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 object detection may not be the same size (image size, gt bbox size, etc.), 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.

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). ../_images/data_pipeline.png pipeline figure

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

Here is a 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, *seg_fields

RandomFlip

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

Pad

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

RandomCrop

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

Normalize

add: img_norm_cfg
update: img

SegRescale

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

Extend and use custom pipelines¶

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

import random
from mmdet.datasets import PIPELINES


@PIPELINES.register_module()
class MyTransform:
    """Add your transform

    Args:
        p (float): Probability of shifts. Default 0.5.
    """

    def __init__(self, p=0.5):
        self.p = p

    def __call__(self, results):
        if random.random() > self.p:
            results['dummy'] = True
        return results

Import and use the pipeline in your config file. Make sure the import is relative to where your train script is located.

custom_imports = dict(imports=['path.to.my_pipeline'], allow_failed_imports=False)

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='MyTransform', p=0.2),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]

Visualize the output of your augmentation pipeline

To visualize the output of your agmentation pipeline, tools/misc/browse_dataset.py can help the user to browse a detection dataset (both images and bounding box annotations) visually, or save the image to a designated directory. More detials can refer to useful_tools