File size: 5,284 Bytes
663494c |
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 |
## Not used in this project
## Provide an implementation for radar feature encoding
import torch
from mmcv.cnn import build_norm_layer
from mmcv.runner import auto_fp16
from mmcv.utils import Registry
from torch import nn
from torch.nn import functional as F
RADAR_ENCODERS = Registry("radar_encoder")
def build_radar_encoder(cfg):
"""Build backbone."""
return RADAR_ENCODERS.build(cfg)
class RFELayer(nn.Module):
"""Radar Feature Encoder layer.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
norm_cfg (dict): Config dict of normalization layers
"""
def __init__(
self,
in_channels,
out_channels,
norm_cfg=dict(type="BN1d", eps=1e-3, momentum=0.01),
):
super(RFELayer, self).__init__()
self.fp16_enabled = False
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.
Args:
inputs (torch.Tensor): Points features of shape (B, M, C).
M is the number of points in
C is the number of channels of point features.
Returns:
the same shape
"""
x = self.linear(inputs) # [B, M, C]
# BMC -> BCM -> BMC
x = self.norm(x.permute(0, 2, 1).contiguous()).permute(0, 2, 1).contiguous()
out = F.relu(x)
return out
@RADAR_ENCODERS.register_module()
class RadarPointEncoder(nn.Module):
def __init__(
self,
in_channels,
out_channels,
norm_cfg=dict(type="BN1d", eps=1e-3, momentum=0.01),
):
super(RadarPointEncoder, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
in_chn = in_channels
layers = []
for out_chn in out_channels:
layer = RFELayer(in_chn, out_chn)
layers.append(layer)
in_chn = out_chn
self.feat_layers = nn.Sequential(*layers)
def forward(self, points):
"""
points: [B, N, C]. N: as max
masks: [B, N, 1]
ret:
out: [B, N, C+1], last channel as 0-1 mask
"""
masks = points[:, :, [-1]]
x = points[:, :, :-1]
for feat_layer in self.feat_layers:
x = feat_layer(x)
out = x * masks
out = torch.cat((x, masks), dim=-1)
return out
@RADAR_ENCODERS.register_module()
class RadarPointEncoderXYAttn(nn.Module):
def __init__(
self,
in_channels,
out_channels,
norm_cfg=dict(type="BN1d", eps=1e-3, momentum=0.01),
):
super(RadarPointEncoderXYAttn, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
in_chn = in_channels
layers = []
for out_chn in out_channels:
layer = RFELayer(in_chn, out_chn)
layers.append(layer)
in_chn = out_chn
trans_encoder_layer = nn.TransformerEncoderLayer(
in_chn, 8, dim_feedforward=2048, dropout=0.1, activation="relu"
)
self.transformer_encoder = torch.nn.TransformerEncoder(
trans_encoder_layer,
num_layers=2,
)
self.feat_layers = nn.Sequential(*layers)
def forward(self, points):
"""
points: [B, N, C]. N: as max
masks: [B, N, 1]
ret:
out: [B, N, C+1], last channel as 0-1 mask
"""
masks = points[:, :, [-1]]
x = points[:, :, :-1]
xy = points[:, :, :2]
for feat_layer in self.feat_layers:
x = feat_layer(x)
x = x.permute(1, 0, 2)
x = self.transformer_encoder(
x, src_key_padding_mask=masks.squeeze(dim=-1).type(torch.bool)
)
x = x.permute(1, 0, 2)
out = x * masks
out = torch.cat((x, masks), dim=-1)
out = torch.cat((xy, out), dim=-1)
return out
@RADAR_ENCODERS.register_module()
class RadarPointEncoderXY(nn.Module):
def __init__(
self,
in_channels,
out_channels,
norm_cfg=dict(type="BN1d", eps=1e-3, momentum=0.01),
):
super(RadarPointEncoderXY, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
in_chn = in_channels
layers = []
for out_chn in out_channels:
layer = RFELayer(in_chn, out_chn)
layers.append(layer)
in_chn = out_chn
self.feat_layers = nn.Sequential(*layers)
def forward(self, points):
"""
points: [B, N, C]. N: as max
masks: [B, N, 1]
ret:
out: [B, N, C+3],
last channel as 0-1 mask
first two channes as xy cords
"""
masks = points[:, :, [-1]]
x = points[:, :, :-1]
xy = points[:, :, :2]
for feat_layer in self.feat_layers:
x = feat_layer(x)
out = x * masks
out = torch.cat((x, masks), dim=-1)
out = torch.cat((xy, out), dim=-1)
return out
|