Fix for perfect match condition in self augmentation match score

algorithms/csd.py

@@ -28,11 +28,11 @@ class CSD(BaseAlgo):
         self.K, m, self.num_classes = 1, H_dim, self.args.out_classes 
         num_domains = self.total_domains
 
-        self.sms = torch.nn.Parameter(torch.normal(0, 1e-3, size=[self.K+1, m, self.num_classes], dtype=torch.float, device='cuda:0'), requires_grad=True)
-        self.sm_biases = torch.nn.Parameter(torch.normal(0, 1e-3, size=[self.K+1, self.num_classes], dtype=torch.float, device='cuda:0'), requires_grad=True)
+        self.sms = torch.nn.Parameter(torch.normal(0, 1e-1, size=[self.K+1, m, self.num_classes], dtype=torch.float, device='cuda:0'), requires_grad=True)
+        self.sm_biases = torch.nn.Parameter(torch.normal(0, 1e-1, size=[self.K+1, self.num_classes], dtype=torch.float, device='cuda:0'), requires_grad=True)
     
         self.embs = torch.nn.Parameter(torch.normal(mean=0., std=1e-1, size=[num_domains, self.K], dtype=torch.float, device='cuda:0'), requires_grad=True)
-        self.cs_wt = torch.nn.Parameter(torch.normal(mean=0, std=1e-3, size=[], dtype=torch.float, device='cuda:0'), requires_grad=True)
+        self.cs_wt = torch.nn.Parameter(torch.normal(mean=.1, std=1e-4, size=[], dtype=torch.float, device='cuda:0'), requires_grad=True)
 
         self.opt= optim.SGD([
                          {'params': filter(lambda p: p.requires_grad, self.phi.parameters()) },
@@ -48,7 +48,7 @@ class CSD(BaseAlgo):
         x = self.phi(x)        
         w_c, b_c = self.sms[0, :, :], self.sm_biases[0, :]
         logits_common = torch.matmul(x, w_c) + b_c
-        
+       
         if eval_case:
             return logits_common
  
@@ -70,7 +70,7 @@ class CSD(BaseAlgo):
         cps = torch.stack([torch.matmul(sms[:, :, _], torch.transpose(sms[:, :, _], 0, 1)) for _ in range(self.num_classes)], dim=0)
         orth_loss = torch.mean((1-diag_tensor)*(cps - diag_tensor)**2)
 
-        loss = 0.5*class_loss + 0.5*specific_loss + orth_loss 
+        loss = class_loss + specific_loss + orth_loss 
         return loss, logits_common
     
     def epoch_callback(self, nepoch, final=False):

algorithms/hybrid.py

@@ -78,7 +78,8 @@ class Hybrid(BaseAlgo):
             for epoch in range(self.args.epochs):    
                 
                 if epoch ==0:
-                    data_match_tensor, label_match_tensor= self.init_erm_phase()            
+#                     data_match_tensor, label_match_tensor= self.init_erm_phase()      
+                    data_match_tensor, label_match_tensor= self.get_match_function(epoch)                    
                 elif epoch % self.args.match_interrupt == 0 and self.args.match_flag:
                     data_match_tensor, label_match_tensor= self.get_match_function(epoch)
 

algorithms/match_dg.py

@@ -154,8 +154,8 @@ class MatchDG(BaseAlgo):
                         pos_feat_match= feat_match[pos_indices]
                         neg_feat_match= feat_match[neg_indices]
 
-                        if pos_feat_match.shape[0] > neg_feat_match.shape[0]:
-                            print('Weird! Positive Matches are more than the negative matches?', pos_feat_match.shape[0], neg_feat_match.shape[0])
+#                         if pos_feat_match.shape[0] > neg_feat_match.shape[0]:
+#                             print('Weird! Positive Matches are more than the negative matches?', pos_feat_match.shape[0], neg_feat_match.shape[0])
 
                         # If no instances of label y_c in the current batch then continue
                         if pos_feat_match.shape[0] ==0 or neg_feat_match.shape[0] == 0:

evaluation/match_eval.py

@@ -45,7 +45,11 @@ class MatchEval(BaseEval):
             base_domain_size= self.test_dataset['base_domain_size']
             domain_size_list= self.test_dataset['domain_size_list']
     
-        inferred_match=1
+        inferred_match=1        
+        # Self Augmentation Match Function evaluation will always follow perfect matches
+        if self.args.match_func_aug_case:
+            self.args.perfect_match= 1
+        
         data_match_tensor, label_match_tensor, indices_matched, perfect_match_rank= get_matched_pairs( self.args, self.cuda, dataset, base_domain_size, total_domains, domain_size_list, self.phi, self.args.match_case, inferred_match )
         
         score= perfect_match_score(indices_matched)