如何实现Pytorch转keras-创新互联
这篇文章主要为大家展示了如何实现Pytorch转keras，内容简而易懂，希望大家可以学习一下，学习完之后肯定会有收获的，下面让小编带大家一起来看看吧。
Pytorch凭借动态图机制，获得了广泛的使用，大有超越tensorflow的趋势，不过在工程应用上，TF仍然占据优势。有的时候我们会遇到这种情况，需要把模型应用到工业中，运用到实际项目上，TF支持的PB文件和TF的C++接口就成为了有效的工具。今天就给大家讲解一下Pytorch转成Keras的方法，进而我们也可以获得Pb文件，因为Keras是支持tensorflow的，我将会在下一篇博客讲解获得Pb文件，并使用Pb文件的方法。
Pytorch To Keras
首先，我们必须有清楚的认识，网上以及github上一些所谓的pytorch转换Keras或者Keras转换成Pytorch的工具代码几乎不能运行或者有使用的局限性（比如仅仅能转换某一些模型），但是我们是可以用这些转换代码中看出一些端倪来，比如二者的参数的尺寸（shape）的形式、channel的排序（first or last）是否一样，掌握到差异性，就能根据这些差异自己编写转换代码，没错，自己编写转换代码，是最稳妥的办法。整个过程也就分为两个部分。笔者将会以Nvidia开源的FlowNet为例，将开源的Pytorch代码转化为Keras模型。
按照Pytorch中模型的结构，编写对应的Keras代码，用keras的函数式API，构建起来会非常方便。
把Pytorch的模型参数，按照层的名称依次赋值给Keras的模型
以上两步虽然看上去简单，但实际我也走了不少弯路。这里一个关键的地方，就是参数的shape在两个框架中是否统一，那当然是不统一的。下面我以FlowNet为例。
Pytorch中的FlowNet代码
我们仅仅展示层名称和层参数，就不把整个结构贴出来了，否则会占很多的空间，形成水文。
先看用Keras搭建的flowNet模型，直接用model.summary()输出模型信息
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 6, 512, 512) 0
__________________________________________________________________________________________________
conv0 (Conv2D) (None, 64, 512, 512) 3520 input_1[0][0]
__________________________________________________________________________________________________
leaky_re_lu_1 (LeakyReLU) (None, 64, 512, 512) 0 conv0[0][0]
__________________________________________________________________________________________________
zero_padding2d_1 (ZeroPadding2D (None, 64, 514, 514) 0 leaky_re_lu_1[0][0]
__________________________________________________________________________________________________
conv1 (Conv2D) (None, 64, 256, 256) 36928 zero_padding2d_1[0][0]
__________________________________________________________________________________________________
leaky_re_lu_2 (LeakyReLU) (None, 64, 256, 256) 0 conv1[0][0]
__________________________________________________________________________________________________
conv1_1 (Conv2D) (None, 128, 256, 256 73856 leaky_re_lu_2[0][0]
__________________________________________________________________________________________________
leaky_re_lu_3 (LeakyReLU) (None, 128, 256, 256 0 conv1_1[0][0]
__________________________________________________________________________________________________
zero_padding2d_2 (ZeroPadding2D (None, 128, 258, 258 0 leaky_re_lu_3[0][0]
__________________________________________________________________________________________________
conv2 (Conv2D) (None, 128, 128, 128 147584 zero_padding2d_2[0][0]
__________________________________________________________________________________________________
leaky_re_lu_4 (LeakyReLU) (None, 128, 128, 128 0 conv2[0][0]
__________________________________________________________________________________________________
conv2_1 (Conv2D) (None, 128, 128, 128 147584 leaky_re_lu_4[0][0]
__________________________________________________________________________________________________
leaky_re_lu_5 (LeakyReLU) (None, 128, 128, 128 0 conv2_1[0][0]
__________________________________________________________________________________________________
zero_padding2d_3 (ZeroPadding2D (None, 128, 130, 130 0 leaky_re_lu_5[0][0]
__________________________________________________________________________________________________
conv3 (Conv2D) (None, 256, 64, 64) 295168 zero_padding2d_3[0][0]
__________________________________________________________________________________________________
leaky_re_lu_6 (LeakyReLU) (None, 256, 64, 64) 0 conv3[0][0]
__________________________________________________________________________________________________
conv3_1 (Conv2D) (None, 256, 64, 64) 590080 leaky_re_lu_6[0][0]
__________________________________________________________________________________________________
leaky_re_lu_7 (LeakyReLU) (None, 256, 64, 64) 0 conv3_1[0][0]
__________________________________________________________________________________________________
zero_padding2d_4 (ZeroPadding2D (None, 256, 66, 66) 0 leaky_re_lu_7[0][0]
__________________________________________________________________________________________________
conv4 (Conv2D) (None, 512, 32, 32) 1180160 zero_padding2d_4[0][0]
__________________________________________________________________________________________________
leaky_re_lu_8 (LeakyReLU) (None, 512, 32, 32) 0 conv4[0][0]
__________________________________________________________________________________________________
conv4_1 (Conv2D) (None, 512, 32, 32) 2359808 leaky_re_lu_8[0][0]
__________________________________________________________________________________________________
leaky_re_lu_9 (LeakyReLU) (None, 512, 32, 32) 0 conv4_1[0][0]
__________________________________________________________________________________________________
zero_padding2d_5 (ZeroPadding2D (None, 512, 34, 34) 0 leaky_re_lu_9[0][0]
__________________________________________________________________________________________________
conv5 (Conv2D) (None, 512, 16, 16) 2359808 zero_padding2d_5[0][0]
__________________________________________________________________________________________________
leaky_re_lu_10 (LeakyReLU) (None, 512, 16, 16) 0 conv5[0][0]
__________________________________________________________________________________________________
conv5_1 (Conv2D) (None, 512, 16, 16) 2359808 leaky_re_lu_10[0][0]
__________________________________________________________________________________________________
leaky_re_lu_11 (LeakyReLU) (None, 512, 16, 16) 0 conv5_1[0][0]
__________________________________________________________________________________________________
zero_padding2d_6 (ZeroPadding2D (None, 512, 18, 18) 0 leaky_re_lu_11[0][0]
__________________________________________________________________________________________________
conv6 (Conv2D) (None, 1024, 8, 8) 4719616 zero_padding2d_6[0][0]
__________________________________________________________________________________________________
leaky_re_lu_12 (LeakyReLU) (None, 1024, 8, 8) 0 conv6[0][0]
__________________________________________________________________________________________________
conv6_1 (Conv2D) (None, 1024, 8, 8) 9438208 leaky_re_lu_12[0][0]
__________________________________________________________________________________________________
leaky_re_lu_13 (LeakyReLU) (None, 1024, 8, 8) 0 conv6_1[0][0]
__________________________________________________________________________________________________
deconv5 (Conv2DTranspose) (None, 512, 16, 16) 8389120 leaky_re_lu_13[0][0]
__________________________________________________________________________________________________
predict_flow6 (Conv2D) (None, 2, 8, 8) 18434 leaky_re_lu_13[0][0]
__________________________________________________________________________________________________
leaky_re_lu_14 (LeakyReLU) (None, 512, 16, 16) 0 deconv5[0][0]
__________________________________________________________________________________________________
upsampled_flow6_to_5 (Conv2DTra (None, 2, 16, 16) 66 predict_flow6[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 1026, 16, 16) 0 leaky_re_lu_11[0][0]
leaky_re_lu_14[0][0]
upsampled_flow6_to_5[0][0]
__________________________________________________________________________________________________
inter_conv5 (Conv2D) (None, 512, 16, 16) 4728320 concatenate_1[0][0]
__________________________________________________________________________________________________
deconv4 (Conv2DTranspose) (None, 256, 32, 32) 4202752 concatenate_1[0][0]
__________________________________________________________________________________________________
predict_flow5 (Conv2D) (None, 2, 16, 16) 9218 inter_conv5[0][0]
__________________________________________________________________________________________________
leaky_re_lu_15 (LeakyReLU) (None, 256, 32, 32) 0 deconv4[0][0]
__________________________________________________________________________________________________
upsampled_flow5_to4 (Conv2DTran (None, 2, 32, 32) 66 predict_flow5[0][0]
__________________________________________________________________________________________________
concatenate_2 (Concatenate) (None, 770, 32, 32) 0 leaky_re_lu_9[0][0]
leaky_re_lu_15[0][0]
upsampled_flow5_to4[0][0]
__________________________________________________________________________________________________
inter_conv4 (Conv2D) (None, 256, 32, 32) 1774336 concatenate_2[0][0]
__________________________________________________________________________________________________
deconv3 (Conv2DTranspose) (None, 128, 64, 64) 1577088 concatenate_2[0][0]
__________________________________________________________________________________________________
predict_flow4 (Conv2D) (None, 2, 32, 32) 4610 inter_conv4[0][0]
__________________________________________________________________________________________________
leaky_re_lu_16 (LeakyReLU) (None, 128, 64, 64) 0 deconv3[0][0]
__________________________________________________________________________________________________
upsampled_flow4_to3 (Conv2DTran (None, 2, 64, 64) 66 predict_flow4[0][0]
__________________________________________________________________________________________________
concatenate_3 (Concatenate) (None, 386, 64, 64) 0 leaky_re_lu_7[0][0]
leaky_re_lu_16[0][0]
upsampled_flow4_to3[0][0]
__________________________________________________________________________________________________
inter_conv3 (Conv2D) (None, 128, 64, 64) 444800 concatenate_3[0][0]
__________________________________________________________________________________________________
deconv2 (Conv2DTranspose) (None, 64, 128, 128) 395328 concatenate_3[0][0]
__________________________________________________________________________________________________
predict_flow3 (Conv2D) (None, 2, 64, 64) 2306 inter_conv3[0][0]
__________________________________________________________________________________________________
leaky_re_lu_17 (LeakyReLU) (None, 64, 128, 128) 0 deconv2[0][0]
__________________________________________________________________________________________________
upsampled_flow3_to2 (Conv2DTran (None, 2, 128, 128) 66 predict_flow3[0][0]
__________________________________________________________________________________________________
concatenate_4 (Concatenate) (None, 194, 128, 128 0 leaky_re_lu_5[0][0]
leaky_re_lu_17[0][0]
upsampled_flow3_to2[0][0]
__________________________________________________________________________________________________
inter_conv2 (Conv2D) (None, 64, 128, 128) 111808 concatenate_4[0][0]
__________________________________________________________________________________________________
predict_flow2 (Conv2D) (None, 2, 128, 128) 1154 inter_conv2[0][0]
__________________________________________________________________________________________________
up_sampling2d_1 (UpSampling2D) (None, 2, 512, 512) 0 predict_flow2[0][0]
另外有需要云服务器可以了解下创新互联scvps.cn，海内外云服务器15元起步，三天无理由+7*72小时售后在线，公司持有idc许可证，提供“云服务器、裸金属服务器、高防服务器、香港服务器、美国服务器、虚拟主机、免备案服务器”等云主机租用服务以及企业上云的综合解决方案，具有“安全稳定、简单易用、服务可用性高、性价比高”等特点与优势，专为企业上云打造定制，能够满足用户丰富、多元化的应用场景需求。
新闻名称：如何实现Pytorch转keras-创新互联
文章位置：http://scyanting.com/article/ccsosc.html
