a  d@sddlmZmZmZmZmZddlZddlmZmZddl m Z ddl m Z m Z ddlmZddlmZddlmZdd lmZmZesd gZGd d d eZdS) )AnyListOptionalSequenceUnionN)Tensortensor)Literal)_total_variation_compute_total_variation_update)Metric) dim_zero_cat)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPETotalVariation.plotcseZdZUdZdZeed<dZeed<dZeed<dZ e ed<e ed <e e ed <e ed <de ed eddfdd Ze ddddZe dddZde ee ee fe eedddZZS)TotalVariationaCompute Total Variation loss (`TV`_). As input to ``forward`` and ``update`` the metric accepts the following input - ``img`` (:class:`~torch.Tensor`): A tensor of shape ``(N, C, H, W)`` consisting of images 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 TV value over sample else returns tensor of shape ``(N,)`` with TV values per sample Args: reduction: a method to reduce metric score over samples - ``'mean'``: takes the mean over samples - ``'sum'``: takes the sum over samples - ``None`` or ``'none'``: return the score per sample kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ValueError: If ``reduction`` is not one of ``'sum'``, ``'mean'``, ``'none'`` or ``None`` Example: >>> import torch >>> from torchmetrics.image import TotalVariation >>> _ = torch.manual_seed(42) >>> tv = TotalVariation() >>> img = torch.rand(5, 3, 28, 28) >>> tv(img) tensor(7546.8018) Ffull_state_updateTis_differentiablehigher_is_bettergplot_lower_bound num_elements score_listscoresum)meanrnoneN) reductionkwargsreturnc sxtjfi||dur*|dvr*td||_|jdgdd|jdtdtjdd d|jd tdtjdd ddS) N)rrrzHExpected argument `reduction` to either be 'sum', 'mean', 'none' or Nonercat)defaultZdist_reduce_fxrr)Zdtyperr) super__init__ ValueErrorrZ add_statertorchfloatint)selfrr __class__^/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/image/tv.pyr#KszTotalVariation.__init__)imgrcCsRt|\}}|jdus |jdkr.|j|n|j|7_|j|7_dS)z0Update current score with batch of input images.Nr)r rrappendrrr)r(r-rrr+r+r,updateUs  zTotalVariation.update)rcCs4|jdus|jdkrt|jn|j}t||j|jS)zCompute final total variation.Nr)rr rrr r)r(rr+r+r,compute^s$zTotalVariation.compute)valaxrcCs |||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 TotalVariation >>> metric = TotalVariation() >>> metric.update(torch.rand(5, 3, 28, 28)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.image import TotalVariation >>> metric = TotalVariation() >>> values = [ ] >>> for _ in range(10): ... values.append(metric(torch.rand(5, 3, 28, 28))) >>> fig_, ax_ = metric.plot(values) )Z_plot)r(r1r2r+r+r,plotcs(r)r)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrr&rrrr rr#r/r0rrrrr3 __classcell__r+r+r)r,rs #       r)typingrrrrrr%rrZtyping_extensionsr Z torchmetrics.functional.image.tvr r Ztorchmetrics.metricr Ztorchmetrics.utilities.datar Ztorchmetrics.utilities.importsrZtorchmetrics.utilities.plotrrZ__doctest_skip__rr+r+r+r,s