Merge 007a1db into cf6f473

configs/pointnet2/metafile.yml

@@ -68,6 +68,7 @@ Models:
  Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_msg_16x2_cosine_250e_scannet_seg-3d-20class/pointnet2_msg_16x2_cosine_250e_scannet_seg-3d-20class_20210514_144009-24477ab1.pth
 
  - Name: pointnet2_ssg_2xb16-cosine-50e_s3dis-seg
+ Alias: pointnet2-ssg_s3dis-seg
  In Collection: PointNet++
  Config: configs/pointnet2/pointnet2_ssg_2xb16-cosine-50e_s3dis-seg.py
  Metadata:

mmdet3d/apis/__init__.py

@@ -3,12 +3,12 @@
  inference_mono_3d_detector,
  inference_multi_modality_detector, inference_segmentor,
  init_model)
-from .inferencers import (BaseDet3DInferencer, LidarDet3DInferencer,
- MonoDet3DInferencer)
+from .inferencers import (Base3DInferencer, LidarDet3DInferencer,
+ LidarSeg3DInferencer, MonoDet3DInferencer)
 
 __all__ = [
  'inference_detector', 'init_model', 'inference_mono_3d_detector',
  'convert_SyncBN', 'inference_multi_modality_detector',
- 'inference_segmentor', 'BaseDet3DInferencer', 'MonoDet3DInferencer',
- 'LidarDet3DInferencer'
+ 'inference_segmentor', 'Base3DInferencer', 'MonoDet3DInferencer',
+ 'LidarDet3DInferencer', 'LidarSeg3DInferencer'
 ]

mmdet3d/apis/inference.py

@@ -76,16 +76,16 @@ def init_model(config: Union[str, Path, Config],
  elif 'CLASSES' in checkpoint.get('meta', {}):
  # < mmdet3d 1.x
  classes = checkpoint['meta']['CLASSES']
- model.dataset_meta = {'CLASSES': classes}
+ model.dataset_meta = {'classes': classes}
 
  if 'PALETTE' in checkpoint.get('meta', {}): # 3D Segmentor
- model.dataset_meta['PALETTE'] = checkpoint['meta']['PALETTE']
+ model.dataset_meta['palette'] = checkpoint['meta']['PALETTE']
  else:
  # < mmdet3d 1.x
- model.dataset_meta = {'CLASSES': config.class_names}
+ model.dataset_meta = {'classes': config.class_names}
 
  if 'PALETTE' in checkpoint.get('meta', {}): # 3D Segmentor
- model.dataset_meta['PALETTE'] = checkpoint['meta']['PALETTE']
+ model.dataset_meta['palette'] = checkpoint['meta']['PALETTE']
 
  model.cfg = config # save the config in the model for convenience
  if device != 'cpu':

mmdet3d/apis/inferencers/__init__.py

@@ -1,8 +1,10 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from .base_det3d_inferencer import BaseDet3DInferencer
+from .base_3d_inferencer import Base3DInferencer
 from .lidar_det3d_inferencer import LidarDet3DInferencer
+from .lidar_seg3d_inferencer import LidarSeg3DInferencer
 from .mono_det3d_inferencer import MonoDet3DInferencer
 
 __all__ = [
- 'BaseDet3DInferencer', 'MonoDet3DInferencer', 'LidarDet3DInferencer'
+ 'Base3DInferencer', 'MonoDet3DInferencer', 'LidarDet3DInferencer',
+ 'LidarSeg3DInferencer'
 ]

mmdet3d/apis/inferencers/base_det3d_inferencer.py → mmdet3d/apis/inferencers/base_3d_inferencer.py

@@ -23,8 +23,8 @@
 ResType = Union[Dict, List[Dict], InstanceData, List[InstanceData]]
 
 
-class BaseDet3DInferencer(BaseInferencer):
- """Base 3D object detection inferencer.
+class Base3DInferencer(BaseInferencer):
+ """Base 3D model inferencer.
 
  Args:
  model (str, optional): Path to the config file or the model name
@@ -39,7 +39,7 @@ class BaseDet3DInferencer(BaseInferencer):
  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, optional): The scope of the model. Defaults to mmdet3d.
+ scope (str): The scope of the model. Defaults to 'mmdet3d'.
  palette (str): Color palette used for visualization. The order of
  priority is palette -> config -> checkpoint. Defaults to 'none'.
  """
@@ -58,7 +58,7 @@ def __init__(self,
  model: Union[ModelType, str, None] = None,
  weights: Optional[str] = None,
  device: Optional[str] = None,
- scope: Optional[str] = 'mmdet3d',
+ scope: str = 'mmdet3d',
  palette: str = 'none') -> None:
  self.palette = palette
  init_default_scope(scope)
@@ -97,16 +97,16 @@ def _init_model(
  elif 'CLASSES' in checkpoint.get('meta', {}):
  # < mmdet3d 1.x
  classes = checkpoint['meta']['CLASSES']
- model.dataset_meta = {'CLASSES': classes}
+ model.dataset_meta = {'classes': classes}
 
  if 'PALETTE' in checkpoint.get('meta', {}): # 3D Segmentor
- model.dataset_meta['PALETTE'] = checkpoint['meta']['PALETTE']
+ model.dataset_meta['palette'] = checkpoint['meta']['PALETTE']
  else:
  # < mmdet3d 1.x
- model.dataset_meta = {'CLASSES': cfg.class_names}
+ model.dataset_meta = {'classes': cfg.class_names}
 
  if 'PALETTE' in checkpoint.get('meta', {}): # 3D Segmentor
- model.dataset_meta['PALETTE'] = checkpoint['meta']['PALETTE']
+ model.dataset_meta['palette'] = checkpoint['meta']['PALETTE']
 
  model.cfg = cfg # save the config in the model for convenience
  model.to(device)
@@ -130,8 +130,8 @@ def _inputs_to_list(
 
  Args:
  inputs (Union[dict, list]): Inputs for the inferencer.
- modality_key (Union[str, List[str]], optional): The key of the
- modality. Defaults to 'points'.
+ modality_key (Union[str, List[str]]): The key of the modality.
+ Defaults to 'points'.
 
  Returns:
  list: List of input for the :meth:`preprocess`.
@@ -187,6 +187,7 @@ def __call__(self,
  pred_out_file: str = '',
  **kwargs) -> dict:
  """Call the inferencer.
+
  Args:
  inputs (InputsType): Inputs for the inferencer.
  return_datasamples (bool): Whether to return results as
@@ -205,14 +206,15 @@ def __call__(self,
  If left as empty, no file will be saved. Defaults to ''.
  print_result (bool): Whether to print the inference result w/o
  visualization to the console. Defaults to False.
- pred_out_file: File to save the inference results w/o
+ pred_out_file (str): File to save the inference results w/o
  visualization. If left as empty, no file will be saved.
  Defaults to ''.
  **kwargs: Other keyword arguments passed to :meth:`preprocess`,
  :meth:`forward`, :meth:`visualize` and :meth:`postprocess`.
  Each key in kwargs should be in the corresponding set of
  ``preprocess_kwargs``, ``forward_kwargs``, ``visualize_kwargs``
  and ``postprocess_kwargs``.
+
  Returns:
  dict: Inference and visualization results.
  """
@@ -240,29 +242,36 @@ def postprocess(
  ) -> Union[ResType, Tuple[ResType, np.ndarray]]:
  """Process the predictions and visualization results from ``forward``
  and ``visualize``.
+
  This method should be responsible for the following tasks:
+
  1. Convert datasamples into a json-serializable dict if needed.
  2. Pack the predictions and visualization results and return them.
  3. Dump or log the predictions.
+
  Args:
  preds (List[Dict]): Predictions of the model.
- visualization (Optional[np.ndarray]): Visualized predictions.
+ visualization (np.ndarray, optional): Visualized predictions.
+ Defaults to None.
  return_datasample (bool): Whether to use Datasample to store
  inference results. If False, dict will be used.
+ Defaults to False.
  print_result (bool): Whether to print the inference result w/o
  visualization to the console. Defaults to False.
- pred_out_file: File to save the inference results w/o
+ pred_out_file (str): File to save the inference results w/o
  visualization. If left as empty, no file will be saved.
  Defaults to ''.
+
  Returns:
  dict: Inference and visualization results with key ``predictions``
  and ``visualization``.
+
  - ``visualization`` (Any): Returned by :meth:`visualize`.
  - ``predictions`` (dict or DataSample): Returned by
-  :meth:`forward` and processed in :meth:`postprocess`.
-  If ``return_datasample=False``, it usually should be a
-  json-serializable dict containing only basic data elements such
-  as strings and numbers.
+ :meth:`forward` and processed in :meth:`postprocess`.
+ If ``return_datasample=False``, it usually should be a
+ json-serializable dict containing only basic data elements such
+ as strings and numbers.
  """
  result_dict = {}
  results = preds
@@ -286,11 +295,18 @@ def pred2dict(self, data_sample: InstanceData) -> Dict:
  It's better to contain only basic data elements such as strings and
  numbers in order to guarantee it's json-serializable.
  """
- pred_instances = data_sample.pred_instances_3d.numpy()
- result = {
- 'bboxes_3d': pred_instances.bboxes_3d.tensor.cpu().tolist(),
- 'labels_3d': pred_instances.labels_3d.tolist(),
- 'scores_3d': pred_instances.scores_3d.tolist()
- }
+ result = {}
+ if 'pred_instances_3d' in data_sample:
+ pred_instances_3d = data_sample.pred_instances_3d.numpy()
+ result = {
+ 'bboxes_3d': pred_instances_3d.bboxes_3d.tensor.cpu().tolist(),
+ 'labels_3d': pred_instances_3d.labels_3d.tolist(),
+ 'scores_3d': pred_instances_3d.scores_3d.tolist()
+ }
+
+ if 'pred_pts_seg' in data_sample:
+ pred_pts_seg = data_sample.pred_pts_seg.numpy()
+ result['pts_semantic_mask'] = \
+ pred_pts_seg.pts_semantic_mask.tolist()
 
  return result

mmdet3d/apis/inferencers/lidar_det3d_inferencer.py

@@ -10,7 +10,7 @@
 
 from mmdet3d.registry import INFERENCERS
 from mmdet3d.utils import ConfigType
-from .base_det3d_inferencer import BaseDet3DInferencer
+from .base_3d_inferencer import Base3DInferencer
 
 InstanceList = List[InstanceData]
 InputType = Union[str, np.ndarray]
@@ -22,7 +22,7 @@
 
 @INFERENCERS.register_module(name='det3d-lidar')
 @INFERENCERS.register_module()
-class LidarDet3DInferencer(BaseDet3DInferencer):
+class LidarDet3DInferencer(Base3DInferencer):
  """The inferencer of LiDAR-based detection.
 
  Args:
@@ -38,8 +38,9 @@ class LidarDet3DInferencer(BaseDet3DInferencer):
  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, optional): The scope of registry.
- palette (str, optional): The palette of visualization.
+ scope (str): The scope of the model. Defaults to 'mmdet3d'.
+ palette (str): Color palette used for visualization. The order of
+ priority is palette -> config -> checkpoint. Defaults to 'none'.
  """
 
  preprocess_kwargs: set = set()
@@ -56,14 +57,17 @@ def __init__(self,
  model: Union[ModelType, str, None] = None,
  weights: Optional[str] = None,
  device: Optional[str] = None,
- scope: Optional[str] = 'mmdet3d',
+ 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
- self.palette = palette
- super().__init__(
- model=model, weights=weights, device=device, scope=scope)
+ super(LidarDet3DInferencer, 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.
@@ -129,6 +133,7 @@ def visualize(self,
  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.