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inter
2025-09-21 20:18:55 +08:00
parent 6c87bbd0dc
commit 2856272d81
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pcdet/models/backbones_3d/spconv_backbone_2d.py Normal file
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from functools import partial
import torch.nn as nn
from ...utils.spconv_utils import replace_feature, spconv
def post_act_block(in_channels, out_channels, kernel_size, indice_key=None, stride=1, padding=0,
conv_type='subm', norm_fn=None):
if conv_type == 'subm':
conv = spconv.SubMConv2d(in_channels, out_channels, kernel_size, bias=False, indice_key=indice_key)
elif conv_type == 'spconv':
conv = spconv.SparseConv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding,
bias=False, indice_key=indice_key)
elif conv_type == 'inverseconv':
conv = spconv.SparseInverseConv2d(in_channels, out_channels, kernel_size, indice_key=indice_key, bias=False)
else:
raise NotImplementedError
m = spconv.SparseSequential(
conv,
norm_fn(out_channels),
nn.ReLU(),
)
return m
def post_act_block_dense(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, norm_fn=None):
m = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding=padding, dilation=dilation, bias=False),
norm_fn(out_channels),
nn.ReLU(),
)
return m
class SparseBasicBlock(spconv.SparseModule):
expansion = 1
def __init__(self, inplanes, planes, stride=1, norm_fn=None, downsample=None, indice_key=None):
super(SparseBasicBlock, self).__init__()
assert norm_fn is not None
bias = norm_fn is not None
self.conv1 = spconv.SubMConv2d(
inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key
)
self.bn1 = norm_fn(planes)
self.relu = nn.ReLU()
self.conv2 = spconv.SubMConv2d(
planes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key
)
self.bn2 = norm_fn(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = replace_feature(out, self.bn1(out.features))
out = replace_feature(out, self.relu(out.features))
out = self.conv2(out)
out = replace_feature(out, self.bn2(out.features))
if self.downsample is not None:
identity = self.downsample(x)
out = replace_feature(out, out.features + identity.features)
out = replace_feature(out, self.relu(out.features))
return out
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, norm_fn=None, downsample=None):
super(BasicBlock, self).__init__()
assert norm_fn is not None
bias = norm_fn is not None
self.conv1 = nn.Conv2d(inplanes, planes, 3, stride=stride, padding=1, bias=bias)
self.bn1 = norm_fn(planes)
self.relu = nn.ReLU()
self.conv2 = nn.Conv2d(planes, planes, 3, stride=stride, padding=1, bias=bias)
self.bn2 = norm_fn(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out = out + identity
out = self.relu(out)
return out
class PillarBackBone8x(nn.Module):
def __init__(self, model_cfg, input_channels, grid_size, **kwargs):
super().__init__()
self.model_cfg = model_cfg
norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
self.sparse_shape = grid_size[[1, 0]]
block = post_act_block
dense_block = post_act_block_dense
self.conv1 = spconv.SparseSequential(
block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm1'),
block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm1'),
)
self.conv2 = spconv.SparseSequential(
# [1600, 1408] <- [800, 704]
block(32, 64, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv2', conv_type='spconv'),
block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),
block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),
)
self.conv3 = spconv.SparseSequential(
# [800, 704] <- [400, 352]
block(64, 128, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv3', conv_type='spconv'),
block(128, 128, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),
block(128, 128, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),
)
self.conv4 = spconv.SparseSequential(
# [400, 352] <- [200, 176]
block(128, 256, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv4', conv_type='spconv'),
block(256, 256, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),
block(256, 256, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),
)
norm_fn = partial(nn.BatchNorm2d, eps=1e-3, momentum=0.01)
self.conv5 = nn.Sequential(
# [200, 176] <- [100, 88]
dense_block(256, 256, 3, norm_fn=norm_fn, stride=2, padding=1),
dense_block(256, 256, 3, norm_fn=norm_fn, padding=1),
dense_block(256, 256, 3, norm_fn=norm_fn, padding=1),
)
self.num_point_features = 256
self.backbone_channels = {
'x_conv1': 32,
'x_conv2': 64,
'x_conv3': 128,
'x_conv4': 256,
'x_conv5': 256
}
def forward(self, batch_dict):
pillar_features, pillar_coords = batch_dict['pillar_features'], batch_dict['pillar_coords']
batch_size = batch_dict['batch_size']
input_sp_tensor = spconv.SparseConvTensor(
features=pillar_features,
indices=pillar_coords.int(),
spatial_shape=self.sparse_shape,
batch_size=batch_size
)
x_conv1 = self.conv1(input_sp_tensor)
x_conv2 = self.conv2(x_conv1)
x_conv3 = self.conv3(x_conv2)
x_conv4 = self.conv4(x_conv3)
x_conv4 = x_conv4.dense()
x_conv5 = self.conv5(x_conv4)
batch_dict.update({
'multi_scale_2d_features': {
'x_conv1': x_conv1,
'x_conv2': x_conv2,
'x_conv3': x_conv3,
'x_conv4': x_conv4,
'x_conv5': x_conv5,
}
})
batch_dict.update({
'multi_scale_2d_strides': {
'x_conv1': 1,
'x_conv2': 2,
'x_conv3': 4,
'x_conv4': 8,
'x_conv5': 16,
}
})
return batch_dict
class PillarRes18BackBone8x(nn.Module):
def __init__(self, model_cfg, input_channels, grid_size, **kwargs):
super().__init__()
self.model_cfg = model_cfg
norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
self.sparse_shape = grid_size[[1, 0]]
block = post_act_block
dense_block = post_act_block_dense
self.conv1 = spconv.SparseSequential(
SparseBasicBlock(32, 32, norm_fn=norm_fn, indice_key='res1'),
SparseBasicBlock(32, 32, norm_fn=norm_fn, indice_key='res1'),
)
self.conv2 = spconv.SparseSequential(
# [1600, 1408] <- [800, 704]
block(32, 64, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv2', conv_type='spconv'),
SparseBasicBlock(64, 64, norm_fn=norm_fn, indice_key='res2'),
SparseBasicBlock(64, 64, norm_fn=norm_fn, indice_key='res2'),
)
self.conv3 = spconv.SparseSequential(
# [800, 704] <- [400, 352]
block(64, 128, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv3', conv_type='spconv'),
SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'),
SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'),
)
self.conv4 = spconv.SparseSequential(
# [400, 352] <- [200, 176]
block(128, 256, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv4', conv_type='spconv'),
SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res4'),
SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res4'),
)
norm_fn = partial(nn.BatchNorm2d, eps=1e-3, momentum=0.01)
self.conv5 = nn.Sequential(
# [200, 176] <- [100, 88]
dense_block(256, 256, 3, norm_fn=norm_fn, stride=2, padding=1),
BasicBlock(256, 256, norm_fn=norm_fn),
BasicBlock(256, 256, norm_fn=norm_fn),
)
self.num_point_features = 256
self.backbone_channels = {
'x_conv1': 32,
'x_conv2': 64,
'x_conv3': 128,
'x_conv4': 256,
'x_conv5': 256
}
def forward(self, batch_dict):
pillar_features, pillar_coords = batch_dict['pillar_features'], batch_dict['pillar_coords']
batch_size = batch_dict['batch_size']
input_sp_tensor = spconv.SparseConvTensor(
features=pillar_features,
indices=pillar_coords.int(),
spatial_shape=self.sparse_shape,
batch_size=batch_size
)
x_conv1 = self.conv1(input_sp_tensor)
x_conv2 = self.conv2(x_conv1)
x_conv3 = self.conv3(x_conv2)
x_conv4 = self.conv4(x_conv3)
x_conv4 = x_conv4.dense()
x_conv5 = self.conv5(x_conv4)
# batch_dict.update({
# 'encoded_spconv_tensor': out,
# 'encoded_spconv_tensor_stride': 8
# })
batch_dict.update({
'multi_scale_2d_features': {
'x_conv1': x_conv1,
'x_conv2': x_conv2,
'x_conv3': x_conv3,
'x_conv4': x_conv4,
'x_conv5': x_conv5,
}
})
batch_dict.update({
'multi_scale_2d_strides': {
'x_conv1': 1,
'x_conv2': 2,
'x_conv3': 4,
'x_conv4': 8,
'x_conv5': 16,
}
})
return batch_dict