Customize Data Pipelines¶
Write a new transform in a file, e.g., in
my_pipeline.py
. It takes a dict as input and returns a dict.import random from mmcv.transforms import BaseTransform from mmdet.registry import TRANSFORMS @TRANSFORMS.register_module() class MyTransform(BaseTransform): """Add your transform Args: p (float): Probability of shifts. Default 0.5. """ def __init__(self, prob=0.5): self.prob = prob def transform(self, results): if random.random() > self.prob: 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) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), dict(type='Resize', scale=(1333, 800), keep_ratio=True), dict(type='RandomFlip', prob=0.5), dict(type='MyTransform', prob=0.2), dict(type='PackDetInputs') ]
Visualize the output of your transforms pipeline
To visualize the output of your transforms 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 details can refer to visualization documentation