TransformBroadcaster

class mmcv.transforms.TransformBroadcaster(transforms: List[Union[Dict, Callable[[Dict], Dict]]], mapping: Optional[Dict] = None, remapping: Optional[Dict] = None, auto_remap: Optional[bool] = None, allow_nonexist_keys: bool = False, share_random_params: bool = False)

A transform wrapper to apply the wrapped transforms to multiple data items. For example, apply Resize to multiple images.

Parameters

• transforms (list[dict | callable]) – Sequence of transform object or config dict to be wrapped.

• mapping (dict) – A dict that defines the input key mapping. Note that to apply the transforms to multiple data items, the outer keys of the target items should be remapped as a list with the standard inner key (The key required by the wrapped transform). See the following example and the document of mmcv.transforms.wrappers.KeyMapper for details.

• remapping (dict) – A dict that defines the output key mapping. The keys and values have the same meanings and rules as in the mapping. Default: None.

• auto_remap (bool, optional) – If True, an inverse of the mapping will be used as the remapping. If auto_remap is not given, it will be automatically set True if ‘remapping’ is not given, and vice versa. Default: None.

• allow_nonexist_keys (bool) – If False, the outer keys in the mapping must exist in the input data, or an exception will be raised. Default: False.

• share_random_params (bool) – If True, the random transform (e.g., RandomFlip) will be conducted in a deterministic way and have the same behavior on all data items. For example, to randomly flip either both input image and ground-truth image, or none. Default: False.

Note

To apply the transforms to each elements of a list or tuple, instead of separating data items, you can map the outer key of the target sequence to the standard inner key. See example 2. example.

Examples

>>> # Example 1: Broadcast to enumerated keys, each contains a single
>>> # data element
>>> pipeline = [
>>>     dict(type='LoadImageFromFile', key='lq'),  # low-quality img
>>>     dict(type='LoadImageFromFile', key='gt'),  # ground-truth img
>>>     # TransformBroadcaster maps multiple outer fields to standard
>>>     # the inner field and process them with wrapped transforms
>>>     # respectively
>>>     dict(type='TransformBroadcaster',
>>>         # case 1: from multiple outer fields
>>>         mapping={'img': ['lq', 'gt']},
>>>         auto_remap=True,
>>>         # share_random_param=True means using identical random
>>>         # parameters in every processing
>>>         share_random_param=True,
>>>         transforms=[
>>>             dict(type='Crop', crop_size=(384, 384)),
>>>             dict(type='Normalize'),
>>>         ])
>>> ]

>>> # Example 2: Broadcast to keys that contains data sequences
>>> pipeline = [
>>>     dict(type='LoadImageFromFile', key='lq'),  # low-quality img
>>>     dict(type='LoadImageFromFile', key='gt'),  # ground-truth img
>>>     # TransformBroadcaster maps multiple outer fields to standard
>>>     # the inner field and process them with wrapped transforms
>>>     # respectively
>>>     dict(type='TransformBroadcaster',
>>>         # case 2: from one outer field that contains multiple
>>>         # data elements (e.g. a list)
>>>         # mapping={'img': 'images'},
>>>         auto_remap=True,
>>>         share_random_param=True,
>>>         transforms=[
>>>             dict(type='Crop', crop_size=(384, 384)),
>>>             dict(type='Normalize'),
>>>         ])
>>> ]

>>> Example 3: Set ignored keys in broadcasting
>>> pipeline = [
>>>        dict(type='TransformBroadcaster',
>>>            # Broadcast the wrapped transforms to multiple images
>>>            # 'lq' and 'gt, but only update 'img_shape' once
>>>            mapping={
>>>                'img': ['lq', 'gt'],
>>>                'img_shape': ['img_shape', ...],
>>>             },
>>>            auto_remap=True,
>>>            share_random_params=True,
>>>            transforms=[
>>>                # `RandomCrop` will modify the field "img",
>>>                # and optionally update "img_shape" if it exists
>>>                dict(type='RandomCrop'),
>>>            ])
>>>    ]

scatter_sequence(data: Dict) → List[Dict]

Scatter the broadcasting targets to a list of inputs of the wrapped transforms.

transform(results: Dict)

Broadcast wrapped transforms to multiple targets.