From 3ca04f057650669ca0bd548663dfc9ca3421a64a Mon Sep 17 00:00:00 2001 From: Jason Ren Date: Mon, 18 Jul 2022 16:43:56 -0400 Subject: [PATCH] Update README.md fix `XDG_CACHE` to `XDG_CACHE_HOME` --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 3738ddee..2769d623 100644 --- a/README.md +++ b/README.md @@ -132,12 +132,12 @@ Torrents](http://academictorrents.com/)) and prepare ImageNet the first time it is used. However, since ImageNet is quite large, this requires a lot of disk space and time. If you already have ImageNet on your disk, you can speed things up by putting the data into -`${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/data/` (which defaults to +`${XDG_CACHE_HOME}/autoencoders/data/ILSVRC2012_{split}/data/` (which defaults to `~/.cache/autoencoders/data/ILSVRC2012_{split}/data/`), where `{split}` is one of `train`/`validation`. It should have the following structure: ``` -${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/data/ +${XDG_CACHE_HOME}/autoencoders/data/ILSVRC2012_{split}/data/ ├── n01440764 │ ├── n01440764_10026.JPEG │ ├── n01440764_10027.JPEG @@ -151,12 +151,12 @@ ${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/data/ If you haven't extracted the data, you can also place `ILSVRC2012_img_train.tar`/`ILSVRC2012_img_val.tar` (or symlinks to them) into -`${XDG_CACHE}/autoencoders/data/ILSVRC2012_train/` / -`${XDG_CACHE}/autoencoders/data/ILSVRC2012_validation/`, which will then be +`${XDG_CACHE_HOME}/autoencoders/data/ILSVRC2012_train/` / +`${XDG_CACHE_HOME}/autoencoders/data/ILSVRC2012_validation/`, which will then be extracted into above structure without downloading it again. Note that this will only happen if neither a folder -`${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/data/` nor a file -`${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/.ready` exist. Remove them +`${XDG_CACHE_HOME}/autoencoders/data/ILSVRC2012_{split}/data/` nor a file +`${XDG_CACHE_HOME}/autoencoders/data/ILSVRC2012_{split}/.ready` exist. Remove them if you want to force running the dataset preparation again.