Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[Feature] Support donwloading datasets from opendatalab #212

Merged
merged 13 commits into from
Jun 26, 2023

Conversation

HAOCHENYE
Copy link
Contributor

@HAOCHENYE HAOCHENYE commented May 11, 2023

Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers.

Motivation

Support downloading datasets from opendatalab and preprocessing the data by the script provided by OpenMMLab downstream repositories.

For example, users can download the target format for specified dataset by the following commands:

mim download mmdet --dataset coco2017

Then users can run training, testing without preparing the dataset by themselves.

The downstream repos should provide the dataset-index.yml in the project root directory like this:

voc2007:
  dataset: PASCAL_VOC2007
  download_root: data
  data_root: data
  script: tools/dataset_converters/scripts/preprocess_voc2007.sh

voc2012:
  dataset: PASCAL_VOC2012
  download_root: data
  data_root: data
  script: tools/dataset_converters/scripts/preprocess_voc2012.sh

coco2017:
  dataset: COCO_2017
  download_root: data
  data_root: data/coco
  script: tools/dataset_converters/scripts/preprocess_coco2017.sh

the script field represents the preparing script:

#!/usr/bin/env bash

DOWNLOAD_DIR=$1
DATA_ROOT=$2

unzip $DOWNLOAD_DIR/COCO_2017/raw/Images/val2017.zip -d $DATA_ROOT
unzip $DOWNLOAD_DIR/COCO_2017/raw/Images/train2017.zip -d $DATA_ROOT
unzip $DOWNLOAD_DIR/COCO_2017/raw/Images/test2017.zip -d $DATA_ROOT/
unzip $DOWNLOAD_DIR/COCO_2017/raw/Images/unlabeled2017.zip -d $DATA_ROOT
unzip $DOWNLOAD_DIR/COCO_2017/raw/Annotations/stuff_annotations_trainval2017.zip -d $DATA_ROOT/
unzip $DOWNLOAD_DIR/COCO_2017/raw/Annotations/panoptic_annotations_trainval2017.zip -d $DATA_ROOT/
unzip $DOWNLOAD_DIR/COCO_2017/raw/Annotations/image_info_unlabeled2017.zip -d $DATA_ROOT/
unzip $DOWNLOAD_DIR/COCO_2017/raw/Annotations/image_info_test2017.zip -d $DATA_ROOT/
unzip $DOWNLOAD_DIR/COCO_2017/raw/Annotations/annotations_trainval2017.zip -d $DATA_ROOT
rm -rf $DATA_ROOT/COCO_2017

Modification

Please briefly describe what modification is made in this PR.

BC-breaking (Optional)

Does the modification introduce changes that break the back-compatibility of the downstream repos?
If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR.

Use cases (Optional)

If this PR introduces a new feature, it is better to list some use cases here, and update the documentation.

Checklist

  1. Pre-commit or other linting tools are used to fix the potential lint issues.
  2. The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness.
  3. If the modification has potential influence on downstream projects, this PR should be tested with downstream projects, like MMDet or MMCls.
  4. The documentation has been modified accordingly, like docstring or example tutorials.

process = subprocess.Popen(
['odl', 'get', src_name, '-d', download_root],
stdin=sys.stdin,
stdout=subprocess.PIPE,
Copy link

@Ezra-Yu Ezra-Yu Jun 6, 2023

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I think stdout=sys.stdout is better, user can know it is downloading, how much has been downloaded, and how much time is needed.

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I initially redirected the output to sys.stdout, but the problem was that I couldn't capture the output of odl in the terminal and return a proper error message in mim download. Of course, this shouldn't have been the responsibility of mim to handle; odl should ensure the readability of its own error messages. Unfortunately, their error handling is not good, and they don't raise an error when there's no login but simply return "401: {"msg":"login required"} to stdout.

Regardless, redirecting the logs to a pipe indeed affected the user experience, so I have reverted the changes.

mim/commands/download.py Outdated Show resolved Hide resolved
mim/commands/download.py Outdated Show resolved Hide resolved
mim/commands/download.py Outdated Show resolved Hide resolved
@HAOCHENYE HAOCHENYE marked this pull request as ready for review June 25, 2023 07:03
mim/commands/download.py Outdated Show resolved Hide resolved
mim/commands/download.py Outdated Show resolved Hide resolved
@zhouzaida zhouzaida changed the title Support donwload dataset [Feature] Support donwloading datasets from opendatalab Jun 26, 2023
@zhouzaida zhouzaida merged commit bc5aec2 into open-mmlab:main Jun 26, 2023
xiexinch added a commit to open-mmlab/mmsegmentation that referenced this pull request Jun 28, 2023
## Motivation

Please describe the motivation of this PR and the goal you want to
achieve through this PR.

## Modification

- add dataset-index.yml

## Dependencies

- [ ] open-mmlab/mim#212
nahidnazifi87 pushed a commit to nahidnazifi87/mmsegmentation_playground that referenced this pull request Apr 5, 2024
## Motivation

Please describe the motivation of this PR and the goal you want to
achieve through this PR.

## Modification

- add dataset-index.yml

## Dependencies

- [ ] open-mmlab/mim#212
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

Successfully merging this pull request may close these issues.

3 participants