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mmpretrain.datasets.transforms.wrappers 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import copy
from typing import Callable, List, Union

from mmcv.transforms import BaseTransform, Compose

from mmpretrain.registry import TRANSFORMS

# Define type of transform or transform config
Transform = Union[dict, Callable[[dict], dict]]


[文档]@TRANSFORMS.register_module() class MultiView(BaseTransform): """A transform wrapper for multiple views of an image. Args: transforms (list[dict | callable], optional): Sequence of transform object or config dict to be wrapped. mapping (dict): A dict that defines the input key mapping. The keys corresponds to the inner key (i.e., kwargs of the ``transform`` method), and should be string type. The values corresponds to the outer keys (i.e., the keys of the data/results), and should have a type of string, list or dict. None means not applying input mapping. 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. Examples: >>> # Example 1: MultiViews 1 pipeline with 2 views >>> pipeline = [ >>> dict(type='MultiView', >>> num_views=2, >>> transforms=[ >>> [ >>> dict(type='Resize', scale=224))], >>> ]) >>> ] >>> # Example 2: MultiViews 2 pipelines, the first with 2 views, >>> # the second with 6 views >>> pipeline = [ >>> dict(type='MultiView', >>> num_views=[2, 6], >>> transforms=[ >>> [ >>> dict(type='Resize', scale=224)], >>> [ >>> dict(type='Resize', scale=224), >>> dict(type='RandomSolarize')], >>> ]) >>> ] """ def __init__(self, transforms: List[List[Transform]], num_views: Union[int, List[int]]) -> None: if isinstance(num_views, int): num_views = [num_views] assert isinstance(num_views, List) assert len(num_views) == len(transforms) self.num_views = num_views self.pipelines = [] for trans in transforms: pipeline = Compose(trans) self.pipelines.append(pipeline) self.transforms = [] for i in range(len(num_views)): self.transforms.extend([self.pipelines[i]] * num_views[i])
[文档] def transform(self, results: dict) -> dict: """Apply transformation to inputs. Args: results (dict): Result dict from previous pipelines. Returns: dict: Transformed results. """ multi_views_outputs = dict(img=[]) for trans in self.transforms: inputs = copy.deepcopy(results) outputs = trans(inputs) multi_views_outputs['img'].append(outputs['img']) results.update(multi_views_outputs) return results
def __repr__(self) -> str: repr_str = self.__class__.__name__ + '(' for i, p in enumerate(self.pipelines): repr_str += f'\nPipeline {i + 1} with {self.num_views[i]} views:\n' repr_str += str(p) repr_str += ')' return repr_str
@TRANSFORMS.register_module() class ApplyToList(BaseTransform): """A transform wrapper to apply the wrapped transforms to a list of items. For example, to load and resize a list of images. Args: transforms (list[dict | callable]): Sequence of transform config dict to be wrapped. scatter_key (str): The key to scatter data dict. If the field is a list, scatter the list to multiple data dicts to do transformation. collate_keys (List[str]): The keys to collate from multiple data dicts. The fields in ``collate_keys`` will be composed into a list after transformation, and the other fields will be adopted from the first data dict. """ def __init__(self, transforms, scatter_key, collate_keys): super().__init__() self.transforms = Compose([TRANSFORMS.build(t) for t in transforms]) self.scatter_key = scatter_key self.collate_keys = set(collate_keys) self.collate_keys.add(self.scatter_key) def transform(self, results: dict): scatter_field = results.get(self.scatter_key) if isinstance(scatter_field, list): scattered_results = [] for item in scatter_field: single_results = copy.deepcopy(results) single_results[self.scatter_key] = item scattered_results.append(self.transforms(single_results)) final_output = scattered_results[0] # merge output list to single output for key in scattered_results[0].keys(): if key in self.collate_keys: final_output[key] = [ single[key] for single in scattered_results ] return final_output else: return self.transforms(results)
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