修改代码结构，提高可读性和可维护性；调整训练输出频率。

改进 self.enhancement_module 为
        self.enhancement_module = WTConv2d(32, 32)

net.py

@@ -248,11 +248,7 @@ class DetailFeatureExtraction(nn.Module):
         super(DetailFeatureExtraction, self).__init__()
         INNmodules = [DetailNode() for _ in range(num_layers)]
         self.net = nn.Sequential(*INNmodules)
-        self.enhancement_module = nn.Sequential(
-            nn.Conv2d(32, 32, kernel_size=3, padding=1, bias=True),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(32, 32, kernel_size=3, padding=1, bias=True),
-        )
+        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

train.py

@@ -222,7 +222,7 @@ 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 step % 100 == 0:
+        if i % 100 == 0:
             sys.stdout.write(
                 "\r[Epoch %d/%d] [Batch %d/%d] [loss: %f] ETA: %.10s"
                 % (