[Dev plan] Re-design the dataset API

The goal of this code refactoring is to make it easier to define new datasets and pre-processing pipelines. After the refactoring, users should be able to add new datasets/pipelines without modifying existing codes, as well as reuse more components. There will be breaking changes and welcome for any discussions.

There will be at least 2 PRs to implement it.

• Use registry to manage datasets. (#924)
• Use a list of transforms to define the data pre-processing pipeline. (#935)

The dataset definition will look like this.

from .coco import CocoDataset
from .registry import DATASETS


@DATASETS.register_module
class MyDataset(CocoDataset):

    CLASSES = ('class1', 'class2', 'class3')

    def __init__(self,
                 ann_file,
                 pipeline,
                 img_prefix=None,
                 seg_prefix=None,
                 proposal_file=None,
                 test_mode=False):
        pass

The config file will look like this.

train_pipeline = [
    dict(type='LoadImage'),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=False),
    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='ImageToTensor', keys=['img']),
    dict(type='ToTensor', keys=['gt_bboxes', 'gt_labels']),
    dict(
        type='ToDataContainer',
        fields=[
            dict(key='img', stack=True),
            dict(key='gt_bboxes'),
            dict(key='gt_labels')
        ]),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
train_set = dict(
    type='CocoDataset',
    ann_file=data_root + 'annotations/instances_train2017.json',
    img_prefix=data_root + 'train2017/',
    pipeline=train_pipeline)

[rwightman]

@hellock Is there any posibility of making data_root overridable via cmd line argument with any changes to the dataset api/config?

In order to use absolute paths for dataset locations and not have data within PWD one must modify/duplicate the config files. For numerous reasons I do not feel that linking datasets into the current dir is a good solution. It works for some people, but has drawbacks.

It’d be nice to be able to python tools/train.py ${CONFIG_FILE} --data_root /abs/path/to/data/root and override the default ./data/dataset prefix. Very similary to how work_dir arg works, and I think it’s a valid option for the same reasons you want to specify work_dir.

A change to suppor this withi minimal impact would require:

1. moving data_root + ann_file/img_prefix concat out of the config py files and into CustomDataset.__init__ with data_root added as an arg so that data_root can be overriden

    def __init__(self,
                 data_root,
                 ann_file,
                 img_prefix,
                 img_scale,
                 ...):
        # prefix of images path
        self.img_prefix = data_root + img_prefix
        ann_file = data_root + ann_file

1. override cfg.data_root from arg in tool scripts as with work_dir

[dsuess]

Additionally, we could refactor the tools directory into entrypoints for the python module. Then you could run e.g.

python -m mmdet train 

from anywhere. This would also fix the current issue that the training/evaluation scripts are not installed when we pip-install mmdet from github directly without cloning it.