def loss(self, outputs, targets, weights, pad_mask, weight, log_sigma):
predictions = outputs.tree.bf.images
gt_match_dists = outputs.gt_match_dists
# Compute likelihood
loss_val = batch_cdist(predictions, targets, reduction='sum')
log_sigmas = log_sigma - WeightsHacker.hack_weights(
torch.ones_like(loss_val)).log()
n = np.prod(predictions.shape[2:])
loss_val = 0.5 * loss_val * torch.pow(torch.exp(
-log_sigmas), 2) + n * (log_sigmas + 0.5 * np.log(2 * np.pi))
# Weigh by matching probability
match_weights = gt_match_dists
match_weights = match_weights * pad_mask[:,
None] # Note, this is now unnecessary since both tree models handle it already
loss_val = loss_val * match_weights * weights
losses = AttrDict()
losses.dense_img_rec = PenaltyLoss(weight,
breakdown=2)(loss_val,
log_error_arr=True,
reduction=[-1, -2])
# if self._hp.top_bias > 0.0:
# losses.n_top_bias_nodes = PenaltyLoss(
# self._hp.supervise_match_weight)(1 - WeightsHacker.get_n_top_bias_nodes(targets, weights))
return losses
def nll(self, estimates, targets, weights=1, log_error_arr=False):
"""
:param estimates: a distribution object
"""
losses = AttrDict()
criterion = NLL(self._hp.dense_img_rec_weight, breakdown=1)
avg_inds = get_dim_inds(targets)[1:]
losses.dense_img_rec = criterion(estimates,
targets,
weights=weights,
reduction=avg_inds,
log_error_arr=log_error_arr)
return losses
