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Tutorial 3: Custom Data Pipelines

Design of Data pipelines

Following typical conventions, we use Dataset and DataLoader for data loading with multiple workers. Dataset returns a dict of data items corresponding the arguments of models’ forward method. Since the data flow estimation may not be the same size, we introduce a new DataContainer type in MMCV to help collect and distribute data of different size. See here for more details.

The data preparation pipeline and the dataset is decomposed. Usually a dataset defines how to process the annotations and a data pipeline defines all the steps to prepare a data dict. A pipeline consists of a sequence of operations. Each operation takes a dict as input and also output a dict for the next transform.

The operations are categorized into data loading, pre-processing, formatting.

Here is a pipeline example for PWC-Net

train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations'),
    dict(type='ColorJitter', brightness=0.5, contrast=0.5, saturation=0.5,
         hue=0.5),
    dict(type='RandomGamma', gamma_range=(0.7, 1.5)),
    dict(type='Normalize', mean=[0., 0., 0.], std=[255., 255., 255.], to_rgb=False),
    dict(type='GaussianNoise', sigma_range=(0, 0.04), clamp_range=(0., 1.)),
    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
    dict(type='RandomFlip', prob=0.5, direction='vertical'),
    dict(type='RandomAffine',
         global_transform=dict(
            translates=(0.05, 0.05),
            zoom=(1.0, 1.5),
            shear=(0.86, 1.16),
            rotate=(-10., 10.)
        ),
         relative_transform=)dict(
            translates=(0.00375, 0.00375),
            zoom=(0.985, 1.015),
            shear=(1.0, 1.0),
            rotate=(-1.0, 1.0)
        ),
    dict(type='RandomCrop', crop_size=(384, 448)),
    dict(type='DefaultFormatBundle'),
    dict(
        type='Collect',
        keys=['imgs', 'flow_gt'],
        meta_keys=['img_fields', 'ann_fields', 'filename1', 'filename2',
                   'ori_filename1', 'ori_filename2', 'filename_flow',
                   'ori_filename_flow', 'ori_shape', 'img_shape',
                   'img_norm_cfg']),
]

For each operation, we list the related dict fields that are added/updated/removed.

Data loading

LoadImageFromFile

  • add: img1, img2, filename1, filename2, img_shape, ori_shape, pad_shape, scale_factor, img_norm_cfg

LoadAnnotations

  • add: flow_gt, filename_flow

Pre-processing

ColorJitter

  • update: img1, img2

RandomGamma

  • update: img1, img2

Normalize

  • update: img1, img2, img_norm_cfg

GaussianNoise

  • update: img1, img2

RandomFlip

  • update: img1, img2, flow_gt

RandomAffine

  • update: img1, img2, flow_gt

RandomCrop

  • update: img1, img2, flow_gt, img_shape

Formatting

DefaultFormatBundle

  • update: img1, img2, flow_gt

Collect

  • add: img_meta (the keys of img_meta is specified by meta_keys)

  • remove: all other keys except for those specified by keys

Extend and use custom pipelines

  1. Write a new pipeline in any file, e.g., my_pipeline.py. It takes a dict as input and return a dict.

    from mmflow.datasets import PIPELINES
    
    @PIPELINES.register_module()
    class MyTransform:
    
        def __call__(self, results):
            results['dummy'] = True
            return results
    
  2. Import the new class.

    from .my_pipeline import MyTransform
    
  3. Use it in config files.

    train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations'),
    dict(type='ColorJitter', brightness=0.5, contrast=0.5, saturation=0.5,
         hue=0.5),
    dict(type='RandomGamma', gamma_range=(0.7, 1.5)),
    dict(type='Normalize', mean=[0., 0., 0.], std=[255., 255., 255.], to_rgb=False),
    dict(type='GaussianNoise', sigma_range=(0, 0.04), clamp_range=(0., 1.)),
    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
    dict(type='RandomFlip', prob=0.5, direction='vertical'),
    dict(type='RandomAffine',
         global_transform=dict(
            translates=(0.05, 0.05),
            zoom=(1.0, 1.5),
            shear=(0.86, 1.16),
            rotate=(-10., 10.)
        ),
         relative_transform=)dict(
            translates=(0.00375, 0.00375),
            zoom=(0.985, 1.015),
            shear=(1.0, 1.0),
            rotate=(-1.0, 1.0)
        ),
    dict(type='RandomCrop', crop_size=(384, 448)),
    dict(type='MyTransform'),
    dict(type='DefaultFormatBundle'),
    dict(
        type='Collect',
        keys=['imgs', 'flow_gt'],
        meta_keys=('img_fields', 'ann_fields', 'filename1', 'filename2',
                   'ori_filename1', 'ori_filename2', 'filename_flow',
                   'ori_filename_flow', 'ori_shape', 'img_shape',
                   'img_norm_cfg'))]
    
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