Merge branch 'master' of gitlab.sz.sensetime.com:open-mmlab/mmdet.3d into fix-train-runtime

mmdet3d/models/detectors/mvx_two_stage.py

@@ -8,7 +8,7 @@ from .. import builder
 from .base import BaseDetector
 
 
-@DETECTORS.register_module
+@DETECTORS.register_module()
 class MVXTwoStageDetector(BaseDetector):
 
     def __init__(self,

mmdet3d/models/detectors/parta2.py

@@ -6,7 +6,7 @@ from mmdet.models import DETECTORS, TwoStageDetector
 from .. import builder
 
 
-@DETECTORS.register_module
+@DETECTORS.register_module()
 class PartA2(TwoStageDetector):
 
     def __init__(self,

mmdet3d/models/detectors/voxelnet.py

@@ -6,7 +6,7 @@ from mmdet.models import DETECTORS, SingleStageDetector
 from .. import builder
 
 
-@DETECTORS.register_module
+@DETECTORS.register_module()
 class VoxelNet(SingleStageDetector):
 
     def __init__(self,
@@ -88,7 +88,7 @@ class VoxelNet(SingleStageDetector):
         return bbox_list
 
 
-@DETECTORS.register_module
+@DETECTORS.register_module()
 class DynamicVoxelNet(VoxelNet):
 
     def __init__(self,

mmdet3d/models/fusion_layers/point_fusion.py

 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from mmcv.cnn import xavier_init
+from mmcv.cnn import ConvModule, xavier_init
 
-from mmdet.ops import ConvModule
 from ..registry import FUSION_LAYERS
 
 
@@ -96,7 +95,7 @@ def point_sample(
     return point_features.squeeze().t()
 
 
-@FUSION_LAYERS.register_module
+@FUSION_LAYERS.register_module()
 class PointFusion(nn.Module):
     """Fuse image features from fused single scale features
     """

mmdet3d/models/middle_encoders/pillar_scatter.py

@@ -4,7 +4,7 @@ from torch import nn
 from ..registry import MIDDLE_ENCODERS
 
 
-@MIDDLE_ENCODERS.register_module
+@MIDDLE_ENCODERS.register_module()
 class PointPillarsScatter(nn.Module):
 
     def __init__(self, in_channels, output_shape):

mmdet3d/models/middle_encoders/sparse_encoder.py

 import torch.nn as nn
+from mmcv.cnn import build_norm_layer
 
 import mmdet3d.ops.spconv as spconv
-from mmdet.ops import build_norm_layer
 from ..registry import MIDDLE_ENCODERS
 
 
-@MIDDLE_ENCODERS.register_module
+@MIDDLE_ENCODERS.register_module()
 class SparseEncoder(nn.Module):
 
     def __init__(self,

mmdet3d/models/middle_encoders/sparse_unet.py

 import torch
 import torch.nn as nn
+from mmcv.cnn import build_norm_layer
 
 import mmdet3d.ops.spconv as spconv
 from mmdet3d.ops import SparseBasicBlock
-from mmdet.ops import build_norm_layer
 from ..registry import MIDDLE_ENCODERS
 
 
-@MIDDLE_ENCODERS.register_module
+@MIDDLE_ENCODERS.register_module()
 class SparseUNet(nn.Module):
 
     def __init__(self,

mmdet3d/models/necks/second_fpn.py

@@ -2,16 +2,15 @@ from functools import partial
 
 import torch
 import torch.nn as nn
-from mmcv.cnn import constant_init, kaiming_init
+from mmcv.cnn import build_norm_layer, constant_init, kaiming_init
 from torch.nn import Sequential
 from torch.nn.modules.batchnorm import _BatchNorm
 
 from mmdet.models import NECKS
-from mmdet.ops import build_norm_layer
 from .. import builder
 
 
-@NECKS.register_module
+@NECKS.register_module()
 class SECONDFPN(nn.Module):
     """Compare with RPN, RPNV2 support arbitrary number of stage.
     """
@@ -64,7 +63,7 @@ class SECONDFPN(nn.Module):
         return [out]
 
 
-@NECKS.register_module
+@NECKS.register_module()
 class SECONDFusionFPN(SECONDFPN):
     """Compare with RPN, RPNV2 support arbitrary number of stage.
     """

mmdet3d/models/voxel_encoders/pillar_encoder.py

 import torch
+from mmcv.cnn import build_norm_layer
 from torch import nn
 
 from mmdet3d.ops import DynamicScatter
-from mmdet.ops import build_norm_layer
 from ..registry import VOXEL_ENCODERS
 from .utils import PFNLayer, get_paddings_indicator
 
 
-@VOXEL_ENCODERS.register_module
+@VOXEL_ENCODERS.register_module()
 class PillarFeatureNet(nn.Module):
 
     def __init__(self,
@@ -118,7 +118,7 @@ class PillarFeatureNet(nn.Module):
         return features.squeeze()
 
 
-@VOXEL_ENCODERS.register_module
+@VOXEL_ENCODERS.register_module()
 class DynamicPillarFeatureNet(PillarFeatureNet):
 
     def __init__(self,
@@ -237,7 +237,7 @@ class DynamicPillarFeatureNet(PillarFeatureNet):
         return voxel_feats, voxel_coors
 
 
-@VOXEL_ENCODERS.register_module
+@VOXEL_ENCODERS.register_module()
 class AlignedPillarFeatureNet(nn.Module):
 
     def __init__(self,

mmdet3d/models/voxel_encoders/utils.py

 import torch
+from mmcv.cnn import build_norm_layer
 from torch import nn
 from torch.nn import functional as F
 
-from mmdet.ops import build_norm_layer
-
 
 class Empty(nn.Module):
 

mmdet3d/models/voxel_encoders/voxel_encoder.py

 import torch
+from mmcv.cnn import build_norm_layer
 from torch import nn
 from torch.nn import functional as F
 
 from mmdet3d.ops import DynamicScatter
-from mmdet.ops import build_norm_layer
 from .. import builder
 from ..registry import VOXEL_ENCODERS
 from .utils import Empty, VFELayer, get_paddings_indicator
 
 
-@VOXEL_ENCODERS.register_module
+@VOXEL_ENCODERS.register_module()
 class VoxelFeatureExtractor(nn.Module):
 
     def __init__(self,
@@ -71,7 +71,7 @@ class VoxelFeatureExtractor(nn.Module):
         return voxelwise
 
 
-@VOXEL_ENCODERS.register_module
+@VOXEL_ENCODERS.register_module()
 class VoxelFeatureExtractorV2(nn.Module):
 
     def __init__(self,
@@ -132,7 +132,7 @@ class VoxelFeatureExtractorV2(nn.Module):
         return voxelwise
 
 
-@VOXEL_ENCODERS.register_module
+@VOXEL_ENCODERS.register_module()
 class VoxelFeatureExtractorV3(nn.Module):
 
     def __init__(self,
@@ -152,7 +152,7 @@ class VoxelFeatureExtractorV3(nn.Module):
         return points_mean.contiguous()
 
 
-@VOXEL_ENCODERS.register_module
+@VOXEL_ENCODERS.register_module()
 class DynamicVFEV3(nn.Module):
 
     def __init__(self,
@@ -170,7 +170,7 @@ class DynamicVFEV3(nn.Module):
         return features, features_coors
 
 
-@VOXEL_ENCODERS.register_module
+@VOXEL_ENCODERS.register_module()
 class DynamicVFE(nn.Module):
 
     def __init__(self,
@@ -318,7 +318,7 @@ class DynamicVFE(nn.Module):
         return voxel_feats, voxel_coors
 
 
-@VOXEL_ENCODERS.register_module
+@VOXEL_ENCODERS.register_module()
 class HardVFE(nn.Module):
 
     def __init__(self,

mmdet3d/ops/__init__.py

@@ -13,7 +13,6 @@ __all__ = [
     'roi_align',
     'get_compiler_version',
     'get_compiling_cuda_version',
-    'build_conv_layer',
     'NaiveSyncBatchNorm1d',
     'NaiveSyncBatchNorm2d',
     'batched_nms',

mmdet3d/ops/norm.py

 import torch
 import torch.distributed as dist
 import torch.nn as nn
+from mmcv.cnn import NORM_LAYERS
 from torch.autograd.function import Function
 
-from mmdet.ops.norm import norm_cfg
-
 
 class AllReduce(Function):
 
@@ -24,6 +23,7 @@ class AllReduce(Function):
         return grad_output
 
 
+@NORM_LAYERS.register_module('naiveSyncBN1d')
 class NaiveSyncBatchNorm1d(nn.BatchNorm1d):
     """Syncronized Batch Normalization for 3D Tensors
 
@@ -68,6 +68,7 @@ class NaiveSyncBatchNorm1d(nn.BatchNorm1d):
         return input * scale + bias
 
 
+@NORM_LAYERS.register_module('naiveSyncBN2d')
 class NaiveSyncBatchNorm2d(nn.BatchNorm2d):
     """Syncronized Batch Normalization for 4D Tensors
 
@@ -110,10 +111,3 @@ class NaiveSyncBatchNorm2d(nn.BatchNorm2d):
         scale = scale.reshape(1, -1, 1, 1)
         bias = bias.reshape(1, -1, 1, 1)
         return input * scale + bias
-
-
-norm_cfg.update({
-    'BN1d': ('bn', nn.BatchNorm1d),
-    'naiveSyncBN2d': ('bn', NaiveSyncBatchNorm2d),
-    'naiveSyncBN1d': ('bn', NaiveSyncBatchNorm1d),
-})

mmdet3d/ops/sparse_block.py

+from mmcv.cnn import build_norm_layer
 from torch import nn
 
 import mmdet3d.ops.spconv as spconv
 from mmdet.models.backbones.resnet import BasicBlock, Bottleneck
-from mmdet.ops import build_norm_layer
-from mmdet.ops.conv import conv_cfg
-
-conv_cfg.update({'SubMConv3d': spconv.SubMConv3d})
 
 
 def conv3x3(in_planes, out_planes, stride=1, indice_key=None):

mmdet3d/ops/spconv/conv.py

@@ -16,6 +16,7 @@ import math
 
 import numpy as np
 import torch
+from mmcv.cnn import CONV_LAYERS
 from torch.nn import init
 from torch.nn.parameter import Parameter
 
@@ -205,6 +206,7 @@ class SparseConvolution(SparseModule):
         return out_tensor
 
 
+@CONV_LAYERS.register_module()
 class SparseConv2d(SparseConvolution):
 
     def __init__(self,
@@ -230,6 +232,7 @@ class SparseConv2d(SparseConvolution):
             indice_key=indice_key)
 
 
+@CONV_LAYERS.register_module()
 class SparseConv3d(SparseConvolution):
 
     def __init__(self,
@@ -255,6 +258,7 @@ class SparseConv3d(SparseConvolution):
             indice_key=indice_key)
 
 
+@CONV_LAYERS.register_module()
 class SparseConv4d(SparseConvolution):
 
     def __init__(self,
@@ -280,6 +284,7 @@ class SparseConv4d(SparseConvolution):
             indice_key=indice_key)
 
 
+@CONV_LAYERS.register_module()
 class SparseConvTranspose2d(SparseConvolution):
 
     def __init__(self,
@@ -306,6 +311,7 @@ class SparseConvTranspose2d(SparseConvolution):
             indice_key=indice_key)
 
 
+@CONV_LAYERS.register_module()
 class SparseConvTranspose3d(SparseConvolution):
 
     def __init__(self,
@@ -332,6 +338,7 @@ class SparseConvTranspose3d(SparseConvolution):
             indice_key=indice_key)
 
 
+@CONV_LAYERS.register_module()
 class SparseInverseConv2d(SparseConvolution):
 
     def __init__(self,
@@ -350,6 +357,7 @@ class SparseInverseConv2d(SparseConvolution):
             indice_key=indice_key)
 
 
+@CONV_LAYERS.register_module()
 class SparseInverseConv3d(SparseConvolution):
 
     def __init__(self,
@@ -368,6 +376,7 @@ class SparseInverseConv3d(SparseConvolution):
             indice_key=indice_key)
 
 
+@CONV_LAYERS.register_module()
 class SubMConv2d(SparseConvolution):
 
     def __init__(self,
@@ -394,6 +403,7 @@ class SubMConv2d(SparseConvolution):
             indice_key=indice_key)
 
 
+@CONV_LAYERS.register_module()
 class SubMConv3d(SparseConvolution):
 
     def __init__(self,
@@ -420,6 +430,7 @@ class SubMConv3d(SparseConvolution):
             indice_key=indice_key)
 
 
+@CONV_LAYERS.register_module()
 class SubMConv4d(SparseConvolution):
 
     def __init__(self,