Source code for mmengine.structures.instance_data
# Copyright (c) OpenMMLab. All rights reserved.
import itertools
from collections.abc import Sized
from typing import List, Union
import numpy as np
import torch
from .base_data_element import BaseDataElement
IndexType = Union[str, slice, int, list, torch.LongTensor,
torch.cuda.LongTensor, torch.BoolTensor,
torch.cuda.BoolTensor, np.ndarray]
# Modified from
# https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/data_structures/instance_data.py # noqa
[docs]class InstanceData(BaseDataElement):
"""Data structure for instance-level annotations or predictions.
Subclass of :class:`BaseDataElement`. All value in `data_fields`
should have the same length. This design refer to
https://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 type(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
>>> 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
pad_shape: (800, 1196, 3)
img_shape: (800, 1216, 3)
DATA FIELDS
det_labels: tensor([2, 3])
det_scores: tensor([0.8, 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
pad_shape: (800, 1216, 3)
img_shape: (800, 1196, 3)
DATA FIELDS
det_labels: tensor([2])
masks: [[11, 21, 31, 41]]
det_scores: tensor([0.8000])
bboxes: tensor([[0.9308, 0.4000, 0.6077, 0.5554]])
polygons: [[1, 2, 3, 4]]
) at 0x7f64ecf0ec40>
>>> print(instance_data[instance_data.det_scores > 1])
<InstanceData(
META INFORMATION
pad_shape: (800, 1216, 3)
img_shape: (800, 1196, 3)
DATA FIELDS
det_labels: tensor([], dtype=torch.int64)
masks: []
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])
bboxes: tensor([[0.7404, 0.6332, 0.1684, 0.9961],
[0.2837, 0.8112, 0.5416, 0.2810],
[0.7404, 0.6332, 0.1684, 0.9961],
[0.2837, 0.8112, 0.5416, 0.2810]])
data:
polygons: [[1, 2, 3, 4], [5, 6, 7, 8],
[1, 2, 3, 4], [5, 6, 7, 8]]
det_scores: tensor([0.8000, 0.7000, 0.8000, 0.7000])
masks: [[11, 21, 31, 41], [51, 61, 71, 81],
[11, 21, 31, 41], [51, 61, 71, 81]]
) at 0x7f203542feb0>
"""
def __setattr__(self, name: str, value: Sized):
"""setattr is only used to set data.
the value must have the attribute of `__len__` and have the same length
of instancedata
"""
if name in ('_metainfo_fields', '_data_fields'):
if not hasattr(self, name):
super().__setattr__(name, value)
else:
raise AttributeError(
f'{name} has been used as a '
f'private attribute, which is immutable. ')
else:
assert isinstance(value,
Sized), 'value must contain `_len__` attribute'
if len(self) > 0:
assert len(value) == len(self), f'the length of ' \
f'values {len(value)} is ' \
f'not consistent with' \
f' the length of this ' \
f':obj:`InstanceData` ' \
f'{len(self)} '
super().__setattr__(name, value)
__setitem__ = __setattr__
def __getitem__(self, item: IndexType) -> 'InstanceData':
"""
Args:
item (str, obj:`slice`,
obj`torch.LongTensor`, obj:`torch.BoolTensor`):
get the corresponding values according to item.
Returns:
obj:`InstanceData`: Corresponding values.
"""
if isinstance(item, list):
item = np.array(item)
if isinstance(item, np.ndarray):
# The default int type of numpy is platform dependent, int32 for
# windows and int64 for linux. `torch.Tensor` requires the index
# should be int64, therefore we simply convert it to int64 here.
# More details in https://github.com/numpy/numpy/issues/9464
item = item.astype(np.int64) if item.dtype == np.int32 else item
item = torch.from_numpy(item)
assert isinstance(
item, (str, slice, int, torch.LongTensor, torch.cuda.LongTensor,
torch.BoolTensor, torch.cuda.BoolTensor))
if isinstance(item, str):
return getattr(self, item)
if type(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))
new_data = self.__class__(metainfo=self.metainfo)
if isinstance(item, torch.Tensor):
assert item.dim() == 1, 'Only support to get the' \
' values along the first dimension.'
if isinstance(item, (torch.BoolTensor, torch.cuda.BoolTensor)):
assert len(item) == len(self), f'The shape of the' \
f' input(BoolTensor)) ' \
f'{len(item)} ' \
f' does not match the shape ' \
f'of the indexed tensor ' \
f'in results_filed ' \
f'{len(self)} at ' \
f'first dimension. '
for k, v in self.items():
if isinstance(v, torch.Tensor):
new_data[k] = v[item]
elif isinstance(v, np.ndarray):
new_data[k] = v[item.cpu().numpy()]
elif isinstance(
v, (str, list, tuple)) or (hasattr(v, '__getitem__')
and hasattr(v, 'cat')):
# convert to indexes from boolTensor
if isinstance(item,
(torch.BoolTensor, torch.cuda.BoolTensor)):
indexes = torch.nonzero(item).view(
-1).cpu().numpy().tolist()
else:
indexes = item.cpu().numpy().tolist()
slice_list = []
if indexes:
for index in indexes:
slice_list.append(slice(index, None, len(v)))
else:
slice_list.append(slice(None, 0, None))
r_list = [v[s] for s in slice_list]
if isinstance(v, (str, list, tuple)):
new_value = r_list[0]
for r in r_list[1:]:
new_value = new_value + r
else:
new_value = v.cat(r_list)
new_data[k] = new_value
else:
raise ValueError(
f'The type of `{k}` is `{type(v)}`, which has no '
'attribute of `cat`, so it does not '
f'support slice with `bool`')
else:
# item is a slice
for k, v in self.items():
new_data[k] = v[item]
return new_data # type:ignore
[docs] @staticmethod
def cat(instances_list: List['InstanceData']) -> 'InstanceData':
"""Concat the instances of all :obj:`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.
Args:
instances_list (list[:obj:`InstanceData`]): A list
of :obj:`InstanceData`.
Returns:
obj:`InstanceData`
"""
assert all(
isinstance(results, InstanceData) for results in instances_list)
assert len(instances_list) > 0
if len(instances_list) == 1:
return instances_list[0]
# metainfo and data_fields must be exactly the
# same for each element to avoid exceptions.
field_keys_list = [
instances.all_keys() for instances in instances_list
]
assert len({len(field_keys) for field_keys in field_keys_list}) \
== 1 and len(set(itertools.chain(*field_keys_list))) \
== len(field_keys_list[0]), 'There are different keys in ' \
'`instances_list`, which may ' \
'cause the cat operation ' \
'to fail. Please make sure all ' \
'elements in `instances_list` ' \
'have the exact same key '
new_data = instances_list[0].__class__(
metainfo=instances_list[0].metainfo)
for k in instances_list[0].keys():
values = [results[k] for results in instances_list]
v0 = values[0]
if isinstance(v0, torch.Tensor):
new_values = torch.cat(values, dim=0)
elif isinstance(v0, np.ndarray):
new_values = np.concatenate(values, axis=0)
elif isinstance(v0, (str, list, tuple)):
new_values = v0[:]
for v in values[1:]:
new_values += v
elif hasattr(v0, 'cat'):
new_values = v0.cat(values)
else:
raise ValueError(
f'The type of `{k}` is `{type(v0)}` which has no '
'attribute of `cat`')
new_data[k] = new_values
return new_data # type:ignore
def __len__(self) -> int:
"""int: the length of InstanceData"""
if len(self._data_fields) > 0:
return len(self.values()[0])
else:
return 0