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f348660 | 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 | from models.bts import upconv
import torch
import torch.nn as nn
import torchvision.models as models
class encoder_radar_sparse_conv(nn.Module):
def __init__(self, params):
# radar encoder for the first stage
super(encoder_radar_sparse_conv, self).__init__()
self.params = params
self.sparse_conv1 = SparseConv(params.radar_input_channels, 16, 7, activation='elu')
self.sparse_conv2 = SparseConv(16, 16, 5, activation='elu')
self.sparse_conv3 = SparseConv(16, 16, 3, activation='elu')
self.sparse_conv4 = SparseConv(16, 3, 3, activation='elu')
if params.encoder_radar == 'resnet34':
self.base_model_radar = models.resnet34(pretrained=False)
self.feat_names = ['relu', 'layer1', 'layer2', 'layer3', 'layer4']
self.feat_out_channels = [64, 64, 128, 256, 512]
elif params.encoder_radar == 'resnet18':
self.base_model_radar = models.resnet18(pretrained=False)
self.feat_names = ['relu', 'layer1', 'layer2', 'layer3', 'layer4']
self.feat_out_channels = [64, 64, 128, 256, 512]
else:
print('Not supported encoder: {}'.format(params.encoder))
def forward(self, x):
mask = (x[:, 0] > 0).float().unsqueeze(1)
feature = x
feature, mask = self.sparse_conv1(feature, mask)
feature, mask = self.sparse_conv2(feature, mask)
feature, mask = self.sparse_conv3(feature, mask)
feature, mask = self.sparse_conv4(feature, mask)
skip_feat = []
i = 1
for k, v in self.base_model_radar._modules.items():
if 'fc' in k or 'avgpool' in k:
continue
feature = v(feature)
if any(x in k for x in self.feat_names):
skip_feat.append(feature)
i = i + 1
return skip_feat
class encoder_radar_sub(nn.Module):
def __init__(self, params):
# radar encoder for the second stage
super(encoder_radar_sub, self).__init__()
self.params = params
import torchvision.models as models
self.conv = torch.nn.Sequential(nn.Conv2d(params.radar_input_channels+1, 3, 3, 1, 1, bias=False),
nn.ELU())
if params.encoder_radar == 'resnet34':
self.base_model_radar = models.resnet34(pretrained=False)
self.feat_names = ['relu', 'layer1', 'layer2', 'layer3', 'layer4']
self.feat_out_channels = [64, 64, 128, 256, 512]
elif params.encoder_radar == 'resnet18':
self.base_model_radar = models.resnet18(pretrained=False)
self.feat_names = ['relu', 'layer1', 'layer2', 'layer3', 'layer4']
self.feat_out_channels = [64, 64, 128, 256, 512]
else:
print('Not supported encoder: {}'.format(params.encoder))
def forward(self, x):
feature = x
feature = self.conv(feature)
skip_feat = []
i = 1
for k, v in self.base_model_radar._modules.items():
if 'fc' in k or 'avgpool' in k:
continue
feature = v(feature)
if any(x in k for x in self.feat_names):
skip_feat.append(feature)
i = i + 1
return skip_feat
class decoder_radar(nn.Module):
def __init__(self, params, feat_out_channels_img, feat_out_channels_radar):
super(decoder_radar, self).__init__()
self.params = params
self.upconv5 = upconv(feat_out_channels_img[4]+feat_out_channels_radar[4], feat_out_channels_radar[4]//2)
self.bn5 = nn.BatchNorm2d(feat_out_channels_radar[4]//2, momentum=0.01, affine=True, eps=1.1e-5)
self.conv5 = torch.nn.Sequential(nn.Conv2d(feat_out_channels_radar[4]//2, feat_out_channels_radar[4]//2, 3, 1, 1, bias=False),
nn.ELU())
self.upconv4 = upconv(feat_out_channels_img[3]+feat_out_channels_radar[3]+feat_out_channels_radar[4]//2, feat_out_channels_radar[3]//2)
self.bn4 = nn.BatchNorm2d(feat_out_channels_radar[3]//2, momentum=0.01, affine=True, eps=1.1e-5)
self.conv4 = torch.nn.Sequential(nn.Conv2d(feat_out_channels_radar[3]//2, feat_out_channels_radar[3]//2, 3, 1, 1, bias=False),
nn.ELU())
self.upconv3 = upconv(feat_out_channels_img[2]+feat_out_channels_radar[2]+feat_out_channels_radar[3]//2, feat_out_channels_radar[2]//2)
self.bn3 = nn.BatchNorm2d(feat_out_channels_radar[2]//2, momentum=0.01, affine=True, eps=1.1e-5)
self.conv3 = torch.nn.Sequential(nn.Conv2d(feat_out_channels_radar[2]//2, feat_out_channels_radar[2]//2, 3, 1, 1, bias=False),
nn.ELU())
self.upconv2 = upconv(feat_out_channels_img[1]+feat_out_channels_radar[1]+feat_out_channels_radar[2]//2, feat_out_channels_radar[1]//2)
self.bn2 = nn.BatchNorm2d(feat_out_channels_radar[1]//2, momentum=0.01, affine=True, eps=1.1e-5)
self.conv2 = torch.nn.Sequential(nn.Conv2d(feat_out_channels_radar[1]//2, feat_out_channels_radar[1]//2, 3, 1, 1, bias=False),
nn.ELU())
self.upconv1 = upconv(feat_out_channels_img[0]+feat_out_channels_radar[0]+feat_out_channels_radar[1]//2, feat_out_channels_radar[0]//2)
self.bn1 = nn.BatchNorm2d(feat_out_channels_radar[0]//2, momentum=0.01, affine=True, eps=1.1e-5)
self.conv1 = torch.nn.Sequential(nn.Conv2d(feat_out_channels_radar[0]//2, feat_out_channels_radar[0]//2, 3, 1, 1, bias=False),
nn.ELU())
# self.get_depth = torch.nn.Sequential(nn.Conv2d(feat_out_channels_radar[0]//2, 1, 3, 1, 1, bias=False),
# nn.Sigmoid())
self.get_depth = torch.nn.Sequential(nn.Conv2d(feat_out_channels_radar[0]//2, 2, 3, 1, 1, bias=False),
nn.Sigmoid())
def forward(self, image_features, radar_features):
img_skip0, img_skip1, img_skip2, img_skip3, img_final = image_features[0], image_features[1], image_features[2], image_features[3], image_features[4]
rad_skip0, rad_skip1, rad_skip2, rad_skip3, rad_final = radar_features[0], radar_features[1], radar_features[2], radar_features[3], radar_features[4]
final = torch.cat([img_final, rad_final], axis=1)
upconv5 = self.upconv5(final)
upconv5 = self.bn5(upconv5)
upconv5 = self.conv5(upconv5)
upconv5 = torch.cat([img_skip3, rad_skip3, upconv5], axis=1)
upconv4 = self.upconv4(upconv5)
upconv4 = self.bn4(upconv4)
upconv4 = self.conv4(upconv4)
upconv4 = torch.cat([img_skip2, rad_skip2, upconv4], axis=1)
upconv3 = self.upconv3(upconv4)
upconv3 = self.bn3(upconv3)
upconv3 = self.conv3(upconv3)
upconv3 = torch.cat([img_skip1, rad_skip1, upconv3], axis=1)
upconv2 = self.upconv2(upconv3)
upconv2 = self.bn2(upconv2)
upconv2 = self.conv2(upconv2)
upconv2 = torch.cat([img_skip0, rad_skip0, upconv2], axis=1)
upconv1 = self.upconv1(upconv2)
upconv1 = self.bn1(upconv1)
upconv1 = self.conv1(upconv1)
# confidence = self.get_depth(upconv1)
# depth = self.params.max_depth * confidence
depth_conf = self.get_depth(upconv1)
depth = self.params.max_depth * depth_conf[:, 0:1]
confidence = depth_conf[:, 1:2]
return confidence, depth
class SparseConv(nn.Module):
def __init__(self,
in_channels,
out_channels,
kernel_size,
activation='relu'):
super().__init__()
padding = kernel_size//2
self.conv = nn.Conv2d(
in_channels,
out_channels,
kernel_size=kernel_size,
padding=padding,
bias=False)
self.bias = nn.Parameter(
torch.zeros(out_channels),
requires_grad=True)
self.sparsity = nn.Conv2d(
in_channels,
out_channels,
kernel_size=kernel_size,
padding=padding,
bias=False)
kernel = torch.FloatTensor(torch.ones([kernel_size, kernel_size])).unsqueeze(0).unsqueeze(0)
self.sparsity.weight = nn.Parameter(
data=kernel,
requires_grad=False)
if activation == 'relu':
self.act = nn.ReLU(inplace=False)
elif activation == 'sigmoid':
self.act = nn.Sigmoid()
elif activation == 'elu':
self.act = nn.ELU()
self.max_pool = nn.MaxPool2d(
kernel_size,
stride=1,
padding=padding)
def forward(self, x, mask):
x = x*mask
x = self.conv(x)
normalizer = 1/(self.sparsity(mask)+1e-8)
x = x * normalizer + self.bias.unsqueeze(0).unsqueeze(2).unsqueeze(3)
x = self.act(x)
mask = self.max_pool(mask)
return x, mask
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