update nas quick start (#584)

docs/zh_cn/quick_start/nas_tutorial.md

@@ -25,7 +25,9 @@
 请确认已正确安装Paddle，导入需要的依赖包。
 ```python
 import paddle
-import paddle.fluid as fluid
+import paddle.nn as nn
+import paddle.nn.functional as F
+import paddle.static as static
 import paddleslim as slim
 import numpy as np
 ```
@@ -38,60 +40,78 @@ sanas = slim.nas.SANAS(configs=[('MobileNetV2Space')], server_addr=("", 8337), s
 ## 3. 构建网络
 根据传入的网络结构构造训练program和测试program。
 ```python
+paddle.enable_static()
 def build_program(archs):
-    train_program = fluid.Program()
-    startup_program = fluid.Program()
-    with fluid.program_guard(train_program, startup_program):
-        data = fluid.data(name='data', shape=[None, 3, 32, 32], dtype='float32')
-        label = fluid.data(name='label', shape=[None, 1], dtype='int64')
+    train_program = static.Program()
+    startup_program = static.Program()
+    with static.program_guard(train_program, startup_program):
+        data = static.data(name='data', shape=[None, 3, 32, 32], dtype='float32')
+        label = static.data(name='label', shape=[None, 1], dtype='int64')
+        gt = paddle.reshape(label, [-1, 1])
         output = archs(data)
-        output = fluid.layers.fc(input=output, size=10)
+        output = static.nn.fc(output, size=10)
 
-        softmax_out = fluid.layers.softmax(input=output, use_cudnn=False)
-        cost = fluid.layers.cross_entropy(input=softmax_out, label=label)
-        avg_cost = fluid.layers.mean(cost)
-        acc_top1 = fluid.layers.accuracy(input=softmax_out, label=label, k=1)
-        acc_top5 = fluid.layers.accuracy(input=softmax_out, label=label, k=5)
-        test_program = fluid.default_main_program().clone(for_test=True)
+        softmax_out = F.softmax(output)
+        cost = F.cross_entropy(softmax_out, label=label)
+        avg_cost = paddle.mean(cost)
+        acc_top1 = paddle.metric.accuracy(input=softmax_out, label=gt, k=1)
+        acc_top5 = paddle.metric.accuracy(input=softmax_out, label=gt, k=5)
+        test_program = static.default_main_program().clone(for_test=True)
 
-        optimizer = fluid.optimizer.Adam(learning_rate=0.1)
+        optimizer = paddle.optimizer.Adam(learning_rate=0.1)
         optimizer.minimize(avg_cost)
 
-        place = fluid.CPUPlace()
-        exe = fluid.Executor(place)
+        place = paddle.CPUPlace()
+        exe = static.Executor(place)
         exe.run(startup_program)
     return exe, train_program, test_program, (data, label), avg_cost, acc_top1, acc_top5
 ```
 
 ## 4. 定义输入数据函数
-使用的数据集为cifar10，paddle框架中`paddle.dataset.cifar`包括了cifar数据集的下载和读取，代码如下：
+为了快速执行该示例，我们使用的数据集为CIFAR10，Paddle框架的`paddle.vision.datasets.Cifar10`包定义了CIFAR10数据的下载和读取。 代码如下：
+
 ```python
-def input_data(inputs):
-    train_reader = paddle.fluid.io.batch(paddle.reader.shuffle(paddle.dataset.cifar.train10(cycle=False), buf_size=1024),batch_size=256)
-    train_feeder = fluid.DataFeeder(inputs, fluid.CPUPlace())
-    eval_reader = paddle.fluid.io.batch(paddle.dataset.cifar.test10(cycle=False), batch_size=256)
-    eval_feeder = fluid.DataFeeder(inputs, fluid.CPUPlace())
-    return train_reader, train_feeder, eval_reader, eval_feeder
+import paddle.vision.transforms as T
+
+def input_data(image, label):
+    transform = T.Compose([T.Normalize([127.5], [127.5])])
+    train_dataset = paddle.vision.datasets.Cifar10(mode="train", transform=transform)
+    train_loader = paddle.io.DataLoader(train_dataset,
+                    places=paddle.CPUPlace(),
+                    feed_list=[image, label],
+                    drop_last=True,
+                    batch_size=64,
+                    return_list=False,
+                    shuffle=True)
+    eval_dataset = paddle.vision.datasets.Cifar10(mode="test", transform=transform)
+    eval_loader = paddle.io.DataLoader(eval_dataset,
+                    places=paddle.CPUPlace(),
+                    feed_list=[image, label],
+                    drop_last=False,
+                    batch_size=64,
+                    return_list=False,
+                    shuffle=False)
+    return train_loader, eval_loader
 ```
 
 ## 5. 定义训练函数
 根据训练program和训练数据进行训练。
 ```python
-def start_train(program, data_reader, data_feeder):
+def start_train(program, data_loader):
     outputs = [avg_cost.name, acc_top1.name, acc_top5.name]
-    for data in data_reader():
-        batch_reward = exe.run(program, feed=data_feeder.feed(data), fetch_list = outputs)
+    for data in data_loader():
+        batch_reward = exe.run(program, feed=data, fetch_list = outputs)
         print("TRAIN: loss: {}, acc1: {}, acc5:{}".format(batch_reward[0], batch_reward[1], batch_reward[2]))
 ```
 
 ## 6. 定义评估函数
 根据评估program和评估数据进行评估。
 ```python
-def start_eval(program, data_reader, data_feeder):
+def start_eval(program, data_loader):
     reward = []
     outputs = [avg_cost.name, acc_top1.name, acc_top5.name]
-    for data in data_reader():
-        batch_reward = exe.run(program, feed=data_feeder.feed(data), fetch_list = outputs)
+    for data in data_loader():
+        batch_reward = exe.run(program, feed=data, fetch_list = outputs)
         reward_avg = np.mean(np.array(batch_reward), axis=1)
         reward.append(reward_avg)
         print("TEST: loss: {}, acc1: {}, acc5:{}".format(batch_reward[0], batch_reward[1], batch_reward[2]))
@@ -112,23 +132,23 @@ archs = sanas.next_archs()[0]
 ### 7.2 构造program
 调用步骤3中的函数，根据4.1中的模型结构构造相应的program。
 ```python
-exe, train_program, eval_program, inputs, avg_cost, acc_top1, acc_top5 = build_program(archs)
+exe, train_program, eval_program, (image, label), avg_cost, acc_top1, acc_top5 = build_program(archs)
 ```
 
 ### 7.3 定义输入数据
 ```python
-train_reader, train_feeder, eval_reader, eval_feeder = input_data(inputs)
+train_loader, eval_loader = input_data(image, label)
 ```
 
 ### 7.4 训练模型
 根据上面得到的训练program和评估数据启动训练。
 ```python
-start_train(train_program, train_reader, train_feeder)
+start_train(train_program, train_loader)
 ```
 ### 7.5 评估模型
 根据上面得到的评估program和评估数据启动评估。
 ```python
-finally_reward = start_eval(eval_program, eval_reader, eval_feeder)
+finally_reward = start_eval(eval_program, eval_loader)
 ```
 ### 7.6 回传当前模型的得分
 ```
@@ -141,16 +161,16 @@ sanas.reward(float(finally_reward[1]))
 for step in range(3):
     archs = sanas.next_archs()[0]
     exe, train_program, eval_program, inputs, avg_cost, acc_top1, acc_top5 = build_program(archs)
-    train_reader, train_feeder, eval_reader, eval_feeder = input_data(inputs)
+    train_loader, eval_loader = input_data(inputs)
 
     current_flops = slim.analysis.flops(train_program)
     if current_flops > 321208544:
         continue
 
     for epoch in range(7):
-        start_train(train_program, train_reader, train_feeder)
+        start_train(train_program, train_loader)
 
-    finally_reward = start_eval(eval_program, eval_reader, eval_feeder)
+    finally_reward = start_eval(eval_program, eval_loader)
 
     sanas.reward(float(finally_reward[1]))
 ```