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'''
* Copyright (c) 2023 Salesforce, Inc.
* All rights reserved.
* SPDX-License-Identifier: Apache License 2.0
* For full license text, see LICENSE.txt file in the repo root or http://www.apache.org/licenses/
* By Can Qin
* Modified from ControlNet repo: https://github.com/lllyasviel/ControlNet
* Copyright (c) 2023 Lvmin Zhang and Maneesh Agrawala
* Modified from MMCV repo: From https://github.com/open-mmlab/mmcv
* Copyright (c) OpenMMLab. All rights reserved.
'''
import collections
from annotator.uniformer.mmcv.utils import build_from_cfg
from ..builder import PIPELINES
@PIPELINES.register_module()
class Compose(object):
"""Compose multiple transforms sequentially.
Args:
transforms (Sequence[dict | callable]): Sequence of transform object or
config dict to be composed.
"""
def __init__(self, transforms):
assert isinstance(transforms, collections.abc.Sequence)
self.transforms = []
for transform in transforms:
if isinstance(transform, dict):
transform = build_from_cfg(transform, PIPELINES)
self.transforms.append(transform)
elif callable(transform):
self.transforms.append(transform)
else:
raise TypeError('transform must be callable or a dict')
def __call__(self, data):
"""Call function to apply transforms sequentially.
Args:
data (dict): A result dict contains the data to transform.
Returns:
dict: Transformed data.
"""
for t in self.transforms:
data = t(data)
if data is None:
return None
return data
def __repr__(self):
format_string = self.__class__.__name__ + '('
for t in self.transforms:
format_string += '\n'
format_string += f' {t}'
format_string += '\n)'
return format_string
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