!952 modify code formats in quantization_aware.md

Merge pull request !952 from lvmingfu/master

tutorials/source_en/advanced_use/quantization_aware.md

@@ -90,63 +90,36 @@ Define a fusion network and replace the specified operators.
 The definition of the original network model LeNet5 is as follows:
 
 ```python
-def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
-    """weight initial for conv layer"""
-    weight = weight_variable()
-    return nn.Conv2d(in_channels, out_channels,
-                     kernel_size=kernel_size, stride=stride, padding=padding,
-                     weight_init=weight, has_bias=False, pad_mode="valid")
-
-
-def fc_with_initialize(input_channels, out_channels):
-    """weight initial for fc layer"""
-    weight = weight_variable()
-    bias = weight_variable()
-    return nn.Dense(input_channels, out_channels, weight, bias)
-
-
-def weight_variable():
-    """weight initial"""
-    return TruncatedNormal(0.02)
-
-
 class LeNet5(nn.Cell):
     """
     Lenet network
 
     Args:
         num_class (int): Num classes. Default: 10.
-
+        num_channel (int): Num channel. Default: 1.
     Returns:
         Tensor, output tensor
     Examples:
-        >>> LeNet(num_class=10)
-
+        >>> LeNet(num_class=10, num_channel=1)
+    
     """
-    def __init__(self, num_class=10, channel=1):
+    def __init__(self, num_class=10, num_channel=1):
         super(LeNet5, self).__init__()
-        self.num_class = num_class
-        self.conv1 = conv(channel, 6, 5)
-        self.conv2 = conv(6, 16, 5)
-        self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
-        self.fc2 = fc_with_initialize(120, 84)
-        self.fc3 = fc_with_initialize(84, self.num_class)
+        self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid')
+        self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
+        self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02))
+        self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02))
+        self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.02))
         self.relu = nn.ReLU()
         self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
         self.flatten = nn.Flatten()
 
     def construct(self, x):
-        x = self.conv1(x)
-        x = self.relu(x)
-        x = self.max_pool2d(x)
-        x = self.conv2(x)
-        x = self.relu(x)
-        x = self.max_pool2d(x)
+        x = self.max_pool2d(self.relu(self.conv1(x)))
+        x = self.max_pool2d(self.relu(self.conv2(x)))
         x = self.flatten(x)
-        x = self.fc1(x)
-        x = self.relu(x)
-        x = self.fc2(x)
-        x = self.relu(x)
+        x = self.relu(self.fc1(x))
+        x = self.relu(self.fc2(x))
         x = self.fc3(x)
         return x
 ```
@@ -168,10 +141,8 @@ class LeNet5(nn.Cell):
         self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
 
     def construct(self, x):
-        x = self.conv1(x)
-        x = self.max_pool2d(x)
-        x = self.conv2(x)
-        x = self.max_pool2d(x)
+        x = self.max_pool2d(self.conv1(x))
+        x = self.max_pool2d(self.conv2(x))
         x = self.flattern(x)
         x = self.fc1(x)
         x = self.fc2(x)

tutorials/source_zh_cn/advanced_use/quantization_aware.md

@@ -90,63 +90,36 @@ MindSpore的感知量化训练是在训练基础上，使用低精度数据替
 原网络模型LeNet5的定义如下所示：
 
 ```python
-def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
-    """weight initial for conv layer"""
-    weight = weight_variable()
-    return nn.Conv2d(in_channels, out_channels,
-                     kernel_size=kernel_size, stride=stride, padding=padding,
-                     weight_init=weight, has_bias=False, pad_mode="valid")
-
-
-def fc_with_initialize(input_channels, out_channels):
-    """weight initial for fc layer"""
-    weight = weight_variable()
-    bias = weight_variable()
-    return nn.Dense(input_channels, out_channels, weight, bias)
-
-
-def weight_variable():
-    """weight initial"""
-    return TruncatedNormal(0.02)
-
-
 class LeNet5(nn.Cell):
     """
     Lenet network
 
     Args:
         num_class (int): Num classes. Default: 10.
-
+        num_channel (int): Num channel. Default: 1.
     Returns:
         Tensor, output tensor
     Examples:
-        >>> LeNet(num_class=10)
-
+        >>> LeNet(num_class=10, num_channel=1)
+    
     """
-    def __init__(self, num_class=10, channel=1):
+    def __init__(self, num_class=10, num_channel=1):
         super(LeNet5, self).__init__()
-        self.num_class = num_class
-        self.conv1 = conv(channel, 6, 5)
-        self.conv2 = conv(6, 16, 5)
-        self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
-        self.fc2 = fc_with_initialize(120, 84)
-        self.fc3 = fc_with_initialize(84, self.num_class)
+        self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid')
+        self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
+        self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02))
+        self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02))
+        self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.02))
         self.relu = nn.ReLU()
         self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
         self.flatten = nn.Flatten()
 
     def construct(self, x):
-        x = self.conv1(x)
-        x = self.relu(x)
-        x = self.max_pool2d(x)
-        x = self.conv2(x)
-        x = self.relu(x)
-        x = self.max_pool2d(x)
+        x = self.max_pool2d(self.relu(self.conv1(x)))
+        x = self.max_pool2d(self.relu(self.conv2(x)))
         x = self.flatten(x)
-        x = self.fc1(x)
-        x = self.relu(x)
-        x = self.fc2(x)
-        x = self.relu(x)
+        x = self.relu(self.fc1(x))
+        x = self.relu(self.fc2(x))
         x = self.fc3(x)
         return x
 ```
@@ -168,10 +141,8 @@ class LeNet5(nn.Cell):
         self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
 
     def construct(self, x):
-        x = self.conv1(x)
-        x = self.max_pool2d(x)
-        x = self.conv2(x)
-        x = self.max_pool2d(x)
+        x = self.max_pool2d(self.conv1(x))
+        x = self.max_pool2d(self.conv2(x))
         x = self.flattern(x)
         x = self.fc1(x)
         x = self.fc2(x)