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). 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 any file, e.g.,
my_pipeline.py
. It takes a dict as input and return a dict.from mmdet.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) 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'), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), ]