About the loss calculation about yolov3_pytorch

[GNAYUOHZ]

Hi, do you test it? can you tell me the result? Thank you

[chrisway613]

Maybe I think you could set

        self.mse_loss = nn.MSELoss()
        self.bce_loss = nn.BCELoss()

to

        self.mse_loss = nn.MSELoss(size_average=False)
        self.bce_loss = nn.BCELoss(size_average=False)

and then calculate losses like this way:

        n_mask = torch.sum(mask)
        n_noobj_mask = torch.sum(noobj_mask)

        loss_x = self.bce_loss(x * mask, tx * mask) / n_mask
        loss_y = self.bce_loss(y * mask, ty * mask) / n_mask
        loss_w = self.mse_loss(w * mask, tw * mask) / n_mask
        loss_h = self.mse_loss(h * mask, th * mask) / n_mask
        loss_conf = self.bce_loss(conf * mask, 1.0 * mask) / n_mask + \
            0.5 * self.bce_loss(conf * noobj_mask, 0.0 * noobj_mask) / n_noobj_mask
        loss_cls = self.bce_loss(pred_cls[mask == 1], tcls[mask == 1]) / n_mask

I think maybe also add the condition when n_mask is zero:
if n_mask:
....
else:
loss_x = torch.zeros(1).cuda()
loss_y = torch.zeros(1).cuda()
loss_w = torch.zeros(1).cuda()
loss_h = torch.zeros(1).cuda()
loss_conf = self.lambda_conf * .5 * self.bce_loss(pred_conf * noobj_mask, noobj_mask * 0.) / n_noobj
loss_cls = torch.zeros(1).cuda()