a  d@szddlmZmZddlmZmZmZddlZddlmZm Z ddl m Z ddl m Z ddlmZmZGdd d e eZdS) )ABCabstractmethod)AnyListOptionalN)Tensortensor)Metric)_check_retrieval_inputs)_flexible_bincount dim_zero_catcseZdZUdZdZeed<dZeed<dZeed<e e ed<e e ed<e e ed <de e e ed d fd d Ze e e d dddZe dddZee e e dddZZS)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_updateindexespredstargetnegN)empty_target_action ignore_indexkwargsreturnc stjfi|d|_d}||vr4td|d||_|durTt|tsTtd||_|jdgdd|jdgdd|jd gdddS) NF)errorskiprposz7Argument `empty_target_action` received a wrong value `z`.z3Argument `ignore_index` must be an integer or None.r)defaultZdist_reduce_fxrr) super__init__allow_non_binary_target ValueErrorr isinstanceintrZ add_state)selfrrrZempty_target_action_options __class__d/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/retrieval/base.pyrMszRetrievalMetric.__init__)rrrrcCsT|durtdt||||j|jd\}}}|j||j||j|dS)zGCheck shape, check and convert dtypes, flatten and add to accumulators.Nz!Argument `indexes` cannot be None)rr)r r rrrappendrr)r#rrrr&r&r'updatees   zRetrievalMetric.update)rcst|j}t|jt|j}t|\}}|||}t| }g}t tj |ddtj ||ddD]h\}}| s|j dkrtd|j dkr|tdq|j dkr|tdqv||||qv|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)ZdimrzC`compute` method was provided with a query with no positive target.rg?rgcsg|]}|qSr&)to).0xrr&r' z+RetrievalMetric.compute..)r rrrtorchsortr detachcputolistzipsplitsumrr r(r_metricstackmeanr*)r#rrindicesZ split_sizesresZ mini_predsZ mini_targetr&r-r'computers(       zRetrievalMetric.compute)rrrcCsdS)zCompute a metric over a predictions and target of a single group. This method should be overridden by subclasses. Nr&)r#rrr&r&r'r8szRetrievalMetric._metric)rN)__name__ __module__ __qualname____doc__rbool__annotations__rrrrstrrr"rrr)r=rr8 __classcell__r&r&r$r'r s& +       #r )abcrrtypingrrrr0rrZ torchmetricsr Ztorchmetrics.utilities.checksr Ztorchmetrics.utilities.datar r r r&r&r&r's