o .wi@sddlmZddlmZmZmZmZddlZddlmZm Z ddl m Z ddl m Z mZddlmZddlmZdd lmZdd lmZmZesKd gZGd d d eZdS))Sequence)AnyListOptionalUnionN)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 <dd e eddeddffdd Zde ddfddZde fddZ dde ee ee fde edefddZZS)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: >>> from torch import rand >>> from torchmetrics.image import TotalVariation >>> 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 reduction)meanrnonekwargsreturnNc sxtjd i||dur|dvrtd||_|jdgdd|jdtdtjdd d|jd tdtjdd ddS) N)rrrzHExpected argument `reduction` to either be 'sum', 'mean', 'none' or Nonercat)defaultdist_reduce_fxrr)dtyperr) super__init__ ValueErrorr add_statertorchfloatint)selfrr __class__r$R/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/image/tv.pyr&KszTotalVariation.__init__imgcCsRt|\}}|jdus|jdkr|j|n |j|7_|j|7_dS)z0Update current score with batch of input images.Nr)r rrappendrrr)r,r0rrr$r$r/updateUs zTotalVariation.updatecCs4|jdus |jdkrt|jn|j}t||j|jS)zCompute final total variation.Nr)rr rrr r)r,rr$r$r/compute^s$zTotalVariation.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 >>> 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) )_plot)r,r4r5r$r$r/plotcs (r)r)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrr*rrrr rr&r2r3rrrrr7 __classcell__r$r$r-r/rs(  "    $  r)collections.abcrtypingrrrrr)rrtyping_extensionsr torchmetrics.functional.image.tvr r torchmetrics.metricr torchmetrics.utilities.datar torchmetrics.utilities.importsrtorchmetrics.utilities.plotrr__doctest_skip__rr$r$r$r/s