修改代码实现，提高代码可读性和可维护性

net.py

@@ -31,7 +31,7 @@ class DropPath(nn.Module):
 
     def forward(self, x):
         return drop_path(x, self.drop_prob, self.training)
-
+# 改点，使用Pooling替换AttentionBase
 class Pooling(nn.Module):
     def __init__(self, kernel_size=3):
         super().__init__()
@@ -44,8 +44,8 @@ class Pooling(nn.Module):
 
 class PoolMlp(nn.Module):
     """
-      Implementation of MLP with 1*1 convolutions.
-      Input: tensor with shape [B, C, H, W]
+    实现基于1x1卷积的MLP模块。
+    输入：形状为[B, C, H, W]的张量。
     """
 
     def __init__(self,
@@ -55,6 +55,17 @@ class PoolMlp(nn.Module):
                  act_layer=nn.GELU,
                  bias=False,
                  drop=0.):
+        """
+        初始化PoolMlp模块。
+
+        参数:
+            in_features (int): 输入特征的数量。
+            hidden_features (int, 可选): 隐藏层特征的数量。默认为None，设置为与in_features相同。
+            out_features (int, 可选): 输出特征的数量。默认为None，设置为与in_features相同。
+            act_layer (nn.Module, 可选): 使用的激活层。默认为nn.GELU。
+            bias (bool, 可选): 是否在卷积层中包含偏置项。默认为False。
+            drop (float, 可选): Dropout比率。默认为0。
+        """
         super().__init__()
         out_features = out_features or in_features
         hidden_features = hidden_features or in_features
@@ -64,6 +75,15 @@ class PoolMlp(nn.Module):
         self.drop = nn.Dropout(drop)
 
     def forward(self, x):
+        """
+        通过PoolMlp模块的前向传播。
+
+        参数:
+            x (torch.Tensor): 形状为[B, C, H, W]的输入张量。
+
+        返回:
+            torch.Tensor: 形状为[B, C, H, W]的输出张量。
+        """
         x = self.fc1(x)  # (B, C, H, W) --> (B, C, H, W)
         x = self.act(x)
         x = self.drop(x)
@@ -71,6 +91,7 @@ class PoolMlp(nn.Module):
         x = self.drop(x)
         return x
 
+
 class BaseFeatureExtraction(nn.Module):
     def __init__(self, dim, pool_size=3, mlp_ratio=4.,
                  act_layer=nn.GELU,
@@ -108,7 +129,7 @@ class BaseFeatureExtraction(nn.Module):
                 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.token_mixer(self.norm1(x))) # 匹配cddfuse
             x = x + self.drop_path(self.poolmlp(self.norm2(x)))
         return x
 
@@ -131,11 +152,9 @@ class InvertedResidualBlock(nn.Module):
             nn.Conv2d(hidden_dim, oup, 1, bias=False),
             # nn.BatchNorm2d(oup),
         )
-
     def forward(self, x):
         return self.bottleneckBlock(x)
 
-
 class DetailNode(nn.Module):
     def __init__(self):
         super(DetailNode, self).__init__()
@@ -163,14 +182,12 @@ class DetailFeatureExtraction(nn.Module):
         super(DetailFeatureExtraction, 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)
 
-
 # =============================================================================
 
 # =============================================================================

requirement.txt

@@ -0,0 +1,5 @@
+
+scipy==1.9.3
+scikit-image==0.19.2
+scikit-learn==1.1.3
+tqdm==4.62.0

test_IVF.py

@@ -13,13 +13,13 @@ logging.basicConfig(level=logging.CRITICAL)
 
 
 os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-ckpt_path= r"models/PFCFuse.pth"
+ckpt_path= r"/home/star/whaiDir/PFCFuse/models/PFCFusion10-05-18-13.pth"
 
-for dataset_name in ["MSRS","TNO","RoadScene"]:
+for dataset_name in ["TNO"]:
     print("\n"*2+"="*80)
     model_name="PFCFuse    "
     print("The test result of "+dataset_name+' :')
-    test_folder=os.path.join('test_img',dataset_name) 
+    test_folder=os.path.join('/home/star/whaiDir/CDDFuse/test_img/',dataset_name)
     test_out_folder=os.path.join('test_result',dataset_name)
 
     device = 'cuda' if torch.cuda.is_available() else 'cpu'
@@ -39,6 +39,7 @@ for dataset_name in ["MSRS","TNO","RoadScene"]:
 
     with torch.no_grad():
         for img_name in os.listdir(os.path.join(test_folder,"ir")):
+            print(img_name)
 
             data_IR=image_read_cv2(os.path.join(test_folder,"ir",img_name),mode='GRAY')[np.newaxis,np.newaxis, ...]/255.0
             data_VIS = cv2.split(image_read_cv2(os.path.join(test_folder, "vi", img_name), mode='YCrCb'))[0][np.newaxis, np.newaxis, ...] / 255.0

train.py

@@ -87,7 +87,7 @@ Loss_ssim = kornia.losses.SSIM(11, reduction='mean')
 HuberLoss = nn.HuberLoss()
 
 # data loader
-trainloader = DataLoader(H5Dataset(r"data/MSRS_train_imgsize_128_stride_200.h5"),
+trainloader = DataLoader(H5Dataset(r"/home/star/whaiDir/CDDFuse/data/MSRS_train_imgsize_128_stride_200.h5"),
                          batch_size=batch_size,
                          shuffle=True,
                          num_workers=0)
@@ -201,13 +201,14 @@ for epoch in range(num_epochs):
         epoch_time = time.time() - prev_time
         prev_time = time.time()
         sys.stdout.write(
-            "\r[Epoch %d/%d] [Batch %d/%d] [loss: %f]"
+            "\r[Epoch %d/%d] [Batch %d/%d] [loss: %f] ETA: %.10s"
             % (
                 epoch,
                 num_epochs,
                 i,
                 len(loader['train']),
                 loss.item(),
+                time_left,
             )
         )