forked from open-mmlab/mmdetection3d
-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
[Feature] Support MultiModalityDet3DInferencer (open-mmlab#2342)
* add multimodality inferencer * add multimodality inferencer * add Loader and Inferencer, fix some mono vis bugs * add UT for multi_modality_inferencer * add calib file in UT * fix type hint * resolve comments * small update * update to Base3DInferencer * fix docstring * minor fix * resolve comments * resolve comments * add suffix check * fix UT
- Loading branch information
1 parent
98d2642
commit c9fd5a6
Showing
14 changed files
with
512 additions
and
18 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
233 changes: 233 additions & 0 deletions
233
mmdet3d/apis/inferencers/multi_modality_det3d_inferencer.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,233 @@ | ||
| # Copyright (c) OpenMMLab. All rights reserved. | ||
| import os.path as osp | ||
| import warnings | ||
| from typing import Dict, List, Optional, Sequence, Union | ||
|
|
||
| import mmcv | ||
| import mmengine | ||
| import numpy as np | ||
| from mmengine.dataset import Compose | ||
| from mmengine.infer.infer import ModelType | ||
| from mmengine.structures import InstanceData | ||
|
|
||
| from mmdet3d.registry import INFERENCERS | ||
| from mmdet3d.utils import ConfigType | ||
| from .base_3d_inferencer import Base3DInferencer | ||
|
|
||
| InstanceList = List[InstanceData] | ||
| InputType = Union[str, np.ndarray] | ||
| InputsType = Union[InputType, Sequence[InputType]] | ||
| PredType = Union[InstanceData, InstanceList] | ||
| ImgType = Union[np.ndarray, Sequence[np.ndarray]] | ||
| ResType = Union[Dict, List[Dict], InstanceData, List[InstanceData]] | ||
|
|
||
|
|
||
| @INFERENCERS.register_module(name='det3d-multi_modality') | ||
| @INFERENCERS.register_module() | ||
| class MultiModalityDet3DInferencer(Base3DInferencer): | ||
| """The inferencer of multi-modality detection. | ||
| Args: | ||
| model (str, optional): Path to the config file or the model name | ||
| defined in metafile. For example, it could be | ||
| "pointpillars_kitti-3class" or | ||
| "configs/pointpillars/pointpillars_hv_secfpn_8xb6-160e_kitti-3d-3class.py". # noqa: E501 | ||
| If model is not specified, user must provide the | ||
| `weights` saved by MMEngine which contains the config string. | ||
| Defaults to None. | ||
| weights (str, optional): Path to the checkpoint. If it is not specified | ||
| and model is a model name of metafile, the weights will be loaded | ||
| from metafile. Defaults to None. | ||
| device (str, optional): Device to run inference. If None, the available | ||
| device will be automatically used. Defaults to None. | ||
| scope (str): The scope of registry. Defaults to 'mmdet3d'. | ||
| palette (str): The palette of visualization. Defaults to 'none'. | ||
| """ | ||
|
|
||
| preprocess_kwargs: set = set() | ||
| forward_kwargs: set = set() | ||
| visualize_kwargs: set = { | ||
| 'return_vis', 'show', 'wait_time', 'draw_pred', 'pred_score_thr', | ||
| 'img_out_dir' | ||
| } | ||
| postprocess_kwargs: set = { | ||
| 'print_result', 'pred_out_file', 'return_datasample' | ||
| } | ||
|
|
||
| def __init__(self, | ||
| model: Union[ModelType, str, None] = None, | ||
| weights: Optional[str] = None, | ||
| device: Optional[str] = None, | ||
| scope: str = 'mmdet3d', | ||
| palette: str = 'none') -> None: | ||
| # A global counter tracking the number of frames processed, for | ||
| # naming of the output results | ||
| self.num_visualized_frames = 0 | ||
| super(MultiModalityDet3DInferencer, self).__init__( | ||
| model=model, | ||
| weights=weights, | ||
| device=device, | ||
| scope=scope, | ||
| palette=palette) | ||
|
|
||
| def _inputs_to_list(self, inputs: Union[dict, list]) -> list: | ||
| """Preprocess the inputs to a list. | ||
| Preprocess inputs to a list according to its type: | ||
| - list or tuple: return inputs | ||
| - dict: the value with key 'points' is | ||
| - Directory path: return all files in the directory | ||
| - other cases: return a list containing the string. The string | ||
| could be a path to file, a url or other types of string according | ||
| to the task. | ||
| Args: | ||
| inputs (Union[dict, list]): Inputs for the inferencer. | ||
| Returns: | ||
| list: List of input for the :meth:`preprocess`. | ||
| """ | ||
| return super()._inputs_to_list(inputs, modality_key=['points', 'img']) | ||
|
|
||
| def _init_pipeline(self, cfg: ConfigType) -> Compose: | ||
| """Initialize the test pipeline.""" | ||
| pipeline_cfg = cfg.test_dataloader.dataset.pipeline | ||
|
|
||
| load_point_idx = self._get_transform_idx(pipeline_cfg, | ||
| 'LoadPointsFromFile') | ||
| load_mv_img_idx = self._get_transform_idx( | ||
| pipeline_cfg, 'LoadMultiViewImageFromFiles') | ||
| if load_mv_img_idx != -1: | ||
| warnings.warn( | ||
| 'LoadMultiViewImageFromFiles is not supported yet in the ' | ||
| 'multi-modality inferencer. Please remove it') | ||
| # Now, we only support ``LoadImageFromFile`` as the image loader in the | ||
| # original piepline. `LoadMultiViewImageFromFiles` is not supported | ||
| # yet. | ||
| load_img_idx = self._get_transform_idx(pipeline_cfg, | ||
| 'LoadImageFromFile') | ||
|
|
||
| if load_point_idx == -1 or load_img_idx == -1: | ||
| raise ValueError( | ||
| 'Both LoadPointsFromFile and LoadImageFromFile must ' | ||
| 'be specified the pipeline, but LoadPointsFromFile is ' | ||
| f'{load_point_idx == -1} and LoadImageFromFile is ' | ||
| f'{load_img_idx}') | ||
|
|
||
| load_cfg = pipeline_cfg[load_point_idx] | ||
| self.coord_type, self.load_dim = load_cfg['coord_type'], load_cfg[ | ||
| 'load_dim'] | ||
| self.use_dim = list(range(load_cfg['use_dim'])) if isinstance( | ||
| load_cfg['use_dim'], int) else load_cfg['use_dim'] | ||
|
|
||
| load_point_args = pipeline_cfg[load_point_idx] | ||
| load_point_args.pop('type') | ||
| load_img_args = pipeline_cfg[load_img_idx] | ||
| load_img_args.pop('type') | ||
|
|
||
| load_idx = min(load_point_idx, load_img_idx) | ||
| pipeline_cfg.pop(max(load_point_idx, load_img_idx)) | ||
|
|
||
| pipeline_cfg[load_idx] = dict( | ||
| type='MultiModalityDet3DInferencerLoader', | ||
| load_point_args=load_point_args, | ||
| load_img_args=load_img_args) | ||
|
|
||
| return Compose(pipeline_cfg) | ||
|
|
||
| def visualize(self, | ||
| inputs: InputsType, | ||
| preds: PredType, | ||
| return_vis: bool = False, | ||
| show: bool = False, | ||
| wait_time: int = 0, | ||
| draw_pred: bool = True, | ||
| pred_score_thr: float = 0.3, | ||
| img_out_dir: str = '') -> Union[List[np.ndarray], None]: | ||
| """Visualize predictions. | ||
| Args: | ||
| inputs (InputsType): Inputs for the inferencer. | ||
| preds (PredType): Predictions of the model. | ||
| return_vis (bool): Whether to return the visualization result. | ||
| Defaults to False. | ||
| show (bool): Whether to display the image in a popup window. | ||
| Defaults to False. | ||
| wait_time (float): The interval of show (s). Defaults to 0. | ||
| draw_pred (bool): Whether to draw predicted bounding boxes. | ||
| Defaults to True. | ||
| pred_score_thr (float): Minimum score of bboxes to draw. | ||
| Defaults to 0.3. | ||
| img_out_dir (str): Output directory of visualization results. | ||
| If left as empty, no file will be saved. Defaults to ''. | ||
| Returns: | ||
| List[np.ndarray] or None: Returns visualization results only if | ||
| applicable. | ||
| """ | ||
| if self.visualizer is None or (not show and img_out_dir == '' | ||
| and not return_vis): | ||
| return None | ||
|
|
||
| if getattr(self, 'visualizer') is None: | ||
| raise ValueError('Visualization needs the "visualizer" term' | ||
| 'defined in the config, but got None.') | ||
|
|
||
| results = [] | ||
|
|
||
| for single_input, pred in zip(inputs, preds): | ||
| points_input = single_input['points'] | ||
| if isinstance(points_input, str): | ||
| pts_bytes = mmengine.fileio.get(points_input) | ||
| points = np.frombuffer(pts_bytes, dtype=np.float32) | ||
| points = points.reshape(-1, self.load_dim) | ||
| points = points[:, self.use_dim] | ||
| pc_name = osp.basename(points_input).split('.bin')[0] | ||
| pc_name = f'{pc_name}.png' | ||
| elif isinstance(points_input, np.ndarray): | ||
| points = points_input.copy() | ||
| pc_num = str(self.num_visualized_frames).zfill(8) | ||
| pc_name = f'pc_{pc_num}.png' | ||
| else: | ||
| raise ValueError('Unsupported input type: ' | ||
| f'{type(points_input)}') | ||
|
|
||
| o3d_save_path = osp.join(img_out_dir, pc_name) \ | ||
| if img_out_dir != '' else None | ||
|
|
||
| img_input = single_input['img'] | ||
| if isinstance(single_input['img'], str): | ||
| img_bytes = mmengine.fileio.get(img_input) | ||
| img = mmcv.imfrombytes(img_bytes) | ||
| img = img[:, :, ::-1] | ||
| img_name = osp.basename(img_input) | ||
| elif isinstance(img_input, np.ndarray): | ||
| img = img_input.copy() | ||
| img_num = str(self.num_visualized_frames).zfill(8) | ||
| img_name = f'{img_num}.jpg' | ||
| else: | ||
| raise ValueError('Unsupported input type: ' | ||
| f'{type(img_input)}') | ||
|
|
||
| out_file = osp.join(img_out_dir, img_name) if img_out_dir != '' \ | ||
| else None | ||
|
|
||
| data_input = dict(points=points, img=img) | ||
| self.visualizer.add_datasample( | ||
| pc_name, | ||
| data_input, | ||
| pred, | ||
| show=show, | ||
| wait_time=wait_time, | ||
| draw_gt=False, | ||
| draw_pred=draw_pred, | ||
| pred_score_thr=pred_score_thr, | ||
| o3d_save_path=o3d_save_path, | ||
| out_file=out_file, | ||
| vis_task='multi-modality_det', | ||
| ) | ||
| results.append(points) | ||
| self.num_visualized_frames += 1 | ||
|
|
||
| return results |
Oops, something went wrong.