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# Customize Data Pipelines
1. Write a new transform in a file, e.g., in `my_pipeline.py`. It takes a dict as input and returns a dict.
```python
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
```
2. Import and use the pipeline in your config file.
Make sure the import is relative to where your train script is located.
```python
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')
]
```
3. 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](../user_guides/visualization.md)
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