[Enhance] Update SECOND benchmark on Waymo (#205)

* Enhance SECOND benchmark on Waymo

* Update log link

configs/_base_/models/hv_second_secfpn_waymo.py

@@ -10,7 +10,7 @@ model = dict(
         max_num_points=10,
         point_cloud_range=[-76.8, -51.2, -2, 76.8, 51.2, 4],
         voxel_size=voxel_size,
-        max_voxels=(60000, 60000)),
+        max_voxels=(80000, 90000)),
     voxel_encoder=dict(type='HardSimpleVFE', num_features=5),
     middle_encoder=dict(
         type='SparseEncoder',

configs/second/README.md

@@ -25,7 +25,7 @@ We implement SECOND and provide the results and checkpoints on KITTI dataset.
 
 |  Backbone | Load Interval | Class | Lr schd | Mem (GB) | Inf time (fps) | mAP@L1 | mAPH@L1 |  mAP@L2 | **mAPH@L2** | Download |
 | :-------: | :-----------: |:-----:| :------:| :------: | :------------: | :----: | :-----: | :-----: | :-----: | :------: |
-| [SECFPN](./hv_second_secfpn_sbn_2x16_2x_waymoD5-3d-3class.py)|5|3 Class|2x|8.12||58.0|53.5|51.5|48.3|[log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/second/hv_second_secfpn_sbn_2x16_2x_waymoD5-3d-3class/hv_second_secfpn_sbn_2x16_2x_waymoD5-3d-3class_20201003_105141.log.json)|
+| [SECFPN](./hv_second_secfpn_sbn_2x16_2x_waymoD5-3d-3class.py)|5|3 Class|2x|8.12||58.0|53.5|51.5|48.3|[log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/second/hv_second_secfpn_sbn_4x8_2x_waymoD5-3d-3class/hv_second_secfpn_sbn_4x8_2x_waymoD5-3d-3class_20201115_112448.log.json)|
 | above @ Car|||2x|8.12||58.5|57.9|51.6|51.1| |
 | above @ Pedestrian|||2x|8.12||63.9|54.9|56.0|48.0| |
 | above @ Cyclist|||2x|8.12||48.6|47.6|46.8|45.8| |

configs/second/hv_second_secfpn_sbn_2x16_2x_waymoD5-3d-3class.py

@@ -17,9 +17,9 @@ db_sampler = dict(
     rate=1.0,
     prepare=dict(
         filter_by_difficulty=[-1],
-        filter_by_min_points=dict(Car=5, Pedestrian=10, Cyclist=10)),
+        filter_by_min_points=dict(Car=5, Pedestrian=5, Cyclist=5)),
     classes=class_names,
-    sample_groups=dict(Car=6, Pedestrian=8, Cyclist=10),
+    sample_groups=dict(Car=15, Pedestrian=10, Cyclist=10),
     points_loader=dict(
         type='LoadPointsFromFile', load_dim=5, use_dim=[0, 1, 2, 3, 4]))
 
@@ -68,7 +68,7 @@ test_pipeline = [
 ]
 
 data = dict(
-    samples_per_gpu=2,
+    samples_per_gpu=4,
     workers_per_gpu=4,
     train=dict(
         type='RepeatDataset',