o  i4@snddlmZddlZddlmZddlmZmZddlm Z dd d Z dd d Z e e dZ GdddeZ dS)) annotationsN)_Loss) gaussian_1dseparable_filtering) LossReductionsigmaintreturn torch.TensorcCs,|dkr td|tt|ddddS)Nr$expecting positive sigma, got sigma=sampledF)r truncatedapprox normalize) ValueErrorrtorchtensorrrZ/home/dell461/cl/sdc2/last_ska_mid/HISourceFinder-master-l/src/monai/losses/multi_scale.pymake_gaussian_kernelsrcsbdkr tdtd}tfddt| |dD}t|}|t|}|S)Nrr csg|] }|ddqS)r).0xrrr sz&make_cauchy_kernel..r)rrrrrange reciprocalsum)rtailkrrrmake_cauchy_kernels $ r#)gaussiancauchycs6eZdZdZddejfdfdd ZdddZZS)MultiScaleLossz This is a wrapper class. It smooths the input and target at different scales before passing them into the wrapped loss function. Adapted from: DeepReg (https://github.com/DeepRegNet/DeepReg) Nr$lossrscales list | Nonekernelstr reductionLossReduction | strr NonecsFtjt|jd|tvrtd|dt||_||_||_dS)z Args: loss: loss function to be wrapped scales: list of scalars or None, if None, do not apply any scaling. kernel: gaussian or cauchy. )r,zgot unsupported kernel type: z only support gaussian and cauchyN) super__init__rvaluekernel_fn_dictr kernel_fnr'r()selfr'r(r*r, __class__rrr01s    zMultiScaleLoss.__init__y_truer y_predc Cs|jdur |||}nDg}|jD]7}|dkr!||||q||t||||g|jdt||||g|jdqtj|dd}|j t j j kr^t |}|S|j t jj krlt|}|S|j t jj kr|td|j d|S)Nrr)dimzUnsupported reduction: z0, available options are ["mean", "sum", "none"].)r(r'appendrr3tondimrstackr,rMEANr1meanSUMr NONEr)r4r7r8r' loss_listsrrrforwardEs,      zMultiScaleLoss.forward) r'rr(r)r*r+r,r-r r.)r7r r8r r r ) __name__ __module__ __qualname____doc__rr>r0rD __classcell__rrr5rr&(s r&)rrr r ) __future__rrtorch.nn.modules.lossrmonai.networks.layersrr monai.utilsrrr#r2r&rrrrs