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model.py
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model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
class LaneNet(nn.Module):
def __init__(
self,
embed_dim=4,
delta_v=0.5,
delta_d=3.0,
scale_lane_line=1.0,
scale_var=1.0,
scale_dist=1.0,
pretrained=False,
**kwargs
):
super(LaneNet, self).__init__()
self.pretrained = pretrained
self.embed_dim = embed_dim
self.delta_v = delta_v
self.delta_d = delta_d
self.net_init()
self.scale_seg = scale_lane_line
self.scale_var = scale_var
self.scale_dist = scale_dist
self.scale_reg = 0
self.seg_loss = nn.CrossEntropyLoss(weight=torch.tensor([0.4, 1.]))
def net_init(self):
self.backbone = models.vgg16_bn(pretrained=self.pretrained).features
# ----------------- process backbone -----------------
for i in [34, 37, 40]:
conv = self.backbone._modules[str(i)]
dilated_conv = nn.Conv2d(
conv.in_channels, conv.out_channels, conv.kernel_size, stride=conv.stride,
padding=tuple(p * 2 for p in conv.padding), dilation=2, bias=(conv.bias is not None)
)
dilated_conv.load_state_dict(conv.state_dict())
self.backbone._modules[str(i)] = dilated_conv
self.backbone._modules.pop('33')
self.backbone._modules.pop('43')
# ----------------- additional conv -----------------
self.layer1 = nn.Sequential(
nn.Conv2d(512, 1024, 3, padding=4, dilation=4, bias=False),
nn.BatchNorm2d(1024),
nn.ReLU(),
nn.Conv2d(1024, 128, 3, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.Conv2d(128, 32, 3, padding=1, bias=False),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.Conv2d(32, 8, 3, padding=1, bias=False),
nn.BatchNorm2d(8),
nn.ReLU(),
nn.Upsample(scale_factor=8, mode='bilinear', align_corners=True)
)
# ----------------- embedding -----------------
self.embedding = nn.Sequential(
nn.Conv2d(8, 8, 1),
nn.BatchNorm2d(8),
nn.ReLU(),
nn.Conv2d(8, self.embed_dim, 1)
)
# ----------------- binary segmentation -----------------
self.binary_seg = nn.Sequential(
nn.Conv2d(8, 8, 1),
nn.BatchNorm2d(8),
nn.ReLU(),
nn.Conv2d(8, 2, 1)
)
def forward(self, img, segLabel=None):
x = self.backbone(img)
x = self.layer1(x)
embedding = self.embedding(x)
binary_seg = self.binary_seg(x)
if segLabel is not None:
var_loss, dist_loss, reg_loss = self.discriminative_loss(embedding, segLabel)
seg_loss = self.seg_loss(binary_seg, torch.gt(segLabel, 0).type(torch.long))
else:
var_loss = torch.tensor(0, dtype=img.dtype, device=img.device)
dist_loss = torch.tensor(0, dtype=img.dtype, device=img.device)
seg_loss = torch.tensor(0, dtype=img.dtype, device=img.device)
loss = seg_loss * self.scale_seg + var_loss * self.scale_var + dist_loss * self.scale_dist
output = {
"embedding": embedding,
"binary_seg": binary_seg,
"loss_seg": seg_loss,
"loss_var": var_loss,
"loss_dist": dist_loss,
"loss": loss
}
return output
def discriminative_loss(self, embedding, seg_gt):
batch_size = embedding.shape[0]
var_loss = torch.tensor(0, dtype=embedding.dtype, device=embedding.device)
dist_loss = torch.tensor(0, dtype=embedding.dtype, device=embedding.device)
reg_loss = torch.tensor(0, dtype=embedding.dtype, device=embedding.device)
for b in range(batch_size):
embedding_b = embedding[b] # (embed_dim, H, W)
seg_gt_b = seg_gt[b]
labels = torch.unique(seg_gt_b)
labels = labels[labels!=0]
num_lanes = len(labels)
if num_lanes==0:
# please refer to issue here: https://github.com/harryhan618/LaneNet/issues/12
_nonsense = embedding.sum()
_zero = torch.zeros_like(_nonsense)
var_loss = var_loss + _nonsense * _zero
dist_loss = dist_loss + _nonsense * _zero
reg_loss = reg_loss + _nonsense * _zero
continue
centroid_mean = []
for lane_idx in labels:
seg_mask_i = (seg_gt_b == lane_idx)
if not seg_mask_i.any():
continue
embedding_i = embedding_b[:, seg_mask_i]
mean_i = torch.mean(embedding_i, dim=1)
centroid_mean.append(mean_i)
# ---------- var_loss -------------
var_loss = var_loss + torch.mean( F.relu(torch.norm(embedding_i-mean_i.reshape(self.embed_dim,1), dim=0) - self.delta_v)**2 ) / num_lanes
centroid_mean = torch.stack(centroid_mean) # (n_lane, embed_dim)
if num_lanes > 1:
centroid_mean1 = centroid_mean.reshape(-1, 1, self.embed_dim)
centroid_mean2 = centroid_mean.reshape(1, -1, self.embed_dim)
dist = torch.norm(centroid_mean1-centroid_mean2, dim=2) # shape (num_lanes, num_lanes)
dist = dist + torch.eye(num_lanes, dtype=dist.dtype, device=dist.device) * self.delta_d # diagonal elements are 0, now mask above delta_d
# divided by two for double calculated loss above, for implementation convenience
dist_loss = dist_loss + torch.sum(F.relu(-dist + self.delta_d)**2) / (num_lanes * (num_lanes-1)) / 2
# reg_loss is not used in original paper
# reg_loss = reg_loss + torch.mean(torch.norm(centroid_mean, dim=1))
var_loss = var_loss / batch_size
dist_loss = dist_loss / batch_size
reg_loss = reg_loss / batch_size
return var_loss, dist_loss, reg_loss