From cfff2bed115c6d054b4827392e3c5ebe987c1cc9 Mon Sep 17 00:00:00 2001
From: shahules786 <shahules786@gmail.com>
Date: Sun, 25 Sep 2022 11:58:43 +0530
Subject: [PATCH] rmv loss

---
 enhancer/utils/loss.py | 65 ------------------------------------------
 1 file changed, 65 deletions(-)
 delete mode 100644 enhancer/utils/loss.py

diff --git a/enhancer/utils/loss.py b/enhancer/utils/loss.py
deleted file mode 100644
index 3c1cc06..0000000
--- a/enhancer/utils/loss.py
+++ /dev/null
@@ -1,65 +0,0 @@
-from turtle import forward
-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}
-
-