fix conf

trainers/promptsrc.py

@@ -220,39 +220,6 @@ class CustomCLIP(nn.Module):
         self.dtype = clip_model.dtype
         self.total_epochs = cfg.OPTIM.MAX_EPOCH
         self.n_cls = len(classnames)
-        
-        confidence_type = getattr(cfg.TRAINER.PROMPTSRC, 'CONFIDENCE_TYPE', 'entropy')
-        self.confidence_type = confidence_type
-        self.temperature = getattr(cfg.TRAINER.PROMPTSRC, 'CONFIDENCE_TEMPERATURE', 1.0)
-        self.momentum = getattr(cfg.TRAINER.PROMPTSRC, 'CONFIDENCE_MOMENTUM', 0.9)
-        self.register_buffer('running_confidence', torch.tensor(0.0))
-        self.steps = 0
-        
-    def compute_confidence(self, logits):
-        if self.confidence_type == 'entropy':
-            probs = F.softmax(logits / self.temperature, dim=1)
-            entropy = -(probs * torch.log(probs + 1e-10)).sum(dim=1)
-            normalized_entropy = entropy / torch.log(torch.tensor(self.n_cls, dtype=logits.dtype, device=logits.device))
-            confidence = 1 - normalized_entropy
-        elif self.confidence_type == 'max_prob':
-            probs = F.softmax(logits / self.temperature, dim=1)
-            confidence = probs.max(dim=1).values
-        elif self.confidence_type == 'margin':
-            probs = F.softmax(logits / self.temperature, dim=1)
-            top2_probs, _ = probs.topk(2, dim=1)
-            confidence = top2_probs[:, 0] - top2_probs[:, 1]
-        elif self.confidence_type == 'max_margin':
-            max_prob = F.softmax(logits / self.temperature, dim=1).max(dim=1).values
-            entropy = -(F.softmax(logits / self.temperature, dim=1) * 
-                       torch.log(F.softmax(logits / self.temperature, dim=1) + 1e-10)).sum(dim=1)
-            normalized_entropy = entropy / torch.log(torch.tensor(self.n_cls, dtype=logits.dtype, device=logits.device))
-            confidence = max_prob * (1 - normalized_entropy)
-        else:
-            probs = F.softmax(logits / self.temperature, dim=1)
-            confidence = probs.max(dim=1).values
-        
-        confidence = confidence.clamp(0, 1)
-        return confidence
 
     def forward(self, image, label=None):
         tokenized_prompts = self.tokenized_prompts
@@ -281,17 +248,7 @@ class CustomCLIP(nn.Module):
         logits_strong = logit_scale * image_features @ text_features_strong.t()
         logits_weak = logit_scale * image_features @ text_features_weak.t()
 
-        confidence = self.compute_confidence(zero_shot_logits)
-        
-        if self.training:
-            confidence_batch = confidence.mean().detach()
-            if self.steps == 0:
-                self.running_confidence = confidence_batch
-            else:
-                self.running_confidence = self.momentum * self.running_confidence + (1 - self.momentum) * confidence_batch
-            self.steps += 1
-        
-        alpha = confidence.unsqueeze(1)
+        alpha = 0.5
 
         logits_final = alpha * logits_strong + (1 - alpha) * logits_weak
 
@@ -498,4 +455,4 @@ class PromptSRC(TrainerX):
 
             print("Loading weights to {} " 'from "{}" (epoch = {})'.format(name, model_path, epoch))
             # set strict=False
-            self._models[name].load_state_dict(state_dict, strict=False)
+            self._models[name].load_state_dict(state_dict, strict=False)