feat(net): 重构特征融合模块并添加新组件

- 新增 BaseFeatureFusion 和 DetailFeatureFusioin 类，用于特征融合
- 更新 ProjectRootManager 配置，使用本地 Python 3.8 环境
- 修改训练数据集路径
- 优化训练日志输出格式

net.py

@@ -198,6 +198,58 @@ class BaseFeatureExtraction(nn.Module):
             x = x + self.drop_path(self.poolmlp(self.norm2(x)))
         return x
 
+class BaseFeatureFusion(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.WTConv2d = WTConv2d(dim, dim)
+        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): # 1 64 128 128
+        if self.use_layer_scale:
+            #  self.layer_scale_1(64,)
+            tmp1 = self.layer_scale_1.unsqueeze(-1).unsqueeze(-1) # 64 1 1
+            normal = self.norm1(x) # 1 64 128 128
+            token_mix = self.token_mixer(normal) # 1 64 128 128
+
+            x = self.WTConv2d(x)
+
+            x = (x +
+                 self.drop_path(
+                    tmp1 * token_mix
+                 )
+                 # 该表达式将 self.layer_scale_1 这个一维张量（或变量）在维度末尾添加两个新的维度，使其从一维变为三维。这通常用于使其能够与三维的特征图进行广播操作，如元素相乘。具体用途可能包括调整卷积层或注意力机制中的权重。
+            )
+            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))) # 匹配cddfuse
+            x = x + self.drop_path(self.poolmlp(self.norm2(x)))
+        return x
+
 class InvertedResidualBlock(nn.Module):
     def __init__(self, inp, oup, expand_ratio):
         super(InvertedResidualBlock, self).__init__()
@@ -262,6 +314,27 @@ class DetailFeatureExtraction(nn.Module):
             z1, z2 = layer(z1, z2)
         return torch.cat((z1, z2), dim=1)
 
+class DetailFeatureFusioin(nn.Module):
+    def __init__(self, num_layers=3):
+        super(DetailFeatureFusioin, self).__init__()
+        INNmodules = [DetailNode() for _ in range(num_layers)]
+        self.net = nn.Sequential(*INNmodules)
+        self.enhancement_module = WTConv2d(32, 32)
+
+    def forward(self, x): # 1 64 128 128
+        z1, z2 = x[:, :x.shape[1] // 2], x[:, x.shape[1] // 2:x.shape[1]] # 1 32 128 128
+        # 增强并添加残差连接
+        enhanced_z1 = self.enhancement_module(z1)
+        enhanced_z2 = self.enhancement_module(z2)
+        # 残差连接
+        z1 = z1 + enhanced_z1
+        z2 = z2 + enhanced_z2
+        for layer in self.net:
+            z1, z2 = layer(z1, z2)
+        return torch.cat((z1, z2), dim=1)
+
+
+
 # =============================================================================
 
 # =============================================================================

train.py

@@ -6,7 +6,8 @@ Import packages
 ------------------------------------------------------------------------------
 '''
 
-from net import Restormer_Encoder, Restormer_Decoder, BaseFeatureExtraction, DetailFeatureExtraction
+from net import Restormer_Encoder, Restormer_Decoder, BaseFeatureExtraction, DetailFeatureExtraction, BaseFeatureFusion, \
+    DetailFeatureFusioin
 from utils.dataset import H5Dataset
 import os
 os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
@@ -85,8 +86,8 @@ device = 'cuda' if torch.cuda.is_available() else 'cpu'
 DIDF_Encoder = nn.DataParallel(Restormer_Encoder()).to(device)
 DIDF_Decoder = nn.DataParallel(Restormer_Decoder()).to(device)
 # BaseFuseLayer = nn.DataParallel(BaseFeatureExtraction(dim=64, num_heads=8)).to(device)
-BaseFuseLayer = nn.DataParallel(BaseFeatureExtraction(dim=64)).to(device)
+BaseFuseLayer = nn.DataParallel(BaseFeatureFusion(dim=64)).to(device)
-DetailFuseLayer = nn.DataParallel(DetailFeatureExtraction(num_layers=1)).to(device)
+DetailFuseLayer = nn.DataParallel(DetailFeatureFusioin(num_layers=1)).to(device)
 
 # optimizer, scheduler and loss function
 optimizer1 = torch.optim.Adam(
@@ -109,7 +110,7 @@ Loss_ssim = kornia.losses.SSIM(11, reduction='mean')
 HuberLoss = nn.HuberLoss()
 
 # data loader
-trainloader = DataLoader(H5Dataset(r"/home/star/whaiDir/CDDFuse/data/MSRS_train_imgsize_128_stride_200.h5"),
+trainloader = DataLoader(H5Dataset(r"/home/star/whaiDir/PFCFuse/data/YYX_sar_opr_data_imgsize_256_stride_200.h5"),
                          batch_size=batch_size,
                          shuffle=True,
                          num_workers=0)
@@ -222,7 +223,7 @@ for epoch in range(num_epochs):
         time_left = datetime.timedelta(seconds=batches_left * (time.time() - prev_time))
         epoch_time = time.time() - prev_time
         prev_time = time.time()
-        if i % 100 == 0:
+
         sys.stdout.write(
                 "\r[Epoch %d/%d] [Batch %d/%d] [loss: %f] ETA: %.10s"
                 % (