single_distiller_api_doc.md

# paddleslim.dist API文档

## merge(teacher_program, student_program, data_name_map, place, scope=fluid.global_scope(), name_prefix='teacher_')

该方法将两个fluid program（teacher_program, student_program）融合为一个program，并将融合得到的program返回。在融合的program中，可以为其中合适的teacher特征图和student特征图添加蒸馏损失函数，从而达到用teacher模型的暗知识（Dark Knowledge）指导student模型学习的目的。

**参数：**

- teacher_program(Program)-定义了teacher模型的paddle program
- student_program(Program)-定义了student模型的paddle program
- data_name_map(dict)-teacher输入接口名与student输入接口名的映射，key为teacher的输入名，value为student的输入名。merge函数将会把这两个模型的输入按对应关系合并在一起，保证teacher与student输入数据相同
- place(fluid.CPUPlace()|fluid.CUDAPlace(N))-该参数表示程序运行在何种设备上，这里的N为GPU对应的ID
- scope(Scope)-该参数表示teacher variables和student variables所使用的作用域，如果不指定将使用默认的全局作用域。默认值：fluid.global_scope()
- name_prefix(str)-为了避免teacher variables和student variables存在同名变量而引起命名冲突，merge函数将统一为teacher variables添加一个名称前缀name_prefix，merge后的program中所有teacher variables都将带有这一名称前缀。默认值：'teacher_'

**返回：**由student_program和teacher_program merge得到的program

**使用示例：**

```python
import paddle.fluid as fluid
import paddleslim.dist as dist
student_program = fluid.Program()
with fluid.program_guard(student_program):
    x = fluid.layers.data(name='x', shape=[1, 28, 28])
    conv = fluid.layers.conv2d(x, 32, 1)
    out = fluid.layers.conv2d(conv, 64, 3, padding=1)
teacher_program = fluid.Program()
with fluid.program_guard(teacher_program):
    y = fluid.layers.data(name='y', shape=[1, 28, 28])
    conv = fluid.layers.conv2d(y, 32, 1)
    conv = fluid.layers.conv2d(conv, 32, 3, padding=1)
    out = fluid.layers.conv2d(conv, 64, 3, padding=1)
data_name_map = {'y':'x'}
USE_GPU = False
place = fluid.CUDAPlace(0) if USE_GPU else fluid.CPUPlace()
main_program = dist.merge(teacher_program, student_program, data_name_map, place)
```



## fsp_loss(teacher_var1_name, teacher_var2_name, student_var1_name, student_var2_name, program=fluid.default_main_program())

fsp_loss为program内的teacher var和student var添加fsp loss，出自论文[A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning](http://openaccess.thecvf.com/content_cvpr_2017/papers/Yim_A_Gift_From_CVPR_2017_paper.pdf)

**参数：**

- teacher_var1_name(str): teacher_var1的名称. 对应的variable是一个形为`[batch_size, x_channel, height, width]`的4-D特征图Tensor，数据类型为float32或float64
- teacher_var2_name(str): teacher_var2的名称. 对应的variable是一个形为`[batch_size, y_channel, height, width]`的4-D特征图Tensor，数据类型为float32或float64。只有y_channel可以与teacher_var1的x_channel不同，其他维度必须与teacher_var1相同
- student_var1_name(str): student_var1的名称. 对应的variable需与teacher_var1尺寸保持一致，是一个形为`[batch_size, x_channel, height, width]`的4-D特征图Tensor，数据类型为float32或float64
- student_var2_name(str): student_var2的名称. 对应的variable需与teacher_var2尺寸保持一致，是一个形为`[batch_size, y_channel, height, width]`的4-D特征图Tensor，数据类型为float32或float64。只有y_channel可以与student_var1的x_channel不同，其他维度必须与student_var1相同
- program(Program): 用于蒸馏训练的fluid program。默认值：fluid.default_main_program()

**返回：**由teacher_var1, teacher_var2, student_var1, student_var2组合得到的fsp_loss

**使用示例：**

```python
import paddle.fluid as fluid
import paddleslim.dist as dist
student_program = fluid.Program()
with fluid.program_guard(student_program):
    x = fluid.layers.data(name='x', shape=[1, 28, 28])
    conv = fluid.layers.conv2d(x, 32, 1, name='s1')
    out = fluid.layers.conv2d(conv, 64, 3, padding=1, name='s2')
teacher_program = fluid.Program()
with fluid.program_guard(teacher_program):
    y = fluid.layers.data(name='y', shape=[1, 28, 28])
    conv = fluid.layers.conv2d(y, 32, 1, name='t1')
    conv = fluid.layers.conv2d(conv, 32, 3, padding=1)
    out = fluid.layers.conv2d(conv, 64, 3, padding=1, name='t2')
data_name_map = {'y':'x'}
USE_GPU = False
place = fluid.CUDAPlace(0) if USE_GPU else fluid.CPUPlace()
main_program = merge(teacher_program, student_program, data_name_map, place)
with fluid.program_guard(main_program):
    distillation_loss = dist.fsp_loss('teacher_t1.tmp_1', 'teacher_t2.tmp_1', 's1.tmp_1', 's2.tmp_1', main_program)
```



## l2_loss(teacher_var_name, student_var_name, program=fluid.default_main_program())

l2_loss为program内的teacher var和student var添加l2 loss

**参数：**

- teacher_var_name(str): teacher_var的名称. 
- student_var_name(str): student_var的名称.
- program(Program): 用于蒸馏训练的fluid program。默认值：fluid.default_main_program()

**返回：**由teacher_var, student_var组合得到的l2_loss

**使用示例：**

```python
import paddle.fluid as fluid
import paddleslim.dist as dist
student_program = fluid.Program()
with fluid.program_guard(student_program):
    x = fluid.layers.data(name='x', shape=[1, 28, 28])
    conv = fluid.layers.conv2d(x, 32, 1, name='s1')
    out = fluid.layers.conv2d(conv, 64, 3, padding=1, name='s2')
teacher_program = fluid.Program()
with fluid.program_guard(teacher_program):
    y = fluid.layers.data(name='y', shape=[1, 28, 28])
    conv = fluid.layers.conv2d(y, 32, 1, name='t1')
    conv = fluid.layers.conv2d(conv, 32, 3, padding=1)
    out = fluid.layers.conv2d(conv, 64, 3, padding=1, name='t2')
data_name_map = {'y':'x'}
USE_GPU = False
place = fluid.CUDAPlace(0) if USE_GPU else fluid.CPUPlace()
main_program = merge(teacher_program, student_program, data_name_map, place)
with fluid.program_guard(main_program):
    distillation_loss = dist.l2_loss('teacher_t2.tmp_1', 's2.tmp_1', main_program)
```



## soft_label_loss(teacher_var_name, student_var_name, program=fluid.default_main_program(), teacher_temperature=1., student_temperature=1.)

soft_label_loss为program内的teacher var和student var添加soft label loss，出自论文[Distilling the Knowledge in a Neural Network](https://arxiv.org/pdf/1503.02531.pdf)

**参数：**

- teacher_var_name(str): teacher_var的名称. 
- student_var_name(str): student_var的名称. 
- program(Program): 用于蒸馏训练的fluid program。默认值：fluid.default_main_program()
- teacher_temperature(float): 对teacher_var进行soft操作的温度值，温度值越大得到的特征图越平滑 
- student_temperature(float): 对student_var进行soft操作的温度值，温度值越大得到的特征图越平滑 

**返回：**由teacher_var, student_var组合得到的soft_label_loss

**使用示例：**

```python
import paddle.fluid as fluid
import paddleslim.dist as dist
student_program = fluid.Program()
with fluid.program_guard(student_program):
    x = fluid.layers.data(name='x', shape=[1, 28, 28])
    conv = fluid.layers.conv2d(x, 32, 1, name='s1')
    out = fluid.layers.conv2d(conv, 64, 3, padding=1, name='s2')
teacher_program = fluid.Program()
with fluid.program_guard(teacher_program):
    y = fluid.layers.data(name='y', shape=[1, 28, 28])
    conv = fluid.layers.conv2d(y, 32, 1, name='t1')
    conv = fluid.layers.conv2d(conv, 32, 3, padding=1)
    out = fluid.layers.conv2d(conv, 64, 3, padding=1, name='t2')
data_name_map = {'y':'x'}
USE_GPU = False
place = fluid.CUDAPlace(0) if USE_GPU else fluid.CPUPlace()
main_program = merge(teacher_program, student_program, data_name_map, place)
with fluid.program_guard(main_program):
    distillation_loss = dist.soft_label_loss('teacher_t2.tmp_1', 's2.tmp_1', main_program, 1., 1.)
```



## loss(loss_func, program=fluid.default_main_program(), **kwargs)

loss函数支持对任意多对teacher_var和student_var使用自定义损失函数

**参数：**

- loss_func(python function): 自定义的损失函数，输入为teacher var和student var，输出为自定义的loss 
- program(Program): 用于蒸馏训练的fluid program。默认值：fluid.default_main_program()
- **kwargs: loss_func输入名与对应variable名称

**返回**：自定义的损失函数loss

**使用示例：**

```python
import paddle.fluid as fluid
import paddleslim.dist as dist
student_program = fluid.Program()
with fluid.program_guard(student_program):
    x = fluid.layers.data(name='x', shape=[1, 28, 28])
    conv = fluid.layers.conv2d(x, 32, 1, name='s1')
    out = fluid.layers.conv2d(conv, 64, 3, padding=1, name='s2')
teacher_program = fluid.Program()
with fluid.program_guard(teacher_program):
    y = fluid.layers.data(name='y', shape=[1, 28, 28])
    conv = fluid.layers.conv2d(y, 32, 1, name='t1')
    conv = fluid.layers.conv2d(conv, 32, 3, padding=1)
    out = fluid.layers.conv2d(conv, 64, 3, padding=1, name='t2')
data_name_map = {'y':'x'}
USE_GPU = False
place = fluid.CUDAPlace(0) if USE_GPU else fluid.CPUPlace()
main_program = merge(teacher_program, student_program, data_name_map, place)
def adaptation_loss(t_var, s_var):
    teacher_channel = t_var.shape[1]
    s_hint = fluid.layers.conv2d(s_var, teacher_channel, 1)
    hint_loss = fluid.layers.reduce_mean(fluid.layers.square(s_hint - t_var))
    return hint_loss
with fluid.program_guard(main_program):
    distillation_loss = dist.loss(main_program, adaptation_loss, t_var='teacher_t2.tmp_1', s_var='s2.tmp_1')
```

## 注意事项

在添加蒸馏loss时会引入新的variable，需要注意新引入的variable不要与student variables命名冲突。这里建议两种用法：

1. 建议与student_program使用同一个命名空间，以避免一些未指定名称的variables(例如tmp_0, tmp_1...)多次定义为同一名称出现命名冲突
2. 建议在添加蒸馏loss时指定一个命名空间前缀，具体用法请参考Paddle官方文档[fluid.name_scope](https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/fluid_cn/name_scope_cn.html#name-scope)