ABLkit/abl/data/structures/base_data_element.py

# Copyright (c) OpenMMLab. All rights reserved.
import copy
from typing import Any, Iterator, Optional, Tuple, Type, Union

import numpy as np
import torch


# Modified from
# https://github.com/open-mmlab/mmengine/blob/main/mmengine/structures/base_data_element.py
class BaseDataElement:
    """A base data interface that supports Tensor-like and dict-like
    operations.

    A typical data elements refer to predicted results or ground truth labels
    on a task, such as predicted bboxes, instance masks, semantic
    segmentation masks, etc. Because groundtruth labels and predicted results
    often have similar properties (for example, the predicted bboxes and the
    groundtruth bboxes), MMEngine uses the same abstract data interface to
    encapsulate predicted results and groundtruth labels, and it is recommended
    to use different name conventions to distinguish them, such as using
    ``gt_instances`` and ``pred_instances`` to distinguish between labels and
    predicted results. Additionally, we distinguish data elements at instance
    level, pixel level, and label level. Each of these types has its own
    characteristics. Therefore, MMEngine defines the base class
    ``BaseDataElement``, and implement ``InstanceData``, ``PixelData``, and
    ``LabelData`` inheriting from ``BaseDataElement`` to represent different
    types of ground truth labels or predictions.

    Another common data element is data example. A data example consists of input
    data (such as an image) and its annotations and predictions. In general,
    an image can have multiple types of annotations and/or predictions at the
    same time (for example, both pixel-level semantic segmentation annotations
    and instance-level detection bboxes annotations). All labels and
    predictions of a training example are often passed between Dataset, Model,
    Visualizer, and Evaluator components. In order to simplify the interface
    between components, we can treat them as a large data element and
    encapsulate them. Such data elements are generally called XXDataSample in
    the OpenMMLab. Therefore, Similar to `nn.Module`, the `BaseDataElement`
    allows `BaseDataElement` as its attribute. Such a class generally
    encapsulates all the data of a example in the algorithm library, and its
    attributes generally are various types of data elements. For example,
    MMDetection is assigned by the BaseDataElement to encapsulate all the data
    elements of the example labeling and prediction of a example in the
    algorithm library.

    The attributes in ``BaseDataElement`` are divided into two parts,
    the ``metainfo`` and the ``data`` respectively.

        - ``metainfo``: Usually contains the
          information about the image such as filename,
          image_shape, pad_shape, etc. The attributes can be accessed or
          modified by dict-like or object-like operations, such as
          ``.`` (for data access and modification), ``in``, ``del``,
          ``pop(str)``, ``get(str)``, ``metainfo_keys()``,
          ``metainfo_values()``, ``metainfo_items()``, ``set_metainfo()`` (for
          set or change key-value pairs in metainfo).

        - ``data``: Annotations or model predictions are
          stored. The attributes can be accessed or modified by
          dict-like or object-like operations, such as
          ``.``, ``in``, ``del``, ``pop(str)``, ``get(str)``, ``keys()``,
          ``values()``, ``items()``. Users can also apply tensor-like
          methods to all :obj:`torch.Tensor` in the ``data_fields``,
          such as ``.cuda()``, ``.cpu()``, ``.numpy()``, ``.to()``,
          ``to_tensor()``, ``.detach()``.

    Args:
        metainfo (dict, optional): A dict contains the meta information
            of single image, such as ``dict(img_shape=(512, 512, 3),
            scale_factor=(1, 1, 1, 1))``. Defaults to None.
        kwargs (dict, optional): A dict contains annotations of single image or
            model predictions. Defaults to None.

    Examples:
        >>> import torch
        >>> from mmengine.structures import BaseDataElement
        >>> gt_instances = BaseDataElement()
        >>> bboxes = torch.rand((5, 4))
        >>> scores = torch.rand((5,))
        >>> img_id = 0
        >>> img_shape = (800, 1333)
        >>> gt_instances = BaseDataElement(
        ...     metainfo=dict(img_id=img_id, img_shape=img_shape),
        ...     bboxes=bboxes, scores=scores)
        >>> gt_instances = BaseDataElement(
        ...     metainfo=dict(img_id=img_id, img_shape=(640, 640)))

        >>> # new
        >>> gt_instances1 = gt_instances.new(
        ...     metainfo=dict(img_id=1, img_shape=(640, 640)),
        ...                   bboxes=torch.rand((5, 4)),
        ...                   scores=torch.rand((5,)))
        >>> gt_instances2 = gt_instances1.new()

        >>> # add and process property
        >>> gt_instances = BaseDataElement()
        >>> gt_instances.set_metainfo(dict(img_id=9, img_shape=(100, 100)))
        >>> assert 'img_shape' in gt_instances.metainfo_keys()
        >>> assert 'img_shape' in gt_instances
        >>> assert 'img_shape' not in gt_instances.keys()
        >>> assert 'img_shape' in gt_instances.all_keys()
        >>> print(gt_instances.img_shape)
        (100, 100)
        >>> gt_instances.scores = torch.rand((5,))
        >>> assert 'scores' in gt_instances.keys()
        >>> assert 'scores' in gt_instances
        >>> assert 'scores' in gt_instances.all_keys()
        >>> assert 'scores' not in gt_instances.metainfo_keys()
        >>> print(gt_instances.scores)
        tensor([0.5230, 0.7885, 0.2426, 0.3911, 0.4876])
        >>> gt_instances.bboxes = torch.rand((5, 4))
        >>> assert 'bboxes' in gt_instances.keys()
        >>> assert 'bboxes' in gt_instances
        >>> assert 'bboxes' in gt_instances.all_keys()
        >>> assert 'bboxes' not in gt_instances.metainfo_keys()
        >>> print(gt_instances.bboxes)
        tensor([[0.0900, 0.0424, 0.1755, 0.4469],
                [0.8648, 0.0592, 0.3484, 0.0913],
                [0.5808, 0.1909, 0.6165, 0.7088],
                [0.5490, 0.4209, 0.9416, 0.2374],
                [0.3652, 0.1218, 0.8805, 0.7523]])

        >>> # delete and change property
        >>> gt_instances = BaseDataElement(
        ...     metainfo=dict(img_id=0, img_shape=(640, 640)),
        ...     bboxes=torch.rand((6, 4)), scores=torch.rand((6,)))
        >>> gt_instances.set_metainfo(dict(img_shape=(1280, 1280)))
        >>> gt_instances.img_shape  # (1280, 1280)
        >>> gt_instances.bboxes = gt_instances.bboxes * 2
        >>> gt_instances.get('img_shape', None)  # (1280, 1280)
        >>> gt_instances.get('bboxes', None)  # 6x4 tensor
        >>> del gt_instances.img_shape
        >>> del gt_instances.bboxes
        >>> assert 'img_shape' not in gt_instances
        >>> assert 'bboxes' not in gt_instances
        >>> gt_instances.pop('img_shape', None)  # None
        >>> gt_instances.pop('bboxes', None)  # None

        >>> # Tensor-like
        >>> cuda_instances = gt_instances.cuda()
        >>> cuda_instances = gt_instances.to('cuda:0')
        >>> cpu_instances = cuda_instances.cpu()
        >>> cpu_instances = cuda_instances.to('cpu')
        >>> fp16_instances = cuda_instances.to(
        ...     device=None, dtype=torch.float16, non_blocking=False,
        ...     copy=False, memory_format=torch.preserve_format)
        >>> cpu_instances = cuda_instances.detach()
        >>> np_instances = cpu_instances.numpy()

        >>> # print
        >>> metainfo = dict(img_shape=(800, 1196, 3))
        >>> gt_instances = BaseDataElement(
        ...     metainfo=metainfo, det_labels=torch.LongTensor([0, 1, 2, 3]))
        >>> example = BaseDataElement(metainfo=metainfo,
        ...                          gt_instances=gt_instances)
        >>> print(example)
        <BaseDataElement(
            META INFORMATION
            img_shape: (800, 1196, 3)
            DATA FIELDS
            gt_instances: <BaseDataElement(
                    META INFORMATION
                    img_shape: (800, 1196, 3)
                    DATA FIELDS
                    det_labels: tensor([0, 1, 2, 3])
                ) at 0x7f0ec5eadc70>
        ) at 0x7f0fea49e130>

        >>> # inheritance
        >>> class DetDataSample(BaseDataElement):
        ...     @property
        ...     def proposals(self):
        ...         return self._proposals
        ...     @proposals.setter
        ...     def proposals(self, value):
        ...         self.set_field(value, '_proposals', dtype=BaseDataElement)
        ...     @proposals.deleter
        ...     def proposals(self):
        ...         del self._proposals
        ...     @property
        ...     def gt_instances(self):
        ...         return self._gt_instances
        ...     @gt_instances.setter
        ...     def gt_instances(self, value):
        ...         self.set_field(value, '_gt_instances',
        ...                        dtype=BaseDataElement)
        ...     @gt_instances.deleter
        ...     def gt_instances(self):
        ...         del self._gt_instances
        ...     @property
        ...     def pred_instances(self):
        ...         return self._pred_instances
        ...     @pred_instances.setter
        ...     def pred_instances(self, value):
        ...         self.set_field(value, '_pred_instances',
        ...                        dtype=BaseDataElement)
        ...     @pred_instances.deleter
        ...     def pred_instances(self):
        ...         del self._pred_instances
        >>> det_example = DetDataSample()
        >>> proposals = BaseDataElement(bboxes=torch.rand((5, 4)))
        >>> det_example.proposals = proposals
        >>> assert 'proposals' in det_example
        >>> assert det_example.proposals == proposals
        >>> del det_example.proposals
        >>> assert 'proposals' not in det_example
        >>> with self.assertRaises(AssertionError):
        ...     det_example.proposals = torch.rand((5, 4))
    """

    def __init__(self, *, metainfo: Optional[dict] = None, **kwargs) -> None:
        self._metainfo_fields: set = set()
        self._data_fields: set = set()

        if metainfo is not None:
            self.set_metainfo(metainfo=metainfo)
        if kwargs:
            self.set_data(kwargs)

    def set_metainfo(self, metainfo: dict) -> None:
        """Set or change key-value pairs in ``metainfo_field`` by parameter
        ``metainfo``.

        Args:
            metainfo (dict): A dict contains the meta information
                of image, such as ``img_shape``, ``scale_factor``, etc.
        """
        assert isinstance(metainfo, dict), f"metainfo should be a ``dict`` but got {type(metainfo)}"
        meta = copy.deepcopy(metainfo)
        for k, v in meta.items():
            self.set_field(name=k, value=v, field_type="metainfo", dtype=None)

    def set_data(self, data: dict) -> None:
        """Set or change key-value pairs in ``data_field`` by parameter
        ``data``.

        Args:
            data (dict): A dict contains annotations of image or
                model predictions.
        """
        assert isinstance(data, dict), f"data should be a `dict` but got {data}"
        for k, v in data.items():
            # Use `setattr()` rather than `self.set_field` to allow `set_data`
            # to set property method.
            setattr(self, k, v)

    def update(self, instance: "BaseDataElement") -> None:
        """The update() method updates the BaseDataElement with the elements
        from another BaseDataElement object.

        Args:
            instance (BaseDataElement): Another BaseDataElement object for
                update the current object.
        """
        assert isinstance(
            instance, BaseDataElement
        ), f"instance should be a `BaseDataElement` but got {type(instance)}"
        self.set_metainfo(dict(instance.metainfo_items()))
        self.set_data(dict(instance.items()))

    def new(self, *, metainfo: Optional[dict] = None, **kwargs) -> "BaseDataElement":
        """Return a new data element with same type. If ``metainfo`` and
        ``data`` are None, the new data element will have same metainfo and
        data. If metainfo or data is not None, the new result will overwrite it
        with the input value.

        Args:
            metainfo (dict, optional): A dict contains the meta information
                of image, such as ``img_shape``, ``scale_factor``, etc.
                Defaults to None.
            kwargs (dict): A dict contains annotations of image or
                model predictions.

        Returns:
            BaseDataElement: A new data element with same type.
        """
        new_data = self.__class__()

        if metainfo is not None:
            new_data.set_metainfo(metainfo)
        else:
            new_data.set_metainfo(dict(self.metainfo_items()))
        if kwargs:
            new_data.set_data(kwargs)
        else:
            new_data.set_data(dict(self.items()))
        return new_data

    def clone(self):
        """Deep copy the current data element.

        Returns:
            BaseDataElement: The copy of current data element.
        """
        clone_data = self.__class__()
        clone_data.set_metainfo(dict(self.metainfo_items()))
        clone_data.set_data(dict(self.items()))
        return clone_data

    def keys(self) -> list:
        """
        Returns:
            list: Contains all keys in data_fields.
        """
        # We assume that the name of the attribute related to property is
        # '_' + the name of the property. We use this rule to filter out
        # private keys.
        # TODO: Use a more robust way to solve this problem
        private_keys = {
            "_" + key
            for key in self._data_fields
            if isinstance(getattr(type(self), key, None), property)
        }
        return list(self._data_fields - private_keys)

    def metainfo_keys(self) -> list:
        """
        Returns:
            list: Contains all keys in metainfo_fields.
        """
        return list(self._metainfo_fields)

    def values(self) -> list:
        """
        Returns:
            list: Contains all values in data.
        """
        return [getattr(self, k) for k in self.keys()]

    def metainfo_values(self) -> list:
        """
        Returns:
            list: Contains all values in metainfo.
        """
        return [getattr(self, k) for k in self.metainfo_keys()]

    def all_keys(self) -> list:
        """
        Returns:
            list: Contains all keys in metainfo and data.
        """
        return self.metainfo_keys() + self.keys()

    def all_values(self) -> list:
        """
        Returns:
            list: Contains all values in metainfo and data.
        """
        return self.metainfo_values() + self.values()

    def all_items(self) -> Iterator[Tuple[str, Any]]:
        """
        Returns:
            iterator: An iterator object whose element is (key, value) tuple
            pairs for ``metainfo`` and ``data``.
        """
        for k in self.all_keys():
            yield (k, getattr(self, k))

    def items(self) -> Iterator[Tuple[str, Any]]:
        """
        Returns:
            iterator: An iterator object whose element is (key, value) tuple
            pairs for ``data``.
        """
        for k in self.keys():
            yield (k, getattr(self, k))

    def metainfo_items(self) -> Iterator[Tuple[str, Any]]:
        """
        Returns:
            iterator: An iterator object whose element is (key, value) tuple
            pairs for ``metainfo``.
        """
        for k in self.metainfo_keys():
            yield (k, getattr(self, k))

    @property
    def metainfo(self) -> dict:
        """dict: A dict contains metainfo of current data element."""
        return dict(self.metainfo_items())

    def __setattr__(self, name: str, value: Any):
        """setattr is only used to set data."""
        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:
            self.set_field(name=name, value=value, field_type="data", dtype=None)

    def __delattr__(self, item: str):
        """Delete the item in dataelement.

        Args:
            item (str): The key to delete.
        """
        if item in ("_metainfo_fields", "_data_fields"):
            raise AttributeError(
                f"{item} has been used as a " "private attribute, which is immutable."
            )
        super().__delattr__(item)
        if item in self._metainfo_fields:
            self._metainfo_fields.remove(item)
        elif item in self._data_fields:
            self._data_fields.remove(item)

    # dict-like methods
    __delitem__ = __delattr__

    def get(self, key, default=None) -> Any:
        """Get property in data and metainfo as the same as python."""
        # Use `getattr()` rather than `self.__dict__.get()` to allow getting
        # properties.
        return getattr(self, key, default)

    def pop(self, *args) -> Any:
        """Pop property in data and metainfo as the same as python."""
        assert len(args) < 3, "``pop`` get more than 2 arguments"
        name = args[0]
        if name in self._metainfo_fields:
            self._metainfo_fields.remove(args[0])
            return self.__dict__.pop(*args)

        elif name in self._data_fields:
            self._data_fields.remove(args[0])
            return self.__dict__.pop(*args)

        # with default value
        elif len(args) == 2:
            return args[1]
        else:
            # don't just use 'self.__dict__.pop(*args)' for only popping key in
            # metainfo or data
            raise KeyError(f"{args[0]} is not contained in metainfo or data")

    def __contains__(self, item: str) -> bool:
        """Whether the item is in dataelement.

        Args:
            item (str): The key to inquire.
        """
        return item in self._data_fields or item in self._metainfo_fields

    def set_field(
        self,
        value: Any,
        name: str,
        dtype: Optional[Union[Type, Tuple[Type, ...]]] = None,
        field_type: str = "data",
    ) -> None:
        """Special method for set union field, used as property.setter
        functions."""
        assert field_type in ["metainfo", "data"]
        if dtype is not None:
            assert isinstance(value, dtype), f"{value} should be a {dtype} but got {type(value)}"

        if field_type == "metainfo":
            if name in self._data_fields:
                raise AttributeError(
                    f"Cannot set {name} to be a field of metainfo "
                    f"because {name} is already a data field"
                )
            self._metainfo_fields.add(name)
        else:
            if name in self._metainfo_fields:
                raise AttributeError(
                    f"Cannot set {name} to be a field of data "
                    f"because {name} is already a metainfo field"
                )
            self._data_fields.add(name)
        super().__setattr__(name, value)

    # Tensor-like methods
    def to(self, *args, **kwargs) -> "BaseDataElement":
        """Apply same name function to all tensors in data_fields."""
        new_data = self.new()
        for k, v in self.items():
            if hasattr(v, "to"):
                v = v.to(*args, **kwargs)
                data = {k: v}
                new_data.set_data(data)
        return new_data

    # Tensor-like methods
    def cpu(self) -> "BaseDataElement":
        """Convert all tensors to CPU in data."""
        new_data = self.new()
        for k, v in self.items():
            if isinstance(v, (torch.Tensor, BaseDataElement)):
                v = v.cpu()
                data = {k: v}
                new_data.set_data(data)
        return new_data

    # Tensor-like methods
    def cuda(self) -> "BaseDataElement":
        """Convert all tensors to GPU in data."""
        new_data = self.new()
        for k, v in self.items():
            if isinstance(v, (torch.Tensor, BaseDataElement)):
                v = v.cuda()
                data = {k: v}
                new_data.set_data(data)
        return new_data

    # Tensor-like methods
    def npu(self) -> "BaseDataElement":
        """Convert all tensors to NPU in data."""
        new_data = self.new()
        for k, v in self.items():
            if isinstance(v, (torch.Tensor, BaseDataElement)):
                v = v.npu()
                data = {k: v}
                new_data.set_data(data)
        return new_data

    def mlu(self) -> "BaseDataElement":
        """Convert all tensors to MLU in data."""
        new_data = self.new()
        for k, v in self.items():
            if isinstance(v, (torch.Tensor, BaseDataElement)):
                v = v.mlu()
                data = {k: v}
                new_data.set_data(data)
        return new_data

    # Tensor-like methods
    def detach(self) -> "BaseDataElement":
        """Detach all tensors in data."""
        new_data = self.new()
        for k, v in self.items():
            if isinstance(v, (torch.Tensor, BaseDataElement)):
                v = v.detach()
                data = {k: v}
                new_data.set_data(data)
        return new_data

    # Tensor-like methods
    def numpy(self) -> "BaseDataElement":
        """Convert all tensors to np.ndarray in data."""
        new_data = self.new()
        for k, v in self.items():
            if isinstance(v, (torch.Tensor, BaseDataElement)):
                v = v.detach().cpu().numpy()
                data = {k: v}
                new_data.set_data(data)
        return new_data

    def to_tensor(self) -> "BaseDataElement":
        """Convert all np.ndarray to tensor in data."""
        new_data = self.new()
        for k, v in self.items():
            data = {}
            if isinstance(v, np.ndarray):
                v = torch.from_numpy(v)
                data[k] = v
            elif isinstance(v, BaseDataElement):
                v = v.to_tensor()
                data[k] = v
            new_data.set_data(data)
        return new_data

    def to_dict(self) -> dict:
        """Convert BaseDataElement to dict."""
        return {
            k: v.to_dict() if isinstance(v, BaseDataElement) else v for k, v in self.all_items()
        }

    def __repr__(self) -> str:
        """Represent the object."""

        def _addindent(s_: str, num_spaces: int) -> str:
            """This func is modified from `pytorch` https://github.com/pytorch/
            pytorch/blob/b17b2b1cc7b017c3daaeff8cc7ec0f514d42ec37/torch/nn/modu
            les/module.py#L29.

            Args:
                s_ (str): The string to add spaces.
                num_spaces (int): The num of space to add.

            Returns:
                str: The string after add indent.
            """
            s = s_.split("\n")
            # don't do anything for single-line stuff
            if len(s) == 1:
                return s_
            first = s.pop(0)
            s = [(num_spaces * " ") + line for line in s]
            s = "\n".join(s)  # type: ignore
            s = first + "\n" + s  # type: ignore
            return s  # type: ignore

        def dump(obj: Any) -> str:
            """Represent the object.

            Args:
                obj (Any): The obj to represent.

            Returns:
                str: The represented str.
            """
            _repr = ""
            if isinstance(obj, dict):
                for k, v in obj.items():
                    _repr += f"\n{k}: {_addindent(dump(v), 4)}"
            elif isinstance(obj, BaseDataElement):
                _repr += "\n\n    META INFORMATION"
                metainfo_items = dict(obj.metainfo_items())
                _repr += _addindent(dump(metainfo_items), 4)
                _repr += "\n\n    DATA FIELDS"
                items = dict(obj.items())
                _repr += _addindent(dump(items), 4)
                classname = obj.__class__.__name__
                _repr = f"<{classname}({_repr}\n) at {hex(id(obj))}>"
            else:
                _repr += repr(obj)
            return _repr

        return dump(self)