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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, Sequence, Union
import numpy as np
import torch
from mmengine.structures import BaseDataElement, InstanceData
from mmengine.utils import is_str
LABEL_TYPE = Union[torch.Tensor, np.ndarray, Sequence, int]
SCORE_TYPE = Union[torch.Tensor, np.ndarray, Sequence, Dict]
def format_label(value: LABEL_TYPE) -> torch.Tensor:
"""Convert various python types to label-format tensor.
Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
:class:`Sequence`, :class:`int`.
Args:
value (torch.Tensor | numpy.ndarray | Sequence | int): Label value.
Returns:
:obj:`torch.Tensor`: The formatted label tensor.
"""
# Handle single number
if isinstance(value, (torch.Tensor, np.ndarray)) and value.ndim == 0:
value = int(value.item())
if isinstance(value, np.ndarray):
value = torch.from_numpy(value).to(torch.long)
elif isinstance(value, Sequence) and not is_str(value):
value = torch.tensor(value).to(torch.long)
elif isinstance(value, int):
value = torch.LongTensor([value])
elif not isinstance(value, torch.Tensor):
raise TypeError(f'Type {type(value)} is not an available label type.')
return value
def format_score(value: SCORE_TYPE) -> Union[torch.Tensor, Dict]:
"""Convert various python types to score-format tensor.
Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
:class:`Sequence`.
Args:
value (torch.Tensor | numpy.ndarray | Sequence | dict):
Score values or dict of scores values.
Returns:
:obj:`torch.Tensor` | dict: The formatted scores.
"""
if isinstance(value, np.ndarray):
value = torch.from_numpy(value).float()
elif isinstance(value, Sequence) and not is_str(value):
value = torch.tensor(value).float()
elif isinstance(value, dict):
for k, v in value.items():
value[k] = format_score(v)
elif not isinstance(value, torch.Tensor):
raise TypeError(f'Type {type(value)} is not an available label type.')
return value
class ActionDataSample(BaseDataElement):
def set_gt_label(self, value: LABEL_TYPE) -> 'ActionDataSample':
"""Set `gt_label``."""
self.set_field(format_label(value), 'gt_label', dtype=torch.Tensor)
return self
def set_pred_label(self, value: LABEL_TYPE) -> 'ActionDataSample':
"""Set ``pred_label``."""
self.set_field(format_label(value), 'pred_label', dtype=torch.Tensor)
return self
def set_pred_score(self, value: SCORE_TYPE) -> 'ActionDataSample':
"""Set score of ``pred_label``."""
score = format_score(value)
self.set_field(score, 'pred_score')
if hasattr(self, 'num_classes'):
assert len(score) == self.num_classes, \
f'The length of score {len(score)} should be '\
f'equal to the num_classes {self.num_classes}.'
else:
self.set_field(
name='num_classes', value=len(score), field_type='metainfo')
return self
@property
def proposals(self):
"""Property of `proposals`"""
return self._proposals
@proposals.setter
def proposals(self, value):
"""Setter of `proposals`"""
self.set_field(value, '_proposals', dtype=InstanceData)
@proposals.deleter
def proposals(self):
"""Deleter of `proposals`"""
del self._proposals
@property
def gt_instances(self):
"""Property of `gt_instances`"""
return self._gt_instances
@gt_instances.setter
def gt_instances(self, value):
"""Setter of `gt_instances`"""
self.set_field(value, '_gt_instances', dtype=InstanceData)
@gt_instances.deleter
def gt_instances(self):
"""Deleter of `gt_instances`"""
del self._gt_instances
@property
def features(self):
"""Setter of `features`"""
return self._features
@features.setter
def features(self, value):
"""Setter of `features`"""
self.set_field(value, '_features', dtype=InstanceData)
@features.deleter
def features(self):
"""Deleter of `features`"""
del self._features
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