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IvanHahan
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Nov 29, 2021
| not_padding = not_padding.unsqueeze(0) | ||
| pred_class = pred_class[not_padding] | ||
| pred_class = pred_class.reshape([1, 2, int(pred_class.size()[0] / 2)]) | ||
| return label_class, pred_class No newline at end of file |
Owner
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шот сложное тут творишь. плюс, захардкожены классы. ты либо все сделай флат, отфильтруй -1, а потом экспанд дименшин сделай, чтобы количество размерностей подходящее было. Либо в лоссах делай умножение на 0, в позициях для -1
| loss = lambda out, labels: loss_ce(out, labels) + loss_dice(out, labels) | ||
| return loss | ||
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Owner
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лямбды тут не нужно использовать. ты ж можешь просто присвоить саму функцию с лоссом.
# Conflicts: # models/losses.py # models/mesh_classifier.py
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