Tutorial 3: Custom 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 pose estimation 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.
The operations are categorized into data loading, pre-processing, formatting, label generating.
Here is an pipeline example for Simple Baseline (ResNet50).
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(type='TopDownHalfBodyTransform', num_joints_half_body=8, prob_half_body=0.3),
dict(type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
For each operation, we list the related dict fields that are added/updated/removed.
Data loading
LoadImageFromFile
Pre-processing
TopDownRandomFlip
- update: img, joints_3d, joints_3d_visible, center
TopDownHalfBodyTransform
TopDownGetRandomScaleRotation
TopDownAffine
- update: img, joints_3d, joints_3d_visible
NormalizeTensor
Generating labels
TopDownGenerateTarget
- add: target, target_weight
Formatting
ToTensor
Collect
- add: img_meta (the keys of img_meta is specified by
meta_keys
)
- remove: all other keys except for those specified by
keys
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 mmpose.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.
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(type='TopDownHalfBodyTransform', num_joints_half_body=8, prob_half_body=0.3),
dict(type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='MyTransform'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]