def hm_svoego_roda_loss(pred, target):
pred_coord = heatmap_to_measure(pred)[0]
target_coord = heatmap_to_measure(target)[0]
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():
return Loss.ZERO()
bce = nn.BCELoss()(pred, target)
if torch.isnan(bce).any():
return Loss.ZERO()
return Loss(bce + nn.MSELoss()(pred_coord, target_coord) * 0.0005)
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)
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))
