a  d? @sddlmZmZmZddlZddlmZmZddlmZddl m Z m Z deeee eded d d Z deeeeeeed ffeeefd ddZdeeeee ee e ffe edeeeeed ffedddZdS))OptionalTupleUnionN)Tensortensor)Literal)rank_zero_warnreduce$@elementwise_mean)r sumnoneN)sum_squared_errorn_obs data_rangebase reductionreturncCs>dt|t||}|dtt|}t||dS)aCompute peak signal-to-noise ratio. Args: sum_squared_error: Sum of square of errors over all observations n_obs: Number of predictions or observations data_range: the range of the data. If None, it is determined from the data (max - min). ``data_range`` must be given when ``dim`` is not None. base: a base of a logarithm to use reduction: a method to reduce metric score over labels. - ``'elementwise_mean'``: takes the mean (default) - ``'sum'``: takes the sum - ``'none'`` or ``None``: no reduction will be applied Example: >>> preds = torch.tensor([[0.0, 1.0], [2.0, 3.0]]) >>> target = torch.tensor([[3.0, 2.0], [1.0, 0.0]]) >>> data_range = target.max() - target.min() >>> sum_squared_error, n_obs = _psnr_update(preds, target) >>> _psnr_compute(sum_squared_error, n_obs, data_range) tensor(2.5527)  )r)torchlogrr )rrrrrZ psnr_base_eZ psnr_valsrk/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/functional/image/psnr.py _psnr_computesr.)predstargetdimrcCs|dur8tt||d}t||jd}||fS||}tj|||d}t|trb|gnt|}|st||jd}n$t| |jd| }| |}||fS)aZUpdate and return variables required to compute peak signal-to-noise ratio. Args: preds: Predicted tensor target: Ground truth tensor dim: Dimensions to reduce PSNR scores over provided as either an integer or a list of integers. Default is None meaning scores will be reduced across all dimensions. Nr)devicer) rr powrZnumelr isinstanceintlistsizeprodZ expand_as)rrrrrdiffZdim_listrrr _psnr_update:s r')rrrrrrrcCs|dur |dkr td|d|durJ|dur8td||}n\t|trtj||d|dd}tj||d|dd}t|d|d}n tt |}t |||d \}}t |||||d S) aCompute the peak signal-to-noise ratio. Args: preds: estimated signal target: groun truth signal data_range: the range of the data. If None, it is determined from the data (max - min). If a tuple is provided then the range is calculated as the difference and input is clamped between the values. The ``data_range`` must be given when ``dim`` is not None. base: a base of a logarithm to use reduction: a method to reduce metric score over labels. - ``'elementwise_mean'``: takes the mean (default) - ``'sum'``: takes the sum - ``'none'`` or None``: no reduction will be applied dim: Dimensions to reduce PSNR scores over provided as either an integer or a list of integers. Default is None meaning scores will be reduced across all dimensions. Return: Tensor with PSNR score Raises: ValueError: If ``dim`` is not ``None`` and ``data_range`` is not provided. Example: >>> from torchmetrics.functional.image import peak_signal_noise_ratio >>> pred = torch.tensor([[0.0, 1.0], [2.0, 3.0]]) >>> target = torch.tensor([[3.0, 2.0], [1.0, 0.0]]) >>> peak_signal_noise_ratio(pred, target) tensor(2.5527) .. note:: Half precision is only support on GPU for this metric Nr zThe `reduction=z.` will not have any effect when `dim` is None.z6The `data_range` must be given when `dim` is not None.r)minmaxr)rr) r ValueErrorr*r)r!tuplerclamprfloatr'r)rrrrrrrrrrrpeak_signal_noise_ratioZs.  r/)r r )N)Nr r N)typingrrrrrrZtyping_extensionsrZtorchmetrics.utilitiesrr r.rr"r'r/rrrrsD  & #