o .wi$@sddlmZddlmZmZmZmZddlmZddl m Z ddl m Z m Z ddlmZddlmZddlmZdd lmZmZdd lmZmZesMd gZesSd d gZGd d d eZdS))Sequence)AnyListOptionalUnion)Tensor)Literal)!_spatial_distortion_index_compute _spatial_distortion_index_update)Metric)rank_zero_warn) dim_zero_cat)_MATPLOTLIB_AVAILABLE_TORCHVISION_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPESpatialDistortionIndex.plotSpatialDistortionIndexc s eZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <e e ed <e e ed <e e ed <e e ed<   d$dedededdeddf fdd Zd e deee fddfddZde fddZ d%d eee ee fd!eedefd"d#ZZS)&ra Compute Spatial Distortion Index (SpatialDistortionIndex_) also now as D_s. The metric is used to compare the spatial distortion between two images. A value of 0 indicates no distortion (optimal value) and corresponds to the case where the high resolution panchromatic image is equal to the low resolution panchromatic image. The metric is defined as: .. math:: D_s = \\sqrt[q]{\frac{1}{L}\\sum_{l=1}^L|Q(\\hat{G_l}, P) - Q(\tilde{G}, \tilde{P})|^q} where :math:`Q` is the universal image quality index (see this :class:`~torchmetrics.image.UniversalImageQualityIndex` for more info), :math:`\\hat{G_l}` is the l-th band of the high resolution multispectral image, :math:`\tilde{G}` is the high resolution panchromatic image, :math:`P` is the high resolution panchromatic image, :math:`\tilde{P}` is the low resolution panchromatic image, :math:`L` is the number of bands and :math:`q` is the order of the norm applied on the difference. 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`): Low resolution panchromatic image of shape ``(N,C,H',W')``. where H and W must be multiple of H' and W'. As output of `forward` and `compute` the metric returns the following output - ``sdi`` (:class:`~torch.Tensor`): if ``reduction!='none'`` returns float scalar tensor with average SDI value over sample else returns tensor of shape ``(N,)`` with SDI values per sample Args: 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 SpatialDistortionIndex >>> preds = rand([16, 3, 32, 32]) >>> target = { ... 'ms': rand([16, 3, 16, 16]), ... 'pan': rand([16, 3, 32, 32]), ... } >>> sdi = SpatialDistortionIndex() >>> sdi(preds, target) tensor(0.0090) Fhigher_is_betterTis_differentiablefull_state_updategplot_lower_boundg?plot_upper_boundpredsmspanpan_lrelementwise_mean norm_order window_size reductionrsumnonekwargsreturnNc stjdi|tdt|tr|dkrtd|d||_t|tr*|dkr2td|d||_d}||vrEtd|d|||_|j d gd d |j d gd d |j d gd d |j dgd d dS)NzMetric `SpatialDistortionIndex` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.rz@Expected `norm_order` to be a positive integer. Got norm_order: .zBExpected `window_size` to be a positive integer. Got window_size: r#z(Expected argument `reduction` be one of z but got rcat)defaultdist_reduce_fxrrr) super__init__r isinstanceint ValueErrorr r!r" add_state)selfr r!r"r&allowed_reductions __class__r,S/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/image/d_s.pyr.fs$zSpatialDistortionIndex.__init__targetcCsd|vrtd|dd|vrtd|d|d}|d}|d}t||||\}}}}|j||j|d|j|dd|vrX|j|ddSdS)aUpdate state with preds and target. Args: preds: High resolution multispectral image. target: A dictionary containing the following keys: - ``'ms'``: low resolution multispectral image. - ``'pan'``: high resolution panchromatic image. - ``'pan_lr'``: (optional) low resolution panchromatic image. Raises: ValueError: If ``target`` doesn't have ``ms`` and ``pan``. rz0Expected `target` to have key `ms`. Got target: r(rz1Expected `target` to have key `pan`. Got target: rN) r1keysgetr rappendrrr)r3rr8rrrr,r,r7updates  zSpatialDistortionIndex.updatecCsxt|j}t|j}t|j}t|jdkrt|jnd}||d}||dur,d|init|||||j|j |j S)z-Compute and returns spatial distortion index.rN)rrr) r rrrlenrr<r r r!r")r3rrrrr8r,r,r7computes    zSpatialDistortionIndex.computevalaxcCs |||S)aPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> # Example plotting a single value >>> from torch import rand >>> from torchmetrics.image import SpatialDistortionIndex >>> preds = rand([16, 3, 32, 32]) >>> target = { ... 'ms': rand([16, 3, 16, 16]), ... 'pan': rand([16, 3, 32, 32]), ... } >>> metric = SpatialDistortionIndex() >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> from torch import rand >>> from torchmetrics.image import SpatialDistortionIndex >>> preds = rand([16, 3, 32, 32]) >>> target = { ... 'ms': rand([16, 3, 16, 16]), ... 'pan': rand([16, 3, 32, 32]), ... } >>> metric = SpatialDistortionIndex() >>> values = [ ] >>> for _ in range(10): ... values.append(metric(preds, target)) >>> fig_, ax_ = metric.plot(values) )_plot)r3r?r@r,r,r7plots 2r)rrr)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrrr0rrr.dictstrr<r>rrrrrrB __classcell__r,r,r5r7r#sF  7         N)collections.abcrtypingrrrrtorchrtyping_extensionsr!torchmetrics.functional.image.d_sr r torchmetrics.metricr torchmetrics.utilitiesr torchmetrics.utilities.datar torchmetrics.utilities.importsrrtorchmetrics.utilities.plotrr__doctest_skip__rr,r,r,r7s