merge conflict

README.md

@@ -84,7 +84,7 @@ Support methods
 - [x] [3DSSD (CVPR'2020)](configs/3dssd/README.md)
 - [x] [Part-A2 (TPAMI'2020)](configs/parta2/README.md)
 - [x] [MVXNet (ICRA'2019)](configs/mvxnet/README.md)
-- [x] [CenterPoint (Arxiv'2020)](configs/centerpoint/README.md)
+- [x] [CenterPoint (CVPR'2021)](configs/centerpoint/README.md)
 - [x] [SSN (ECCV'2020)](configs/ssn/README.md)
 
 | | ResNet | ResNeXt | SENet |PointNet++ | HRNet | RegNetX | Res2Net |

README_zh-CN.md

@@ -83,7 +83,7 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代
 - [x] [3DSSD (CVPR'2020)](configs/3dssd/README.md)
 - [x] [Part-A2 (TPAMI'2020)](configs/parta2/README.md)
 - [x] [MVXNet (ICRA'2019)](configs/mvxnet/README.md)
-- [x] [CenterPoint (Arxiv'2020)](configs/centerpoint/README.md)
+- [x] [CenterPoint (CVPR'2021)](configs/centerpoint/README.md)
 - [x] [SSN (ECCV'2020)](configs/ssn/README.md)
 
 | | ResNet | ResNeXt | SENet |PointNet++ | HRNet | RegNetX | Res2Net |

configs/centerpoint/README.md

@@ -27,11 +27,11 @@ We follow the below style to name config files. Contributors are advised to foll
 
 `{dataset}`: dataset like nus-3d, kitti-3d, lyft-3d, scannet-3d, sunrgbd-3d. We also indicate the number of classes we are using if there exist multiple settings, e.g., kitti-3d-3class and kitti-3d-car means training on KITTI dataset with 3 classes and single class, respectively.
 ```
-@article{yin2020center,
- title={Center-based 3d object detection and tracking},
+@article{yin2021center,
+ title={Center-based 3D Object Detection and Tracking},
  author={Yin, Tianwei and Zhou, Xingyi and Kr{\"a}henb{\"u}hl, Philipp},
- journal={arXiv preprint arXiv:2006.11275},
- year={2020}
+ journal={CVPR},
+ year={2021},
 }
 ```
 

mmdet3d/core/evaluation/__init__.py

@@ -1,5 +1,9 @@
 from .indoor_eval import indoor_eval
 from .kitti_utils import kitti_eval, kitti_eval_coco_style
 from .lyft_eval import lyft_eval
+from .seg_eval import seg_eval
 
-__all__ = ['kitti_eval_coco_style', 'kitti_eval', 'indoor_eval', 'lyft_eval']
+__all__ = [
+ 'kitti_eval_coco_style', 'kitti_eval', 'indoor_eval', 'lyft_eval',
+ 'seg_eval'
+]

mmdet3d/core/evaluation/seg_eval.py

@@ -0,0 +1,121 @@
+import numpy as np
+from mmcv.utils import print_log
+from terminaltables import AsciiTable
+
+
+def fast_hist(preds, labels, num_classes):
+ """Compute the confusion matrix for every batch.
+
+ Args:
+ preds (np.ndarray): Prediction labels of points with shape of
+ (num_points, ).
+ labels (np.ndarray): Ground truth labels of points with shape of
+ (num_points, ).
+ num_classes (int): number of classes
+
+ Returns:
+ np.ndarray: Calculated confusion matrix.
+ """
+
+ k = (labels >= 0) & (labels < num_classes)
+ bin_count = np.bincount(
+ num_classes * labels[k].astype(int) + preds[k],
+ minlength=num_classes**2)
+ return bin_count[:num_classes**2].reshape(num_classes, num_classes)
+
+
+def per_class_iou(hist):
+ """Compute the per class iou.
+
+ Args:
+ hist(np.ndarray): Overall confusion martix
+ (num_classes, num_classes ).
+
+ Returns:
+ np.ndarray: Calculated per class iou
+ """
+
+ return np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))
+
+
+def get_acc(hist):
+ """Compute the overall accuracy.
+
+ Args:
+ hist(np.ndarray): Overall confusion martix
+ (num_classes, num_classes ).
+
+ Returns:
+ float: Calculated overall acc
+ """
+
+ return np.diag(hist).sum() / hist.sum()
+
+
+def get_acc_cls(hist):
+ """Compute the class average accuracy.
+
+ Args:
+ hist(np.ndarray): Overall confusion martix
+ (num_classes, num_classes ).
+
+ Returns:
+ float: Calculated class average acc
+ """
+
+ return np.nanmean(np.diag(hist) / hist.sum(axis=1))
+
+
+def seg_eval(gt_labels, seg_preds, label2cat, logger=None):
+ """Semantic Segmentation Evaluation.
+
+ Evaluate the result of the Semantic Segmentation.
+
+ Args:
+ gt_labels (list[torch.Tensor]): Ground truth labels.
+ seg_preds (list[torch.Tensor]): Predtictions
+ label2cat (dict): Map from label to category.
+ logger (logging.Logger | str | None): The way to print the mAP
+ summary. See `mmdet.utils.print_log()` for details. Default: None.
+
+ Return:
+ dict[str, float]: Dict of results.
+ """
+ assert len(seg_preds) == len(gt_labels)
+
+ hist_list = []
+ for i in range(len(seg_preds)):
+ hist_list.append(
+ fast_hist(seg_preds[i].numpy().astype(int),
+ gt_labels[i].numpy().astype(int), len(label2cat)))
+ iou = per_class_iou(sum(hist_list))
+ miou = np.nanmean(iou)
+ acc = get_acc(sum(hist_list))
+ acc_cls = get_acc_cls(sum(hist_list))
+
+ header = ['classes']
+ for i in range(len(label2cat)):
+ header.append(label2cat[i])
+ header.extend(['miou', 'acc', 'acc_cls'])
+
+ ret_dict = dict()
+ table_columns = [['results']]
+ for i in range(len(label2cat)):
+ ret_dict[label2cat[i]] = float(iou[i])
+ table_columns.append([f'{iou[i]:.4f}'])
+ ret_dict['miou'] = float(miou)
+ ret_dict['acc'] = float(acc)
+ ret_dict['acc_cls'] = float(acc_cls)
+
+ table_columns.append([f'{miou:.4f}'])
+ table_columns.append([f'{acc:.4f}'])
+ table_columns.append([f'{acc_cls:.4f}'])
+
+ table_data = [header]
+ table_rows = list(zip(*table_columns))
+ table_data += table_rows
+ table = AsciiTable(table_data)
+ table.inner_footing_row_border = True
+ print_log('\n' + table.table, logger=logger)
+
+ return ret_dict

tests/test_metrics/test_seg_eval.py

@@ -0,0 +1,35 @@
+import numpy as np
+import pytest
+import torch
+
+from mmdet3d.core.evaluation.seg_eval import seg_eval
+
+
+def test_indoor_eval():
+ if not torch.cuda.is_available():
+ pytest.skip()
+ seg_preds = [
+ torch.Tensor(
+ [0, 0, 1, 0, 2, 1, 3, 1, 1, 0, 2, 2, 2, 2, 1, 3, 0, 3, 3, 3])
+ ]
+ gt_labels = [
+ torch.Tensor(
+ [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3])
+ ]
+
+ label2cat = {
+ 0: 'car',
+ 1: 'bicycle',
+ 2: 'motorcycle',
+ 3: 'truck',
+ }
+ ret_value = seg_eval(gt_labels, seg_preds, label2cat)
+
+ assert np.isclose(ret_value['car'], 0.428571429)
+ assert np.isclose(ret_value['bicycle'], 0.428571429)
+ assert np.isclose(ret_value['motorcycle'], 0.6666667)
+ assert np.isclose(ret_value['truck'], 0.6666667)
+
+ assert np.isclose(ret_value['acc'], 0.7)
+ assert np.isclose(ret_value['acc_cls'], 0.7)
+ assert np.isclose(ret_value['miou'], 0.547619048)