def ddd_decode(heat, rot, dim, pc_range, ground, wh=None, reg=None, K=40):
batch, cat, height, width = heat.size()
# heat = torch.sigmoid(heat)
# perform nms on heatmaps
heat = _nms(heat)
scores, inds, clses, ys, xs = _topk(heat, K=K)
reg = _tranpose_and_gather_feat(reg, inds)
reg = reg.view(batch, K, 2)
xs = xs.view(batch, K, 1) + reg[:, :, 0:1]
ys = ys.view(batch, K, 1) + reg[:, :, 1:2]
rot = _tranpose_and_gather_feat(rot, inds)
rot = rot.view(batch, K, 8)
dim = _tranpose_and_gather_feat(dim, inds)
dim = dim.view(batch, K, 3)
clses = clses.view(batch, K, 1).float()
scores = scores.view(batch, K, 1)
xs = xs.view(batch, K, 1)
ys = ys.view(batch, K, 1)
# correct the x,y,z dimensions
xmin, ymin, zmin, xmax, ymax, _ = pc_range
xs = xs * (xmax - xmin) / (width - 1) + xmin
ys = ys * (ymax - ymin) / (height - 1) + ymin
lowest = torch.tensor(ground, device="cuda:0",
dtype=torch.float32) #.unsqueeze(1).unsqueeze(1)
zs = lowest[:, None, None] + dim[:, :, 2:3] / 2 # add gound plane here
return torch.cat([xs, ys, zs, dim, rot, scores, clses], dim=2)
def forward(self, output, mask, ind, target): pred = _tranpose_and_gather_feat(output, ind) mask = mask.float() # loss = F.l1_loss(pred * mask, target * mask, reduction='elementwise_mean') loss = F.l1_loss(pred * mask, target * mask, size_average=False) loss = loss / (mask.sum() + 1e-4) return loss
def forward(self, output, mask, ind, target): pred = _tranpose_and_gather_feat(output, ind) mask = mask.unsqueeze(2).expand_as(pred).float() # loss = F.l1_loss(pred * mask, target * mask, reduction='elementwise_mean') pred = pred / (target + 1e-4) target = target * 0 + 1 loss = F.l1_loss(pred * mask, target * mask, size_average=False) loss = loss / (mask.sum() + 1e-4) return loss
def forward(self, output, mask, ind, rotbin, rotres): pred = _tranpose_and_gather_feat(output, ind) loss = compute_rot_loss(pred, rotbin, rotres, mask) return loss
def forward(self, output, mask, ind, target): pred = _tranpose_and_gather_feat(output, ind) mask = mask.unsqueeze(2).expand_as(pred).float() loss = F.l1_loss(pred * mask, target * mask, reduction='elementwise_mean') return loss
def forward(self, output, mask, ind, target): pred = _tranpose_and_gather_feat(output, ind) loss = _reg_loss(pred, target, mask) return loss