Add dygraph models

dygraph/mnist/README.md

+# MNIST
+当我们学习编程的时候，编写的第一个程序一般是实现打印"Hello World"。而机器学习（或深度学习）的入门教程，一般都是 MNIST 数据库上的手写识别问题。原因是手写识别属于典型的图像分类问题，比较简单，同时MNIST数据集也很完备。
+本页将介绍如何使用PaddlePaddle在DyGraph模式下实现MNIST，包括[安装](#installation)、[训练](#training-a-model)、[输出](#log)、[参数保存](#save)、[模型评估](#evaluation)。
+
+---
+## 内容
+- [安装](#installation)
+- [训练](#training-a-model)
+- [输出](#log)
+
+## 安装
+
+在当前目录下运行样例代码需要PadddlePaddle Fluid的v1.4.0或以上的版本。如果你的运行环境中的PaddlePaddle低于此版本，请根据安装文档中的说明来更新PaddlePaddle。
+
+## 训练
+教程中使用`paddle.dataset.mnist`数据集作为训练数据，可以通过如下的方式启动训练：
+```
+env CUDA_VISIBLE_DEVICES=0 python mnist_dygraph.py
+```
+
+## 输出
+执行训练开始后，将得到类似如下的输出。
+```
+Loss at epoch 0 step 0: [2.3043773]
+Loss at epoch 0 step 100: [0.20764539]
+Loss at epoch 0 step 200: [0.18648806]
+Loss at epoch 0 step 300: [0.10279777]
+Loss at epoch 0 step 400: [0.03940877]
+...
+```
+
+## 参数保存
+调用`fluid.dygraph.save_persistables()`接口可以把模型的参数进行保存。
+```python
+fluid.dygraph.save_persistables(mnist.state_dict(), "save_dir")
+```
+
+## 测试
+执行`mnist.eval()`可以切换至评估状态，即不更新只使用参数进行训练，通过这种方式进行测试或者评估。
+```python
+mnist.eval()
+```
+
+## 模型评估
+我们使用手写数据集中的一张图片来进行评估。为了区别训练模型，我们使用`with fluid.dygraph.guard()`来切换到一个新的参数空间，然后构建一个用于评估的网络`mnist_infer`，并通过`mnist_infer.load_dict()`来加载使用`fluid.dygraph.load_persistables`读取的参数。然后用`mnist_infer.eval()`切换到评估。
+```python
+with fluid.dygraph.guard():
+
+    mnist_infer = MNIST("mnist")
+    # load checkpoint
+    mnist_infer.load_dict(
+        fluid.dygraph.load_persistables("save_dir"))
+
+    # start evaluate mode
+    mnist_infer.eval()
+```
+如果无意外，将可以看到预测的结果：
+```text
+Inference result of image/infer_3.png is: 3
+```
+
+

dygraph/mnist/mnist_dygraph.py

+# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import numpy as np
+from PIL import Image
+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.base import to_variable
+
+
+class SimpleImgConvPool(fluid.dygraph.Layer):
+    def __init__(self,
+                 name_scope,
+                 num_channels,
+                 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 = Conv2D(
+            self.full_name(),
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=conv_stride,
+            padding=conv_padding,
+            dilation=conv_dilation,
+            groups=conv_groups,
+            param_attr=None,
+            bias_attr=None,
+            use_cudnn=use_cudnn)
+
+        self._pool2d = 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
+
+
+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(), 1, 20, 5, 2, 2, act="relu")
+
+        self._simple_img_conv_pool_2 = SimpleImgConvPool(
+            self.full_name(), 20, 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 = 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
+
+
+def test_train(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 = to_variable(dy_x_data)
+        label = 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 train_mnist():
+    epoch_num = 5
+    BATCH_SIZE = 64
+    with fluid.dygraph.guard():
+
+        mnist = MNIST("mnist")
+        adam = AdamOptimizer(learning_rate=0.001)
+        train_reader = paddle.batch(
+            paddle.dataset.mnist.train(), batch_size=BATCH_SIZE, drop_last=True)
+        test_reader = paddle.batch(
+            paddle.dataset.mnist.test(), 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(BATCH_SIZE, 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.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_train(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_persistables(mnist.state_dict(), "save_dir")
+        print("checkpoint saved")
+
+    with fluid.dygraph.guard():
+
+        mnist_infer = MNIST("mnist")
+        # load checkpoint
+        mnist_infer.load_dict(
+            fluid.dygraph.load_persistables("save_dir"))
+        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(to_variable(tensor_img))
+        lab = np.argsort(results.numpy())
+        print("Inference result of image/infer_3.png is: %d" % lab[0][-1])
+
+
+if __name__ == '__main__':
+    train_mnist()

dygraph/resnet/README.md

+DyGraph模式下Residual Network实现
+========
+
+简介
+--------
+Residual Network（ResNet）是常用的图像分类模型。我们实现了在paddlepaddle的DyGraph模式下相应的实现。可以对比原先静态图下实现（[Residual Network](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification/models)）来了解paddle中DyGraph模式。
+运行本目录下的程序示例需要使用PaddlePaddle develop最新版本。如果您的PaddlePaddle安装版本低于此要求，请按照[安装文档](http://www.paddlepaddle.org/docs/develop/documentation/zh/build_and_install/pip_install_cn.html)中的说明更新PaddlePaddle安装版本。
+
+
+## 代码结构
+```
+└── train.py     # 训练脚本。
+```
+
+## 使用的数据
+
+教程中使用`paddle.dataset.flowers`数据集作为训练数据，该数据集通过`paddle.dataset`模块自动下载到本地。
+
+## 训练测试Residual Network
+
+在GPU单卡上训练Residual Network:
+
+```
+env CUDA_VISIBLE_DEVICES=0 python train.py
+```
+
+这里`CUDA_VISIBLE_DEVICES=0`表示是执行在0号设备卡上，请根据自身情况修改这个参数。
+
+## 输出
+执行训练开始后，将得到类似如下的输出。每一轮`batch`训练将会打印当前epoch、step以及loss值。当前默认执行`epoch=10`, `batch_size=8`。您可以调整参数以得到更好的训练效果，同时也意味着消耗更多的内存（显存）以及需要花费更长的时间。
+```text
+epoch id: 0, batch step: 0, loss: 4.951202
+epoch id: 0, batch step: 1, loss: 5.268410
+epoch id: 0, batch step: 2, loss: 5.123999
+```

dygraph/resnet/train.py

+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.layer_helper import LayerHelper
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, FC
+from paddle.fluid.dygraph.base import to_variable
+
+batch_size = 8
+epoch = 10
+
+
+def optimizer_setting():
+    return fluid.optimizer.SGD(learning_rate=0.01)
+
+
+class ConvBNLayer(fluid.dygraph.Layer):
+    def __init__(self,
+                 name_scope,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 stride=1,
+                 groups=1,
+                 act=None):
+        super(ConvBNLayer, self).__init__(name_scope)
+
+        self._conv = Conv2D(
+            self.full_name(),
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=groups,
+            act=None,
+            bias_attr=None)
+
+        self._batch_norm = BatchNorm(self.full_name(), num_filters, act=act)
+
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+
+        return y
+
+
+class BottleneckBlock(fluid.dygraph.Layer):
+    def __init__(self,
+                 name_scope,
+                 num_channels,
+                 num_filters,
+                 stride,
+                 shortcut=True):
+        super(BottleneckBlock, self).__init__(name_scope)
+
+        self.conv0 = ConvBNLayer(
+            self.full_name(),
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=1,
+            act='relu')
+        self.conv1 = ConvBNLayer(
+            self.full_name(),
+            num_channels=num_filters,
+            num_filters=num_filters,
+            filter_size=3,
+            stride=stride,
+            act='relu')
+        self.conv2 = ConvBNLayer(
+            self.full_name(),
+            num_channels=num_filters,
+            num_filters=num_filters * 4,
+            filter_size=1,
+            act=None)
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                self.full_name(),
+                num_channels=num_channels,
+                num_filters=num_filters * 4,
+                filter_size=1,
+                stride=stride)
+
+        self.shortcut = shortcut
+
+        self._num_channels_out = num_filters * 4
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+        conv2 = self.conv2(conv1)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+
+        y = fluid.layers.elementwise_add(x=short, y=conv2)
+
+        layer_helper = LayerHelper(self.full_name(), act='relu')
+        return layer_helper.append_activation(y)
+
+
+class ResNet(fluid.dygraph.Layer):
+    def __init__(self, name_scope, layers=50, class_dim=102):
+        super(ResNet, self).__init__(name_scope)
+
+        self.layers = layers
+        supported_layers = [50, 101, 152]
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(supported_layers, layers)
+
+        if layers == 50:
+            depth = [3, 4, 6, 3]
+        elif layers == 101:
+            depth = [3, 4, 23, 3]
+        elif layers == 152:
+            depth = [3, 8, 36, 3]
+        num_filters = [64, 128, 256, 512]
+
+        self.conv = ConvBNLayer(
+            self.full_name(),
+            num_channels=3,
+            num_filters=64,
+            filter_size=7,
+            stride=2,
+            act='relu')
+        self.pool2d_max = Pool2D(
+            self.full_name(),
+            pool_size=3,
+            pool_stride=2,
+            pool_padding=1,
+            pool_type='max')
+
+        self.bottleneck_block_list = []
+        num_channels = 64
+        for block in range(len(depth)):
+            shortcut = False
+            for i in range(depth[block]):
+                bottleneck_block = self.add_sublayer(
+                    'bb_%d_%d' % (block, i),
+                    BottleneckBlock(
+                        self.full_name(),
+                        num_channels=num_channels,
+                        num_filters=num_filters[block],
+                        stride=2 if i == 0 and block != 0 else 1,
+                        shortcut=shortcut))
+                num_channels = bottleneck_block._num_channels_out
+                self.bottleneck_block_list.append(bottleneck_block)
+                shortcut = True
+
+        self.pool2d_avg = Pool2D(
+            self.full_name(), pool_size=7, pool_type='avg', global_pooling=True)
+
+        import math
+        stdv = 1.0 / math.sqrt(2048 * 1.0)
+
+        self.out = FC(self.full_name(),
+                      size=class_dim,
+                      act='softmax',
+                      param_attr=fluid.param_attr.ParamAttr(
+                          initializer=fluid.initializer.Uniform(-stdv, stdv)))
+
+    def forward(self, inputs):
+        y = self.conv(inputs)
+        y = self.pool2d_max(y)
+        for bottleneck_block in self.bottleneck_block_list:
+            y = bottleneck_block(y)
+        y = self.pool2d_avg(y)
+        y = self.out(y)
+        return y
+
+
+def train_resnet():
+    with fluid.dygraph.guard():
+        resnet = ResNet("resnet")
+        optimizer = optimizer_setting()
+        train_reader = paddle.batch(
+            paddle.dataset.flowers.train(),
+            batch_size=batch_size)
+
+        for eop in range(epoch):
+            for batch_id, data in enumerate(train_reader()):
+                dy_x_data = np.array(
+                    [x[0].reshape(3, 224, 224) for x in data]).astype('float32')
+                if len(np.array([x[1] for x in data]).astype('int64')) != batch_size:
+                    continue
+                y_data = np.array([x[1] for x in data]).astype('int64').reshape(
+                    batch_size, 1)
+
+                img = to_variable(dy_x_data)
+                label = to_variable(y_data)
+                label._stop_gradient = True
+
+                out = resnet(img)
+                loss = fluid.layers.cross_entropy(input=out, label=label)
+                avg_loss = fluid.layers.mean(x=loss)
+
+                dy_out = avg_loss.numpy()
+                avg_loss.backward()
+
+                optimizer.minimize(avg_loss)
+                resnet.clear_gradients()
+
+                print("epoch id: %d, batch step: %d, loss: %f" % (eop, batch_id, dy_out))
+
+
+if __name__ == '__main__':
+    train_resnet()

dygraph/transformer/README.md

+## 简介
+
+### 任务说明
+
+机器翻译（machine translation, MT）是利用计算机将一种自然语言(源语言)转换为另一种自然语言(目标语言)的过程，输入为源语言句子，输出为相应的目标语言的句子。本示例是机器翻译主流模型 Transformer 的实现和相关介绍。
+
+### 数据集说明
+
+我们使用公开的 [WMT'16 EN-DE 数据集](http://www.statmt.org/wmt16/translation-task.html)训练
+
+
+可以将下载好的wmt16数据集放在`~/.cache/paddle/dataset/wmt16/`目录下
+
+### 安装说明
+
+1. paddle安装
+
+   本项目依赖于 Paddlepaddle Fluid 1.4.1，请参考安装指南进行安装。
+
+2. 安装代码
+3. 环境依赖
+
+### 执行训练：
+利用python解释器执行train.py即可
+
+### 执行效果
+
+    W0422 13:25:53.853921 116144 device_context.cc:261] Please NOTE: device: 0, CUDA Capability: 35, Driver API Version: 9.0, Runtime API Version: 8.0
+    W0422 13:25:53.861614 116144 device_context.cc:269] device: 0, cuDNN Version: 7.0.
+
+    pass num : 0, batch_id: 10, dy_graph avg loss: [9.033163]
+    pass num : 0, batch_id: 20, dy_graph avg loss: [8.869838]
+    pass num : 0, batch_id: 30, dy_graph avg loss: [8.635877]
+    pass num : 0, batch_id: 40, dy_graph avg loss: [8.460026]
+    pass num : 0, batch_id: 50, dy_graph avg loss: [8.293438]
+    pass num : 0, batch_id: 60, dy_graph avg loss: [8.138791]
+    pass num : 0, batch_id: 70, dy_graph avg loss: [7.9594088]
+    pass num : 0, batch_id: 80, dy_graph avg loss: [7.7303553]
+    pass num : 0, batch_id: 90, dy_graph avg loss: [7.6716228]
+    pass num : 0, batch_id: 100, dy_graph avg loss: [7.611051]
+    pass num : 0, batch_id: 110, dy_graph avg loss: [7.4179897]
+    pass num : 0, batch_id: 120, dy_graph avg loss: [7.318419]
+
+## 进阶使用
+
+### 模型原理介绍
+
+Transformer 是论文 [Attention Is All You Need](https://arxiv.org/abs/1706.03762) 中提出的用以完成机器翻译（machine translation, MT）等序列到序列（sequence to sequence, Seq2Seq）学习任务的一种全新网络结构。其同样使用了 Seq2Seq 任务中典型的编码器-解码器（Encoder-Decoder）的框架结构，但相较于此前广泛使用的循环神经网络（Recurrent Neural Network, RNN），其完全使用注意力（Attention）机制来实现序列到序列的建模，整体网络结构如图1所示。
+
+<p align="center">
+<img src="../../PaddleNLP/neural_machine_translation/transformer/images/transformer_network.png" height=400 hspace='10'/> <br />
+图 1. Transformer 网络结构图
+</p>
+
+Encoder 由若干相同的 layer 堆叠组成，每个 layer 主要由多头注意力（Multi-Head Attention）和全连接的前馈（Feed-Forward）网络这两个 sub-layer 构成。
+- Multi-Head Attention 在这里用于实现 Self-Attention，相比于简单的 Attention 机制，其将输入进行多路线性变换后分别计算 Attention 的结果，并将所有结果拼接后再次进行线性变换作为输出。参见图2，其中 Attention 使用的是点积（Dot-Product），并在点积后进行了 scale 的处理以避免因点积结果过大进入 softmax 的饱和区域。
+- Feed-Forward 网络会对序列中的每个位置进行相同的计算（Position-wise），其采用的是两次线性变换中间加以 ReLU 激活的结构。
+
+此外，每个 sub-layer 后还施以 [Residual Connection](http://openaccess.thecvf.com/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf) 和 [Layer Normalization](https://arxiv.org/pdf/1607.06450.pdf) 来促进梯度传播和模型收敛。
+
+<p align="center">
+<img src="../../PaddleNLP/neural_machine_translation/transformer/images/multi_head_attention.png" height=300 hspace='10'/> <br />
+图 2. Multi-Head Attention
+</p>
+
+Decoder 具有和 Encoder 类似的结构，只是相比于组成 Encoder 的 layer ，在组成 Decoder 的 layer 中还多了一个 Multi-Head Attention 的 sub-layer 来实现对 Encoder 输出的 Attention，这个 Encoder-Decoder Attention 在其他 Seq2Seq 模型中也是存在的。