Reformat docstrings in code

mmdet3d/models/roi_heads/roi_extractors/single_roiaware_extractor.py

 import torch
-import torch.nn as nn
+from torch import nn as nn
 
 from mmdet3d import ops
 from mmdet.models.builder import ROI_EXTRACTORS
@@ -7,7 +7,7 @@ from mmdet.models.builder import ROI_EXTRACTORS
 
 @ROI_EXTRACTORS.register_module()
 class Single3DRoIAwareExtractor(nn.Module):
-    """Point-wise roi-aware Extractor
+    """Point-wise roi-aware Extractor.
 
     Extract Point-wise roi features.
 
@@ -29,7 +29,7 @@ class Single3DRoIAwareExtractor(nn.Module):
         return roi_layers
 
     def forward(self, feats, coordinate, batch_inds, rois):
-        """Extract point-wise roi features
+        """Extract point-wise roi features.
 
         Args:
             feats (FloatTensor): point-wise features with

mmdet3d/models/voxel_encoders/pillar_encoder.py

@@ -83,7 +83,7 @@ class PillarFeatureNet(nn.Module):
         self.point_cloud_range = point_cloud_range
 
     def forward(self, features, num_points, coors):
-        """Forward function
+        """Forward function.
 
         Args:
             features (torch.Tensor): Point features or raw points in shape
@@ -136,7 +136,7 @@ class PillarFeatureNet(nn.Module):
 
 @VOXEL_ENCODERS.register_module()
 class DynamicPillarFeatureNet(PillarFeatureNet):
-    """Pillar Feature Net using dynamic voxelization
+    """Pillar Feature Net using dynamic voxelization.
 
     The network prepares the pillar features and performs forward pass
     through PFNLayers. The main difference is that it is used for
@@ -205,7 +205,7 @@ class DynamicPillarFeatureNet(PillarFeatureNet):
             voxel_size, point_cloud_range, average_points=True)
 
     def map_voxel_center_to_point(self, pts_coors, voxel_mean, voxel_coors):
-        """Map the centers of voxels to its corresponding points
+        """Map the centers of voxels to its corresponding points.
 
         Args:
             pts_coors (torch.Tensor): The coordinates of each points, shape
@@ -244,7 +244,7 @@ class DynamicPillarFeatureNet(PillarFeatureNet):
         return center_per_point
 
     def forward(self, features, coors):
-        """Forward function
+        """Forward function.
 
         Args:
             features (torch.Tensor): Point features or raw points in shape

mmdet3d/models/voxel_encoders/utils.py

@@ -28,7 +28,7 @@ def get_paddings_indicator(actual_num, max_num, axis=0):
 
 
 class VFELayer(nn.Module):
-    """ Voxel Feature Encoder layer.
+    """Voxel Feature Encoder layer.
 
     The voxel encoder is composed of a series of these layers.
     This module do not support average pooling and only support to use
@@ -59,7 +59,7 @@ class VFELayer(nn.Module):
         self.linear = nn.Linear(in_channels, out_channels, bias=False)
 
     def forward(self, inputs):
-        """Forward function
+        """Forward function.
 
         Args:
             inputs (torch.Tensor): Voxels features of shape (N, M, C).
@@ -100,7 +100,7 @@ class VFELayer(nn.Module):
 
 
 class PFNLayer(nn.Module):
-    """ Pillar Feature Net Layer.
+    """Pillar Feature Net Layer.
 
     The Pillar Feature Net is composed of a series of these layers, but the
     PointPillars paper results only used a single PFNLayer.
@@ -136,7 +136,7 @@ class PFNLayer(nn.Module):
         self.mode = mode
 
     def forward(self, inputs, num_voxels=None, aligned_distance=None):
-        """Forward function
+        """Forward function.
 
         Args:
             inputs (torch.Tensor): Pillar/Voxel inputs with shape (N, M, C).

mmdet3d/models/voxel_encoders/voxel_encoder.py

@@ -10,7 +10,7 @@ from .utils import VFELayer, get_paddings_indicator
 
 @VOXEL_ENCODERS.register_module()
 class HardSimpleVFE(nn.Module):
-    """Simple voxel feature encoder used in SECOND
+    """Simple voxel feature encoder used in SECOND.
 
     It simply averages the values of points in a voxel.
     """
@@ -19,7 +19,7 @@ class HardSimpleVFE(nn.Module):
         super(HardSimpleVFE, self).__init__()
 
     def forward(self, features, num_points, coors):
-        """Forward function
+        """Forward function.
 
         Args:
             features (torch.Tensor): point features in shape
@@ -39,7 +39,7 @@ class HardSimpleVFE(nn.Module):
 
 @VOXEL_ENCODERS.register_module()
 class DynamicSimpleVFE(nn.Module):
-    """Simple dynamic voxel feature encoder used in DV-SECOND
+    """Simple dynamic voxel feature encoder used in DV-SECOND.
 
     It simply averages the values of points in a voxel.
     But the number of points in a voxel is dynamic and varies.
@@ -57,7 +57,7 @@ class DynamicSimpleVFE(nn.Module):
 
     @torch.no_grad()
     def forward(self, features, coors):
-        """Forward function
+        """Forward function.
 
         Args:
             features (torch.Tensor): point features in shape
@@ -76,7 +76,7 @@ class DynamicSimpleVFE(nn.Module):
 
 @VOXEL_ENCODERS.register_module()
 class DynamicVFE(nn.Module):
-    """Dynamic Voxel feature encoder used in DV-SECOND
+    """Dynamic Voxel feature encoder used in DV-SECOND.
 
     It encodes features of voxels and their points. It could also fuse
     image feature into voxel features in a point-wise manner.
@@ -211,7 +211,7 @@ class DynamicVFE(nn.Module):
                 points=None,
                 img_feats=None,
                 img_metas=None):
-        """Forward functions
+        """Forward functions.
 
         Args:
             features (torch.Tensor): Features of voxels, shape is NxC.
@@ -274,7 +274,7 @@ class DynamicVFE(nn.Module):
 
 @VOXEL_ENCODERS.register_module()
 class HardVFE(nn.Module):
-    """Voxel feature encoder used in DV-SECOND
+    """Voxel feature encoder used in DV-SECOND.
 
     It encodes features of voxels and their points. It could also fuse
     image feature into voxel features in a point-wise manner.
@@ -374,7 +374,7 @@ class HardVFE(nn.Module):
                 coors,
                 img_feats=None,
                 img_metas=None):
-        """Forward functions
+        """Forward functions.
 
         Args:
             features (torch.Tensor): Features of voxels, shape is MxNxC.

mmdet3d/ops/ball_query/ball_query.py

@@ -5,7 +5,7 @@ from . import ball_query_ext
 
 
 class BallQuery(Function):
-    """Ball Query
+    """Ball Query.
 
     Find nearby points in spherical space.
     """

mmdet3d/ops/furthest_point_sample/furthest_point_sample.py

@@ -7,8 +7,8 @@ from . import furthest_point_sample_ext
 class FurthestPointSampling(Function):
     """Furthest Point Sampling.
 
-    Uses iterative furthest point sampling to select a set of
-    features whose corresponding points have the furthest distance.
+    Uses iterative furthest point sampling to select a set of features whose
+    corresponding points have the furthest distance.
     """
 
     @staticmethod

mmdet3d/ops/gather_points/gather_points.py

@@ -5,7 +5,7 @@ from . import gather_points_ext
 
 
 class GatherPoints(Function):
-    """Gather Points
+    """Gather Points.
 
     Gather points with given index.
     """

mmdet3d/ops/group_points/group_points.py

-from typing import Tuple
-
 import torch
-import torch.nn as nn
+from torch import nn as nn
 from torch.autograd import Function
+from typing import Tuple
 
 from ..ball_query import ball_query
 from . import group_points_ext
@@ -49,7 +48,7 @@ class QueryAndGroup(nn.Module):
             assert self.uniform_sample
 
     def forward(self, points_xyz, center_xyz, features=None):
-        """forward
+        """forward.
 
         Args:
             points_xyz (Tensor): (B, N, 3) xyz coordinates of the features.

mmdet3d/ops/interpolate/three_interpolate.py

-from typing import Tuple
-
 import torch
 from torch.autograd import Function
+from typing import Tuple
 
 from . import interpolate_ext
 
@@ -11,7 +10,7 @@ class ThreeInterpolate(Function):
     @staticmethod
     def forward(ctx, features: torch.Tensor, indices: torch.Tensor,
                 weight: torch.Tensor) -> torch.Tensor:
-        """Performs weighted linear interpolation on 3 features
+        """Performs weighted linear interpolation on 3 features.
 
         Args:
             features (Tensor): (B, C, M) Features descriptors to be
@@ -40,7 +39,7 @@ class ThreeInterpolate(Function):
     def backward(
         ctx, grad_out: torch.Tensor
     ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """Backward of three interpolate
+        """Backward of three interpolate.
 
         Args:
             grad_out (Tensor): (B, C, N) tensor with gradients of outputs

mmdet3d/ops/interpolate/three_nn.py

-from typing import Tuple
-
 import torch
 from torch.autograd import Function
+from typing import Tuple
 
 from . import interpolate_ext
 
@@ -11,7 +10,8 @@ class ThreeNN(Function):
     @staticmethod
     def forward(ctx, target: torch.Tensor,
                 source: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        """Find the top-3 nearest neighbors of the target set from the source set.
+        """Find the top-3 nearest neighbors of the target set from the source
+        set.
 
         Args:
             target (Tensor): shape (B, N, 3), points set that needs to

mmdet3d/ops/norm.py

 import torch
-import torch.distributed as dist
-import torch.nn as nn
 from mmcv.cnn import NORM_LAYERS
+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):
 
 @NORM_LAYERS.register_module('naiveSyncBN1d')
 class NaiveSyncBatchNorm1d(nn.BatchNorm1d):
-    """Syncronized Batch Normalization for 3D Tensors
+    """Syncronized Batch Normalization for 3D Tensors.
 
     Note:
         This implementation is modified from
@@ -70,7 +70,7 @@ class NaiveSyncBatchNorm1d(nn.BatchNorm1d):
 
 @NORM_LAYERS.register_module('naiveSyncBN2d')
 class NaiveSyncBatchNorm2d(nn.BatchNorm2d):
-    """Syncronized Batch Normalization for 4D Tensors
+    """Syncronized Batch Normalization for 4D Tensors.
 
     Note:
         This implementation is modified from

mmdet3d/ops/pointnet_modules/point_fp_module.py

-from typing import List
-
 import torch
-import torch.nn as nn
 from mmcv.cnn import ConvModule
+from torch import nn as nn
+from typing import List
 
 from mmdet3d.ops import three_interpolate, three_nn
 

mmdet3d/ops/pointnet_modules/point_sa_module.py

-from typing import List
-
 import torch
-import torch.nn as nn
-import torch.nn.functional as F
 from mmcv.cnn import ConvModule
+from torch import nn as nn
+from torch.nn import functional as F
+from typing import List
 
 from mmdet3d.ops import (GroupAll, QueryAndGroup, furthest_point_sample,
                          gather_points)
 
 
 class PointSAModuleMSG(nn.Module):
-    """Point set abstraction module with multi-scale grouping used in Pointnets.
+    """Point set abstraction module with multi-scale grouping used in
+    Pointnets.
 
     Args:
         num_point (int): Number of points.

mmdet3d/ops/roiaware_pool3d/roiaware_pool3d.py

 import mmcv
 import torch
-import torch.nn as nn
+from torch import nn as nn
 from torch.autograd import Function
 
 from . import roiaware_pool3d_ext
@@ -24,7 +24,7 @@ class RoIAwarePool3d(nn.Module):
         self.mode = pool_method_map[mode]
 
     def forward(self, rois, pts, pts_feature):
-        """RoIAwarePool3d module forward
+        """RoIAwarePool3d module forward.
 
         Args:
             rois (torch.Tensor): [N, 7],in LiDAR coordinate,
@@ -46,7 +46,7 @@ class RoIAwarePool3dFunction(Function):
     @staticmethod
     def forward(ctx, rois, pts, pts_feature, out_size, max_pts_per_voxel,
                 mode):
-        """RoIAwarePool3d function forward
+        """RoIAwarePool3d function forward.
 
         Args:
             rois (torch.Tensor): [N, 7], in LiDAR coordinate,
@@ -89,7 +89,7 @@ class RoIAwarePool3dFunction(Function):
 
     @staticmethod
     def backward(ctx, grad_out):
-        """RoIAwarePool3d function forward
+        """RoIAwarePool3d function forward.
 
         Args:
             grad_out (torch.Tensor): [N, out_x, out_y, out_z, C]

mmdet3d/ops/spconv/conv.py

@@ -11,9 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 import math
-
 import numpy as np
 import torch
 from mmcv.cnn import CONV_LAYERS

mmdet3d/ops/spconv/modules.py

@@ -11,11 +11,9 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 import sys
-from collections import OrderedDict
-
 import torch
+from collections import OrderedDict
 from torch import nn
 
 from .structure import SparseConvTensor
@@ -46,9 +44,8 @@ def _mean_update(vals, m_vals, t):
 
 
 class SparseModule(nn.Module):
-    """ place holder,
-        All module subclass from this will take sptensor in SparseSequential.
-    """
+    """place holder, All module subclass from this will take sptensor in
+    SparseSequential."""
     pass
 
 
@@ -140,7 +137,9 @@ class SparseSequential(SparseModule):
         return input
 
     def fused(self):
-        """don't use this. no effect.
+        """don't use this.
+
+        no effect.
         """
         from .conv import SparseConvolution
         mods = [v for k, v in self._modules.items()]
@@ -189,16 +188,14 @@ class SparseSequential(SparseModule):
 
 
 class ToDense(SparseModule):
-    """convert SparseConvTensor to NCHW dense tensor.
-    """
+    """convert SparseConvTensor to NCHW dense tensor."""
 
     def forward(self, x: SparseConvTensor):
         return x.dense()
 
 
 class RemoveGrid(SparseModule):
-    """remove pre-allocated grid buffer.
-    """
+    """remove pre-allocated grid buffer."""
 
     def forward(self, x: SparseConvTensor):
         x.grid = None

mmdet3d/ops/spconv/structure.py

@@ -4,9 +4,9 @@ import torch
 
 def scatter_nd(indices, updates, shape):
     """pytorch edition of tensorflow scatter_nd.
-    this function don't contain except handle code. so use this carefully
-    when indice repeats, don't support repeat add which is supported
-    in tensorflow.
+
+    this function don't contain except handle code. so use this carefully when
+    indice repeats, don't support repeat add which is supported in tensorflow.
     """
     ret = torch.zeros(*shape, dtype=updates.dtype, device=updates.device)
     ndim = indices.shape[-1]

mmdet3d/ops/spconv/test_utils.py

@@ -11,10 +11,8 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
-import unittest
-
 import numpy as np
+import unittest
 
 
 class TestCase(unittest.TestCase):
@@ -26,6 +24,7 @@ class TestCase(unittest.TestCase):
 
     def assertAllEqual(self, a, b):
         """Asserts that two numpy arrays have the same values.
+
         Args:
         a: the expected numpy ndarray or anything can be converted to one.
         b: the actual numpy ndarray or anything can be converted to one.
@@ -56,6 +55,7 @@ class TestCase(unittest.TestCase):
 
     def assertAllClose(self, a, b, rtol=1e-6, atol=1e-6):
         """Asserts that two numpy arrays, or dicts of same, have near values.
+
         This does not support nested dicts.
         Args:
         a: The expected numpy ndarray (or anything can be converted to one), or

mmdet3d/utils/collect_env.py

-import os.path as osp
-import subprocess
-import sys
-from collections import defaultdict
-
 import cv2
 import mmcv
+import subprocess
+import sys
 import torch
 import torchvision
+from collections import defaultdict
+from os import path as osp
 
 import mmdet
 import mmdet3d

tests/test_config.py

@@ -4,7 +4,7 @@ from mmdet.core import BitmapMasks, PolygonMasks
 
 
 def _get_config_directory():
-    """ Find the predefined detector config directory """
+    """Find the predefined detector config directory."""
     try:
         # Assume we are running in the source mmdetection repo
         repo_dpath = dirname(dirname(__file__))
@@ -19,10 +19,10 @@ def _get_config_directory():
 
 
 def test_config_build_detector():
-    """
-    Test that all detection models defined in the configs can be initialized.
-    """
+    """Test that all detection models defined in the configs can be
+    initialized."""
     from mmcv import Config
+
     from mmdet3d.models import build_detector
 
     config_dpath = _get_config_directory()
@@ -74,10 +74,10 @@ def test_config_build_detector():
 
 
 def test_config_build_pipeline():
-    """
-    Test that all detection models defined in the configs can be initialized.
-    """
+    """Test that all detection models defined in the configs can be
+    initialized."""
     from mmcv import Config
+
     from mmdet3d.datasets.pipelines import Compose
 
     config_dpath = _get_config_directory()
@@ -102,14 +102,15 @@ def test_config_build_pipeline():
 
 
 def test_config_data_pipeline():
-    """
-    Test whether the data pipeline is valid and can process corner cases.
+    """Test whether the data pipeline is valid and can process corner cases.
+
     CommandLine:
         xdoctest -m tests/test_config.py test_config_build_data_pipeline
     """
+    import numpy as np
     from mmcv import Config
+
     from mmdet3d.datasets.pipelines import Compose
-    import numpy as np
 
     config_dpath = _get_config_directory()
     print('Found config_dpath = {!r}'.format(config_dpath))
@@ -262,7 +263,7 @@ def _check_roi_head(config, head):
 
 
 def _check_roi_extractor(config, roi_extractor, prev_roi_extractor=None):
-    import torch.nn as nn
+    from torch import nn as nn
     if isinstance(roi_extractor, nn.ModuleList):
         if prev_roi_extractor:
             prev_roi_extractor = prev_roi_extractor[0]
@@ -289,7 +290,7 @@ def _check_roi_extractor(config, roi_extractor, prev_roi_extractor=None):
 
 
 def _check_mask_head(mask_cfg, mask_head):
-    import torch.nn as nn
+    from torch import nn as nn
     if isinstance(mask_cfg, list):
         for single_mask_cfg, single_mask_head in zip(mask_cfg, mask_head):
             _check_mask_head(single_mask_cfg, single_mask_head)
@@ -307,7 +308,7 @@ def _check_mask_head(mask_cfg, mask_head):
 
 
 def _check_bbox_head(bbox_cfg, bbox_head):
-    import torch.nn as nn
+    from torch import nn as nn
     if isinstance(bbox_cfg, list):
         for single_bbox_cfg, single_bbox_head in zip(bbox_cfg, bbox_head):
             _check_bbox_head(single_bbox_cfg, single_bbox_head)
@@ -357,7 +358,7 @@ def _check_parta2_roi_extractor(config, roi_extractor):
 
 
 def _check_parta2_bbox_head(bbox_cfg, bbox_head):
-    import torch.nn as nn
+    from torch import nn as nn
     if isinstance(bbox_cfg, list):
         for single_bbox_cfg, single_bbox_head in zip(bbox_cfg, bbox_head):
             _check_bbox_head(single_bbox_cfg, single_bbox_head)