annotator/uniformer/mmdet/datasets/custom.py

import os.path as osp
import warnings
from collections import OrderedDict

import mmcv
import numpy as np
from mmcv.utils import print_log
from torch.utils.data import Dataset

from mmdet.core import eval_map, eval_recalls
from .builder import DATASETS
from .pipelines import Compose


@DATASETS.register_module()
class CustomDataset(Dataset):
    """Custom dataset for detection.

    The annotation format is shown as follows. The `ann` field is optional for
    testing.

    .. code-block:: none

        [
            {
                'filename': 'a.jpg',
                'width': 1280,
                'height': 720,
                'ann': {
                    'bboxes': <np.ndarray> (n, 4) in (x1, y1, x2, y2) order.
                    'labels': <np.ndarray> (n, ),
                    'bboxes_ignore': <np.ndarray> (k, 4), (optional field)
                    'labels_ignore': <np.ndarray> (k, 4) (optional field)
                }
            },
            ...
        ]

    Args:
        ann_file (str): Annotation file path.
        pipeline (list[dict]): Processing pipeline.
        classes (str | Sequence[str], optional): Specify classes to load.
            If is None, ``cls.CLASSES`` will be used. Default: None.
        data_root (str, optional): Data root for ``ann_file``,
            ``img_prefix``, ``seg_prefix``, ``proposal_file`` if specified.
        test_mode (bool, optional): If set True, annotation will not be loaded.
        filter_empty_gt (bool, optional): If set true, images without bounding
            boxes of the dataset's classes will be filtered out. This option
            only works when `test_mode=False`, i.e., we never filter images
            during tests.
    """

    CLASSES = None

    def __init__(self,
                 ann_file,
                 pipeline,
                 classes=None,
                 data_root=None,
                 img_prefix='',
                 seg_prefix=None,
                 proposal_file=None,
                 test_mode=False,
                 filter_empty_gt=True):
        self.ann_file = ann_file
        self.data_root = data_root
        self.img_prefix = img_prefix
        self.seg_prefix = seg_prefix
        self.proposal_file = proposal_file
        self.test_mode = test_mode
        self.filter_empty_gt = filter_empty_gt
        self.CLASSES = self.get_classes(classes)

        # join paths if data_root is specified
        if self.data_root is not None:
            if not osp.isabs(self.ann_file):
                self.ann_file = osp.join(self.data_root, self.ann_file)
            if not (self.img_prefix is None or osp.isabs(self.img_prefix)):
                self.img_prefix = osp.join(self.data_root, self.img_prefix)
            if not (self.seg_prefix is None or osp.isabs(self.seg_prefix)):
                self.seg_prefix = osp.join(self.data_root, self.seg_prefix)
            if not (self.proposal_file is None
                    or osp.isabs(self.proposal_file)):
                self.proposal_file = osp.join(self.data_root,
                                              self.proposal_file)
        # load annotations (and proposals)
        self.data_infos = self.load_annotations(self.ann_file)

        if self.proposal_file is not None:
            self.proposals = self.load_proposals(self.proposal_file)
        else:
            self.proposals = None

        # filter images too small and containing no annotations
        if not test_mode:
            valid_inds = self._filter_imgs()
            self.data_infos = [self.data_infos[i] for i in valid_inds]
            if self.proposals is not None:
                self.proposals = [self.proposals[i] for i in valid_inds]
            # set group flag for the sampler
            self._set_group_flag()

        # processing pipeline
        self.pipeline = Compose(pipeline)

    def __len__(self):
        """Total number of samples of data."""
        return len(self.data_infos)

    def load_annotations(self, ann_file):
        """Load annotation from annotation file."""
        return mmcv.load(ann_file)

    def load_proposals(self, proposal_file):
        """Load proposal from proposal file."""
        return mmcv.load(proposal_file)

    def get_ann_info(self, idx):
        """Get annotation by index.

        Args:
            idx (int): Index of data.

        Returns:
            dict: Annotation info of specified index.
        """

        return self.data_infos[idx]['ann']

    def get_cat_ids(self, idx):
        """Get category ids by index.

        Args:
            idx (int): Index of data.

        Returns:
            list[int]: All categories in the image of specified index.
        """

        return self.data_infos[idx]['ann']['labels'].astype(np.int).tolist()

    def pre_pipeline(self, results):
        """Prepare results dict for pipeline."""
        results['img_prefix'] = self.img_prefix
        results['seg_prefix'] = self.seg_prefix
        results['proposal_file'] = self.proposal_file
        results['bbox_fields'] = []
        results['mask_fields'] = []
        results['seg_fields'] = []

    def _filter_imgs(self, min_size=32):
        """Filter images too small."""
        if self.filter_empty_gt:
            warnings.warn(
                'CustomDataset does not support filtering empty gt images.')
        valid_inds = []
        for i, img_info in enumerate(self.data_infos):
            if min(img_info['width'], img_info['height']) >= min_size:
                valid_inds.append(i)
        return valid_inds

    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.
        """
        self.flag = np.zeros(len(self), dtype=np.uint8)
        for i in range(len(self)):
            img_info = self.data_infos[i]
            if img_info['width'] / img_info['height'] > 1:
                self.flag[i] = 1

    def _rand_another(self, idx):
        """Get another random index from the same group as the given index."""
        pool = np.where(self.flag == self.flag[idx])[0]
        return np.random.choice(pool)

    def __getitem__(self, idx):
        """Get training/test data after pipeline.

        Args:
            idx (int): Index of data.

        Returns:
            dict: Training/test data (with annotation if `test_mode` is set \
                True).
        """

        if self.test_mode:
            return self.prepare_test_img(idx)
        while True:
            data = self.prepare_train_img(idx)
            if data is None:
                idx = self._rand_another(idx)
                continue
            return data

    def prepare_train_img(self, idx):
        """Get training data and annotations after pipeline.

        Args:
            idx (int): Index of data.

        Returns:
            dict: Training data and annotation after pipeline with new keys \
                introduced by pipeline.
        """

        img_info = self.data_infos[idx]
        ann_info = self.get_ann_info(idx)
        results = dict(img_info=img_info, ann_info=ann_info)
        if self.proposals is not None:
            results['proposals'] = self.proposals[idx]
        self.pre_pipeline(results)
        return self.pipeline(results)

    def prepare_test_img(self, idx):
        """Get testing data  after pipeline.

        Args:
            idx (int): Index of data.

        Returns:
            dict: Testing data after pipeline with new keys introduced by \
                pipeline.
        """

        img_info = self.data_infos[idx]
        results = dict(img_info=img_info)
        if self.proposals is not None:
            results['proposals'] = self.proposals[idx]
        self.pre_pipeline(results)
        return self.pipeline(results)

    @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.

        Returns:
            tuple[str] or list[str]: Names of categories of the dataset.
        """
        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, results, **kwargs):
        """Place holder to format result to dataset specific output."""

    def evaluate(self,
                 results,
                 metric='mAP',
                 logger=None,
                 proposal_nums=(100, 300, 1000),
                 iou_thr=0.5,
                 scale_ranges=None):
        """Evaluate the dataset.

        Args:
            results (list): Testing results of the dataset.
            metric (str | list[str]): Metrics to be evaluated.
            logger (logging.Logger | None | str): Logger used for printing
                related information during evaluation. Default: None.
            proposal_nums (Sequence[int]): Proposal number used for evaluating
                recalls, such as recall@100, recall@1000.
                Default: (100, 300, 1000).
            iou_thr (float | list[float]): IoU threshold. Default: 0.5.
            scale_ranges (list[tuple] | None): Scale ranges for evaluating mAP.
                Default: None.
        """

        if not isinstance(metric, str):
            assert len(metric) == 1
            metric = metric[0]
        allowed_metrics = ['mAP', 'recall']
        if metric not in allowed_metrics:
            raise KeyError(f'metric {metric} is not supported')
        annotations = [self.get_ann_info(i) for i in range(len(self))]
        eval_results = OrderedDict()
        iou_thrs = [iou_thr] if isinstance(iou_thr, float) else iou_thr
        if metric == 'mAP':
            assert isinstance(iou_thrs, list)
            mean_aps = []
            for iou_thr in iou_thrs:
                print_log(f'\n{"-" * 15}iou_thr: {iou_thr}{"-" * 15}')
                mean_ap, _ = eval_map(
                    results,
                    annotations,
                    scale_ranges=scale_ranges,
                    iou_thr=iou_thr,
                    dataset=self.CLASSES,
                    logger=logger)
                mean_aps.append(mean_ap)
                eval_results[f'AP{int(iou_thr * 100):02d}'] = round(mean_ap, 3)
            eval_results['mAP'] = sum(mean_aps) / len(mean_aps)
        elif metric == 'recall':
            gt_bboxes = [ann['bboxes'] for ann in annotations]
            recalls = eval_recalls(
                gt_bboxes, results, proposal_nums, iou_thr, logger=logger)
            for i, num in enumerate(proposal_nums):
                for j, iou in enumerate(iou_thrs):
                    eval_results[f'recall@{num}@{iou}'] = recalls[i, j]
            if recalls.shape[1] > 1:
                ar = recalls.mean(axis=1)
                for i, num in enumerate(proposal_nums):
                    eval_results[f'AR@{num}'] = ar[i]
        return eval_results