InstanceData — mmengine 0.10.7 documentation (original) (raw)
class mmengine.structures.InstanceData(*, metainfo=None, **kwargs)[source]¶
Data structure for instance-level annotations or predictions.
Subclass of BaseDataElement. All value in data_fieldsshould have the same length. This design refer tohttps://github.com/facebookresearch/detectron2/blob/master/detectron2/structures/instances.py # noqa E501 InstanceData also support extra functions: index, slice and cat for data field. The type of value in data field can be base data structure such as torch.Tensor, numpy.ndarray, list, str, tuple, and can be customized data structure that has __len__, __getitem__ and cat attributes.
Examples
custom data structure
class TmpObject: ... def init(self, tmp) -> None: ... assert isinstance(tmp, list) ... self.tmp = tmp ... def len(self): ... return len(self.tmp) ... def getitem(self, item): ... if isinstance(item, int): ... if item >= len(self) or item < -len(self): # type:ignore ... raise IndexError(f'Index {item} out of range!') ... else: ... # keep the dimension ... item = slice(item, None, len(self)) ... return TmpObject(self.tmp[item]) ... @staticmethod ... def cat(tmp_objs): ... assert all(isinstance(results, TmpObject) for results in tmp_objs) ... if len(tmp_objs) == 1: ... return tmp_objs[0] ... tmp_list = [tmp_obj.tmp for tmp_obj in tmp_objs] ... tmp_list = list(itertools.chain(*tmp_list)) ... new_data = TmpObject(tmp_list) ... return new_data ... def __repr__(self): ... return str(self.tmp) from mmengine.structures import InstanceData import numpy as np import torch img_meta = dict(img_shape=(800, 1196, 3), pad_shape=(800, 1216, 3)) instance_data = InstanceData(metainfo=img_meta) 'img_shape' in instance_data True instance_data.det_labels = torch.LongTensor([2, 3]) instance_data["det_scores"] = torch.Tensor([0.8, 0.7]) instance_data.bboxes = torch.rand((2, 4)) instance_data.polygons = TmpObject([[1, 2, 3, 4], [5, 6, 7, 8]]) len(instance_data) 2 print(instance_data) <InstanceData( META INFORMATION img_shape: (800, 1196, 3) pad_shape: (800, 1216, 3) DATA FIELDS det_labels: tensor([2, 3]) det_scores: tensor([0.8000, 0.7000]) bboxes: tensor([[0.4997, 0.7707, 0.0595, 0.4188], [0.8101, 0.3105, 0.5123, 0.6263]]) polygons: [[1, 2, 3, 4], [5, 6, 7, 8]] ) at 0x7fb492de6280> sorted_results = instance_data[instance_data.det_scores.sort().indices] sorted_results.det_scores tensor([0.7000, 0.8000]) print(instance_data[instance_data.det_scores > 0.75]) <InstanceData( META INFORMATION img_shape: (800, 1196, 3) pad_shape: (800, 1216, 3) DATA FIELDS det_labels: tensor([2]) det_scores: tensor([0.8000]) bboxes: tensor([[0.4997, 0.7707, 0.0595, 0.4188]]) polygons: [[1, 2, 3, 4]] ) at 0x7f64ecf0ec40> print(instance_data[instance_data.det_scores > 1]) <InstanceData( META INFORMATION img_shape: (800, 1196, 3) pad_shape: (800, 1216, 3) DATA FIELDS det_labels: tensor([], dtype=torch.int64) det_scores: tensor([]) bboxes: tensor([], size=(0, 4)) polygons: [] ) at 0x7f660a6a7f70> print(instance_data.cat([instance_data, instance_data])) <InstanceData( META INFORMATION img_shape: (800, 1196, 3) pad_shape: (800, 1216, 3) DATA FIELDS det_labels: tensor([2, 3, 2, 3]) det_scores: tensor([0.8000, 0.7000, 0.8000, 0.7000]) bboxes: tensor([[0.4997, 0.7707, 0.0595, 0.4188], [0.8101, 0.3105, 0.5123, 0.6263], [0.4997, 0.7707, 0.0595, 0.4188], [0.8101, 0.3105, 0.5123, 0.6263]]) polygons: [[1, 2, 3, 4], [5, 6, 7, 8], [1, 2, 3, 4], [5, 6, 7, 8]] ) at 0x7f203542feb0>
Parameters:
metainfo (dict | None) –
static cat(instances_list)[source]¶
Concat the instances of all InstanceData in the list.
Note: To ensure that cat returns as expected, make sure that all elements in the list must have exactly the same keys.
Parameters:
instances_list (list[InstanceData]) – A list of InstanceData.
Returns:
Return type: