fix lint & delete unused

configs/_base_/models/multiview_dfm.py

@@ -89,8 +89,7 @@
                 pos_iou_thr=0.6,
                 neg_iou_thr=0.45,
                 min_pos_iou=0.45,
-                ignore_iof_thr=-1),
-
+                ignore_iof_thr=-1)
         ],
         allowed_border=0,
         pos_weight=-1,

configs/pgd/pgd_r101_fpn_gn-head_dcn_8xb3-2x_waymoD3-mv-mono3d.py

@@ -108,4 +108,4 @@
 train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=24, val_interval=24)
 val_cfg = dict(type='ValLoop')
 test_cfg = dict(type='TestLoop')
-auto_scale_lr = dict(enable=False, base_batch_size=48)
+auto_scale_lr = dict(enable=False, base_batch_size=48)

mmdet3d/datasets/convert_utils.py

@@ -10,7 +10,7 @@
 from shapely.geometry import MultiPoint, box
 from shapely.geometry.polygon import Polygon
 
-from mmdet3d.structures import Box3DMode, LiDARInstance3DBoxes, points_cam2img
+from mmdet3d.structures import Box3DMode, CameraInstance3DBoxes, points_cam2img
 from mmdet3d.structures.ops import box_np_ops
 
 kitti_categories = ('Pedestrian', 'Cyclist', 'Car', 'Van', 'Truck',
@@ -318,23 +318,21 @@ def get_kitti_style_2d_boxes(info: dict,
 def convert_annos(info: dict, cam_idx: int) -> dict:
     """Convert front-cam anns to i-th camera (KITTI-style info)."""
     rect = info['calib']['R0_rect'].astype(np.float32)
-    if cam_idx == 0:
-        lidar2cami = info['calib']['Tr_velo_to_cam'].astype(np.float32)
-    else:
-        lidar2cami = info['calib'][f'Tr_velo_to_cam{cam_idx}'].astype(
-            np.float32)
+    lidar2cam0 = info['calib']['Tr_velo_to_cam'].astype(np.float32)
+    lidar2cami = info['calib'][f'Tr_velo_to_cam{cam_idx}'].astype(np.float32)
     annos = info['annos']
     converted_annos = copy.deepcopy(annos)
     loc = annos['location']
     dims = annos['dimensions']
     rots = annos['rotation_y']
     gt_bboxes_3d = np.concatenate([loc, dims, rots[..., np.newaxis]],
                                   axis=1).astype(np.float32)
-    # BC-breaking: gt_bboxes_3d is already in lidar coordinates
+    # convert gt_bboxes_3d to velodyne coordinates
+    gt_bboxes_3d = CameraInstance3DBoxes(gt_bboxes_3d).convert_to(
+        Box3DMode.LIDAR, np.linalg.inv(rect @ lidar2cam0), correct_yaw=True)
     # convert gt_bboxes_3d to cam coordinates
-    gt_bboxes_3d = LiDARInstance3DBoxes(gt_bboxes_3d).convert_to(
+    gt_bboxes_3d = gt_bboxes_3d.convert_to(
         Box3DMode.CAM, rect @ lidar2cami, correct_yaw=True).numpy()
-
     converted_annos['location'] = gt_bboxes_3d[:, :3]
     converted_annos['dimensions'] = gt_bboxes_3d[:, 3:6]
     converted_annos['rotation_y'] = gt_bboxes_3d[:, 6]

mmdet3d/datasets/transforms/transforms_3d.py

@@ -2071,7 +2071,6 @@ def _crop_data(self,
                 offset_w = np.random.randint(
                     self.rel_offset_w[0] * margin_w,
                     self.rel_offset_w[1] * margin_w + 1)
-                # offset_h, offset_w = 0, 0
             else:
                 offset_w, offset_h = results['img_crop_offset']
 

mmdet3d/datasets/waymo_dataset.py

@@ -249,7 +249,6 @@ def parse_data_info(self, info: dict) -> Union[dict, List[dict]]:
             new_image_info = {}
             new_image_info[self.default_cam_key] = \
                 info['images'][self.default_cam_key]
-            # cam_prefix = self.data_prefix[self.default_cam_key]
             info['images'] = new_image_info
             info['instances'] = info['cam_instances'][self.default_cam_key]
             return Det3DDataset.parse_data_info(self, info)
@@ -258,7 +257,6 @@ def parse_data_info(self, info: dict) -> Union[dict, List[dict]]:
             # Convert frame-based infos to multi-view image-based
             data_list = []
             for (cam_key, img_info) in info['images'].items():
-                # camera_info = deepcopy(info)
                 camera_info = dict()
                 camera_info['sample_idx'] = info['sample_idx']
                 camera_info['timestamp'] = info['timestamp']

mmdet3d/evaluation/metrics/waymo_metric.py

@@ -61,7 +61,6 @@ def __init__(self,
         self.load_type = load_type
         self.result_prefix = result_prefix
         self.format_only = format_only
-        self.result_prefix = result_prefix
         if self.format_only:
             assert result_prefix is not None, 'result_prefix must be not '
             'None when format_only is True, otherwise the result files will '

mmdet3d/models/detectors/dfm.py

@@ -57,7 +57,8 @@ def __init__(self,
                  test_cfg=None,
                  pretrained=None,
                  init_cfg=None):
-        super().__init__(data_preprocessor= data_preprocessor,init_cfg=init_cfg)
+        super().__init__(
+            data_preprocessor=data_preprocessor, init_cfg=init_cfg)
         self.backbone = MODELS.build(backbone)
         self.neck = MODELS.build(neck)
         if backbone_stereo is not None:

mmdet3d/models/detectors/multiview_dfm.py

@@ -1,18 +1,19 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+from typing import Union
+
 import numpy as np
 import torch
+from mmengine.structures import InstanceData
 from torch import Tensor
-from typing import Union
 
 from mmdet3d.models.layers.fusion_layers.point_fusion import (point_sample,
                                                               voxel_sample)
 from mmdet3d.registry import MODELS, TASK_UTILS
 from mmdet3d.structures.bbox_3d.utils import get_lidar2img
 from mmdet3d.structures.det3d_data_sample import SampleList
-from mmengine.structures import InstanceData
-from mmdet3d.utils import ConfigType, OptConfigType
+from mmdet3d.utils import ConfigType, OptConfigType, OptInstanceList
 from .dfm import DfM
-from mmdet3d.utils.typing_utils import OptConfigType, OptInstanceList
+
 
 @MODELS.register_module()
 class MultiViewDfM(DfM):
@@ -356,7 +357,7 @@ def feature_transformation(self, batch_feats, batch_img_metas, num_views,
         return transform_feats
 
     def loss(self, batch_inputs: Tensor,
-                batch_data_samples: SampleList) -> Union[dict, tuple]:
+             batch_data_samples: SampleList) -> Union[dict, tuple]:
         """Calculate losses from a batch of inputs dict and data samples.
 
         Args:
@@ -393,7 +394,7 @@ def predict(self, batch_inputs: Tensor,
             batch_data_samples (List[:obj:`Det3DDataSample`]): The Data
                 samples. It usually includes information such as
                 `gt_instance_3d`, `gt_panoptic_seg_3d` and `gt_sem_seg_3d`.
-        
+
         Returns:
             list[:obj:`Det3DDataSample`]: Detection results of the
             input samples. Each Det3DDataSample usually contain
@@ -409,14 +410,15 @@ def predict(self, batch_inputs: Tensor,
         """
         feats = self.extract_feat(batch_inputs, batch_data_samples)
         bev_feat = feats[0]
-        results_list = self.bbox_head_3d.predict([bev_feat], batch_data_samples)
+        results_list = self.bbox_head_3d.predict([bev_feat],
+                                                 batch_data_samples)
         predictions = self.add_pred_to_datasample(batch_data_samples,
                                                   results_list)
         return predictions
 
     def _forward(self,
-                batch_inputs: Tensor,
-                batch_data_samples: SampleList = None):
+                 batch_inputs: Tensor,
+                 batch_data_samples: SampleList = None):
         """Network forward process.
 
         Usually includes backbone, neck and head forward without any post-
@@ -426,12 +428,11 @@ def _forward(self,
         bev_feat = feats[0]
         self.bbox_head.forward(bev_feat, batch_data_samples)
 
-
     def add_pred_to_datasample(
-    self,
-    data_samples: SampleList,
-    data_instances_3d: OptInstanceList = None,
-    data_instances_2d: OptInstanceList = None,
+        self,
+        data_samples: SampleList,
+        data_instances_3d: OptInstanceList = None,
+        data_instances_2d: OptInstanceList = None,
     ) -> SampleList:
         """Convert results list to `Det3DDataSample`.
 

projects/DSVT/configs/dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class.py

@@ -228,56 +228,9 @@
     result_prefix='./dsvt_pred')
 test_evaluator = val_evaluator
 
-# vis_backends = [dict(type='LocalVisBackend'), dict(type='WandbVisBackend')]
 vis_backends = [dict(type='LocalVisBackend')]
 visualizer = dict(
     type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
-lr = 1e-5
-# This schedule is mainly used by models on nuScenes dataset
-# max_norm=10 is better for SECOND
-optim_wrapper = dict(
-    type='OptimWrapper',
-    optimizer=dict(type='AdamW', lr=lr, weight_decay=0.05, betas=(0.9, 0.99)),
-    clip_grad=dict(max_norm=10, norm_type=2))
-# learning rate
-param_scheduler = [
-    dict(
-        type='CosineAnnealingLR',
-        T_max=1.2,
-        eta_min=lr * 100,
-        begin=0,
-        end=1.2,
-        by_epoch=True,
-        convert_to_iter_based=True),
-    dict(
-        type='CosineAnnealingLR',
-        T_max=10.8,
-        eta_min=lr * 1e-4,
-        begin=1.2,
-        end=12,
-        by_epoch=True,
-        convert_to_iter_based=True),
-    # momentum scheduler
-    dict(
-        type='CosineAnnealingMomentum',
-        T_max=1.2,
-        eta_min=0.85,
-        begin=0,
-        end=1.2,
-        by_epoch=True,
-        convert_to_iter_based=True),
-    dict(
-        type='CosineAnnealingMomentum',
-        T_max=10.8,
-        eta_min=0.95,
-        begin=1.2,
-        end=12,
-        by_epoch=True,
-        convert_to_iter_based=True)
-]
-
-# runtime settings
-train_cfg = dict(by_epoch=True, max_epochs=12, val_interval=1)
 
 # schedules
 lr = 1e-5

tools/dataset_converters/kitti_converter.py

@@ -5,7 +5,6 @@
 import mmcv
 import mmengine
 import numpy as np
-from mmengine import logging, print_log
 from nuscenes.utils.geometry_utils import view_points
 
 from mmdet3d.structures import points_cam2img
@@ -250,12 +249,6 @@ def create_waymo_info_file(data_path,
         max_sweeps (int, optional): Max sweeps before the detection frame
             to be used. Default: 5.
     """
-    print_log(
-        'Deprecation Warning: related functions has been migrated to '
-        '`Waymo2KITTI.create_waymo_info_file`. It will be removed in '
-        'the future!',
-        logger='current',
-        level=logging.WARNING)
     imageset_folder = Path(data_path) / 'ImageSets'
     train_img_ids = _read_imageset_file(str(imageset_folder / 'train.txt'))
     val_img_ids = _read_imageset_file(str(imageset_folder / 'val.txt'))

tools/dataset_converters/kitti_data_utils.py

@@ -6,7 +6,6 @@
 
 import mmengine
 import numpy as np
-from mmengine import logging, print_log
 from PIL import Image
 from skimage import io
 
@@ -350,12 +349,6 @@ def __init__(self,
         self.relative_path = relative_path
         self.with_imageshape = with_imageshape
         self.max_sweeps = max_sweeps
-        print_log(
-            'Deprecation Warning: `WaymoInfoGatherer` has been migrated to '
-            '`Waymo2KITTI.create_waymo_info_file`. It will be removed in '
-            'the future!',
-            logger='current',
-            level=logging.WARNING)
 
     def gather_single(self, idx):
         root_path = Path(self.path)