[Fix]: fix mmcv.model to mmengine.model (#1750)

mmdet3d/models/layers/norm.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import torch
-from mmcv.cnn import NORM_LAYERS
 from mmcv.runner import force_fp32
+from mmengine.registry import MODELS
 from torch import distributed as dist
 from torch import nn as nn
 from torch.autograd.function import Function
@@ -25,7 +25,7 @@ class AllReduce(Function):
         return grad_output
 
 
-@NORM_LAYERS.register_module('naiveSyncBN1d')
+@MODELS.register_module('naiveSyncBN1d')
 class NaiveSyncBatchNorm1d(nn.BatchNorm1d):
     """Synchronized Batch Normalization for 3D Tensors.
 
@@ -98,7 +98,7 @@ class NaiveSyncBatchNorm1d(nn.BatchNorm1d):
         return output
 
 
-@NORM_LAYERS.register_module('naiveSyncBN2d')
+@MODELS.register_module('naiveSyncBN2d')
 class NaiveSyncBatchNorm2d(nn.BatchNorm2d):
     """Synchronized Batch Normalization for 4D Tensors.
 

mmdet3d/models/layers/spconv/__init__.py

 # Copyright (c) OpenMMLab. All rights reserved.
-from .compat_spconv2 import register_spconv2
+from .overwrite_spconv import register_spconv2
 
 try:
     import spconv

mmdet3d/models/layers/spconv/compat_spconv2.py → mmdet3d/models/layers/spconv/overwrite_spconv/write_spconv2.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import itertools
 
-from mmcv.cnn.bricks.registry import CONV_LAYERS
+from mmengine.registry import MODELS
 from torch.nn.parameter import Parameter
 
 
@@ -17,47 +17,28 @@ def register_spconv2():
     except ImportError:
         return False
     else:
-        CONV_LAYERS._register_module(SparseConv2d, 'SparseConv2d', force=True)
-        CONV_LAYERS._register_module(SparseConv3d, 'SparseConv3d', force=True)
-        CONV_LAYERS._register_module(SparseConv4d, 'SparseConv4d', force=True)
+        MODELS._register_module(SparseConv2d, 'SparseConv2d', force=True)
+        MODELS._register_module(SparseConv3d, 'SparseConv3d', force=True)
+        MODELS._register_module(SparseConv4d, 'SparseConv4d', force=True)
 
-        CONV_LAYERS._register_module(
+        MODELS._register_module(
             SparseConvTranspose2d, 'SparseConvTranspose2d', force=True)
-        CONV_LAYERS._register_module(
+        MODELS._register_module(
             SparseConvTranspose3d, 'SparseConvTranspose3d', force=True)
 
-        CONV_LAYERS._register_module(
+        MODELS._register_module(
             SparseInverseConv2d, 'SparseInverseConv2d', force=True)
-        CONV_LAYERS._register_module(
+        MODELS._register_module(
             SparseInverseConv3d, 'SparseInverseConv3d', force=True)
 
-        CONV_LAYERS._register_module(SubMConv2d, 'SubMConv2d', force=True)
-        CONV_LAYERS._register_module(SubMConv3d, 'SubMConv3d', force=True)
-        CONV_LAYERS._register_module(SubMConv4d, 'SubMConv4d', force=True)
+        MODELS._register_module(SubMConv2d, 'SubMConv2d', force=True)
+        MODELS._register_module(SubMConv3d, 'SubMConv3d', force=True)
+        MODELS._register_module(SubMConv4d, 'SubMConv4d', force=True)
+        SparseModule._version = 2
         SparseModule._load_from_state_dict = _load_from_state_dict
-        SparseModule._save_to_state_dict = _save_to_state_dict
         return True
 
 
-def _save_to_state_dict(self, destination, prefix, keep_vars):
-    """Rewrite this func to compat the convolutional kernel weights between
-    spconv 1.x in MMCV and 2.x in spconv2.x.
-
-    Kernel weights in MMCV spconv has shape in (D,H,W,in_channel,out_channel) ,
-    while those in spcon2.x is in (out_channel,D,H,W,in_channel).
-    """
-    for name, param in self._parameters.items():
-        if param is not None:
-            param = param if keep_vars else param.detach()
-            if name == 'weight':
-                dims = list(range(1, len(param.shape))) + [0]
-                param = param.permute(*dims)
-            destination[prefix + name] = param
-    for name, buf in self._buffers.items():
-        if buf is not None and name not in self._non_persistent_buffers_set:
-            destination[prefix + name] = buf if keep_vars else buf.detach()
-
-
 def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
                           missing_keys, unexpected_keys, error_msgs):
     """Rewrite this func to compat the convolutional kernel weights between
@@ -66,6 +47,7 @@ def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
     Kernel weights in MMCV spconv has shape in (D,H,W,in_channel,out_channel) ,
     while those in spcon2.x is in (out_channel,D,H,W,in_channel).
     """
+    version = local_metadata.get('version', None)
     for hook in self._load_state_dict_pre_hooks.values():
         hook(state_dict, prefix, local_metadata, strict, missing_keys,
              unexpected_keys, error_msgs)
@@ -83,9 +65,10 @@ def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
             # 0.3.* to version 0.4+
             if len(param.shape) == 0 and len(input_param.shape) == 1:
                 input_param = input_param[0]
-            dims = [len(input_param.shape) - 1] + list(
-                range(len(input_param.shape) - 1))
-            input_param = input_param.permute(*dims)
+            if version != 2:
+                dims = [len(input_param.shape) - 1] + list(
+                    range(len(input_param.shape) - 1))
+                input_param = input_param.permute(*dims)
             if input_param.shape != param.shape:
                 # local shape should match the one in checkpoint
                 error_msgs.append(

mmdet3d/models/layers/transformer.py

 # Copyright (c) OpenMMLab. All rights reserved.
-from mmcv.cnn.bricks.registry import ATTENTION
-from mmcv.cnn.bricks.transformer import POSITIONAL_ENCODING, MultiheadAttention
+from mmcv.cnn.bricks.transformer import MultiheadAttention
+from mmengine.registry import MODELS
 from torch import nn as nn
 
 
-@ATTENTION.register_module()
+@MODELS.register_module()
 class GroupFree3DMHA(MultiheadAttention):
     """A warpper for torch.nn.MultiheadAttention for GroupFree3D.
 
@@ -108,7 +108,7 @@ class GroupFree3DMHA(MultiheadAttention):
             **kwargs)
 
 
-@POSITIONAL_ENCODING.register_module()
+@MODELS.register_module()
 class ConvBNPositionalEncoding(nn.Module):
     """Absolute position embedding with Conv learning.