[Fix] fix pillars encode (#1689)

* fix pillars encode

* fix all configs

* fix load key
Co-authored-by: VVsssssk <shenkun@pjlab.org.cn>

configs/centerpoint/centerpoint_0075voxel_second_secfpn_dcn_4x8_cyclic_flip-tta_20e_nus.py

@@ -31,13 +31,9 @@ test_pipeline = [
                 translation_std=[0, 0, 0]),
             dict(type='RandomFlip3D', sync_2d=False),
             dict(
-                type='PointsRangeFilter', point_cloud_range=point_cloud_range),
-            dict(
-                type='DefaultFormatBundle3D',
-                class_names=class_names,
-                with_label=False),
-            dict(type='Collect3D', keys=['points'])
-        ])
+                type='PointsRangeFilter', point_cloud_range=point_cloud_range)
+        ]),
+    dict(type='Pack3DDetInputs', keys=['points'])
 ]
 
 data = dict(

configs/centerpoint/centerpoint_0075voxel_second_secfpn_dcn_4x8_cyclic_tta_20e_nus.py

@@ -33,13 +33,9 @@ test_pipeline = [
                 translation_std=[0, 0, 0]),
             dict(type='RandomFlip3D', sync_2d=False),
             dict(
-                type='PointsRangeFilter', point_cloud_range=point_cloud_range),
-            dict(
-                type='DefaultFormatBundle3D',
-                class_names=class_names,
-                with_label=False),
-            dict(type='Collect3D', keys=['points'])
-        ])
+                type='PointsRangeFilter', point_cloud_range=point_cloud_range)
+        ]),
+    dict(type='Pack3DDetInputs', keys=['points'])
 ]
 
 data = dict(

configs/centerpoint/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_flip-tta_20e_nus.py

@@ -33,12 +33,8 @@ test_pipeline = [
             dict(type='RandomFlip3D', sync_2d=False),
             dict(
                 type='PointsRangeFilter', point_cloud_range=point_cloud_range),
-            dict(
-                type='DefaultFormatBundle3D',
-                class_names=class_names,
-                with_label=False),
-            dict(type='Collect3D', keys=['points'])
-        ])
+        ]),
+    dict(type='Pack3DDetInputs', keys=['points'])
 ]
 
 data = dict(

configs/centerpoint/centerpoint_01voxel_second_secfpn_4x8_cyclic_20e_nus.py

@@ -112,23 +112,7 @@ test_pipeline = [
         ]),
     dict(type='Pack3DDetInputs', keys=['points'])
 ]
-# construct a pipeline for data and gt loading in show function
-# please keep its loading function consistent with test_pipeline (e.g. client)
-eval_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='LIDAR',
-        load_dim=5,
-        use_dim=5,
-    ),
-    dict(
-        type='LoadPointsFromMultiSweeps',
-        sweeps_num=9,
-        use_dim=[0, 1, 2, 3, 4],
-        pad_empty_sweeps=True,
-        remove_close=True),
-    dict(type='Pack3DDetInputs', keys=['points'])
-]
+
 train_dataloader = dict(
     _delete_=True,
     batch_size=4,

configs/centerpoint/centerpoint_02pillar_second_secfpn_4x8_cyclic_20e_nus.py

@@ -111,18 +111,7 @@ test_pipeline = [
         ]),
     dict(type='Pack3DDetInputs', keys=['points'])
 ]
-# construct a pipeline for data and gt loading in show function
-# please keep its loading function consistent with test_pipeline (e.g. client)
-eval_pipeline = [
-    dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=5, use_dim=5),
-    dict(
-        type='LoadPointsFromMultiSweeps',
-        sweeps_num=9,
-        use_dim=[0, 1, 2, 3, 4],
-        pad_empty_sweeps=True,
-        remove_close=True),
-    dict(type='Pack3DDetInputs', keys=['points'])
-]
+
 train_dataloader = dict(
     _delete_=True,
     batch_size=4,

mmdet3d/models/detectors/centerpoint.py

 # Copyright (c) OpenMMLab. All rights reserved.
 from typing import Optional
 
-import torch
-
-from mmdet3d.models.test_time_augs import merge_aug_bboxes_3d
 from mmdet3d.registry import MODELS
 from .mvx_two_stage import MVXTwoStageDetector
 
@@ -70,118 +67,3 @@ class CenterPoint(MVXTwoStageDetector):
                              pts_bbox_head, img_roi_head, img_rpn_head,
                              train_cfg, test_cfg, init_cfg, data_preprocessor,
                              **kwargs)
-
-    # TODO support this
-    def aug_test_pts(self, feats, img_metas, rescale=False):
-        """Test function of point cloud branch with augmentaiton.
-
-        The function implementation process is as follows:
-
-            - step 1: map features back for double-flip augmentation.
-            - step 2: merge all features and generate boxes.
-            - step 3: map boxes back for scale augmentation.
-            - step 4: merge results.
-
-        Args:
-            feats (list[torch.Tensor]): Feature of point cloud.
-            img_metas (list[dict]): Meta information of samples.
-            rescale (bool, optional): Whether to rescale bboxes.
-                Default: False.
-
-        Returns:
-            dict: Returned bboxes consists of the following keys:
-
-                - boxes_3d (:obj:`LiDARInstance3DBoxes`): Predicted bboxes.
-                - scores_3d (torch.Tensor): Scores of predicted boxes.
-                - labels_3d (torch.Tensor): Labels of predicted boxes.
-        """
-        raise NotImplementedError
-        # only support aug_test for one sample
-        outs_list = []
-        for x, img_meta in zip(feats, img_metas):
-            outs = self.pts_bbox_head(x)
-            # merge augmented outputs before decoding bboxes
-            for task_id, out in enumerate(outs):
-                for key in out[0].keys():
-                    if img_meta[0]['pcd_horizontal_flip']:
-                        outs[task_id][0][key] = torch.flip(
-                            outs[task_id][0][key], dims=[2])
-                        if key == 'reg':
-                            outs[task_id][0][key][:, 1, ...] = 1 - outs[
-                                task_id][0][key][:, 1, ...]
-                        elif key == 'rot':
-                            outs[task_id][0][
-                                key][:, 0,
-                                     ...] = -outs[task_id][0][key][:, 0, ...]
-                        elif key == 'vel':
-                            outs[task_id][0][
-                                key][:, 1,
-                                     ...] = -outs[task_id][0][key][:, 1, ...]
-                    if img_meta[0]['pcd_vertical_flip']:
-                        outs[task_id][0][key] = torch.flip(
-                            outs[task_id][0][key], dims=[3])
-                        if key == 'reg':
-                            outs[task_id][0][key][:, 0, ...] = 1 - outs[
-                                task_id][0][key][:, 0, ...]
-                        elif key == 'rot':
-                            outs[task_id][0][
-                                key][:, 1,
-                                     ...] = -outs[task_id][0][key][:, 1, ...]
-                        elif key == 'vel':
-                            outs[task_id][0][
-                                key][:, 0,
-                                     ...] = -outs[task_id][0][key][:, 0, ...]
-
-            outs_list.append(outs)
-
-        preds_dicts = dict()
-        scale_img_metas = []
-
-        # concat outputs sharing the same pcd_scale_factor
-        for i, (img_meta, outs) in enumerate(zip(img_metas, outs_list)):
-            pcd_scale_factor = img_meta[0]['pcd_scale_factor']
-            if pcd_scale_factor not in preds_dicts.keys():
-                preds_dicts[pcd_scale_factor] = outs
-                scale_img_metas.append(img_meta)
-            else:
-                for task_id, out in enumerate(outs):
-                    for key in out[0].keys():
-                        preds_dicts[pcd_scale_factor][task_id][0][key] += out[
-                            0][key]
-
-        aug_bboxes = []
-
-        for pcd_scale_factor, preds_dict in preds_dicts.items():
-            for task_id, pred_dict in enumerate(preds_dict):
-                # merge outputs with different flips before decoding bboxes
-                for key in pred_dict[0].keys():
-                    preds_dict[task_id][0][key] /= len(outs_list) / len(
-                        preds_dicts.keys())
-            bbox_list = self.pts_bbox_head.get_bboxes(
-                preds_dict, img_metas[0], rescale=rescale)
-            bbox_list = [
-                dict(boxes_3d=bboxes, scores_3d=scores, labels_3d=labels)
-                for bboxes, scores, labels in bbox_list
-            ]
-            aug_bboxes.append(bbox_list[0])
-
-        if len(preds_dicts.keys()) > 1:
-            # merge outputs with different scales after decoding bboxes
-            merged_bboxes = merge_aug_bboxes_3d(aug_bboxes, scale_img_metas,
-                                                self.pts_bbox_head.test_cfg)
-            return merged_bboxes
-        else:
-            for key in bbox_list[0].keys():
-                bbox_list[0][key] = bbox_list[0][key].to('cpu')
-            return bbox_list[0]
-
-    # TODO support this
-    def aug_test(self, points, img_metas, imgs=None, rescale=False):
-        raise NotImplementedError
-        """Test function with augmentaiton."""
-        img_feats, pts_feats = self.extract_feats(points, img_metas, imgs)
-        bbox_list = dict()
-        if pts_feats and self.with_pts_bbox:
-            pts_bbox = self.aug_test_pts(pts_feats, img_metas, rescale)
-            bbox_list.update(pts_bbox=pts_bbox)
-        return [bbox_list]

mmdet3d/models/voxel_encoders/pillar_encoder.py

@@ -91,7 +91,7 @@ class PillarFeatureNet(nn.Module):
         self.point_cloud_range = point_cloud_range
 
     @force_fp32(out_fp16=True)
-    def forward(self, features, num_points, coors):
+    def forward(self, features, num_points, coors, *args, **kwargs):
         """Forward function.
 
         Args: