models/loss.py

import torch
from torch import nn
import torch.nn.functional as F
from utils.geometry_utils import edge_acc


class CornerCriterion(nn.Module):
    def __init__(self, image_size):
        super().__init__()
        self.loss_rate = 9

    def forward(self, outputs_s1, targets, gauss_targets, epoch=0):
        # Compute the acc first, use the acc to guide the setup of loss weight
        preds_s1 = (outputs_s1 >= 0.5).float()
        pos_target_ids = torch.where(targets == 1)
        correct = (preds_s1[pos_target_ids] == targets[pos_target_ids]).float().sum()
        recall_s1 = correct / len(pos_target_ids[0])

        rate = self.loss_rate

        loss_weight = (gauss_targets > 0.5).float() * rate + 1
        loss_s1 = F.binary_cross_entropy(outputs_s1, gauss_targets, weight=loss_weight, reduction='none')
        loss_s1 = loss_s1.sum(-1).sum(-1).mean()

        return loss_s1, recall_s1


class EdgeCriterion(nn.Module):
    def __init__(self):
        super().__init__()
        self.edge_loss = nn.CrossEntropyLoss(weight=torch.tensor([0.33, 1.0]).cuda(), reduction='none')

    def forward(self, logits_s1, logits_s2_hybrid, logits_s2_rel, s2_ids, s2_edge_mask, edge_labels, edge_lengths,
                edge_mask, s2_gt_values):
        # loss for edge filtering
        s1_losses = self.edge_loss(logits_s1, edge_labels)
        s1_losses[torch.where(edge_mask == True)] = 0
        s1_losses = s1_losses[torch.where(s1_losses > 0)].sum() / edge_mask.shape[0]
        gt_values = torch.ones_like(edge_mask).long() * 2
        s1_acc = edge_acc(logits_s1, edge_labels, edge_lengths, gt_values)

        # loss for stage-2
        s2_labels = torch.gather(edge_labels, 1, s2_ids)

        # the image-aware decoder
        s2_losses_hybrid = self.edge_loss(logits_s2_hybrid, s2_labels)
        s2_losses_hybrid[torch.where((s2_edge_mask == True) | (s2_gt_values != 2))] = 0
        # aggregate the loss into the final scalar
        s2_losses_hybrid = s2_losses_hybrid[torch.where(s2_losses_hybrid > 0)].sum() / s2_edge_mask.shape[0]
        s2_edge_lengths = (s2_edge_mask == 0).sum(dim=-1)
        # compute edge-level acc
        s2_acc_hybrid = edge_acc(logits_s2_hybrid, s2_labels, s2_edge_lengths, s2_gt_values)

        # the geom-only decoder
        s2_losses_rel = self.edge_loss(logits_s2_rel, s2_labels)
        s2_losses_rel[torch.where((s2_edge_mask == True) | (s2_gt_values != 2))] = 0
        # aggregate the loss into the final scalar
        s2_losses_rel = s2_losses_rel[torch.where(s2_losses_rel > 0)].sum() / s2_edge_mask.shape[0]
        s2_edge_lengths = (s2_edge_mask == 0).sum(dim=-1)
        # compute edge-level f1-score
        s2_acc_rel = edge_acc(logits_s2_rel, s2_labels, s2_edge_lengths, s2_gt_values)

        return s1_losses, s1_acc, s2_losses_hybrid, s2_acc_hybrid, s2_losses_rel, s2_acc_rel