custom_3d.py

import mmcv
import numpy as np
import tempfile
from os import path as osp
from torch.utils.data import Dataset

from mmdet.datasets import DATASETS
from ..core.bbox import get_box_type
from .pipelines import Compose


@DATASETS.register_module()
class Custom3DDataset(Dataset):
    """Customized 3D dataset.

    This is the base dataset of SUNRGB-D, ScanNet, nuScenes, and KITTI
    dataset.

    Args:
        data_root (str): Path of dataset root.
        ann_file (str): Path of annotation file.
        pipeline (list[dict], optional): Pipeline used for data processing.
            Defaults to None.
        classes (tuple[str], optional): Classes used in the dataset.
            Defaults to None.
        modality (dict, optional): Modality to specify the sensor data used
            as input. Defaults to None.
        box_type_3d (str, optional): Type of 3D box of this dataset.
            Based on the `box_type_3d`, the dataset will encapsulate the box
            to its original format then converted them to `box_type_3d`.
            Defaults to 'LiDAR'. Available options includes

            - 'LiDAR': Box in LiDAR coordinates.
            - 'Depth': Box in depth coordinates, usually for indoor dataset.
            - 'Camera': Box in camera coordinates.
        filter_empty_gt (bool, optional): Whether to filter empty GT.
            Defaults to True.
        test_mode (bool, optional): Whether the dataset is in test mode.
            Defaults to False.
    """

    def __init__(self,
                 data_root,
                 ann_file,
                 pipeline=None,
                 classes=None,
                 modality=None,
                 box_type_3d='LiDAR',
                 filter_empty_gt=True,
                 test_mode=False):
        super().__init__()
        self.data_root = data_root
        self.ann_file = ann_file
        self.test_mode = test_mode
        self.modality = modality
        self.filter_empty_gt = filter_empty_gt
        self.box_type_3d, self.box_mode_3d = get_box_type(box_type_3d)

        self.CLASSES = self.get_classes(classes)
        self.cat2id = {name: i for i, name in enumerate(self.CLASSES)}
        self.data_infos = self.load_annotations(self.ann_file)

        if pipeline is not None:
            self.pipeline = Compose(pipeline)

        # set group flag for the sampler
        if not self.test_mode:
            self._set_group_flag()

    def load_annotations(self, ann_file):
        """Load annotations from ann_file.

        Args:
            ann_file (str): Path of the annotation file.

        Returns:
            list[dict]: List of annotations.
        """
        return mmcv.load(ann_file)

    def get_data_info(self, index):
        """Get data info according to the given index.

        Args:
            index (int): Index of the sample data to get.

        Returns:
            dict: Data information that will be passed to the data \
                preprocessing pipelines. It includes the following keys:

                - sample_idx (str): Sample index.
                - pts_filename (str): Filename of point clouds.
                - file_name (str): Filename of point clouds.
                - ann_info (dict): Annotation info.
        """
        info = self.data_infos[index]
        sample_idx = info['point_cloud']['lidar_idx']
        pts_filename = osp.join(self.data_root, info['pts_path'])

        input_dict = dict(
            pts_filename=pts_filename,
            sample_idx=sample_idx,
            file_name=pts_filename)

        if not self.test_mode:
            annos = self.get_ann_info(index)
            input_dict['ann_info'] = annos
            if self.filter_empty_gt and ~(annos['gt_labels_3d'] != -1).any():
                return None
        return input_dict

    def pre_pipeline(self, results):
        """Initialization before data preparation.

        Args:
            results (dict): Dict before data preprocessing.

                - img_fields (list): Image fields.
                - bbox3d_fields (list): 3D bounding boxes fields.
                - pts_mask_fields (list): Mask fields of points.
                - pts_seg_fields (list): Mask fields of point segments.
                - bbox_fields (list): Fields of bounding boxes.
                - mask_fields (list): Fields of masks.
                - seg_fields (list): Segment fields.
                - box_type_3d (str): 3D box type.
                - box_mode_3d (str): 3D box mode.
        """
        results['img_fields'] = []
        results['bbox3d_fields'] = []
        results['pts_mask_fields'] = []
        results['pts_seg_fields'] = []
        results['bbox_fields'] = []
        results['mask_fields'] = []
        results['seg_fields'] = []
        results['box_type_3d'] = self.box_type_3d
        results['box_mode_3d'] = self.box_mode_3d

    def prepare_train_data(self, index):
        """Training data preparation.

        Args:
            index (int): Index for accessing the target data.

        Returns:
            dict: Training data dict of the corresponding index.
        """
        input_dict = self.get_data_info(index)
        if input_dict is None:
            return None
        self.pre_pipeline(input_dict)
        example = self.pipeline(input_dict)
        if self.filter_empty_gt and \
                (example is None or
                    ~(example['gt_labels_3d']._data != -1).any()):
            return None
        return example

    def prepare_test_data(self, index):
        """Prepare data for testing.

        Args:
            index (int): Index for accessing the target data.

        Returns:
            dict: Testing data dict of the corresponding index.
        """
        input_dict = self.get_data_info(index)
        self.pre_pipeline(input_dict)
        example = self.pipeline(input_dict)
        return example

    @classmethod
    def get_classes(cls, classes=None):
        """Get class names of current dataset.

        Args:
            classes (Sequence[str] | str | None): If classes is None, use
                default CLASSES defined by builtin dataset. If classes is a
                string, take it as a file name. The file contains the name of
                classes where each line contains one class name. If classes is
                a tuple or list, override the CLASSES defined by the dataset.

        Return:
            list[str]: A list of class names.
        """
        if classes is None:
            return cls.CLASSES

        if isinstance(classes, str):
            # take it as a file path
            class_names = mmcv.list_from_file(classes)
        elif isinstance(classes, (tuple, list)):
            class_names = classes
        else:
            raise ValueError(f'Unsupported type {type(classes)} of classes.')

        return class_names

    def format_results(self,
                       outputs,
                       pklfile_prefix=None,
                       submission_prefix=None):
        """Format the results to pkl file.

        Args:
            outputs (list[dict]): Testing results of the dataset.
            pklfile_prefix (str | None): The prefix of pkl files. It includes
                the file path and the prefix of filename, e.g., "a/b/prefix".
                If not specified, a temp file will be created. Default: None.

        Returns:
            tuple: (outputs, tmp_dir), outputs is the detection results, \
                tmp_dir is the temporal directory created for saving json \
                files when ``jsonfile_prefix`` is not specified.
        """
        if pklfile_prefix is None:
            tmp_dir = tempfile.TemporaryDirectory()
            pklfile_prefix = osp.join(tmp_dir.name, 'results')
            out = f'{pklfile_prefix}.pkl'
        mmcv.dump(outputs, out)
        return outputs, tmp_dir

    def evaluate(self,
                 results,
                 metric=None,
                 iou_thr=(0.25, 0.5),
                 logger=None,
                 show=False,
                 out_dir=None):
        """Evaluate.

        Evaluation in indoor protocol.

        Args:
            results (list[dict]): List of results.
            metric (str | list[str]): Metrics to be evaluated.
            iou_thr (list[float]): AP IoU thresholds.
            show (bool): Whether to visualize.
                Default: False.
            out_dir (str): Path to save the visualization results.
                Default: None.

        Returns:
            dict: Evaluation results.
        """
        from mmdet3d.core.evaluation import indoor_eval
        assert isinstance(
            results, list), f'Expect results to be list, got {type(results)}.'
        assert len(results) > 0, 'Expect length of results > 0.'
        assert len(results) == len(self.data_infos)
        assert isinstance(
            results[0], dict
        ), f'Expect elements in results to be dict, got {type(results[0])}.'
        gt_annos = [info['annos'] for info in self.data_infos]
        label2cat = {i: cat_id for i, cat_id in enumerate(self.CLASSES)}
        ret_dict = indoor_eval(
            gt_annos,
            results,
            iou_thr,
            label2cat,
            logger=logger,
            box_type_3d=self.box_type_3d,
            box_mode_3d=self.box_mode_3d)
        if show:
            self.show(results, out_dir)

        return ret_dict

    def __len__(self):
        """Return the length of data infos.

        Returns:
            int: Length of data infos.
        """
        return len(self.data_infos)

    def _rand_another(self, idx):
        """Randomly get another item with the same flag.

        Returns:
            int: Another index of item with the same flag.
        """
        pool = np.where(self.flag == self.flag[idx])[0]
        return np.random.choice(pool)

    def __getitem__(self, idx):
        """Get item from infos according to the given index.

        Returns:
            dict: Data dictionary of the corresponding index.
        """
        if self.test_mode:
            return self.prepare_test_data(idx)
        while True:
            data = self.prepare_train_data(idx)
            if data is None:
                idx = self._rand_another(idx)
                continue
            return data

    def _set_group_flag(self):
        """Set flag according to image aspect ratio.

        Images with aspect ratio greater than 1 will be set as group 1,
        otherwise group 0. In 3D datasets, they are all the same, thus are all
        zeros.
        """
        self.flag = np.zeros(len(self), dtype=np.uint8)