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:

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.

from .my_pipeline import MyFlip

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

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.

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.