From 08b88c0c2b9752362eab478b4698cc74f2aee6e4 Mon Sep 17 00:00:00 2001
From: Bai Yifan <me@ethanbai.com>
Date: Tue, 5 Jan 2021 11:30:53 +0800
Subject: [PATCH] distillation tutorial update (#578) (#583)

---
 .../quick_start/distillation_tutorial.md      | 84 +++++++++++--------
 1 file changed, 48 insertions(+), 36 deletions(-)

diff --git a/docs/zh_cn/quick_start/distillation_tutorial.md b/docs/zh_cn/quick_start/distillation_tutorial.md
index aa2dd3e9..6a55a157 100755
--- a/docs/zh_cn/quick_start/distillation_tutorial.md
+++ b/docs/zh_cn/quick_start/distillation_tutorial.md
@@ -13,67 +13,69 @@
 
 ## 1. 导入依赖
 
-PaddleSlim依赖Paddle1.7版本，请确认已正确安装Paddle，然后按以下方式导入Paddle和PaddleSlim:
+PaddleSlim依赖Paddle2.0版本，请确认已正确安装Paddle，然后按以下方式导入Paddle和PaddleSlim:
 
 ```
 import paddle
-import paddle.fluid as fluid
+import numpy as np
 import paddleslim as slim
+paddle.enable_static()
 ```
 
 ## 2. 定义student_program和teacher_program
 
-本教程在MNIST数据集上进行知识蒸馏的训练和验证，输入图片尺寸为`[1, 28, 28]`，输出类别数为10。
+本教程在CIFAR数据集上进行知识蒸馏的训练和验证，输入图片尺寸为`[3, 32, 32]`，输出类别数为10。
 选择`ResNet50`作为teacher对`MobileNet`结构的student进行蒸馏训练。
 
 ```python
 model = slim.models.MobileNet()
-student_program = fluid.Program()
-student_startup = fluid.Program()
-with fluid.program_guard(student_program, student_startup):
-    image = fluid.data(
-        name='image', shape=[None] + [1, 28, 28], dtype='float32')
-    label = fluid.data(name='label', shape=[None, 1], dtype='int64')
+student_program = paddle.static.Program()
+student_startup = paddle.static.Program()
+with paddle.static.program_guard(student_program, student_startup):
+    image = paddle.static.data(
+        name='image', shape=[None, 3, 32, 32], dtype='float32')
+    label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
+    gt = paddle.reshape(label, [-1, 1])
     out = model.net(input=image, class_dim=10)
-    cost = fluid.layers.cross_entropy(input=out, label=label)
-    avg_cost = fluid.layers.mean(x=cost)
-    acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
-    acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
+    cost = paddle.nn.functional.loss.cross_entropy(input=out, label=gt)
+    avg_cost = paddle.mean(x=cost)
+    acc_top1 = paddle.metric.accuracy(input=out, label=gt, k=1)
+    acc_top5 = paddle.metric.accuracy(input=out, label=gt, k=5)
 ```
 
 
 
 ```python
-model = slim.models.ResNet50()
-teacher_program = fluid.Program()
-teacher_startup = fluid.Program()
-with fluid.program_guard(teacher_program, teacher_startup):
-    with fluid.unique_name.guard():
-        image = fluid.data(
-            name='image', shape=[None] + [1, 28, 28], dtype='float32')
+teacher_model = slim.models.ResNet50()
+teacher_program = paddle.static.Program()
+teacher_startup = paddle.static.Program()
+with paddle.static.program_guard(teacher_program, teacher_startup):
+    with paddle.utils.unique_name.guard():
+        image = paddle.static.data(
+            name='image', shape=[None, 3, 32, 32], dtype='float32')
         predict = teacher_model.net(image, class_dim=10)
-exe = fluid.Executor(fluid.CPUPlace())
+exe = paddle.static.Executor(paddle.CPUPlace())
 exe.run(teacher_startup)
 ```
 
 ## 3. 选择特征图
 
-我们可以用student_的list_vars方法来观察其中全部的Variables，从中选出一个或多个变量（Variable）来拟合teacher相应的变量。
+我们可以用student_的list_vars方法来观察其中全部的Tensor，从中选出一个或多个变量（Tensor）来拟合teacher相应的变量。
 
 ```python
-# get all student variables
+# get all student tensor
 student_vars = []
 for v in student_program.list_vars():
     student_vars.append((v.name, v.shape))
-#uncomment the following lines to observe student's variables for distillation
+#uncomment the following lines to observe student's tensor for distillation
 #print("="*50+"student_model_vars"+"="*50)
 #print(student_vars)
 
-# get all teacher variables
+# get all teacher tensor
 teacher_vars = []
 for v in teacher_program.list_vars():
     teacher_vars.append((v.name, v.shape))
-#uncomment the following lines to observe teacher's variables for distillation
+#uncomment the following lines to observe teacher's tensor for distillation
 #print("="*50+"teacher_model_vars"+"="*50)
 #print(teacher_vars)
 ```
@@ -81,33 +83,43 @@ for v in teacher_program.list_vars():
 经过筛选我们可以看到，teacher_program中的'bn5c_branch2b.output.1.tmp_3'和student_program的'depthwise_conv2d_11.tmp_0'尺寸一致，可以组成蒸馏损失函数。
 
 ## 4. 合并program (merge)并添加蒸馏loss
-merge操作将student_program和teacher_program中的所有Variables和Op都将被添加到同一个Program中，同时为了避免两个program中有同名变量会引起命名冲突，merge也会为teacher_program中的Variables添加一个同一的命名前缀name_prefix，其默认值是'teacher_'
+merge操作将student_program和teacher_program中的所有Tensor和Op都将被添加到同一个Program中，同时为了避免两个program中有同名变量会引起命名冲突，merge也会为teacher_program中的Tensor添加一个同一的命名前缀name_prefix，其默认值是'teacher_'
 
 为了确保teacher网络和student网络输入的数据是一样的，merge操作也会对两个program的输入数据层进行合并操作，所以需要指定一个数据层名称的映射关系data_name_map，key是teacher的输入数据名称，value是student的
 
 ```python
 data_name_map = {'image': 'image'}
-main = slim.dist.merge(teacher_program, student_program, data_name_map, fluid.CPUPlace())
-with fluid.program_guard(student_program, student_startup):
+main = slim.dist.merge(teacher_program, student_program, data_name_map, paddle.CPUPlace())
+with paddle.static.program_guard(student_program, student_startup):
     l2_loss = slim.dist.l2_loss('teacher_bn5c_branch2b.output.1.tmp_3', 'depthwise_conv2d_11.tmp_0', student_program)
     loss = l2_loss + avg_cost
-    opt = fluid.optimizer.Momentum(0.01, 0.9)
+    opt = paddle.optimizer.Momentum(0.01, 0.9)
     opt.minimize(loss)
 exe.run(student_startup)
 ```
 
 ## 5. 模型训练
 
-为了快速执行该示例，我们选取简单的MNIST数据，Paddle框架的`paddle.dataset.mnist`包定义了MNIST数据的下载和读取。 代码如下：
+为了快速执行该示例，我们选取简单的CIFAR数据，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
-train_reader = paddle.fluid.io.batch(
-    paddle.dataset.mnist.train(), batch_size=128, drop_last=True)
-train_feeder = fluid.DataFeeder(['image', 'label'], fluid.CPUPlace(), student_program)
+import paddle.vision.transforms as T
+transform = T.Compose([T.Transpose(), T.Normalize([127.5], [127.5])])
+train_dataset = paddle.vision.datasets.Cifar10(
+    mode="train", backend="cv2", 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)
 ```
 
 ```python
-for data in train_reader():
-    acc1, acc5, loss_np = exe.run(student_program, feed=train_feeder.feed(data), fetch_list=[acc_top1.name, acc_top5.name, loss.name])
+for idx, data in enumerate(train_loader):
+    acc1, acc5, loss_np = exe.run(student_program, feed=data, fetch_list=[acc_top1.name, acc_top5.name, loss.name])
     print("Acc1: {:.6f}, Acc5: {:.6f}, Loss: {:.6f}".format(acc1.mean(), acc5.mean(), loss_np.mean()))
 ```
-- 
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