modify lenet network

model_zoo/official/cv/lenet/src/lenet.py

@@ -14,27 +14,6 @@
 # ============================================================================
 """LeNet."""
 import mindspore.nn as nn
-from mindspore.common.initializer import TruncatedNormal
-
-
-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):
@@ -43,6 +22,7 @@ class LeNet5(nn.Cell):
 
     Args:
         num_class (int): Num classes. Default: 10.
+        channel (int): Num classes. Default: 1.
 
     Returns:
         Tensor, output tensor
@@ -53,26 +33,20 @@ class LeNet5(nn.Cell):
     def __init__(self, num_class=10, 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(channel, 6, 5, pad_mode='valid')
+        self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
+        self.fc1 = nn.Dense(16 * 5 * 5, 120)
+        self.fc2 = nn.Dense(120, 84)
+        self.fc3 = nn.Dense(84, self.num_class)
         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