Ah sadly its commercial code, so i cant release it, here are some relavent pieces though similar to the CRNN codebase,

t is the sequence tensor, right padding with 0, and l is the length tensor of all the sequences in the batch, exactly same as how pytorch CTC loss needs it as the input.

text = torch.IntTensor(opt.batchSize * 5)
length = torch.IntTensor(opt.batchSize)
criterion = ACELabelSmoothingLoss()

def loadData(v, data):
    v.resize_as_(data).copy_(data, non_blocking=False)

def LossFunction(y_pred, y):
    y_pred = y_pred.permute(1, 0, 2).contiguous()  # time, Batch, n_vocab
    t, l = converter.encode(y)
    loadData(text, t)
    loadData(length, l)
    preds_size = torch.IntTensor([y_pred.size(0)] * y_pred.size(1))
    return criterion(y_pred.to(device), text, preds_size, length)

This is how the loss is backpropagated

        x, y = batch
        y_pred = model(x)
        loss = LossFunction(y_pred, y)
        loss.backward()

This is how i am calculating exact accuracy, accuracy class is from pytorch-ignite, and essentially just calculates _num_correct / _num_examples

     class ExactAccuracy(Accuracy):

        @torch.no_grad()
        def update(self, output):
            y_pred, y = output
            y_pred = y_pred.permute(1, 0, 2)
            preds_size = torch.IntTensor([y_pred.size(0)] * y_pred.size(1))
            _, preds = y_pred.max(2)
            preds = preds.transpose(1, 0).contiguous().view(-1)
            sim_preds = converter.decode(
                preds.tolist(), preds_size.tolist(), raw=False)
            batch_edit_accuracy = 0
            for pred, target in zip(sim_preds, y):
                correct = 1 if pred == target else 0
                batch_edit_accuracy += correct
            self._num_correct += batch_edit_accuracy
            self._num_examples += y_pred.size(1)

where converter.encode is essentially same as https://github.com/meijieru/crnn.pytorch/blob/master/utils.py#L32,

Hope this shed some more light and helps you.

accuracy about LS-ACELoss #1

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