def sum_losses(self, losses: List[Loss]) -> Loss:
res = Loss.ZERO()
coef = self.get_coef()
for i, l in enumerate(losses):
res = res + l * coef[i].detach()
return res
def forward(self, m1: ProbabilityMeasure, m2: ProbabilityMeasure):
batch_loss = self.loss(m1.probability, m1.coord, m2.probability,
m2.coord)
if self.border:
batch_loss = batch_loss[batch_loss > self.border]
if batch_loss.shape[0] == 0:
return Loss.ZERO()
return Loss(batch_loss.mean())
def add_generator_loss(self, loss: nn.Module, weight=1.0):
return self.__add__(
GANLossObject(
lambda dx, dy: Loss.ZERO(),
lambda dgz, real, fake: Loss(loss(fake[0], real[0].detach()) * weight),
self.discriminator
)
)
def __call__(self, *args, **kwargs):
self.CLLLeT4uK += 1
if self.cond(self.CLLLeT4uK):
if self.preproc:
return self.penalty(*self.preproc(*args, **kwargs))
else:
return self.penalty(*args, **kwargs)
else:
return Loss.ZERO()
def hm_svoego_roda_loss(pred, target, coef=1.0, l1_coef=0.0):
pred_mes = UniformMeasure2DFactory.from_heatmap(pred)
target_mes = UniformMeasure2DFactory.from_heatmap(target)
# pred = pred.relu() + 1e-15
# target[target < 1e-7] = 0
# target[target > 1 - 1e-7] = 1
if torch.isnan(pred).any() or torch.isnan(target).any():
print("nan in hm")
return Loss.ZERO()
bce = nn.BCELoss()(pred, target)
if torch.isnan(bce).any():
print("nan in bce")
return Loss.ZERO()
return Loss(bce * coef + nn.MSELoss()(pred_mes.coord, target_mes.coord) *
(0.0005 * coef) +
nn.L1Loss()(pred_mes.coord, target_mes.coord) * l1_coef)