动态图机制-DyGraph

PaddlePaddle的DyGraph模式是一种动态的图执行机制,可以立即执行结果,无需构建整个图。同时,和以往静态的执行计算图不同,DyGraph模式下您的所有操作可以立即获得执行结果,而不必等待所构建的计算图全部执行完成,这样可以让您更加直观地构建PaddlePaddle下的深度学习任务,以及进行模型的调试,同时还减少了大量用于构建静态计算图的代码,使得您编写、调试网络的过程变得更加便捷。

PaddlePaddle DyGraph是一个更加灵活易用的模式,可提供:

  • 更加灵活便捷的代码组织结构: 使用python的执行控制流程和面向对象的模型设计
  • 更加便捷的调试功能: 直接使用python的打印方法即时打印所需要的结果,从而检查正在运行的模型结果便于测试更改
  • 和静态执行图通用的模型代码:同样的模型代码可以使用更加便捷的DyGraph调试,执行,同时也支持使用原有的静态图模式执行

有关的动态图机制更多的实际模型示例请参考Paddle/models/dygraph

设置和基本用法

  1. 升级到最新的PaddlePaddle 1.6.0:
pip install -q --upgrade paddlepaddle==1.6.0
  1. 使用fluid.dygraph.guard(place=None) 上下文:
import paddle.fluid as fluid
with fluid.dygraph.guard():
    # write your executable dygraph code here             

现在您就可以在fluid.dygraph.guard()上下文环境中使用DyGraph的模式运行网络了,DyGraph将改变以往PaddlePaddle的执行方式: 现在他们将会立即执行,并且将计算结果返回给Python。

Dygraph将非常适合和Numpy一起使用,使用fluid.dygraph.to_variable(x)将会将ndarray转换为fluid.Variable,而使用fluid.Variable.numpy()将可以把任意时刻获取到的计算结果转换为Numpyndarray

x = np.ones([2, 2], np.float32)
with fluid.dygraph.guard():
    inputs = []
    for _ in range(10):
        inputs.append(fluid.dygraph.to_variable(x))
    ret = fluid.layers.sums(inputs)
    print(ret.numpy())

得到输出:

[[10. 10.]
[10. 10.]]
这里创建了一系列ndarray的输入,执行了一个sum操作之后,我们可以直接将运行的结果打印出来

然后通过调用reduce_sum后使用Variable.backward()方法执行反向,使用Variable.gradient()方法即可获得反向网络执行完成后的梯度值的ndarray形式:

loss = fluid.layers.reduce_sum(ret)
loss.backward()
print(loss.gradient())

得到输出 :

[1.]

基于DyGraph构建网络

  1. 编写一段用于DyGraph执行的Object-Oriented-Designed, PaddlePaddle模型代码主要由以下三个部分组成: 请注意,如果您设计的这一层结构是包含参数的,则必须要使用继承自fluid.dygraph.Layer的Object-Oriented-Designed的类来描述该层的行为。

    1. 建立一个可以在DyGraph模式中执行的,Object-Oriented的网络,需要继承自fluid.dygraph.Layer,其中需要调用基类的__init__方法,并且实现带有参数name_scope(用来标识本层的名字)的__init__构造函数,在构造函数中,我们通常会执行一些例如参数初始化,子网络初始化的操作,执行这些操作时不依赖于输入的动态信息:

      class MyLayer(fluid.dygraph.Layer):
          def __init__(self, name_scope):
              super(MyLayer, self).__init__(name_scope)
      
    2. 实现一个forward(self, *inputs)的执行函数,该函数将负责执行实际运行时网络的执行逻辑, 该函数将会在每一轮训练/预测中被调用,这里我们将执行一个简单的relu -> elementwise add -> reduce sum

          def forward(self, inputs):
              x = fluid.layers.relu(inputs)
              self._x_for_debug = x
              x = fluid.layers.elementwise_mul(x, x)
              x = fluid.layers.reduce_sum(x)
              return [x]
      
  2. fluid.dygraph.guard()中执行:

    1. 使用Numpy构建输入:

      np_inp = np.array([1.0, 2.0, -1.0], dtype=np.float32)
      
    2. 转换输入的ndarrayVariable, 并执行前向网络获取返回值: 使用fluid.dygraph.to_variable(np_inp)转换Numpy输入为DyGraph接收的输入,然后使用my_layer(var_inp)[0]调用callable object并且获取了x作为返回值,利用x.numpy()方法直接获取了执行得到的xndarray返回值。

      with fluid.dygraph.guard():
          var_inp = fluid.dygraph.to_variable(np_inp)
          my_layer = MyLayer("my_layer")
          x = my_layer(var_inp)[0]
          dy_out = x.numpy()
      
    3. 计算梯度:自动微分对于实现机器学习算法(例如用于训练神经网络的反向传播)来说很有用, 使用x.backward()方法可以从某个fluid.Varaible开始执行反向网络,同时利用my_layer._x_for_debug.gradient()获取了网络中x梯度的ndarray 返回值:

          x.backward()
          dy_grad = my_layer._x_for_debug.gradient()
      

完整代码如下:

import paddle.fluid as fluid
import numpy as np

class MyLayer(fluid.dygraph.Layer):
    def __init__(self, name_scope):
        super(MyLayer, self).__init__(name_scope)
        self.fc = fluid.dygraph.nn.FC(self.full_name(), size=12)
    
    def forward(self, inputs):
        x = self.fc(inputs)
        x = fluid.layers.relu(x)
        self._x_for_debug = x
        x = fluid.layers.elementwise_mul(x, x)
        x = fluid.layers.reduce_sum(x)
        return [x]

if __name__ == '__main__':
    np_inp = np.array([[1.0, 2.0, -1.0]], dtype=np.float32)
    with fluid.dygraph.guard():
        var_inp = fluid.dygraph.to_variable(np_inp)
        my_layer = MyLayer("my_layer")
        x = my_layer(var_inp)[0]
        dy_out = x.numpy()
        x.backward()
        dy_grad = my_layer._x_for_debug.gradient()
        my_layer.clear_gradients()  # 将参数梯度清零以保证下一轮训练的正确性

关于自动剪枝

每个 Variable 都有一个 stop_gradient 属性,可以用于细粒度地在反向梯度计算时排除部分子图,以提高效率。

如果OP只有一个输入需要梯度,那么该OP的输出也需要梯度。 相反,只有当OP的所有输入都不需要梯度时,该OP的输出也不需要梯度。 在所有的 Variable 都不需要梯度的子图中,反向计算就不会进行计算了。

在动态图模式下,除参数以外的所有 Variablestop_gradient 属性默认值都为 True,而参数的 stop_gradient 属性默认值为 False。 该属性用于自动剪枝,避免不必要的反向运算。

例如:

import paddle.fluid as fluid
import numpy as np

with fluid.dygraph.guard():
    x = fluid.dygraph.to_variable(np.random.randn(5, 5))  # 默认stop_gradient=True
    y = fluid.dygraph.to_variable(np.random.randn(5, 5))  # 默认stop_gradient=True
    z = fluid.dygraph.to_variable(np.random.randn(5, 5))
    z.stop_gradient = False
    a = x + y
    a.stop_gradient  # True
    b = a + z
    b.stop_gradient  # False

当你想冻结你的模型的一部分,或者你事先知道你不会使用某些参数的梯度的时候,这个功能是非常有用的。

例如:

import paddle.fluid as fluid
import numpy as np

with fluid.dygraph.guard():
    value0 = np.arange(26).reshape(2, 13).astype("float32")
    value1 = np.arange(6).reshape(2, 3).astype("float32")
    value2 = np.arange(10).reshape(2, 5).astype("float32")
    fc = fluid.FC("fc1", size=5, dtype="float32")
    fc2 = fluid.FC("fc2", size=3, dtype="float32")
    a = fluid.dygraph.to_variable(value0)
    b = fluid.dygraph.to_variable(value1)
    c = fluid.dygraph.to_variable(value2)
    out1 = fc(a)
    out2 = fc2(b)
    out1.stop_gradient = True  # 将不会对out1这部分子图做反向计算
    out = fluid.layers.concat(input=[out1, out2, c], axis=1)
    out.backward()
    # 可以发现这里fc参数的梯度都为0
    assert (fc._w.gradient() == 0).all()
    assert (out1.gradient() == 0).all()

使用DyGraph训练模型

接下来我们将以“手写数字识别”这个最基础的模型为例,展示如何利用DyGraph模式搭建并训练一个模型:

有关手写数字识别的相关理论知识请参考PaddleBook中的内容,我们在这里默认您已经了解了该模型所需的深度学习理论知识。

  1. 准备数据,我们使用paddle.dataset.mnist作为训练所需要的数据集:

    train_reader = paddle.batch(
    paddle.dataset.mnist.train(), batch_size=BATCH_SIZE, drop_last=True)
    
  2. 构建网络,虽然您可以根据之前的介绍自己定义所有的网络结构,但是您也可以直接使用fluid.dygraph.Layer当中我们为您定制好的一些基础网络结构,这里我们利用fluid.dygraph.Conv2D以及fluid.dygraph.Pool2d构建了基础的SimpleImgConvPool

    class SimpleImgConvPool(fluid.dygraph.Layer):
        def __init__(self,
                     name_scope,
                     num_filters,
                     filter_size,
                     pool_size,
                     pool_stride,
                     pool_padding=0,
                     pool_type='max',
                     global_pooling=False,
                     conv_stride=1,
                     conv_padding=0,
                     conv_dilation=1,
                     conv_groups=1,
                     act=None,
                     use_cudnn=False,
                     param_attr=None,
                     bias_attr=None):
            super(SimpleImgConvPool, self).__init__(name_scope)
    
            self._conv2d = fluid.dygraph.Conv2D(
                self.full_name(),
                num_filters=num_filters,
                filter_size=filter_size,
                stride=conv_stride,
                padding=conv_padding,
                dilation=conv_dilation,
                groups=conv_groups,
                param_attr=param_attr,
                bias_attr=bias_attr,
                act=act,
                use_cudnn=use_cudnn)
    
            self._pool2d = fluid.dygraph.Pool2D(
                self.full_name(),
                pool_size=pool_size,
                pool_type=pool_type,
                pool_stride=pool_stride,
                pool_padding=pool_padding,
                global_pooling=global_pooling,
                use_cudnn=use_cudnn)
    
        def forward(self, inputs):
            x = self._conv2d(inputs)
            x = self._pool2d(x)
            return x
    

    注意: 构建网络时子网络的定义和使用请在__init__中进行, 而子网络的执行则在forward函数中进行

  3. 利用已经构建好的SimpleImgConvPool组成最终的MNIST网络:

    class MNIST(fluid.dygraph.Layer):
        def __init__(self, name_scope):
            super(MNIST, self).__init__(name_scope)
    
            self._simple_img_conv_pool_1 = SimpleImgConvPool(
                self.full_name(), 20, 5, 2, 2, act="relu")
    
            self._simple_img_conv_pool_2 = SimpleImgConvPool(
                self.full_name(), 50, 5, 2, 2, act="relu")
    
            pool_2_shape = 50 * 4 * 4
            SIZE = 10
            scale = (2.0 / (pool_2_shape**2 * SIZE))**0.5
            self._fc = fluid.dygraph.FC(self.full_name(),
                                        10,
                                        param_attr=fluid.param_attr.ParamAttr(
                                            initializer=fluid.initializer.NormalInitializer(
                                                loc=0.0, scale=scale)),
                                        act="softmax")
    
        def forward(self, inputs, label=None):
            x = self._simple_img_conv_pool_1(inputs)
            x = self._simple_img_conv_pool_2(x)
            x = self._fc(x)
            if label is not None:
                acc = fluid.layers.accuracy(input=x, label=label)
                return x, acc
            else:
                return x
    
  4. fluid.dygraph.guard()中定义配置好的MNIST网络结构,此时即使没有训练也可以在fluid.dygraph.guard()中调用模型并且检查输出:

    with fluid.dygraph.guard():
        mnist = MNIST("mnist")
        id, data = list(enumerate(train_reader()))[0]
        dy_x_data = np.array(
            [x[0].reshape(1, 28, 28)
             for x in data]).astype('float32')
        img = fluid.dygraph.to_variable(dy_x_data)
        print("result is: {}".format(mnist(img).numpy()))
    

    输出:

    result is: [[0.10135901 0.1051138  0.1027941  ... 0.0972859  0.10221873 0.10165327]
            [0.09735426 0.09970362 0.10198303 ... 0.10134517 0.10179105 0.10025002]
            [0.09539858 0.10213123 0.09543551 ... 0.10613529 0.10535969 0.097991  ]
            ...
            [0.10120598 0.0996111  0.10512722 ... 0.10067689 0.10088114 0.10071224]
            [0.09889644 0.10033772 0.10151272 ... 0.10245881 0.09878646 0.101483  ]
            [0.09097178 0.10078511 0.10198414 ... 0.10317434 0.10087223 0.09816764]]
    
  5. 构建训练循环,在每一轮参数更新完成后我们调用mnist.clear_gradients()来重置梯度:

    with fluid.dygraph.guard():
        epoch_num = 5		
        BATCH_SIZE = 64
        train_reader = paddle.batch(
            paddle.dataset.mnist.train(), batch_size=32, drop_last=True)
        mnist = MNIST("mnist")
        adam = fluid.optimizer.AdamOptimizer(learning_rate=0.001)
        for epoch in range(epoch_num):
            for batch_id, data in enumerate(train_reader()):
                dy_x_data = np.array([x[0].reshape(1, 28, 28)
                                      for x in data]).astype('float32')
                y_data = np.array(
                    [x[1] for x in data]).astype('int64').reshape(-1, 1)
    
                img = fluid.dygraph.to_variable(dy_x_data)
                label = fluid.dygraph.to_variable(y_data)
    
                cost = mnist(img)
    
                loss = fluid.layers.cross_entropy(cost, label)
                avg_loss = fluid.layers.mean(loss)
    
                if batch_id % 100 == 0 and batch_id is not 0:
                    print("epoch: {}, batch_id: {}, loss is: {}".format(epoch, batch_id, avg_loss.numpy()))
                avg_loss.backward()
                adam.minimize(avg_loss)
                mnist.clear_gradients()
    
  6. 变量及优化器

    模型的参数或者任何您希望检测的值可以作为变量封装在类中,然后通过对象获取并使用numpy()方法获取其ndarray的输出, 在训练过程中您可以使用mnist.parameters()来获取到网络中所有的参数,也可以指定某一个Layer的某个参数或者parameters()来获取该层的所有参数,使用numpy()方法随时查看参数的值

    反向运行后调用之前定义的Adam优化器对象的minimize方法进行参数更新:

    with fluid.dygraph.guard():
        epoch_num = 5
        BATCH_SIZE = 64
    
        mnist = MNIST("mnist")
        adam = fluid.optimizer.AdamOptimizer(learning_rate=0.001)
        train_reader = paddle.batch(
            paddle.dataset.mnist.train(), batch_size= BATCH_SIZE, drop_last=True)
    
        np.set_printoptions(precision=3, suppress=True)
        for epoch in range(epoch_num):
            for batch_id, data in enumerate(train_reader()):
                dy_x_data = np.array(
                    [x[0].reshape(1, 28, 28)
                     for x in data]).astype('float32')
                y_data = np.array(
                    [x[1] for x in data]).astype('int64').reshape(BATCH_SIZE, 1)
    
                img = fluid.dygraph.to_variable(dy_x_data)
                label = fluid.dygraph.to_variable(y_data)
                label.stop_gradient = True
    
                cost = mnist(img)
                loss = fluid.layers.cross_entropy(cost, label)
                avg_loss = fluid.layers.mean(loss)
    
                dy_out = avg_loss.numpy()
    
                avg_loss.backward()
                adam.minimize(avg_loss)
                mnist.clear_gradients()
    
                dy_param_value = {}
                for param in mnist.parameters():
                    dy_param_value[param.name] = param.numpy()
    
                if batch_id % 20 == 0:
                    print("Loss at step {}: {}".format(batch_id, avg_loss.numpy()))
        print("Final loss: {}".format(avg_loss.numpy()))
        print("_simple_img_conv_pool_1_conv2d W's mean is: {}".format(mnist._simple_img_conv_pool_1._conv2d._filter_param.numpy().mean()))
        print("_simple_img_conv_pool_1_conv2d Bias's mean is: {}".format(mnist._simple_img_conv_pool_1._conv2d._bias_param.numpy().mean()))
    

    输出:

     ```
     Loss at step 0: [2.302]
     Loss at step 20: [1.616]
     Loss at step 40: [1.244]
     Loss at step 60: [1.142]
     Loss at step 80: [0.911]
     Loss at step 100: [0.824]
     Loss at step 120: [0.774]
     Loss at step 140: [0.626]
     Loss at step 160: [0.609]
     Loss at step 180: [0.627]
     Loss at step 200: [0.466]
     Loss at step 220: [0.499]
     Loss at step 240: [0.614]
     Loss at step 260: [0.585]
     Loss at step 280: [0.503]
     Loss at step 300: [0.423]
     Loss at step 320: [0.509]
     Loss at step 340: [0.348]
     Loss at step 360: [0.452]
     Loss at step 380: [0.397]
     Loss at step 400: [0.54]
     Loss at step 420: [0.341]
     Loss at step 440: [0.337]
     Loss at step 460: [0.155]
     Final loss: [0.164]
     _simple_img_conv_pool_1_conv2d W's mean is: 0.00606656912714
     _simple_img_conv_pool_1_conv2d Bias's mean is: -3.4576318285e-05
     ```
    
  7. 性能

在使用fluid.dygraph.guard()时可以通过传入fluid.CUDAPlace(0)或者fluid.CPUPlace()来选择执行DyGraph的设备,通常如果不做任何处理将会自动适配您的设备。

使用多卡训练模型

目前PaddlePaddle支持通过多进程方式进行多卡训练,即每个进程对应一张卡。训练过程中,在第一次执行前向操作时,如果该操作需要参数,则会将0号卡的参数Broadcast到其他卡上,确保各个卡上的参数一致;在计算完反向操作之后,将产生的参数梯度在所有卡之间进行聚合;最后在各个GPU卡上分别进行参数更新。

place = fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id)
with fluid.dygraph.guard(place):

    strategy = fluid.dygraph.parallel.prepare_context()
    mnist = MNIST("mnist")
    adam = AdamOptimizer(learning_rate=0.001)
    mnist = fluid.dygraph.parallel.DataParallel(mnist, strategy)

    train_reader = paddle.batch(
        paddle.dataset.mnist.train(), batch_size=BATCH_SIZE, drop_last=True)
    train_reader = fluid.contrib.reader.distributed_batch_reader(
            train_reader)

    for epoch in range(epoch_num):
        for batch_id, data in enumerate(train_reader()):
            dy_x_data = np.array([x[0].reshape(1, 28, 28)
                                  for x in data]).astype('float32')
            y_data = np.array(
                [x[1] for x in data]).astype('int64').reshape(-1, 1)

            img = to_variable(dy_x_data)
            label = to_variable(y_data)
            label.stop_gradient = True

            cost, acc = mnist(img, label)

            loss = fluid.layers.cross_entropy(cost, label)
            avg_loss = fluid.layers.mean(loss)

            avg_loss = mnist.scale_loss(avg_loss)
            avg_loss.backward()
            mnist.apply_collective_grads()
            
            adam.minimize(avg_loss)
            mnist.clear_gradients()
            if batch_id % 100 == 0 and batch_id is not 0:
                print("epoch: {}, batch_id: {}, loss is: {}".format(epoch, batch_id, avg_loss.numpy()))

动态图单卡训练转多卡训练需要修改的地方主要有四处:

  1. 需要从环境变量获取设备的ID,即:

    place = fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id)
    
  2. 需要对原模型做一些预处理,即:

    strategy = fluid.dygraph.parallel.prepare_context()
    mnist = MNIST("mnist")
    adam = AdamOptimizer(learning_rate=0.001)
    mnist = fluid.dygraph.parallel.DataParallel(mnist, strategy)
    
  3. 数据读取,必须确保每个进程读取的数据是不同的,即所有进程读取数据的交集为空,所有进程读取数据的并集是完整的数据集:

    train_reader = paddle.batch(
        paddle.dataset.mnist.train(), batch_size=BATCH_SIZE, drop_last=True)
    train_reader = fluid.contrib.reader.distributed_batch_reader(
        train_reader)
    
  4. 需要对loss进行调整,以及对参数的梯度进行聚合,即:

    avg_loss = mnist.scale_loss(avg_loss)
    avg_loss.backward()
    mnist.apply_collective_grads()
    

Paddle动态图多进程多卡模型训练启动时需要指定使用的GPU,即如果使用0,1,2,3卡,启动方式如下:

python -m paddle.distributed.launch --selected_gpus=0,1,2,3 --log_dir ./mylog train.py 

输出结果为:

-----------  Configuration Arguments -----------
cluster_node_ips: 127.0.0.1
log_dir: ./mylog
node_ip: 127.0.0.1
print_config: True
selected_gpus: 0,1,2,3
started_port: 6170
training_script: train.py
training_script_args: ['--use_data_parallel', '1']
use_paddlecloud: True
------------------------------------------------
trainers_endpoints: 127.0.0.1:6170,127.0.0.1:6171,127.0.0.1:6172,127.0.0.1:6173 , node_id: 0 , current_node_ip: 127.0.0.1 , num_nodes: 1 , node_ips: ['127.0.0.1'] , nranks: 4

此时,程序会将每个进程的输出log导出到./mylog路径下:

.
├── mylog
│   ├── workerlog.0
│   ├── workerlog.1
│   ├── workerlog.2
│   └── workerlog.3
└── train.py

如果不指定--log_dir,程序会将打印出所有进程的输出,即:

-----------  Configuration Arguments -----------
cluster_node_ips: 127.0.0.1
log_dir: None
node_ip: 127.0.0.1
print_config: True
selected_gpus: 0,1,2,3
started_port: 6170
training_script: train.py
training_script_args: ['--use_data_parallel', '1']
use_paddlecloud: True
------------------------------------------------
trainers_endpoints: 127.0.0.1:6170,127.0.0.1:6171,127.0.0.1:6172,127.0.0.1:6173 , node_id: 0 , current_node_ip: 127.0.0.1 , num_nodes: 1 , node_ips: ['127.0.0.1'] , nranks: 4
grep: warning: GREP_OPTIONS is deprecated; please use an alias or script
grep: warning: GREP_OPTIONS is deprecated; please use an alias or script
grep: warning: GREP_OPTIONS is deprecated; please use an alias or script
grep: warning: GREP_OPTIONS is deprecated; please use an alias or script
I0923 09:32:36.423513 56410 nccl_context.cc:120] init nccl context nranks: 4 local rank: 1 gpu id: 1
I0923 09:32:36.425287 56411 nccl_context.cc:120] init nccl context nranks: 4 local rank: 2 gpu id: 2
I0923 09:32:36.429337 56409 nccl_context.cc:120] init nccl context nranks: 4 local rank: 0 gpu id: 0
I0923 09:32:36.429440 56412 nccl_context.cc:120] init nccl context nranks: 4 local rank: 3 gpu id: 3
W0923 09:32:42.594097 56412 device_context.cc:198] Please NOTE: device: 3, CUDA Capability: 70, Driver API Version: 9.0, Runtime API Version: 9.0
W0923 09:32:42.605836 56412 device_context.cc:206] device: 3, cuDNN Version: 7.5.
W0923 09:32:42.632463 56410 device_context.cc:198] Please NOTE: device: 1, CUDA Capability: 70, Driver API Version: 9.0, Runtime API Version: 9.0
W0923 09:32:42.637948 56410 device_context.cc:206] device: 1, cuDNN Version: 7.5.
W0923 09:32:42.648674 56411 device_context.cc:198] Please NOTE: device: 2, CUDA Capability: 70, Driver API Version: 9.0, Runtime API Version: 9.0
W0923 09:32:42.654021 56411 device_context.cc:206] device: 2, cuDNN Version: 7.5.
W0923 09:32:43.048696 56409 device_context.cc:198] Please NOTE: device: 0, CUDA Capability: 70, Driver API Version: 9.0, Runtime API Version: 9.0
W0923 09:32:43.053236 56409 device_context.cc:206] device: 0, cuDNN Version: 7.5.
start data reader (trainers_num: 4, trainer_id: 2)
start data reader (trainers_num: 4, trainer_id: 3)
start data reader (trainers_num: 4, trainer_id: 1)
start data reader (trainers_num: 4, trainer_id: 0)
Loss at epoch 0 step 0: [0.57390565]
Loss at epoch 0 step 0: [0.57523954]
Loss at epoch 0 step 0: [0.575606]
Loss at epoch 0 step 0: [0.5767452]

模型参数的保存

动态图由于模型和优化器在不同的对象中存储,模型参数和优化器信息要分别存储。

在模型训练中可以使用 paddle.fluid.dygraph.save_dygraph(state_dict, model_path) 来保存模型参数的dict或优化器信息的dict。

同样可以使用 paddle.fluid.dygraph.load_dygraph(model_path) 获取保存的模型参数的dict和优化器信息的dict。

再使用your_modle_object.set_dict(para_dict)接口来恢复保存的模型参数从而达到继续训练的目的。

以及使用your_optimizer_object.set_dict(opti_dict)接口来恢复保存的优化器中的learning rate decay值。

下面的代码展示了如何在“手写数字识别”任务中保存参数并且读取已经保存的参数来继续训练。

import paddle.fluid as fluid

with fluid.dygraph.guard():
    epoch_num = 5
    BATCH_SIZE = 64

    mnist = MNIST("mnist")
    adam = fluid.optimizer.Adam(learning_rate=0.001)
    train_reader = paddle.batch(
        paddle.dataset.mnist.train(), batch_size= BATCH_SIZE, drop_last=True)

    np.set_printoptions(precision=3, suppress=True)
    dy_param_init_value={}
    for epoch in range(epoch_num):
        for batch_id, data in enumerate(train_reader()):
            dy_x_data = np.array(
                [x[0].reshape(1, 28, 28)
                 for x in data]).astype('float32')
            y_data = np.array(
                [x[1] for x in data]).astype('int64').reshape(BATCH_SIZE, 1)

            img = fluid.dygraph.to_variable(dy_x_data)
            label = fluid.dygraph.to_variable(y_data)
            label.stop_gradient = True

            cost = mnist(img)
            loss = fluid.layers.cross_entropy(cost, label)
            avg_loss = fluid.layers.mean(loss)

            dy_out = avg_loss.numpy()

            avg_loss.backward()
            adam.minimize(avg_loss)
            if batch_id == 20:
                fluid.dygraph.save_dygraph(mnist.state_dict(), "paddle_dy")
            mnist.clear_gradients()

            if batch_id == 20:
                for param in mnist.parameters():
                    dy_param_init_value[param.name] = param.numpy()
                model, _ = fluid.dygraph.load_dygraph("paddle_dy")
                mnist.set_dict(model)
                break
        if epoch == 0:
            break
    restore = mnist.parameters()
    # check save and load

    success = True
    for value in restore:
        if (not np.array_equal(value.numpy(), dy_param_init_value[value.name])) or (not np.isfinite(value.numpy().all())) or (np.isnan(value.numpy().any())):
            success = False
    print("model save and load success? {}".format(success))

需要注意的是,如果采用多卡训练,只需要一个进程对模型参数进行保存,因此在保存模型参数时,需要进行指定保存哪个进程的参数,比如

    if fluid.dygraph.parallel.Env().local_rank == 0:
        fluid.dygraph.save_dygraph(mnist.state_dict(), "paddle_dy")

模型评估

当我们需要在DyGraph模式下利用搭建的模型进行预测任务,可以使用YourModel.eval()接口,在之前的手写数字识别模型中我们使用mnist.eval()来启动预测模式(我们默认在fluid.dygraph.guard()上下文中是训练模式),在预测的模式下,DyGraph将只会执行前向的预测网络,而不会进行自动求导并执行反向网络:

下面的代码展示了如何使用DyGraph模式训练一个用于执行“手写数字识别”任务的模型并保存,并且利用已经保存好的模型进行预测。

我们在fluid.dygraph.guard()上下文中进行了模型的保存和训练,值得注意的是,当我们需要在训练的过程中进行预测时需要使用YourModel.eval()切换到预测模式,并且在预测完成后使用YourModel.train()切换回训练模式继续训练。

我们在inference_mnist 中启用另一个fluid.dygraph.guard(),并在其上下文中load之前保存的checkpoint进行预测,同样的在执行预测前需要使用YourModel.eval()来切换到预测模式。

def test_mnist(reader, model, batch_size):
    acc_set = []
    avg_loss_set = []
    for batch_id, data in enumerate(reader()):
        dy_x_data = np.array([x[0].reshape(1, 28, 28)
                              for x in data]).astype('float32')
        y_data = np.array(
            [x[1] for x in data]).astype('int64').reshape(batch_size, 1)

        img = fluid.dygraph.to_variable(dy_x_data)
        label = fluid.dygraph.to_variable(y_data)
        label.stop_gradient = True
        prediction, acc = model(img, label)
        loss = fluid.layers.cross_entropy(input=prediction, label=label)
        avg_loss = fluid.layers.mean(loss)
        acc_set.append(float(acc.numpy()))
        avg_loss_set.append(float(avg_loss.numpy()))

        # get test acc and loss
    acc_val_mean = np.array(acc_set).mean()
    avg_loss_val_mean = np.array(avg_loss_set).mean()

    return avg_loss_val_mean, acc_val_mean

def inference_mnist():
    with fluid.dygraph.guard():
        mnist_infer = MNIST("mnist")
        # load checkpoint
        model_dict, _ = fluid.dygraph.load_dygraph("paddle_dy")
        mnist_infer.load_dict(model_dict)
        print("checkpoint loaded")

        # start evaluate mode
        mnist_infer.eval()

        def load_image(file):
            im = Image.open(file).convert('L')
            im = im.resize((28, 28), Image.ANTIALIAS)
            im = np.array(im).reshape(1, 1, 28, 28).astype(np.float32)
            im = im / 255.0 * 2.0 - 1.0
            return im

        cur_dir = os.path.dirname(os.path.realpath(__file__))
        tensor_img = load_image(cur_dir + '/image/infer_3.png')

        results = mnist_infer(fluid.dygraph.to_variable(tensor_img))
        lab = np.argsort(results.numpy())
        print("Inference result of image/infer_3.png is: %d" % lab[0][-1])

with fluid.dygraph.guard():
    epoch_num = 1
    BATCH_SIZE = 64
    mnist = MNIST("mnist")
    adam = fluid.optimizer.AdamOptimizer(learning_rate=0.001)
    test_reader = paddle.batch(
        paddle.dataset.mnist.test(), batch_size=BATCH_SIZE, drop_last=True)

    train_reader = paddle.batch(
        paddle.dataset.mnist.train(),
        batch_size=BATCH_SIZE,
        drop_last=True)

    for epoch in range(epoch_num):
        for batch_id, data in enumerate(train_reader()):
            dy_x_data = np.array([x[0].reshape(1, 28, 28)
                                  for x in data]).astype('float32')
            y_data = np.array(
                [x[1] for x in data]).astype('int64').reshape(-1, 1)

            img = fluid.dygraph.to_variable(dy_x_data)
            label = fluid.dygraph.to_variable(y_data)
            label.stop_gradient = True

            cost, acc = mnist(img, label)

            loss = fluid.layers.cross_entropy(cost, label)
            avg_loss = fluid.layers.mean(loss)

            avg_loss.backward()

            adam.minimize(avg_loss)
            # save checkpoint
            mnist.clear_gradients()
            if batch_id % 100 == 0:
                print("Loss at epoch {} step {}: {:}".format(
                    epoch, batch_id, avg_loss.numpy()))

        mnist.eval()
        test_cost, test_acc = test_mnist(test_reader, mnist, BATCH_SIZE)
        mnist.train()
        print("Loss at epoch {} , Test avg_loss is: {}, acc is: {}".format(
            epoch, test_cost, test_acc))

    fluid.dygraph.save_dygraph(mnist.state_dict(), "paddle_dy")
    print("checkpoint saved")

    inference_mnist()

输出:

Loss at epoch 0 step 0: [2.2991252]
Loss at epoch 0 step 100: [0.15491392]
Loss at epoch 0 step 200: [0.13315125]
Loss at epoch 0 step 300: [0.10253005]
Loss at epoch 0 step 400: [0.04266362]
Loss at epoch 0 step 500: [0.08894891]
Loss at epoch 0 step 600: [0.08999012]
Loss at epoch 0 step 700: [0.12975612]
Loss at epoch 0 step 800: [0.15257305]
Loss at epoch 0 step 900: [0.07429226]
Loss at epoch 0 , Test avg_loss is: 0.05995981965082674, acc is: 0.9794671474358975
checkpoint saved
No optimizer loaded. If you didn't save optimizer, please ignore this. The program can still work with new optimizer. 
checkpoint loaded
Inference result of image/infer_3.png is: 3

编写兼容的模型

以上一步中手写数字识别的例子为例,动态图的模型代码可以直接用于静态图中作为模型代码,执行时,直接使用PaddlePaddle静态图执行方式即可,这里以静态图中的executor为例, 模型代码可以直接使用之前的模型代码,执行时使用Executor执行即可

epoch_num = 1
BATCH_SIZE = 64
exe = fluid.Executor(fluid.CPUPlace())

mnist = MNIST("mnist")
sgd = fluid.optimizer.SGDOptimizer(learning_rate=1e-3)
train_reader = paddle.batch(
    paddle.dataset.mnist.train(), batch_size=BATCH_SIZE, drop_last=True)
img = fluid.layers.data(
    name='pixel', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
cost = mnist(img)
loss = fluid.layers.cross_entropy(cost, label)
avg_loss = fluid.layers.mean(loss)
sgd.minimize(avg_loss)

out = exe.run(fluid.default_startup_program())

for epoch in range(epoch_num):
    for batch_id, data in enumerate(train_reader()):
        static_x_data = np.array(
            [x[0].reshape(1, 28, 28)
             for x in data]).astype('float32')
        y_data = np.array(
            [x[1] for x in data]).astype('int64').reshape([BATCH_SIZE, 1])

        fetch_list = [avg_loss.name]
        out = exe.run(
            fluid.default_main_program(),
            feed={"pixel": static_x_data,
                  "label": y_data},
            fetch_list=fetch_list)

        static_out = out[0]

        if batch_id % 100 == 0 and batch_id is not 0:
            print("epoch: {}, batch_id: {}, loss: {}".format(epoch, batch_id, static_out))