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from typing import Dict, Sequence, Union
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import numpy as np
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import torch
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from mmengine.structures import BaseDataElement, InstanceData
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from mmengine.utils import is_str
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LABEL_TYPE = Union[torch.Tensor, np.ndarray, Sequence, int]
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SCORE_TYPE = Union[torch.Tensor, np.ndarray, Sequence, Dict]
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def format_label(value: LABEL_TYPE) -> torch.Tensor:
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"""Convert various python types to label-format tensor.
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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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Returns:
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:obj:`torch.Tensor`: The formatted label tensor.
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"""
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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 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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return value
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def format_score(value: SCORE_TYPE) -> Union[torch.Tensor, Dict]:
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"""Convert various python types to score-format tensor.
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Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
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:class:`Sequence`.
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Args:
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value (torch.Tensor | numpy.ndarray | Sequence | dict):
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Score values or dict of scores values.
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Returns:
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:obj:`torch.Tensor` | dict: The formatted scores.
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"""
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if isinstance(value, np.ndarray):
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value = torch.from_numpy(value).float()
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elif isinstance(value, Sequence) and not is_str(value):
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value = torch.tensor(value).float()
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elif isinstance(value, dict):
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for k, v in value.items():
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value[k] = format_score(v)
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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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return value
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class ActionDataSample(BaseDataElement):
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def set_gt_label(self, value: LABEL_TYPE) -> 'ActionDataSample':
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"""Set `gt_label``."""
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self.set_field(format_label(value), 'gt_label', dtype=torch.Tensor)
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return self
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def set_pred_label(self, value: LABEL_TYPE) -> 'ActionDataSample':
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"""Set ``pred_label``."""
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self.set_field(format_label(value), 'pred_label', dtype=torch.Tensor)
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return self
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def set_pred_score(self, value: SCORE_TYPE) -> 'ActionDataSample':
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"""Set score of ``pred_label``."""
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score = format_score(value)
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self.set_field(score, 'pred_score')
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if hasattr(self, 'num_classes'):
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assert len(score) == self.num_classes, \
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f'The length of score {len(score)} should be '\
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f'equal to the num_classes {self.num_classes}.'
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else:
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self.set_field(
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name='num_classes', value=len(score), field_type='metainfo')
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return self
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@property
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def proposals(self):
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"""Property of `proposals`"""
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return self._proposals
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@proposals.setter
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def proposals(self, value):
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"""Setter of `proposals`"""
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self.set_field(value, '_proposals', dtype=InstanceData)
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@proposals.deleter
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def proposals(self):
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"""Deleter of `proposals`"""
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del self._proposals
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@property
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def gt_instances(self):
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"""Property of `gt_instances`"""
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return self._gt_instances
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@gt_instances.setter
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def gt_instances(self, value):
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"""Setter of `gt_instances`"""
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self.set_field(value, '_gt_instances', dtype=InstanceData)
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@gt_instances.deleter
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def gt_instances(self):
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"""Deleter of `gt_instances`"""
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del self._gt_instances
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@property
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def features(self):
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"""Setter of `features`"""
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return self._features
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@features.setter
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def features(self, value):
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"""Setter of `features`"""
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self.set_field(value, '_features', dtype=InstanceData)
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@features.deleter
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def features(self):
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"""Deleter of `features`"""
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del self._features
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