File size: 12,210 Bytes
2cd560a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 |
from functools import partial
import torch
import torch.nn as nn
import spconv.pytorch as spconv
from spconv.core import ConvAlgo
def replace_feature(out, new_features):
return out.replace_feature(new_features)
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.SubMConv3d(in_channels, out_channels, kernel_size, bias=False, indice_key=indice_key, algo=ConvAlgo.Native)
elif conv_type == 'spconv':
conv = spconv.SparseConv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding,
bias=False, indice_key=indice_key, algo=ConvAlgo.Native)
elif conv_type == 'inverseconv':
conv = spconv.SparseInverseConv3d(in_channels, out_channels, kernel_size, indice_key=indice_key, bias=False, algo=ConvAlgo.Native)
else:
raise NotImplementedError
m = spconv.SparseSequential(
conv,
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.SubMConv3d(
inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key, algo=ConvAlgo.Native
)
self.bn1 = norm_fn(planes)
self.relu = nn.ReLU()
self.conv2 = spconv.SubMConv3d(
planes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key, algo=ConvAlgo.Native
)
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 VoxelResBackBone8xVoxelNeXt(nn.Module):
def __init__(self, input_channels, grid_size, **kwargs):
super().__init__()
norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
spconv_kernel_sizes = [3, 3, 3, 3]
channels = [16, 32, 64, 128, 128]
out_channel = 128
self.sparse_shape = grid_size[::-1] + [1, 0, 0]
self.conv_input = spconv.SparseSequential(
spconv.SubMConv3d(input_channels, channels[0], 3, padding=1, bias=False, indice_key='subm1', algo=ConvAlgo.Native),
norm_fn(channels[0]),
nn.ReLU(),
)
block = post_act_block
self.conv1 = spconv.SparseSequential(
SparseBasicBlock(channels[0], channels[0], norm_fn=norm_fn, indice_key='res1'),
SparseBasicBlock(channels[0], channels[0], norm_fn=norm_fn, indice_key='res1'),
)
self.conv2 = spconv.SparseSequential(
# [1600, 1408, 41] <- [800, 704, 21]
block(channels[0], channels[1], spconv_kernel_sizes[0], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[0]//2), indice_key='spconv2', conv_type='spconv'),
SparseBasicBlock(channels[1], channels[1], norm_fn=norm_fn, indice_key='res2'),
SparseBasicBlock(channels[1], channels[1], norm_fn=norm_fn, indice_key='res2'),
)
self.conv3 = spconv.SparseSequential(
# [800, 704, 21] <- [400, 352, 11]
block(channels[1], channels[2], spconv_kernel_sizes[1], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[1]//2), indice_key='spconv3', conv_type='spconv'),
SparseBasicBlock(channels[2], channels[2], norm_fn=norm_fn, indice_key='res3'),
SparseBasicBlock(channels[2], channels[2], norm_fn=norm_fn, indice_key='res3'),
)
self.conv4 = spconv.SparseSequential(
# [400, 352, 11] <- [200, 176, 6]
block(channels[2], channels[3], spconv_kernel_sizes[2], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[2]//2), indice_key='spconv4', conv_type='spconv'),
SparseBasicBlock(channels[3], channels[3], norm_fn=norm_fn, indice_key='res4'),
SparseBasicBlock(channels[3], channels[3], norm_fn=norm_fn, indice_key='res4'),
)
self.conv5 = spconv.SparseSequential(
# [200, 176, 6] <- [100, 88, 3]
block(channels[3], channels[4], spconv_kernel_sizes[3], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[3]//2), indice_key='spconv5', conv_type='spconv'),
SparseBasicBlock(channels[4], channels[4], norm_fn=norm_fn, indice_key='res5'),
SparseBasicBlock(channels[4], channels[4], norm_fn=norm_fn, indice_key='res5'),
)
self.conv6 = spconv.SparseSequential(
# [200, 176, 6] <- [100, 88, 3]
block(channels[4], channels[4], spconv_kernel_sizes[3], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[3]//2), indice_key='spconv6', conv_type='spconv'),
SparseBasicBlock(channels[4], channels[4], norm_fn=norm_fn, indice_key='res6'),
SparseBasicBlock(channels[4], channels[4], norm_fn=norm_fn, indice_key='res6'),
)
self.conv_out = spconv.SparseSequential(
# [200, 150, 5] -> [200, 150, 2]
spconv.SparseConv2d(channels[3], out_channel, 3, stride=1, padding=1, bias=False, indice_key='spconv_down2', algo=ConvAlgo.Native),
norm_fn(out_channel),
nn.ReLU(),
)
self.shared_conv = spconv.SparseSequential(
spconv.SubMConv2d(out_channel, out_channel, 3, stride=1, padding=1, bias=True, algo=ConvAlgo.Native),
nn.BatchNorm1d(out_channel),
nn.ReLU(True),
)
self.forward_ret_dict = {}
self.num_point_features = out_channel
self.backbone_channels = {
'x_conv1': channels[0],
'x_conv2': channels[1],
'x_conv3': channels[2],
'x_conv4': channels[3]
}
def bev_out(self, x_conv, index):
features_cat = x_conv.features
indices_cat = x_conv.indices[:, [0, 2, 3]]
spatial_shape = x_conv.spatial_shape[1:]
indices_unique, _inv = torch.unique(indices_cat, dim=0, return_inverse=True)
features_unique = features_cat.new_zeros((indices_unique.shape[0], features_cat.shape[1]))
features_unique.index_add_(0, _inv, features_cat)
perm = torch.arange(_inv.size(0), dtype=_inv.dtype, device=_inv.device)
perm = _inv.new_empty(indices_unique.size(0)).scatter_(0, _inv, perm)
index_out = index[perm]
x_out = spconv.SparseConvTensor(
features=features_unique,
indices=indices_unique,
spatial_shape=spatial_shape,
batch_size=x_conv.batch_size
)
return x_out, index_out
def track_voxels_2d(self, x, x_downsample, index, kernel_size=3):
_step = int(kernel_size//2)
kernel_offsets = [[i, j] for i in range(-_step, _step+1) for j in range(-_step, _step+1)]
#kernel_offsets.remove([0, 0])
kernel_offsets = torch.Tensor(kernel_offsets).to(x.indices.device)
batch_size = x.batch_size
index_batch = []
indices_batch = []
for b in range(batch_size):
batch_index = x.indices[:, 0]==b
indices_ori = x.indices[batch_index]
features_ori = index[batch_index]
features_fore = features_ori
coords_fore = indices_ori
voxel_kerels_imp = kernel_offsets.unsqueeze(0).repeat(features_fore.shape[0],1, 1)
indices_fore_kernels = coords_fore[:, 1:].unsqueeze(1).repeat(1, kernel_offsets.shape[0], 1)
indices_with_imp = indices_fore_kernels + voxel_kerels_imp
features_fore = features_fore.repeat(1, kernel_offsets.shape[0])
selected_indices = indices_with_imp
spatial_indices = (selected_indices[:, :, 0] >=0) * (selected_indices[:, :, 1] >=0) * \
(selected_indices[:, :, 0] < x.spatial_shape[0]) * (selected_indices[:, :, 1] < x.spatial_shape[1])
selected_indices = selected_indices[spatial_indices]
features_fore = features_fore[spatial_indices].view(-1, 1)
selected_indices = torch.cat([torch.ones((selected_indices.shape[0], 1), device=features_fore.device)*b, selected_indices], dim=1)
features_fore, coords_fore = features_fore, selected_indices
index_batch.append(features_fore)
indices_batch.append(coords_fore)
index_batch = torch.cat(index_batch)
indices_batch = torch.cat(indices_batch)
return self.index_from_sparse(index_batch, indices_batch, x_downsample, True)
def index_from_sparse(self, feature, indices, x_target, _2d=False):
sparse_index = spconv.SparseConvTensor(
features=feature,
indices=indices.int(),
spatial_shape=x_target.spatial_shape,
batch_size=x_target.batch_size
)
dense_index = sparse_index.dense()
indices_downsample = x_target.indices.long()
if _2d:
index_downsample = dense_index[indices_downsample[:, 0], :, indices_downsample[:, 1], indices_downsample[:, 2]]
else:
index_downsample = dense_index[indices_downsample[:, 0], :, indices_downsample[:, 1], indices_downsample[:, 2], indices_downsample[:, 3]]
return index_downsample
def forward(self, batch_dict):
"""
Args:
batch_dict:
batch_size: int
vfe_features: (num_voxels, C)
voxel_coords: (num_voxels, 4), [batch_idx, z_idx, y_idx, x_idx]
Returns:
batch_dict:
encoded_spconv_tensor: sparse tensor
"""
voxel_features, voxel_coords = batch_dict['voxel_features'], batch_dict['voxel_coords']
batch_size = batch_dict['batch_size']
input_sp_tensor = spconv.SparseConvTensor(
features=voxel_features,
indices=voxel_coords.int(),
spatial_shape=self.sparse_shape,
batch_size=batch_size
)
x = self.conv_input(input_sp_tensor)
x_conv1 = self.conv1(x)
x_conv2 = self.conv2(x_conv1)
x_conv3 = self.conv3(x_conv2)
x_conv4 = self.conv4(x_conv3)
x_conv5 = self.conv5(x_conv4)
x_conv6 = self.conv6(x_conv5)
x_conv5.indices[:, 1:] *= 2
x_conv6.indices[:, 1:] *= 4
x_conv4 = x_conv4.replace_feature(torch.cat([x_conv4.features, x_conv5.features, x_conv6.features]))
x_conv4.indices = torch.cat([x_conv4.indices, x_conv5.indices, x_conv6.indices])
index6_out = torch.arange(x_conv4.indices.shape[0], device=x_conv4.indices.device).unsqueeze(-1)
out_bevout, index_bevout = self.bev_out(x_conv4, index6_out)
out = self.conv_out(out_bevout)
index_out = self.track_voxels_2d(out_bevout, out, index_bevout)
out = self.shared_conv(out)
batch_dict.update({
'encoded_spconv_tensor': out,
'encoded_spconv_tensor_stride': 8,
'out_voxels': x_conv4.indices[index_out.squeeze(-1)],
})
batch_dict.update({
'multi_scale_3d_features': {
'x_conv1': x_conv1,
'x_conv2': x_conv2,
'x_conv3': x_conv3,
'x_conv4': x_conv4,
}
})
batch_dict.update({
'multi_scale_3d_strides': {
'x_conv1': 1,
'x_conv2': 2,
'x_conv3': 4,
'x_conv4': 8,
}
})
return batch_dict
|