分类： 神经网络

高性能 PyTorch 训练：性能瓶颈的调查与分析

PyTorch

在 2014 年之前，神经网络与深度学习还没有大规模地应用于工业界。研究者们开发了一些基本而有效的工具包，来搭建神经网络。其中的代表就是 Caffe、Torch 和 Theano。由于当时的研究主流方向是卷积神经网络 (CNN) 在计算机视觉 (CV) 中的应用，所以这些框架主要关注的是 layers。这种设计完全可以满足研究者们拼接不同卷积层、了解不同神经网络结构效果的目的。

而在之后，随着循环神经网络 (RNN) 和自然语言处理 (NLP) 的兴起，以 layers 为“first class citizen”的工具包们就开始力不从心了。而工业界也开始对模型构建、训练以及部署的效率提出了新的要求。随着以 Google、Microsoft、Facebook、Amazon 等巨头的加入，以数据流 (Data Flow) 为中心的体系被提出，TensorFlow、CNTK、MXNet、PyTorch 等新一代的深度学习框架应运而生。

PyTorch 是一个基于 Torch 库的开源机器学习库，用于计算机视觉和自然语言处理等应用，主要由 Facebook 的人工智能研究实验室 (FAIR) 开发。它是在修改后的 BSD 2.0 许可协议下发布的免费开源软件。

正如同它名字的前缀一般，PyTorch 主要采用 Python 语言接口。与 TensorFlow 1.x 相比，PyTorch 的编写方式更简单自然，API 更 pythonic，对 debug 也更加友好。因此，PyTorch 在学术界赢得了更多的拥趸，近年来顶级会议中，PyTorch 的代码提交量遥遥领先于第二名的 TensorFlow (Keras)。而在工业界，PyTorch 后来居上，已经逐渐和 TensorFlow 分庭抗礼。

很多学术界最新的成果都是以 PyTorch 构建的，并被作者开源在了 GitHub。但也有很多声音表示 PyTorch 在训练中比 TensorFlow 更慢。

高性能 PyTorch 的训练流程是什么样的？是产生最高准确率的模型？是最快的运行速度？是易于理解和扩展？还是容易并行化？

答案是，上述所有。

结合我自己给 PyTorch 提速的经历，本文将给出一些提升 PyTorch 性能的方向。当然，作为本文的读者，您需要对 Linux 操作系统和 PyTorch 足够熟悉。

了解瓶颈所在

首先，当感受到训练缓慢时，我们应当检查系统的状态来得知代码的性能被哪些因素限制了。计算，是一个由应用程序（代码）、存储设备（硬盘、内存）、运算设备（CPU、GPU）共同参与的过程，有着非常明显的木桶效应，即其中任意一个环节都有可能成为性能瓶颈的来源。

工具

此时，熟悉一些运维工具可以有效地帮助你了解当前整个计算机以及各个硬件设备的工作状态。只有将性能瓶颈定位到 CPU、GPU、I/O 或是代码中，才能开始解决问题。

htop

htop 是一个跨平台的交互式流程查看器。

htop 允许垂直和水平滚动进程列表，以查看它们的完整命令行以及内存和CPU消耗等相关信息。显示的信息可以通过图形设置进行配置，并且可以交互地进行排序和过滤。与进程相关的任务（例如终止和更新）可以在不输入其 PID 的情况下完成。

从 htop 顶部的信息集合中，可以监视 CPU 和内存的使用情况。

一个好的高性能程序，应当尽可能多地进行异步运算，来充分发挥多核 CPU 的能力。同时，尽量多地使用内存，能够大大提高数据的交流效率。当然，这并不意味着你可以把所有的 CPU 和内存资源耗尽，这将使系统不能够正常调度资源，反而拖累计算。

在所有的 Linux 发行版中，你都可以直接从软件仓库中安装 htop，例如：

$ sudo apt install htop # Ubuntu/Debian

$ sudo yum install htop # RHEL/CentOS

$ sudo zypper install htop # openSUSE

$ sudo pacman -Syyu htop # Archlinux/Manjaro
iotop 和 iostat

iotop 是用来监视每个命令所占用的 I/O 情况的命令行应用程序。

iostat 则是属于 sysstat 工具包中的一个组件，可以监视外部存储设备（硬盘）当前的 I/O 情况。

安装命令分别为：

$ sudo apt install iotop 

$ sudo apt install syssat

需要注意的是，因为涉及到 I/O 情况的监视，所以以上两款程序均需要 root 权限才能正常运行。

nvidia-smi

NVIDIA System Management Interface 是基于 NVIDIA Management Library (NVML) 的命令行应用程序，旨在帮助管理和监视 NVIDIA GPU 设备。

一般来说，GPU 的流处理器使用率越高，就说明 GPU 是在以更高的效率运转的。设备的当前功率也能从侧面反映这个问题。换言之，如果你发现你的流处理器利用率低于 50%，则说明模型没能很好地利用 GPU 的并行能力。

在通过 sh 脚本安装 NVIDIA GPU 驱动后，nvidia-smi 会被自动安装。如果是从发行版的仓库中安装的驱动，可以尝试在软件源中搜索安装 nvidia-smi。

nvtop

nvtop 代表 NVIDIA top，由开发者 Maxime Schmitt 发布于 GitHub，是一款用于观察和记录 NVIDIA GPU 使用情况的 (h)top 任务监视器。你会发现 nvtop 有着与 htop 非常相似的 UI。

它可以用于 GPU，并以曲线图的形式输出在一段时间内 GPU 流处理器和显存使用情况的变化。

相比于 nvidia-smi，nvtop 会关注到更关键的设备信息，并给出其在时间序列上的变化情况。

你可以参考 Syllo/nvtop 中的描述自行编译安装 nvtop。

py-spy

py-spy 是一个针对 Python 程序的采样分析器。

它使您可以直观地看到 Python 程序正在花费时间，而无需重新启动程序或以任何方式修改代码。 py-spy 的开销非常低：为了提高速度，它是用 Rust 编写的，并且不会在所分析的 Python 程序相同的进程中运行。这意味着对生产 Python 代码使用 py-spy 是安全的。

py-spy 可以生成如下的 SVG 图像，来帮助你统计每一个 package、model 甚至每一个 function 在运行时所耗费的时间。

py-spy 同样能够以 top 的方式实时显示 Python 程序中哪些函数花费的时间最多。

只需要在 pypi 中安装即可：

$ pip install py-spy

问题分析与解决思路

有了以上工具所收集的信息，我们就可以开始分析限制程序性能发挥的原因了。

PyTorch Workflow

而首先，我们要了解 PyTorch 的工作流程。

因为 PyTorch 使用 Python 接口，同时在底层调用了相当多的 C 库，所以在使用 PyTorch 时，很多细节对用户是不暴露的。实际上，在常见的训练过程中，用户和 PyTorch 一起，大致完成了以下的步骤：

  1. 构建模型。将编写好的模型类实例化为 nn.Module 对象。
  2. 准备数据。将训练数据和测试数据进行预处理，然后组织为 Dataloader 的形式，并设置好数据增强方案。
  3. 定义 Loss Function 和 Optimizer。
  4. 主训练循环。

其中，主训练循环决定了网络经过多少次完整的数据集，即我们常说的 epoch：

  1. 从 Dataloader 中提取当前 batch 的数据。一般 Dataloader 中只记录了数据的 index 信息，所以每次训练循环时，对应的数据都会从硬盘被读取到内存，然后再从内存放入显存中，交由 GPU 进行后续步骤。
  2. 数据经过模型。
  3. 将得到的输出送到 Loss Function 中计算损失，随后进行 backward 求导。
  4. Optimizer 执行梯度下降，更新参数。

一般来说，PyTorch 训练的过程的快慢决定于主训练循环。主循环中的每一步都将被执行上万次乃至几十万次，任何的效率提升都能够带来极大的收益。

CPU 瓶颈

CPU 和 GPU 的计算特点，决定了它们不同的功用：CPU 具有更高的主频和精度，适合于进行串行任务；GPU 拥有几千到上万个 Stream 核心，可以进行大规模的并行任务。

所以，对于数据增强等没有相互依赖的任务交给 CPU 来进行，很大程度上会拖慢训练的进程。在每次的数据导入时，都会产生一定时间的等待。这是一种非常普遍的 CPU 瓶颈，即将不适合 CPU 的任务交给它来处理。

GPU 瓶颈

如果你熟悉梯度下降 (Gradient Descent) 的原理，那么你一定能够理解 batch size 对训练速度的影响。梯度下降将一个 batch 中的平均梯度作为总体梯度方向的近似，进行一次参数更新。Batch size 越大，那么 GPU 内同时并行计算的数据也就越多，相应的训练速度会有很大的提升。

Batch size 的设定对最终的训练结果有一定的影响，但是在一定范围内的调整并不会产生非常大的扰动。

主流观点中，在不过分影响最终的模型性能的前提下，batch size 的选取以最大化利用显存和流处理器为佳。

I/O 瓶颈

I/O 瓶颈是最常见、最普遍的训练效率影响因素。

正如上文中所描述的，数据将会在硬盘、内存和显存中不断地转移和复制。不同存储设备的读写速度，可能有几个数量级上的差异。

出现 I/O 瓶颈的标志主要有：

  1. 系统 I/O 读写（尤其是硬盘读写）占用过高；
  2. 内存占用和 CPU 占用都普遍偏低；
  3. 显存占用较高的情况下，流处理器的利用率过低。

将数据预读入内存中、异步进行数据加载都是有效的解决方案。一个简单稳定的方式是直接使用 DALI 库。

NVIDIA Data Loading Library (DALI) 是一个可移植的开源库，用于解码和增强图像、视频和语音，以加速深度学习应用程序。DALI 通过重叠训练和预处理减少了延迟和训练时间，缓解了瓶颈。它为流行的深度学习框架中内置的数据加载器和数据迭代器提供了一个插件，便于集成或重定向到不同的框架。

用图像训练神经网络需要开发人员首先对这些图像进行归一化处理。此外，图像通常会被压缩以节省存储空间。因此，开发人员构建了多阶段数据处理流程，包括加载、解码、裁剪、调整大小和许多其他增强算子。这些目前在 CPU 上执行的数据处理流水线已经成为瓶颈，限制了整体吞吐量。

DALI 是内置数据加载器和数据迭代器的高性能替代品。开发人员现在可以在 GPU 上运行他们的数据处理工作。

参考链接

高性能 PyTorch 训练 (1)：性能瓶颈的调查与分析

使用word2vec训练中文维基百科

word2vec是Google于2013年开源推出的一个用于获取词向量的工具包，关于它的介绍，可以先看词向量工具word2vec的学习。

获取和处理中文语料

维基百科的中文语料库质量高、领域广泛而且开放，非常适合作为语料用来训练。相关链接：

有了语料后我们需要将其提取出来，因为wiki百科中的数据是以XML格式组织起来的，所以我们需要寻求些方法。查询之后发现有两种主要的方式：gensim的wikicorpus库，以及wikipedia Extractor。

WikiExtractor

Wikipedia Extractor是一个用Python写的维基百科抽取器，使用非常方便。下载之后直接使用这条命令即可完成抽取，运行时间很快。执行以下命令。

相关链接：

相关命令：

$ git clone https://github.com/attardi/wikiextractor.git

$ python ./wikiextractor/WikiExtractor.py -b 2048M -o extracted zhwiki-latest-pages-articles.xml.bz2

相关说明：

  • -b 2048M表示的是以128M为单位进行切分，默认是1M。
  • extracted：需要将提取的文件存放的路径；
  • zhwiki-latest-pages-articles.xml.bz2：需要进行提取的.bz2文件的路径

二次处理：

通过Wikipedia Extractor处理时会将一些特殊标记的内容去除了，但有时这些并不影响我们的使用场景，所以只要把抽取出来的标签和一些空括号、「」、『』、空书名号等去除掉即可。

import re
import sys
import codecs
 
 
def filte(input_file):
    p1 = re.compile('[（\(][，；。？！\s]*[）\)]')
    p2 = re.compile('《》')
    p3 = re.compile('「')
    p4 = re.compile('」')
    p5 = re.compile('<doc (.*)>')
    p6 = re.compile('</doc>')
    p7 = re.compile('『』')
    p8 = re.compile('『')
    p9 = re.compile('』')
    p10 = re.compile('-\{.*?(zh-hans|zh-cn):([^;]*?)(;.*?)?\}-')
    outfile = codecs.open('std_' + input_file, 'w', 'utf-8')
    with codecs.open(input_file, 'r', 'utf-8') as myfile:
        for line in myfile:
            line = p1.sub('', line)
            line = p2.sub('', line)
            line = p3.sub('“', line)
            line = p4.sub('”', line)
            line = p5.sub('', line)
            line = p6.sub('', line)
            line = p7.sub('', line)
            line = p8.sub('“', line)
            line = p9.sub('”', line)
            line = p10.sub('', line)
            outfile.write(line)
    outfile.close()
 
 
if __name__ == '__main__':
    input_file = sys.argv[1]
    filte(input_file)

保存后执行 python filte.py wiki_00 即可进行二次处理。

gensim的wikicorpus库

转化程序：

# -*- coding: utf-8 -*-
from gensim.corpora import WikiCorpus
import os
 
 
class Config:
    data_path = '/home/qw/CodeHub/Word2Vec/zhwiki'
    zhwiki_bz2 = 'zhwiki-latest-pages-articles.xml.bz2'
    zhwiki_raw = 'zhwiki_raw.txt'
 
 
def data_process(_config):
    i = 0
    output = open(os.path.join(_config.data_path, _config.zhwiki_raw), 'w')
    wiki = WikiCorpus(os.path.join(_config.data_path, _config.zhwiki_bz2), lemmatize=False, dictionary={})
    for text in wiki.get_texts():
        output.write(' '.join(text) + '\n')
        i += 1
        if i % 10000 == 0:
            print('Saved ' + str(i) + ' articles')
    output.close()
    print('Finished Saved ' + str(i) + ' articles')
 
 
config = Config()
data_process(config)

化繁为简

维基百科的中文数据是繁简混杂的，里面包含大陆简体、台湾繁体、港澳繁体等多种不同的数据。有时候在一篇文章的不同段落间也会使用不同的繁简字。这里使用opencc来进行转换。

$ opencc -i zhwiki_raw.txt -o zhswiki_raw.txt -c t2s.json

中文分词

这里直接使用jieba分词的命令行进行处理：

$ python -m jieba -d " " ./zhswiki_raw.txt >./zhswiki_cut.txt

转换成 utf-8 格式

非 UTF-8 字符会被删除

$ iconv -c -t UTF-8 -o zhwiki.utf8.txt zhwiki.zhs.txt

参考链接：https://github.com/lzhenboy/word2vec-Chinese

继续阅读使用word2vec训练中文维基百科

在ubuntu 18.04(GeForce GTX 760 4GB显存)使用Pytorch Pix2PixGAN(CUDA-10.1)

1. 参照 pytorch 1.0.1在ubuntu 18.04(GeForce GTX 760)编译(CUDA-10.1) 建立 `pytorch 1.0.1` 的编译环境，并解决编译时遇到的问题。

2. 依旧是推荐在 Anaconda 上建立独立的编译环境，然后执行编译：

$ sudo apt-get install git

# conda remove -n Pix2Pix --all

$ conda create -n Pix2Pix -y python=3.6.8 pip

$ source activate Pix2Pix

$ conda install numpy pyyaml mkl=2019.1 mkl-include=2019.1 setuptools cmake cffi typing pybind11

$ conda install ninja

# magma-cuda90 magma-cuda91 magma-cuda92 会编译失败 
$ conda install -c pytorch magma-cuda101

$ git clone https://github.com/pytorch/pytorch

$ cd pytorch

# pytorch 1.0.1 版本支持“Compute Capability” 低于3.0版本的硬件，pytorch 1.2.0需要至少3.5版本的硬件才可以正常运行
# https://github.com/pytorch/pytorch/blob/v1.3.0/torch/utils/cpp_extension.py
$ git checkout v1.0.1 -b v1.0.1

$ git submodule sync

$ git submodule update --init --recursive

$ export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}

# 如果不需要使用cuda的话，这里还要加上一句：export NO_CUDA=1

$ python setup.py clean

# 卸载以前安装的pytorch
$ conda uninstall pytorch

# 从Nvidia开发网站查询到自己硬件对应的“Compute Capability” 
# 比如 “GeForce GTX 760” 对应 “3.0” 计算能力，能力不正确会导致运行异常
# RuntimeError: cuda runtime error (48) : no kernel image is available for execution on the device

$ python setup.py install

# 对于开发者模式，可以使用
# python setup.py build develop

# 一定要退出 pytorch 的编译目录，在pytorch代码目录下执行命令会出现异常
$ cd ..

# 退出环境 
$ conda deactivate

编译出错信息，参考 pytorch 1.0.1在ubuntu 18.04(GeForce GTX 760)编译(CUDA-10.1) 里面的介绍解决。

3. 编译安装 TorchVision

$ sudo apt-get install git

# 进入运行环境
$ source activate Pix2Pix

$ git clone https://github.com/pytorch/vision.git

# 也可本站下载一份拷贝 wget http://www.mobibrw.com/wp-content/uploads/2019/11/vision.zip

$ cd vision

$ git checkout v0.2.1 -b v0.2.1

$ python setup.py install

# 退出环境 
$ conda deactivate

4. 检出 CycleGAN and pix2pix in PyTorch 的代码，并安装依赖

# 进入运行环境
$ source activate Pix2Pix

$ git clone https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix.git

# 也可本站下载 wget http://www.mobibrw.com/wp-content/uploads/2019/12/pytorch-CycleGAN-and-pix2pix.zip

$ cd pytorch-CycleGAN-and-pix2pix

# 下载人脸替换部分的数据集
$ bash datasets/download_pix2pix_dataset.sh facades

# 也可本站下载然后自己参照脚本解压缩到指定目录 http://www.mobibrw.com/wp-content/uploads/2019/12/facades.tar.gz

# 安装依赖
$ pip install pillow==6.2.1
$ pip install dominate==2.4.0
$ pip install visdom==0.1.8.9

# 修正错误 models/networks.py
# TypeError: cuda() got an unexpected keyword argument 'device_id'
$ sed -i "s/netG\.cuda(device_id=gpu_ids\[0\])/netG.cuda(gpu_ids[0])/g" models/networks.py

$ sed -i "s/netD\.cuda(device_id=gpu_ids\[0\])/netD.cuda(gpu_ids[0])/g" models/networks.py

$ sed -i "s/network\.cuda(device_id=gpu_ids\[0\])/network.cuda(gpu_ids[0])/g" models/base_model.py

# 开启WEB服务,主要是第一次运行需要下载部分辅助软件包，
# 训练之前需要执行，否则下面训练的时候会报错
$ python -m visdom.server & 

# 等待屏幕上出现 “You can navigate to http://localhost:8097” 代表服务启动成功

# 执行训练
$ bash scripts/train_pix2pix.sh

执行训练的时候，如果出现如下错误：

Traceback (most recent call last):
  File "train.py", line 47, in <module>
    errors = model.get_current_errors()
  File "~/pytorch-CycleGAN-and-pix2pix/models/pix2pix_model.py", line 122, in get_current_errors
    return OrderedDict([('G_GAN', self.loss_G_GAN.data[0]),
IndexError: invalid index of a 0-dim tensor. Use tensor.item() to convert a 0-dim tensor to a Python number

这个原因是由于 PyTorch 版本差异造成的,（作者在 `Pytorch 0.4.1` 版本上测试，我们在 `Pytorch 1.0.1` 版本上测试），执行如下命令修复：

#loss_G_GAN.data[0] 替换为 loss_G_GAN.item()

$ sed -i "s/self\.loss_G_GAN\.data\[0]/self.loss_G_GAN.item()/g" models/pix2pix_model.py

$ sed -i "s/self\.loss_G_L1\.data\[0]/self.loss_G_L1.item()/g" models/pix2pix_model.py

$ sed -i "s/self\.loss_D_real\.data\[0]/self.loss_D_real.item()/g" models/pix2pix_model.py

$ sed -i "s/self\.loss_D_fake\.data\[0]/self.loss_D_fake.item()/g" models/pix2pix_model.py

5. 测试训练结果

$ bash scripts/test_pix2pix.sh

# 观察结果需要打开 ./results/facades_pix2pix/test_latest/index.html

参考链接

在ubuntu 18.04(GeForce GTX 760 4GB显存)使用MaskTextSpotter(CUDA-10.1)进行训练

参考 在ubuntu 18.04(GeForce GTX 760 4GB显存)编译/测试MaskTextSpotter(CUDA-10.1) 建立能运行的测试环境。

由于测试集使用的是 icdar2013 ,因此，务必保证已经可以在 icdar2013 数据集中进行测试。

接下来就是进行数据训练：

1. 修改训练脚本，默认情况下，训练脚本中使用了 8 张卡进行训练，我们只有一张卡，因此要调整训练参数

$ cd MaskTextSpotter

$ export ROOT_PATH=`pwd`

$ sed -i 's/nproc_per_node=8/nproc_per_node=1/g' train.sh

2. 下载训练集 MaskTextSpotter 默认使用的是 SynthText 数据集进行训练，需要先下载这个数据集，大约 40GB

$ mkdir datasets

$ cd datasets

$ sudo apt-get install aria2

$ aria2c -c -j16 -s16 -x16 --follow-torrent=mem -o 'hyperai.torrent' 'https://hyper.ai/tracker/download?torrent=7783'

# 也可下载种子文件 wget http://www.mobibrw.com/wp-content/uploads/2019/11/SynthText.zip

3. 解压缩 SynthText 数据集到指定目录

$ mkdir synthtext

$ unzip SynthText/data/SynthText.zip -d synthtext

# 目录改名
$ mv synthtext/SynthText synthtext/train_images

4. 下载转换后的 SynthText 数据集索引文件，上面解压缩出来的索引是 .mat 扩展名的文件，我们需要转换成 MaskTextSpotter 需要的数据索引文件，作者提供了一份已经转换好的文件，我们直接下载并使用这个文件即可,这个文件大概要 1.6GB 的样子。

$ aria2 -c https://1drv.ms/u/s!ArsnjfK83FbXgb5vgOOVPYywgCWuQw?e=UPuNTa

# 解压缩到指定目录
$ tar -xvf SynthText_GT_E2E.tar.gz -C synthtext

# 目录改名
$ mv synthtext/SynthText_GT_E2E synthtext/train_gts

5. 生成训练文件 train_list.txt 

import os

path = 'train_images'

train_list = 'train_list.txt'

tf = open(train_list, 'w')

for root, dirs, files in os.walk(path):
    files = [f for f in files if not f[0] == '.']
    dirs[:] = [d for d in dirs if not d[0] == '.']
    # use files and dirs
    for file_name in files:
        fn = os.path.join(root, file_name)
        fn = fn.replace('./', '')
        fn = fn.replace(path + '/', '')
        ext = os.path.splitext(fn)[1]
        if '.jpg' == ext :  
            tf.write(fn + '\n')

tf.close()

执行脚本，生成文件

$ cd synthtext

$ python gen_train.py

执行测试

$ cd $ROOT_PATH

# 减少一次性加载图片数量，解决“OSError: [Errno 24] Too many open files”
# 参数设置为 0 代表从主进程加载图片资源
$ sed -i "s/NUM_WORKERS: 4/NUM_WORKERS: 0/g" configs/pretrain.yaml

# 调整训练参数，对于单个GPU来说，默认参数太大了，会导致GPU内存不足
# 解决 “RuntimeError: CUDA out of memory.”
$ sed -i "s/IMS_PER_BATCH: 8/IMS_PER_BATCH: 1/g" configs/pretrain.yaml

# 修正错误 “AttributeError: module 'torch' has no attribute 'bool'”
# 从Pytorch 1.2开始，torch.uint8被修改为torch.bool,如果是低于 Pytorch 1.2的版本
# 需要修改为torch.uint8
$ sed -i "s/torch.bool/torch.uint8/g" maskrcnn_benchmark/modeling/rpn/inference.py
$ sed -i "s/torch.bool/torch.uint8/g" maskrcnn_benchmark/modeling/balanced_positive_negative_sampler.py


# 修改SOLVER设置上的GPU相关参数
# https://github.com/facebookresearch/Detectron/blob/master/configs/getting_started/tutorial_1gpu_e2e_faster_rcnn_R-50-FPN.yaml#L14
# 官方参考建议单个GPU的学习速率是0.0025但是实际运行中会报错，调整为0.0015可以正常运行
$ sed -i "s/BASE_LR: 0.01/BASE_LR: 0.0015/g" configs/pretrain.yaml

# 4GB 显存设置为 8 ，8GB显存可以设置为64/128 
$ sed -i "s/MASK_BATCH_SIZE_PER_IM: 512/MASK_BATCH_SIZE_PER_IM: 8/g" configs/pretrain.yaml

# 目前在RTX 2070 Super 8GB显存版本上测试来看，使用
# “WEIGHT: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl” 
# 的配置情况下，BASE_LR可以设置为 0.0025 , MASK_BATCH_SIZE_PER_IM 可以设置为 128 

# 进入运行环境
$ source activate MaskTextSpotter

$ bash train.sh

注意，我们在 configs/pretrain.yaml 加载的权重文件是 "WEIGHT: "./outputs/finetune/model_finetune.pth" ,这个权重文件是从 SynthText 训练得来的，那么这个"model_finetune.pth"是怎么生成的呢？

作者没有详细介绍，我们从 masktextspotter.caffe2 项目的配置文件中可以知道，这个文件其实是从 " WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl" 开始生成的。这个文件也可以从本站下载 R-50.pkl

R-50.pkl: converted copy of MSRA’s original ResNet-50 model

具体配置文件内容参考如下：

MODEL:
  TYPE: generalized_rcnn
  CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
  NUM_CLASSES: 2
  FASTER_RCNN: True
  MASK_ON: True
  NAME: shrink++
NUM_GPUS: 1
SOLVER:
  WEIGHT_DECAY: 0.0001
  LR_POLICY: steps_with_decay
  BASE_LR: 0.005   #synth
  GAMMA: 0.1
  MAX_ITER: 200000
  STEPS: [0, 120000]
FPN:
  FPN_ON: True
  MULTILEVEL_ROIS: True
  MULTILEVEL_RPN: True
  USE_DEFORMABLE: False
  RPN_ASPECT_RATIOS: (0.5, 1, 2)
FAST_RCNN:
  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
  ROI_XFORM_METHOD: RoIAlign
  ROI_XFORM_RESOLUTION: 7
  ROI_XFORM_SAMPLING_RATIO: 2
MRCNN:
  ROI_MASK_HEAD: text_mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs
  RESOLUTION: 28  # (output mask resolution) default 14
  RESOLUTION_H: 32
  RESOLUTION_W: 128
  ROI_XFORM_METHOD: RoIAlign
  ROI_XFORM_RESOLUTION: 14  # default 7
  ROI_XFORM_RESOLUTION_H: 16
  ROI_XFORM_RESOLUTION_W: 64
  ROI_XFORM_SAMPLING_RATIO: 2  # default 0
  DILATION: 1  # default 2
  CONV_INIT: MSRAFill  # default GaussianFill
  IS_E2E: True
  MASK_BATCH_SIZE_PER_IM: 16
  WEIGHT_LOSS_MASK: 1.0
  WEIGHT_LOSS_CHAR_BOX: 1.0
  WEIGHT_WH: True  ## default is false 

TRAIN:
  BATCH_SIZE_PER_IM: 512
  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
  AUTO_RESUME: True
  SNAPSHOT_ITERS: 10000

  ##################### pre-train on synth ##########################
  WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
  DATASETS: ('synth_train', )
  SCALES: (800,)
  MAX_SIZE: 1333
  IMS_PER_BATCH: 2
  ASPECT_GROUPING: True
  MIX_TRAIN: False
  USE_CHARANNS: [True]

  ######################  Fine tune   #################################
  # MIX_TRAIN: True
  # WEIGHTS: ./train/synth_train/pretrain_model/model_iter159999.pkl
  # DATASETS: ('totaltext_train', 'scut-eng-char_train', 'synth_train', 'icdar2013_train', 'icdar2015_train')
  # USE_CHARANNS: [False, True, True, True, False]
  # # the ratios of synth, icdar2013, icdar2015 is 2:1:1, defaultly
  # # MIX_RATIOS: [0.125, 0.125, 0.5, 0.125, 0.125]
  # MIX_RATIOS: [1.0 / 6, 1.0 / 6, 1.0 / 3, 1.0 / 6, 1.0 / 6]
  # SCALES: (600, 800, 1000)
  # MAX_SIZE: 1333
  # # # SCALES: (800,)
  # # # MAX_SIZE: 1333
  # IMS_PER_BATCH: 1
  # ASPECT_GROUPING: False

IMAGE:
  aug: False
  saturation_prob: 0.5
  saturation_lower: 0.5
  saturation_upper: 1.5
  hue_prob: 0.5
  hue_delta: 18
  lighting_noise_prob: 0.5
  contrast_prob: 0.5
  contrast_lower: 0.5
  contrast_upper: 1.5
  brightness_prob: 0.5
  brightness_delta: 32
  rotate_prob: 0.5
  rotate_delta: 15
TEST:
  OUTPUT_POLYGON: False # only set to True for totaltext
  WEIGHTS: ./train/shrink++_finetune/model_iter79999.pkl
  DATASETS: ('icdar2015_test',)
  SCALES: (1000,)
  MAX_SIZE: 3333
  NMS: 0.5
  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
  RPN_POST_NMS_TOP_N: 1000
  VIS: False
  SCORE_THRESH: 0.2
  BBOX_AUG:
    ENABLED: False
    SCORE_HEUR: UNION  # AVG NOTE: cannot use AVG for e2e model
    COORD_HEUR: UNION  # AVG NOTE: cannot use AVG for e2e model
    H_FLIP: False
    SCALES: (800,)
    MAX_SIZE: 2000
    SCALE_H_FLIP: False
    SCALE_SIZE_DEP: False
    AREA_TH_LO: 2500   # 50^2
    AREA_TH_HI: 32400  # 180^2
    ASPECT_RATIOS: ()
    ASPECT_RATIO_H_FLIP: False
  MASK_AUG:
    ENABLED: False
    HEUR: SOFT_AVG
    H_FLIP: False
    SCALES: (1600,)
    MAX_SIZE: 3333
    SCALE_H_FLIP: False
    SCALE_SIZE_DEP: False
    AREA_TH: 32400  # 180^2
    ASPECT_RATIOS: ()
    ASPECT_RATIO_H_FLIP: False
  BBOX_VOTE:
    ENABLED: True
    VOTE_TH: 0.9
  SOFT_NMS:
    ENABLED: False
OUTPUT_DIR: .

其实我们直接删除或者注释掉权重文件加载部分也是可以的。只是，如果想要复现原作者的测试成果的话，我们最好使用相同的配置信息。

对于 4GB 显存的机器来说，由于显存非常有限，导致非常可能在运行的途中出现 "RuntimeError: CUDA out of memory." ,目前测试来看，继续执行命令即可。

训练结果存储在 outputs/pretrain 目录下，训练结果会在训练到一定阶段之后，存储到这个目录下。

如果出现类似如下错误，请适当减少学习速率 BASE_LR

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [32,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
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~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [31,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
Traceback (most recent call last):
  File "tools/train_net.py", line 173, in <module>
    main()
  File "tools/train_net.py", line 166, in main
    model = train(cfg, args.local_rank, args.distributed)
  File "tools/train_net.py", line 76, in train
    cfg,
  File "~/MaskTextSpotter/maskrcnn_benchmark/engine/trainer.py", line 66, in do_train
    loss_dict = model(images, targets)
  File "~/.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py", line 50, in forward
    proposals, proposal_losses = self.rpn(images, features, targets)
  File "~/.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/rpn/rpn.py", line 94, in forward
    return self._forward_train(anchors, objectness, rpn_box_regression, targets)
  File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/rpn/rpn.py", line 110, in _forward_train
    anchors, objectness, rpn_box_regression, targets
  File "~/.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/rpn/inference.py", line 138, in forward
    sampled_boxes.append(self.forward_for_single_feature_map(a, o, b))
  File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/rpn/inference.py", line 113, in forward_for_single_feature_map
    boxlist = remove_small_boxes(boxlist, self.min_size)
  File "~/MaskTextSpotter/maskrcnn_benchmark/structures/boxlist_ops.py", line 46, in remove_small_boxes
    (ws >= min_size) & (hs >= min_size)
RuntimeError: copy_if failed to synchronize: cudaErrorAssert: device-side assert triggered

参考链接

Tensorflow中same padding和valid padding

  • Same Convolution Padding

我之前学习吴恩达老师的课程时，了解到的same padding是指在输入周围填充0，以使卷积操作后输入输出大小相同。而在tensorflow中的same padding却不是这样的。

要理解tensorflow中的same padding是如何操作的，先考虑一维卷积的情况。

ni和no分别表示输入和输出的大小，k为kernel大小，s为stride步长。那么在same padding中，no由ni和s二者确定：no = ceil(ni / s)

比如，假设ni为11，s为2，那么就得到no为6。而s若为1，则输入输出大小相等。

现在已经确定好了输出no的大小，接下来就要确定如何对输入ni进行pad来得到目标输出大小。也就是要找到满足下面公式的pi：

继续阅读Tensorflow中same padding和valid padding

卷积神经网络在ARM-CPU上的推断计算综述

摘要

  深度学习在计算机视觉领域大放异彩，许多在传统方法下无法解决的问题正在被一一攻克。然而，高昂的计算成本也极大地限制了深度学习的使用，在移动端设备、嵌入式设备等计算资源比较拮据的平台上其计算密集的特性尤为突出。本文对现阶段，工业界所使用的深度学习推断阶段的软件操作库、计算框架等，做了充分调研，由底向上勾勒出深度学习推断阶段的技术轮廓。本文主要工作如下：

  1. 总结深度学习推断阶段的主要操作，指出其性能瓶颈所在；
  2. 从软件库层面总结现阶段可用的开源库；
  3. 对比各层次的软件库，做出总结。

深度学习推断的主要操作

  对于大部分的卷积神经网络而言，卷积层是最消耗时间的部分，而全连接层则是参数量最多的部分[2]。 如下图所示[10]为 2012 年获得imagenet冠军的深度神经网络结构Alexnet分别在GPU和CPU进行推断的性能benchmark，由图可以看出，在CPU上卷积层和全连接层占用了95%的计算时间，而在CPU上卷积层和全连接层占用了89%的时间，如何高效地进行卷积层和全连接层的计算成为提升深度学习推断性能的关键点。

继续阅读卷积神经网络在ARM-CPU上的推断计算综述

卷积与反卷积、步长(stride)与重叠(overlap)及output的大小

1. 卷积神经网络的基础概念

        卷积神经网络是一种专门用来处理具有类似网络结果的数据的神经网络。至少在网络的一层中使用卷积运算来替代一般的矩阵乘法运算的神经网络。

        最核心的几个思想：稀疏交互、参数共享、等变表示（通俗成为平移不变性）。根本目的说白了就是为了节省运算时间和空间。那接下来看一下是怎么实现的。

1.0 卷积

         用一张图展示一下，卷积的计算。element-wise multiply 然后再相加。

继续阅读卷积与反卷积、步长(stride)与重叠(overlap)及output的大小

国内访问Coursera网站上的教学视频

最近在通过 `Coursera` 网站学习 `Andrew Ng` 的 `机器学习` 课程，但是发现网站上的视频无法正常播放。

抓包分析发现课程的视频是通过 `Amazon` 的 `cloudfront.net` 进行加速的，解决方法就是指定一个不被屏蔽的 `IP`地址即可。

在 `/etc/hosts` 中指定解析地址，如下：

52.84.246.90 d3c33hcgiwev3.cloudfront.net

参考链接

Coursera的视频为什么打不开了？