o oi8@sddlZddlmZmZmZmZddlZddlmZddlm Z ddl m Z Gddde Z Gdd d e Z Gd d d e ZGd d d e ZGddde ZGddde ZdS)N)AnyCallableListUnion)Tensor)Metric) dim_zero_catc seZdZUdZeed<dZdZdZ dde e e fde ee fde e e fd effd d Zd e e efd efddZde e efd dfddZd efddZZS)BaseAggregatoraBase class for aggregation metrics. Args: fn: string specifying the reduction function default_value: default tensor value to use for the metric state nan_strategy: options: - ``'error'``: if any `nan` values are encounted will give a RuntimeError - ``'warn'``: if any `nan` values are encounted will give a warning and continue - ``'ignore'``: all `nan` values are silently removed - a float: if a float is provided will impude any `nan` values with this value kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ValueError: If ``nan_strategy`` is not one of ``error``, ``warn``, ``ignore`` or a float valueNFerrorfn default_value nan_strategykwargsc sXtjdi|d}||vrt|tstd|d|d||_|jd||ddS)N)r warnignorez6Arg `nan_strategy` should either be a float or one of z but got .r defaultdist_reduce_fx)super__init__ isinstancefloat ValueErrorr add_state)selfr r rrallowed_nan_strategy __class__rV/home/ubuntu/SoloSpeech/.venv/lib/python3.10/site-packages/torchmetrics/aggregation.pyr0szBaseAggregator.__init__xreturncCst|tstj|tj|jd}t|}|rH|jdkr!t d|jdkr5t dt ||}| S|jdkrC||}| S|j||<| S)zConverts input x to a tensor if not already and afterwards checks for nans that either give an error, warning or just ignored.)dtypedevicer z Encounted `nan` values in tensorrz2Encounted `nan` values in tensor. Will be removed.r)rrtorch as_tensorfloat32r%isnananyr RuntimeErrorwarningsr UserWarningr)rr"nansrrr!_cast_and_nan_check_inputBs        z(BaseAggregator._cast_and_nan_check_inputcCsdS)zOverwrite in child class.Nrrr rrr!updateVszBaseAggregator.updatecCs|jSzCompute the aggregated value.)r rrrr!computeZszBaseAggregator.compute)r )__name__ __module__ __qualname____doc__r__annotations__is_differentiablehigher_is_betterfull_state_updaterrstrrrrrr/r1r4 __classcell__rrrr!r s&    r cTeZdZdZdZ d deeefdeffdd Z deee fd d fd d Z Z S) MaxMetrica"Aggregate a stream of value into their maximum value. As input to ``forward`` and ``update`` the metric accepts the following input - ``value`` (:class:`~float` or :class:`~torch.Tensor`): a single float or an tensor of float values with arbitary shape ``(...,)``. As output of `forward` and `compute` the metric returns the following output - ``agg`` (:class:`~torch.Tensor`): scalar float tensor with aggregated maximum value over all inputs received Args: nan_strategy: options: - ``'error'``: if any `nan` values are encounted will give a RuntimeError - ``'warn'``: if any `nan` values are encounted will give a warning and continue - ``'ignore'``: all `nan` values are silently removed - a float: if a float is provided will impude any `nan` values with this value kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ValueError: If ``nan_strategy`` is not one of ``error``, ``warn``, ``ignore`` or a float Example: >>> import torch >>> from torchmetrics import MaxMetric >>> metric = MaxMetric() >>> metric.update(1) >>> metric.update(torch.tensor([2, 3])) >>> metric.compute() tensor(3.) Trrrc s(tjdttd |fi|dS)Nmaxinfrrr&tensorrrrrrrr!rs zMaxMetric.__init__r r#NcC0||}|rt|jt||_dSdSzUpdate state with data. Args: value: Either a float or tensor containing data. Additional tensor dimensions will be flattened N)r/numelr&rAr r0rrr!r1 zMaxMetric.updater r5r6r7r8r<rr=rrrrr1r>rrrr!r@_" " r@cr?) MinMetrica"Aggregate a stream of value into their minimum value. As input to ``forward`` and ``update`` the metric accepts the following input - ``value`` (:class:`~float` or :class:`~torch.Tensor`): a single float or an tensor of float values with arbitary shape ``(...,)``. As output of `forward` and `compute` the metric returns the following output - ``agg`` (:class:`~torch.Tensor`): scalar float tensor with aggregated minimum value over all inputs received Args: nan_strategy: options: - ``'error'``: if any `nan` values are encounted will give a RuntimeError - ``'warn'``: if any `nan` values are encounted will give a warning and continue - ``'ignore'``: all `nan` values are silently removed - a float: if a float is provided will impude any `nan` values with this value kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ValueError: If ``nan_strategy`` is not one of ``error``, ``warn``, ``ignore`` or a float Example: >>> import torch >>> from torchmetrics import MinMetric >>> metric = MinMetric() >>> metric.update(1) >>> metric.update(torch.tensor([2, 3])) >>> metric.compute() tensor(1.) Trrrc s&tjdttd|fi|dS)NminrBrCrErrr!rs  zMinMetric.__init__r r#NcCrFrG)r/rHr&rNr r0rrr!r1rIzMinMetric.updaterJrKrrrr!rMrLrMcsPeZdZdZ d deeefdeffdd Zdeee fdd fd d Z Z S) SumMetricaAggregate a stream of value into their sum. As input to ``forward`` and ``update`` the metric accepts the following input - ``value`` (:class:`~float` or :class:`~torch.Tensor`): a single float or an tensor of float values with arbitary shape ``(...,)``. As output of `forward` and `compute` the metric returns the following output - ``agg`` (:class:`~torch.Tensor`): scalar float tensor with aggregated sum over all inputs received Args: nan_strategy: options: - ``'error'``: if any `nan` values are encounted will give a RuntimeError - ``'warn'``: if any `nan` values are encounted will give a warning and continue - ``'ignore'``: all `nan` values are silently removed - a float: if a float is provided will impude any `nan` values with this value kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ValueError: If ``nan_strategy`` is not one of ``error``, ``warn``, ``ignore`` or a float Example: >>> import torch >>> from torchmetrics import SumMetric >>> metric = SumMetric() >>> metric.update(1) >>> metric.update(torch.tensor([2, 3])) >>> metric.compute() tensor(6.) rrrc s"tjdtd|fi|dS)Nsum)rrr&rDrErrr!rs zSumMetric.__init__r r#NcCs,||}|r|j|7_dSdSrG)r/rHr rPr0rrr!r1s zSumMetric.updaterJ) r5r6r7r8rr=rrrrr1r>rrrr!rOs$ " rOcs^eZdZdZ ddeeefdeffdd Zdeee fdd fd d Z de fd d Z Z S) CatMetricaConcatenate a stream of values. As input to ``forward`` and ``update`` the metric accepts the following input - ``value`` (:class:`~float` or :class:`~torch.Tensor`): a single float or an tensor of float values with arbitary shape ``(...,)``. As output of `forward` and `compute` the metric returns the following output - ``agg`` (:class:`~torch.Tensor`): scalar float tensor with concatenated values over all input received Args: nan_strategy: options: - ``'error'``: if any `nan` values are encounted will give a RuntimeError - ``'warn'``: if any `nan` values are encounted will give a warning and continue - ``'ignore'``: all `nan` values are silently removed - a float: if a float is provided will impude any `nan` values with this value kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ValueError: If ``nan_strategy`` is not one of ``error``, ``warn``, ``ignore`` or a float Example: >>> import torch >>> from torchmetrics import CatMetric >>> metric = CatMetric() >>> metric.update(1) >>> metric.update(torch.tensor([2, 3])) >>> metric.compute() tensor([1., 2., 3.]) rrrc stjdg|fi|dS)Ncat)rrrErrr!r7szCatMetric.__init__r r#NcCs&||}|r|j|dSdSrG)r/rHr appendr0rrr!r1>s zCatMetric.updatecCs"t|jtr|jrt|jS|jSr2)rr listrr3rrr!r4Is zCatMetric.computerJ r5r6r7r8rr=rrrrr1r4r>rrrr!rRs$  rRcsleZdZdZ ddeeefdeffdd Zddeee fd eee fd d fd d Z d e fddZ Z S) MeanMetricaAggregate a stream of value into their mean value. As input to ``forward`` and ``update`` the metric accepts the following input - ``value`` (:class:`~float` or :class:`~torch.Tensor`): a single float or an tensor of float values with arbitary shape ``(...,)``. - ``weight`` (:class:`~float` or :class:`~torch.Tensor`): a single float or an tensor of float value with arbitary shape ``(...,)``. Needs to be broadcastable with the shape of ``value`` tensor. As output of `forward` and `compute` the metric returns the following output - ``agg`` (:class:`~torch.Tensor`): scalar float tensor with aggregated (weighted) mean over all inputs received Args: nan_strategy: options: - ``'error'``: if any `nan` values are encounted will give a RuntimeError - ``'warn'``: if any `nan` values are encounted will give a warning and continue - ``'ignore'``: all `nan` values are silently removed - a float: if a float is provided will impude any `nan` values with this value kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ValueError: If ``nan_strategy`` is not one of ``error``, ``warn``, ``ignore`` or a float Example: >>> from torchmetrics import MeanMetric >>> metric = MeanMetric() >>> metric.update(1) >>> metric.update(torch.tensor([2, 3])) >>> metric.compute() tensor(2.) rrrc s8tjdtd|fi||jdtddddS)NrPrQweightr)rrr&rDrrErrr!rtszMeanMetric.__init__?r rXr#NcCs^||}||}|dkrdSt||j}|j||7_|j|7_dS)aUpdate state with data. Args: value: Either a float or tensor containing data. Additional tensor dimensions will be flattened weight: Either a float or tensor containing weights for calculating the average. Shape of weight should be able to broadcast with the shape of `value`. Default to `1.0` corresponding to simple harmonic average. rN)r/rHr& broadcast_toshaper rPrX)rr rXrrr!r1s  zMeanMetric.updatecCs |j|jSr2)r rXr3rrr!r4s zMeanMetric.computerJ)rYrVrrrr!rWPs% ( rW)r,typingrrrrr&rtorchmetrics.metricrtorchmetrics.utilities.datarr r@rMrOrRrWrrrr!s    G==;<