脚本专栏

Pytorch转keras的有效方法,以FlowNet为例讲解
脚本专栏 2024/11/8 佚名
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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]

再看看Pytorch搭建的flownet模型

 (conv0): Sequential(
 (0): Conv2d(6, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
 )
 (conv1): Sequential(
 (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
 )
 (conv1_1): Sequential(
 (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
 )
 (conv2): Sequential(
 (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
 )
 (conv2_1): Sequential(
 (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
 )
 (conv3): Sequential(
 (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
 )
 (conv3_1): Sequential(
 (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
 )
 (conv4): Sequential(
 (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
 )
 (conv4_1): Sequential(
 (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
 )
 (conv5): Sequential(
 (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
 )
 (conv5_1): Sequential(
 (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
 )
 (conv6): Sequential(
 (0): Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
 )
 (conv6_1): Sequential(
 (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
 )
 (deconv5): Sequential(
 (0): ConvTranspose2d(1024, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
 )
 (deconv4): Sequential(
 (0): ConvTranspose2d(1026, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
 )
 (deconv3): Sequential(
 (0): ConvTranspose2d(770, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
 )
 (deconv2): Sequential(
 (0): ConvTranspose2d(386, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
 )
 (inter_conv5): Sequential(
 (0): Conv2d(1026, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
 )
 (inter_conv4): Sequential(
 (0): Conv2d(770, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
 )
 (inter_conv3): Sequential(
 (0): Conv2d(386, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
 )
 (inter_conv2): Sequential(
 (0): Conv2d(194, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
 )
 (predict_flow6): Conv2d(1024, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
 (predict_flow5): Conv2d(512, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
 (predict_flow4): Conv2d(256, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
 (predict_flow3): Conv2d(128, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
 (predict_flow2): Conv2d(64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
 (upsampled_flow6_to_5): ConvTranspose2d(2, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
 (upsampled_flow5_to_4): ConvTranspose2d(2, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
 (upsampled_flow4_to_3): ConvTranspose2d(2, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
 (upsampled_flow3_to_2): ConvTranspose2d(2, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
 (upsample1): Upsample(scale_factor=4.0, mode=bilinear)
)
conv0 Sequential(
 (0): Conv2d(6, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
)
conv0.0 Conv2d(6, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
conv0.1 LeakyReLU(negative_slope=0.1, inplace)
conv1 Sequential(
 (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
)
conv1.0 Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
conv1.1 LeakyReLU(negative_slope=0.1, inplace)
conv1_1 Sequential(
 (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
)
conv1_1.0 Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
conv1_1.1 LeakyReLU(negative_slope=0.1, inplace)
conv2 Sequential(
 (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
)
conv2.0 Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
conv2.1 LeakyReLU(negative_slope=0.1, inplace)
conv2_1 Sequential(
 (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
)
conv2_1.0 Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
conv2_1.1 LeakyReLU(negative_slope=0.1, inplace)
conv3 Sequential(
 (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
)
conv3.0 Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
conv3.1 LeakyReLU(negative_slope=0.1, inplace)
conv3_1 Sequential(
 (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
)
conv3_1.0 Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
conv3_1.1 LeakyReLU(negative_slope=0.1, inplace)
conv4 Sequential(
 (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
)
conv4.0 Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
conv4.1 LeakyReLU(negative_slope=0.1, inplace)
conv4_1 Sequential(
 (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
)
conv4_1.0 Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
conv4_1.1 LeakyReLU(negative_slope=0.1, inplace)
conv5 Sequential(
 (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
)
conv5.0 Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
conv5.1 LeakyReLU(negative_slope=0.1, inplace)
conv5_1 Sequential(
 (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
)
conv5_1.0 Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
conv5_1.1 LeakyReLU(negative_slope=0.1, inplace)
conv6 Sequential(
 (0): Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
)
conv6.0 Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
conv6.1 LeakyReLU(negative_slope=0.1, inplace)
conv6_1 Sequential(
 (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
)
conv6_1.0 Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
conv6_1.1 LeakyReLU(negative_slope=0.1, inplace)
deconv5 Sequential(
 (0): ConvTranspose2d(1024, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
)
deconv5.0 ConvTranspose2d(1024, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
deconv5.1 LeakyReLU(negative_slope=0.1, inplace)
deconv4 Sequential(
 (0): ConvTranspose2d(1026, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
)
deconv4.0 ConvTranspose2d(1026, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
deconv4.1 LeakyReLU(negative_slope=0.1, inplace)
deconv3 Sequential(
 (0): ConvTranspose2d(770, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
)
deconv3.0 ConvTranspose2d(770, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
deconv3.1 LeakyReLU(negative_slope=0.1, inplace)
deconv2 Sequential(
 (0): ConvTranspose2d(386, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
 (1): LeakyReLU(negative_slope=0.1, inplace)
)
deconv2.0 ConvTranspose2d(386, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
deconv2.1 LeakyReLU(negative_slope=0.1, inplace)
inter_conv5 Sequential(
 (0): Conv2d(1026, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
inter_conv5.0 Conv2d(1026, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
inter_conv4 Sequential(
 (0): Conv2d(770, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
inter_conv4.0 Conv2d(770, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
inter_conv3 Sequential(
 (0): Conv2d(386, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
inter_conv3.0 Conv2d(386, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
inter_conv2 Sequential(
 (0): Conv2d(194, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)

因为Pytorch模型用name_modules()输出不是按顺序的，动态图机制决定了只有在有数据流动之后才知道走过的路径。所以上面的顺序也是乱的。但我想表明的是，我用Keras搭建的模型确实是根据官方开源的Pytorch模型搭建的。

模型搭建完毕之后，就到了关键的步骤：给Keras模型赋值。

给Keras模型赋值

这个步骤其实注意三个点

Pytorch是channels_first的，Keras默认是channels_last，在代码开头加上这两句：

K.set_image_data_format(‘channels_first')
K.set_learning_phase(0)

众所周知，卷积层的权重是一个4维张量，那么，在Pytorch和keras中，卷积核的权重的形式是否一致的，那自然是不一致的，要不然我为啥还要写这一点。那么就涉及到Pytorch权重的变形。

既然卷积层权重形式在两个框架是不一致的，转置卷积自然也是不一致的。

我们先看看卷积层在两个框架中的形式

keras的卷积层权重形式

我们用以下代码看keras卷积层权重形式

 for l in model.layers:
  print(l.name)
  for i, w in enumerate(l.get_weights()):
   print('%d'%i , w.shape)

第一个卷积层输出如下 0之后是卷积权重的shape，1之后的是偏置项

conv0
0 (3, 3, 6, 64)
1 (64,)

所以Keras的卷积层权重形式是[ height, width, input_channels, out_channels]

Pytorch的卷积层权重形式

 net = FlowNet2SD()
 for n, m in net.named_parameters():
  print(n)
  print(m.data.size())

conv0.0.weight
torch.Size([64, 6, 3, 3])
conv0.0.bias
torch.Size([64])

用上面的代码得到所有层的参数的shape，同样找到第一个卷积层的参数，查看shape。

通过对比我们可以发现，Pytorch的卷积层shape是[ out_channels, input_channels, height, width]的形式。

那么我们在取出Pytorch权重之后，需要用np.transpose改变一下权重的排序，才能送到Keras模型对应的层上。

Keras中转置卷积权重形式

deconv4
0 (4, 4, 256, 1026)
1 (256,)

代码仍然和上面一样，找到转置卷积的对应的位置，查看一下

可以看出在Keras中，转置卷积形式是 [ height, width, out_channels, input_channels]

Pytorch中转置卷积权重形式

deconv4.0.weight
torch.Size([1026, 256, 4, 4])
deconv4.0.bias
torch.Size([256])

代码仍然和上面一样，找到转置卷积的对应的位置，查看一下

可以看出在Pytorch中，转置卷积形式是 [ input_channels,out_channels,height, width]

小结

对于卷积层来说，Pytorch的权重需要使用

np.transpose(weight.data.numpy(), [2, 3, 1, 0]）

才能赋值给keras模型对应的层的权重。

对于转置卷积来说，通过对比其实也是一样的。不信你去试试嘛。O(∩_∩)O哈哈~

对于偏置项，两种模块都是一维的向量，不需要处理。

有的情况还可能需要通道颠倒一下，但是很少需要这样做。

weights[::-1,::-1,:,:]

赋值

结束了预处理之后，我们就进入第二步，开始赋值了。

先看预处理的代码：

for k,v in weights_from_torch.items():
 if 'bias' not in k:
  weights_from_torch[k] = v.data.numpy().transpose(2, 3, 1, 0)

赋值代码我只截了一部分供大家参考：

k_model = k_model()
for layer in k_model.layers:
 current_layer_name = layer.name
 if current_layer_name=='conv0':
  weights = [weights_from_torch['conv0.0.weight'],weights_from_torch['conv0.0.bias']]
  layer.set_weights(weights)
 elif current_layer_name=='conv1':
  weights = [weights_from_torch['conv1.0.weight'],weights_from_torch['conv1.0.bias']]
  layer.set_weights(weights)
 elif current_layer_name=='conv1_1':
  weights = [weights_from_torch['conv1_1.0.weight'],weights_from_torch['conv1_1.0.bias']]
  layer.set_weights(weights)

首先就是定义Keras模型，用layers获得所有层的迭代器。

遍历迭代器，对一个层赋予相应的值。

赋值需要用save_weights，其参数需要是一个列表，形式和get_weights的返回结果一致，即 [ conv_weights, bias_weights]

最后祝愿大家能实现自己模型的迁移。工程开源在了个人Github，有详细的使用介绍，并且包含使用数据，大家可以直接运行。

以上这篇Pytorch转keras的有效方法,以FlowNet为例讲解就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。

标签：
Pytorch,keras,FlowNet
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