Tutorial 3: Customize 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 in semantic segmentation 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 and test-time augmentation.

Here is an pipeline example for PSPNet.

img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 1024)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations'),
    dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='PhotoMetricDistortion'),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(2048, 1024),
        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            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_semantic_seg, seg_fields

Pre-processing¶

Resize

add: scale, scale_idx, pad_shape, scale_factor, keep_ratio
update: img, img_shape, *seg_fields

RandomFlip

add: flip
update: img, *seg_fields

Pad

add: pad_fixed_size, pad_size_divisor
update: img, pad_shape, *seg_fields

RandomCrop

update: img, pad_shape, *seg_fields

Normalize

add: img_norm_cfg
update: img

SegRescale

update: gt_semantic_seg

PhotoMetricDistortion

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, 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

Extend and use custom pipelines¶

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

from mmseg.datasets import PIPELINES

@PIPELINES.register_module()
class MyTransform:

    def __call__(self, results):
        results['dummy'] = True
        return results

Import the new class.
```
from .my_pipeline import MyTransform
```

Use it in config files.

img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 1024)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations'),
    dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='PhotoMetricDistortion'),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
    dict(type='MyTransform'),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
]