update build_once for mnist (#4103)

test=develop

dygraph/mnist/train.py

@@ -21,7 +21,7 @@ import os
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid.optimizer import AdamOptimizer
-from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear
 from paddle.fluid.dygraph.base import to_variable
 
 
@@ -41,7 +41,6 @@ def parse_args():
 
 class SimpleImgConvPool(fluid.dygraph.Layer):
     def __init__(self,
-                 name_scope,
                  num_channels,
                  num_filters,
                  filter_size,
@@ -58,10 +57,10 @@ class SimpleImgConvPool(fluid.dygraph.Layer):
                  use_cudnn=False,
                  param_attr=None,
                  bias_attr=None):
-        super(SimpleImgConvPool, self).__init__(name_scope)
+        super(SimpleImgConvPool, self).__init__()
 
         self._conv2d = Conv2D(
-            self.full_name(),
+            num_channels=num_channels,
             num_filters=num_filters,
             filter_size=filter_size,
             stride=conv_stride,
@@ -74,7 +73,6 @@ class SimpleImgConvPool(fluid.dygraph.Layer):
             use_cudnn=use_cudnn)
 
         self._pool2d = Pool2D(
-            self.full_name(),
             pool_size=pool_size,
             pool_type=pool_type,
             pool_stride=pool_stride,
@@ -89,20 +87,19 @@ class SimpleImgConvPool(fluid.dygraph.Layer):
 
 
 class MNIST(fluid.dygraph.Layer):
-    def __init__(self, name_scope):
-        super(MNIST, self).__init__(name_scope)
+    def __init__(self):
+        super(MNIST, self).__init__()
 
         self._simple_img_conv_pool_1 = SimpleImgConvPool(
-            self.full_name(), 1, 20, 5, 2, 2, act="relu")
+            1, 20, 5, 2, 2, act="relu")
 
         self._simple_img_conv_pool_2 = SimpleImgConvPool(
-            self.full_name(), 20, 50, 5, 2, 2, act="relu")
+            20, 50, 5, 2, 2, act="relu")
 
-        pool_2_shape = 50 * 4 * 4
+        self.pool_2_shape = 50 * 4 * 4
         SIZE = 10
-        scale = (2.0 / (pool_2_shape**2 * SIZE))**0.5
-        self._fc = FC(self.full_name(),
-                      10,
+        scale = (2.0 / (self.pool_2_shape**2 * SIZE))**0.5
+        self._fc = Linear(self.pool_2_shape, 10,
                       param_attr=fluid.param_attr.ParamAttr(
                           initializer=fluid.initializer.NormalInitializer(
                               loc=0.0, scale=scale)),
@@ -111,6 +108,7 @@ class MNIST(fluid.dygraph.Layer):
     def forward(self, inputs, label=None):
         x = self._simple_img_conv_pool_1(inputs)
         x = self._simple_img_conv_pool_2(x)
+        x = fluid.layers.reshape(x, shape=[-1, self.pool_2_shape])
         x = self._fc(x)
         if label is not None:
             acc = fluid.layers.accuracy(input=x, label=label)
@@ -148,7 +146,7 @@ def inference_mnist():
     place = fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id) \
         if args.use_data_parallel else fluid.CUDAPlace(0)
     with fluid.dygraph.guard(place):
-        mnist_infer = MNIST("mnist")
+        mnist_infer = MNIST()
         # load checkpoint
         model_dict, _ = fluid.load_dygraph("save_temp")
         mnist_infer.set_dict(model_dict)
@@ -188,7 +186,7 @@ def train_mnist(args):
 
         if args.use_data_parallel:
             strategy = fluid.dygraph.parallel.prepare_context()
-        mnist = MNIST("mnist")
+        mnist = MNIST()
         adam = AdamOptimizer(learning_rate=0.001)
         if args.use_data_parallel:
             mnist = fluid.dygraph.parallel.DataParallel(mnist, strategy)