a  d "@sddlmZddlmZmZmZmZmZddlZddl m Z ddlm Z ddl m Z ddlmZddlmZdd lmZmZdd lmZesd gZdeee d ddZGdddeZdS)deepcopy)AnyDictOptionalSequenceUnionN)apply_to_collection)Tensor) ModuleList)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE) WrapperMetricBootStrapper.plotpoisson)sizesampling_strategyreturncCsb|dkr8tjd}||f}t|j|ddS|dkrVtjt||ddSt dd S) zResample a tensor along its first dimension with replacement. Args: size: number of samples sampling_strategy: the strategy to use for sampling, either ``'poisson'`` or ``'multinomial'`` Returns: resampled tensor rrdim multinomialT)Z num_samples replacementzUnknown sampling strategyN) torch distributionsZPoissonsampleZarangeZrepeat_interleavelongrZones ValueError)rrpnr"l/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/wrappers/bootstrapping.py_bootstrap_samplers  r$c seZdZUdZdZeeed<dee eeee e e fee edd fd d Zeedd d d Zee e fdddZeeed fdd Zdee e ee feeedddZZS) BootStrapperaUsing `Turn a Metric into a Bootstrapped`_. That can automate the process of getting confidence intervals for metric values. This wrapper class basically keeps multiple copies of the same base metric in memory and whenever ``update`` or ``forward`` is called, all input tensors are resampled (with replacement) along the first dimension. Args: base_metric: base metric class to wrap num_bootstraps: number of copies to make of the base metric for bootstrapping mean: if ``True`` return the mean of the bootstraps std: if ``True`` return the standard diviation of the bootstraps quantile: if given, returns the quantile of the bootstraps. Can only be used with pytorch version 1.6 or higher raw: if ``True``, return all bootstrapped values sampling_strategy: Determines how to produce bootstrapped samplings. Either ``'poisson'`` or ``multinomial``. If ``'possion'`` is chosen, the number of times each sample will be included in the bootstrap will be given by :math:`n\sim Poisson(\lambda=1)`, which approximates the true bootstrap distribution when the number of samples is large. If ``'multinomial'`` is chosen, we will apply true bootstrapping at the batch level to approximate bootstrapping over the hole dataset. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example:: >>> from pprint import pprint >>> from torchmetrics.wrappers import BootStrapper >>> from torchmetrics.classification import MulticlassAccuracy >>> _ = torch.manual_seed(123) >>> base_metric = MulticlassAccuracy(num_classes=5, average='micro') >>> bootstrap = BootStrapper(base_metric, num_bootstraps=20) >>> bootstrap.update(torch.randint(5, (20,)), torch.randint(5, (20,))) >>> output = bootstrap.compute() >>> pprint(output) {'mean': tensor(0.2205), 'std': tensor(0.0859)} Tfull_state_update NFr) base_metricnum_bootstrapsmeanstdquantilerawrkwargsrc  stjfi|tts*tdtfddt|D|_||_||_ ||_ ||_ ||_ d} || vrtd| d|||_ dS)NzKExpected base metric to be an instance of torchmetrics.Metric but received csg|] }tqSr"r).0_r(r"r# lz)BootStrapper.__init__..)rrz5Expected argument ``sampling_strategy`` to be one of z but recieved )super__init__ isinstancer rr rangemetricsr)r*r+r,r-r) selfr(r)r*r+r,r-rr.Zallowed_sampling __class__r1r#r5[s&  zBootStrapper.__init__)argsr.rc Ost|jD]}t|tt}tt|tt}t|dkr@|d}nt|dkrV|d}ntdt||jd |j }t|tt j d|d}t|tt j d|d} |j |j|i| q dS)ztUpdate the state of the base metric. Any tensor passed in will be bootstrapped along dimension 0. rzMNone of the input contained tensors, so could not determine the sampling size)r)rindexN)r7r)r r lenlistrr$rtoZdevicerZ index_selectr8update) r9r<r.idxZ args_sizesZ kwargs_sizesrZ sample_idxnew_argsZ new_kwargsr"r"r#rA|s     zBootStrapper.update)rcCsxtjdd|jDdd}i}|jr4|jdd|d<|jrJ|jdd|d<|jdurft||j|d<|jrt||d <|S) zCompute the bootstrapped metric values. Always returns a dict of tensors, which can contain the following keys: ``mean``, ``std``, ``quantile`` and ``raw`` depending on how the class was initialized. cSsg|] }|qSr")compute)r/mr"r"r#r2r3z(BootStrapper.compute..rrr*r+Nr,r-)rstackr8r*r+r,r-)r9Z computed_valsZ output_dictr"r"r#rDs zBootStrapper.computecstt|j|i|S)z9Use the original forward method of the base metric class.)r4rforward)r9r<r.r:r"r#rGszBootStrapper.forward)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.wrappers import BootStrapper >>> from torchmetrics.regression import MeanSquaredError >>> metric = BootStrapper(MeanSquaredError(), num_bootstraps=20) >>> metric.update(torch.randn(100,), torch.randn(100,)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.wrappers import BootStrapper >>> from torchmetrics.regression import MeanSquaredError >>> metric = BootStrapper(MeanSquaredError(), num_bootstraps=20) >>> values = [ ] >>> for _ in range(3): ... values.append(metric(torch.randn(100,), torch.randn(100,))) >>> fig_, ax_ = metric.plot(values) )Z_plot)r9rHrIr"r"r#plots*r)r'TTNFr)NN)__name__ __module__ __qualname____doc__r&rbool__annotations__r intrfloatr strrr5rArrDrGrrrrJ __classcell__r"r"r:r#r%6s6 "!r%)r)copyrtypingrrrrrrZlightning_utilitiesr r Ztorch.nnr Ztorchmetrics.metricr Ztorchmetrics.utilities.importsr Ztorchmetrics.utilities.plotrrZtorchmetrics.wrappers.abstractrZ__doctest_skip__rQrSr$r%r"r"r"r#s$