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from typing import Optional, Union |
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import torch |
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from torch import Tensor, tensor |
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from typing_extensions import Literal |
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from torchmetrics.utilities import rank_zero_warn, reduce |
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def _psnr_compute( |
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sum_squared_error: Tensor, |
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num_obs: Tensor, |
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data_range: Tensor, |
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base: float = 10.0, |
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reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean", |
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) -> Tensor: |
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"""Compute peak signal-to-noise ratio. |
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Args: |
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sum_squared_error: Sum of square of errors over all observations |
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num_obs: Number of predictions or observations |
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data_range: the range of the data. If None, it is determined from the data (max - min). |
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``data_range`` must be given when ``dim`` is not None. |
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base: a base of a logarithm to use |
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reduction: a method to reduce metric score over labels. |
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- ``'elementwise_mean'``: takes the mean (default) |
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- ``'sum'``: takes the sum |
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- ``'none'`` or ``None``: no reduction will be applied |
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Example: |
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>>> preds = torch.tensor([[0.0, 1.0], [2.0, 3.0]]) |
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>>> target = torch.tensor([[3.0, 2.0], [1.0, 0.0]]) |
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>>> data_range = target.max() - target.min() |
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>>> sum_squared_error, num_obs = _psnr_update(preds, target) |
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>>> _psnr_compute(sum_squared_error, num_obs, data_range) |
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tensor(2.5527) |
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""" |
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psnr_base_e = 2 * torch.log(data_range) - torch.log(sum_squared_error / num_obs) |
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psnr_vals = psnr_base_e * (10 / torch.log(tensor(base))) |
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return reduce(psnr_vals, reduction=reduction) |
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def _psnr_update( |
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preds: Tensor, |
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target: Tensor, |
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dim: Optional[Union[int, tuple[int, ...]]] = None, |
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) -> tuple[Tensor, Tensor]: |
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"""Update and return variables required to compute peak signal-to-noise ratio. |
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Args: |
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preds: Predicted tensor |
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target: Ground truth tensor |
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dim: Dimensions to reduce PSNR scores over provided as either an integer or a list of integers. |
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Default is None meaning scores will be reduced across all dimensions. |
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""" |
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if not preds.is_floating_point(): |
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preds = preds.to(torch.float32) |
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if not target.is_floating_point(): |
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target = target.to(torch.float32) |
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if dim is None: |
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sum_squared_error = torch.sum(torch.pow(preds - target, 2)) |
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num_obs = tensor(target.numel(), device=target.device) |
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return sum_squared_error, num_obs |
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diff = preds - target |
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sum_squared_error = torch.sum(diff * diff, dim=dim) |
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dim_list = [dim] if isinstance(dim, int) else list(dim) |
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if not dim_list: |
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num_obs = tensor(target.numel(), device=target.device) |
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else: |
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num_obs = tensor(target.size(), device=target.device)[dim_list].prod() |
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num_obs = num_obs.expand_as(sum_squared_error) |
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return sum_squared_error, num_obs |
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def peak_signal_noise_ratio( |
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preds: Tensor, |
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target: Tensor, |
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data_range: Optional[Union[float, tuple[float, float]]] = None, |
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base: float = 10.0, |
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reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean", |
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dim: Optional[Union[int, tuple[int, ...]]] = None, |
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) -> Tensor: |
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"""Compute the peak signal-to-noise ratio. |
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Args: |
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preds: estimated signal |
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target: groun truth signal |
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data_range: |
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the range of the data. If None, it is determined from the data (max - min). If a tuple is provided then |
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the range is calculated as the difference and input is clamped between the values. |
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The ``data_range`` must be given when ``dim`` is not None. |
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base: a base of a logarithm to use |
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reduction: a method to reduce metric score over labels. |
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- ``'elementwise_mean'``: takes the mean (default) |
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- ``'sum'``: takes the sum |
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- ``'none'`` or None``: no reduction will be applied |
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dim: |
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Dimensions to reduce PSNR scores over provided as either an integer or a list of integers. Default is |
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None meaning scores will be reduced across all dimensions. |
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Return: |
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Tensor with PSNR score |
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Raises: |
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ValueError: |
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If ``dim`` is not ``None`` and ``data_range`` is not provided. |
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Example: |
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>>> from torchmetrics.functional.image import peak_signal_noise_ratio |
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>>> pred = torch.tensor([[0.0, 1.0], [2.0, 3.0]]) |
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>>> target = torch.tensor([[3.0, 2.0], [1.0, 0.0]]) |
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>>> peak_signal_noise_ratio(pred, target) |
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tensor(2.5527) |
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.. attention:: |
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Half precision is only support on GPU for this metric. |
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""" |
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if dim is None and reduction != "elementwise_mean": |
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rank_zero_warn(f"The `reduction={reduction}` will not have any effect when `dim` is None.") |
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if data_range is None: |
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if dim is not None: |
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raise ValueError("The `data_range` must be given when `dim` is not None.") |
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data_range = target.max() - target.min() |
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elif isinstance(data_range, tuple): |
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preds = torch.clamp(preds, min=data_range[0], max=data_range[1]) |
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target = torch.clamp(target, min=data_range[0], max=data_range[1]) |
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data_range = tensor(data_range[1] - data_range[0]) |
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else: |
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data_range = tensor(float(data_range)) |
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sum_squared_error, num_obs = _psnr_update(preds, target, dim=dim) |
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return _psnr_compute(sum_squared_error, num_obs, data_range, base=base, reduction=reduction) |
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