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這篇文章主要為大家展示了如何實現Pytorch轉keras，內容簡而易懂，希望大家可以學習一下，學習完之后肯定會有收獲的，下面讓小編帶大家一起來看看吧。
Pytorch憑借動態圖機制，獲得了廣泛的使用，大有超越tensorflow的趨勢，不過在工程應用上，TF仍然占據優勢。有的時候我們會遇到這種情況，需要把模型應用到工業中，運用到實際項目上，TF支持的PB文件和TF的C++接口就成為了有效的工具。今天就給大家講解一下Pytorch轉成Keras的方法，進而我們也可以獲得Pb文件，因為Keras是支持tensorflow的，我將會在下一篇博客講解獲得Pb文件，并使用Pb文件的方法。
Pytorch To Keras
首先，我們必須有清楚的認識，網上以及github上一些所謂的pytorch轉換Keras或者Keras轉換成Pytorch的工具代碼幾乎不能運行或者有使用的局限性（比如僅僅能轉換某一些模型），但是我們是可以用這些轉換代碼中看出一些端倪來，比如二者的參數的尺寸（shape）的形式、channel的排序（first or last）是否一樣，掌握到差異性，就能根據這些差異自己編寫轉換代碼，沒錯，自己編寫轉換代碼，是最穩妥的辦法。整個過程也就分為兩個部分。筆者將會以Nvidia開源的FlowNet為例，將開源的Pytorch代碼轉化為Keras模型。
按照Pytorch中模型的結構，編寫對應的Keras代碼，用keras的函數式API，構建起來會非常方便。
把Pytorch的模型參數，按照層的名稱依次賦值給Keras的模型
以上兩步雖然看上去簡單，但實際我也走了不少彎路。這里一個關鍵的地方，就是參數的shape在兩個框架中是否統一，那當然是不統一的。下面我以FlowNet為例。
Pytorch中的FlowNet代碼
我們僅僅展示層名稱和層參數，就不把整個結構貼出來了，否則會占很多的空間，形成水文。
先看用Keras搭建的flowNet模型，直接用model.summary()輸出模型信息
__________________________________________________________________________________________________
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)) )
因為Pytorch模型用name_modules()輸出不是按順序的，動態圖機制決定了只有在有數據流動之后才知道走過的路徑。所以上面的順序也是亂的。但我想表明的是，我用Keras搭建的模型確實是根據官方開源的Pytorch模型搭建的。
模型搭建完畢之后，就到了關鍵的步驟：給Keras模型賦值。
給Keras模型賦值
這個步驟其實注意三個點
Pytorch是channels_first的，Keras默認是channels_last，在代碼開頭加上這兩句：
K.set_image_data_format(‘channels_first')
K.set_learning_phase(0)
眾所周知，卷積層的權重是一個4維張量，那么，在Pytorch和keras中，卷積核的權重的形式是否一致的，那自然是不一致的，要不然我為啥還要寫這一點。那么就涉及到Pytorch權重的變形。
既然卷積層權重形式在兩個框架是不一致的，轉置卷積自然也是不一致的。
我們先看看卷積層在兩個框架中的形式
keras的卷積層權重形式
我們用以下代碼看keras卷積層權重形式
for l in model.layers:
print(l.name)
for i, w in enumerate(l.get_weights()):
print('%d'%i , w.shape)第一個卷積層輸出如下 0之后是卷積權重的shape，1之后的是偏置項
conv0
0 (3, 3, 6, 64)
1 (64,)
所以Keras的卷積層權重形式是[ height, width, input_channels, out_channels]
Pytorch的卷積層權重形式
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])
用上面的代碼得到所有層的參數的shape，同樣找到第一個卷積層的參數，查看shape。
通過對比我們可以發現，Pytorch的卷積層shape是[ out_channels, input_channels, height, width]的形式。
那么我們在取出Pytorch權重之后，需要用np.transpose改變一下權重的排序，才能送到Keras模型對應的層上。
Keras中轉置卷積權重形式
deconv4
0 (4, 4, 256, 1026)
1 (256,)
代碼仍然和上面一樣，找到轉置卷積的對應的位置，查看一下
可以看出在Keras中，轉置卷積形式是 [ height, width, out_channels, input_channels]
Pytorch中轉置卷積權重形式
deconv4.0.weight
torch.Size([1026, 256, 4, 4])
deconv4.0.bias
torch.Size([256])
代碼仍然和上面一樣，找到轉置卷積的對應的位置，查看一下
可以看出在Pytorch中，轉置卷積形式是 [ input_channels,out_channels,height, width]
小結
對于卷積層來說，Pytorch的權重需要使用
np.transpose(weight.data.numpy(), [2, 3, 1, 0]）
才能賦值給keras模型對應的層的權重。
對于轉置卷積來說，通過對比其實也是一樣的。不信你去試試嘛。O(∩_∩)O哈哈~
對于偏置項，兩種模塊都是一維的向量，不需要處理。
有的情況還可能需要通道顛倒一下，但是很少需要這樣做。
weights[::-1,::-1,:,:]
賦值
結束了預處理之后，我們就進入第二步，開始賦值了。
先看預處理的代碼：
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)
賦值代碼我只截了一部分供大家參考：
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)
首先就是定義Keras模型，用layers獲得所有層的迭代器。
遍歷迭代器，對一個層賦予相應的值。
賦值需要用save_weights，其參數需要是一個列表，形式和get_weights的返回結果一致，即 [ conv_weights, bias_weights]
以上就是關于如何實現Pytorch轉keras的內容，如果你們有學習到知識或者技能，可以把它分享出去讓更多的人看到。
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