It doesn't work on my data, why don't you provide a pretrained model? about aggregation-cross-entropy HOT 5 OPEN

+1，all the test results is "blank"---------

from aggregation-cross-entropy.

@LJXLJXLJX Have you solved it?

from aggregation-cross-entropy.

I pretrained a model using ctcloss and it works well. Then I loaded the weights and continued to train with the aceloss. The losses seemed to be coming down, but the test results were terrible, almost all wrong.

Here is my implementation of ACELoss.

device = torch.device("cuda:" + cfg.TRAIN.GPU_ID if torch.cuda.is_available() else "cpu")
class ACELoss(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, input_, target, target_lens):
        w, bs, num_class = input_.size()
        aggragetions = torch.zeros(bs, cfg.ARCH.NUM_CLASS)
        for i in range(bs):
            idx = 0
            for j in range(target_lens[i]):
                aggragetions[i][target[idx]] += 1
                idx += 1
            aggragetions[i][0] = w - target_lens[i]
        target = aggragetions.to(device)

        input_ = input_ + 1e-10
        input_ = torch.sum(input_, 0)
        input_ = input_ / w
        target = target / w

        loss = (-torch.sum(torch.log(input_) * target)) / bs
        return loss

your idxseems wrong

from aggregation-cross-entropy.

I pretrained a model using ctcloss and it works well. Then I loaded the weights and continued to train with the aceloss. The losses seemed to be coming down, but the test results were terrible, almost all wrong.
Here is my implementation of ACELoss.

device = torch.device("cuda:" + cfg.TRAIN.GPU_ID if torch.cuda.is_available() else "cpu")
class ACELoss(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, input_, target, target_lens):
        w, bs, num_class = input_.size()
        aggragetions = torch.zeros(bs, cfg.ARCH.NUM_CLASS)
        for i in range(bs):
            idx = 0
            for j in range(target_lens[i]):
                aggragetions[i][target[idx]] += 1
                idx += 1
            aggragetions[i][0] = w - target_lens[i]
        target = aggragetions.to(device)

        input_ = input_ + 1e-10
        input_ = torch.sum(input_, 0)
        input_ = input_ / w
        target = target / w

        loss = (-torch.sum(torch.log(input_) * target)) / bs
        return loss

your idxseems wrong

Hello, I checked it, and I think it is not wrong.

from aggregation-cross-entropy.

I pretrained a model using ctcloss and it works well. Then I loaded the weights and continued to train with the aceloss. The losses seemed to be coming down, but the test results were terrible, almost all wrong.

Here is my implementation of ACELoss.

device = torch.device("cuda:" + cfg.TRAIN.GPU_ID if torch.cuda.is_available() else "cpu")
class ACELoss(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, input_, target, target_lens):
        w, bs, num_class = input_.size()
        aggragetions = torch.zeros(bs, cfg.ARCH.NUM_CLASS)
        for i in range(bs):
            idx = 0
            for j in range(target_lens[i]):
                aggragetions[i][target[idx]] += 1
                idx += 1
            aggragetions[i][0] = w - target_lens[i]
        target = aggragetions.to(device)

        input_ = input_ + 1e-10
        input_ = torch.sum(input_, 0)
        input_ = input_ / w
        target = target / w

        loss = (-torch.sum(torch.log(input_) * target)) / bs
        return loss

what is the difference between cfg.ARCH.NUM_CLASS and num_class? whether contain the blank?

from aggregation-cross-entropy.