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基于PaddlePaddle的强化学习算法CycleGAN Fork 72 收藏
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基于PaddlePaddle的强化学习算法CycleGAN Fork 72 收藏
生成对抗网络(Generative Adversarial Network[1], 简称GAN) 是一种非监督学习的方式，通过让两个神经网络相互博弈的方法进行学习，该方法由lan Goodfellow等人在2014年提出。生成对抗网络由一个生成网络和一个判别网络组成，生成网络从潜在的空间(latent space)中随机采样作为输入，其输出结果需要尽量模仿训练集中的真实样本。判别网络的输入为真实样本或生成网络的输出，其目的是将生成网络的输出从真实样本中尽可能的分辨出来。而生成网络则尽可能的欺骗判别网络，两个网络相互对抗，不断调整参数。 生成对抗网络常用于生成以假乱真的图片。此外，该方法还被用于生成影片，三维物体模型等。
下载安装命令 ## CPU版本安装命令 pip install -f https://paddlepaddle.org.cn/pip/oschina/cpu paddlepaddle ## GPU版本安装命令 pip install -f https://paddlepaddle.org.cn/pip/oschina/gpu paddlepaddle-gpu
CycleGAN可以利用非成对的图片进行图像翻译，即输入为两种不同风格的不同图片，自动进行风格转换。传统的GAN是单向生成，而CycleGAN是互相生成，网络是个环形，所以命名为Cycle。并且CycleGAN一个非常实用的地方就是输入的两张图片可以是任意的两张图片，也就是unpaired。
CycleGAN由两个生成网络和两个判别网络组成，生成网络A是输入A类风格的图片输出B类风格的图片，生成网络B是输入B类风格的图片输出A类风格的图片。生成网络中编码部分的网络结构都是采用convolution-norm-ReLU作为基础结构，解码部分的网络结构由transpose convolution-norm-ReLU组成，判别网络基本是由convolution-norm-leaky_ReLU作为基础结构。生成网络损失函数由LSGAN的损失函数，重构损失和自身损失组成，判别网络的损失函数由LSGAN的损失函数组成。CycleGAN的结构如下： Cycle-Gan总结构有四个网络，第一个网络为生成（转化）网络命名为G:X---->Y；第二个网络为生成（转化）网络命名为F:Y--->X；第三个网络为对抗网络命名为Dx，鉴别输入图像是不是X；第四个网络为对抗网络命名为Dy，鉴别输入图像是不是Y。
如上图，以马（X）和斑马（Y）为例，G网络将马的图像转化为斑马图像；F网络将斑马的图像转化为马的图像；Dx网络鉴别输入的图像是不是马；Dy网络鉴别输入图像是不是斑马；
这四个网络仅有两个网络结构，即G和F都是生成（转化）网络，这两者的网络结构相同，Dx和Dy都是对抗性网络，这两者的网络结构相同。CycleGAN 效果展示：
In[1]
#代码结构
# ├── data_reader.py # 读取、处理数据。
# ├── layers.py # 封装定义基础的layers。
# ├── model.py # 定义基础生成网络和判别网络。
# ├── trainer.py # 构造loss和训练网络。
# ├── train.py # 训练脚本。
# └── infer.py # 预测脚本。
In[2]
!cd /home/aistudio/data/data10040/ && unzip -qo horse2zebra.zip本项目使用 horse2zebra 数据集 来进行模型的训练测试工作，horse2zebra训练集包含1069张野马图片，1336张斑马图片。测试集包含121张野马图片和141张斑马图片。
In[3]
# 数据准备
# 本教程使用 horse2zebra 数据集 来进行模型的训练测试工作，horse2zebra训练集包含1069张野马图片，1336张斑马图片。测试集包含121张野马图片和141张斑马图片。
# 以下路径结构：
# data
# |-- horse2zebra
# | |-- testA
# | |-- testA.txt
# | |-- testB
# | |-- testB.txt
# | |-- trainA
# | |-- trainA.txt
# | |-- trainB
# | `-- trainB.txt
# 以上数据文件中，data文件夹需要放在训练脚本train.py同级目录下。testA为存放野马测试图片的文件夹，testB为存放斑马测试图片的文件夹，testA.txt和testB.txt分别为野马和斑马测试图片路径列表文件，格式如下：
# testA/n02381460_9243.jpg
# testA/n02381460_9244.jpg
# testA/n02381460_9245.jpg
# 训练数据组织方式与测试数据相同。
In[4]
#安装scipy
!pip install imageio
!pip install scipy==1.2.1Looking in indexes: https://pypi.mirrors.ustc.edu.cn/simple/
Requirement already satisfied: imageio in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (2.6.1)
Requirement already satisfied: numpy in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from imageio) (1.16.4)
Requirement already satisfied: pillow in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from imageio) (6.2.0)
Looking in indexes: https://pypi.mirrors.ustc.edu.cn/simple/
Collecting scipy==1.2.1
WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='pypi.mirrors.ustc.edu.cn', port=443): Read timed out. (read timeout=15)")': /simple/scipy/
WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='mirrors.ustc.edu.cn', port=443): Read timed out. (read timeout=15)")': /pypi/web/simple/scipy/
WARNING: Retrying (Retry(total=3, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='mirrors.ustc.edu.cn', port=443): Read timed out. (read timeout=15)")': /pypi/web/simple/scipy/
WARNING: Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='mirrors.ustc.edu.cn', port=443): Read timed out. (read timeout=15)")': /pypi/web/simple/scipy/
WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ConnectTimeoutError(<pip._vendor.urllib3.connection.VerifiedHTTPSConnection object at 0x7f658b56e450>, 'Connection to mirrors.tuna.tsinghua.edu.cn timed out. (connect timeout=15)')': /pypi/web/simple/scipy/
Downloading https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/3e/7e/5cee36eee5b3194687232f6150a89a38f784883c612db7f4da2ab190980d/scipy-1.2.1-cp37-cp37m-manylinux1_x86_64.whl (24.8MB)
|████████████████████████████████| 24.8MB 141kB/s eta 0:00:01
Requirement already satisfied: numpy>=1.8.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scipy==1.2.1) (1.16.4)
Installing collected packages: scipy
Found existing installation: scipy 1.3.0
Uninstalling scipy-1.3.0:
Successfully uninstalled scipy-1.3.0
Successfully installed scipy-1.2.1
In[7]
#训练
#在GPU单卡上训练:
!python cycle_gan/train.py --epoch=2 --use_gpu True----------- Configuration Arguments ----------- batch_size: 1 epoch: 2 init_model: None output: ./output profile: False run_ce: False run_test: True save_checkpoints: True use_gpu: 1 ------------------------------------------------ W0316 17:50:21.449174 346 device_context.cc:237] Please NOTE: device: 0, CUDA Capability: 70, Driver API Version: 10.1, Runtime API Version: 9.0 W0316 17:50:21.453552 346 device_context.cc:245] device: 0, cuDNN Version: 7.3. I0316 17:50:23.286839 346 parallel_executor.cc:440] The Program will be executed on CUDA using ParallelExecutor, 1 cards are used, so 1 programs are executed in parallel. I0316 17:50:23.343647 346 build_strategy.cc:365] SeqOnlyAllReduceOps:0, num_trainers:1 I0316 17:50:23.495808 346 parallel_executor.cc:375] Garbage collection strategy is enabled, when FLAGS_eager_delete_tensor_gb = 0 I0316 17:50:24.629184 346 parallel_executor.cc:440] The Program will be executed on CUDA using ParallelExecutor, 1 cards are used, so 1 programs are executed in parallel. I0316 17:50:24.633793 346 build_strategy.cc:365] SeqOnlyAllReduceOps:0, num_trainers:1 I0316 17:50:24.757025 346 parallel_executor.cc:440] The Program will be executed on CUDA using ParallelExecutor, 1 cards are used, so 1 programs are executed in parallel. I0316 17:50:24.810863 346 build_strategy.cc:365] SeqOnlyAllReduceOps:0, num_trainers:1 I0316 17:50:26.055040 346 parallel_executor.cc:440] The Program will be executed on CUDA using ParallelExecutor, 1 cards are used, so 1 programs are executed in parallel. I0316 17:50:26.059253 346 build_strategy.cc:365] SeqOnlyAllReduceOps:0, num_trainers:1 epoch0; batch0; g_A_loss: 14.300308227539062; d_B_loss: 1.703646183013916; g_B_loss: 13.938850402832031; d_A_loss: 1.6837800741195679; Batch_time_cost: 2.89 epoch0; batch50; g_A_loss: 6.143564224243164; d_B_loss: 0.43444156646728516; g_B_loss: 5.804959297180176; d_A_loss: 0.3155096173286438; Batch_time_cost: 0.12 epoch0; batch100; g_A_loss: 6.256138324737549; d_B_loss: 0.2702423632144928; g_B_loss: 5.725310325622559; d_A_loss: 0.3236050307750702; Batch_time_cost: 0.12 epoch0; batch150; g_A_loss: 7.066346645355225; d_B_loss: 0.31580042839050293; g_B_loss: 6.818117141723633; d_A_loss: 0.4519233703613281; Batch_time_cost: 0.13 epoch0; batch200; g_A_loss: 6.909765243530273; d_B_loss: 0.3620782196521759; g_B_loss: 7.228463172912598; d_A_loss: 0.36587968468666077; Batch_time_cost: 0.13 epoch0; batch250; g_A_loss: 5.192142009735107; d_B_loss: 0.24265910685062408; g_B_loss: 5.763035297393799; d_A_loss: 1.1450282335281372; Batch_time_cost: 0.13 epoch0; batch300; g_A_loss: 5.316933631896973; d_B_loss: 0.19533368945121765; g_B_loss: 4.987677097320557; d_A_loss: 0.22845549881458282; Batch_time_cost: 0.13 epoch0; batch350; g_A_loss: 7.186776638031006; d_B_loss: 0.16458189487457275; g_B_loss: 5.839504241943359; d_A_loss: 0.1668826937675476; Batch_time_cost: 0.12 epoch0; batch400; g_A_loss: 5.885252952575684; d_B_loss: 0.30592358112335205; g_B_loss: 5.449808120727539; d_A_loss: 0.1534576117992401; Batch_time_cost: 0.13 epoch0; batch450; g_A_loss: 6.440225124359131; d_B_loss: 0.11404794454574585; g_B_loss: 5.590849876403809; d_A_loss: 0.1966126412153244; Batch_time_cost: 0.13 epoch0; batch500; g_A_loss: 6.872323036193848; d_B_loss: 0.19951260089874268; g_B_loss: 6.571059226989746; d_A_loss: 0.23495124280452728; Batch_time_cost: 0.13 epoch0; batch550; g_A_loss: 5.0895466804504395; d_B_loss: 0.16031894087791443; g_B_loss: 5.121213912963867; d_A_loss: 0.25687021017074585; Batch_time_cost: 0.13 epoch0; batch600; g_A_loss: 6.153958797454834; d_B_loss: 0.21091848611831665; g_B_loss: 6.268405914306641; d_A_loss: 0.11446988582611084; Batch_time_cost: 0.13 epoch0; batch650; g_A_loss: 8.350132942199707; d_B_loss: 0.13534343242645264; g_B_loss: 8.49114990234375; d_A_loss: 0.17544472217559814; Batch_time_cost: 0.13 epoch0; batch700; g_A_loss: 4.763492584228516; d_B_loss: 0.25141626596450806; g_B_loss: 4.048842430114746; d_A_loss: 0.1611996442079544; Batch_time_cost: 0.13 epoch0; batch750; g_A_loss: 6.567712783813477; d_B_loss: 0.2876878082752228; g_B_loss: 6.591218948364258; d_A_loss: 0.17310750484466553; Batch_time_cost: 0.13 epoch0; batch800; g_A_loss: 4.201617240905762; d_B_loss: 0.28061026334762573; g_B_loss: 4.326346397399902; d_A_loss: 0.17125019431114197; Batch_time_cost: 0.13 epoch0; batch850; g_A_loss: 6.599902153015137; d_B_loss: 0.33524584770202637; g_B_loss: 5.3691182136535645; d_A_loss: 0.16773417592048645; Batch_time_cost: 0.12 epoch0; batch900; g_A_loss: 7.22122049331665; d_B_loss: 0.0716591626405716; g_B_loss: 6.713944435119629; d_A_loss: 0.16595764458179474; Batch_time_cost: 0.12 epoch0; batch950; g_A_loss: 6.072809219360352; d_B_loss: 0.17901702225208282; g_B_loss: 5.398962020874023; d_A_loss: 0.17636674642562866; Batch_time_cost: 0.13 epoch0; batch1000; g_A_loss: 5.913625717163086; d_B_loss: 0.0724739134311676; g_B_loss: 4.69740629196167; d_A_loss: 0.27375420928001404; Batch_time_cost: 0.13 epoch0; batch1050; g_A_loss: 9.782687187194824; d_B_loss: 0.12104632705450058; g_B_loss: 9.672157287597656; d_A_loss: 0.14316022396087646; Batch_time_cost: 0.13 epoch0; batch1100; g_A_loss: 4.571684837341309; d_B_loss: 0.06749674677848816; g_B_loss: 4.33621883392334; d_A_loss: 0.17319558560848236; Batch_time_cost: 0.13 epoch0; batch1150; g_A_loss: 5.194060802459717; d_B_loss: 0.13884581625461578; g_B_loss: 5.199503421783447; d_A_loss: 0.1418939232826233; Batch_time_cost: 0.13 epoch0; batch1200; g_A_loss: 4.909609794616699; d_B_loss: 0.12311942875385284; g_B_loss: 4.3402299880981445; d_A_loss: 0.2318917214870453; Batch_time_cost: 0.13 epoch0; batch1250; g_A_loss: 5.436093330383301; d_B_loss: 0.19272129237651825; g_B_loss: 6.465127944946289; d_A_loss: 0.23860839009284973; Batch_time_cost: 0.13 epoch0; batch1300; g_A_loss: 8.133463859558105; d_B_loss: 0.0819319561123848; g_B_loss: 7.404263496398926; d_A_loss: 0.10251586139202118; Batch_time_cost: 0.13 cycle_gan/train.py:134: DeprecationWarning: `imsave` is deprecated! `imsave` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0. Use ``imageio.imwrite`` instead. (fake_B_temp + 1) * 127.5).astype(np.uint8)) cycle_gan/train.py:136: DeprecationWarning: `imsave` is deprecated! `imsave` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0. Use ``imageio.imwrite`` instead. (fake_A_temp + 1) * 127.5).astype(np.uint8)) cycle_gan/train.py:138: DeprecationWarning: `imsave` is deprecated! `imsave` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0. Use ``imageio.imwrite`` instead. (cyc_A_temp + 1) * 127.5).astype(np.uint8)) cycle_gan/train.py:140: DeprecationWarning: `imsave` is deprecated! `imsave` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0. Use ``imageio.imwrite`` instead. (cyc_B_temp + 1) * 127.5).astype(np.uint8)) cycle_gan/train.py:142: DeprecationWarning: `imsave` is deprecated! `imsave` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0. Use ``imageio.imwrite`` instead. (input_A_temp + 1) * 127.5).astype(np.uint8)) cycle_gan/train.py:144: DeprecationWarning: `imsave` is deprecated! `imsave` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0. Use ``imageio.imwrite`` instead. (input_B_temp + 1) * 127.5).astype(np.uint8)) #保存生成的图 saved checkpoint to ./output/checkpoints/ epoch1; batch0; g_A_loss: 5.827764987945557; d_B_loss: 0.07218477874994278; g_B_loss: 5.5165019035339355; d_A_loss: 0.3245842158794403; Batch_time_cost: 0.12 epoch1; batch50; g_A_loss: 5.765468597412109; d_B_loss: 0.167547345161438; g_B_loss: 5.18796443939209; d_A_loss: 0.15456309914588928; Batch_time_cost: 0.12 epoch1; batch100; g_A_loss: 5.45959997177124; d_B_loss: 0.09713760763406754; g_B_loss: 4.907477855682373; d_A_loss: 0.06172751635313034; Batch_time_cost: 0.12 epoch1; batch150; g_A_loss: 6.784292221069336; d_B_loss: 0.11054498702287674; g_B_loss: 6.478621959686279; d_A_loss: 0.44314247369766235; Batch_time_cost: 0.12 epoch1; batch200; g_A_loss: 7.896561145782471; d_B_loss: 0.16192974150180817; g_B_loss: 7.6382269859313965; d_A_loss: 0.10663460940122604; Batch_time_cost: 0.12 epoch1; batch250; g_A_loss: 6.924862384796143; d_B_loss: 0.029113346710801125; g_B_loss: 6.796955585479736; d_A_loss: 0.07325638085603714; Batch_time_cost: 0.12 epoch1; batch300; g_A_loss: 4.884801864624023; d_B_loss: 0.050231873989105225; g_B_loss: 4.698901176452637; d_A_loss: 0.14451992511749268; Batch_time_cost: 0.13 epoch1; batch350; g_A_loss: 5.315099239349365; d_B_loss: 0.17729483544826508; g_B_loss: 5.134288787841797; d_A_loss: 0.18281573057174683; Batch_time_cost: 0.13 epoch1; batch400; g_A_loss: 7.244136810302734; d_B_loss: 0.2588624954223633; g_B_loss: 6.518290042877197; d_A_loss: 0.03540463373064995; Batch_time_cost: 0.12 epoch1; batch450; g_A_loss: 5.102941513061523; d_B_loss: 0.03143639117479324; g_B_loss: 4.737099647521973; d_A_loss: 0.22579550743103027; Batch_time_cost: 0.13 epoch1; batch500; g_A_loss: 6.038484573364258; d_B_loss: 0.07545653730630875; g_B_loss: 5.054262638092041; d_A_loss: 0.1474056839942932; Batch_time_cost: 0.12 epoch1; batch550; g_A_loss: 5.1528449058532715; d_B_loss: 0.05942108482122421; g_B_loss: 5.586222171783447; d_A_loss: 0.1414523869752884; Batch_time_cost: 0.13 epoch1; batch600; g_A_loss: 7.961068630218506; d_B_loss: 0.07359579205513; g_B_loss: 7.495856285095215; d_A_loss: 0.08565278351306915; Batch_time_cost: 0.12 epoch1; batch650; g_A_loss: 4.3616814613342285; d_B_loss: 0.17840822041034698; g_B_loss: 3.6735024452209473; d_A_loss: 0.15866130590438843; Batch_time_cost: 0.13 epoch1; batch700; g_A_loss: 4.804023742675781; d_B_loss: 0.18827767670154572; g_B_loss: 4.755307197570801; d_A_loss: 0.2991688549518585; Batch_time_cost: 0.13 epoch1; batch750; g_A_loss: 5.776893138885498; d_B_loss: 0.24969376623630524; g_B_loss: 5.4265851974487305; d_A_loss: 0.1804438978433609; Batch_time_cost: 0.13 epoch1; batch800; g_A_loss: 8.234237670898438; d_B_loss: 0.328776478767395; g_B_loss: 7.330533027648926; d_A_loss: 0.07429933547973633; Batch_time_cost: 0.13 epoch1; batch850; g_A_loss: 7.487462520599365; d_B_loss: 0.08431682735681534; g_B_loss: 7.30022668838501; d_A_loss: 0.1268233209848404; Batch_time_cost: 0.13 epoch1; batch900; g_A_loss: 3.5420680046081543; d_B_loss: 0.15772652626037598; g_B_loss: 2.79952335357666; d_A_loss: 0.2009810507297516; Batch_time_cost: 0.13 epoch1; batch950; g_A_loss: 6.323566436767578; d_B_loss: 0.09212709963321686; g_B_loss: 5.999871730804443; d_A_loss: 0.1642218381166458; Batch_time_cost: 0.12 epoch1; batch1000; g_A_loss: 4.416383743286133; d_B_loss: 0.4296090602874756; g_B_loss: 3.9065892696380615; d_A_loss: 0.14740586280822754; Batch_time_cost: 0.13 epoch1; batch1050; g_A_loss: 4.462809085845947; d_B_loss: 0.5468143820762634; g_B_loss: 4.7028489112854; d_A_loss: 0.10133746266365051; Batch_time_cost: 0.12 epoch1; batch1100; g_A_loss: 6.614782810211182; d_B_loss: 0.09065119922161102; g_B_loss: 6.871949672698975; d_A_loss: 0.265675812959671; Batch_time_cost: 0.12 epoch1; batch1150; g_A_loss: 4.825323104858398; d_B_loss: 0.029798883944749832; g_B_loss: 4.237264633178711; d_A_loss: 0.15145432949066162; Batch_time_cost: 0.13 epoch1; batch1200; g_A_loss: 5.156582355499268; d_B_loss: 0.31561416387557983; g_B_loss: 5.017688274383545; d_A_loss: 0.1431412398815155; Batch_time_cost: 0.13 epoch1; batch1250; g_A_loss: 5.140244007110596; d_B_loss: 0.12650391459465027; g_B_loss: 4.05291223526001; d_A_loss: 0.2101406753063202; Batch_time_cost: 0.12 epoch1; batch1300; g_A_loss: 3.725010633468628; d_B_loss: 0.25355270504951477; g_B_loss: 4.305200576782227; d_A_loss: 0.31700998544692993; Batch_time_cost: 0.13 saved checkpoint to ./output/checkpoints/
In[8]
#应用固化的模型(训练10轮)进行预测,结果保存在output，训练150轮，可以达到上面简介模块展示的效果
!python cycle_gan/infer.py \
--init_model="output/freeze" \
--input="./data/data10040/horse2zebra/testA/n02381460_4260.jpg" \
--input_style A \
--output="output/freeze_infer_result"
# 可视化转换前后的效果
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import cv2
img= cv2.imread('data/data10040/horse2zebra/testA/n02381460_4260.jpg')
result_img= cv2.imread('output/freeze_infer_result/fake_n02381460_4260.jpg')
plt.subplot(1, 2, 1)
plt.imshow(img)
plt.subplot(1, 2, 2)
plt.imshow(result_img)
plt.show()----------- Configuration Arguments ----------- init_model: output/freeze input: ./data/data10040/horse2zebra/testA/n02381460_4260.jpg input_style: A output: output/freeze_infer_result use_gpu: True ------------------------------------------------ /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/executor.py:804: UserWarning: There are no operators in the program to be executed. If you pass Program manually, please use fluid.program_guard to ensure the current Program is being used. warnings.warn(error_info) W0316 17:58:40.541411 441 device_context.cc:237] Please NOTE: device: 0, CUDA Capability: 70, Driver API Version: 10.1, Runtime API Version: 9.0 W0316 17:58:40.545682 441 device_context.cc:245] device: 0, cuDNN Version: 7.3. cycle_gan/infer.py:63: DeprecationWarning: `imsave` is deprecated! `imsave` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0. Use ``imageio.imwrite`` instead. (fake_temp + 1) * 127.5).astype(np.uint8))
使用AI Studio一键上手实践项目吧：https://aistudio.baidu.com/aistudio/projectdetail/169459
下载安装命令 ## CPU版本安装命令 pip install -f https://paddlepaddle.org.cn/pip/oschina/cpu paddlepaddle ## GPU版本安装命令 pip install -f https://paddlepaddle.org.cn/pip/oschina/gpu paddlepaddle-gpu
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