a  d.!@sddlmZddlmZmZmZmZmZddlZddlm Z m Z ddl m Z ddl mZmZddlmZddlmZdd lmZdd lmZmZesd gZGd d d eZdS))partial)AnyOptionalSequenceTupleUnionN)Tensortensor)Literal) _psnr_compute _psnr_update)Metric)rank_zero_warn)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEPeakSignalNoiseRatio.plotc seZdZUdZdZeed<dZeed<dZeed<dZ e ed<e ed <e ed <de e e ee e ffe ede e eeedffed dfdd Ze e d dddZe dddZde e e ee fe eedddZZS)PeakSignalNoiseRatioa `Compute Peak Signal-to-Noise Ratio`_ (PSNR). .. math:: \text{PSNR}(I, J) = 10 * \log_{10} \left(\frac{\max(I)^2}{\text{MSE}(I, J)}\right) Where :math:`\text{MSE}` denotes the `mean-squared-error`_ function. As input to ``forward`` and ``update`` the metric accepts the following input - ``preds`` (:class:`~torch.Tensor`): Predictions from model of shape ``(N,C,H,W)`` - ``target`` (:class:`~torch.Tensor`): Ground truth values of shape ``(N,C,H,W)`` As output of `forward` and `compute` the metric returns the following output - ``psnr`` (:class:`~torch.Tensor`): if ``reduction!='none'`` returns float scalar tensor with average PSNR value over sample else returns tensor of shape ``(N,)`` with PSNR values per sample Args: data_range: the range of the data. If None, it is determined from the data (max - min). If a tuple is provided then the range is calculated as the difference and input is clamped between the values. The ``data_range`` must be given when ``dim`` is not None. base: a base of a logarithm to use. reduction: a method to reduce metric score over labels. - ``'elementwise_mean'``: takes the mean (default) - ``'sum'``: takes the sum - ``'none'`` or ``None``: no reduction will be applied dim: Dimensions to reduce PSNR scores over, provided as either an integer or a list of integers. Default is None meaning scores will be reduced across all dimensions and all batches. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ValueError: If ``dim`` is not ``None`` and ``data_range`` is not given. Example: >>> from torchmetrics.image import PeakSignalNoiseRatio >>> psnr = PeakSignalNoiseRatio() >>> preds = torch.tensor([[0.0, 1.0], [2.0, 3.0]]) >>> target = torch.tensor([[3.0, 2.0], [1.0, 0.0]]) >>> psnr(preds, target) tensor(2.5527) Tis_differentiablehigher_is_betterFfull_state_updateplot_lower_bound min_target max_targetN$@elementwise_mean)rsumnoneN.) data_rangebase reductiondimkwargsreturnc s`tjfi||dur2|dkr2td|d|durd|jdtddd|jdtd ddn |jdgd d|jdgd dd|_|dur|durtd d|_|jd tdtj d|jd tdtj dn`t |t r|jdt|d|d ddt tj|d |dd|_n|jdtt|dd||_||_t |trVt |n||_dS)NrzThe `reduction=z.` will not have any effect when `dim` is None.sum_squared_errorrr)defaultZdist_reduce_fxtotalrcatz6The `data_range` must be given when `dim` is not None.rrrmean)minmax)super__init__rZ add_stater clamping_fn ValueErrorrtorchr+r, isinstancetuplerclampfloatr r!rr")selfrr r!r"r# __class__`/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/image/psnr.pyr.Vs,  zPeakSignalNoiseRatio.__init__)predstargetr$cCs|jdur||}||}t|||jd\}}|jdur|jdurnt||j|_t| |j |_ |j |7_ |j |7_ n|j ||j |dS)z*Update state with predictions and targets.N)r")r/r r"rr1Zminimumr+rmaximumr,rr%r'append)r6r;r<r%Zn_obsr9r9r:update}s      zPeakSignalNoiseRatio.update)r$cCsv|jdur|jn |j|j}|jdur4|j}|j}n,tdd|jD}tdd|jD}t||||j |j dS)z.Compute peak signal-to-noise ratio over state.NcSsg|] }|qSr9flatten.0valuesr9r9r: z0PeakSignalNoiseRatio.compute..cSsg|] }|qSr9r@rBr9r9r:rErF)r r!) rrrr"r%r'r1r(r r r!)r6rr%r'r9r9r:computes zPeakSignalNoiseRatio.compute)valaxr$cCs |||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 >>> import torch >>> from torchmetrics.image import PeakSignalNoiseRatio >>> metric = PeakSignalNoiseRatio() >>> preds = torch.tensor([[0.0, 1.0], [2.0, 3.0]]) >>> target = torch.tensor([[3.0, 2.0], [1.0, 0.0]]) >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.image import PeakSignalNoiseRatio >>> metric = PeakSignalNoiseRatio() >>> preds = torch.tensor([[0.0, 1.0], [2.0, 3.0]]) >>> target = torch.tensor([[3.0, 2.0], [1.0, 0.0]]) >>> values = [ ] >>> for _ in range(10): ... values.append(metric(preds, target)) >>> fig_, ax_ = metric.plot(values) )Z_plot)r6rHrIr9r9r:plots,r)NrrN)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrr5rrrrr intrr.r?rGrrrrJ __classcell__r9r9r7r:rs4 .    ' r) functoolsrtypingrrrrrr1rr Ztyping_extensionsr Z"torchmetrics.functional.image.psnrr r Ztorchmetrics.metricr Ztorchmetrics.utilitiesrZtorchmetrics.utilities.importsrZtorchmetrics.utilities.plotrrZ__doctest_skip__rr9r9r9r:s