深海游弋的鱼 – 默默的点滴

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。

ImageNet 数据集是目前世界上图像识别最大的数据库，根据 WordNet 层次结构（目前仅限物体）组织，主要用于机器视觉领域的图像分类和目标检测。其中层次结构的每个节点由数百和数千个图像描绘，每个节点平均有超过 500 个图像，有大约 1500 万张图片，2.2 万类。 ImageNet 数据集于 2009 年由斯坦福大学的李飞飞等人在视觉科学学会（VSS）首次发布，而后自 2010 年起一年一度的 ImageNet 大规模视觉识别挑战（ILSVRC）挑战赛不断完善 ImageNet 数据集。

ImageNet.torrent 需要占用磁盘空间 860.55 GB

参考链接

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

$ 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 https://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

$ 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 https://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 https://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 https://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

# 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 https://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 https://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

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

#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

$ 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

$ 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 https://www.mobibrw.com/wp-content/uploads/2019/11/SynthText.zip

$ 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 https://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

$ 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

$ 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()

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

$ 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

$ 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: .

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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.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [33,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [34,0,0] 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: [2,0,0], thread: [36,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [37,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [38,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [39,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [40,0,0] 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: [2,0,0], thread: [42,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [43,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [44,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [45,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [46,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [47,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [48,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [49,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [50,0,0] 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: [2,0,0], thread: [53,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
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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

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分类： Deep Learning

使用word2vec训练中文维基百科

获取和处理中文语料

ImageNet(2010-2017)图像识别数据集

参考链接

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

参考链接

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

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