a hs @sJddlZddlmmZddlmZd ejejeee ejdddZ dS) N)_log_api_usage_once?none)inputstargetsalphagamma reductionreturnc Csd|krdks.n|dkr.td|dtjsJtjsJttt|}tj ||dd}||d|d|}|d||}|dkr||d|d|} | |}|dkrn4|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') ValueErrortorchZjitZ is_scripting is_tracingrsigmoid_focal_lossZsigmoidFZ binary_cross_entropy_with_logitsrr) rrrr r pZce_lossZp_tZlossZalpha_trH/var/www/auris/lib/python3.9/site-packages/torchvision/ops/focal_loss.pyrs*    r)rrr) rZtorch.nn.functionalnnZ functionalrutilsrZTensorfloatstrrrrrrs