submodule/sapiens/engine/mmengine/structures/instance_data.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import itertools
from collections.abc import Sized
from typing import Any, List, Union

import numpy as np
import torch

from mmengine.device import get_device
from .base_data_element import BaseDataElement

BoolTypeTensor: Union[Any]
LongTypeTensor: Union[Any]

if get_device() == 'npu':
    BoolTypeTensor = Union[torch.BoolTensor, torch.npu.BoolTensor]
    LongTypeTensor = Union[torch.LongTensor, torch.npu.LongTensor]
elif get_device() == 'mlu':
    BoolTypeTensor = Union[torch.BoolTensor, torch.mlu.BoolTensor]
    LongTypeTensor = Union[torch.LongTensor, torch.mlu.LongTensor]
else:
    BoolTypeTensor = Union[torch.BoolTensor, torch.cuda.BoolTensor]
    LongTypeTensor = Union[torch.LongTensor, torch.cuda.LongTensor]

IndexType: Union[Any] = Union[str, slice, int, list, LongTypeTensor,
                              BoolTypeTensor, np.ndarray]


# Modified from
# https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/data_structures/instance_data.py # noqa
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 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>
    """

    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 '
                                     'private attribute, which is immutable.')

        else:
            assert isinstance(value,
                              Sized), 'value must contain `__len__` attribute'

            if len(self) > 0:
                assert len(value) == len(self), 'The length of ' \
                                                f'values {len(value)} is ' \
                                                'not consistent with ' \
                                                'the length of this ' \
                                                ':obj:`InstanceData` ' \
                                                f'{len(self)}'
            super().__setattr__(name, value)

    __setitem__ = __setattr__

    def __getitem__(self, item: IndexType) -> 'InstanceData':
        """
        Args:
            item (str, int, list, :obj:`slice`, :obj:`numpy.ndarray`,
                :obj:`torch.LongTensor`, :obj:`torch.BoolTensor`):
                Get the corresponding values according to item.

        Returns:
            :obj:`InstanceData`: Corresponding values.
        """
        assert isinstance(item, IndexType.__args__)
        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)

        if isinstance(item, str):
            return getattr(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))

        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, BoolTypeTensor.__args__):
                assert len(item) == len(self), 'The shape of the ' \
                                               'input(BoolTensor) ' \
                                               f'{len(item)} ' \
                                               'does not match the shape ' \
                                               'of the indexed tensor ' \
                                               'in results_field ' \
                                               f'{len(self)} at ' \
                                               '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, BoolTypeTensor.__args__):
                        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 '
                        '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

    @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