Merge branch 'master_temp' into indoor_loading

# Conflicts:
#	tools/data_converter/sunrgbd_data_utils.py

mmdet3d/core/bbox/box_torch_ops.py

@@ -21,9 +21,9 @@ def corners_nd(dims, origin=0.5):
             where x0 < x1, y0 < y1, z0 < z1
     """
     ndim = int(dims.shape[1])
-    corners_norm = np.stack(
-        np.unravel_index(np.arange(2**ndim), [2] * ndim),
-        axis=1).astype(dims.dtype)
+    corners_norm = torch.from_numpy(
+        np.stack(np.unravel_index(np.arange(2**ndim), [2] * ndim), axis=1)).to(
+            device=dims.device, dtype=dims.dtype)
     # now corners_norm has format: (2d) x0y0, x0y1, x1y0, x1y1
     # (3d) x0y0z0, x0y0z1, x0y1z0, x0y1z1, x1y0z0, x1y0z1, x1y1z0, x1y1z1
     # so need to convert to a format which is convenient to do other computing.
@@ -34,7 +34,7 @@ def corners_nd(dims, origin=0.5):
         corners_norm = corners_norm[[0, 1, 3, 2]]
     elif ndim == 3:
         corners_norm = corners_norm[[0, 1, 3, 2, 4, 5, 7, 6]]
-    corners_norm = corners_norm - np.array(origin, dtype=dims.dtype)
+    corners_norm = corners_norm - dims.new_tensor(origin)
     corners = dims.reshape([-1, 1, ndim]) * corners_norm.reshape(
         [1, 2**ndim, ndim])
     return corners
@@ -190,3 +190,23 @@ def rotation_2d(points, angles):
     rot_cos = torch.cos(angles)
     rot_mat_T = torch.stack([[rot_cos, -rot_sin], [rot_sin, rot_cos]])
     return torch.einsum('aij,jka->aik', points, rot_mat_T)
+
+
+def enlarge_box3d_lidar(boxes3d, extra_width):
+    """Enlarge the length, width and height of input boxes
+
+    Args:
+        boxes3d (torch.float32 or numpy.float32): bottom_center with
+            shape [N, 7], (x, y, z, w, l, h, ry) in LiDAR coords
+        extra_width (float): a fix number to add
+
+    Returns:
+        torch.float32 or numpy.float32: enlarged boxes
+    """
+    if isinstance(boxes3d, np.ndarray):
+        large_boxes3d = boxes3d.copy()
+    else:
+        large_boxes3d = boxes3d.clone()
+    large_boxes3d[:, 3:6] += extra_width * 2
+    large_boxes3d[:, 2] -= extra_width  # bottom center z minus extra_width
+    return large_boxes3d

mmdet3d/core/bbox/coders/__init__.py

-from .box_coder import Residual3DBoxCoder
+from mmdet.core.bbox import build_bbox_coder
+from .delta_xywh_bbox_coder import DeltaXYZWLHRBBoxCoder
 
-__all__ = ['Residual3DBoxCoder']
+__all__ = ['build_bbox_coder', 'DeltaXYZWLHRBBoxCoder']

mmdet3d/core/bbox/coders/box_coder.py → mmdet3d/core/bbox/coders/delta_xywh_bbox_coder.py

-import numpy as np
 import torch
 
+from mmdet.core.bbox import BaseBBoxCoder
+from mmdet.core.bbox.builder import BBOX_CODERS
 
-class Residual3DBoxCoder(object):
 
-    def __init__(self, code_size=7, mean=None, std=None):
-        super().__init__()
-        self.code_size = code_size
-        self.mean = mean
-        self.std = std
-
-    @staticmethod
-    def encode_np(boxes, anchors):
-        """
-        :param boxes: (N, 7) x, y, z, w, l, h, r
-        :param anchors: (N, 7)
-        :return:
-        """
-        # need to convert boxes to z-center format
-        xa, ya, za, wa, la, ha, ra = np.split(anchors, 7, axis=-1)
-        xg, yg, zg, wg, lg, hg, rg = np.split(boxes, 7, axis=-1)
-        zg = zg + hg / 2
-        za = za + ha / 2
-        diagonal = np.sqrt(la**2 + wa**2)  # 4.3
-        xt = (xg - xa) / diagonal
-        yt = (yg - ya) / diagonal
-        zt = (zg - za) / ha  # 1.6
-        lt = np.log(lg / la)
-        wt = np.log(wg / wa)
-        ht = np.log(hg / ha)
-        rt = rg - ra
-        return np.concatenate([xt, yt, zt, wt, lt, ht, rt], axis=-1)
-
-    @staticmethod
-    def decode_np(box_encodings, anchors):
-        """
-        :param box_encodings: (N, 7) x, y, z, w, l, h, r
-        :param anchors: (N, 7)
-        :return:
-        """
-        # need to convert box_encodings to z-bottom format
-        xa, ya, za, wa, la, ha, ra = np.split(anchors, 7, axis=-1)
-        xt, yt, zt, wt, lt, ht, rt = np.split(box_encodings, 7, axis=-1)
+@BBOX_CODERS.register_module()
+class DeltaXYZWLHRBBoxCoder(BaseBBoxCoder):
 
-        za = za + ha / 2
-        diagonal = np.sqrt(la**2 + wa**2)
-        xg = xt * diagonal + xa
-        yg = yt * diagonal + ya
-        zg = zt * ha + za
-
-        lg = np.exp(lt) * la
-        wg = np.exp(wt) * wa
-        hg = np.exp(ht) * ha
-        rg = rt + ra
-        zg = zg - hg / 2
-        return np.concatenate([xg, yg, zg, wg, lg, hg, rg], axis=-1)
+    def __init__(self, code_size=7):
+        super(DeltaXYZWLHRBBoxCoder, self).__init__()
+        self.code_size = code_size
 
     @staticmethod
-    def encode_torch(anchors, boxes, means, stds):
+    def encode(anchors, boxes):
         """
         :param boxes: (N, 7+n) x, y, z, w, l, h, r, velo*
         :param anchors: (N, 7+n)
@@ -85,7 +40,7 @@ class Residual3DBoxCoder(object):
         return torch.cat([xt, yt, zt, wt, lt, ht, rt, *cts], dim=-1)
 
     @staticmethod
-    def decode_torch(anchors, box_encodings, means, stds):
+    def decode(anchors, box_encodings):
         """
         :param box_encodings: (N, 7 + n) x, y, z, w, l, h, r
         :param anchors: (N, 7)

mmdet3d/core/bbox/iou_calculators/iou3d_calculator.py

+import torch
+
 from mmdet3d.ops.iou3d import boxes_iou3d_gpu
 from mmdet.core.bbox import bbox_overlaps
-from mmdet.core.bbox.iou_calculators.registry import IOU_CALCULATORS
+from mmdet.core.bbox.iou_calculators.builder import IOU_CALCULATORS
 from .. import box_torch_ops
 
 
-@IOU_CALCULATORS.register_module
+@IOU_CALCULATORS.register_module()
 class BboxOverlapsNearest3D(object):
     """Nearest 3D IoU Calculator"""
 
@@ -18,7 +20,7 @@ class BboxOverlapsNearest3D(object):
         return repr_str
 
 
-@IOU_CALCULATORS.register_module
+@IOU_CALCULATORS.register_module()
 class BboxOverlaps3D(object):
     """3D IoU Calculator"""
 
@@ -33,18 +35,22 @@ class BboxOverlaps3D(object):
 
 
 def bbox_overlaps_nearest_3d(bboxes1, bboxes2, mode='iou', is_aligned=False):
-    '''
-    :param bboxes1: Tensor, shape (N, 7) [x, y, z, h, w, l, ry]?
-    :param bboxes2: Tensor, shape (M, 7) [x, y, z, h, w, l, ry]?
-    :param mode: mode (str): "iou" (intersection over union) or iof
+    """Calculate nearest 3D IoU
+
+    Args:
+        bboxes1: Tensor, shape (N, 7+N) [x, y, z, h, w, l, ry, v]
+        bboxes2: Tensor, shape (M, 7+N) [x, y, z, h, w, l, ry, v]
+        mode: mode (str): "iou" (intersection over union) or iof
             (intersection over foreground).
-    :return: iou: (M, N) not support aligned mode currently
-    rbboxes: [N, 5(x, y, xdim, ydim, rad)] rotated bboxes
-    '''
-    rbboxes1_bev = bboxes1.index_select(
-        dim=-1, index=bboxes1.new_tensor([0, 1, 3, 4, 6]).long())
-    rbboxes2_bev = bboxes2.index_select(
-        dim=-1, index=bboxes1.new_tensor([0, 1, 3, 4, 6]).long())
+
+    Return:
+        iou: (M, N) not support aligned mode currently
+    """
+    assert bboxes1.size(-1) >= 7
+    assert bboxes2.size(-1) >= 7
+    column_index1 = bboxes1.new_tensor([0, 1, 3, 4, 6], dtype=torch.long)
+    rbboxes1_bev = bboxes1.index_select(dim=-1, index=column_index1)
+    rbboxes2_bev = bboxes2.index_select(dim=-1, index=column_index1)
 
     # Change the bboxes to bev
     # box conversion and iou calculation in torch version on CUDA
@@ -57,14 +63,18 @@ def bbox_overlaps_nearest_3d(bboxes1, bboxes2, mode='iou', is_aligned=False):
 
 
 def bbox_overlaps_3d(bboxes1, bboxes2, mode='iou'):
-    '''
+    """Calculate 3D IoU using cuda implementation
 
-    :param bboxes1: Tensor, shape (N, 7) [x, y, z, h, w, l, ry]
-    :param bboxes2: Tensor, shape (M, 7) [x, y, z, h, w, l, ry]
-    :param mode: mode (str): "iou" (intersection over union) or
+    Args:
+        bboxes1: Tensor, shape (N, 7) [x, y, z, h, w, l, ry]
+        bboxes2: Tensor, shape (M, 7) [x, y, z, h, w, l, ry]
+        mode: mode (str): "iou" (intersection over union) or
             iof (intersection over foreground).
-    :return: iou: (M, N) not support aligned mode currently
-    '''
+
+    Return:
+        iou: (M, N) not support aligned mode currently
+    """
     # TODO: check the input dimension meanings,
     #  this is inconsistent with that in bbox_overlaps_nearest_3d
+    assert bboxes1.size(-1) == bboxes2.size(-1) == 7
     return boxes_iou3d_gpu(bboxes1, bboxes2, mode)

mmdet3d/core/evaluation/kitti_utils/eval.py

@@ -681,7 +681,6 @@ def kitti_eval(gt_annos,
         # mAP threshold array: [num_minoverlap, metric, class]
         # mAP result: [num_class, num_diff, num_minoverlap]
         curcls_name = class_to_name[curcls]
-        ret_dict[curcls_name] = {}
         for i in range(min_overlaps.shape[0]):
             # prepare results for print
             result += ('{} AP@{:.2f}, {:.2f}, {:.2f}:\n'.format(
@@ -702,18 +701,17 @@ def kitti_eval(gt_annos,
 
             # prepare results for logger
             for idx in range(3):
-                postfix = '{}_{}'.format(difficulty[idx], min_overlaps[i, idx,
-                                                                       j])
+                if i == 0:
+                    postfix = f'{difficulty[idx]}_strict'
+                else:
+                    postfix = f'{difficulty[idx]}_loose'
+                prefix = f'KITTI/{curcls_name}'
                 if mAP3d is not None:
-                    ret_dict[curcls_name]['3D_{}'.format(postfix)] = mAP3d[j,
-                                                                           idx,
-                                                                           i]
+                    ret_dict[f'{prefix}_3D_{postfix}'] = mAP3d[j, idx, i]
                 if mAPbev is not None:
-                    ret_dict[curcls_name]['BEV_{}'.format(postfix)] = mAPbev[
-                        j, idx, i]
+                    ret_dict[f'{prefix}_BEV_{postfix}'] = mAPbev[j, idx, i]
                 if mAPbbox is not None:
-                    ret_dict[curcls_name]['2D_{}'.format(postfix)] = mAPbbox[
-                        j, idx, i]
+                    ret_dict[f'{prefix}_2D_{postfix}'] = mAPbbox[j, idx, i]
 
     # calculate mAP over all classes if there are multiple classes
     if len(current_classes) > 1:
@@ -735,14 +733,14 @@ def kitti_eval(gt_annos,
         # prepare results for logger
         ret_dict['Overall'] = dict()
         for idx in range(3):
-            postfix = '{}'.format(difficulty[idx])
+            postfix = f'{difficulty[idx]}'
             if mAP3d is not None:
-                ret_dict['Overall']['3D_{}'.format(postfix)] = mAP3d[idx, 0]
+                ret_dict[f'KITTI/Overall_3D_{postfix}'] = mAP3d[idx, 0]
             if mAPbev is not None:
-                ret_dict['Overall']['BEV_{}'.format(postfix)] = mAPbev[idx, 0]
+                ret_dict[f'KITTI/Overall_BEV_{postfix}'] = mAPbev[idx, 0]
             if mAPbbox is not None:
-                ret_dict['Overall']['2D_{}'.format(postfix)] = mAPbbox[idx, 0]
-    print(result)
+                ret_dict[f'KITTI/Overall_2D_{postfix}'] = mAPbbox[idx, 0]
+
     return result, ret_dict
 
 

mmdet3d/core/optimizer/cocktail_constructor.py

 from mmcv.utils import build_from_cfg
 
+from mmdet3d.utils import get_root_logger
 from mmdet.core.optimizer import OPTIMIZER_BUILDERS, OPTIMIZERS
-from mmdet.utils import get_root_logger
 from .cocktail_optimizer import CocktailOptimizer
 
 
-@OPTIMIZER_BUILDERS.register_module
+@OPTIMIZER_BUILDERS.register_module()
 class CocktailOptimizerConstructor(object):
     """Special constructor for cocktail optimizers.
 

mmdet3d/core/optimizer/cocktail_optimizer.py

@@ -3,7 +3,7 @@ from torch.optim import Optimizer
 from mmdet.core.optimizer import OPTIMIZERS
 
 
-@OPTIMIZERS.register_module
+@OPTIMIZERS.register_module()
 class CocktailOptimizer(Optimizer):
     """Cocktail Optimizer that contains multiple optimizers
 

mmdet3d/datasets/__init__.py

-from mmdet.datasets.registry import DATASETS
+from mmdet.datasets.builder import DATASETS
 from .builder import build_dataset
 from .dataset_wrappers import RepeatFactorDataset
 from .kitti2d_dataset import Kitti2DDataset
 from .kitti_dataset import KittiDataset
 from .loader import DistributedGroupSampler, GroupSampler, build_dataloader
-from .nuscenes2d_dataset import NuScenes2DDataset
 from .nuscenes_dataset import NuScenesDataset
+from .pipelines import (GlobalRotScale, ObjectNoise, ObjectRangeFilter,
+                        ObjectSample, PointShuffle, PointsRangeFilter,
+                        RandomFlip3D)
 
 __all__ = [
     'KittiDataset', 'GroupSampler', 'DistributedGroupSampler',
     'build_dataloader', 'RepeatFactorDataset', 'DATASETS', 'build_dataset',
-    'CocoDataset', 'Kitti2DDataset', 'NuScenesDataset', 'NuScenes2DDataset'
+    'CocoDataset', 'Kitti2DDataset', 'NuScenesDataset', 'ObjectSample',
+    'RandomFlip3D', 'ObjectNoise', 'GlobalRotScale', 'PointShuffle',
+    'ObjectRangeFilter', 'PointsRangeFilter', 'Collect3D'
 ]

mmdet3d/datasets/builder.py

 import copy
 
+from mmcv.utils import build_from_cfg
+
 from mmdet.datasets import DATASETS, ConcatDataset, RepeatDataset
-from mmdet.utils import build_from_cfg
 from .dataset_wrappers import RepeatFactorDataset
 
 

mmdet3d/datasets/dataset_wrappers.py

@@ -7,7 +7,7 @@ from mmdet.datasets import DATASETS
 
 
 # Modified from https://github.com/facebookresearch/detectron2/blob/41d475b75a230221e21d9cac5d69655e3415e3a4/detectron2/data/samplers/distributed_sampler.py#L57 # noqa
-@DATASETS.register_module
+@DATASETS.register_module()
 class RepeatFactorDataset(object):
     """A wrapper of repeated dataset with repeat factor.
 

mmdet3d/datasets/kitti2d_dataset.py

@@ -4,7 +4,7 @@ import numpy as np
 from mmdet.datasets import DATASETS, CustomDataset
 
 
-@DATASETS.register_module
+@DATASETS.register_module()
 class Kitti2DDataset(CustomDataset):
 
     CLASSES = ('car', 'pedestrian', 'cyclist')

mmdet3d/datasets/kitti_dataset.py

 import copy
 import os
-import pickle
+import os.path as osp
+import tempfile
 
 import mmcv
 import numpy as np
 import torch
 import torch.utils.data as torch_data
+from mmcv.utils import print_log
 
 from mmdet.datasets import DATASETS
-from mmdet.datasets.pipelines import Compose
 from ..core.bbox import box_np_ops
+from .pipelines import Compose
 from .utils import remove_dontcare
 
 
-@DATASETS.register_module
+@DATASETS.register_module()
 class KittiDataset(torch_data.Dataset):
 
     CLASSES = ('car', 'pedestrian', 'cyclist')
@@ -43,8 +45,7 @@ class KittiDataset(torch_data.Dataset):
         self.pcd_limit_range = [0, -40, -3, 70.4, 40, 0.0]
 
         self.ann_file = ann_file
-        with open(ann_file, 'rb') as f:
-            self.kitti_infos = pickle.load(f)
+        self.kitti_infos = mmcv.load(ann_file)
 
         # set group flag for the sampler
         if not self.test_mode:
@@ -262,37 +263,76 @@ class KittiDataset(torch_data.Dataset):
         inds = np.array(inds, dtype=np.int64)
         return inds
 
-    def reformat_bbox(self, outputs, out=None):
+    def format_results(self,
+                       outputs,
+                       pklfile_prefix=None,
+                       submission_prefix=None):
+        if pklfile_prefix is None:
+            tmp_dir = tempfile.TemporaryDirectory()
+            pklfile_prefix = osp.join(tmp_dir.name, 'results')
+        else:
+            tmp_dir = None
+
         if not isinstance(outputs[0][0], dict):
             sample_idx = [
                 info['image']['image_idx'] for info in self.kitti_infos
             ]
             result_files = self.bbox2result_kitti2d(outputs, self.class_names,
-                                                    sample_idx, out)
+                                                    sample_idx, pklfile_prefix,
+                                                    submission_prefix)
         else:
             result_files = self.bbox2result_kitti(outputs, self.class_names,
-                                                  out)
-        return result_files
+                                                  pklfile_prefix,
+                                                  submission_prefix)
+        return result_files, tmp_dir
+
+    def evaluate(self,
+                 results,
+                 metric=None,
+                 logger=None,
+                 pklfile_prefix=None,
+                 submission_prefix=None,
+                 result_names=['pts_bbox']):
+        """Evaluation in KITTI protocol.
 
-    def evaluate(self, result_files, eval_types=None):
+        Args:
+            results (list): Testing results of the dataset.
+            metric (str | list[str]): Metrics to be evaluated.
+            logger (logging.Logger | str | None): Logger used for printing
+                related information during evaluation. Default: None.
+            pklfile_prefix (str | None): The prefix of pkl files. It includes
+                the file path and the prefix of filename, e.g., "a/b/prefix".
+                If not specified, a temp file will be created. Default: None.
+            submission_prefix (str | None): The prefix of submission datas.
+                If not specified, the submission data will not be generated.
+
+        Returns:
+            dict[str: float]
+        """
+        result_files, tmp_dir = self.format_results(results, pklfile_prefix)
         from mmdet3d.core.evaluation import kitti_eval
         gt_annos = [info['annos'] for info in self.kitti_infos]
-        if eval_types == 'img_bbox':
+        if metric == 'img_bbox':
             ap_result_str, ap_dict = kitti_eval(
                 gt_annos, result_files, self.class_names, eval_types=['bbox'])
         else:
             ap_result_str, ap_dict = kitti_eval(gt_annos, result_files,
                                                 self.class_names)
-        return ap_result_str, ap_dict
-
-    def bbox2result_kitti(self, net_outputs, class_names, out=None):
-        if out:
-            output_dir = out[:-4] if out.endswith(('.pkl', '.pickle')) else out
-            result_dir = output_dir + '/data'
-            mmcv.mkdir_or_exist(result_dir)
+        print_log('\n' + ap_result_str, logger=logger)
+        if tmp_dir is not None:
+            tmp_dir.cleanup()
+        return ap_dict
+
+    def bbox2result_kitti(self,
+                          net_outputs,
+                          class_names,
+                          pklfile_prefix=None,
+                          submission_prefix=None):
+        if submission_prefix is not None:
+            mmcv.mkdir_or_exist(submission_prefix)
 
         det_annos = []
-        print('Converting prediction to KITTI format')
+        print('\nConverting prediction to KITTI format')
         for idx, pred_dicts in enumerate(
                 mmcv.track_iter_progress(net_outputs)):
             annos = []
@@ -346,9 +386,9 @@ class KittiDataset(torch_data.Dataset):
                         anno = {k: np.stack(v) for k, v in anno.items()}
                         annos.append(anno)
 
-                    if out:
-                        cur_det_file = result_dir + '/%06d.txt' % sample_idx
-                        with open(cur_det_file, 'w') as f:
+                    if submission_prefix is not None:
+                        curr_file = f'{submission_prefix}/{sample_idx:06d}.txt'
+                        with open(curr_file, 'w') as f:
                             bbox = anno['bbox']
                             loc = anno['location']
                             dims = anno['dimensions']  # lhw -> hwl
@@ -386,9 +426,9 @@ class KittiDataset(torch_data.Dataset):
 
             det_annos += annos
 
-        if out:
-            if not out.endswith(('.pkl', '.pickle')):
-                out = '{}.pkl'.format(out)
+        if pklfile_prefix is not None:
+            if not pklfile_prefix.endswith(('.pkl', '.pickle')):
+                out = f'{pklfile_prefix}.pkl'
             mmcv.dump(det_annos, out)
             print('Result is saved to %s' % out)
 
@@ -398,7 +438,8 @@ class KittiDataset(torch_data.Dataset):
                             net_outputs,
                             class_names,
                             sample_ids,
-                            out=None):
+                            pklfile_prefix=None,
+                            submission_prefix=None):
         """Convert results to kitti format for evaluation and test submission
 
         Args:
@@ -406,6 +447,8 @@ class KittiDataset(torch_data.Dataset):
             class_nanes (List[String]): A list of class names
             sample_idx (List[Int]): A list of samples' index,
                 should have the same length as net_outputs.
+            pklfile_prefix (str | None): The prefix of pkl file.
+            submission_prefix (str | None): The prefix of submission file.
 
         Return:
             List([dict]): A list of dict have the kitti format
@@ -469,17 +512,20 @@ class KittiDataset(torch_data.Dataset):
                 [sample_idx] * num_example, dtype=np.int64)
             det_annos += annos
 
-        if out:
+        if pklfile_prefix is not None:
+            # save file in pkl format
+            pklfile_path = (
+                pklfile_prefix[:-4] if pklfile_prefix.endswith(
+                    ('.pkl', '.pickle')) else pklfile_prefix)
+            mmcv.dump(det_annos, pklfile_path)
+
+        if submission_prefix is not None:
             # save file in submission format
-            output_dir = out[:-4] if out.endswith(('.pkl', '.pickle')) else out
-            result_dir = output_dir + '/data'
-            mmcv.mkdir_or_exist(result_dir)
-            out = '{}.pkl'.format(result_dir)
-            mmcv.dump(det_annos, out)
-            print('Result is saved to {}'.format(out))
+            mmcv.mkdir_or_exist(submission_prefix)
+            print(f'Saving KITTI submission to {submission_prefix}')
             for i, anno in enumerate(det_annos):
                 sample_idx = sample_ids[i]
-                cur_det_file = result_dir + '/%06d.txt' % sample_idx
+                cur_det_file = f'{submission_prefix}/{sample_idx:06d}.txt'
                 with open(cur_det_file, 'w') as f:
                     bbox = anno['bbox']
                     loc = anno['location']
@@ -497,7 +543,7 @@ class KittiDataset(torch_data.Dataset):
                                 anno['score'][idx]),
                             file=f,
                         )
-            print('Result is saved to {}'.format(result_dir))
+            print('Result is saved to {}'.format(submission_prefix))
 
         return det_annos
 

mmdet3d/datasets/nuscenes2d_dataset.py

-from pycocotools.coco import COCO
-
-from mmdet3d.core.evaluation.coco_utils import getImgIds
-from mmdet.datasets import DATASETS, CocoDataset
-
-
-@DATASETS.register_module
-class NuScenes2DDataset(CocoDataset):
-
-    CLASSES = ('car', 'truck', 'trailer', 'bus', 'construction_vehicle',
-               'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone',
-               'barrier')
-
-    def load_annotations(self, ann_file):
-        if not self.class_names:
-            self.class_names = self.CLASSES
-        self.coco = COCO(ann_file)
-        # send class_names into the get id
-        # in case we only need to train on several classes
-        # by default self.class_names = CLASSES
-        self.cat_ids = self.coco.getCatIds(catNms=self.class_names)
-
-        self.cat2label = {
-            cat_id: i  # + 1 rm +1 here thus the 0-79 are fg, 80 is bg
-            for i, cat_id in enumerate(self.cat_ids)
-        }
-        # send cat ids to the get img id
-        # in case we only need to train on several classes
-        if len(self.cat_ids) < len(self.CLASSES):
-            self.img_ids = getImgIds(self.coco, catIds=self.cat_ids)
-        else:
-            self.img_ids = self.coco.getImgIds()
-        img_infos = []
-        for i in self.img_ids:
-            info = self.coco.loadImgs([i])[0]
-            info['filename'] = info['file_name']
-            img_infos.append(info)
-        return img_infos

mmdet3d/datasets/nuscenes_dataset.py

@@ -9,11 +9,11 @@ import torch.utils.data as torch_data
 from nuscenes.utils.data_classes import Box as NuScenesBox
 
 from mmdet.datasets import DATASETS
-from mmdet.datasets.pipelines import Compose
 from ..core.bbox import box_np_ops
+from .pipelines import Compose
 
 
-@DATASETS.register_module
+@DATASETS.register_module()
 class NuScenesDataset(torch_data.Dataset):
     NumPointFeatures = 4  # xyz, timestamp. set 4 to use kitti pretrain
     NameMapping = {

mmdet3d/datasets/pipelines/__init__.py

+from mmdet.datasets.pipelines import Compose
+from .dbsampler import DataBaseSampler, MMDataBaseSampler
+from .formating import DefaultFormatBundle, DefaultFormatBundle3D
+from .loading import LoadMultiViewImageFromFiles, LoadPointsFromFile
 from .train_aug import (GlobalRotScale, ObjectNoise, ObjectRangeFilter,
                         ObjectSample, PointShuffle, PointsRangeFilter,
                         RandomFlip3D)
 
 __all__ = [
     'ObjectSample', 'RandomFlip3D', 'ObjectNoise', 'GlobalRotScale',
-    'PointShuffle', 'ObjectRangeFilter', 'PointsRangeFilter', 'Collect3D'
+    'PointShuffle', 'ObjectRangeFilter', 'PointsRangeFilter', 'Collect3D',
+    'Compose', 'LoadMultiViewImageFromFiles', 'LoadPointsFromFile',
+    'DefaultFormatBundle', 'DefaultFormatBundle3D', 'DataBaseSampler',
+    'MMDataBaseSampler'
 ]

mmdet3d/datasets/pipelines/dbsampler.py

@@ -52,7 +52,7 @@ class BatchSampler:
         return [self._sampled_list[i] for i in indices]
 
 
-@OBJECTSAMPLERS.register_module
+@OBJECTSAMPLERS.register_module()
 class DataBaseSampler(object):
 
     def __init__(self, info_path, root_path, rate, prepare, object_rot_range,
@@ -68,7 +68,7 @@ class DataBaseSampler(object):
             db_infos = pickle.load(f)
 
         # filter database infos
-        from mmdet3d.apis import get_root_logger
+        from mmdet3d.utils import get_root_logger
         logger = get_root_logger()
         for k, v in db_infos.items():
             logger.info(f'load {len(v)} {k} database infos')
@@ -255,7 +255,7 @@ class DataBaseSampler(object):
         return valid_samples
 
 
-@OBJECTSAMPLERS.register_module
+@OBJECTSAMPLERS.register_module()
 class MMDataBaseSampler(DataBaseSampler):
 
     def __init__(self,

mmdet3d/datasets/pipelines/formating.py

 import numpy as np
 from mmcv.parallel import DataContainer as DC
 
+from mmdet.datasets.builder import PIPELINES
 from mmdet.datasets.pipelines import to_tensor
-from mmdet.datasets.registry import PIPELINES
 
 PIPELINES._module_dict.pop('DefaultFormatBundle')
 
 
-@PIPELINES.register_module
+@PIPELINES.register_module()
 class DefaultFormatBundle(object):
     """Default formatting bundle.
 
@@ -59,7 +59,7 @@ class DefaultFormatBundle(object):
         return self.__class__.__name__
 
 
-@PIPELINES.register_module
+@PIPELINES.register_module()
 class Collect3D(object):
 
     def __init__(self,
@@ -90,7 +90,7 @@ class Collect3D(object):
             self.keys, self.meta_keys)
 
 
-@PIPELINES.register_module
+@PIPELINES.register_module()
 class DefaultFormatBundle3D(DefaultFormatBundle):
     """Default formatting bundle.
 

mmdet3d/datasets/pipelines/indoor_sample.py

+import numpy as np
+
+from mmdet.datasets.builder import PIPELINES
+
+
+@PIPELINES.register_module()
+class PointSample(object):
+    """Point Sample.
+
+    Sampling data to a certain number.
+
+    Args:
+        name (str): Name of the dataset.
+        num_points (int): Number of points to be sampled.
+    """
+
+    def __init__(self, num_points):
+        self.num_points = num_points
+
+    def points_random_sampling(self,
+                               points,
+                               num_samples,
+                               replace=None,
+                               return_choices=False):
+        """Points Random Sampling.
+
+        Sample points to a certain number.
+
+        Args:
+            points (ndarray): 3D Points.
+            num_samples (int): Number of samples to be sampled.
+            replace (bool): Whether the sample is with or without replacement.
+            return_choices (bool): Whether return choice.
+
+        Returns:
+            points (ndarray): 3D Points.
+            choices (ndarray): The generated random samples
+        """
+        if replace is None:
+            replace = (points.shape[0] < num_samples)
+        choices = np.random.choice(
+            points.shape[0], num_samples, replace=replace)
+        if return_choices:
+            return points[choices], choices
+        else:
+            return points[choices]
+
+    def __call__(self, results):
+        points = results.get('points', None)
+        points, choices = self.points_random_sampling(
+            points, self.num_points, return_choices=True)
+        pts_instance_mask = results.get('pts_instance_mask', None)
+        pts_semantic_mask = results.get('pts_semantic_mask', None)
+        results['points'] = points
+
+        if pts_instance_mask is not None and pts_semantic_mask is not None:
+            pts_instance_mask = pts_instance_mask[choices]
+            pts_semantic_mask = pts_semantic_mask[choices]
+            results['pts_instance_mask'] = pts_instance_mask
+            results['pts_semantic_mask'] = pts_semantic_mask
+
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += '(num_points={})'.format(self.num_points)
+        return repr_str

mmdet3d/datasets/pipelines/loading.py

@@ -3,10 +3,10 @@ import os.path as osp
 import mmcv
 import numpy as np
 
-from mmdet.datasets.registry import PIPELINES
+from mmdet.datasets.builder import PIPELINES
 
 
-@PIPELINES.register_module
+@PIPELINES.register_module()
 class LoadPointsFromFile(object):
 
     def __init__(self, points_dim=4, with_reflectivity=True):
@@ -31,7 +31,7 @@ class LoadPointsFromFile(object):
         return repr_str
 
 
-@PIPELINES.register_module
+@PIPELINES.register_module()
 class LoadMultiViewImageFromFiles(object):
     """ Load multi channel images from a list of separate channel files.
     Expects results['filename'] to be a list of filenames

mmdet3d/datasets/pipelines/train_aug.py

+import mmcv
 import numpy as np
+from mmcv.utils import build_from_cfg
 
 from mmdet3d.core.bbox import box_np_ops
-from mmdet3d.utils import build_from_cfg
+from mmdet.datasets.builder import PIPELINES
 from mmdet.datasets.pipelines import RandomFlip
-from mmdet.datasets.registry import PIPELINES
 from ..registry import OBJECTSAMPLERS
 from .data_augment_utils import noise_per_object_v3_
 
 
-@PIPELINES.register_module
+@PIPELINES.register_module()
 class RandomFlip3D(RandomFlip):
     """Flip the points & bbox.
 
@@ -34,7 +35,42 @@ class RandomFlip3D(RandomFlip):
         return gt_bboxes_3d, points
 
     def __call__(self, input_dict):
-        super(RandomFlip3D, self).__call__(input_dict)
+        # filp 2D image and its annotations
+        if 'flip' not in input_dict:
+            flip = True if np.random.rand() < self.flip_ratio else False
+            input_dict['flip'] = flip
+        if 'flip_direction' not in input_dict:
+            input_dict['flip_direction'] = self.direction
+        if input_dict['flip']:
+            # flip image
+            if 'img' in input_dict:
+                if isinstance(input_dict['img'], list):
+                    input_dict['img'] = [
+                        mmcv.imflip(
+                            img, direction=input_dict['flip_direction'])
+                        for img in input_dict['img']
+                    ]
+                else:
+                    input_dict['img'] = mmcv.imflip(
+                        input_dict['img'],
+                        direction=input_dict['flip_direction'])
+            # flip bboxes
+            for key in input_dict.get('bbox_fields', []):
+                input_dict[key] = self.bbox_flip(input_dict[key],
+                                                 input_dict['img_shape'],
+                                                 input_dict['flip_direction'])
+            # flip masks
+            for key in input_dict.get('mask_fields', []):
+                input_dict[key] = [
+                    mmcv.imflip(mask, direction=input_dict['flip_direction'])
+                    for mask in input_dict[key]
+                ]
+
+            # flip segs
+            for key in input_dict.get('seg_fields', []):
+                input_dict[key] = mmcv.imflip(
+                    input_dict[key], direction=input_dict['flip_direction'])
+
         if self.sync_2d:
             input_dict['pcd_flip'] = input_dict['flip']
         else:
@@ -50,8 +86,12 @@ class RandomFlip3D(RandomFlip):
             input_dict['points'] = points
         return input_dict
 
+    def __repr__(self):
+        return self.__class__.__name__ + '(flip_ratio={}, sync_2d={})'.format(
+            self.flip_ratio, self.sync_2d)
+
 
-@PIPELINES.register_module
+@PIPELINES.register_module()
 class ObjectSample(object):
 
     def __init__(self, db_sampler, sample_2d=False):
@@ -128,7 +168,7 @@ class ObjectSample(object):
         return self.__class__.__name__
 
 
-@PIPELINES.register_module
+@PIPELINES.register_module()
 class ObjectNoise(object):
 
     def __init__(self,
@@ -167,7 +207,7 @@ class ObjectNoise(object):
         return repr_str
 
 
-@PIPELINES.register_module
+@PIPELINES.register_module()
 class GlobalRotScale(object):
 
     def __init__(self,
@@ -241,7 +281,7 @@ class GlobalRotScale(object):
         return repr_str
 
 
-@PIPELINES.register_module
+@PIPELINES.register_module()
 class PointShuffle(object):
 
     def __call__(self, input_dict):
@@ -252,7 +292,7 @@ class PointShuffle(object):
         return self.__class__.__name__
 
 
-@PIPELINES.register_module
+@PIPELINES.register_module()
 class ObjectRangeFilter(object):
 
     def __init__(self, point_cloud_range):
@@ -304,7 +344,7 @@ class ObjectRangeFilter(object):
         return repr_str
 
 
-@PIPELINES.register_module
+@PIPELINES.register_module()
 class PointsRangeFilter(object):
 
     def __init__(self, point_cloud_range):