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Datasets
The datasets folder under mmselfsup contains all kinds of modules, related to loading data.
It can be roughly split into three parts, namely,
- cutomized datasets to read images
- cutomized dataset samplers to read index before loading images
- data transforms, e.g.
RandomResizedCrop, to augment data before feeding into models.
In this tutorial, we will explain the above three parts in details.
Datasets
OpenMMLab provides a lot of off-the-shelf datasets, and all these datasets inherit the BaseDataset
implemented in MMEngine. To have a full knowledge about all these functionalities implemented in
BaseDataset, we recommend interested readers to refer to the documents in MMEngine. ImageNet, ADE20KDataset and CocoDataset are the three commonly used datasets MMSelfSup. Before using them, you should refactor your local folder according to
the following format.
Refactor your datasets
To use these existing datasets, you need to refactor your datasets into following dataset format.
mmselfsup
├── mmselfsup
├── tools
├── configs
├── docs
├── data
│ ├── imagenet
│ │ ├── meta
│ │ ├── train
│ │ ├── val
│ │
│ │── ade
│ │ ├── ADEChallengeData2016
│ │ │ ├── annotations
│ │ │ │ ├── training
│ │ │ │ ├── validation
│ │ │ ├── images
│ │ │ │ ├── training
│ │ │ │ ├── validation
│ │
│ │── coco
│ │ ├── annotations
│ │ ├── train2017
│ │ ├── val2017
│ │ ├── test2017
For more details about the annotation files and the structure of each subfolder, you can consult MMClassfication, MMSegmentation and MMDetection.
Use datasets from other MM-repos in your config
# Use ImageNet dataset from MMClassification
# Use ImageNet in your dataloader
# For simplicity, we only provide the config related to importting ImageNet
# from MMClassification, instead of the full configuration for the dataloader.
# The ``mmcls`` prefix tells the ``Registry`` to search ``ImageNet`` in
# MMClassification
train_dataloader=dict(dataset=dict(type='mmcls.ImageNet', ...), ...)
# Use ADE20KDataset dataset from MMSegmentation
# Use ADE20KDataset in your dataloader
# For simplicity, we only provide the config related to importting ADE20KDataset
# from MMSegmentation, instead of the full configuration for the dataloader.
# The ``mmseg`` prefix tells the ``Registry`` to search ``ADE20KDataset`` in
# MMSegmentation
train_dataloader=dict(dataset=dict(type='mmseg.ADE20KDataset', ...), ...)
# Use CocoDataset in your dataloader
# For simplicity, we only provide the config related to importting CocoDataset
# from MMDetection, instead of the full configuration for the dataloader.
# The ``mmdet`` prefix tells the ``Registry`` to search ``CocoDataset`` in
# MMDetection
train_dataloader=dict(dataset=dict(type='mmdet.CocoDataset', ...), ...)
# Use dataset in MMSelfSup, for example ``DeepClusterImageNet``
train_dataloader=dict(dataset=dict(type='DeepClusterImageNet', ...), ...)
Till now, we have introduced two key steps, in order to use existing datasets successfully. We hope you can
grasp the basic idea about how to use datasets in MMSelfSup. If you want to create you customized datasets, you can refer to
another useful document, add_datasets.
Samplers
In pytorch, Sampler is used to sample the index of data before loading. MMEngine has already implemented DefaultSampler and
InfiniteSampler. In most situation, we can directly use them, instead of implementing customized sampler. But the DeepClusterSampler is a special case, in which we implement the unique index sampling logic. We recommend interested user to refer to the API doc for more details about this sampler. If you want to implement your customized sampler, you can follow DeepClusterSamplerand implement it under the folder of samplers.
Transforms
In short, transform refer to data augmentation in MM-repos and we compose a series of transforms into a list, called pipeline.
MMCV already provides some useful transforms, covering most of scenarios. But every MM-repo defines their own transforms, following
the User Guide in MMCV. Concretely, every
customized dataset: i) inherits BaseTransform,
ii) overwrite the transform function and implement your key logic in it. In MMSelfSup, we implement these transforms below:
For interested users, you can refer to the API doc to have a full understanding of these transforms. Now, we have introduced the basic concepts about transform. If you want to know how to use them in your config or implement your customed transforms, you can refer to transforms and add_transforms.