Update fp16 models and performance (#178)

* Support to train using FP16

* fix type inconsistency error on naive syncBN

* resolve comments

* clean nan check

* clean nan check

* clean unnecessary modification

* update model link

configs/fp16/README.md

@@ -13,11 +13,16 @@ Mixed precision training for PointNet-based methods will be supported in the fut
 ### SECOND on KITTI dataset
 |  Backbone   |Class| Lr schd | FP32 Mem (GB) | FP16 Mem (GB) | FP32 mAP | FP16 mAP |Download |
 | :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: | :------: |
-|    [SECFPN](./hv_second_secfpn_fp16_6x8_80e_kitti-3d-car.py)| Car |cyclic 80e|5.4|2.9|79.07|78.72||
-|    [SECFPN](./hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py)| 3 Class |cyclic 80e|5.4|2.9|64.41|67.4||
+|    [SECFPN](./hv_second_secfpn_fp16_6x8_80e_kitti-3d-car.py)| Car |cyclic 80e|5.4|2.9|79.07|78.72|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car_20200924_211301-1f5ad833.pth)| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car_20200924_211301.log.json)|
+|    [SECFPN](./hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py)| 3 Class |cyclic 80e|5.4|2.9|64.41|67.4|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class_20200925_110059-05f67bdf.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class_20200925_110059.log.json)|
 
 ### PointPillars on nuScenes dataset
+|  Backbone   | Lr schd | FP32 Mem (GB) | FP16 Mem (GB) | FP32 mAP | FP32 NDS| FP16 mAP | FP16 NDS| Download |
+| :---------: | :-----: | :------: | :------------: | :----: |:----: | :----: |:----: | :------: |
+|[SECFPN](./hv_pointpillars_secfpn_sbn-all_fp16_2x8_2x_nus-3d.py)|2x|16.4|8.37|35.17|49.7|35.19|50.27|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_pointpillars_secfpn_sbn-all_fp16_2x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_fp16_2x8_2x_nus-3d_20201020_222626-c3f0483e.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_pointpillars_secfpn_sbn-all_fp16_2x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_fp16_2x8_2x_nus-3d_20201020_222626.log.json)|
+|[FPN](./hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d.py)|2x|16.4|8.40|40.0|53.3|39.26|53.26|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d_20201021_120719-269f9dd6.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d_20201021_120719.log.json)|
 
-
-**Note**: With mixed precision training, we can train PointPillars with RegNet-400mf on 8 Titan XP GPUS with batch size of 2.
+**Note**:
+1. With mixed precision training, we can train PointPillars with nuScenes dataset on 8 Titan XP GPUS with batch size of 2.
 This will cause OOM error without mixed precision training.
+2. The loss scale for PointPillars on nuScenes dataset is specifically tuned to avoid the loss to be Nan. We find 32 is more stable than 512, though loss scale 32 still cause Nan sometimes.

configs/fp16/hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d.py

 _base_ = '../pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d.py'
 data = dict(samples_per_gpu=2, workers_per_gpu=2)
-# fp16 settings
-fp16 = dict(loss_scale=512.)
+# fp16 settings, the loss scale is specifically tuned to avoid Nan
+fp16 = dict(loss_scale=32.)

configs/fp16/hv_pointpillars_regnet-400mf_fpn_sbn-all_fp16_2x8_2x_nus-3d.py

 _base_ = '../regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d.py'
 data = dict(samples_per_gpu=2, workers_per_gpu=2)
-# fp16 settings
-fp16 = dict(loss_scale=512.)
+# fp16 settings, the loss scale is specifically tuned to avoid Nan
+fp16 = dict(loss_scale=32.)

configs/fp16/hv_pointpillars_secfpn_sbn-all_fp16_2x8_2x_nus-3d.py

 _base_ = '../pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d.py'
 data = dict(samples_per_gpu=2, workers_per_gpu=2)
-# fp16 settings
-fp16 = dict(loss_scale=512.)
+# fp16 settings, the loss scale is specifically tuned to avoid Nan
+fp16 = dict(loss_scale=32.)