Delete layers.py

layers.py

@@ -1,42 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from utils import enumerate_spans
-from torch.nn.utils.rnn import pad_sequence
-
-class SpanEnumerationLayer(nn.Module):
-    def __init__(self, *args, **kwargs) -> None:
-        super().__init__(*args, **kwargs)
-
-
-    def compute_embeddings(self,embeddings, enumerations, operation = 'sum'):
-    
-      computed_embeddings = []
-
-      for enumeration, embedding in zip(enumerations, embeddings):
-          
-          output_embeddings = []
-          dim_size = embedding.shape[-1]
-          
-          for idx in range(len(enumeration)):
-            
-            x1,x2 = enumeration[idx]
-            output_tensor = embedding[x1:x2+1].sum(dim=0)
-
-            if(operation == 'average'):
-              divisor = abs((x2+1)-x1)
-              output_tensor=torch.div(output_tensor, divisor)  
-
-            output_embeddings.append(output_tensor)
-          computed_embeddings.append(torch.stack(output_embeddings))
-
-      return computed_embeddings
-              
-    def forward(self, embeddings, lengths):
-      
-      enumerations = enumerate_spans(lengths)  
-      computed_embeddings = self.compute_embeddings(embeddings, enumerations)
-
-      return computed_embeddings, enumerations
-