vandit15 / class-balanced-loss-pytorch Goto Github PK
View Code? Open in Web Editor NEWPytorch implementation of the paper "Class-Balanced Loss Based on Effective Number of Samples"
License: MIT License
Pytorch implementation of the paper "Class-Balanced Loss Based on Effective Number of Samples"
License: MIT License
Hi @vandit15,
Thanks for sharing your code.
In line 73, weights = weights / np.sum(weights) * no_of_classes
, why is no_of_classes included here to normalise the weights? Any help would be thankful.
Thanks
does someone has implement focal_loss with softmax correctly?could you recommend its link for me?thank you very much
Hi, Inside the CBloss you are using binary cross-entropy, so why not using cross-entropy ? Could you explain? Thank you
I should compute samples_per_cls of whole dataset or each batch? If there is 0 in samples_per_cls of each batch, the loss will be nan.
Hi, thanks for your code sharing!
I am now trying to understand how do you implement and I have something want to discuss.
In your main function you have provide us with a toy example, I noticed that you have set samples_per_cls equals [2,3,1,2,2] which indicates label 0 has 2 samples , am i right?
But you have random labels,so should I count samples of each class every times ?
I would be appreciated if you can answer my question as quick as possible. Have a good day!
shouldn't it be F.cross_entropy instead?
It seems that you forgot to include weights for sigmoid loss
Hey can this be applied to multi label problems?
Thanks
@vandit15 thanks for open-sourcing the code , is it possible to use Cb loss in the object detection or segmentation architecture ?? did you experiment it with any std architecture as yolo retina, deeplab ?? since i am planning on using to our custom object detection architecture so
should be alpha: A float tensor of size [num_classes]
Instead calculating a weight for each batch, applying to class using pos_weight argument in torch.nn.BCELoss(pos_weights=weights)
Simply, https://github.com/vandit15/Class-balanced-loss-pytorch/blob/master/class_balanced_loss.py#L71-L82
Are those line of codes same with
effective_num = 1.0 - np.power(beta, samples_per_cls)
weights = (1.0 - beta) / np.array(effective_num)
weights = weights / np.sum(weights) * no_of_classes
loss = torch.nn.BCELoss(reduction='mean', pos_weight=weights)
this code?
Thanks for sharing your code.
I have 4D tensor..
I just understand your code. Thanks!
Hi, I wonder if this implementation is correct?
When I run the code with softmax, I get weights like this:
tensor([[0.8824, 0.8824, 0.8824, 0.8824, 0.8824],
[0.8824, 0.8824, 0.8824, 0.8824, 0.8824],
[0.8824, 0.8824, 0.8824, 0.8824, 0.8824],
[0.8824, 0.8824, 0.8824, 0.8824, 0.8824],
[1.7646, 1.7646, 1.7646, 1.7646, 1.7646],
[1.7646, 1.7646, 1.7646, 1.7646, 1.7646],
[0.8824, 0.8824, 0.8824, 0.8824, 0.8824],
[0.8824, 0.8824, 0.8824, 0.8824, 0.8824],
[0.8824, 0.8824, 0.8824, 0.8824, 0.8824],
[0.8824, 0.8824, 0.8824, 0.8824, 0.8824]])
where each sample enjoin the same weights.
This is weird, right? According to the original paper, we should have different weight for each class (column), right?
Thanks for sharing code.I have modified the code. I want to calculate the multivariate classification. The code is as follows. I can always report errors. I wonder if you have tried it?
pred = logits.log_softmax(dim=1)
cb_loss = F.cross_etropy(input=pred, target=labels, weights=weights)
Thanks.
thank you for sharing Pytorch implementation,
I was wondering if we need to specify "reduction="none"" for the "binary_cross_entropy_with_logits" function here since we are going to do weighting and reduction on our own later?
original:
cb_loss = F.binary_cross_entropy_with_logits(input = logits,target = labels_one_hot, weights = weights)
update:
cb_loss = F.binary_cross_entropy_with_logits(input = logits,target = labels_one_hot, weight = weights)
Thanks for the resource, it worked great!!
Hi, i'm interested in your work! Now i have problem, why your implement code of focal loss use "modulator = torch.exp(-gamma * labels * logits - gamma * torch.log(1 + torch.exp(-1.0 * logits)))" ?
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