66 lines
1.9 KiB
Python
66 lines
1.9 KiB
Python
from turtle import forward
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import torch
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import torch.nn as nn
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class mean_squared_error(nn.Module):
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def __init__(self,reduction="mean"):
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super().__init__()
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self.loss_fun = nn.MSELoss(reduction=reduction)
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def forward(self,prediction:torch.Tensor, target: torch.Tensor):
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if prediction.size() != target.size() or target.ndim < 3:
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raise TypeError(f"""Inputs must be of the same shape (batch_size,channels,samples)
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got {prediction.size()} and {target.size()} instead""")
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return self.loss_fun(prediction, target)
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class mean_absolute_error(nn.Module):
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def __init__(self,reduction="mean"):
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super().__init__()
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self.loss_fun = nn.L1Loss(reduction=reduction)
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def forward(self, prediction:torch.Tensor, target: torch.Tensor):
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if prediction.size() != target.size() or target.ndim < 3:
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raise TypeError(f"""Inputs must be of the same shape (batch_size,channels,samples)
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got {prediction.size()} and {target.size()} instead""")
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return self.loss_fun(prediction, target)
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class Avergeloss(nn.Module):
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def __init__(self,losses):
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super().__init__()
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self.valid_losses = nn.ModuleList()
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for loss in losses:
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loss = self.validate_loss(loss)
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self.valid_losses.append(loss())
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def validate_loss(self,loss:str):
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if loss not in LOSS_MAP.keys():
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raise ValueError(f"Invalid loss function {loss}, available loss functions are {LOSS_MAP.keys()}")
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else:
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return LOSS_MAP[loss]
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def forward(self,prediction:torch.Tensor, target:torch.Tensor):
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loss = 0.0
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for loss_fun in self.valid_losses:
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loss += loss_fun(prediction, target)
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return loss
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LOSS_MAP = {"mea":mean_absolute_error, "mse": mean_squared_error}
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