#torch.save, - and ``monai.data.utils.SUPPORTED_PICKLE_MOD``. hash_transform: a callable to compute hash from the transform information when caching. This may reduce errors due to transforms changing during experiments. Default to None (no hash). Other options are `pickle_hashing` and `json_hashing` functions from `monai.data.utils`.