Thank you for sharing, I refer BalancedMSE/synthetic_benchmark/loss.py; Modified loss function in distributed run regression, after training the result is very low is my code modified by mistake? Below is the code for my change, which can explain why

Thanks for your work.

def bmc_loss_md(pred, target, noise_var): I = torch.eye(pred.shape[-1]) logits = MVN(pred.unsqueeze(1), noise_var*I).log_prob(target.unsqueeze(0)) loss = F.cross_entropy(logits, torch.arange(pred.shape[0])) loss = loss * (2 * noise_var).detach() return loss

My size of pred and target are [30,3,256,256] when running this code "loss = F.cross_entropy(logits, torch.arange(pred.shape[0]))",i got a error because "torch.arange(pred.shape[0])"is 1d,and logits is 4d.

How can i solve this error

def bmc_loss_md(pred, target, noise_var): I = torch.eye(pred.shape[-1]).cuda() logits = MVN(pred.unsqueeze(1), noise_var*I) logits = logits.log_prob(target.unsqueeze(0)) loss = F.cross_entropy(logits, torch.arange(pred.shape[0])) loss = loss * (2 * noise_var).detach() return loss 用于ssrnet年龄回归任务。 在这一段代码中，loss = F.cross_entropy(logits, torch.arange(pred.shape[0]))　报错： IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)　

回归任务不就是一维的吗。

The bmcloss equals 0.0 when I added BMCloss to my project with minibatch==1. I noticed that bmcloss is calculated by treating the regression predictions in a batch as multiclass classification. But since the size of each of my samples is different, my minibatch can only be set to 1. Is it not feasible to apply bmcloss in this case?

@jiawei-ren I wondered how should I apply Balanced-MSE for the D-dimensional regression task? I checked the tutorial code, the example code is formatted for one-dimensional regression.

def bmc_loss(pred, target, noise_var):
    logits = - (pred - target.T).pow(2) / (2 * noise_var)
    loss = F.cross_entropy(logits, torch.arange(pred.shape[0]))
    loss = loss * (2 * noise_var).detach()  # optional: restore the loss scale

    return loss

For example, the pred shape is (N, D), and the target is also (N, D). N is the batch size, and D is the dimension for regressing tasks. If following the above example, there is an error in the F.cross_entropy function.

Hi. I apply bMSE on my task which is a single-dimensional regression task. I found that the performance on train set is weird. The prediction of relatively low value data point is very high. In all, the performance on train set is bad. So it is normal? Looking forward to your reply!

The shape of logits in bmc loss is [batch, batch] which means both pred and target have the size [batch, 1]. For dense prediction, do we have to flat the 2d-image to a 1d vector with an operation of unsqueeze(-1)?

Thank you for your work.

gmm = {k: gmm[k].reshape(1, -1).expand(pred.shape[0], -1) for k in gmm}
mse_term = F.mse_loss(pred, target, reduction='none') / 2 / noise_var + 0.5 * noise_var.log()
sum_var = gmm['variances'] + noise_var
balancing_term = - 0.5 * sum_var.log() - 0.5 * (pred - gmm['means']).pow(2) / sum_var + gmm['weights'].log()
balancing_term = torch.logsumexp(balancing_term, dim=-1, keepdim=True)

when I use gai loss, my pred size is [128], but the data of gmm is [128, 8] after expand. How I solve this, looking forward to your recovery