o .wi @sddlZddlmZddlmZddlmZddlmZdededeeeffd d Z ddeded e deddef ddZ ddeded e deddef ddZ dS)N)Tensor)Literal)universal_image_quality_index)reducepredstargetreturncCs|j|jkrtd|jd|jdt|jdkr'td|jd|jd|jdd|jddkrBtd |jd|jd||fS) zUpdate and returns variables required to compute Spectral Distortion Index. Args: preds: Low resolution multispectral image target: High resolution fused image z>Expected `ms` and `fused` to have the same data type. Got ms: z and fused: .z@Expected `preds` and `target` to have BxCxHxW shape. Got preds: z and target: NzNExpected `preds` and `target` to have same batch and channel sizes.Got preds: )dtype TypeErrorlenshape ValueErrorrrrc/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/functional/image/d_lambda.py!_spectral_distortion_index_updates" relementwise_meanp reduction)rsumnonecsjd}tj||fjd}tj||fjd}t|D]}||d}|dkr*qdd||dddddf|ddd} tjfddt|d|Ddd} ddt| | d d jdD} t | d|||ddf<dd||dddddf|ddd} tjfd dt|d|Ddd} d dt| | d d jdD} t | d|||ddf<q||j }||j }t t |||} |dkrt | d |} nt d ||dt | d |} t| |S)aCompute Spectral Distortion Index (SpectralDistortionIndex_). Args: preds: Low resolution multispectral image target: High resolution fused image p: a parameter to emphasize large spectral difference reduction: a method to reduce metric score over labels. - ``'elementwise_mean'``: takes the mean (default) - ``'sum'``: takes the sum - ``'none'``: no reduction will be applied Example: >>> from torch import rand >>> preds = rand([16, 3, 16, 16]) >>> target = rand([16, 3, 16, 16]) >>> preds, target = _spectral_distortion_index_update(preds, target) >>> _spectral_distortion_index_compute(preds, target) tensor(0.0234) r)devicerNc0g|]}dd||dddddfqSNrr.0r)rrr U0z6_spectral_distortion_index_compute..)dimcSg|]}|qSrmeanrsrrrr!Vr)rcrrrr)rrrr!\r"cSr$rr%r'rrrr!]r)g?)rtorchzerosrrangerepeatcatrsplitstackTpowabsrr)rrrrlengthm1m2knumstack1stack2scorediffoutputrrr"_spectral_distortion_index_compute0s4   0&0&  & r>cCs>t|tr |dkrtd|dt||\}}t||||S)a}Calculate `Spectral Distortion Index`_ (SpectralDistortionIndex_) also known as D_lambda. Metric is used to compare the spectral distortion between two images. Args: preds: Low resolution multispectral image target: High resolution fused image p: Large spectral differences reduction: a method to reduce metric score over labels. - ``'elementwise_mean'``: takes the mean (default) - ``'sum'``: takes the sum - ``'none'``: no reduction will be applied Return: Tensor with SpectralDistortionIndex score Raises: TypeError: If ``preds`` and ``target`` don't have the same data type. ValueError: If ``preds`` and ``target`` don't have ``BxCxHxW shape``. ValueError: If ``p`` is not a positive integer. Example: >>> from torch import rand >>> from torchmetrics.functional.image import spectral_distortion_index >>> preds = rand([16, 3, 16, 16]) >>> target = rand([16, 3, 16, 16]) >>> spectral_distortion_index(preds, target) tensor(0.0234) rz.Expected `p` to be a positive integer. Got p: r ) isinstanceintrrr>)rrrrrrrspectral_distortion_indexms(rA)rr) r*rtyping_extensionsr!torchmetrics.functional.image.uqir"torchmetrics.utilities.distributedrtuplerr@r>rArrrrs@     @