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[Refactor] Dataset tutorial for 1.x (#1932)

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* modify scripts dirname

* update links
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谢昕辰 2022-08-18 22:02:33 +08:00 committed by MeowZheng
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docs/en/user_guides/2_dataset_prepare.md
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@ -145,12 +145,12 @@ mmsegmentation
The data could be found [here](https://www.cityscapes-dataset.com/downloads/) after registration. The data could be found [here](https://www.cityscapes-dataset.com/downloads/) after registration.
By convention, `**labelTrainIds.png` are used for cityscapes training. By convention, `**labelTrainIds.png` are used for cityscapes training.
We provided a [scripts](https://github.com/open-mmlab/mmsegmentation/blob/master/tools/convert_datasets/cityscapes.py) based on [cityscapesscripts](https://github.com/mcordts/cityscapesScripts) We provided a [scripts](https://github.com/open-mmlab/mmsegmentation/blob/master/tools/dataset_converters/cityscapes.py) based on [cityscapesscripts](https://github.com/mcordts/cityscapesScripts)
to generate `**labelTrainIds.png`. to generate `**labelTrainIds.png`.
```shell ```shell
# --nproc means 8 process for conversion, which could be omitted as well. # --nproc means 8 process for conversion, which could be omitted as well.
python tools/convert_datasets/cityscapes.py data/cityscapes --nproc 8 python tools/dataset_converters/cityscapes.py data/cityscapes --nproc 8
``` ```
### Pascal VOC ### Pascal VOC
@ -162,10 +162,10 @@ If you would like to use augmented VOC dataset, please run following command to
```shell ```shell
# --nproc means 8 process for conversion, which could be omitted as well. # --nproc means 8 process for conversion, which could be omitted as well.
python tools/convert_datasets/voc_aug.py data/VOCdevkit data/VOCdevkit/VOCaug --nproc 8 python tools/dataset_converters/voc_aug.py data/VOCdevkit data/VOCdevkit/VOCaug --nproc 8
``` ```
Please refer to [concat dataset](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/tutorials/customize_datasets.md#concatenate-dataset) for details about how to concatenate them and train them together. Please refer to [concat dataset](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/docs/en/advanced_guides/datasets.md) for details about how to concatenate them and train them together.
### ADE20K ### ADE20K
@ -181,7 +181,7 @@ To split the training and validation set from original dataset, you may download
If you would like to use Pascal Context dataset, please install [Detail](https://github.com/zhanghang1989/detail-api) and then run the following command to convert annotations into proper format. If you would like to use Pascal Context dataset, please install [Detail](https://github.com/zhanghang1989/detail-api) and then run the following command to convert annotations into proper format.
```shell ```shell
python tools/convert_datasets/pascal_context.py data/VOCdevkit data/VOCdevkit/VOC2010/trainval_merged.json python tools/dataset_converters/pascal_context.py data/VOCdevkit data/VOCdevkit/VOC2010/trainval_merged.json
``` ```
### COCO Stuff 10k ### COCO Stuff 10k
@ -199,7 +199,7 @@ wget http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/cocostu
unzip cocostuff-10k-v1.1.zip unzip cocostuff-10k-v1.1.zip
# --nproc means 8 process for conversion, which could be omitted as well. # --nproc means 8 process for conversion, which could be omitted as well.
python tools/convert_datasets/coco_stuff10k.py /path/to/coco_stuff10k --nproc 8 python tools/dataset_converters/coco_stuff10k.py /path/to/coco_stuff10k --nproc 8
``` ```
By convention, mask labels in `/path/to/coco_stuff164k/annotations/*2014/*_labelTrainIds.png` are used for COCO Stuff 10k training and testing. By convention, mask labels in `/path/to/coco_stuff164k/annotations/*2014/*_labelTrainIds.png` are used for COCO Stuff 10k training and testing.
@ -221,7 +221,7 @@ unzip val2017.zip -d images/
unzip stuffthingmaps_trainval2017.zip -d annotations/ unzip stuffthingmaps_trainval2017.zip -d annotations/
# --nproc means 8 process for conversion, which could be omitted as well. # --nproc means 8 process for conversion, which could be omitted as well.
python tools/convert_datasets/coco_stuff164k.py /path/to/coco_stuff164k --nproc 8 python tools/dataset_converters/coco_stuff164k.py /path/to/coco_stuff164k --nproc 8
``` ```
By convention, mask labels in `/path/to/coco_stuff164k/annotations/*2017/*_labelTrainIds.png` are used for COCO Stuff 164k training and testing. By convention, mask labels in `/path/to/coco_stuff164k/annotations/*2017/*_labelTrainIds.png` are used for COCO Stuff 164k training and testing.
@ -235,7 +235,7 @@ The training and validation set of CHASE DB1 could be download from [here](https
To convert CHASE DB1 dataset to MMSegmentation format, you should run the following command: To convert CHASE DB1 dataset to MMSegmentation format, you should run the following command:
```shell ```shell
python tools/convert_datasets/chase_db1.py /path/to/CHASEDB1.zip python tools/dataset_converters/chase_db1.py /path/to/CHASEDB1.zip
``` ```
The script will make directory structure automatically. The script will make directory structure automatically.
@ -247,7 +247,7 @@ The training and validation set of DRIVE could be download from [here](https://d
To convert DRIVE dataset to MMSegmentation format, you should run the following command: To convert DRIVE dataset to MMSegmentation format, you should run the following command:
```shell ```shell
python tools/convert_datasets/drive.py /path/to/training.zip /path/to/test.zip python tools/dataset_converters/drive.py /path/to/training.zip /path/to/test.zip
``` ```
The script will make directory structure automatically. The script will make directory structure automatically.
@ -259,7 +259,7 @@ First, download [healthy.zip](https://www5.cs.fau.de/fileadmin/research/datasets
To convert HRF dataset to MMSegmentation format, you should run the following command: To convert HRF dataset to MMSegmentation format, you should run the following command:
```shell ```shell
python tools/convert_datasets/hrf.py /path/to/healthy.zip /path/to/healthy_manualsegm.zip /path/to/glaucoma.zip /path/to/glaucoma_manualsegm.zip /path/to/diabetic_retinopathy.zip /path/to/diabetic_retinopathy_manualsegm.zip python tools/dataset_converters/hrf.py /path/to/healthy.zip /path/to/healthy_manualsegm.zip /path/to/glaucoma.zip /path/to/glaucoma_manualsegm.zip /path/to/diabetic_retinopathy.zip /path/to/diabetic_retinopathy_manualsegm.zip
``` ```
The script will make directory structure automatically. The script will make directory structure automatically.
@ -271,7 +271,7 @@ First, download [stare-images.tar](http://cecas.clemson.edu/~ahoover/stare/probi
To convert STARE dataset to MMSegmentation format, you should run the following command: To convert STARE dataset to MMSegmentation format, you should run the following command:
```shell ```shell
python tools/convert_datasets/stare.py /path/to/stare-images.tar /path/to/labels-ah.tar /path/to/labels-vk.tar python tools/dataset_converters/stare.py /path/to/stare-images.tar /path/to/labels-ah.tar /path/to/labels-vk.tar
``` ```
The script will make directory structure automatically. The script will make directory structure automatically.
@ -302,10 +302,10 @@ wget https://zenodo.org/record/5706578/files/Test.zip
For LoveDA dataset, please run the following command to download and re-organize the dataset. For LoveDA dataset, please run the following command to download and re-organize the dataset.
```shell ```shell
python tools/convert_datasets/loveda.py /path/to/loveDA python tools/dataset_converters/loveda.py /path/to/loveDA
``` ```
Using trained model to predict test set of LoveDA and submit it to server can be found [here](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/inference.md). Using trained model to predict test set of LoveDA and submit it to server can be found [here](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/docs/en/user_guides/3_inference.md).
More details about LoveDA can be found [here](https://github.com/Junjue-Wang/LoveDA). More details about LoveDA can be found [here](https://github.com/Junjue-Wang/LoveDA).
@ -320,7 +320,7 @@ The '2_Ortho_RGB.zip' and '5_Labels_all_noBoundary.zip' are required.
For Potsdam dataset, please run the following command to download and re-organize the dataset. For Potsdam dataset, please run the following command to download and re-organize the dataset.
```shell ```shell
python tools/convert_datasets/potsdam.py /path/to/potsdam python tools/dataset_converters/potsdam.py /path/to/potsdam
``` ```
In our default setting, it will generate 3456 images for training and 2016 images for validation. In our default setting, it will generate 3456 images for training and 2016 images for validation.
@ -336,7 +336,7 @@ The 'ISPRS_semantic_labeling_Vaihingen.zip' and 'ISPRS_semantic_labeling_Vaihing
For Vaihingen dataset, please run the following command to download and re-organize the dataset. For Vaihingen dataset, please run the following command to download and re-organize the dataset.
```shell ```shell
python tools/convert_datasets/vaihingen.py /path/to/vaihingen python tools/dataset_converters/vaihingen.py /path/to/vaihingen
``` ```
In our default setting (`clip_size` =512, `stride_size`=256), it will generate 344 images for training and 398 images for validation. In our default setting (`clip_size` =512, `stride_size`=256), it will generate 344 images for training and 398 images for validation.
@ -372,7 +372,7 @@ You may need to follow the following structure for dataset preparation after dow
``` ```
```shell ```shell
python tools/convert_datasets/isaid.py /path/to/iSAID python tools/dataset_converters/isaid.py /path/to/iSAID
``` ```
In our default setting (`patch_width`=896, `patch_height`=896,　`overlap_area`=384), it will generate 33978 images for training and 11644 images for validation. In our default setting (`patch_width`=896, `patch_height`=896,　`overlap_area`=384), it will generate 33978 images for training and 11644 images for validation.