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psnr.py
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psnr.py
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# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Optional, Sequence, Tuple, Union
import torch
from torch import Tensor, tensor
from torchmetrics.functional.image.psnr import _psnr_compute, _psnr_update
from torchmetrics.metric import Metric
from torchmetrics.utilities import rank_zero_warn
class PSNR(Metric):
r"""
Computes `peak signal-to-noise ratio <https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio>`_ (PSNR):
.. math:: \text{PSNR}(I, J) = 10 * \log_{10} \left(\frac{\max(I)^2}{\text{MSE}(I, J)}\right)
Where :math:`\text{MSE}` denotes the `mean-squared-error`_ function.
Args:
data_range:
the range of the data. If None, it is determined from the data (max - min).
The ``data_range`` must be given when ``dim`` is not None.
base: a base of a logarithm to use (default: 10)
reduction: a method to reduce metric score over labels.
- ``'elementwise_mean'``: takes the mean (default)
- ``'sum'``: takes the sum
- ``'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 and all batches.
compute_on_step:
Forward only calls ``update()`` and return None if this is set to False. default: True
dist_sync_on_step:
Synchronize metric state across processes at each ``forward()``
before returning the value at the step. default: False
process_group:
Specify the process group on which synchronization is called. default: None (which selects the entire world)
Raises:
ValueError:
If ``dim`` is not ``None`` and ``data_range`` is not given.
Example:
>>> from torchmetrics import PSNR
>>> psnr = PSNR()
>>> preds = torch.tensor([[0.0, 1.0], [2.0, 3.0]])
>>> target = torch.tensor([[3.0, 2.0], [1.0, 0.0]])
>>> psnr(preds, target)
tensor(2.5527)
.. note::
Half precision is only support on GPU for this metric
"""
min_target: Tensor
max_target: Tensor
def __init__(
self,
data_range: Optional[float] = None,
base: float = 10.0,
reduction: str = "elementwise_mean",
dim: Optional[Union[int, Tuple[int, ...]]] = None,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
) -> None:
super().__init__(
compute_on_step=compute_on_step,
dist_sync_on_step=dist_sync_on_step,
process_group=process_group,
)
if dim is None and reduction != "elementwise_mean":
rank_zero_warn(f"The `reduction={reduction}` will not have any effect when `dim` is None.")
if dim is None:
self.add_state("sum_squared_error", default=tensor(0.0), dist_reduce_fx="sum")
self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
else:
self.add_state("sum_squared_error", default=[])
self.add_state("total", default=[])
if data_range is None:
if dim is not None:
# Maybe we could use `torch.amax(target, dim=dim) - torch.amin(target, dim=dim)` in PyTorch 1.7 to
# calculate `data_range` in the future.
raise ValueError("The `data_range` must be given when `dim` is not None.")
self.data_range = None
self.add_state("min_target", default=tensor(0.0), dist_reduce_fx=torch.min)
self.add_state("max_target", default=tensor(0.0), dist_reduce_fx=torch.max)
else:
self.add_state("data_range", default=tensor(float(data_range)), dist_reduce_fx="mean")
self.base = base
self.reduction = reduction
self.dim = tuple(dim) if isinstance(dim, Sequence) else dim
def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore
"""Update state with predictions and targets.
Args:
preds: Predictions from model
target: Ground truth values
"""
sum_squared_error, n_obs = _psnr_update(preds, target, dim=self.dim)
if self.dim is None:
if self.data_range is None:
# keep track of min and max target values
self.min_target = min(target.min(), self.min_target)
self.max_target = max(target.max(), self.max_target)
self.sum_squared_error += sum_squared_error
self.total += n_obs
else:
self.sum_squared_error.append(sum_squared_error)
self.total.append(n_obs)
def compute(self) -> Tensor:
"""Compute peak signal-to-noise ratio over state."""
if self.data_range is not None:
data_range = self.data_range
else:
data_range = self.max_target - self.min_target
if self.dim is None:
sum_squared_error = self.sum_squared_error
total = self.total
else:
sum_squared_error = torch.cat([values.flatten() for values in self.sum_squared_error])
total = torch.cat([values.flatten() for values in self.total])
return _psnr_compute(sum_squared_error, total, data_range, base=self.base, reduction=self.reduction)