[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/task_modules/voxel/voxel_generator.py

@@ -2,7 +2,10 @@
 import numba
 import numpy as np
 
+from mmdet3d.registry import MODELS
 
+
+@MODELS.register_module()
 class VoxelGenerator(object):
     """Voxel generator in numpy implementation.
 

mmdet3d/models/voxel_encoders/pillar_encoder.py

@@ -2,7 +2,6 @@
 import torch
 from mmcv.cnn import build_norm_layer
 from mmcv.ops import DynamicScatter
-from mmcv.runner import force_fp32
 from torch import nn
 
 from mmdet3d.registry import MODELS
@@ -90,7 +89,6 @@ class PillarFeatureNet(nn.Module):
         self.z_offset = self.vz / 2 + point_cloud_range[2]
         self.point_cloud_range = point_cloud_range
 
-    @force_fp32(out_fp16=True)
     def forward(self, features, num_points, coors, *args, **kwargs):
         """Forward function.
 
@@ -272,7 +270,6 @@ class DynamicPillarFeatureNet(PillarFeatureNet):
         center_per_point = canvas[:, voxel_index.long()].t()
         return center_per_point
 
-    @force_fp32(out_fp16=True)
     def forward(self, features, coors):
         """Forward function.
 

mmdet3d/models/voxel_encoders/utils.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import torch
 from mmcv.cnn import build_norm_layer
-from mmcv.runner import auto_fp16
 from torch import nn
 from torch.nn import functional as F
 
@@ -61,7 +60,6 @@ class VFELayer(nn.Module):
         self.norm = build_norm_layer(norm_cfg, out_channels)[1]
         self.linear = nn.Linear(in_channels, out_channels, bias=False)
 
-    @auto_fp16(apply_to=('inputs'), out_fp32=True)
     def forward(self, inputs):
         """Forward function.
 
@@ -142,7 +140,6 @@ class PFNLayer(nn.Module):
         assert mode in ['max', 'avg']
         self.mode = mode
 
-    @auto_fp16(apply_to=('inputs'), out_fp32=True)
     def forward(self, inputs, num_voxels=None, aligned_distance=None):
         """Forward function.
 

mmdet3d/models/voxel_encoders/voxel_encoder.py

@@ -2,7 +2,6 @@
 import torch
 from mmcv.cnn import build_norm_layer
 from mmcv.ops import DynamicScatter
-from mmcv.runner import force_fp32
 from torch import Tensor, nn
 
 from mmdet3d.registry import MODELS
@@ -25,7 +24,6 @@ class HardSimpleVFE(nn.Module):
         self.num_features = num_features
         self.fp16_enabled = False
 
-    @force_fp32(out_fp16=True)
     def forward(self, features: Tensor, num_points: Tensor, coors: Tensor,
                 *args, **kwargs) -> Tensor:
         """Forward function.
@@ -66,7 +64,6 @@ class DynamicSimpleVFE(nn.Module):
         self.fp16_enabled = False
 
     @torch.no_grad()
-    @force_fp32(out_fp16=True)
     def forward(self, features, coors, *args, **kwargs):
         """Forward function.
 
@@ -386,7 +383,6 @@ class HardVFE(nn.Module):
         if fusion_layer is not None:
             self.fusion_layer = builder.build_fusion_layer(fusion_layer)
 
-    @force_fp32(out_fp16=True)
     def forward(self,
                 features,
                 num_points,

mmdet3d/structures/det3d_data_sample.py

@@ -2,7 +2,7 @@
 from typing import Dict, List, Optional, Tuple, Union
 
 import torch
-from mmengine.data import InstanceData
+from mmengine.structures import InstanceData
 
 from mmdet.structures import DetDataSample
 from .point_data import PointData
@@ -55,7 +55,7 @@ class Det3DDataSample(DetDataSample):
             evaluator and do the online evaluation.
 
     Examples:
-    >>> from mmengine.data import InstanceData
+    >>> from mmengine.structures import InstanceData
 
     >>> from mmdet3d.structures import Det3DDataSample
     >>> from mmdet3d.structures import BaseInstance3DBoxes

mmdet3d/structures/point_data.py

@@ -4,7 +4,7 @@ from typing import Union
 
 import numpy as np
 import torch
-from mmengine.data import BaseDataElement
+from mmengine.structures import BaseDataElement
 
 IndexType = Union[str, slice, int, list, torch.LongTensor,
                   torch.cuda.LongTensor, torch.BoolTensor,

mmdet3d/utils/collect_env.py

 # Copyright (c) OpenMMLab. All rights reserved.
-from mmcv.utils import collect_env as collect_base_env
-from mmcv.utils import get_git_hash
+from mmengine.utils import get_git_hash
+from mmengine.utils.dl_utils import collect_env as collect_base_env
 
 import mmdet
 import mmdet3d

mmdet3d/utils/compat_cfg.py

@@ -2,7 +2,7 @@
 import copy
 import warnings
 
-from mmcv import ConfigDict
+from mmengine import ConfigDict
 
 
 def compat_cfg(cfg):

mmdet3d/utils/typing.py

@@ -3,9 +3,9 @@
 from typing import List, Optional, Union
 
 from mmengine.config import ConfigDict
-from mmengine.data import InstanceData
+from mmengine.structures import InstanceData
 
-from mmdet.models.task_modules import SamplingResult
+from mmdet.models.task_modules.samplers import SamplingResult
 
 # Type hint of config data
 ConfigType = Union[ConfigDict, dict]

mmdet3d/visualization/local_visualizer.py

@@ -17,7 +17,7 @@ except ImportError:
     raise ImportError(
         'Please run "pip install open3d" to install open3d first.')
 
-from mmengine.data import InstanceData
+from mmengine.structures import InstanceData
 from mmengine.visualization.utils import check_type, tensor2ndarray
 
 from mmdet3d.registry import VISUALIZERS
@@ -67,7 +67,7 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
     Examples:
         >>> import numpy as np
         >>> import torch
-        >>> from mmengine.data import InstanceData
+        >>> from mmengine.structures import InstanceData
         >>> from mmdet3d.structures import Det3DDataSample
         >>> from mmdet3d.visualization import Det3DLocalVisualizer
 

tests/test_datasets/test_kitti_dataset.py

@@ -3,8 +3,8 @@
 import numpy as np
 import torch
 from mmcv.transforms.base import BaseTransform
-from mmengine.data import InstanceData
 from mmengine.registry import TRANSFORMS
+from mmengine.structures import InstanceData
 
 from mmdet3d.datasets import KittiDataset
 from mmdet3d.structures import Det3DDataSample, LiDARInstance3DBoxes

tests/test_datasets/test_lyft_dataset.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import numpy as np
 from mmcv.transforms.base import BaseTransform
-from mmengine.data import InstanceData
 from mmengine.registry import TRANSFORMS
+from mmengine.structures import InstanceData
 
 from mmdet3d.datasets import LyftDataset
 from mmdet3d.structures import Det3DDataSample, LiDARInstance3DBoxes

tests/test_datasets/test_nuscenes_dataset.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import numpy as np
 from mmcv.transforms.base import BaseTransform
-from mmengine.data import InstanceData
 from mmengine.registry import TRANSFORMS
+from mmengine.structures import InstanceData
 
 from mmdet3d.datasets import NuScenesDataset
 from mmdet3d.structures import Det3DDataSample, LiDARInstance3DBoxes

tests/test_evaluation/test_metrics/test_kitti_metric.py

 import numpy as np
 import pytest
 import torch
-from mmengine.data import InstanceData
+from mmengine.structures import InstanceData
 
 from mmdet3d.evaluation.metrics import KittiMetric
 from mmdet3d.structures import Det3DDataSample, LiDARInstance3DBoxes

tests/test_models/test_dense_heads/test_anchor3d_head.py

@@ -3,7 +3,7 @@ from unittest import TestCase
 
 import torch
 from mmengine import Config
-from mmengine.data import InstanceData
+from mmengine.structures import InstanceData
 
 from mmdet3d import *  # noqa
 from mmdet3d.models.dense_heads import Anchor3DHead

tests/test_models/test_dense_heads/test_fcos_mono3d_head.py

 # Copyright (c) OpenMMLab. All rights reserved.
 from unittest import TestCase
 
-import mmcv
+import mmengine
 import numpy as np
 import torch
-from mmengine.data import InstanceData
+from mmengine.structures import InstanceData
 
 from mmdet3d.models.dense_heads import FCOSMono3DHead
 from mmdet3d.structures import CameraInstance3DBoxes
@@ -39,8 +39,8 @@ class TestFCOSMono3DHead(TestCase):
             min_bbox_size=0,
             max_per_img=200)
 
-        train_cfg = mmcv.Config(train_cfg)
-        test_cfg = mmcv.Config(test_cfg)
+        train_cfg = mmengine.Config(train_cfg)
+        test_cfg = mmengine.Config(test_cfg)
 
         fcos_mono3d_head = FCOSMono3DHead(
             num_classes=10,

tests/test_models/test_dense_heads/test_pgd_head.py

 # Copyright (c) OpenMMLab. All rights reserved.
 from unittest import TestCase
 
-import mmcv
+import mmengine
 import numpy as np
 import torch
-from mmengine.data import InstanceData
+from mmengine.structures import InstanceData
 
 from mmdet3d.models.dense_heads import PGDHead
 from mmdet3d.structures import CameraInstance3DBoxes
@@ -40,8 +40,8 @@ class TestFGDHead(TestCase):
             min_bbox_size=0,
             max_per_img=20)
 
-        train_cfg = mmcv.Config(train_cfg)
-        test_cfg = mmcv.Config(test_cfg)
+        train_cfg = mmengine.Config(train_cfg)
+        test_cfg = mmengine.Config(test_cfg)
 
         pgd_head = PGDHead(
             num_classes=3,

tests/test_models/test_dense_heads/test_smoke_mono3d_head.py

@@ -3,7 +3,7 @@ from unittest import TestCase
 
 import numpy as np
 import torch
-from mmengine.data import InstanceData
+from mmengine.structures import InstanceData
 
 from mmdet3d.models.dense_heads import SMOKEMono3DHead
 from mmdet3d.structures import CameraInstance3DBoxes

tests/test_structures/test_det3d_data_sample.py

@@ -4,7 +4,7 @@ from unittest import TestCase
 import numpy as np
 import pytest
 import torch
-from mmengine.data import InstanceData
+from mmengine.structures import InstanceData
 
 from mmdet3d.structures import Det3DDataSample, PointData
 

tests/utils/model_utils.py

@@ -37,7 +37,7 @@ def _get_config_directory():
 
 def _get_config_module(fname):
     """Load a configuration as a python module."""
-    from mmcv import Config
+    from mmengine import Config
     config_dpath = _get_config_directory()
     config_fpath = join(config_dpath, fname)
     config_mod = Config.fromfile(config_fpath)
@@ -62,11 +62,11 @@ def _get_detector_cfg(fname):
     These are deep copied to allow for safe modification of parameters without
     influencing other tests.
     """
-    import mmcv
+    import mmengine
     config = _get_config_module(fname)
     model = copy.deepcopy(config.model)
-    train_cfg = mmcv.Config(copy.deepcopy(config.model.train_cfg))
-    test_cfg = mmcv.Config(copy.deepcopy(config.model.test_cfg))
+    train_cfg = mmengine.Config(copy.deepcopy(config.model.train_cfg))
+    test_cfg = mmengine.Config(copy.deepcopy(config.model.test_cfg))
 
     model.update(train_cfg=train_cfg)
     model.update(test_cfg=test_cfg)