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Data preparation pipeline

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.

We present a classical pipeline in the following figure. The blue blocks are pipeline operations. With the pipeline going on, each operator can add new keys (marked as green) to the result dict or update the existing keys (marked as orange). pipeline figure

The operations are categorized into data loading, pre-processing, formatting and test-time augmentation.

Here is an pipeline example for Faster R-CNN.

img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    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='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]

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

Data loading

LoadImageFromFile

  • add: img, img_shape, ori_shape

LoadAnnotations

  • add: gt_bboxes, gt_bboxes_ignore, gt_labels, gt_masks, gt_semantic_seg, bbox_fields, mask_fields

LoadProposals

  • add: proposals

Pre-processing

Resize

  • add: scale, scale_idx, pad_shape, scale_factor, keep_ratio
  • update: img, img_shape, *bbox_fields, *mask_fields

RandomFlip

  • add: flip
  • update: img, *bbox_fields, *mask_fields

Pad

  • add: pad_fixed_size, pad_size_divisor
  • update: img, pad_shape, *mask_fields

RandomCrop

  • update: img, pad_shape, gt_bboxes, gt_labels, gt_masks, *bbox_fields

Normalize

  • add: img_norm_cfg
  • update: img

SegResizeFlipPadRescale

  • update: gt_semantic_seg

PhotoMetricDistortion

  • update: img

Expand

  • update: img, gt_bboxes

MinIoURandomCrop

  • update: img, gt_bboxes, gt_labels

Corrupt

  • update: img

Formatting

ToTensor

  • update: specified by keys.

ImageToTensor

  • update: specified by keys.

Transpose

  • update: specified by keys.

ToDataContainer

  • update: specified by fields.

DefaultFormatBundle

  • update: img, proposals, gt_bboxes, gt_bboxes_ignore, gt_labels, gt_masks, gt_semantic_seg

Collect

  • add: img_meta (the keys of img_meta is specified by meta_keys)
  • remove: all other keys except for those specified by keys

Test time augmentation

MultiScaleFlipAug

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