fix basemodule and init_weights (#1714)

mmdet3d/models/backbones/base_pointnet.py

@@ -2,7 +2,7 @@
 import warnings
 from abc import ABCMeta
 
-from mmcv.runner import BaseModule
+from mmengine.model import BaseModule
 
 
 class BasePointNet(BaseModule, metaclass=ABCMeta):

mmdet3d/models/backbones/dgcnn.py

 # Copyright (c) OpenMMLab. All rights reserved.
-from mmcv.runner import BaseModule, auto_fp16
+from mmengine.model import BaseModule
 from torch import nn as nn
 
 from mmdet3d.models.layers import DGCNNFAModule, DGCNNGFModule
@@ -71,7 +71,6 @@ class DGCNNBackbone(BaseModule):
         self.FA_module = DGCNNFAModule(
             mlp_channels=cur_fa_mlps, act_cfg=act_cfg)
 
-    @auto_fp16(apply_to=('points', ))
     def forward(self, points):
         """Forward pass.
 

mmdet3d/models/backbones/dla.py

@@ -3,7 +3,7 @@ import warnings
 
 import torch
 from mmcv.cnn import build_conv_layer, build_norm_layer
-from mmcv.runner import BaseModule
+from mmengine.model import BaseModule
 from torch import nn
 
 from mmdet3d.registry import MODELS

mmdet3d/models/backbones/multi_backbone.py

@@ -4,7 +4,7 @@ import warnings
 
 import torch
 from mmcv.cnn import ConvModule
-from mmcv.runner import BaseModule, auto_fp16
+from mmengine.model import BaseModule
 from torch import nn as nn
 
 from mmdet3d.models.builder import build_backbone
@@ -90,7 +90,6 @@ class MultiBackbone(BaseModule):
                           'please use "init_cfg" instead')
             self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
 
-    @auto_fp16()
     def forward(self, points):
         """Forward pass.
 

mmdet3d/models/backbones/second.py

@@ -2,7 +2,7 @@
 import warnings
 
 from mmcv.cnn import build_conv_layer, build_norm_layer
-from mmcv.runner import BaseModule
+from mmengine.model import BaseModule
 from torch import nn as nn
 
 from mmdet3d.registry import MODELS

mmdet3d/models/decode_heads/decode_head.py

@@ -3,8 +3,8 @@ from abc import ABCMeta, abstractmethod
 from typing import List
 
 import torch
-from mmcv.cnn import normal_init
-from mmcv.runner import BaseModule, auto_fp16
+from mmengine.model import BaseModule
+from mmengine.model.utils import normal_init
 from torch import Tensor
 from torch import nn as nn
 
@@ -94,7 +94,6 @@ class Base3DDecodeHead(BaseModule, metaclass=ABCMeta):
         super().init_weights()
         normal_init(self.conv_seg, mean=0, std=0.01)
 
-    @auto_fp16()
     @abstractmethod
     def forward(self, feats_dict: dict):
         """Placeholder of forward function."""

mmdet3d/models/dense_heads/anchor_free_mono3d_head.py

@@ -3,7 +3,8 @@ from abc import abstractmethod
 from typing import Any, List, Sequence, Tuple, Union
 
 import torch
-from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
+from mmcv.cnn import ConvModule
+from mmengine.model.utils import bias_init_with_prob, normal_init
 from torch import Tensor
 from torch import nn as nn
 

mmdet3d/models/dense_heads/base_3d_dense_head.py

@@ -4,10 +4,10 @@ from typing import List, Optional, Tuple
 
 import numpy as np
 import torch
-from mmcv.cnn.utils.weight_init import constant_init
 from mmengine.config import ConfigDict
 from mmengine.data import InstanceData
 from mmengine.model import BaseModule
+from mmengine.model.utils import constant_init
 from torch import Tensor
 
 from mmdet3d.models.layers import box3d_multiclass_nms

mmdet3d/models/dense_heads/base_conv_bbox_head.py

 # Copyright (c) OpenMMLab. All rights reserved.
 from mmcv.cnn import ConvModule
 from mmcv.cnn.bricks import build_conv_layer
-from mmcv.runner import BaseModule
+from mmengine.model import BaseModule
 from torch import nn as nn
 
 from mmdet3d.registry import MODELS

mmdet3d/models/dense_heads/base_mono3d_dense_head.py

@@ -2,8 +2,8 @@
 from abc import ABCMeta, abstractmethod
 from typing import Optional, Tuple
 
-from mmcv.runner import BaseModule
 from mmengine.config import ConfigDict
+from mmengine.model import BaseModule
 from torch import Tensor
 
 from mmdet3d.structures.det3d_data_sample import SampleList

mmdet3d/models/dense_heads/centerpoint_head.py

@@ -4,8 +4,8 @@ from typing import Dict, List, Optional, Tuple, Union
 
 import torch
 from mmcv.cnn import ConvModule, build_conv_layer
-from mmcv.runner import BaseModule, force_fp32
 from mmengine import InstanceData
+from mmengine.model import BaseModule
 from torch import Tensor, nn
 
 from mmdet3d.models.utils import (clip_sigmoid, draw_heatmap_gaussian,
@@ -608,7 +608,6 @@ class CenterHead(BaseModule):
         losses = self.loss_by_feat(outs, batch_gt_instance_3d)
         return losses
 
-    @force_fp32(apply_to=('preds_dicts'))
     def loss_by_feat(self, preds_dicts: Tuple[List[dict]],
                      batch_gt_instances_3d: List[InstanceData], *args,
                      **kwargs):

mmdet3d/models/dense_heads/fcos_mono3d_head.py

@@ -3,8 +3,9 @@ from typing import List, Optional, Sequence, Tuple
 
 import numpy as np
 import torch
-from mmcv.cnn import Scale, normal_init
+from mmcv.cnn import Scale
 from mmengine.data import InstanceData
+from mmengine.model.utils import normal_init
 from torch import Tensor
 from torch import nn as nn
 

mmdet3d/models/dense_heads/groupfree3d_head.py

@@ -4,13 +4,14 @@ from typing import Dict, List, Optional, Tuple
 
 import numpy as np
 import torch
-from mmcv.cnn import ConvModule, xavier_init
+from mmcv.cnn import ConvModule
 from mmcv.cnn.bricks.transformer import (build_positional_encoding,
                                          build_transformer_layer)
 from mmcv.ops import PointsSampler as Points_Sampler
 from mmcv.ops import gather_points
-from mmcv.runner import BaseModule
 from mmengine import InstanceData
+from mmengine.model import BaseModule
+from mmengine.model.utils import xavier_init
 from torch import Tensor
 from torch import nn as nn
 from torch.nn import functional as F

mmdet3d/models/dense_heads/monoflex_head.py

@@ -2,9 +2,9 @@
 from typing import List, Optional, Tuple, Union
 
 import torch
-from mmcv.cnn import xavier_init
 from mmengine.config import ConfigDict
 from mmengine.data import InstanceData
+from mmengine.model.utils import xavier_init
 from torch import Tensor
 from torch import nn as nn
 

mmdet3d/models/dense_heads/pgd_head.py

@@ -3,8 +3,9 @@ from typing import List, Optional, Tuple
 
 import numpy as np
 import torch
-from mmcv.cnn import Scale, bias_init_with_prob, normal_init
+from mmcv.cnn import Scale
 from mmengine.data import InstanceData
+from mmengine.model.utils import bias_init_with_prob, normal_init
 from torch import Tensor
 from torch import nn as nn
 from torch.nn import functional as F

mmdet3d/models/dense_heads/point_rpn_head.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import torch
-from mmcv.runner import BaseModule, force_fp32
+from mmengine.model import BaseModule
 from torch import nn as nn
 
 from mmdet3d.models.builder import build_loss
@@ -124,7 +124,6 @@ class PointRPNHead(BaseModule):
             batch_size, -1, self._get_reg_out_channels())
         return point_box_preds, point_cls_preds
 
-    @force_fp32(apply_to=('bbox_preds'))
     def loss(self,
              bbox_preds,
              cls_preds,

mmdet3d/models/dense_heads/vote_head.py

@@ -4,8 +4,8 @@ from typing import Dict, List, Optional, Tuple, Union
 import numpy as np
 import torch
 from mmcv.ops import furthest_point_sample
-from mmcv.runner import BaseModule
 from mmengine import ConfigDict, InstanceData
+from mmengine.model import BaseModule
 from torch import Tensor
 from torch.nn import functional as F
 

mmdet3d/models/layers/dgcnn_modules/dgcnn_fa_module.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import torch
 from mmcv.cnn import ConvModule
-from mmcv.runner import BaseModule, force_fp32
+from mmengine.model import BaseModule
 from torch import nn as nn
 
 
@@ -39,7 +39,6 @@ class DGCNNFAModule(BaseModule):
                     norm_cfg=norm_cfg,
                     act_cfg=act_cfg))
 
-    @force_fp32()
     def forward(self, points):
         """forward.
 

mmdet3d/models/layers/dgcnn_modules/dgcnn_fp_module.py

 # Copyright (c) OpenMMLab. All rights reserved.
 from mmcv.cnn import ConvModule
-from mmcv.runner import BaseModule, force_fp32
+from mmengine.model import BaseModule
 from torch import nn as nn
 
 
@@ -38,7 +38,6 @@ class DGCNNFPModule(BaseModule):
                     norm_cfg=norm_cfg,
                     act_cfg=act_cfg))
 
-    @force_fp32()
     def forward(self, points):
         """forward.
 

mmdet3d/models/layers/edge_fusion_module.py

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