190 lines
4.8 KiB
Python
190 lines
4.8 KiB
Python
import torch.nn.functional as F
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import torch
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from pytorch_lightning.metrics.metric import Metric
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__all__ = ['MSE', 'RMSE', 'MAE', 'RMSLE']
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class MSE(Metric):
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"""
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Computes the mean squared loss.
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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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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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super().__init__(name='mse')
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if reduction == 'elementwise_mean':
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reduction = 'mean'
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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 F.mse_loss(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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"""
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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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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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super().__init__(name='rmse')
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if reduction == 'elementwise_mean':
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reduction = 'mean'
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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 torch.sqrt(F.mse_loss(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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"""
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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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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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super().__init__(name='mae')
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if reduction == 'elementwise_mean':
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reduction = 'mean'
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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 F.l1_loss(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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"""
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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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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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super().__init__(name='rmsle')
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if reduction == 'elementwise_mean':
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reduction = 'mean'
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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 F.mse_loss(torch.log(pred + 1), torch.log(target + 1), self.reduction)
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