Remove old code

library/model_utils.py

@@ -1,6 +1,5 @@
 import torch
 from torch import nn, Tensor
-import torch.nn.functional as F
 
 
 class AID(nn.Module):
@@ -17,63 +16,3 @@ class AID(nn.Module):
             pos_part = (x >= 0).float() * x * self.p
             neg_part = (x < 0).float() * x * (1 - self.p)
             return pos_part + neg_part
-
-
-# class AID(nn.Module):
-#     def __init__(self, dropout_prob=0.9):
-#         super().__init__()
-#         self.p = dropout_prob
-#
-#     def forward(self, x: Tensor):
-#         if self.training:
-#             # Use boolean masks and torch.where for better efficiency
-#             pos_mask = x > 0
-#
-#             # Process positive values (keep with probability p)
-#             pos_vals = torch.where(pos_mask, x, torch.zeros_like(x))
-#             pos_dropped = F.dropout(pos_vals, p=1 - self.p, training=True)
-#             if self.p > 0:
-#                 pos_dropped = pos_dropped / self.p
-#
-#             # Process negative values (keep with probability 1-p)
-#             neg_vals = torch.where(~pos_mask, x, torch.zeros_like(x))
-#             neg_dropped = F.dropout(neg_vals, p=self.p, training=True)
-#             if (1 - self.p) > 0:
-#                 neg_dropped = neg_dropped / (1 - self.p)
-#
-#             return pos_dropped + neg_dropped
-#         else:
-#             # Simplified test-time behavior
-#             return torch.where(x > 0, self.p * x, (1 - self.p) * (-x))
-
-
-# class AID_GELU(nn.Module):
-#     def __init__(self, p=0.9, approximate="none"):
-#         super().__init__()
-#         self.p = p
-#         self.gelu = nn.GELU(approximate=approximate)
-#
-#     def forward(self, x):
-#         # Apply GELU first
-#         x = self.gelu(x)
-#
-#         if self.training:
-#             # Create masks once and reuse
-#             pos_mask = x > 0
-#
-#             # Process positive values (keep with probability p)
-#             pos_vals = torch.where(pos_mask, x, torch.zeros_like(x))
-#             pos_dropped = F.dropout(pos_vals, p=1 - self.p, training=True)
-#             if self.p > 0:
-#                 pos_dropped = pos_dropped / self.p
-#
-#             # Process negative values (keep with probability 1-p)
-#             neg_vals = torch.where(~pos_mask, x, torch.zeros_like(x))
-#             neg_dropped = F.dropout(neg_vals, p=self.p, training=True)
-#             if (1 - self.p) > 0:
-#                 neg_dropped = neg_dropped / (1 - self.p)
-#
-#             return pos_dropped + neg_dropped
-#         else:
-#             # Test time behavior - simplify with direct where operations
-#             return torch.where(x > 0, self.p * x, (1 - self.p) * x)