a  dz@sddlZddlmZddlZddlmZddlmZddlmZddl m Z eeeeeeeee eeeeeeefd dd Z eeeeed d d Z eeed ddZdS)N)Tuple)Tensor) _check_data_shape_to_num_outputs)rank_zero_warn)_check_same_shape) predstargetmean_xmean_yvar_xvar_ycorr_xyn_prior num_outputsreturnc Cs"t||t||||jd} |dkp2| dk} | rn|||d|| } |||d|| } n|d} |d} || 7}| r||| ||d7}||| ||d7}n,||d| d7}||d| d7}||| ||d7}| }| }||||||fS)a[Update and returns variables required to compute Pearson Correlation Coefficient. Check for same shape of input tensors. Args: preds: estimated scores target: ground truth scores mean_x: current mean estimate of x tensor mean_y: current mean estimate of y tensor var_x: current variance estimate of x tensor var_y: current variance estimate of y tensor corr_xy: current covariance estimate between x and y tensor n_prior: current number of observed observations num_outputs: Number of outputs in multioutput setting r)rrshapemeansumvar) rrr r r r r rrZn_obsZcondZmx_newZmy_newrs/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/functional/regression/pearson.py_pearson_corrcoef_updates&     r)r r r nbrcCs||d}||d}||d}|jtjkrP|jtdkrP|}|}tt|jj}||k s|||k rt d|jdt ||| }t |ddS)aCompute the final pearson correlation based on accumulated statistics. Args: var_x: variance estimate of x tensor var_y: variance estimate of y tensor corr_xy: covariance estimate between x and y tensor nb: number of observations rcpuzThe variance of predictions or target is close to zero. This can cause instability in Pearson correlationcoefficient, leading to wrong results. Consider re-scaling the input if possible or computing using alarger dtype (currently using z).gg?)dtypetorchfloat16deviceZbfloat16mathsqrtZfinfoZepsanyr UserWarningZsqueezeclamp)r r r rboundZcorrcoefrrr_pearson_corrcoef_computePs    r%)rrrc Cs|jdkr|jdnd}tj||j|jd}|||}}}|||}}} t|||||||| |jdkrdn|jdd \} } }}}} t|||| S)aCompute pearson correlation coefficient. Args: preds: estimated scores target: ground truth scores Example (single output regression): >>> from torchmetrics.functional.regression import pearson_corrcoef >>> target = torch.tensor([3, -0.5, 2, 7]) >>> preds = torch.tensor([2.5, 0.0, 2, 8]) >>> pearson_corrcoef(preds, target) tensor(0.9849) Example (multi output regression): >>> from torchmetrics.functional.regression import pearson_corrcoef >>> target = torch.tensor([[3, -0.5], [2, 7]]) >>> preds = torch.tensor([[2.5, 0.0], [2, 8]]) >>> pearson_corrcoef(preds, target) tensor([1., 1.]) r)rr)r) ndimrrzerosrrclonerr%) rrd_tempr r r r r r_rrrpearson_corrcoefus&r.)rtypingrrrZ(torchmetrics.functional.regression.utilsrZtorchmetrics.utilitiesrZtorchmetrics.utilities.checksrintrr%r.rrrrs0      8 %