fix mmlp model

MOAFModels.py

@@ -260,6 +260,18 @@ class MOAFWithMMLP(nn.Module):
             self._create_regressor_head() for _ in range(num_lenses)
         ])
 
+        # 注册物镜参数基准张量（不可学习）
+        lens_params_base = torch.tensor([
+            [10, 0.25, 1.0000],   # obj1
+            [10, 0.30, 1.0000],   # obj2
+            [20, 0.70, 1.0000],   # obj3
+            [20, 0.80, 1.0000],   # obj4
+            [40, 0.65, 1.0000],   # obj5
+            [100, 0.80, 1.0000],  # obj6
+            [100, 1.25, 1.4730]   # obj7
+        ], dtype=torch.float32)
+        self.register_buffer('lens_params_base', lens_params_base)
+
     def _create_regressor_head(self):
         return nn.Sequential(
             nn.Flatten(),
@@ -269,48 +281,29 @@ class MOAFWithMMLP(nn.Module):
         )
 
     def _find_lens_id(self, params):
-        batch_size = params.shape[0]
+        # 扩展维度以进行广播计算
-        lens_ids = []
+        params_expanded = params.unsqueeze(1)  # [batch_size, 1, 3]
+        base_expanded = self.lens_params_base.unsqueeze(0)  # [1, 7, 3]
         
-        for i in range(batch_size):
+        # 计算欧氏距离（完全向量化）
-            mag, na, rix = params[i][0].item(), params[i][1].item(), params[i][2].item()
+        distances = torch.sqrt(torch.sum((params_expanded - base_expanded) ** 2, dim=2))  # [batch_size, 7]
         
-            # 直接的条件匹配（类似于switch-case）
+        # 找到最小距离的索引
-            if mag == 10 and na == 0.25 and rix == 1.0000:
+        lens_ids = torch.argmin(distances, dim=1)  # [batch_size]
-                lens_ids.append(0)  # obj1
+        return lens_ids
-            elif mag == 10 and na == 0.30 and rix == 1.0000:
-                lens_ids.append(1)  # obj2
-            elif mag == 20 and na == 0.70 and rix == 1.0000:
-                lens_ids.append(2)  # obj3
-            elif mag == 20 and na == 0.80 and rix == 1.0000:
-                lens_ids.append(3)  # obj4
-            elif mag == 40 and na == 0.65 and rix == 1.0000:
-                lens_ids.append(4)  # obj5
-            elif mag == 100 and na == 0.80 and rix == 1.0000:
-                lens_ids.append(5)  # obj6
-            elif mag == 100 and na == 1.25 and rix == 1.4730:
-                lens_ids.append(6)  # obj7
-            else:
-                lens_ids.append(0)
-        
-        return torch.tensor(lens_ids, dtype=torch.long, device=params.device)
 
     def forward(self, image, params):
         x = self.features(image)
         x = self.avgpool(x)
         
         lens_ids = self._find_lens_id(params)
-        batch_size = params.size(0)
+        lens_ids = torch.clamp(lens_ids, 0, self.num_lenses - 1)
-        outputs = []
+        batch_size = x.size(0)
+        all_outputs = []
         for i in range(batch_size):
-            current_lens_id = lens_ids[i]
+            head_idx = lens_ids[i]
-            # 确保lens_id在有效范围内
+            output = self.regressors[head_idx](x[i].unsqueeze(0))
-            if current_lens_id < 0 or current_lens_id >= self.num_lenses:
+            all_outputs.append(output)
-                current_lens_id = 0
         
-            # 选择对应的回归头
+        return torch.cat(all_outputs, dim=0)
-            head_output = self.regressors[current_lens_id](x[i].unsqueeze(0))
-            outputs.append(head_output)
-        
-        return torch.cat(outputs, dim=0)
 

MOAFTrainDDP.py

@@ -128,6 +128,9 @@ def fit(rank, world_size, cfg):
                 print_with_timestamp("Model Checkpoint Params Not Exist")
 
     # DDP 化模型
+    if "mmlp" in model_type:
+        model = DDP(model, device_ids=[rank], output_device=rank, find_unused_parameters=True)
+    else:
         model = DDP(model, device_ids=[rank], output_device=rank, find_unused_parameters=False)
     if rank == 0:
         print_with_timestamp("Model Wrapped with DDP")