I am struggling to understand how pytorch keeps track of which loss belongs to which output. Let me explain (all the code used below comes from this implementation of a text segmentation algorithm, which uses a Fully Convolutional Net: [https://github.com/princewang1994/TextSnake.pytorch]).
The last layers of the CNN looks like this:
        self.predict = nn.Sequential(
            nn.Conv2d(16, 16, kernel_size=3, stride=1, padding=1),
            nn.Conv2d(16, self.output_channel, kernel_size=1, stride=1, padding=0)
        ) # with self.output_channel = 7


Basically the CNN has 7 outputs. Then the algorithm is computing the losses:
class TextLoss(nn.Module):

    def __init__(self):
        super().__init__()

    def ohem(self, predict, target, train_mask, negative_ratio=3.):
        pos = (target * train_mask).byte()
        neg = ((1 - target) * train_mask).byte()

        n_pos = pos.float().sum()

        if n_pos.item() > 0:
            loss_pos = F.cross_entropy(predict[pos], target[pos], reduction='sum')
            loss_neg = F.cross_entropy(predict[neg], target[neg], reduction='none')
            n_neg = min(int(neg.float().sum().item()), int(negative_ratio * n_pos.float()))
        else:
            loss_pos = 0.
            loss_neg = F.cross_entropy(predict[neg], target[neg], reduction='none')
            n_neg = 100
        loss_neg, _ = torch.topk(loss_neg, n_neg)

        return (loss_pos + loss_neg.sum()) / (n_pos + n_neg).float()

    def forward(self, input, tr_mask, tcl_mask, sin_map, cos_map, radii_map, train_mask):
        """
        calculate textsnake loss
        :param input: (Variable), network predict, (BS, 7, H, W)
        :param tr_mask: (Variable), TR target, (BS, H, W)
        :param tcl_mask: (Variable), TCL target, (BS, H, W)
        :param sin_map: (Variable), sin target, (BS, H, W)
        :param cos_map: (Variable), cos target, (BS, H, W)
        :param radii_map: (Variable), radius target, (BS, H, W)
        :param train_mask: (Variable), training mask, (BS, H, W)
        :return: loss_tr, loss_tcl, loss_radii, loss_sin, loss_cos
        """

        tr_pred = input[:, :2].permute(0, 2, 3, 1).contiguous().view(-1, 2)  # (BSxHxW, 2)
        tcl_pred = input[:, 2:4].permute(0, 2, 3, 1).contiguous().view(-1, 2)  # (BSxHxW, 2)
        sin_pred = input[:, 4].contiguous().view(-1)  # (BSxHxW,)
        cos_pred = input[:, 5].contiguous().view(-1)  # (BSxHxW,)

        # regularize sin and cos: sum to 1
        scale = torch.sqrt(1.0 / (sin_pred ** 2 + cos_pred ** 2))
        sin_pred = sin_pred * scale
        cos_pred = cos_pred * scale

        radii_pred = input[:, 6].contiguous().view(-1)  # (BSxHxW,)
        train_mask = train_mask.view(-1)  # (BSxHxW,)

        tr_mask = tr_mask.contiguous().view(-1)
        tcl_mask = tcl_mask.contiguous().view(-1)
        radii_map = radii_map.contiguous().view(-1)
        sin_map = sin_map.contiguous().view(-1)
        cos_map = cos_map.contiguous().view(-1)

        # loss_tr = F.cross_entropy(tr_pred[train_mask], tr_mask[train_mask].long())
        loss_tr = self.ohem(tr_pred, tr_mask.long(), train_mask.long())

        loss_tcl = 0.
        tr_train_mask = train_mask * tr_mask
        if tr_train_mask.sum().item() > 0:
            loss_tcl = F.cross_entropy(tcl_pred[tr_train_mask], tcl_mask[tr_train_mask].long())

        # geometry losses
        loss_radii, loss_sin, loss_cos = 0., 0., 0.
        tcl_train_mask = train_mask * tcl_mask
        if tcl_train_mask.sum().item() > 0:
            ones = radii_map.new(radii_pred[tcl_mask].size()).fill_(1.).float()
            loss_radii = F.smooth_l1_loss(radii_pred[tcl_mask] / radii_map[tcl_mask], ones)
            loss_sin = F.smooth_l1_loss(sin_pred[tcl_mask], sin_map[tcl_mask])
            loss_cos = F.smooth_l1_loss(cos_pred[tcl_mask], cos_map[tcl_mask])

        return loss_tr, loss_tcl, loss_radii, loss_sin, loss_cos

Finally, it’s adding up all the the losses before backprograting the total loss.
I have 2 questions:
1/ Regarding the ohem loss (first function of the class TextLoss): 2 of the 7 outputs are inputed to this function (as ‘predict’), and it is nowhere specified what which output should be, but the algo still manages to train them as expected. Also when inspecting the output tensor (i.e. the error), it only has one value, so how does pytorch know how to train differently the two masks?
2/ Similarly, the sum of all the errors is a tensor with a single value, so how does pytorch know which error corresponds to each output?