# Adding New Data Transforms

## Customization data transformation

The customized data transformation must inherited from `BaseTransform` and implement `transform` function.
Here we use a simple flipping transformation as example:

```python
import random
import mmcv
from mmcv.transforms import BaseTransform, TRANSFORMS

@TRANSFORMS.register_module()
class MyFlip(BaseTransform):
    def __init__(self, direction: str):
        super().__init__()
        self.direction = direction

    def transform(self, results: dict) -> dict:
        img = results['img']
        results['img'] = mmcv.imflip(img, direction=self.direction)
        return results
```

Moreover, import the new class.

```python
from .my_pipeline import MyFlip
```

Thus, we can instantiate a `MyFlip` object and use it to process the data dict.

```python
import numpy as np

transform = MyFlip(direction='horizontal')
data_dict = {'img': np.random.rand(224, 224, 3)}
data_dict = transform(data_dict)
processed_img = data_dict['img']
```

Or, we can use `MyFlip` transformation in data pipeline in our config file.

```python
pipeline = [
    ...
    dict(type='MyFlip', direction='horizontal'),
    ...
]
```

Note that if you want to use `MyFlip` in config, you must ensure the file containing `MyFlip` is imported during runtime.