Update densenet

densenet

@@ -34,6 +34,7 @@ class BottleNeck(tl.layers.Module):
         y = tf.keras.layers.concatenate([x, y], axis=-1)
         return y
 
+# 构建密集块
 class DenseBlock(tl.layers.Module):
     def __init__(self, num_layers, growth_rate, drop_rate=0.5):
         super(DenseBlock, self).__init__()
@@ -49,7 +50,7 @@ class DenseBlock(tl.layers.Module):
             x = layer(x)
         return x
 
-
+# 构建过渡层
 class TransitionLayer(tl.layers.Module):
     def __init__(self, out_channels):
         super(TransitionLayer, self).__init__()
@@ -69,9 +70,10 @@ class TransitionLayer(tl.layers.Module):
         x = self.pool(x)
         return x
 
-class DenseNet_121(tl.layers.Module):
+# DenseNet-121,169,201,264模型
+class DenseNet(tl.layers.Module):
     def __init__(self, num_init_features, growth_rate, block_layers, compression_rate, drop_rate):
-        super(DenseNet_121, self).__init__()
+        super(DenseNet, self).__init__()
         self.conv = tl.layers.Conv2d(n_filter=num_init_features,
                                            filter_size=(7, 7),
                                            strides=(2,2),
@@ -97,6 +99,7 @@ class DenseNet_121(tl.layers.Module):
 
         self.avgpool = tl.layers.GlobalMeanPool2d()
         self.fc = tl.layers.Dense(n_units=10,act=tl.softmax(logits=()))
+        
     def forward(self, inputs):
         x = self.conv(inputs)
         x = self.bn(x)
@@ -116,7 +119,7 @@ class DenseNet_121(tl.layers.Module):
 
         return x
 
-
+# DenseNet-100模型
 class DenseNet_100(tl.layers.Module):
     def __init__(self, num_init_features, growth_rate, block_layers, compression_rate, drop_rate):
         super(DenseNet_100, self).__init__()
@@ -145,6 +148,7 @@ class DenseNet_100(tl.layers.Module):
 
         self.avgpool = tl.layers.GlobalMeanPool2d()
         self.fc = tl.layers.Dense(n_units=10,act=tl.softmax(logits=()))
+        
     def forward(self, inputs):
         x = self.conv(inputs)
         x = self.bn(x)
@@ -167,17 +171,18 @@ class DenseNet_100(tl.layers.Module):
 
 def densenet(x):
     if x == 'densenet-121':
-        return DenseNet_121(num_init_features=64, growth_rate=32, block_layers=[6, 12, 24, 16], compression_rate=0.5,
+        return DenseNet(num_init_features=64, growth_rate=32, block_layers=[6, 12, 24, 16], compression_rate=0.5,
                             drop_rate=0.5)
     elif x == 'densenet-169':
-        return DenseNet_121(num_init_features=64, growth_rate=32, block_layers=[6 , 12, 32, 32], compression_rate=0.5,
+        return DenseNet(num_init_features=64, growth_rate=32, block_layers=[6 , 12, 32, 32], compression_rate=0.5,
                             drop_rate=0.5)
     elif x == 'densenet-201':
-        return DenseNet_121(num_init_features=64, growth_rate=32, block_layers=[6, 12, 48, 32], compression_rate=0.5,
+        return DenseNet(num_init_features=64, growth_rate=32, block_layers=[6, 12, 48, 32], compression_rate=0.5,
                             drop_rate=0.5)
     elif x == 'densenet-264':
-        return DenseNet_121(num_init_features=64, growth_rate=32, block_layers=[6, 12, 64, 48], compression_rate=0.5,
+        return DenseNet(num_init_features=64, growth_rate=32, block_layers=[6, 12, 64, 48], compression_rate=0.5,
                             drop_rate=0.5)
     elif x=='densenet-100':
         return DenseNet_100(num_init_features=64, growth_rate=12, block_layers=[16, 16, 16], compression_rate=0.5,
                             drop_rate=0.5)
+