199 lines
6.7 KiB
Python
199 lines
6.7 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from numbers import Number
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from typing import Sequence, Union
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import mmcv
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import numpy as np
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import torch
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from mmengine.data import BaseDataElement, LabelData
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def format_label(value: Union[torch.Tensor, np.ndarray, Sequence, int],
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num_classes: int = None) -> LabelData:
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"""Convert label of various python types to :obj:`mmengine.LabelData`.
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Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
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:class:`Sequence`, :class:`int`.
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Args:
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value (torch.Tensor | numpy.ndarray | Sequence | int): Label value.
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num_classes (int, optional): The number of classes. If not None, set
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it to the metainfo. Defaults to None.
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Returns:
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:obj:`mmengine.LabelData`: The foramtted label data.
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"""
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# Handle single number
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if isinstance(value, (torch.Tensor, np.ndarray)) and value.ndim == 0:
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value = int(value.item())
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if isinstance(value, np.ndarray):
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value = torch.from_numpy(value).to(torch.long)
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elif isinstance(value, Sequence) and not mmcv.is_str(value):
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value = torch.tensor(value).to(torch.long)
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elif isinstance(value, int):
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value = torch.LongTensor([value])
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elif not isinstance(value, torch.Tensor):
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raise TypeError(f'Type {type(value)} is not an available label type.')
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metainfo = {}
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if num_classes is not None:
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metainfo['num_classes'] = num_classes
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if value.max() >= num_classes:
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raise ValueError(f'The label data ({value}) should not '
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f'exceed num_classes ({num_classes}).')
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label = LabelData(label=value, metainfo=metainfo)
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return label
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class ClsDataSample(BaseDataElement):
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"""A data structure interface of classification task.
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It's used as interfaces between different components.
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Meta field:
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img_shape (Tuple): The shape of the corresponding input image.
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Used for visualization.
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ori_shape (Tuple): The original shape of the corresponding image.
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Used for visualization.
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num_classes (int): The number of all categories.
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Used for label format conversion.
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Data field:
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gt_label (LabelData): The ground truth label.
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pred_label (LabelData): The predicted label.
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scores (torch.Tensor): The outputs of model.
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logits (torch.Tensor): The outputs of model without softmax nor
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sigmoid.
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Examples:
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>>> import torch
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>>> from mmcls.engine import ClsDataSample
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>>>
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>>> img_meta = dict(img_shape=(960, 720), num_classes=5)
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>>> data_sample = ClsDataSample(metainfo=img_meta)
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>>> data_sample.set_gt_label(3)
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>>> print(data_sample)
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<ClsDataSample(
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META INFORMATION
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num_classes = 5
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img_shape = (960, 720)
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DATA FIELDS
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gt_label: <LabelData(
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META INFORMATION
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num_classes: 5
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DATA FIELDS
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label: tensor([3])
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) at 0x7f21fb1b9190>
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) at 0x7f21fb1b9880>
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>>> # For multi-label data
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>>> data_sample.set_gt_label([0, 1, 4])
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>>> print(data_sample.gt_label)
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<LabelData(
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META INFORMATION
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num_classes: 5
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DATA FIELDS
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label: tensor([0, 1, 4])
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) at 0x7fd7d1b41970>
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>>> # Set one-hot format score
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>>> score = torch.tensor([0.1, 0.1, 0.6, 0.1, 0.1])
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>>> data_sample.set_pred_score(score)
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>>> print(data_sample.pred_label)
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<LabelData(
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META INFORMATION
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num_classes: 5
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DATA FIELDS
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score: tensor([0.1, 0.1, 0.6, 0.1, 0.1])
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) at 0x7fd7d1b41970>
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"""
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def set_gt_label(
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self, value: Union[np.ndarray, torch.Tensor, Sequence[Number], Number]
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) -> 'ClsDataSample':
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"""Set label of ``gt_label``."""
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label = format_label(value, self.get('num_classes'))
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if 'gt_label' in self:
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self.gt_label.label = label.label
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else:
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self.gt_label = label
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return self
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def set_gt_score(self, value: torch.Tensor) -> 'ClsDataSample':
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"""Set score of ``gt_label``."""
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assert isinstance(value, torch.Tensor), \
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f'The value should be a torch.Tensor but got {type(value)}.'
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assert value.ndim == 1, \
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f'The dims of value should be 1, but got {value.ndim}.'
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if 'num_classes' in self:
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assert value.size(0) == self.num_classes, \
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f"The length of value ({value.size(0)}) doesn't "\
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f'match the num_classes ({self.num_classes}).'
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metainfo = {'num_classes': self.num_classes}
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else:
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metainfo = {'num_classes': value.size(0)}
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if 'gt_label' in self:
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self.gt_label.score = value
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else:
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self.gt_label = LabelData(score=value, metainfo=metainfo)
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return self
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def set_pred_label(
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self, value: Union[np.ndarray, torch.Tensor, Sequence[Number], Number]
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) -> 'ClsDataSample':
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"""Set label of ``pred_label``."""
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label = format_label(value, self.get('num_classes'))
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if 'pred_label' in self:
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self.pred_label.label = label.label
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else:
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self.pred_label = label
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return self
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def set_pred_score(self, value: torch.Tensor) -> 'ClsDataSample':
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"""Set score of ``pred_label``."""
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assert isinstance(value, torch.Tensor), \
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f'The value should be a torch.Tensor but got {type(value)}.'
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assert value.ndim == 1, \
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f'The dims of value should be 1, but got {value.ndim}.'
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if 'num_classes' in self:
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assert value.size(0) == self.num_classes, \
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f"The length of value ({value.size(0)}) doesn't "\
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f'match the num_classes ({self.num_classes}).'
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metainfo = {'num_classes': self.num_classes}
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else:
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metainfo = {'num_classes': value.size(0)}
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if 'pred_label' in self:
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self.pred_label.score = value
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else:
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self.pred_label = LabelData(score=value, metainfo=metainfo)
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return self
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@property
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def gt_label(self):
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return self._gt_label
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@gt_label.setter
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def gt_label(self, value: LabelData):
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self.set_field(value, '_gt_label', dtype=LabelData)
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@gt_label.deleter
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def gt_label(self):
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del self._gt_label
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@property
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def pred_label(self):
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return self._pred_label
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@pred_label.setter
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def pred_label(self, value: LabelData):
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self.set_field(value, '_pred_label', dtype=LabelData)
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@pred_label.deleter
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def pred_label(self):
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del self._pred_label
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