feat(net): 增加 SAR 图像处理支持

- 新增 BaseFeatureExtractionSAR 和 DetailFeatureExtractionSAR 模块
- 修改 DIDF_Encoder 类，支持 SAR 图像输入
- 更新测试和训练脚本，增加 SAR 图像处理相关逻辑

net.py

@@ -112,6 +112,48 @@ class BaseFeatureExtraction(nn.Module):
             x = x + self.drop_path(self.poolmlp(self.norm2(x)))
         return x
 
+class BaseFeatureExtractionSAR(nn.Module):
+    def __init__(self, dim, pool_size=3, mlp_ratio=4.,
+                 act_layer=nn.GELU,
+                 # norm_layer=nn.LayerNorm,
+                 drop=0., drop_path=0.,
+                 use_layer_scale=True, layer_scale_init_value=1e-5):
+
+        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.norm2 = LayerNorm(dim, 'WithBias')
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.poolmlp = PoolMlp(in_features=dim, hidden_features=mlp_hidden_dim,
+                               act_layer=act_layer, drop=drop)
+
+        # The following two techniques are useful to train deep PoolFormers.
+        self.drop_path = DropPath(drop_path) if drop_path > 0. \
+            else nn.Identity()
+        self.use_layer_scale = use_layer_scale
+
+        if use_layer_scale:
+            self.layer_scale_1 = nn.Parameter(
+                torch.ones(dim, dtype=torch.float32) * layer_scale_init_value)
+
+            self.layer_scale_2 = nn.Parameter(
+                torch.ones(dim, dtype=torch.float32) * layer_scale_init_value)
+
+    def forward(self, x):
+        if self.use_layer_scale:
+            x = x + self.drop_path(
+                self.layer_scale_1.unsqueeze(-1).unsqueeze(-1)
+                * self.token_mixer(self.norm1(x)))
+            x = x + self.drop_path(
+                self.layer_scale_2.unsqueeze(-1).unsqueeze(-1)
+                * self.poolmlp(self.norm2(x)))
+        else:
+            x = x + self.drop_path(self.token_mixer(self.norm1(x)))
+            x = x + self.drop_path(self.poolmlp(self.norm2(x)))
+        return x
+
+
 
 class InvertedResidualBlock(nn.Module):
     def __init__(self, inp, oup, expand_ratio):
@@ -171,6 +213,19 @@ class DetailFeatureExtraction(nn.Module):
         return torch.cat((z1, z2), dim=1)
 
 
+class DetailFeatureExtractionSAR(nn.Module):
+    def __init__(self, num_layers=3):
+        super(DetailFeatureExtractionSAR, self).__init__()
+        INNmodules = [DetailNode() for _ in range(num_layers)]
+        self.net = nn.Sequential(*INNmodules)
+
+    def forward(self, x):
+        z1, z2 = x[:, :x.shape[1] // 2], x[:, x.shape[1] // 2:x.shape[1]]
+        for layer in self.net:
+            z1, z2 = layer(z1, z2)
+        return torch.cat((z1, z2), dim=1)
+
+
 # =============================================================================
 
 # =============================================================================
@@ -352,14 +407,23 @@ class Restormer_Encoder(nn.Module):
             *[TransformerBlock(dim=dim, num_heads=heads[0], ffn_expansion_factor=ffn_expansion_factor,
                                bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[0])])
         self.baseFeature = BaseFeatureExtraction(dim=dim)
-
         self.detailFeature = DetailFeatureExtraction()
 
-    def forward(self, inp_img):
+        self.baseFeatureSar= BaseFeatureExtractionSAR(dim=dim)
+        self.detailFeatureSar = DetailFeatureExtractionSAR()
+
+
+
+    def forward(self, inp_img, sar_img=False):
         inp_enc_level1 = self.patch_embed(inp_img)
         out_enc_level1 = self.encoder_level1(inp_enc_level1)
+
+        if sar_img:
             base_feature = self.baseFeature(out_enc_level1)
             detail_feature = self.detailFeature(out_enc_level1)
+        else:
+            base_feature= self.baseFeature(out_enc_level1)
+            detail_feature = self.detailFeature(out_enc_level1)
         return base_feature, detail_feature, out_enc_level1
 
 

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/whaiFusion11-15-17-48.pth"
+ckpt_path= r"/home/star/whaiDir/PFCFuse/models/whaiFusion11-15-22-09.pth"
 
 for dataset_name in ["sar"]:
     print("\n"*2+"="*80)
-    model_name="PFCFuse 最基本版本   "
+    model_name="PFCFuse Enhance "
     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)

train.py

@@ -149,7 +149,7 @@ for epoch in range(num_epochs):
 
         if epoch < epoch_gap: #Phase I
             feature_V_B, feature_V_D, _ = DIDF_Encoder(data_VIS)
-            feature_I_B, feature_I_D, _ = DIDF_Encoder(data_IR)
+            feature_I_B, feature_I_D, _ = DIDF_Encoder(data_IR,sar_img=True)
             data_VIS_hat, _ = DIDF_Decoder(data_VIS, feature_V_B, feature_V_D)
             data_IR_hat, _ = DIDF_Decoder(data_IR, feature_I_B, feature_I_D)