feat(net): 替换 token_mixer 为 SCSA

- 移除 Pooling 层，使用 SCSA（Spectral Correlation and Spatial Attention）层作为 token_mixer
- 更新模型架构，以适应新的 SCSA 层
- 修改测试脚本，针对特定数据集进行测试

net.py

@@ -6,6 +6,7 @@ import torch.utils.checkpoint as checkpoint
 from timm.models.layers import DropPath, to_2tuple, trunc_normal_
 from einops import rearrange
 
+from componets.SCSA import SCSA
 
 def drop_path(x, drop_prob: float = 0., training: bool = False):
     if drop_prob == 0. or not training:
@@ -101,7 +102,8 @@ class BaseFeatureExtractionSAR(nn.Module):
         super().__init__()
 
         self.norm1 = LayerNorm(dim, 'WithBias')
-        self.token_mixer = Pooling(kernel_size=pool_size)  # vits是msa，MLPs是mlp，这个用pool来替代
+        # self.token_mixer = Pooling(kernel_size=pool_size)  # vits是msa，MLPs是mlp，这个用pool来替代
+        self.token_mixer = SCSA(dim=dim, head_num=8)
         self.norm2 = LayerNorm(dim, 'WithBias')
         mlp_hidden_dim = int(dim * mlp_ratio)
         self.poolmlp = PoolMlp(in_features=dim, hidden_features=mlp_hidden_dim,

test_IVF.py

@@ -17,11 +17,11 @@ current_time = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
 
 
 os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-ckpt_path= r"/home/star/whaiDir/PFCFuse/models/whaiFusion10-08-16-20.pth"
+ckpt_path= r"/home/star/whaiDir/PFCFuse/models/whaiFusion11-15-11-11.pth"
 
-for dataset_name in ["TNO","RoadScene"]:
+for dataset_name in ["sar"]:
     print("\n"*2+"="*80)
-    model_name="PFCFuse    "
+    model_name="whai 修改SCSA分支    "
     print("The test result of "+dataset_name+' :')
     test_folder = os.path.join('test_img', dataset_name)
     test_out_folder=os.path.join('test_result',current_time,dataset_name)