my_pipeline.py
,它以一个字典作为输入并且输出一个字典: 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
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')
]
如果想要可视化数据增强处理流程的结果,可以使用 tools/misc/browse_dataset.py
直观
地浏览检测数据集(图像和标注信息),或将图像保存到指定目录。
使用方法请参考可视化文档