o yi@szddlmZmZddlmZmZmZddlZddlmZm Z ddl m Z ddl m Z ddlmZmZGdd d e eZdS) )ABCabstractmethod)AnyListOptionalN)Tensortensor)Metric)_check_retrieval_inputs)_flexible_bincount dim_zero_catc seZdZUdZdZeed<dZeed<dZeed<e e ed<e e ed<e e ed < dd e d e e dedd ffdd Zde d e de dd fddZde fddZede d e de fddZZS)RetrievalMetricaWorks 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 .. note:: ``indexes``, ``preds`` and ``target`` must have the same dimension and will be flatten to single dimension once provided. .. note:: Predictions will be first grouped by ``indexes`` and then the real metric, defined by overriding the `_metric` method, will be computed as the mean of the scores over each query. As output to ``forward`` and ``compute`` the metric returns the following output: - ``metric`` (:class:`~torch.Tensor`): A tensor as computed by ``_metric`` if the number of positive targets is at least 1, otherwise behave as specified by ``self.empty_target_action``. Args: empty_target_action: Specify what to do with queries that do not have at least a positive or negative (depend on metric) 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. 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. Fis_differentiableThigher_is_betterfull_state_updateindexespredstargetnegNempty_target_action ignore_indexkwargsreturnc stjd i|d|_d}||vrtd|d||_|dur*t|ts*td||_|jdgdd|jdgdd|jd gdddS) NF)errorskiprposz7Argument `empty_target_action` received a wrong value `z`.z3Argument `ignore_index` must be an integer or None.r)defaultdist_reduce_fxrr) super__init__allow_non_binary_target ValueErrorr isinstanceintr add_state)selfrrrempty_target_action_options __class__rO/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/retrieval/base.pyr LszRetrievalMetric.__init__cCsT|durtdt||||j|jd\}}}|j||j||j|dS)zGCheck shape, check and convert dtypes, flatten and add to accumulators.Nz!Argument `indexes` cannot be None)r!r)r"r r!rrappendrr)r&rrrrrr*updateds  zRetrievalMetric.updatecst|j}t|jt|j}t|\}}|||}t| }g}t tj |ddtj ||ddD]4\}}| sf|j dkrLtd|j dkrY|tdq;|j dkre|tdq;||||q;|rtfd d |DStdS) 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 positive targets is at least 1, otherwise behave as specified by ``self.empty_target_action``. r)dimrzC`compute` method was provided with a query with no positive target.rg?rgcsg|]}|qSr)to).0xrrr* sz+RetrievalMetric.compute..)r rrrtorchsortr detachcputolistzipsplitsumrr"r+r_metricstackmeanr.)r&rrindices split_sizesres mini_preds mini_targetrr1r*computeqs*       .zRetrievalMetric.computecCsdS)zCompute a metric over a predictions and target of a single group. This method should be overridden by subclasses. Nr)r&rrrrr*r;szRetrievalMetric._metric)rN)__name__ __module__ __qualname____doc__rbool__annotations__rrrrstrrr$rr r,rCrr; __classcell__rrr(r*r s.  *      " r )abcrrtypingrrrr3rr torchmetricsr torchmetrics.utilities.checksr torchmetrics.utilities.datar r r rrrr*s