o .wi%@sddlmZddlmZmZmZmZddlmZddl m Z ddl m Z m Z ddlmZmZddlmZddlmZdd lmZdd lmZmZdd lmZmZesUd gZes[d d gZGdd d eZdS))Sequence)AnyListOptionalUnion)Tensor)Literal)"_spectral_distortion_index_compute!_spectral_distortion_index_update)!_spatial_distortion_index_compute _spatial_distortion_index_update)Metric)rank_zero_warn) dim_zero_cat)_MATPLOTLIB_AVAILABLE_TORCHVISION_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEQualityWithNoReference.plotQualityWithNoReferencecseZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <e e ed <e e ed <e e ed <e e ed<     d&de de dedededdeddffdd Zd e deee fddfddZde fd d!Z d'd"eee ee fd#eedefd$d%ZZS)(raCompute Quality with No Reference (QualityWithNoReference_) also now as QNR. The metric is used to compare the joint spectral and spatial distortion between two images. As input to ``forward`` and ``update`` the metric accepts the following input - ``preds`` (:class:`~torch.Tensor`): High resolution multispectral image of shape ``(N,C,H,W)``. - ``target`` (:class:`~Dict`): A dictionary containing the following keys: - ``ms`` (:class:`~torch.Tensor`): Low resolution multispectral image of shape ``(N,C,H',W')``. - ``pan`` (:class:`~torch.Tensor`): High resolution panchromatic image of shape ``(N,C,H,W)``. - ``pan_lr`` (:class:`~torch.Tensor`): (optional) Low resolution panchromatic image of shape ``(N,C,H',W')``. where H and W must be multiple of H' and W'. When ``pan_lr`` is ``None``, a uniform filter will be applied on ``pan`` to produce a degraded image. The degraded image is then resized to match the size of ``ms`` and served as ``pan_lr`` in the calculation. As output of `forward` and `compute` the metric returns the following output - ``qnr`` (:class:`~torch.Tensor`): if ``reduction!='none'`` returns float scalar tensor with average QNR value over sample else returns tensor of shape ``(N,)`` with QNR values per sample Args: alpha: Relevance of spectral distortion. beta: Relevance of spatial distortion. norm_order: Order of the norm applied on the difference. window_size: Window size of the filter applied to degrade the high resolution panchromatic image. 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 kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example: >>> from torch import rand >>> from torchmetrics.image import QualityWithNoReference >>> preds = rand([16, 3, 32, 32]) >>> target = { ... 'ms': rand([16, 3, 16, 16]), ... 'pan': rand([16, 3, 32, 32]), ... } >>> qnr = QualityWithNoReference() >>> qnr(preds, target) tensor(0.9694) Thigher_is_betteris_differentiableFfull_state_updategplot_lower_boundg?plot_upper_boundpredsmspanpan_lrelementwise_meanalphabeta norm_order window_size reductionr!sumnonekwargsreturnNc s,tjdi|tdt|ttfr|dkr td|d||_t|ttfr.|dkr6td|d||_t|trB|dkrJtd|d||_ t|trV|dkr^td|d||_ d}||vrqtd |d |||_ |j d gd d |j dgd d |j dgd d |j dgd d dS)NzMetric `QualityWithNoReference` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.rz>Expected `alpha` to be a non-negative real number. Got alpha: .z>> # Example plotting a single value >>> from torch import rand >>> from torchmetrics.image import QualityWithNoReference >>> preds = rand([16, 3, 32, 32]) >>> target = { ... 'ms': rand([16, 3, 16, 16]), ... 'pan': rand([16, 3, 32, 32]), ... } >>> metric = QualityWithNoReference() >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> from torch import rand >>> from torchmetrics.image import QualityWithNoReference >>> preds = rand([16, 3, 32, 32]) >>> target = { ... 'ms': rand([16, 3, 16, 16]), ... 'pan': rand([16, 3, 32, 32]), ... } >>> metric = QualityWithNoReference() >>> values = [ ] >>> for _ in range(10): ... values.append(metric(preds, target)) >>> fig_, ax_ = metric.plot(values) )_plot)r8rFrGr0r0r<plots 2r)rrrr r!)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrr5rrrr4rrr2dictstrrArErrrrrrI __classcell__r0r0r:r<r$sR  2        $ N)collections.abcrtypingrrrrtorchrtyping_extensionsr&torchmetrics.functional.image.d_lambdar r !torchmetrics.functional.image.d_sr r torchmetrics.metricr torchmetrics.utilitiesrtorchmetrics.utilities.datartorchmetrics.utilities.importsrrtorchmetrics.utilities.plotrr__doctest_skip__rr0r0r0r<s