Yhs rddlZddlmcmZddlmZ d dejdejded ed e d ejf d Z dS)N)_log_api_usage_once?noneinputstargetsalphagamma reductionreturnc\d|cxkrdksn|dkrtd|dtjs2tjst t tj|}tj ||d}||zd|z d|z zz}|d|z |zz}|dkr||zd|z d|z zz} | |z}|dkrnI|dkr| }n.|d kr| }ntd |d |S) a Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. Args: inputs (Tensor): A float tensor of arbitrary shape. The predictions for each example. targets (Tensor): A float tensor with the same shape as inputs. Stores the binary classification label for each element in inputs (0 for the negative class and 1 for the positive class). alpha (float): Weighting factor in range [0, 1] to balance positive vs negative examples or -1 for ignore. Default: ``0.25``. gamma (float): Exponent of the modulating factor (1 - p_t) to balance easy vs hard examples. Default: ``2``. reduction (string): ``'none'`` | ``'mean'`` | ``'sum'`` ``'none'``: No reduction will be applied to the output. ``'mean'``: The output will be averaged. ``'sum'``: The output will be summed. Default: ``'none'``. Returns: Loss tensor with the reduction option applied. rzInvalid alpha value: z4. alpha must be in the range [0,1] or -1 for ignore.r)r meansumz$Invalid Value for arg 'reduction': 'z3 Supported reduction modes: 'none', 'mean', 'sum') ValueErrortorchjit is_scripting is_tracingrsigmoid_focal_losssigmoidF binary_cross_entropy_with_logitsrr) rrr r r pce_lossp_tlossalpha_ts l/var/www/tools.fuzzalab.pt/emblema-extractor/venv/lib/python3.11/site-packages/torchvision/ops/focal_loss.pyrrsc: OOOO!OOOO"llllmmm 9 ! ! # #0EI,@,@,B,B0./// fA0FSSSG g+Q1w;/ /C q3w5( )D zz'/QY1w;$??~F f  yy{{ e  xxzz r9 r r r    K)rrr) rtorch.nn.functionalnn functionalrutilsrTensorfloatstrrr!r r*s ''''''  66 L6 \6 6  6  6  \ 666666r!