[Enhance] add README and copyright pre-commmit (#1134)

* add precommmit to check readme

* update

* fix typos

.pre-commit-config.yaml

@@ -43,3 +43,9 @@ repos:
     hooks:
       - id: docformatter
         args: ["--in-place", "--wrap-descriptions", "79"]
+  - repo: https://github.com/open-mmlab/pre-commit-hooks
+    rev: master  # Use the ref you want to point at
+    hooks:
+      - id: check-algo-readme
+      - id: check-copyright
+        args: ["mmdet3d"]  # replace the dir_to_check with your expected directory to check

configs/nuimages/metafile.yml

-Collections:
-  - Name: Mask R-CNN
-    Metadata:
-      Training Data: nuImages
-      Training Techniques:
-        - SGD with Momentum
-      Architecture:
-        - RoI Align
-        - RPN
-    README: configs/nuimages/README.md
-    Code:
-      Version: v0.6.0
-
 Models:
   - Name: mask_rcnn_r50_fpn_1x_nuim
     In Collection: Mask R-CNN

configs/regnet/metafile.yml

-Collections:
-  - Name: RegNetX
-    Metadata:
-      Training Techniques:
-        - AdamW
-      Training Resources: 8x V100 GPUs
-      Architecture:
-        - Faster R-CNN
-        - Hard Voxelization
-    Paper:
-      URL: https://arxiv.org/abs/2003.13678
-      Title: 'Designing Network Design Spaces'
-    README: configs/regnet/README.md
-    Code:
-      URL: https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/models/backbones/nostem_regnet.py#L7
-      Version: v0.5.0
-
 Models:
   - Name: hv_pointpillars_regnet-400mf_secfpn_sbn-all_4x8_2x_nus-3d
-    In Collection: RegNetX
+    In Collection: PointPillars
     Config: configs/regnet/hv_pointpillars_regnet-400mf_secfpn_sbn-all_4x8_2x_nus-3d.py
     Metadata:
       Training Data: nuScenes
       Training Memory (GB): 16.4
+      Architecture:
+        - RegNetX
+        - Hard Voxelization
     Results:
       - Task: 3D Object Detection
         Dataset: nuScenes
@@ -31,11 +17,14 @@ Models:
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_secfpn_sbn-all_4x8_2x_nus-3d_20200620_230334-53044f32.pth
 
   - Name: hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d
-    In Collection: RegNetX
+    In Collection: PointPillars
     Config: configs/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d.py
     Metadata:
       Training Data: nuScenes
       Training Memory (GB): 17.3
+      Architecture:
+        - RegNetX
+        - Hard Voxelization
     Results:
       - Task: 3D Object Detection
         Dataset: nuScenes
@@ -45,11 +34,14 @@ Models:
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d_20200620_230239-c694dce7.pth
 
   - Name: hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d
-    In Collection: RegNetX
+    In Collection: PointPillars
     Config: configs/regnet/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d.py
     Metadata:
       Training Data: nuScenes
       Training Memory (GB): 24.0
+      Architecture:
+        - RegNetX
+        - Hard Voxelization
     Results:
       - Task: 3D Object Detection
         Dataset: nuScenes
@@ -59,11 +51,14 @@ Models:
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d_20200629_050311-dcd4e090.pth
 
   - Name: hv_pointpillars_regnet-400mf_secfpn_sbn-all_2x8_2x_lyft-3d
-    In Collection: RegNetX
+    In Collection: PointPillars
     Config: configs/regnet/hv_pointpillars_regnet-400mf_secfpn_sbn-all_2x8_2x_lyft-3d.py
     Metadata:
       Training Data: Lyft
       Training Memory (GB): 15.9
+      Architecture:
+        - RegNetX
+        - Hard Voxelization
     Results:
       - Task: 3D Object Detection
         Dataset: Lyft
@@ -73,11 +68,14 @@ Models:
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_secfpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_regnet-400mf_secfpn_sbn-all_2x8_2x_lyft-3d_20210524_092151-42513826.pth
 
   - Name: hv_pointpillars_regnet-400mf_fpn_sbn-all_2x8_2x_lyft-3d
-    In Collection: RegNetX
+    In Collection: PointPillars
     Config: configs/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_2x8_2x_lyft-3d.py
     Metadata:
       Training Data: Lyft
       Training Memory (GB): 13.0
+      Architecture:
+        - RegNetX
+        - Hard Voxelization
     Results:
       - Task: 3D Object Detection
         Dataset: Lyft

mmdet3d/core/bbox/coders/fcos3d_bbox_coder.py

+# Copyright (c) OpenMMLab. All rights reserved.
 import numpy as np
 import torch
 

mmdet3d/core/bbox/coders/monoflex_bbox_coder.py

+# Copyright (c) OpenMMLab. All rights reserved.
 import numpy as np
 import torch
 from torch.nn import functional as F

mmdet3d/core/bbox/coders/pgd_bbox_coder.py

+# Copyright (c) OpenMMLab. All rights reserved.
 import numpy as np
 import torch
 from torch.nn import functional as F

mmdet3d/core/bbox/coders/point_xyzwhlr_bbox_coder.py

+# Copyright (c) OpenMMLab. All rights reserved.
 import numpy as np
 import torch
 

mmdet3d/core/bbox/coders/smoke_bbox_coder.py

+# Copyright (c) OpenMMLab. All rights reserved.
 import numpy as np
 import torch
 

mmdet3d/core/utils/array_converter.py

+# Copyright (c) OpenMMLab. All rights reserved.
 import functools
 import numpy as np
 import torch

mmdet3d/models/backbones/dla.py

+# Copyright (c) OpenMMLab. All rights reserved.
 import torch
 import warnings
 from mmcv.cnn import build_conv_layer, build_norm_layer

mmdet3d/models/dense_heads/monoflex_head.py

+# Copyright (c) OpenMMLab. All rights reserved.
 import torch
 from mmcv.cnn import xavier_init
 from torch import nn as nn

mmdet3d/models/dense_heads/pgd_head.py

+# Copyright (c) OpenMMLab. All rights reserved.
 import numpy as np
 import torch
 from mmcv.cnn import Scale, bias_init_with_prob, normal_init

mmdet3d/models/dense_heads/point_rpn_head.py

+# Copyright (c) OpenMMLab. All rights reserved.
 import torch
 from mmcv.runner import BaseModule, force_fp32
 from torch import nn as nn

mmdet3d/models/dense_heads/smoke_mono3d_head.py

+# Copyright (c) OpenMMLab. All rights reserved.
 import torch
 from torch.nn import functional as F
 

mmdet3d/models/detectors/point_rcnn.py

+# Copyright (c) OpenMMLab. All rights reserved.
 import torch
 
 from mmdet.models import DETECTORS

mmdet3d/models/detectors/smoke_mono3d.py

+# Copyright (c) OpenMMLab. All rights reserved.
 from mmdet.models.builder import DETECTORS
 from .single_stage_mono3d import SingleStageMono3DDetector
 

mmdet3d/models/losses/uncertain_smooth_l1_loss.py

+# Copyright (c) OpenMMLab. All rights reserved.
 import torch
 from torch import nn as nn
 

mmdet3d/models/model_utils/edge_fusion_module.py

+# Copyright (c) OpenMMLab. All rights reserved.
 from mmcv.cnn import ConvModule
 from mmcv.runner import BaseModule
 from torch import nn as nn

mmdet3d/models/necks/dla_neck.py

+# Copyright (c) OpenMMLab. All rights reserved.
 import math
 import numpy as np
 from mmcv.cnn import ConvModule, build_conv_layer

mmdet3d/models/necks/pointnet2_fp_neck.py

+# Copyright (c) OpenMMLab. All rights reserved.
 from mmcv.runner import BaseModule
 from torch import nn as nn