Add modules before mg_head in centerpoint

mmdet3d/models/middle_encoders/sparse_encoder.py

@@ -1,6 +1,6 @@
 from torch import nn as nn
 
-from mmdet3d.ops import make_sparse_convmodule
+from mmdet3d.ops import SparseBasicBlock, make_sparse_convmodule
 from mmdet3d.ops import spconv as spconv
 from ..registry import MIDDLE_ENCODERS
 
@@ -18,6 +18,7 @@ class SparseEncoder(nn.Module):
  encoder_channels (tuple[tuple[int]]):
  Convolutional channels of each encode block.
  encoder_paddings (tuple[tuple[int]]): Paddings of each encode block.
+ block_type (str): Type of the block to use.
  """
 
  def __init__(self,
@@ -30,8 +31,10 @@ def __init__(self,
  encoder_channels=((16, ), (32, 32, 32), (64, 64, 64), (64, 64,
  64)),
  encoder_paddings=((1, ), (1, 1, 1), (1, 1, 1), ((0, 1, 1), 1,
- 1))):
+ 1)),
+ block_type='submblock'):
  super().__init__()
+ assert block_type in ['submblock', 'basicblock']
  self.sparse_shape = sparse_shape
  self.in_channels = in_channels
  self.order = order
@@ -66,7 +69,10 @@ def __init__(self,
  conv_type='SubMConv3d')
 
  encoder_out_channels = self.make_encoder_layers(
- make_sparse_convmodule, norm_cfg, self.base_channels)
+ make_sparse_convmodule,
+ norm_cfg,
+ self.base_channels,
+ block_type=block_type)
 
  self.conv_out = make_sparse_convmodule(
  encoder_out_channels,
@@ -111,17 +117,25 @@ def forward(self, voxel_features, coors, batch_size):
 
  return spatial_features
 
- def make_encoder_layers(self, make_block, norm_cfg, in_channels):
+ def make_encoder_layers(self,
+ make_block,
+ norm_cfg,
+ in_channels,
+ block_type='submblock',
+ conv_cfg=dict(type='SubMConv3d')):
  """make encoder layers using sparse convs.
 
  Args:
  make_block (method): A bounded function to build blocks.
  norm_cfg (dict[str]): Config of normalization layer.
  in_channels (int): The number of encoder input channels.
+ block_type (str): Type of the block to use.
+ conv_cfg (dict): Config of conv layer.
 
  Returns:
  int: The number of encoder output channels.
  """
+ assert block_type in ['submblock', 'basicblock']
  self.encoder_layers = spconv.SparseSequential()
 
  for i, blocks in enumerate(self.encoder_channels):
@@ -130,7 +144,7 @@ def make_encoder_layers(self, make_block, norm_cfg, in_channels):
  padding = tuple(self.encoder_paddings[i])[j]
  # each stage started with a spconv layer
  # except the first stage
- if i != 0 and j == 0:
+ if i != 0 and j == 0 and block_type == 'submblock':
  blocks_list.append(
  make_block(
  in_channels,
@@ -141,6 +155,26 @@ def make_encoder_layers(self, make_block, norm_cfg, in_channels):
  padding=padding,
  indice_key=f'spconv{i + 1}',
  conv_type='SparseConv3d'))
+ elif block_type == 'basicblock':
+ if j == len(blocks) - 1 and i != len(
+ self.encoder_channels) - 1:
+ blocks_list.append(
+ make_block(
+ in_channels,
+ out_channels,
+ 3,
+ norm_cfg=norm_cfg,
+ stride=2,
+ padding=padding,
+ indice_key=f'spconv{i + 1}',
+ conv_type='SparseConv3d'))
+ else:
+ blocks_list.append(
+ SparseBasicBlock(
+ out_channels,
+ out_channels,
+ norm_cfg=norm_cfg,
+ conv_cfg=conv_cfg))
  else:
  blocks_list.append(
  make_block(

mmdet3d/models/necks/second_fpn.py

@@ -1,6 +1,7 @@
+import numpy as np
 import torch
-from mmcv.cnn import (build_norm_layer, build_upsample_layer, constant_init,
- is_norm, kaiming_init)
+from mmcv.cnn import (build_conv_layer, build_norm_layer, build_upsample_layer,
+ constant_init, is_norm, kaiming_init)
 from torch import nn as nn
 
 from mmdet.models import NECKS
@@ -11,19 +12,24 @@ class SECONDFPN(nn.Module):
  """FPN used in SECOND/PointPillars/PartA2/MVXNet.
 
  Args:
- in_channels (list[int]): Input channels of multi-scale feature maps
- out_channels (list[int]): Output channels of feature maps
- upsample_strides (list[int]): Strides used to upsample the feature maps
- norm_cfg (dict): Config dict of normalization layers
- upsample_cfg (dict): Config dict of upsample layers
+ in_channels (list[int]): Input channels of multi-scale feature maps.
+ out_channels (list[int]): Output channels of feature maps.
+ upsample_strides (list[int]): Strides used to upsample the
+ feature maps.
+ norm_cfg (dict): Config dict of normalization layers.
+ upsample_cfg (dict): Config dict of upsample layers.
+ conv_cfg (dict): Config dict of conv layers.
+ use_conv_for_no_stride (bool): Whether to use conv when stride is 1.
  """
 
  def __init__(self,
  in_channels=[128, 128, 256],
  out_channels=[256, 256, 256],
  upsample_strides=[1, 2, 4],
  norm_cfg=dict(type='BN', eps=1e-3, momentum=0.01),
- upsample_cfg=dict(type='deconv', bias=False)):
+ upsample_cfg=dict(type='deconv', bias=False),
+ conv_cfg=dict(type='Conv2d', bias=False),
+ use_conv_for_no_stride=False):
  # if for GroupNorm,
  # cfg is dict(type='GN', num_groups=num_groups, eps=1e-3, affine=True)
  super(SECONDFPN, self).__init__()
@@ -33,12 +39,23 @@ def __init__(self,
 
  deblocks = []
  for i, out_channel in enumerate(out_channels):
- upsample_layer = build_upsample_layer(
- upsample_cfg,
- in_channels=in_channels[i],
- out_channels=out_channel,
- kernel_size=upsample_strides[i],
- stride=upsample_strides[i])
+ stride = upsample_strides[i]
+ if stride > 1 or (stride == 1 and not use_conv_for_no_stride):
+ upsample_layer = build_upsample_layer(
+ upsample_cfg,
+ in_channels=in_channels[i],
+ out_channels=out_channel,
+ kernel_size=upsample_strides[i],
+ stride=upsample_strides[i])
+ else:
+ stride = np.round(1 / stride).astype(np.int64)
+ upsample_layer = build_conv_layer(
+ conv_cfg,
+ in_channels=in_channels[i],
+ out_channels=out_channel,
+ kernel_size=stride,
+ stride=stride)
+
  deblock = nn.Sequential(upsample_layer,
  build_norm_layer(norm_cfg, out_channel)[1],
  nn.ReLU(inplace=True))

mmdet3d/models/voxel_encoders/pillar_encoder.py

@@ -31,6 +31,8 @@ class PillarFeatureNet(nn.Module):
  Defaults to dict(type='BN1d', eps=1e-3, momentum=0.01).
  mode (str, optional): The mode to gather point features. Options are
  'max' or 'avg'. Defaults to 'max'.
+ legacy (bool): Whether to use the new behavior or
+ the original behavior.
  """
 
  def __init__(self,
@@ -42,9 +44,11 @@ def __init__(self,
  voxel_size=(0.2, 0.2, 4),
  point_cloud_range=(0, -40, -3, 70.4, 40, 1),
  norm_cfg=dict(type='BN1d', eps=1e-3, momentum=0.01),
- mode='max'):
+ mode='max',
+ legacy=False):
  super(PillarFeatureNet, self).__init__()
  assert len(feat_channels) > 0
+ self.egacy = legacy
  if with_cluster_center:
  in_channels += 3
  if with_voxel_center:
@@ -89,7 +93,7 @@ def forward(self, features, num_points, coors):
  features (torch.Tensor): Point features or raw points in shape
  (N, M, C).
  num_points (torch.Tensor): Number of points in each pillar.
- coors (torch.Tensor): Coordinates of each voxel
+ coors (torch.Tensor): Coordinates of each voxel.
 
  Returns:
  torch.Tensor: Features of pillars.
@@ -104,14 +108,24 @@ def forward(self, features, num_points, coors):
  features_ls.append(f_cluster)
 
  # Find distance of x, y, and z from pillar center
+ dtype = features.dtype
  if self._with_voxel_center:
- f_center = features[:, :, :2]
- f_center[:, :, 0] = f_center[:, :, 0] - (
- coors[:, 3].type_as(features).unsqueeze(1) * self.vx +
- self.x_offset)
- f_center[:, :, 1] = f_center[:, :, 1] - (
- coors[:, 2].type_as(features).unsqueeze(1) * self.vy +
- self.y_offset)
+ if self.legacy:
+ f_center = torch.zeros_like(features[:, :, :2])
+ f_center[:, :, 0] = features[:, :, 0] - (
+ coors[:, 3].to(dtype).unsqueeze(1) * self.vx +
+ self.x_offset)
+ f_center[:, :, 1] = features[:, :, 1] - (
+ coors[:, 2].to(dtype).unsqueeze(1) * self.vy +
+ self.y_offset)
+ else:
+ f_center = features[:, :, :2]
+ f_center[:, :, 0] = f_center[:, :, 0] - (
+ coors[:, 3].type_as(features).unsqueeze(1) * self.vx +
+ self.x_offset)
+ f_center[:, :, 1] = f_center[:, :, 1] - (
+ coors[:, 2].type_as(features).unsqueeze(1) * self.vy +
+ self.y_offset)
  features_ls.append(f_center)
 
  if self._with_distance:

mmdet3d/models/voxel_encoders/voxel_encoder.py

@@ -13,10 +13,14 @@ class HardSimpleVFE(nn.Module):
  """Simple voxel feature encoder used in SECOND.
 
  It simply averages the values of points in a voxel.
+
+ Args:
+ num_features (int): Number of features to use. Default: 4.
  """
 
- def __init__(self):
+ def __init__(self, num_features=4):
  super(HardSimpleVFE, self).__init__()
+ self.num_features = num_features
 
  def forward(self, features, num_points, coors):
  """Forward function.
@@ -32,7 +36,7 @@ def forward(self, features, num_points, coors):
  Returns:
  torch.Tensor: Mean of points inside each voxel in shape (N, 3(4))
  """
- points_mean = features[:, :, :4].sum(
+ points_mean = features[:, :, :self.num_features].sum(
  dim=1, keepdim=False) / num_points.type_as(features).view(-1, 1)
  return points_mean.contiguous()
 

tests/test_middle_encoders.py

@@ -0,0 +1,26 @@
+import pytest
+import torch
+
+from mmdet3d.models.builder import build_middle_encoder
+
+
+def test_sparse_encoder():
+ if not torch.cuda.is_available():
+ pytest.skip('test requires GPU and torch+cuda')
+ sparse_encoder_cfg = dict(
+ type='SparseEncoder',
+ in_channels=5,
+ sparse_shape=[40, 1024, 1024],
+ order=('conv', 'norm', 'act'),
+ encoder_channels=((16, 16, 32), (32, 32, 64), (64, 64, 128), (128,
+ 128)),
+ encoder_paddings=((1, 1, 1), (1, 1, 1), (1, 1, 1), (1, 1, 1), (1, 1,
+ 1)),
+ option='basicblock')
+
+ sparse_encoder = build_middle_encoder(sparse_encoder_cfg).cuda()
+ voxel_features = torch.rand([207842, 5]).cuda()
+ coors = torch.randint(0, 4, [207842, 4]).cuda()
+
+ ret = sparse_encoder(voxel_features, coors, 4)
+ assert ret.shape == torch.Size([4, 256, 128, 128])

tests/test_necks.py

@@ -0,0 +1,32 @@
+import torch
+
+from mmdet3d.models.builder import build_backbone, build_neck
+
+
+def test_centerpoint_rpn():
+ second_cfg = dict(
+ type='SECOND',
+ in_channels=64,
+ out_channels=[64, 128, 256],
+ layer_nums=[3, 5, 5],
+ layer_strides=[2, 2, 2],
+ norm_cfg=dict(type='BN', eps=1e-3, momentum=0.01),
+ conv_cfg=dict(type='Conv2d', bias=False))
+
+ second = build_backbone(second_cfg)
+
+ centerpoint_fpn_cfg = dict(
+ type='SECONDFPN',
+ in_channels=[64, 128, 256],
+ out_channels=[128, 128, 128],
+ upsample_strides=[0.5, 1, 2],
+ norm_cfg=dict(type='BN', eps=1e-3, momentum=0.01),
+ upsample_cfg=dict(type='deconv', bias=False),
+ use_conv_for_no_stride=True)
+
+ second_fpn = build_neck(centerpoint_fpn_cfg)
+
+ input = torch.rand([4, 64, 512, 512])
+ sec_output = second(input)
+ output = second_fpn(sec_output)
+ assert output[0].shape == torch.Size([4, 384, 128, 128])

tests/test_voxel_encoders.py

@@ -0,0 +1,43 @@
+import numpy as np
+import torch
+
+from mmdet3d.models.builder import build_voxel_encoder
+
+
+def _set_seed():
+ torch.manual_seed(0)
+ torch.backends.cudnn.deterministic = True
+ torch.backends.cudnn.benchmark = False
+ np.random.seed(0)
+
+
+def test_pillar_feature_net():
+ _set_seed()
+ pillar_feature_net_cfg = dict(
+ type='PillarFeatureNet',
+ in_channels=5,
+ feat_channels=[64],
+ with_distance=False,
+ voxel_size=(0.2, 0.2, 8),
+ point_cloud_range=(-51.2, -51.2, -5.0, 51.2, 51.2, 3.0),
+ norm_cfg=dict(type='BN1d', eps=1e-3, momentum=0.01),
+ )
+
+ pillar_feature_net = build_voxel_encoder(pillar_feature_net_cfg)
+
+ features = torch.rand([97297, 20, 5])
+ num_voxels = torch.randint(1, 100, [97297])
+ coors = torch.randint(0, 100, [97297, 4])
+
+ features = pillar_feature_net(features, num_voxels, coors)
+ assert features.shape == torch.Size([97297, 64])
+
+
+def test_hard_simple_VFE():
+ hard_simple_VFE_cfg = dict(type='HardSimpleVFE', num_features=5)
+ hard_simple_VFE = build_voxel_encoder(hard_simple_VFE_cfg)
+ features = torch.rand([240000, 10, 5])
+ num_voxels = torch.randint(1, 10, [240000])
+
+ outputs = hard_simple_VFE(features, num_voxels, None)
+ assert outputs.shape == torch.Size([240000, 5])