Tutorial 4: Customize Data Pipelines¶
In this tutorial, we will introduce some methods about the design of data pipelines, and how to customize and extend your own data pipelines for the project.
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 action recognition & localization may not be the same size (image size, gt bbox size, etc.),
The DataContainer
in MMCV is used to help collect and distribute data of different sizes.
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 operation.
We present a typical 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).
The operations are categorized into data loading, pre-processing and formatting.
Here is a pipeline example for TSN.
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)
train_pipeline = [
dict(type='SampleFrames', clip_len=1, frame_interval=1, num_clips=3),
dict(type='RawFrameDecode', io_backend='disk'),
dict(type='Resize', scale=(-1, 256)),
dict(
type='MultiScaleCrop',
input_size=224,
scales=(1, 0.875, 0.75, 0.66),
random_crop=False,
max_wh_scale_gap=1),
dict(type='Resize', scale=(224, 224), keep_ratio=False),
dict(type='Flip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCHW'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs', 'label'])
]
val_pipeline = [
dict(
type='SampleFrames',
clip_len=1,
frame_interval=1,
num_clips=3,
test_mode=True),
dict(type='RawFrameDecode', io_backend='disk'),
dict(type='Resize', scale=(-1, 256)),
dict(type='CenterCrop', crop_size=224),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCHW'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs'])
]
test_pipeline = [
dict(
type='SampleFrames',
clip_len=1,
frame_interval=1,
num_clips=25,
test_mode=True),
dict(type='RawFrameDecode', io_backend='disk'),
dict(type='Resize', scale=(-1, 256)),
dict(type='TenCrop', crop_size=224),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCHW'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs'])
]
We have supported some lazy operators and encourage users to apply them.
Lazy ops record how the data should be processed, but it will postpone the processing on the raw data until the raw data forward Fuse
stage.
Specifically, lazy ops avoid frequent reading and modification operation on the raw data, but process the raw data once in the final Fuse stage, thus accelerating data preprocessing.
Here is a pipeline example applying lazy ops.
train_pipeline = [
dict(type='SampleFrames', clip_len=32, frame_interval=2, num_clips=1),
dict(type='RawFrameDecode', decoding_backend='turbojpeg'),
# The following three lazy ops only process the bbox of frames without
# modifying the raw data.
dict(type='Resize', scale=(-1, 256), lazy=True),
dict(
type='MultiScaleCrop',
input_size=224,
scales=(1, 0.8),
random_crop=False,
max_wh_scale_gap=0,
lazy=True),
dict(type='Resize', scale=(224, 224), keep_ratio=False, lazy=True),
# Lazy operator `Flip` only record whether a frame should be fliped and the
# flip direction.
dict(type='Flip', flip_ratio=0.5, lazy=True),
# Processing the raw data once in Fuse stage.
dict(type='Fuse'),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs', 'label'])
]
For each operation, we list the related dict fields that are added/updated/removed below, where *
means the key may not be affected.
Data loading¶
SampleFrames
add: frame_inds, clip_len, frame_interval, num_clips, *total_frames
DenseSampleFrames
add: frame_inds, clip_len, frame_interval, num_clips, *total_frames
PyAVDecode
add: imgs, original_shape
update: *frame_inds
DecordDecode
add: imgs, original_shape
update: *frame_inds
OpenCVDecode
add: imgs, original_shape
update: *frame_inds
RawFrameDecode
add: imgs, original_shape
update: *frame_inds
Pre-processing¶
RandomCrop
add: crop_bbox, img_shape
update: imgs
RandomResizedCrop
add: crop_bbox, img_shape
update: imgs
MultiScaleCrop
add: crop_bbox, img_shape, scales
update: imgs
Resize
add: img_shape, keep_ratio, scale_factor
update: imgs
Flip
add: flip, flip_direction
update: imgs, label
Normalize
add: img_norm_cfg
update: imgs
CenterCrop
add: crop_bbox, img_shape
update: imgs
ThreeCrop
add: crop_bbox, img_shape
update: imgs
TenCrop
add: crop_bbox, img_shape
update: imgs
Formatting¶
ToTensor
update: specified by
keys
.
ImageToTensor
update: specified by
keys
.
Transpose
update: specified by
keys
.
Collect
add: img_metas (the keys of img_metas is specified by
meta_keys
)remove: all other keys except for those specified by
keys
It is noteworthy that the first key, commonly imgs
, will be used as the main key to calculate the batch size.
FormatShape
add: input_shape
update: imgs
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 mmaction.datasets import PIPELINES @PIPELINES.register_module() class MyTransform: def __call__(self, results): results['key'] = value 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='DenseSampleFrames', clip_len=8, frame_interval=8, num_clips=1), dict(type='RawFrameDecode', io_backend='disk'), dict(type='MyTransform'), # use a custom pipeline dict(type='Normalize', **img_norm_cfg), dict(type='FormatShape', input_format='NCTHW'), dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), dict(type='ToTensor', keys=['imgs', 'label']) ]