updates
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@ -160,8 +160,8 @@ class YOLOLayer(nn.Module):
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lh = 5 * MSELoss(h[mask], th[mask])
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lconf = 1.5 * BCEWithLogitsLoss1(pred_conf[mask], mask[mask].float())
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# lcls = nM * CrossEntropyLoss(pred_cls[mask], torch.argmax(tcls, 1))
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lcls = nM * BCEWithLogitsLoss2(pred_cls[mask], tcls.float())
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lcls = nM * CrossEntropyLoss(pred_cls[mask], torch.argmax(tcls, 1))
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# lcls = nM * BCEWithLogitsLoss2(pred_cls[mask], tcls.float())
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else:
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lx, ly, lw, lh, lcls, lconf = FT([0]), FT([0]), FT([0]), FT([0]), FT([0]), FT([0])
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