a  d@sddlmZmZmZmZddlZddlmZmZddlm Z ddl m Z ddl m Z mZddlmZddlmZmZes~d gZGd d d e ZdS) )AnyOptionalSequenceUnionN)Tensortensor)retrieval_fall_out)RetrievalMetric)_flexible_bincount dim_zero_cat)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPERetrievalFallOut.plotcseZdZUdZdZeed<dZeed<dZeed<dZ e ed<dZ e ed <de e ee eed d fd d ZedddZeeedddZde eeeefe eedddZZS)RetrievalFallOuta3 Compute `Fall-out`_. Works with binary target data. Accepts float predictions from a model output. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, ...)`` - ``target`` (:class:`~torch.Tensor`): A long or bool tensor of shape ``(N, ...)`` - ``indexes`` (:class:`~torch.Tensor`): A long tensor of shape ``(N, ...)`` which indicate to which query a prediction belongs As output to ``forward`` and ``compute`` the metric returns the following output: - ``fo@k`` (:class:`~torch.Tensor`): A tensor with the computed metric All ``indexes``, ``preds`` and ``target`` must have the same dimension and will be flatten at the beginning, so that for example, a tensor of shape ``(N, M)`` is treated as ``(N * M, )``. Predictions will be first grouped by ``indexes`` and then will be computed as the mean of the metric over each query. Args: empty_target_action: Specify what to do with queries that do not have at least a negative ``target``. Choose from: - ``'neg'``: those queries count as ``0.0`` (default) - ``'pos'``: those queries count as ``1.0`` - ``'skip'``: skip those queries; if all queries are skipped, ``0.0`` is returned - ``'error'``: raise a ``ValueError`` ignore_index: Ignore predictions where the target is equal to this number. top_k: Consider only the top k elements for each query (default: `None`, which considers them all) kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ValueError: If ``empty_target_action`` is not one of ``error``, ``skip``, ``neg`` or ``pos``. ValueError: If ``ignore_index`` is not `None` or an integer. ValueError: If ``top_k`` is not ``None`` or not an integer greater than 0. Example: >>> from torchmetrics.retrieval import RetrievalFallOut >>> indexes = tensor([0, 0, 0, 1, 1, 1, 1]) >>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2]) >>> target = tensor([False, False, True, False, True, False, True]) >>> fo = RetrievalFallOut(top_k=2) >>> fo(preds, target, indexes=indexes) tensor(0.5000) Fis_differentiablehigher_is_betterfull_state_updateplot_lower_bound?plot_upper_boundposN)empty_target_action ignore_indextop_kkwargsreturnc sDtjf||d||dur:t|tr2|dks:td||_dS)N)rrrz,`top_k` has to be a positive integer or None)super__init__ isinstanceint ValueErrorr)selfrrrr __class__h/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/retrieval/fall_out.pyrWszRetrievalFallOut.__init__)rcst|j}t|jt|j}t|\}}|||}t| }g}t tj |ddtj ||ddD]l\}}d| s|j dkrtd|j dkr|tdq|j dkr|td qv||||qv|rtfd d |DStd S) aFirst concat state ``indexes``, ``preds`` and ``target`` since they were stored as lists. After that, compute list of groups that will help in keeping together predictions about the same query. Finally, for each group compute the `_metric` if the number of negative targets is at least 1, otherwise behave as specified by `self.empty_target_action`. r)ZdimerrorzC`compute` method was provided with a query with no negative target.rrnegrcsg|]}|qSr&)to).0xpredsr&r' z,RetrievalFallOut.compute..)r indexesr/targettorchsortr detachcputolistzipsplitsumrr"appendr_metricstackmeanr+)r#r2r3indicesZ split_sizesresZ mini_predsZ mini_targetr&r.r'computehs(        zRetrievalFallOut.compute)r/r3rcCst|||jdS)N)r)rr)r#r/r3r&r&r'r=szRetrievalFallOut._metric)valaxrcCs |||S)a[Plot 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 >>> import torch >>> from torchmetrics.retrieval import RetrievalFallOut >>> # Example plotting a single value >>> metric = RetrievalFallOut() >>> metric.update(torch.rand(10,), torch.randint(2, (10,)), indexes=torch.randint(2,(10,))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> import torch >>> from torchmetrics.retrieval import RetrievalFallOut >>> # Example plotting multiple values >>> metric = RetrievalFallOut() >>> values = [] >>> for _ in range(10): ... values.append(metric(torch.rand(10,), torch.randint(2, (10,)), indexes=torch.randint(2,(10,)))) >>> fig, ax = metric.plot(values) )Z_plot)r#rCrDr&r&r'plots(r)rNN)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrstrrr!rrrrBr=rrr rrE __classcell__r&r&r$r'rs. 3     #r)typingrrrrr4rrZ*torchmetrics.functional.retrieval.fall_outrZtorchmetrics.retrieval.baser Ztorchmetrics.utilities.datar r Ztorchmetrics.utilities.importsr Ztorchmetrics.utilities.plotr rZ__doctest_skip__rr&r&r&r's