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Zero
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# 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 os.path as osp
from typing import List, Optional
from mmengine.dataset import BaseDataset
from mmengine.fileio import load
from mmengine.utils import is_abs
from ..registry import DATASETS
@DATASETS.register_module()
class BaseDetDataset(BaseDataset):
"""Base dataset for detection.
Args:
proposal_file (str, optional): Proposals file path. Defaults to None.
file_client_args (dict): Arguments to instantiate the
corresponding backend in mmdet <= 3.0.0rc6. Defaults to None.
backend_args (dict, optional): Arguments to instantiate the
corresponding backend. Defaults to None.
return_classes (bool): Whether to return class information
for open vocabulary-based algorithms. Defaults to False.
"""
def __init__(self,
*args,
seg_map_suffix: str = '.png',
proposal_file: Optional[str] = None,
file_client_args: dict = None,
backend_args: dict = None,
return_classes: bool = False,
**kwargs) -> None:
self.seg_map_suffix = seg_map_suffix
self.proposal_file = proposal_file
self.backend_args = backend_args
self.return_classes = return_classes
if file_client_args is not None:
raise RuntimeError(
'The `file_client_args` is deprecated, '
'please use `backend_args` instead, please refer to'
'https://github.com/open-mmlab/mmdetection/blob/main/configs/_base_/datasets/coco_detection.py' # noqa: E501
)
super().__init__(*args, **kwargs)
def full_init(self) -> None:
"""Load annotation file and set ``BaseDataset._fully_initialized`` to
True.
If ``lazy_init=False``, ``full_init`` will be called during the
instantiation and ``self._fully_initialized`` will be set to True. If
``obj._fully_initialized=False``, the class method decorated by
``force_full_init`` will call ``full_init`` automatically.
Several steps to initialize annotation:
- load_data_list: Load annotations from annotation file.
- load_proposals: Load proposals from proposal file, if
`self.proposal_file` is not None.
- filter data information: Filter annotations according to
filter_cfg.
- slice_data: Slice dataset according to ``self._indices``
- serialize_data: Serialize ``self.data_list`` if
``self.serialize_data`` is True.
"""
if self._fully_initialized:
return
# load data information
self.data_list = self.load_data_list()
# get proposals from file
if self.proposal_file is not None:
self.load_proposals()
# filter illegal data, such as data that has no annotations.
self.data_list = self.filter_data()
# Get subset data according to indices.
if self._indices is not None:
self.data_list = self._get_unserialized_subset(self._indices)
# serialize data_list
if self.serialize_data:
self.data_bytes, self.data_address = self._serialize_data()
self._fully_initialized = True
def load_proposals(self) -> None:
"""Load proposals from proposals file.
The `proposals_list` should be a dict[img_path: proposals]
with the same length as `data_list`. And the `proposals` should be
a `dict` or :obj:`InstanceData` usually contains following keys.
- bboxes (np.ndarry): Has a shape (num_instances, 4),
the last dimension 4 arrange as (x1, y1, x2, y2).
- scores (np.ndarry): Classification scores, has a shape
(num_instance, ).
"""
# TODO: Add Unit Test after fully support Dump-Proposal Metric
if not is_abs(self.proposal_file):
self.proposal_file = osp.join(self.data_root, self.proposal_file)
proposals_list = load(
self.proposal_file, backend_args=self.backend_args)
assert len(self.data_list) == len(proposals_list)
for data_info in self.data_list:
img_path = data_info['img_path']
# `file_name` is the key to obtain the proposals from the
# `proposals_list`.
file_name = osp.join(
osp.split(osp.split(img_path)[0])[-1],
osp.split(img_path)[-1])
proposals = proposals_list[file_name]
data_info['proposals'] = proposals
def get_cat_ids(self, idx: int) -> List[int]:
"""Get COCO category ids by index.
Args:
idx (int): Index of data.
Returns:
List[int]: All categories in the image of specified index.
"""
instances = self.get_data_info(idx)['instances']
return [instance['bbox_label'] for instance in instances]
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