[Fix] replace mmcv's function and modules imported with mmengine's and sync...

[Fix] replace mmcv's function and modules imported with mmengine's and sync the latest mmengine (#1765)

* replace mmcv to mmengine

* fix

* fix comments

mmdet3d/models/detectors/sassd.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import torch
 from mmcv.ops import Voxelization
-from mmcv.runner import force_fp32
 from torch.nn import functional as F
 
 from mmdet3d.models.test_time_augs import merge_aug_bboxes_3d
@@ -52,7 +51,6 @@ class SASSD(SingleStage3DDetector):
         return x, point_misc
 
     @torch.no_grad()
-    @force_fp32()
     def voxelize(self, points):
         """Apply hard voxelization to points."""
         voxels, coors, num_points = [], [], []

mmdet3d/models/detectors/votenet.py

 # Copyright (c) OpenMMLab. All rights reserved.
 from typing import Dict, List, Optional, Union
 
-from mmengine import InstanceData
+from mmengine.structures import InstanceData
 from torch import Tensor
 
 from mmdet3d.registry import MODELS

mmdet3d/models/layers/norm.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import torch
-from mmcv.runner import force_fp32
 from mmengine.registry import MODELS
 from torch import distributed as dist
 from torch import nn as nn
@@ -48,10 +47,6 @@ class NaiveSyncBatchNorm1d(nn.BatchNorm1d):
         super().__init__(*args, **kwargs)
         self.fp16_enabled = False
 
-    # customized normalization layer still needs this decorator
-    # to force the input to be fp32 and the output to be fp16
-    # TODO: make mmcv fp16 utils handle customized norm layers
-    @force_fp32(out_fp16=True)
     def forward(self, input):
         """
         Args:
@@ -121,10 +116,6 @@ class NaiveSyncBatchNorm2d(nn.BatchNorm2d):
         super().__init__(*args, **kwargs)
         self.fp16_enabled = False
 
-    # customized normalization layer still needs this decorator
-    # to force the input to be fp32 and the output to be fp16
-    # TODO: make mmcv fp16 utils handle customized norm layers
-    @force_fp32(out_fp16=True)
     def forward(self, input):
         """
         Args:

mmdet3d/models/layers/paconv/paconv.py

@@ -4,7 +4,7 @@ import copy
 import torch
 from mmcv.cnn import ConvModule, build_activation_layer, build_norm_layer
 from mmcv.ops import assign_score_withk as assign_score_cuda
-from mmengine.model.utils import constant_init
+from mmengine.model import constant_init
 from torch import nn as nn
 from torch.nn import functional as F
 

mmdet3d/models/layers/pointnet_modules/builder.py

 # Copyright (c) OpenMMLab. All rights reserved.
-from mmcv.utils import Registry
+from mmengine.registry import Registry
 
 SA_MODULES = Registry('point_sa_module')
 

mmdet3d/models/layers/transformer.py

@@ -20,7 +20,7 @@ class GroupFree3DMHA(MultiheadAttention):
         proj_drop (float, optional): A Dropout layer. Defaults to 0.0.
         dropout_layer (obj:`ConfigDict`, optional): The dropout_layer used
             when adding the shortcut.
-        init_cfg (obj:`mmcv.ConfigDict`, optional): The Config for
+        init_cfg (obj:`mmengine.ConfigDict`, optional): The Config for
             initialization. Default: None.
         batch_first (bool, optional): Key, Query and Value are shape of
             (batch, n, embed_dim)

mmdet3d/models/layers/vote_module.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import torch
-from mmcv import is_tuple_of
 from mmcv.cnn import ConvModule
+from mmengine import is_tuple_of
 from torch import nn as nn
 
 from mmdet3d.models.builder import build_loss

mmdet3d/models/middle_encoders/pillar_scatter.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import torch
-from mmcv.runner import auto_fp16
 from torch import nn
 
 from mmdet3d.registry import MODELS
@@ -25,7 +24,6 @@ class PointPillarsScatter(nn.Module):
         self.in_channels = in_channels
         self.fp16_enabled = False
 
-    @auto_fp16(apply_to=('voxel_features', ))
     def forward(self, voxel_features, coors, batch_size=None):
         """Foraward function to scatter features."""
         # TODO: rewrite the function in a batch manner

mmdet3d/models/middle_encoders/sparse_encoder.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import torch
 from mmcv.ops import points_in_boxes_all, three_interpolate, three_nn
-from mmcv.runner import auto_fp16
 from torch import nn as nn
 
 from mmdet3d.models.layers import SparseBasicBlock, make_sparse_convmodule
@@ -104,7 +103,6 @@ class SparseEncoder(nn.Module):
             indice_key='spconv_down2',
             conv_type='SparseConv3d')
 
-    @auto_fp16(apply_to=('voxel_features', ))
     def forward(self, voxel_features, coors, batch_size):
         """Forward of SparseEncoder.
 
@@ -266,7 +264,6 @@ class SparseEncoderSASSD(SparseEncoder):
         self.point_cls = nn.Linear(64, 1, bias=False)
         self.point_reg = nn.Linear(64, 3, bias=False)
 
-    @auto_fp16(apply_to=('voxel_features', ))
     def forward(self, voxel_features, coors, batch_size, test_mode=False):
         """Forward of SparseEncoder.
 

mmdet3d/models/roi_heads/bbox_heads/h3d_bbox_head.py

@@ -3,8 +3,8 @@ from typing import Dict, List, Optional, Tuple
 
 import torch
 from mmcv.cnn import ConvModule
-from mmengine import InstanceData
 from mmengine.model import BaseModule
+from mmengine.structures import InstanceData
 from torch import Tensor
 from torch import nn as nn
 from torch.nn import functional as F

mmdet3d/models/roi_heads/bbox_heads/parta2_bbox_head.py

@@ -4,8 +4,8 @@ from typing import List
 import numpy as np
 import torch
 from mmcv.cnn import ConvModule
-from mmengine.data import InstanceData
-from mmengine.model.utils import normal_init
+from mmengine.model import normal_init
+from mmengine.structures import InstanceData
 from torch import Tensor
 
 from mmdet3d.models import make_sparse_convmodule

mmdet3d/models/roi_heads/bbox_heads/point_rcnn_bbox_head.py

@@ -3,8 +3,7 @@ import numpy as np
 import torch
 from mmcv.cnn import ConvModule
 from mmcv.cnn.bricks import build_conv_layer
-from mmengine.model import BaseModule
-from mmengine.model.utils import normal_init
+from mmengine.model import BaseModule, normal_init
 from torch import nn as nn
 
 from mmdet3d.models.layers import nms_bev, nms_normal_bev

mmdet3d/models/roi_heads/h3d_roi_head.py

 # Copyright (c) OpenMMLab. All rights reserved.
 from typing import Dict, List
 
-from mmengine import InstanceData
+from mmengine.structures import InstanceData
 from torch import Tensor
 
 from mmdet3d.registry import MODELS

mmdet3d/models/roi_heads/mask_heads/primitive_head.py

@@ -4,8 +4,8 @@ from typing import Dict, List, Optional
 import torch
 from mmcv.cnn import ConvModule
 from mmcv.ops import furthest_point_sample
-from mmengine import InstanceData
 from mmengine.model import BaseModule
+from mmengine.structures import InstanceData
 from torch import nn as nn
 from torch.nn import functional as F
 

mmdet3d/models/roi_heads/part_aggregation_roi_head.py

 # Copyright (c) OpenMMLab. All rights reserved.
 from typing import Dict, List, Tuple
 
-from mmcv import ConfigDict
+from mmengine import ConfigDict
 from torch import Tensor
 from torch.nn import functional as F
 

mmdet3d/models/task_modules/anchor/anchor_3d_generator.py

 # Copyright (c) OpenMMLab. All rights reserved.
-import mmcv
+import mmengine
 import torch
 
 from mmdet3d.registry import TASK_UTILS
@@ -44,7 +44,7 @@ class Anchor3DRangeGenerator(object):
                  custom_values=(),
                  reshape_out=True,
                  size_per_range=True):
-        assert mmcv.is_list_of(ranges, list)
+        assert mmengine.is_list_of(ranges, list)
         if size_per_range:
             if len(sizes) != len(ranges):
                 assert len(ranges) == 1
@@ -52,7 +52,7 @@ class Anchor3DRangeGenerator(object):
             assert len(ranges) == len(sizes)
         else:
             assert len(ranges) == 1
-        assert mmcv.is_list_of(sizes, list)
+        assert mmengine.is_list_of(sizes, list)
         assert isinstance(scales, list)
 
         self.sizes = sizes

mmdet3d/models/task_modules/assigners/max_3d_iou_assigner.py

 # Copyright (c) OpenMMLab. All rights reserved.
 from typing import Optional, Union
 
-from mmengine.data import InstanceData
+from mmengine.structures import InstanceData
 
 from mmdet3d.registry import TASK_UTILS
 from mmdet.models.task_modules import AssignResult, MaxIoUAssigner
@@ -101,7 +101,7 @@ class Max3DIoUAssigner(MaxIoUAssigner):
             :obj:`AssignResult`: The assign result.
 
         Example:
-            >>> from mmengine.data import InstanceData
+            >>> from mmengine.structures import InstanceData
             >>> self = MaxIoUAssigner(0.5, 0.5)
             >>> pred_instances = InstanceData()
             >>> pred_instances.priors = torch.Tensor([[0, 0, 10, 10],

mmdet3d/models/task_modules/samplers/pseudosample.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import torch
-from mmengine.data import InstanceData
+from mmengine.structures import InstanceData
 
 from mmdet3d.registry import TASK_UTILS
 from mmdet.models.task_modules import AssignResult

mmdet3d/models/task_modules/voxel/__init__.py

 # Copyright (c) OpenMMLab. All rights reserved.
-from .builder import build_voxel_generator
 from .voxel_generator import VoxelGenerator
 
-__all__ = ['build_voxel_generator', 'VoxelGenerator']
+__all__ = ['VoxelGenerator']

mmdet3d/models/task_modules/voxel/builder.py

-# Copyright (c) OpenMMLab. All rights reserved.
-import mmcv
-
-from . import voxel_generator
-
-
-def build_voxel_generator(cfg, **kwargs):
-    """Builder of voxel generator."""
-    if isinstance(cfg, voxel_generator.VoxelGenerator):
-        return cfg
-    elif isinstance(cfg, dict):
-        return mmcv.runner.obj_from_dict(
-            cfg, voxel_generator, default_args=kwargs)
-    else:
-        raise TypeError('Invalid type {} for building a sampler'.format(
-            type(cfg)))