294 lines
7.5 KiB
Python
294 lines
7.5 KiB
Python
from typing import Sequence
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import torch
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from pytorch_lightning.metrics.functional.regression import (
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mae,
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mse,
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psnr,
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rmse,
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rmsle,
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ssim
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)
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from pytorch_lightning.metrics.metric import Metric
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class MSE(Metric):
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"""
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Computes the mean squared loss.
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Example:
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>>> pred = torch.tensor([0., 1, 2, 3])
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>>> target = torch.tensor([0., 1, 2, 2])
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>>> metric = MSE()
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>>> metric(pred, target)
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tensor(0.2500)
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"""
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def __init__(
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self,
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reduction: str = 'elementwise_mean',
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):
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"""
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Args:
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reduction: a method for reducing mse over labels (default: takes the mean)
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Available reduction methods:
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- elementwise_mean: takes the mean
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- none: pass array
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- sum: add elements
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"""
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super().__init__(name='mse')
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self.reduction = reduction
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def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
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"""
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Actual metric computation
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Args:
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pred: predicted labels
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target: ground truth labels
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Return:
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A Tensor with the mse loss.
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"""
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return mse(pred, target, self.reduction)
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class RMSE(Metric):
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"""
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Computes the root mean squared loss.
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Example:
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>>> pred = torch.tensor([0., 1, 2, 3])
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>>> target = torch.tensor([0., 1, 2, 2])
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>>> metric = RMSE()
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>>> metric(pred, target)
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tensor(0.5000)
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"""
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def __init__(
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self,
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reduction: str = 'elementwise_mean',
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):
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"""
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Args:
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reduction: a method for reducing mse over labels (default: takes the mean)
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Available reduction methods:
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- elementwise_mean: takes the mean
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- none: pass array
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- sum: add elements
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"""
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super().__init__(name='rmse')
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self.reduction = reduction
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def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
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"""
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Actual metric computation
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Args:
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pred: predicted labels
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target: ground truth labels
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Return:
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A Tensor with the rmse loss.
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"""
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return rmse(pred, target, self.reduction)
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class MAE(Metric):
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"""
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Computes the root mean absolute loss or L1-loss.
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Example:
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>>> pred = torch.tensor([0., 1, 2, 3])
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>>> target = torch.tensor([0., 1, 2, 2])
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>>> metric = MAE()
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>>> metric(pred, target)
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tensor(0.2500)
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"""
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def __init__(
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self,
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reduction: str = 'elementwise_mean',
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):
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"""
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Args:
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reduction: a method for reducing mse over labels (default: takes the mean)
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Available reduction methods:
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- elementwise_mean: takes the mean
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- none: pass array
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- sum: add elements
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"""
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super().__init__(name='mae')
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self.reduction = reduction
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def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
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"""
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Actual metric computation
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Args:
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pred: predicted labels
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target: ground truth labels
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Return:
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A Tensor with the mae loss.
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"""
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return mae(pred, target, self.reduction)
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class RMSLE(Metric):
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"""
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Computes the root mean squared log loss.
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Example:
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>>> pred = torch.tensor([0., 1, 2, 3])
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>>> target = torch.tensor([0., 1, 2, 2])
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>>> metric = RMSLE()
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>>> metric(pred, target)
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tensor(0.0207)
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"""
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def __init__(
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self,
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reduction: str = 'elementwise_mean',
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):
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"""
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Args:
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reduction: a method for reducing mse over labels (default: takes the mean)
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Available reduction methods:
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- elementwise_mean: takes the mean
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- none: pass array
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- sum: add elements
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"""
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super().__init__(name='rmsle')
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self.reduction = reduction
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def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
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"""
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Actual metric computation
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Args:
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pred: predicted labels
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target: ground truth labels
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Return:
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A Tensor with the rmsle loss.
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"""
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return rmsle(pred, target, self.reduction)
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class PSNR(Metric):
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"""
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Computes the peak signal-to-noise ratio
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Example:
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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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>>> metric = PSNR()
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>>> metric(pred, target)
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tensor(2.5527)
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"""
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def __init__(
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self,
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data_range: float = None,
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base: int = 10,
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reduction: str = 'elementwise_mean'
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):
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"""
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Args:
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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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base: a base of a logarithm to use (default: 10)
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reduction: method for reducing psnr (default: takes the mean)
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Available reduction methods:
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- elementwise_mean: takes the mean
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- none: pass array
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- sum: add elements
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"""
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super().__init__(name='psnr')
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self.data_range = data_range
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self.base = float(base)
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self.reduction = reduction
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def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
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"""
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Actual metric computation
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Args:
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pred: predicted labels
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target: ground truth labels
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Return:
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A Tensor with psnr score.
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"""
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return psnr(pred, target, self.data_range, self.base, self.reduction)
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class SSIM(Metric):
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"""
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Computes Structual Similarity Index Measure
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Example:
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>>> pred = torch.rand([16, 1, 16, 16])
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>>> target = pred * 1.25
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>>> metric = SSIM()
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>>> metric(pred, target)
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tensor(0.9520)
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"""
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def __init__(
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self,
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kernel_size: Sequence[int] = (11, 11),
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sigma: Sequence[float] = (1.5, 1.5),
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reduction: str = "elementwise_mean",
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data_range: float = None,
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k1: float = 0.01,
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k2: float = 0.03
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):
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"""
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Args:
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kernel_size: Size of the gaussian kernel. Default: (11, 11)
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sigma: Standard deviation of the gaussian kernel. Default: (1.5, 1.5)
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reduction: A method for reducing ssim. Default: ``elementwise_mean``
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Available reduction methods:
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- elementwise_mean: takes the mean
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- none: pass away
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- sum: add elements
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data_range: Range of the image. If ``None``, it is determined from the image (max - min)
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k1: Parameter of SSIM. Default: 0.01
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k2: Parameter of SSIM. Default: 0.03
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"""
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super().__init__(name="ssim")
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self.kernel_size = kernel_size
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self.sigma = sigma
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self.reduction = reduction
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self.data_range = data_range
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self.k1 = k1
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self.k2 = k2
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def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
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"""
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Actual metric computation
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Args:
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pred: Estimated image
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target: Ground truth image
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Return:
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torch.Tensor: SSIM Score
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"""
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return ssim(pred, target, self.kernel_size, self.sigma, self.reduction, self.data_range, self.k1, self.k2)
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