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from collections.abc import Sequence |
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from functools import partial |
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from typing import Any, 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.functional.image.psnr import _psnr_compute, _psnr_update |
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from torchmetrics.metric import Metric |
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from torchmetrics.utilities import rank_zero_warn |
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from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE |
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from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE |
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if not _MATPLOTLIB_AVAILABLE: |
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__doctest_skip__ = ["PeakSignalNoiseRatio.plot"] |
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class PeakSignalNoiseRatio(Metric): |
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r"""`Compute Peak Signal-to-Noise Ratio`_ (PSNR). |
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.. math:: \text{PSNR}(I, J) = 10 * \log_{10} \left(\frac{\max(I)^2}{\text{MSE}(I, J)}\right) |
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Where :math:`\text{MSE}` denotes the `mean-squared-error`_ function. |
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As input to ``forward`` and ``update`` the metric accepts the following input |
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- ``preds`` (:class:`~torch.Tensor`): Predictions from model of shape ``(N,C,H,W)`` |
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- ``target`` (:class:`~torch.Tensor`): Ground truth values of shape ``(N,C,H,W)`` |
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As output of `forward` and `compute` the metric returns the following output |
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- ``psnr`` (:class:`~torch.Tensor`): if ``reduction!='none'`` returns float scalar tensor with average PSNR value |
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over sample else returns tensor of shape ``(N,)`` with PSNR values per sample |
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Args: |
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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 and all batches. |
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kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. |
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Raises: |
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ValueError: |
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If ``dim`` is not ``None`` and ``data_range`` is not given. |
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Example: |
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>>> from torchmetrics.image import PeakSignalNoiseRatio |
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>>> psnr = PeakSignalNoiseRatio() |
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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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>>> psnr(preds, target) |
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tensor(2.5527) |
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""" |
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is_differentiable: bool = True |
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higher_is_better: bool = True |
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full_state_update: bool = False |
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plot_lower_bound: float = 0.0 |
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min_target: Tensor |
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max_target: Tensor |
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def __init__( |
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self, |
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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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**kwargs: Any, |
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) -> None: |
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super().__init__(**kwargs) |
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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 dim is None: |
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self.add_state("sum_squared_error", default=tensor(0.0), dist_reduce_fx="sum") |
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self.add_state("total", default=tensor(0), dist_reduce_fx="sum") |
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else: |
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self.add_state("sum_squared_error", default=[], dist_reduce_fx="cat") |
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self.add_state("total", default=[], dist_reduce_fx="cat") |
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self.clamping_fn = 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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self.data_range = None |
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self.add_state("min_target", default=tensor(0.0), dist_reduce_fx=torch.min) |
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self.add_state("max_target", default=tensor(0.0), dist_reduce_fx=torch.max) |
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elif isinstance(data_range, tuple): |
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self.add_state("data_range", default=tensor(data_range[1] - data_range[0]), dist_reduce_fx="mean") |
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self.clamping_fn = partial(torch.clamp, min=data_range[0], max=data_range[1]) |
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else: |
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self.add_state("data_range", default=tensor(float(data_range)), dist_reduce_fx="mean") |
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self.base = base |
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self.reduction = reduction |
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self.dim = tuple(dim) if isinstance(dim, Sequence) else dim |
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def update(self, preds: Tensor, target: Tensor) -> None: |
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"""Update state with predictions and targets.""" |
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if self.clamping_fn is not None: |
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preds = self.clamping_fn(preds) |
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target = self.clamping_fn(target) |
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sum_squared_error, num_obs = _psnr_update(preds, target, dim=self.dim) |
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if self.dim is None: |
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if self.data_range is None: |
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self.min_target = torch.minimum(target.min(), self.min_target) |
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self.max_target = torch.maximum(target.max(), self.max_target) |
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if not isinstance(self.sum_squared_error, Tensor): |
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raise TypeError( |
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f"Expected `self.sum_squared_error` to be a Tensor, but got {type(self.sum_squared_error)}" |
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) |
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if not isinstance(self.total, Tensor): |
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raise TypeError(f"Expected `self.total` to be a Tensor, but got {type(self.total)}") |
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self.sum_squared_error += sum_squared_error |
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self.total += num_obs |
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else: |
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if not isinstance(self.sum_squared_error, list): |
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raise TypeError( |
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f"Expected `self.sum_squared_error` to be a list, but got {type(self.sum_squared_error)}" |
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) |
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if not isinstance(self.total, list): |
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raise TypeError(f"Expected `self.total` to be a list, but got {type(self.total)}") |
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self.sum_squared_error.append(sum_squared_error) |
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self.total.append(num_obs) |
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def compute(self) -> Tensor: |
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"""Compute peak signal-to-noise ratio over state.""" |
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data_range = self.data_range if self.data_range is not None else self.max_target - self.min_target |
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if isinstance(self.sum_squared_error, torch.Tensor): |
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sum_squared_error = self.sum_squared_error |
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elif isinstance(self.sum_squared_error, list): |
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sum_squared_error = torch.cat([value.flatten() for value in self.sum_squared_error]) |
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else: |
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raise TypeError("Expected sum_squared_error to be a Tensor or a list of Tensors") |
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if isinstance(self.total, torch.Tensor): |
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total = self.total |
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elif isinstance(self.total, list): |
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total = torch.cat([value.flatten() for value in self.total]) |
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else: |
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raise TypeError("Expected total to be a Tensor or a list of Tensors") |
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return _psnr_compute(sum_squared_error, total, data_range, base=self.base, reduction=self.reduction) |
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def plot( |
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self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None |
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) -> _PLOT_OUT_TYPE: |
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"""Plot a single or multiple values from the metric. |
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Args: |
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val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. |
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If no value is provided, will automatically call `metric.compute` and plot that result. |
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ax: An matplotlib axis object. If provided will add plot to that axis |
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Returns: |
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Figure and Axes object |
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Raises: |
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ModuleNotFoundError: |
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If `matplotlib` is not installed |
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.. plot:: |
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:scale: 75 |
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>>> # Example plotting a single value |
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>>> import torch |
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>>> from torchmetrics.image import PeakSignalNoiseRatio |
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>>> metric = PeakSignalNoiseRatio() |
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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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>>> metric.update(preds, target) |
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>>> fig_, ax_ = metric.plot() |
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.. plot:: |
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:scale: 75 |
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>>> # Example plotting multiple values |
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>>> import torch |
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>>> from torchmetrics.image import PeakSignalNoiseRatio |
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>>> metric = PeakSignalNoiseRatio() |
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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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>>> values = [ ] |
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>>> for _ in range(10): |
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... values.append(metric(preds, target)) |
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>>> fig_, ax_ = metric.plot(values) |
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""" |
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return self._plot(val, ax) |
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