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.
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.
SampleFrames
DenseSampleFrames
PyAVDecode
DecordDecode
OpenCVDecode
RawFrameDecode
RandomCrop
RandomResizedCrop
MultiScaleCrop
Resize
Flip
Normalize
CenterCrop
ThreeCrop
TenCrop
ToTensor
keys
.ImageToTensor
keys
.Transpose
keys
.Collect
meta_keys
)keys
It is noteworthy that the first key, commonly imgs
, will be used as the main key to calculate the batch size.
FormatShape
Write a new pipeline in any file, e.g., my_pipeline.py
. It takes a dict as input and return a dict.
```python
from mmaction.datasets import PIPELINES
@PIPELINES.register_module()
class MyTransform:
def __call__(self, results):
results['key'] = value
return results
```
Import the new class.
python
from .my_pipeline import MyTransform
Use it in config files.
python
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'])
]