From 8825d086bc0ece5b30ef44d81adeed93b6792940 Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Mon, 18 May 2020 16:57:55 +0800 Subject: [PATCH] add hapi readme --- README.md | 199 ++++++++++++++++++++++++++++++++++++++- image/hapi_gif.gif | Bin 0 -> 468015 bytes image/hapi_transform.png | Bin 0 -> 123632 bytes image/new_hapi.png | Bin 0 -> 758654 bytes image/paddlevision.png | Bin 0 -> 130669 bytes 5 files changed, 197 insertions(+), 2 deletions(-) create mode 100644 image/hapi_gif.gif create mode 100644 image/hapi_transform.png create mode 100644 image/new_hapi.png create mode 100644 image/paddlevision.png diff --git a/README.md b/README.md index d692df5..be924a3 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,197 @@ -# hapi -hapi is a High-level API that supports both static and dynamic execution modes (still under development) +# 简介 + +PaddleHapi是飞桨新推出的高层API,PaddleHapi是对飞桨API的进一步封装与升级,提供了更加简洁易用的API,进一步提升了飞桨的易学易用性,并增强飞桨的功能。 + +PaddleHapi面向从深度学习小白到资深开发者的所有人群,对于AI初学者来说,使用PaddleHapi可以简单快速的构建深度学习项目,对于资深开发者来说,可以使用PaddleHapi快速完成算法迭代。 + +PaddleHapi具有以下特点: +- 易学易用: 高层API是对普通动态图API的进一步封装和优化,同时保持与普通API的兼容性,高层API使用更加易学易用,同样的实现使用高层API可以节省大量的代码。 +- 低代码开发: 使用飞桨高层API的一个明显特点是,用户可编程代码量大大缩减。 +- 动静转换: 高层API支持动静转换,用户只需要改一行代码即可实现将动态图代码在静态图模式下训练,既方便用户使用动态图调试模型,又提升了模型训练效率。 + + + +在功能增强与使用方式上,高层API有以下升级: +1. 模型训练方式升级: 高层API中封装了Model类,继承了Model类的神经网络可以仅用几行代码完成模型的训练。 +2. 新增图像处理模块transform: 飞桨新增了图像预处理模块,其中包含十数种数据处理函数,基本涵盖了常用的数据处理、数据增强方法。 +3. 提供常用的神经网络模型可供调用: 高层API中集成了计算机视觉领域和自然语言处理领域常用模型,包括但不限于mobilenet、resnet、yolov3、cyclegan、bert、transformer、seq2seq等等。同时发布了对应模型的预训练模型,用户可以直接使用这些模型或者在此基础上完成二次开发。 + + +![](./image/hapi_gif.gif) + + +## 目录 + +* [特性]() +* [快速使用]() +* [新增功能]() +* [使用示例]() + + +## 特性 + +### 易学易用 + +高层API基于飞桨动态图实现,兼容飞桨动态图的所有功能,既秉承了动态图易学、易用、易调试的特点,又对飞桨的动态图做了进一步的封装与优化。 + +### 低代码开发 + +相比较与动态图的算法实现,使用高层API实现的算法可编程代码量更少,原始的动态图训练代码需要20多行代码才能完成模型的训练,使用高层API后,仅用8行代码即可实现相同的功能。 + +使用普通API与高层API实现手写字符识别对比如下图,左边是普通动态图API的实现,右边是使用高层API的实现,可以明显发现,使用高层API的代码量更少。 +![](./new_hapi.png) + + +### 动静统一 + +高层API中实现了动静统一,用户无需感知到静态图、动态图的区别,只需要改一行代码即可实现将动态图代码在静态图模式下训练。动态图更方便调试模型,静态图的训练方式训练效率更高。 + +高层API默认采用静态图的训练方式,我们可以使用 fluid.enable_dygraph() 切换到动态图模式下运行。 + +``` +fluid.CUDAPlace() +# 一行代码切换动态图训练模式 +fluid.enable_dygraph(place) + +# 声明网络结构 +model = Mnist("mnist") +# 定义优化器 +optimizer = fluid.optimizer.SGDOptimizer(learning_rate=0.001, parameter_list=model.parameters()) +# 调用prepare() 完成训练的配置 +model.prepare(optimizer, CrossEntropy(), Accuracy(), inputs, labels, device='gpu') +# 调用 fit(),启动模型的训练 +model.fit(train_dataset, val_dataset, batch_size=100, epochs=1, log_freq=100, save_dir="./output/") +``` + +## 快速使用 + +以mnist手写字符识别为例,介绍飞桨高层API的使用方式。 + +### 1. 搭建网络结构 + +使用高层API组建网络与动态图的组网方式基本相同,唯一的区别在于,使用高层API组建网络需要继承Model这个类,而普通的动态图组网是需要继承dygraph.Layer类。 + +高层API组网方式如下 +``` +from paddle.incubate.hapi.model import Model, Input +from paddle.incubate.hapi.loss import CrossEntropy + +class Mnist(Model): + def __init__(self, name_scope): + super(Mnist, self).__init__() + self.fc = Linear(input_dim=784, output_dim=10, act="softmax") + + # 定义网络结构的前向计算过程 + def forward(self, inputs): + outputs = self.fc(inputs) + return outputs + +``` + +### 2. 训练准备 + +在开始训练前,需要定义优化器、损失函数、度量函数,准备数据等等。这些过程均可以在高层API Model类中的prepare函数中完成。 + +``` +# 定义输入数据格式 +inputs = [Input([None, 784], 'float32', name='image')] +labels = [Input([None, 1], 'int64', name='label')] + +# 声明网络结构 +model = Mnist("mnist") +optimizer = fluid.optimizer.SGDOptimizer(learning_rate=0.001, parameter_list=model.parameters()) +# 使用高层API,prepare() 完成训练的配置 +model.prepare(optimizer, CrossEntropy(), Accuracy(), inputs, labels, device='gpu') +``` + +### 3. 启动训练 + +使用高层API完成训练迭代过程时,使用一行代码即可构建双层循环程序,去控制训练的轮数和数据读取过程。 + +``` +from paddle.incubate.hapi.datasets.mnist import MNIST as MnistDataset +# 定义数据读取器 +train_dataset = MnistDataset(mode='train') +val_dataset = MnistDataset(mode='test') +# 启动训练 +model.fit(train_dataset, val_dataset, batch_size=100, epochs=10, log_freq=100, save_dir="./output/") +``` + +高层API中通过fit函数完成训练的循环过程,只需要设置训练的数据读取器、batchsize大小,迭代的轮数epoch、训练日志打印频率log_freq,保存模型的路径即可。 + +## 新增功能 + +除了使用高层API实现一行代码启动训练外,还新增了以下功能: +- transform 数据增强模块 +- paddlevision 模型调用模块 + +### transform +vision.transform。图像预处理模块transform包括一系列的图像增强与图像处理实现,对处理计算机视觉相关的任务有很大帮助。 + +下表中列出Transform支持的数据处理和数据增强API,如下所示: + +| transform的数据处理实现 | 函数功能 | | +| :-------- | :----- | :---- | +| Compose | 组合多种数据变换 | +| Resize | 将图像转换为固定大小 | +| RandomResizedCrop | 根据输入比例对图像做随机剪切,然后resize到指定大小 | +| CenterCrop | 以图像的中心为中心对图像做剪切 | | +| CenterCropResize | 对图像做padding,padding后的图像做centercrop,然后resize到指定大小| | +| RandomHorizontalFlip | 随机对图像做水平翻转 | | +| RandomVerticalFlip | 随机对图像做垂直翻转 | | +| Permute | 将数据的的维度换位 | | +| Normalize | 用指定的均值和标准差对数据做归一化 | | +| GaussianNoise | 给数据增加高斯噪声 | | +| BrightnessTransform | 调整输入图像的亮度 | | +| SaturationTransform | 调整输入图像的饱和度 | | +| ContrastTransform | 调整输入图像的对比度 | | +| HueTransform | 调整图像的色调 | | +| ColorJitter | 随机调整图像的亮度、饱和度、对比度、和色调| | + +使用方法如下: +``` + +from paddle.incubate.hapi.vision.transforms import transforms +import cv2 + +img_path = "./output/sample.jpg" +img = cv2.imread(img_path) + +# 使用Compose 将可以将多个数据增强函数组合在一起 +trans_funcs = transforms.Compose([transforms.RandomResizedCrop(224), + transforms.RandomHorizontalFlip(), + transforms.BrightnessTransform(0.2)]) +label = None +img_processed, label = trans_funcs(img, label) + +``` + +上述代码的效果图如下: + +![](./image/hapi_transform.png) + + +### paddlevision + +paddlevision中包含了高层API对常用模型的封装,包括ResNet、VGG、MobileNet、yoloV3、darknet、BMN +transformer等等。使用这些现有的模型,可以快速的完成神经网络的训练、finetune等。 + +使用paddlevision中的模型可以简单快速的构建一个深度学习任务,比如13代码即可实现resnet在Imagenet数据集上的训练: + +![](./image/paddlevision.png) + + + +## 更多使用示例 + +更多的高层API使用示例请参考: +- [bert](https://github.com/PaddlePaddle/hapi/tree/master/examples/bert) +- [image classification](https://github.com/PaddlePaddle/hapi/tree/master/examples/image_classification) +- [BMN](https://github.com/PaddlePaddle/hapi/tree/master/examples/bmn) +- [cycleGAN](https://github.com/PaddlePaddle/hapi/tree/master/examples/cyclegan) +- [ocr](https://github.com/PaddlePaddle/hapi/tree/master/examples/ocr) +- [TSM](https://github.com/PaddlePaddle/hapi/tree/master/examples/tsm) +- [yolov3](https://github.com/PaddlePaddle/hapi/tree/master/examples/yolov3) +- [transformer](https://github.com/PaddlePaddle/hapi/tree/master/examples/transformer) +- [seq2seq](https://github.com/PaddlePaddle/hapi/tree/master/examples/seq2seq) +- [style-transfer](https://github.com/PaddlePaddle/hapi/tree/master/examples/style-transfer) diff --git a/image/hapi_gif.gif b/image/hapi_gif.gif new file mode 100644 index 0000000000000000000000000000000000000000..5d261d2f375e1800858c71bf12158a73edb7d5e9 GIT binary patch literal 468015 zcma%?<8xha*tYlH*)e{xZQHhuHn#01X_Cf`8{4+6#nFAmQ8J$%f|pe@jg%8Q0^JPcGlip2{trJ3{)09$VV) zy?>nF`C|A6orgwUz6>P3&Q(7a=}*r-J(e1$XJ5ysT;0d5Em=IFwY}f39_2-*^>~-M_zoFDSm* 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