mv loss from utils

enhancer/loss.py

@@ -0,0 +1,64 @@
+import torch
+import torch.nn as nn
+
+
+class mean_squared_error(nn.Module):
+
+    def __init__(self,reduction="mean"):
+        super().__init__()
+
+        self.loss_fun = nn.MSELoss(reduction=reduction)
+
+    def forward(self,prediction:torch.Tensor, target: torch.Tensor):
+
+        if prediction.size() != target.size() or target.ndim < 3:
+            raise TypeError(f"""Inputs must be of the same shape (batch_size,channels,samples) 
+                            got {prediction.size()} and {target.size()} instead""")
+
+        return self.loss_fun(prediction, target)
+
+class mean_absolute_error(nn.Module):
+
+    def __init__(self,reduction="mean"):
+        super().__init__()
+
+        self.loss_fun = nn.L1Loss(reduction=reduction)
+
+    def forward(self, prediction:torch.Tensor, target: torch.Tensor):
+
+        if prediction.size() != target.size() or target.ndim < 3:
+            raise TypeError(f"""Inputs must be of the same shape (batch_size,channels,samples) 
+                            got {prediction.size()} and {target.size()} instead""")
+
+        return self.loss_fun(prediction, target)
+
+class Avergeloss(nn.Module):
+
+    def __init__(self,losses):
+        super().__init__()
+
+        self.valid_losses = nn.ModuleList()
+        for loss in losses:
+            loss = self.validate_loss(loss)
+            self.valid_losses.append(loss())
+
+
+    def validate_loss(self,loss:str):
+        if loss not in LOSS_MAP.keys():
+            raise ValueError(f"Invalid loss function {loss}, available loss functions are {LOSS_MAP.keys()}")
+        else:
+            return LOSS_MAP[loss]
+
+    def forward(self,prediction:torch.Tensor, target:torch.Tensor):
+        loss = 0.0
+        for loss_fun in self.valid_losses:
+            loss += loss_fun(prediction, target)
+        
+        return loss
+
+            
+
+
+LOSS_MAP = {"mea":mean_absolute_error, "mse": mean_squared_error}
+
+