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.
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